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Sample: Definition, Types, Formula & Examples

How often do researchers look for the right survey respondents, either for a market research study or an existing survey in the field? The sample or the respondents of this research may be selected from a set of customers or users that are known or unknown.

You may often know your typical respondent profile but don’t have access to the respondents to complete your research study. At such times, researchers and research teams reach out to specialized organizations to access their panel of respondents or buy respondents from them to complete research studies and surveys.

These could be general population respondents that match demographic criteria or respondents based on specific criteria. Such respondents are imperative to the success of research studies.

This article discusses in detail the different types of samples, sampling methods, and examples of each. It also mentions the steps to calculate the size, the details of an online sample, and the advantages of using them.

Content Index

• What is a sample?

Probability sampling methodologies with examples

Non-probability sampling methodologies with examples.

• How to determine a sample size
• Calculating sample size
• Sampling advantages

What is a Sample?

A sample is a smaller set of data that a researcher chooses or selects from a larger population using a pre-defined selection bias method. These elements are known as sample points, sampling units, or observations.

Creating a sample is an efficient method of conducting research . Researching the whole population is often impossible, costly, and time-consuming. Hence, examining the sample provides insights the researcher can apply to the entire population.

For example, if a cell phone manufacturer wants to conduct a feature research study among students in US Universities. An in-depth research study must be conducted if the researcher is looking for features that the students use, features they would like to see, and the price they are willing to pay.

This step is imperative to understand the features that need development, the features that require an upgrade, the device’s pricing, and the go-to-market strategy.

In 2016/17 alone, there were 24.7 million students enrolled in universities across the US. It is impossible to research all these students; the time spent would make the new device redundant, and the money spent on development would render the study useless.

Creating a sample of universities by geographical location and further creating a sample of these students from these universities provides a large enough number of students for research.

Typically, the population for market research is enormous. Making an enumeration of the whole population is practically impossible. The sample usually represents a manageable size of this population. Researchers then collect data from these samples through surveys, polls, and questionnaires and extrapolate this data analysis to the broader community.

LEARN ABOUT: Survey Sampling

Types of Samples: Selection methodologies with examples

The process of deriving a sample is called a sampling method. Sampling forms an integral part of the research design as this method derives the quantitative and qualitative data that can be collected as part of a research study. Sampling methods are characterized into two distinct approaches: probability sampling and non-probability sampling.

Probability sampling is a method of deriving a sample where the objects are selected from a population-based on probability theory. This method includes everyone in the population, and everyone has an equal chance of being selected. Hence, there is no bias whatsoever in this type of sample.

Each person in the population can subsequently be a part of the research. The selection criteria are decided at the outset of the market research study and form an important component of research.

LEARN ABOUT:   Action Research

Probability sampling can be further classified into four distinct types of samples. They are:

• Simple random sampling: The most straightforward way of selecting a sample is simple random sampling . In this method, each member has an equal chance of participating in the study. The objects in this sample population are chosen randomly, and each member has the same probability of being selected. For example, if a university dean would like to collect feedback from students about their perception of the teachers and level of education, all 1000 students in the University could be a part of this sample. Any 100 students can be selected randomly to be a part of this sample.
• Cluster sampling: Cluster sampling is a type of sampling method where the respondent population is divided into equal clusters. Clusters are identified and included in a sample based on defining demographic parameters such as age, location, sex, etc. This makes it extremely easy for a survey creator to derive practical inferences from the feedback. For example, if the FDA wants to collect data about adverse side effects from drugs, they can divide the mainland US into distinctive cluster analysis , like states. Research studies are then administered to respondents in these clusters. This type of generating a sample makes the data collection in-depth and provides easy-to-consume and act-upon, insights.
• Systematic sampling: Systematic sampling is a sampling method where the researcher chooses respondents at equal intervals from a population. The approach to selecting the sample is to pick a starting point and then pick respondents at a pre-defined sample interval. For example, while selecting 1,000 volunteers for the Olympics from an application list of 10,000 people, each applicant is given a count of 1 to 10,000. Then starting from 1 and selecting each respondent with an interval of 10, a sample of 1,000 volunteers can be obtained.
• Stratified random sampling: Stratified random sampling is a method of dividing the respondent population into distinctive but pre-defined parameters in the research design phase. In this method, the respondents don’t overlap but collectively represent the whole population. For example, a researcher looking to analyze people from different socioeconomic backgrounds can distinguish respondents by their annual salaries. This forms smaller groups of people or samples, and then some objects from these samples can be used for the research study.

LEARN ABOUT: Purposive Sampling

The non-probability sampling method uses the researcher’s discretion to select a sample. This type of sample is derived mostly from the researcher’s or statistician’s ability to get to this sample.

This type of sampling is used for preliminary research where the primary objective is to derive a hypothesis about the topic in research. Here each member does not have an equal chance of being a part of the sample population, and those parameters are known only post-selection to the sample.

We can classify non-probability sampling into four distinct types of samples. They are:

• Convenience sampling: Convenience sampling , in easy terms, stands for the convenience of a researcher accessing a respondent. There is no scientific method for deriving this sample. Researchers have nearly no authority over selecting the sample elements, and it’s purely done based on proximity and not representativeness.

This non-probability sampling method is used when there is time and costs limitations in collecting feedback. For example, researchers that are conducting a mall-intercept survey to understand the probability of using a fragrance from a perfume manufacturer. In this sampling method, the sample respondents are chosen based on their proximity to the survey desk and willingness to participate in the research.

• Judgemental/purposive sampling: The judgemental or purposive sampling method is a method of developing a sample purely on the basis and discretion of the researcher purely, based on the nature of the study along with his/her understanding of the target audience. This sampling method selects people who only fit the research criteria and end objectives, and the remaining are kept out.

For example, if the research topic is understanding what University a student prefers for Masters, if the question asked is “Would you like to do your Masters?” anything other than a response, “Yes” to this question, everyone else is excluded from this study.

• Snowball sampling: Snowball sampling or chain-referral sampling is defined as a non-probability sampling technique in which the samples have rare traits. This is a sampling technique in which existing subjects provide referrals to recruit samples required for a research study.

For example, while collecting feedback about a sensitive topic like AIDS, respondents aren’t forthcoming with information. In this case, the researcher can recruit people with an understanding or knowledge of such people and collect information from them or ask them to collect information.

• Quota sampling: Quota sampling is a method of collecting a sample where the researcher has the liberty to select a sample based on their strata. The primary characteristic of this method is that two people cannot exist under two different conditions. For example, when a shoe manufacturer would like to understand millennials’ perception of the brand with other parameters like comfort, pricing, etc. It selects only females who are millennials for this study as the research objective is to collect feedback about women’s shoes.

How to determine a Sample Size

As we have learned above, the right sample size determination is essential for the success of data collection in a market research study. But is there a correct number for the sample size? What parameters decide the sample size? What are the distribution methods of the survey?

To understand all of this and make an informed calculation of the right sample size, it is first essential to understand four important variables that form the basic characteristics of a sample. They are:

• Population size: The population size is all the people that can be considered for the research study. This number, in most cases, runs into huge amounts. For example, the population of the United States is 327 million. But in market research, it is impossible to consider all of them for the research study.
• The margin of error (confidence interval): The margin of error is depicted by a percentage that is a statistical inference about the confidence of what number of the population depicts the actual views of the whole population. This percentage helps towards the statistical analysis in selecting a sample and how much sampling error in this would be acceptable.

LEARN ABOUT: Research Process Steps

• Confidence level: This metric measures where the actual mean falls within a confidence interval. The most common confidence intervals are 90%, 95%, and 99%.
• Standard deviation: This metric covers the variance in a survey. A safe number to consider is .5, which would mean that the sample size has to be that large.

Calculating Sample Size

To calculate the sample size, you need the following parameters.

• Z-score: The Z-score value can be found   here .
• Standard deviation
• Margin of error
• Confidence level

To calculate use the sample size, use this formula:

Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Consider the confidence level of 90%, standard deviation of .6 and margin of error, +/-4%

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603 respondents are needed and that becomes your sample size.

Try our sample size calculator to give population, margin of error calculator , and confidence level.

LEARN MORE: Population vs Sample

Sampling Advantages

As shown above, there are many advantages to sampling. Some of the most significant advantages are:

• Reduced cost & time: Since using a sample reduces the number of people that have to be reached out to, it reduces cost and time. Imagine the time saved between researching with a population of millions vs. conducting a research study using a sample.
• Reduced resource deployment: It is obvious that if the number of people involved in a research study is much lower due to the sample, the resources required are also much less. The workforce needed to research the sample is much less than the workforce needed to study the whole population .
• Accuracy of data: Since the sample indicates the population, the data collected is accurate. Also, since the respondent is willing to participate, the survey dropout rate is much lower, which increases the validity and accuracy of the data.
• Intensive & exhaustive data: Since there are lesser respondents, the data collected from a sample is intense and thorough. More time and effort are given to each respondent rather than collecting data from many people.
• Apply properties to a larger population: Since the sample is indicative of the broader population, it is safe to say that the data collected and analyzed from the sample can be applied to the larger population, which would hold true.

To collect accurate data for research, filter bad panelists, and eliminate sampling bias by applying different control measures. If you need any help arranging a sample audience for your next market research project, contact us at [email protected] . We have more than 22 million panelists across the world!

In conclusion, a sample is a subset of a population that is used to represent the characteristics of the entire population. Sampling is essential in research and data analysis to make inferences about a population based on a smaller group of individuals. There are different types of sampling, such as probability sampling, non-probability sampling, and others, each with its own advantages and disadvantages.

Choosing the right sampling method depends on the research question, budget, and resources is important. Furthermore, the sample size plays a crucial role in the accuracy and generalizability of the findings.

This article has provided a comprehensive overview of the definition, types, formula, and examples of sampling. By understanding the different types of sampling and the formulas used to calculate sample size, researchers and analysts can make more informed decisions when conducting research and data unit of analysis .

Sampling is an important tool that enables researchers to make inferences about a population based on a smaller group of individuals. With the right sampling method and sample size, researchers can ensure that their findings are accurate and generalizable to the population.

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

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• What is a sample in research: Definition, examples & tips

Researchers can conduct studies on large populations. It is highly unusual for researchers to be able to get information from every member of a group of individuals they are studying. If you are researching a large population, you can pick a sample . 

The population that will participate in the study is the sample. Using samples, researchers may perform their experiments more quickly and with more manageable data. This article will explain the definition of a sample in research, what a sample is in statistics with examples, how researchers choose a sample, and how to determine the correct sample size for your research with all details.

• What is a sample?

A sample is a condensed, controllable representation of a larger group . It is a subgroup of people with traits from a wider population . When the population size is too large for the test to include all potential participants or observations, samples are utilized in statistical testing. 

The definition of a sample

To put it simply:  a sample is a more manageable and compact version of a bigger group. A sampler population possesses the traits of a bigger group. A sample is utilized in statistical analysis when the population size is too big to include all individuals or observations in the test.

A sample is an analytical subset of a larger population in statistics . The sample should be representative of the population as a whole and should not show bias toward any particular characteristic. The researcher gains knowledge from the sample that can be applied to the entire population.  

• How do researchers choose a sample?

Sampling is an essential component of the research design as it gathers information that can be used in a research study. Probability sampling and non probability sampling are the essential methodologies that define sampling techniques. 

Sampling methodologies

Probability sampling

Probability sampling is a sampling technique that entails randomly picking a sample or a section of the population. It is also known as random sampling . When procedures are established to guarantee that each unit within a population has an equal probability of being picked , this is known as random selection.  Here are 4 types of probability sampling designs that are frequently used. 

1 - Simple random sampling

Simple random sampling takes a random selection from the whole population with an equal probability of selection for each unit. The most typical method of choosing a random sample is the one. 

Consider creating a list of every person in the population and giving them a number. Using a random number table, random number table, or random number generator, you choose samples at random from this population. 

2 - Stratified sampling

Stratified sampling randomly chooses a sample from one or more strata or population subgroups . Each group is distinguished from the others based on a shared trait, such as age, gender, color, and religion. 

By doing this, you can ensure that your sample population sufficiently represents each subgroup of a particular community. For example, if you divide a student population by university majors, Architecture, Linguistics, and Teaching departments, students are three different tiers within that population. 

3 - Cluster sampling

The cluster sampling method divides the population into clusters , which are smaller groupings. Then, you choose a sample of people at random from these clusters. Large or geographically distributed populations are frequently studied using cluster sampling. 

For example, you may divide all cities into neighborhoods or clusters and then choose the areas with the most significant population while filtering by mobile device users to see how well your goods perform across a city.

4 - Systematic sampling

When using systematic sampling , units are chosen at regular intervals beginning at a random point , drawing a random sample from the target population. Every member of the population is assigned a number in systematic sampling ,  but rather than being a random selection procedure, people are picked out at predetermined intervals. 

For example, while 1000 vaccine volunteers are selected from a list of 5000 applicants, each applicant is given a number from 1 to 5000. A sample of 1000 volunteers can then be obtained by starting at 1 and selecting each participant on 10 to an item scale.

Nonprobability sampling

When the number of units in the population is either unknown or difficult to identify individuals , nonprobability sampling approaches are utilized in quantitative and qualitative research. Additionally, it is employed when you wish to limit the results’ applicability to a particular group or organization rather than the broader populace. 

Besides the advantages of non-probability sampling, the most significant disadvantage is the possibility of sampling bias. As the sample selection process unfairly favors some population members over others. Here are some types of nonprobability sampling:

1. Convenience sampling

Convenience sampling comprises those who are easiest to research by the researcher. Researchers selected these samples only because they are simple to compile , and they did not think to choose a sample representative of the total population. 

For example, researchers conducted a shopping mall response survey to understand a product manufacturer's likelihood of customers using the products. In this sampling method, sample participants are selected based on their proximity to the survey table and their willingness to participate in the research.  

2. Snowball sampling

Snowball sampling is used to recruit participants through other participants if the population is difficult to reach. As you interact with additional individuals, your network of contacts "snowballs" in size.

For example, you are looking into local homeless people's experiences. Since there is no list of every homeless person in the city, probability sampling is not an option. One of the persons you meet agrees to participate in the research, and the homeless person refers you to other local homeless people he knows.

3. Purposive sampling

Purposive sampling is frequently employed in qualitative research when the researcher prefers to learn in-depth information about a particular phenomenon versus drawing general conclusions from statistics or when the population is relatively tiny and focused.

For instance, a researcher wants to learn more about how people with persistent headaches live. In such instances, they can choose a sample of people diagnosed with persistent headaches using purposive sampling. 

• How to determine the right sample size

The sample size is crucial for reliable, statistically meaningful results and a smooth research operation. You should learn the fundamentals of the statistics involved to select the appropriate sample size , considering a few distinct elements that may affect your study.

1. Population size

The population size is the total number of individuals that can be included in the study. To determine the appropriate population size, you should be clear about who belongs or doesn’t belong in your group. 

2. The margin of error (confidence interval)

Errors are inevitable in research studies. The margin of error is represented by a percentage, which is a statistical inference about the confidence that the number of respondents accurately represents the opinions of the whole population.  

3. Confidence level

The confidence level value measures your degree of certainty on how closely a sample reflects the total population within your chosen margin of error. The most prevalent are the 90%, 95%, and 99% confidence intervals.

4. Standard deviation

The standard deviation indicates how much variation you can expect in your responses. A safe value to use as a guide is 0.5 , which denotes that significant sample size is required.

Sample size formula

You may select the appropriate sample size by considering various factors affecting your study. You may compute the sample using an online calculator or read on to learn how to do it by hand.

1. Discover the Z-score

The Z-score displays how far a certain ratio deviates from the mean by standard deviation. You should translate your degree of confidence into a Z-score.

For the most typical confidence levels, the Z-scores are as follows:

• 90% Z-score = 1.645
• 95% Z-score = 1.96
• 99% Z-score = 2.576

2. Apply the formula for the sample size

Use the following formula to perform the calculation manually. 

• N = population size
•  e = Margin of error 
•  z = z-score
•  p = standard of deviation

For example, you select a 95% confidence level. Let the population size be 1000, and the margin of level be 5. Based on these data, your sample size would be 370.

• Frequently asked questions about sample

A sample is a particular group from which you will gather data. You should employ a sample when your population is sizable , spread geographically , or challenging . The population, sample, and sample frame are different from each other. Here are the frequently asked questions about the sample.

Population vs. sample

Sample and population are closely related concepts, so they can often be confused. We will explain the differences between them so that you can distinguish between the sample and population. 

Population refers to the entire group of individuals about which you want to draw conclusions. On the other hand, sample refers to the group of people you will collect data from.

A sample is more manageable, minor, and representative of a bigger group. The sample size is always less than the total population size. When a population is too vast for all the members or observations to be included in the test, a sample is employed in statistical analysis.

Sample vs. sample frame

A sample is a group of participants chosen from a broader population of interest; it is an essential component of the research. On the other hand, sample frames are crucial for  researchers to maintain organization and guarantee that the most recent data for a population is being used. Here are the differences between sample and sample frame: 

The sample is a smaller group of people or units chosen from a larger population for a survey or research project. In contrast, a sample frame is an exhaustive enumeration of all the elements or people that comprise the population from which the sample is taken. 

The sample is a subset of the population's elements chosen for research, whereas the sample frame is a comprehensive list or inventory of all population items.

• Key points to takeaway

In conclusion, a sample is a group or subset of persons or things chosen from a broader population to study or assess particular traits or behaviors. To guarantee that every member of the population has an equal chance of being chosen, the sample should be representative of the people from which it is collected or selected using a random sampling procedure. 

Selecting the appropriate sample technique based on the research topic , budget , and available resources . Additionally, the accuracy and generalizability of the results are greatly influenced by the sample size. 

This article has explained what a sample is in research methodology, what sample is in research examples, and how to determine the correct sample size. You can learn more about the research by reading this article.

Sena is a content writer at forms.app. She likes to read and write articles on different topics. Sena also likes to learn about different cultures and travel. She likes to study and learn different languages. Her specialty is linguistics, surveys, survey questions, and sampling methods.

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What Is a Sample?

Understanding samples, special considerations, sampling methods, types of sampling, examples of samples, the bottom line.

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Sample: What It Means in Statistics, Types, and Examples

Investopedia / Theresa Chiechi

The term sample refers to a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger  population . Samples are used in statistical testing when population sizes are too large to include all possible members or observations. A sample should represent the population as a whole and not reflect any bias toward a specific attribute.

There are several  sampling  techniques used by researchers and statisticians, each with benefits and drawbacks. The issue of sampling has taken mainstream attention with the advent of artificial intelligence and the data it is trained on. Now, the debate is intense around whether the sampling made in the data chosed to train AI is not biased towards some segments of the population, some actors, some information, some ideas, some regions, and so on.

Key Takeaways

• A sample is used in statistics as an analytic subset of a larger population.
• Using samples allows researchers to conduct timely their studies with more manageable data.
• Randomly drawn samples do not have much bias if they are large enough, but achieving such a sample may be expensive and time-consuming.
• In simple random sampling, every entity in the population is identical, while stratified random sampling divides the overall population into smaller groups. 

A population is the total number of observations (i.e., individuals, animals, items, data, etc.) contained in a given group or context. A sample is a portion, part, or fraction of the whole group, and acts as a subset of that population. Samples are used in a variety of settings where research is conducted. Scientists, marketers , government agencies, economists, and research groups are among those who use samples for their studies and measurements.

Using whole populations for research comes with challenges. Researchers may have problems gaining access to entire populations. And, because of the nature of some studies, researchers may have difficulties getting the results they need in a timely fashion. This is why samples are used. Using a smaller group to represent the entire population can still produce valid results while reducing time and resources.

Samples must resemble the broader population to make accurate inferences or predictions. All the participants in the sample should share the same characteristics and qualities. So, if the study is about male college freshmen, the sample should be a small percentage of males that fit this description. Similarly, if a research group conducts a study on the sleep patterns of single women over 50, the sample should only include women within this demographic .

Consider a team of academic researchers who want to know how many students studied for less than 40 hours for the CFA exam and still passed. Since more than 200,000 people take the exam globally each year, reaching out to every exam participant would burn time and resources.

In fact, by the time the data from the population is collected and analyzed, a couple of years would have passed, making the analysis worthless since a new population would have emerged. What the researchers can do instead is take a representative population and get data from this sample.

To achieve an unbiased sample, the selection has to be random so everyone from the population has an equal and likely chance of being added to the sample group. This is similar to a lottery draw and is the basis for simple random sampling .

A sample is an unbiased number of observations taken from a population. For an unbiased sample, the selection must be random so that everyone in the population has an equal chance of being added to the group.

Sampling methods refer to the way samples are chosen from the general population. Researchers can use one of two sampling methods to conduct their studies:

• Probability Sampling: There is no deliberate choice in probability sampling. That's why it's also referred to as random sampling. Because there is no bias involved, probability sampling can be time-consuming and, at times, costly.
• Non-Probability Sampling: Researchers who use this sampling method deliberately choose their samples. This makes it a non-random sampling method. Since it isn't random, only a certain portion of the population has a chance to participate in the study. Samples are chosen based on certain factors, including location or convenience.

Now that you know the methods of sampling, it's important to understand the different types of sampling that statisticians and researchers can use. We've highlighted just a few kinds of sampling below.

Simple Random Sampling

Simple random sampling is ideal if every entity in the population is identical. If the researchers don’t care whether their sample subjects are all male or all female or a combination of both sexes in some form, simple random sampling may be a good selection technique.

Let's say 200,000 test-takers sat for the CFA exam in 2021, out of which 40% were women and 60% were men. The random sample drawn from the population should, therefore, have 400 women and 600 men for a total of 1,000 test-takers.

Systematic Sampling

Systematic sampling is a form of probability sampling. Similar to simple random sampling, it involves choosing random samples within a fixed periodic interval. Researchers calculate the interval by dividing the total population by the required sample size.

Unlike simple random sampling, systematic sampling is more efficient when it comes to time and cost. There is also a lower risk of data being manipulated.

This type of sampling is best used when:

• There is some order in the population
• When the population is large and known, especially when time and resources are limited
• When the sample is evenly spread across the population

Stratified Random Sampling

But what about cases where knowing the ratio of men to women who passed a test after studying for less than 40 hours is important? Here, a stratified random sample would be preferable to a simple random sample.

This type of sampling, also referred to as proportional random sampling or quota random sampling, divides the overall population into smaller groups. These are known as strata. People within the strata share similar characteristics.

What if age was an important factor that researchers wanted to include in their data? Using the stratified random sampling technique, they could create layers or strata for each age group. The selection would have to be random so everyone in the bracket has a likely chance of being included.

For example, two participants, Alex and David, are 22 and 24 years old, respectively. The sample selection cannot pick one over the other based on some preferential mechanism. They both should have an equal chance of being selected from their age group. The strata could look something like this:

From the table, the population has been divided into age groups. For example, 30,000 people within the age range of 20 to 24 years old took the CFA exam in 2021. Using this same proportion, the sample group will have (30,000 ÷ 200,000) × 1,000 = 150 test-takers that fall within this group. Alex or David—or both or neither—may be included among the 150 random exam participants of the sample.

There are many more strata that could be compiled when deciding on a sample size. Some researchers might populate the job functions, countries, marital status, etc., of the test-takers when deciding how to create the sample.

Cluster Sampling

Cluster sampling is a form of random sampling. Clusters are defined as different subsets of the larger population. Individual samples within the cluster have similar characteristics. Cluster sampling is commonly used when there are large populations that are spread out, making it expensive and time-consuming to study each subject.

There are a few steps to cluster sampling:

• Understand and identify the population that is being studied.
• Create the cluster. This means dividing the entire population into groups and choosing random samples from those groups to study.
• Select the sample from the clusters.
• Researchers conduct their study by interviewing the samples. Once this is done, data is collected and analyzed.

As noted above, cluster sampling can save time and money. But, there are certain disadvantages to using this type of sampling. For instance, researchers may be biased when they choose their clusters and samples. As such, the samples may not accurately represent the population at large.

In 2022, the population of the world was nearly 7.95 billion, out of which 49.7% were female and 50% were male. The total number of people in any given country can also be a population size. The total number of students in a city can be taken as a population, and the total number of dogs in a city is also a population size. Samples can be taken from these populations for research purposes.

Following our CFA exam example, the researchers could take a sample of 1,000 CFA participants from the total 200,000 test-takers—the population—and run the required data on this number. The mean of this sample would be taken to estimate the average of CFA exam takers who passed even though they only studied for less than 40 hours.

The sample group taken should not be biased. This means that if the sample mean of the 1,000 CFA exam participants is 50, the population mean of the 200,000 test takers should also be approximately 50.

Why Do Analysts Use Samples Instead of Measuring the Population?

Often, a population is too large or extensive in order to measure every member and measuring each member would be expensive and time-consuming. A sample allows for inferences to be made about the population using statistical methods.

What Is a Simple Random Sample?

This sampling method uses respondents or data points that are randomly selected from the larger population. With a large enough sample size, a random sample removes bias.

Why Do Random Samples Allow for Inference?

The laws of statistics imply that accurate measurements and assessments can be made about a population by using a sample. Analysis of variance (ANOVA) , linear regression , and more advanced modeling techniques are valid because of the law of large numbers and the central limit theorem .

How Large of a Sample Do You Need?

This will depend on the size of the population and the type of analysis you'd like to do (e.g., what confidence intervals you are using). Power analysis is a technique for mathematically evaluating the smallest sample size needed based on your needs. Another rule of thumb is that your sample should be large enough, but no more than 10% as large as the population.

Sampling can help us understand the nuances of large populations. It is a cost-effective way for researchers to study them while saving time. Because it can be difficult to study large groups, marketers, scientists, governments, and other researchers use smaller subsets—known as samples—to analyze and make important decisions.

Sage Publishing. " Introduction to Statistics, Chapter 1 ," Pages 4-5.

CFA Institute. " 1963 - 2022 Candidate Examination Results ."

Virginia Tech Library. " Significant Statistics: 1.5 Sampling Techniques and Ethics ."

The World Bank Group. " Population, Female (% of Total Population) ."

The World Bank Group. " Population, Male (% of Total Population) ."

The World Bank Group. " Population, Total ."

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Chapter 8 Sampling

Sampling is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviors within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest. Improper and biased sampling is the primary reason for often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every U.S. Presidential elections.

The Sampling Process

Figure 8.1. The sampling process

The sampling process comprises of several stage. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organization, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meets certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyze the behavior of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behavior over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organizations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalizable to the population. For instance, if your target population is organizational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalizable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganization and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms in general, most of which are medium and small-sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P large-cap, mid-cap, or small-cap lists, but includes publicly traded firms (and not private firms) and hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to the success rate of a new “quit smoking” program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular “quit smoking” program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalizability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability Sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: (1) every unit in the population has a known non-zero probability of being sampled, and (2) the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Simple random sampling. In this technique, all possible subsets of a population (more accurately, of a sampling frame) are given an equal probability of being selected. The probability of selecting any set of n units out of a total of N units in a sampling frame is N C n . Hence, sample statistics are unbiased estimates of population parameters, without any weighting. Simple random sampling involves randomly selecting respondents from a sampling frame, but with large sampling frames, usually a table of random numbers or a computerized random number generator is used. For instance, if you wish to select 200 firms to survey from a list of 1000 firms, if this list is entered into a spreadsheet like Excel, you can use Excel’s RAND() function to generate random numbers for each of the 1000 clients on that list. Next, you sort the list in increasing order of their corresponding random number, and select the first 200 clients on that sorted list. This is the simplest of all probability sampling techniques; however, the simplicity is also the strength of this technique. Because the sampling frame is not subdivided or partitioned, the sample is unbiased and the inferences are most generalizable amongst all probability sampling techniques.

Systematic sampling. In this technique, the sampling frame is ordered according to some criteria and elements are selected at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every k th element from that point onwards, where k = N / n , where k is the ratio of sampling frame size N and the desired sample size n , and is formally called the sampling ratio . It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first k elements on the list. In our previous example of selecting 200 firms from a list of 1000 firms, you can sort the 1000 firms in increasing (or decreasing) order of their size (i.e., employee count or annual revenues), randomly select one of the first five firms on the sorted list, and then select every fifth firm on the list. This process will ensure that there is no overrepresentation of large or small firms in your sample, but rather that firms of all sizes are generally uniformly represented, as it is in your sampling frame. In other words, the sample is representative of the population, at least on the basis of the sorting criterion.

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called “strata”), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1000 firms, you can start by categorizing the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favor of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of sample within each subgroup does not reflect the proportions in the sampling frame (or the population of interest), and the smaller subgroup (large-sized firms) is over-sampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the “large” group, 60 from the “medium” group and 120 from the “small” group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach is weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into “clusters” (usually along geographic boundaries), randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every official in those counties. However, depending on between- cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalizable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorize a sampling frame of firms into “high profitable” firms and “low profitable firms” based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Such matched-pairs sampling technique is often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools; within each school, select a simple random sample of grade levels; and within each grade level, select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-Probability Sampling

Nonprobability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, nonprobability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalized back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping center and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centers. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping center such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping center close to a university will attract primarily university students with unique purchase habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalizability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalizable inferences.

Quota sampling. In this technique, the population is segmented into mutually-exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70% Caucasians, 15% Hispanic-Americans, and 13% African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping center and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population. Non-proportional quota sampling is less restrictive in that you don’t have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African- Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping center in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population but may be useful in that it allows capturing the opinions of small and underrepresented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample an group of corporate accountants who are familiar with this act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalizable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also do network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of Sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not “statistic” because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical (and equal to the population parameter), and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Figure 8.2. Sample Statistic.

The mean value of a sample statistic in a sampling distribution is presumed to be an estimate of the unknown population parameter. Based on the spread of this sampling distribution (i.e., based on standard error), it is also possible to estimate confidence intervals for that prediction population parameter. Confidence interval is the estimated probability that a population parameter lies within a specific interval of sample statistic values. All normal distributions tend to follow a 68-95-99 percent rule (see Figure 8.4), which says that over 68% of the cases in the distribution lie within one standard deviation of the mean value (µ + 1σ), over 95% of the cases in the distribution lie within two standard deviations of the mean (µ + 2σ), and over 99% of the cases in the distribution lie within three standard deviations of the mean value (µ + 3σ). Since a sampling distribution with an infinite number of samples will approach a normal distribution, the same 68-95-99 rule applies, and it can be said that:

• (Sample statistic + one standard error) represents a 68% confidence interval for the population parameter.
• (Sample statistic + two standard errors) represents a 95% confidence interval for the population parameter.
• (Sample statistic + three standard errors) represents a 99% confidence interval for the population parameter.

Figure 8.3. The sampling distribution.

A sample is “biased” (i.e., not representative of the population) if its sampling distribution cannot be estimated or if the sampling distribution violates the 68-95-99 percent rule. As an aside, note that in most regression analysis where we examine the significance of regression coefficients with p<0.05, we are attempting to see if the sampling statistic (regression coefficient) predicts the corresponding population parameter (true effect size) with a 95% confidence interval. Interestingly, the “six sigma” standard attempts to identify manufacturing defects outside the 99% confidence interval or six standard deviations (standard deviation is represented using the Greek letter sigma), representing significance testing at p<0.01.

Figure 8.4. The 68-95-99 percent rule for confidence interval.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Statistics By Jim

Making statistics intuitive

Sampling Methods: Different Types in Research

By Jim Frost 2 Comments

What Are Sampling Methods?

Sampling methods are the processes by which you draw a sample from a population . When performing research, you’re typically interested in the results for an entire population. Unfortunately, they are almost always too large to study fully. Consequently, researchers use samples to draw conclusions about a population—the process of making statistical inferences.

A population is the complete set of individuals that you’re studying. A sample is the subset of the population that you actually measure, test, or evaluate and base your results. Sampling methods are how you obtain your sample.

Before beginning your study, carefully define the population because your results apply to the target population. You can define your population as narrowly as necessary to meet the needs of your study—for example, adult Swedish women who are otherwise healthy but have osteoporosis. Then choose your sampling method.

Learn more about populations and samples , inferential vs. descriptive statistics and populations and parameters .

In research and inferential statistics , sampling methods are a vital issue. How you draw your sample affects how much you can trust the results! If your sample doesn’t reflect the population, your results might not be valid. It’s a crucial part of experimental design .

In this post, learn more about sampling methods, which ones produce representative samples, and the pros and cons of each procedure.

Probability vs Non-Probability Sampling Methods

Sampling methods have the following two broad categories:

• Probability sampling : Entails random selection and typically, but not always, requires a list of the entire population.
• Non-probability sampling : Does not use random selection but some other process, such as convenience. Usually does not sample from the whole population.

Probability sampling is typically more difficult and costly to implement, but, in exchange, these processes tend to increase validity by producing representative samples. In short, you can make valid conclusions about the population.  A statistical inference is when you use a sample to learn about a population. Learn more about Making Statistical Inferences .

On the other hand, non-probability sampling methods are often easier and less expensive, but the trade-off is that the validity of your conclusions is questionable. You might not be able to trust the results. Sampling bias is more likely to occur.

Learn more about Validity in Research and Psychology: Types & Examples and Internal and External Validity .

Probability Sampling Methods

Given the benefits of using representative samples, you’ll typically want to use a probability sampling method whenever possible. Let’s go over the standard methods. They each have pros and cons. Click the links to learn more about each sampling method and see examples. Learn more about representative samples .

To use a probability method, you’ll first need to develop a sampling frame, which lists all members of your target population. Then you can use one of the following methods.

Learn more about Sampling Frames: Definition, Examples & Uses .

Simple Random Sampling (SRS)

In simple random sampling (SRS), researchers take a complete list of the population and randomly select participants from it. All population members have an equal likelihood of being selected. Out of all sampling methods, statisticians consider this one to be the gold standard for producing representative samples. It’s entirely random, leaving little room for accidentally biasing the results.

However, this sampling method has some drawbacks.

First and foremost, this method can be pretty unwieldy and require abundant resources. For one thing, it requires a list of all population members, which can be a tremendous hurdle by itself. Attempting to perform SRS with an incomplete population list causes undercoverage bias and a nonrepresentative sample.

Furthermore, while random selection is beneficial, it also ensures that the subjects are maximally dispersed, making them harder to contact.

SRS can exclude smaller but crucial subpopulations purely by chance. Additionally, this approach produces less precise estimates for subgroups and the differences between subgroups than some other probability sampling methods.

Learn more about Simple Random Sampling  and Undercoverage Bias: Definition & Examples

Systematic Sampling

Systematic sampling is similar to SRS but attempts to ease some of the difficulties for researchers. There are several versions of this method.

One form uses a complete list of the population. The researchers randomly select the first subject and then move down the list choosing every X th subject rather than using a randomized technique.

The other form does not use a complete list of the population. This sampling method is suitable for populations that are tough to document, such as the homeless, because a comprehensive list won’t exist. The essential requirement for this sampling method is knowing how to locate them. While it’s not perfect, it’s a feasible option when you can’t obtain the full list.

Suppose you want to survey theater patrons but lack a complete list. Instead, you can use systematic sampling and recruit every 20th person who exits the theater. This approach works because they leave randomly.

This sampling method has some disadvantages. The form that uses a complete list of the population can closely mirror the results of simple random sampling. However, the non-randomness increases the potential for manipulation, even if accidentally. Additionally, patterns in the list can unintentionally create a non-representative sample.

The form that doesn’t use a list has more potential problems. Namely, it increases the potential for missing subgroups and acquiring a non-representative sample. This sampling method increases the knowledge you must have about the population and their habits. Without that knowledge, you won’t be able to find subjects that reflect the whole population.

Learn more about Systematic Sampling .

Stratified Sampling

In stratified sampling, researchers divide a population into similar subpopulations (strata). Then they randomly sample from the strata.

This sampling method can guarantee the presence of small but vital subpopulations in the sample. Relative to SRS, this method can increase the precision of subgroup estimates and the differences between subgroups. In short, it helps researchers gain a better understanding of the subgroups. Dividing the whole population into smaller, more similar subsets can also reduce costs and simplify data collection.

The drawbacks are that this sampling method requires additional upfront knowledge and planning. The researchers must know enough about the subgroups to devise an effective strata scheme. Then they must have sufficient information about all population members to assign them to the correct strata.

Learn more about Stratified Sampling .

Cluster Sampling

Like stratified sampling, the cluster sampling method divides the whole population into smaller groups. However, unlike strata, each cluster mirrors the full diversity present in the population. Then the researchers randomly sample from some of these clusters.

The primary benefit of this sampling method is that it reduces the costs of studying large, geographically dispersed populations. Using this method, researchers don’t need to sample the entire geographic region but only certain areas because they know individual clusters are similar to the population. Additionally, they don’t need to develop a list of potential subjects for clusters from which they’re not sampling. These considerations can significantly reduce planning, administrative, and travel costs.

When researchers can’t create a list of the entire population, cluster sampling can be an excellent choice.

On the downside, cluster sampling increases the design complexity. Researchers must understand how well each cluster approximates the whole population. If the clusters don’t fully represent the population, results can be biased. In real-world studies, clusters tend to be naturally occurring groups that don’t mirror the population, which reduces the ability to draw valid conclusions.

Learn more about Cluster Sampling .

Non-Probability Sampling Methods

Non-probability sampling methods don’t use random selection, and they typically don’t use a complete population list. While these methods are simpler and less expensive, your results are more likely to be biased, reducing your ability to make sound conclusions.

Researchers often use non-probability sampling methods for exploratory research, pilot studies, and qualitative research . These sampling methods provide quick and rough assessments, help work kinks out of measurement instruments and procedures, and help refine the design for a more rigorous study in the future.

Below are several standard non-probability sampling methods:

• Convenience sampling : The main criteria for recruiting subjects are those who are easy to contact and willing to participate. There are no inclusion requirements. Online polls are a type of convenience sampling. Learn more about Convenience Sampling .
• Quota Sampling : Non-random selection of subjects from population subgroups that the researchers define. Learn more about Quota Sampling .
• Purposive sampling : Investigators use subject-area knowledge to handpick a sample they think will help their study. Learn more about Purposive Sampling .
• Snowball sampling : Researchers use subjects to find and recruit other subjects. This method is helpful when a population is hard to contact. When recruits help you find more recruits, and those help find even more, and so on, the total number snowballs. Learn more about Snowball Sampling .

As you can see, there are many sampling methods. Each one has benefits and disadvantages. When designing a study, evaluate the nature of your target population, your research goals, and the available time and resources to choose your sampling method. After deciding between the sampling methods, calculate your sample size using a power analysis .

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards (nih.gov)

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May 17, 2022 at 12:38 am

I think stratified sampling will work __ mke two groups as stratas _ then use SRS to obtain a complete sample .

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hi.what sampling technique will i use if my respondents are 1st yr college students awardees vs non awardees of different courses?

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How and Why Sampling Is Used in Psychology Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Nusha Ashjaee  

• Why Use Samples
• Probability Samples
• Nonprobability Samples

Sampling Errors

In statistics, a sample is a subset of a population that is used to represent the entire group as a whole. When doing psychology research, it is often impractical to survey every member of a particular population because the number of people is simply too large. To make inferences about the characteristics of a population, psychology researchers use a random sample .

Keep reading to learn about how samples are used in psychology research, the different types of samples, and the errors that may occur when using samples.

Why Psychology Researchers Use Samples

When researching an aspect of the human mind or ​ human behavior , psychology researchers can rarely collect data from every single affected individual. Instead, they use a smaller sample of individuals to represent the larger group.

The goal when choosing a sample is to make sure it represents the entire group accurately. This means that the sample should reflect the diverse characteristics present in the total population. The sample must accurately represent the population in question so researchers can generalize their results to the larger group with statistical analysis .

In psychological research and other types of social science research , experimenters typically rely on a few different sampling methods. These can be grouped into probability and nonprobability samples.

Types of Probability Samples

Probability sampling means every individual in a population stands a chance of being selected. Because probability sampling uses random selection , every subset of the population has an equal chance of being represented in the sample.

Probability samples are more representative of large populations and researchers are better able to generalize their results to the group as a whole when they use probability samples.

Simple Random Sampling

Simple random sampling is, as the name suggests, the simplest type of probability sampling. Psychology researchers take every individual in a population and randomly select individuals to compose their sample, often by using some type of computer program or random number generator.

Stratified Random Sampling

Stratified random sampling involves separating the population into subgroups and then taking a simple random sample from each of these subgroups. For example, researchers may divide the population into subgroups based on race, sex, or age, and then take a simple random sample of each of these groups.

Stratified random sampling often provides greater statistical accuracy than simple random sampling because it ensures each of the subgroups is accurately represented in the sample.

Cluster Sampling

Cluster sampling involves dividing a population into smaller clusters, often based on geographic location. A random sample of these clusters is then selected, and all of the subjects within the cluster are measured.

For example, imagine you are doing a study on school principals in your state. Collecting data from every single school principal would be cost-prohibitive and time-consuming. But, if you were to use a cluster sampling method, you would randomly select five counties from your state and then collect data from every subject in each of those five counties to create a representative sample.

Probability sampling methods allow psychology researchers to get a more representative sample. Techniques that might be used include simple random sampling, stratified random sampling, and cluster sampling.

Types of Nonprobability Samples

Nonprobability sampling involves selecting participants using methods that do not give every subset of a population an equal chance of being represented. For example, a study may recruit participants from an already established group of volunteers.

One problem with this type of sample is that volunteers might differ from non-volunteers on certain variables, which can make it difficult to generalize the results to the entire population.

Convenience Sampling

Convenience sampling involves selecting participants for a study based on what is most convenient–people who are easily accessible and have the time. If you have ever volunteered for a psychology study conducted through your university's psychology department, then you have participated in a study that relied on a convenience sample.

Studies that rely on asking for volunteers or using clinical samples available to the researcher are also examples of convenience samples.

Purposive Sampling

Purposive sampling involves seeking out individuals who meet certain criteria. For example, a researcher might be interested in learning how college graduates between the ages of 20 and 35 feel about a topic. In purposive sampling, the researcher might conduct telephone interviews to intentionally seek out people who meet their criteria.

Quota Sampling

Quota sampling involves intentionally sampling specific proportions of each subgroup within a population. For example, political pollsters might be interested in researching the opinions of a population on a certain political issue. If they use simple random sampling, they might miss certain subsets of the population by chance.

Instead, they establish criteria to assign each subgroup a certain percentage of the sample. This differs from stratified sampling because, to find individuals within each subgroup, researchers use non-random methods to fill the quotas for each subgroup.

Nonprobability sampling can also be used when selecting a sample in psychology research. Such methods are less representative of the general population. Techniques include convenience sampling, purposive sampling, and quota sampling.

Sampling errors are differences between what is present in a population and what is present in a sample. Because sampling cannot include every single individual in a population, errors can occur. This can ultimately have an impact on the results of psychology research.

While it is impossible to know exactly how great the difference between the population and sample may be, researchers can statistically estimate the size of the sampling errors. In political polls, for example, you might often hear of the margin of errors expressed by certain confidence levels.

In general, the larger the sample size, the smaller the level of error. This is simply because the closer the sample is to the size of the total population, the more likely it is to accurately capture all of the characteristics of the population.

The only way to completely eliminate sampling error is to collect data from the entire population, which is often simply too costly and time-consuming. Sampling errors can be minimized, however, by using randomized probability testing and large sample size.

Samples are important in psychology research because they allow scientists to study what is happening in a larger population without having to reach every individual in the entire group.

Different types of samples can be used depending on what researchers are studying and the resources they have available to collect data. Probability samples tend to be more representative of the larger group. Nonprobability samples, on the other hand, tend to involve selecting participants based on availability and studying specific subsets of a larger group, which is less representative of the larger group.

Sampling errors can occur, however, with any type of sampling. To minimize errors, researchers strive to use large, representative samples.

Valliant R, Dever J. Estimating propensity adjustments for volunteer web surveys . Sociol Methods Res . 2011;40(1):105-137. doi:10.1177/0049124110392533

Lin L. Bias caused by sampling error in meta-analysis with small sample sizes . PLoS ONE . 2018;13(9):e0204056. doi:10.1371/journal.pone.0204056

Goodwin CJ. Research In Psychology: Methods and Design, 12th ed . John Wiley and Sons.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• How it works

Sampling Methods – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 25, 2023

If you are performing research on a large community, organisation, or country, then it may not be possible to collect data individually from each participant. To deal with this issue, you can use a group of a specific number of participants, and this group is referred to as a sample .

The method you apply for selecting your participants is known as the  sampling method . It helps in concluding the entire population based on the  outcomes of the research .

Example: If you want to research China’s entire population, it isn’t easy to gather information from 1.38 billion people. You can use a sampling method by conducting your research on a specific number of participants and drawing a conclusion about the entire population based on your study’s outcomes.

Uses of Sampling Method

The sampling method is used to:

• Gather data from a large group of population.
• Counter check on data collection.
• Speed up tabulation and publication of results.
• Increase the efficiency of the research.
• Conduct experimental research
• Obtain data for researches on population census.

What is the Difference between Population and Sample? 

Before starting with the sampling methods, it is important to understand the difference between sample and population.

It is a group selected from the target population when you aim to study a large population. This group is considered as the representative of the overall targeted population.

Example: Sample of 20 female cricketers.

If you add the set of individuals with specific characteristics according to the research requirements, the resulting group is called the population. 

Example: The income of government teachers in India

Sampling Frame Vs. Sampling Size

Sampling frame.

A list of all the elements from a population is known as the sampling frame.

For instance, you are selecting a telephone directory of students or a list of social media users.

This information can be gathered by contacting any commercial organisation. Sometimes some errors are also possible in the sampling frame due to its discrepancy in selecting samples.

Sampling Size

It is considered a subset of the population as it is selected to make the inference to the original population of a study. The chances of accuracy are depended on the size of the population. The larger the size, the more accurate the study is.

When it comes to census, the sample size is the same or parallel with the population size. But to maintain the budget and to consider the time frame, only a representative class is selected.

Methods of Sampling

There are usually two methods of sampling which are used widely. These are considered the best methods:

• Probability Method
• Non-Probability Method

Probability Method 

This method of sampling is conducted by using the method of randomisation. In this method, each individual has an equal and independent opportunity to be selected.  It has further sub-categories.

• Simple Random Methods
• Stratified Methods
• Systematic Method
• Cluster Method
• Multi-Stage

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Simple Random Method

The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling. 

Example: You want to identify how much time people spend on social media. You need to randomly select the participants and assign a specific number of hours to spend on social media.

Example: You want to find out the benefits of a balanced diet. You need to select the participants randomly and assign a balanced diet.

Systematic Sampling Method

In this type of sampling, method participants are selected according to the fixed period interval and starting point. The fixed period interval can be calculated by dividing the sample size by the respective population size. 

Example: Framingham study , which includes selecting every second person from a list of two residents.

Stratified Method

Stratified sampling is a random selection of the participants by dividing them into strata and randomly selecting the participants from each level.

Example: You want to identify how much time people spend on social media. You need to divide the participants into groups based on their age and then assign a specific number of hours to spend on social media.

Example: You want to find out the benefits of a balanced diet. You need to divide participants into various groups based on their age, gender, and health conditions and assigned them to each group’s treatment group.

Matching Method

Even though if participants are selected randomly, they can be assigned to the various groups of comparison. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.

Cluster Sampling

It is a kind of sampling where the population is converted into sub-groups called clusters. These sub-groups or clusters are then selected randomly as a sample. The selected group should have all the characteristics of other groups. 

Example: You want to check high school students’ communication skills, and there are more than 50 schools in the city. You can’t visit each school to gather information. In such situations, you can select any five schools, and these schools will be your clusters.

Non-probability Sampling

Non-probability sampling techniques are often appropriate for exploratory and  qualitative research . This type of sample is not to test a  hypothesis  about a broad population but to develop an initial understanding of a small or under-researched population.

This type of sampling is different from probability, as its criteria are unique. The samples are not selected randomly; rather, these samples are selected according to the researcher’s ability. This might result in a biased result, and participants may find it difficult to be a part of the sample. Still, this is a prevalent method. It has the following types:

• Purposive type sampling
• Referral sampling

Convenience Sampling

Quota sampling.

Reading material: ResearchProspect has also published a very detailed guide about inductive and deductive reasoning for students.

Purposive Sampling

This type of sampling is based on the aims of the research. Therefore, only such elements of the population will be selected, which are according to the research’s purpose.

Example: You want to find out the opinion of people about jobs and businesses. You can select a few participants interested in doing 9-5 jobs and a few interested in doing business.

Referral Sampling

This type of sampling is used where the population is not defined or rare. In this technique, one participant is selected according to defined criteria. After that, the same selected participant is asked to refer to other samples fulfilling the study’s criteria. In this way, it goes enlarging its size with the help of the referral. 

Example: You can use it while conducting a study on the victims of physical harassment at workplaces. No matter how smoothly you approach them, not all women respond openly to your questions as they feel uncomfortable, or they get afraid of being humiliated. You can select the people from these victims’ circles (ex: their colleagues, friends, relatives) to get in touch with them and gather the required information for your research.

This type of sampling is applied according to availability. If the samples are not available easily, and the research is getting costly, this technique is applied to select the samples as per convenience. 

Example: You want to research the election campaigns. In this situation, you need to gather information from the available candidates (political leaders, media persons, voters) whenever and wherever you get any chance to meet them; otherwise, you will need to wait for the next election campaign.

This type of sampling is done when some standards are already adjusted. In this sampling, the representatives are selected from the population. This selected sample should resemble all the characteristics traits of the population. The size of the sample should reflect the. The participants are selected until sufficient information is gathered. 

Example: You want to identify and compare the high school’s academic performance, and you are allowed to select only 100 participants as per the standards of your study. You can select 25 students of the ninth standard, 25 students of the tenth standard, 25 students of the eleventh standard, and 25 students of the twelfth standard.

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Advantages of Sampling Method

Sampling has many advantages, such as:

• It saves a lot of time, as contacting the entire population would be difficult and time-consuming.
• It’s cost-effective.
• It has greater scope and adaptability.
• It provides accurate results.
• It can be managed easily.

Disadvantages of Sampling Method

• It may cause a feeling of discrimination among the participants who are not selected for the study.
• The researcher needs to be skilled, experienced, and qualified to ensure efficient sampling.
• It requires a lot of time, and results may not be reliable.

Frequently Asked Questions

What is sampling and its types.

Sampling is the process of selecting a subset of individuals or items from a larger population to gather data. Types include:

• Random Sampling: Each member has an equal chance.
• Stratified Sampling: Divides population into groups for proportional representation.
• Systematic Sampling: Every nth member is chosen.
• Cluster Sampling: Population is divided into clusters; random clusters are selected.
• Convenience Sampling: Convenient individuals are chosen.
• Snowball Sampling: Existing subjects refer new ones.

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Educational Research Basics by Del Siegle

Selecting Subjects for Survey Research…

Sampling (Selecting Subjects) . ..

The main purpose of survey research is to describe the characteristics of a population. This is usually accomplished by collecting data from a sample. Therefore, the first step in sampling is to define the population.

POPULATION–> The population is the group consisting of all people to whom we (as researchers) wish to apply our findings. lf we were interested in the reading level of 3rd graders in Connecticut, the population would be all third graders in Connecticut. The data (information) we collect from populations are called PARAMETERS and are said to be DESCRIPTIVE. We label the number of subjects (observations) in a population with an upper case N (N=300). The first step in sampling is to define the population (3rd graders in Connecticut). The actual population to whom the researcher wishes to apply his or her findings is called the TARGET population. Often the TARGET population is not available, and the research must use an ACCESSIBLE POPULATIONS. In this case, the researcher can only apply (generalize) his or her findings to that group.

SAMPLE–> Subsets of people are usually used to conduct studies. These subsets are called samples. The samples are used to represent the population from which they were drawn. The data we collect from samples are called STATISTICS and are said to be INFERENTIAL (because we are making inferences about the POPULATION with data collected from the SAMPLE). We label the number of subjects (observations) in a sample with a lower case n (n=25).

Statistics are used to effectively communicate numerical information to other people. In statistics we are…

• …Looking at RELATIONSHIPS among (between) characteristics (i.e., salary & job satisfaction; food consumption & energy) — Correlation Research (which we study in a different unit) is an example of research involving relationships.
• …Looking at DIFFERENCES between (among) groups (i.e., males & females; experiment & control) — Experimental Research (which we study in a different unit) is an example of research that looks at differences.
• …Looking to DESCRIBE the characteristics of the population from data collected from a sample — Survey Research.   The two major types of surveys are cross-sectional survey and longitudinal survey (trend, cohort, and panel studies) .

Inferential statistics are used to determine how likely it is that characteristics exhibited by a sample of people are an accurate description of those characteristics exhibited by the population of people from which the sample was drawn.

The term statistically significant (p < .05) is used merely as a way of indicating the chances are at least 95 out of 100 that the findings obtained from the sample of people who participated in the study are similar to what the findings would be if one were actually able to carry out the study with the entire population. In other words, with p< . 05 we believe that if we repeated our study 100 times with different samples from a population where there really was no difference (or relationship), that the results we found with our sample would occur just by chance less than 5 in 100 times.

The first step in selecting a sample is to define the population to which one wishes to generalize the results of a study. Unfortunately, one may not be able to collect data from his or her TARGET POPULATION. In this case, an ACCESSIBLE POPULATION is used. If the latter is used, care must be taken not to generalize beyond the ACCESSIBLE POPULATION.

  -The sample is drawn from the population

• -Data is collected from the sample
• -Statistics are used to determine how likely the sample results are reflective of the population

A number of different strategies can be used to select a sample. Each of the strategies has strengths and weaknesses. There are times when the research results from the sample cannot be applied to the population because threats to external validity exist with the study. The most important aspect of sampling is that the sample represents the population.

*CHOOSING A SAMPLE*

• SIMPLE RANDOM SAMPLING – Each subject in the population has an equal chance of being selected
• STRATIFIED RANDOM SAMPLING – A representative number of subjects from various subgroups
• TWO STAGE CLUSTER RANDOM SAMPLING – Samples chosen from pre-existing groups
• SYSTEMATIC SAMPLING – Selection of every nth (i.e., 5th) subject in the population
• CONVENIENCE SAMPLING – Subjects are easily accessible
• PURPOSIVE SAMPLING – Subjects are selected because of some characteristic

  SIMPLE RANDOM SAMPLING – Each subject in the population has an equal chance of being selected regardless of what other subjects have or will be selected. While this is desirable, it may not be possible.

A random number table or computer program (random generator) is often employed to generate a list of random numbers to use.

A simple procedure is to place the names from the population is a hat and draw out the number of names one wishes to use for a sample.

STRATIFIED RANDOM SAMPLING – A representative number of subjects from various subgroups is randomly selected.

Suppose we wish to study computer use of educators in the Hartford system. Assume we want the teaching level (elementary, middle school, and high school) in our sample to be proportional to what exists in the population of Hartford teachers.

First we must determine what percentage of the teachers in the Hartford system are elementary, middle school, and high school. For this example, we will use 50%, 20% and 30% respectively. Because those percentages exist in our population, we want our sample to have the same percentages.

Let’s also assume that we want to sample 200 teachers. Since 50% of those teachers need to be elementary teachers, we need 100 elementary teachers in our sample (200 X .50). To achieve this, we obtain a list of all of the elementary teachers in the system. From that list we randomly select 100.

Similarly, we use a list of all of the middle school teachers and randomly select 40 (20% of 200). We do the same for the high school teachers and select 60.

The sample we selected is exactly proportional to the population with regards to teaching level. If we had not used STRATIFIED RANDOM SAMPLING we might have reached a similar proportion, or by chance, we might have had over representation of one of the groups.

However, the main reason we do stratified is to better understand each of the subgroups . Therefore, researchers may over sample some of the subgroups and then weight the results so they are still proportional. The reason we oversample is because we need a large enough sample to represent the subgroup.

CLUSTER RANDOM SAMPLING – Samples chosen from pre-existing groups. Groups are selected and then the individuals in those groups are used for the study.

If we wished to know the attitude of fifth graders in Connecticut about reading, it might be difficult and costly to visit each fifth grade in the state to collect our data. We could randomly select 10 schools (our clusters) and survey the students in those schools. Each school in the state would have an equal chance of being selected, but only the students at the selected schools would be surveyed.

An extension of the Cluster Random Sample is the TWO-STAGE CLUSTER RANDOM SAMPLE. ln this situation, the clusters (classes in our example) are randomly selected and then students within those clusters are randomly selected.

SYSTEMATIC SAMPLING -Systematic sampling is an easier procedure than random sampling when you have a large population and the names of the targeted population are available. Systematic sampling involves selection of every nth (e.g., 5th) subject in the population to be in the sample.

Suppose you had a list of 10,000 voters in your school district and you wished to sample 400 voters to see if they supported special funding for a new school program.

We divide the number in the population (10,000) by the size of the sample we wish to use (400) and we get the interval we need to use when selecting subjects (25). In order to select 400 subjects, we need to select every 25 person on the list.

Before we start selecting subjects, we need to select a random starting point on the list. That starting point must be with one of the first 25 names on the list for this example. We would use a random table or generator to determine the starting point. Once we have the starting point, we select that subject and every 25th subject after that on the list.

CONVENIENCE SAMPLING – Subjects are selected because they are easily accessible. This is one of the weakest sampling procedures. An example might be surveying students in one’s class. Generalization to a population can seldom be made with this procedure.

“Researchers often need to select a convenience sample or face the possibility that they will be unable to do the study. Although a sample randomly drawn from a population ls more desirable, it usually is better to do a study with a convenience sample than to do no study at all– assuming, of course, that the sample suits the purpose of the study” {Gall, Borg, & Gall, 1996, p. 228).

Gall, M. D., Borg, W.R., & Gall, J.P. (1996). Educational Research: An Introduction. White Plains, NY: Longman.

PURPOSIVE SAMPLING-Subjects are selected because of some characteristic. Patton (1990) has proposed the following cases of purposive sampling. Purposive sampling is popular in qualitative research. Note: These categories are provided only for additional information for EPSY 5601 students.

• Extreme or Deviant Case – Learning from highly unusual manifestations of the phenomenon of interest, such as outstanding success/notable failures, top of the class/dropouts, exotic events,
• Intensity – Information-rich cases that manifest the phenomenon intensely, but not extremely, such as good students/poor students, above average/below
• Maximum Variation – Purposefully picking a wide range of variation on dimensions of interest…documents unique or diverse variations that have emerged in adapting to different conditions. Identifies important common patterns that cut across
• Homogeneous – Focuses, reduces variation, simplifies analysis, facilitates group interviewing.
• Typical Case – Illustrates or highlights what is typical, normal,
• Stratified Purposeful – Illustrates characteristics of particular subgroups of interest; facilitates
• Critical Case – Permits logical generalization and maximum application of information to other cases because if it’s true of this once case it’s likely to be true of a!I other
• Snowball or Chain – Identifies cases of interest from people who know people who know people who know what cases are information-rich, that is, good examples for study, good interview
• Criterion – Picking all cases that meet some criterion, such as all children abused in a treatment facility. Quality assurance.
• Theory-Based or Operational Construct – Finding manifestations of a theoretical construct of interest so as to elaborate and examine the
• Confirming or Disconfirming – Elaborating and deepening initial analysis, seeking exceptions, testing variation.
• Opportunistic – Following new leads during fieldwork, taking advantage of the unexpected, flexibility.
• Random Purposeful – (still small sample size) Adds credibility to sample when potential purposeful sample is larger than one can handle. Reduces judgment within a purposeful category. (Not for generalizations or representativeness.)
• Politically Important Cases -Attracts attention to the study {or avoids attracting undesired attention by purposefully eliminating from the sample politically sensitive cases).
• Convenience – Saves time, money, and Poorest rational; lowest credibility. Yields information-poor cases.
• Combination or Mixed Purposeful – Triangulation, flexibility, meets multiple interests and needs. (Patton, 1990)

Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage Publications.

Sample Size

Larger Samples are needed when…  

• a large number of uncontrolled variables are interacting unpredictably
• the total sample is to be divided into several subsamples (the researcher is interested in also studying subgroups within the sample)
• the population is made up of a wide range of variables and characteristics
• differences in the results (effect size) are expected to be small
• high attrition of subjects is expected

Sample Sizes for Surveys

The number of subjects you select (use a sample size calculator to determine this) will influence how confident you can be that your results depict the population from which the sample was drawn.

The confidence interval is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a confidence interval of 4 and 47% percent of your sample picks an answer you can be “sure” that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer.

The confidence level tells you how sure you can be. It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain of the confidence interval; the 99% confidence level means you can be 99% certain of the confidence interval. Most researchers use the 95% confidence level.

When you put the confidence level and the confidence interval together, you can say that you are 95% sure that the true percentage of the population is between 43% and 51%.

The wider the confidence interval you are willing to accept, the more certain you can be that the whole population answers would be within that range. For example, if  you asked a sample of 1000 people in a city which brand of cola they preferred, and 60% said Brand A, you can be very certain that between 40 and 80% of all the people in the city actually do prefer that brand, but you cannot be so sure that between 59 and 61% of the people in the city prefer the brand.

Factors that Affect Confidence Intervals

There are three factors that determine the size of the confidence interval for a given confidence level. These are: sample size, percentage difference, and population size.

The larger your sample, the more confident you can be that their answers truly reflect the population. This indicates that for a given confidence level, the larger your sample size, the smaller your confidence interval. However, the relationship is not linear (i.e., doubling the sample size does not half the confidence interval).

Percentage Difference

Your accuracy also depends on the percentage of your sample that picks a particular answer. If 99% of your sample said “Yes” and 1% said “No” the chances of error are remote, irrespective of sample size. However, if the percentages are 51% and 49% the chances of error are much greater. It is easier to be sure of extreme answers than of middle-of-the-road ones.

When determining the sample size needed for a given level of accuracy you must use the worst case percentage (50%). You should also use this percentage if you want to determine a general level of accuracy for a sample you already have. To determine the confidence interval for a specific answer your sample has given, you use the percentage of the sample that selected that answer, which if it different than 50%, gives a smaller interval.

Population Size

How many people are there in the group your sample represents? This may be the number of people in a city you are studying, the number of people who buy new cars, etc. Often you may not know the exact population size. This is not a problem. The mathematics of probability proves the size of the population is irrelevant, unless the size of the sample exceeds a few percent of the total population you are examining. This means that a sample of 500 people is equally useful in examining the opinions of a state of 15,000,000 as it would a city of 100,000. For this reason, a sample calculator ignores the population size when it is “large” or unknown. Population size is only likely to be a factor when you work with a relatively small and known group of people.

Note : The confidence interval calculations assume you have a genuine random sample of the relevant population. If your sample is not truly random, you cannot rely on the intervals. Non-random samples usually result from some flaw in the sampling procedure. An example of such a flaw is to only call people during the day, and miss almost everyone who works. For most purposes, the non-working population cannot be assumed to accurately represent the entire (working and non-working) population.Information about confidence intervals was obtained from The Survey System

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Research Methods: Definition & Types of Sampling

• Research Methods: Definition & Types…

Definition of sampling:

“In research terms, a sample is a group of people, objects, or items that are taken from a larger population for measurement. The sample should be representative of the population to ensure that we can generalize the findings from the research sample to the population as a whole”.

Type of sampling:

There are two major sampling types:

• Probabilistic.
• Nonprobability.

NONPROBABILISTIC SAMPLING:

“In the context of nonprobabilistic sampling, the likelihood of selecting some individuals from the target population is null. This type of sampling does not render a representative sample; therefore, the observed results are usually not generalizable to the target population”. Still, unrepresentative samples may be useful for some specific research objectives and may help answer particular research questions, as well as contribute to the generation of new hypotheses.

• The different types of nonprobabilistic sampling are detailed below:
• Convenience sampling :

“ The participants are consecutively selected in order of appearance according to their convenient accessibility (also known as consecutive sampling).”

The sampling process comes to an end when the total amount of participants (sample saturation) and/or the time limit (time saturation) are reached. Randomized clinical trials are usually based on convenience sampling. After sampling, participants are usually randomly allocated to the intervention or control group (randomization). Although randomization is a probabilistic process to obtain two comparable groups (treatment and control), the samples used in these studies are generally not representative of the target population.

• Purposive sampling:

“This is used when a diverse sample is necessary or the opinion of experts in a particular field is the topic of interest.”

This technique was used in the study by Roubille et al, in which recommendations for the treatment of comorbidities in patients with rheumatoid arthritis, psoriasis, and psoriatic arthritis were made based on the opinion of a group of experts.

• Quota sampling:

“According to this sampling technique, the population is first classified by characteristics such as gender, age, etc. Subsequently, sampling units are selected to complete each quota.”

For example, in the study by Larkin et al., the combination of vemurafenib and cobimetinib versus placebo was tested in patients with locally advanced melanoma, stage IIIC or IV, with BRAF mutation. The study recruited 495 patients from 135 health centers located in several countries. In this type of study, each center has a “quota” of patients.

• “Snowball” sampling:

“In this case, the researcher selects an initial group of individuals. Then, these participants indicate other potential members with similar characteristics to take part in the study.”

This is frequently used in studies investigating special populations , for example, those including illicit drug users, as was the case of the study by Gonçalves et al, which assessed 27 users of cocaine and crack in combination with marijuana.

PROBABILISTIC SAMPLING:

“In the context of probabilistic sampling, all units of the target population have a nonzero probability to take part in the study.”

If all participants are equally likely to be selected in the study, equiprobability sampling is being used, and the odds of being selected by the research team may be expressed by the formula : P=1/N, where P equals the probability of taking part in the study and N corresponds to the size of the target population. The main types of probabilistic sampling are described below.

• Simple random sampling:

“In this case, we have a full list of sample units or participants (sample basis), and we randomly select individuals using a table of random numbers”.

An example is a study by Pimenta et al, in which the authors obtained a listing from the Health Department of all elderly enrolled in the Family Health Strategy and, by simple random sampling, selected a sample of 449 participants.

• Systematic random sampling:

“In this case , participants are selected from fixed intervals previously defined from a ranked list of participants”.

For example, in the study of Kelbore et al, children who were assisted at the PediatricDermatology Service were selected to evaluate factors associated with atopic dermatitis, selecting always the second child by consulting order.

• Stratified sampling:

“In this type of sampling, the target population is first divided into separate sections. Then, samples are selected within each section, either through simple or systematic sampling.

The total number of individuals to be selected in each section can be fixed or proportional to the size of each section. Each individual may be equally likely to be selected to participate in the study.”

However, the fixed method usually involves the use of sampling weights in the statistical analysis (inverse of the probability of selection or 1/P).

An example is a study conducted in South Australia to investigate factors associated with vitamin D deficiency in preschool children. Using the national census as the sample frame, households were randomly selected in each stratum and all children in the age group of interest identified in the selected houses were investigated.

• Cluster sampling :

In this type of probabilistic sampling, groups such as health facilities, schools, etc., are sampled. In the above-mentioned study, the selection of households is an example of cluster sampling.

• Complex or multi-stage sampling:

“This probabilistic sampling method combines different strategies in the selection of the sample units”.

An example is the study of Duquia et al. to assess the prevalence and factors associated with the use of sunscreen in adults. The sampling process included two stages.

Using the 2000 Brazilian demographic census as a sampling frame, all 404 census tracts from Pelotas (Southern Brazil) were listed in ascending order of family income. A sample of 120 tracts was systematically selected (first sampling stage units).

In the second stage, 12 households in each of these census tract (second sampling stage units) were systematically drawn. All adult residents in these households were included in the study (third sampling stage units). All these stages have to be +considered in the statistical analysis to provide correct estimates.

NON RESPONDENTS :

Frequently, sample sizes are increased by 10% to compensate for potential nonresponses (refusals/losses). Let us imagine that in a study to assess the prevalence of premalignant skin lesions there is a higher percentage of nonrespondents among men (10%) than among women (1%).

If the highest percentage of nonresponse occurs because these men are not at home during the scheduled visits, and these participants are more likely to be exposed to the sun, the number of skin lesions will be underestimated.

For this reason, it is strongly recommended to collect and describe some basic characteristics of nonrespondents (sex, age, etc.) so they can be compared to the respondents to evaluate whether the results may have been affected by this systematic error.

Often, in study protocols, refusal to participate or sign the informed consent is considered an “exclusion criteria”. However, this is not correct, as these individuals are eligible for the study and need to be reported as “nonrespondents”.

SAMPLING METHOD ACCORDING TO THE TYPE OF STUDY:

In general, clinical trials aim to obtain a homogeneous sample that is not necessarily representative of any target population.

Clinical trials often recruit those participants who are most likely to benefit from the intervention. Thus, the more strict criteria for inclusion and exclusion of subjects in clinical trials often make it difficult to locate participants: after verification of the eligibility criteria, just one out of ten possible candidates will enter the study.

Therefore, clinical trials usually show limitations to generalize the results to the entire population of patients with the disease, but only to those with similar characteristics to the sample included in the study. These peculiarities in clinical trials justify the necessity of conducting multicenter and/or global studies to accelerate the recruitment rate and to reach, in a shorter time, the number of patients required for the study.

In turn , in observational studies to build a solid sampling plan is important because of the great heterogeneity usually observed in the target population.

Therefore, this heterogeneity has to be also reflected in the sample. A cross-sectional population-based study aiming to assess disease estimates or identify risk factors often uses complex probabilistic sampling, because the sample representativeness is crucial.

However, in a case-control study, we face the challenge of selecting two different samples for the same study. One sample is formed by the cases, which are identified based on the diagnosis of the disease of interest.

The other consists of controls, which need to be representative of the population that originated the cases. Improper selection of control individuals may introduce selection bias in the results. Thus, the concern with representativeness in this type of study is established based on the relationship between cases and controls (comparability).

In group studies , individuals are recruited based on the exposure (exposed and unexposed subjects), and they are followed over time to evaluate the occurrence of the outcome of interest.

At baseline, the sample can be selected from a representative sample (population-based cohort studies) or a non-representative sample. However, in the successive follow-ups of the cohort member, study participants must be a representative sample of those included in the baseline. In this type of study, losses over time may cause follow-up bias.

How we can select research participants?

• Introduction:

During the planning phase, you thought about which community members would best be able to provide the information you want, or if you are looking at issues within your organization or department, which staff members can provide the information.

As well as these questions there are many other decisions you will need to make when selecting your participants. This section provides you with a list of issues that you will need to consider before making the final decisions regarding study participants.

• Method of selection (sampling):

There are many methods for selecting your participants, and the type of sampling will depend on how you will use the information. Focus group results cannot usually be used to describe how an entire population would respond to the same questions, so the type of sampling used in studies designed to describe whole populations is not necessary.

The common (and simplest) method for selecting participants for focus groups is called “purposive” or “convenience” sampling. This means that you select those members of the community who you think will provide you with the best information. It need not be a random selection; indeed, a random sample may be foolish.

For example, if you are investigating why leprosy patients do not always present for medication, it would seem more “convenient” and more useful to select those patients, relatives, and staff involved in the leprosy program. A random sample of the whole community may not provide you with a single person with leprosy!

• Who can provide the best information?

Do not forget to think carefully about all aspects of the problem and be creative when deciding who can provide you with the best information. People in positions of power and authority, or with technical skills, are not necessarily the best people to talk to if you are interested in community attitudes and beliefs.

Sometimes less obvious people can be extremely useful. Try to think of all the members of your community that could have some knowledge or influence on the problem.

If you do not understand the community well enough to know who can be of most use, do not be afraid to ask local health staff, local leaders, or simply members of the community that you have access to. Never rely only on your ideas about a problem, particularly where you are studying people’s attitudes and beliefs. You could be on the wrong track completely by viewing things from your own experiences.

• What will the composition be in each focus group?

As focus groups are discussions among people with similar characteristics, it is important to ensure that participants in any one group have something in common with each other. The reason for this is simple. People talk more openly if they are in a group of people who share the same background or experiences. For example, suppose you are interested in sexual practices in a project concerned with community education to prevent HIV/AIDS.

A group that included both young single women and older married women might not be very successful; the young women may feel obliged to discuss “acceptable” practices rather than their true range of experiences and behaviors. Participants with different backgrounds and experiences can restrict the openness of discussion within the group. Given this, you need to

think about the status of participants in the community, their socio-economic status, educational background, religion, sex, age, and so on, considering which characteristics might most influence a free and natural discussion.

• How many groups are necessary?

In general, once the focus groups cease to provide you with new information, then you do not need to conduct any more sessions. Sometimes this may occur after only two or three sessions with each grouping of participants; sometimes you may need to run six, seven, or more before you are satisfied. If this is the first time your team has used focus groups, then you need to allow also for a few practice sessions that may not provide you with the quality of information you require.

You should group “types” of people together. This is probably obvious, but worth mentioning. Say, for example, in a study of leprosy, you have identified as target groups for focus group discussions local health workers, traditional healers, adult patients, caretakers of young people with leprosy, and other members of families with leprosy patients. It would be most appropriate to conduct focus groups separately for each group.

However, do not get too complicated in your selection process. This is a very easy mistake to make! In the above example, you already have identified five separate groups of participants.

If you now decided that sex, education and residence might all inhibit discussion, and so decided to interview women and men separately, to interview those with and without formal education, and to interview rural and urban dwellers separately, and you aim to hold three focus groups for each group of participants, you’d end up with 120 focus groups! Use your common sense about the criteria for selection.

Ask yourself some basic questions. Will separating leprosy patients according to education, for example, really provide you with more clues to understanding their presentation for therapy?

• How many participants do we want to select?

After deciding who it is you want to include in the project, you need to decide how many people you will want to contact for each session. Focus groups work well with around four to twelve people.

Groups with more than eight can be difficult to control, but the decision on how many you want in each group will depend on how your particular community groups together and conducts discussions in natural community settings.

If you have decided on eight participants for each group, it is still advisable to invite ten people, in case some do not arrive at the session. Be careful though not to over-recruit. In many communities, it would not be acceptable to turn away participants who had already arrived.

• How do we contact the participants?

This will depend, again, on the community with which you are working. Simply observe the local custom in your area. This usually involves contacting local leaders first, providing an explanation of the study, and gaining permission to work in that village or location.

It could also involve meeting with local health workers. Provided you approach such people appropriately, they will usually be happy to help you to locate individuals for the focus groups.

How much notice you give the participants of the focus groups will vary according to the logistics of gaining access to the community. It is ideal to notify the participants the week before and then provide a reminder the day before.

In many situations, this is not possible, and in some cases, participants have been successfully recruited one hour before the session! You need to consider your participants’ daily routine and take into account the ease or difficulty for them to attend the session. They are making a sacrifice to assist you, and this should always be recognized and allowed for.

When the participants are contacted for the first time, they should be provided with information about the study (without actually discussing the focus group questions or directly stating the aim of the study, as this may reduce the quality of the session), about why they have been selected, and how the results will be used.

For example, you might introduce a study on perceptions of disease in an area with a high prevalence of schistosomiasis by explaining that you are interested in the health problems of the study community, that to understand these you need to talk to as many people as possible, that you hope to learn from their own experience of health and illness, and that the information you gather will be used to help formulate plans to try to ensure better health.

At this time you will also need to check whether anything will need to be provided to help the participant attend, like child care or transport. Personal contact by the project team is strongly recommended as this can show the participants that their contribution is considered important.

How we can sample planes in research?

• Definition :

“A sampling plan is a term widely used in research studies that provide an outline based on which research is conducted. It tells which category is to be surveyed, what should be the sample size and how the respondents should be chosen out of the population.”

• Three major decision while making sample plane:

The sampling plan is a base from which the research starts and includes the following three major decisions:

• What should be the Sampling unit i.e. choosing the category of the population to be surveyed is the first and the foremost decision in a sampling plan that initiates the research? E.g. In the case of the banking industry, should the sampling unit consist of current account holders, saving account holders, or both? Should it include male or female account holders? These decisions once made the then sampling frame is designed to give everyone in the target population an equal chance of being sampled.
• The second decision in the sampling plan is determining the size of the sample i.e. how many objects in the sample area to be surveyed. Generally, “the larger the sample size, the more is the reliability” and therefore, researchers try to cover as many samples as possible.
• The final decision that completes the sampling plan is selecting the sampling procedure i.e. which method can be used such that every object in the population has an equal chance of being selected. Generally, the researchers use probability sampling to determine the objects to be chosen as these represent the sample more accurately.

Define population and sample:

• Population:

“A population is the entire group that you want to conclude about”.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible, and cooperative.

“A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.”

And what you understand from the sample and population in research?

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc. And the sample is a small portion of that population that suits your research problem and that sample represents the whole population.

References:

https://www.thh.nhs.uk/documents/_Departments/Research/InfoSheets/16_sampling_research.pdf

https://agriquora.com/sampling/

https://healthunlocked.com/psp/posts/144362665/research-study-participants-wanted

https://www.skillsyouneed.com/learn/sampling-sample-design.htmlhttps://www.questionpro.com/blog/types-of-sampling-for-social-research/

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Research Paper Examples

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A Sample Research Paper on Child Abuse

A research paper represents the pinnacle of academic investigation, a scholarly manuscript that encapsulates a detailed study, analysis, or argument based on extensive independent research. It is an embodiment of the researcher’s ability to synthesize a wealth of information, draw insightful conclusions, and contribute novel perspectives to the existing body of knowledge within a specific field. At its core, a research paper strives to push the boundaries of what is known, challenging existing theories and proposing new insights that could potentially reshape the understanding of a particular subject area.

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At iResearchNet, we understand the pivotal role that research papers play in the academic and professional development of students. With this understanding at our core, we offer comprehensive writing services that cater to the intricate process of research paper creation. Our services are designed to guide students through every stage of the writing process, from initial research to final submission, ensuring clarity, coherence, and scholarly rigor.

The Need for Research Paper Writing Services

Navigating the complexities of academic writing and research can be a daunting task for many students. The challenges of identifying credible sources, synthesizing information, adhering to academic standards, and articulating arguments cohesively are significant. Furthermore, the pressures of tight deadlines and the high stakes of academic success can exacerbate the difficulties faced by students. iResearchNet’s research paper writing services are crafted to address these challenges head-on, providing expert assistance that empowers students to achieve their academic goals with confidence.

Why Choose iResearchNet

Selecting the right partner for research paper writing is a pivotal decision for students and researchers aiming for academic excellence. iResearchNet stands out as the premier choice for several compelling reasons, each designed to meet the diverse needs of our clientele and ensure their success.

• Expert Writers : At iResearchNet, we pride ourselves on our team of expert writers who are not only masters in their respective fields but also possess a profound understanding of academic writing standards. With advanced degrees and extensive experience, our writers bring depth, insight, and precision to each paper, ensuring that your work is informed by the latest research and methodologies.
• Top Quality : Quality is the cornerstone of our services. We adhere to rigorous quality control processes to ensure that every paper we deliver meets the highest standards of academic excellence. Our commitment to quality means thorough research, impeccable writing, and meticulous proofreading, resulting in work that not only meets but exceeds expectations.
• Customized Solutions : Understanding that each research project has its unique challenges and requirements, iResearchNet offers customized solutions tailored to your specific needs. Whether you’re grappling with a complex research topic, a tight deadline, or specific formatting guidelines, our team is equipped to provide personalized support that aligns with your objectives.
• Affordable Prices : We believe that access to high-quality research paper writing services should not be prohibitive. iResearchNet offers competitive pricing structures designed to provide value without compromising on quality. Our transparent pricing model ensures that you know exactly what you are paying for, with no hidden costs or surprises.
• Timely Delivery : Meeting deadlines is critical in academic writing, and at iResearchNet, we take this seriously. Our efficient processes and dedicated team ensure that your paper is delivered on time, every time, allowing you to meet your academic deadlines with confidence.
• 24/7 Support : Our commitment to your success is reflected in our round-the-clock support. Whether you have a question about your order, need to communicate with your writer, or require assistance with any aspect of our service, our friendly and knowledgeable support team is available 24/7 to assist you.
• Money-Back Guarantee : Your satisfaction is our top priority. iResearchNet offers a money-back guarantee, ensuring that if for any reason you are not satisfied with the work delivered, you are entitled to a refund. This policy underscores our confidence in the quality of our services and our dedication to your success.

Choosing iResearchNet for your research paper writing needs means partnering with a trusted provider committed to excellence, innovation, and customer satisfaction. Our unparalleled blend of expert writers, top-quality work, customized solutions, affordability, timely delivery, 24/7 support, and a money-back guarantee makes us the ideal choice for students and researchers seeking to elevate their academic performance.

How It Works: iResearchNet’s Streamlined Process

Navigating the process of obtaining a top-notch research paper has never been more straightforward, thanks to iResearchNet’s streamlined approach. Our user-friendly system ensures that from the moment you decide to place your order to the final receipt of your custom-written paper, every step is seamless, transparent, and tailored to your needs. Here’s how our comprehensive process works:

• Place Your Order : Begin your journey to academic success by visiting our website and filling out the order form. Here, you’ll provide details about your research paper, including the topic, academic level, number of pages, formatting style, and any specific instructions or requirements. This initial step is crucial for us to understand your needs fully and match you with the most suitable writer.
• Make Payment : Once your order details are confirmed, you’ll proceed to the payment section. Our platform offers a variety of secure payment options, ensuring that your transaction is safe and hassle-free. Our transparent pricing policy means you’ll know exactly what you’re paying for upfront, with no hidden fees.
• Choose Your Writer : After payment, you’ll have the opportunity to choose a writer from our team of experts. Our writers are categorized based on their fields of expertise, academic qualifications, and customer feedback ratings. This step empowers you to select the writer who best matches your research paper’s requirements, ensuring a personalized and targeted approach to your project.
• Receive Your Work : Our writer will commence work on your research paper, adhering to the specified guidelines and timelines. Throughout this process, you’ll have the ability to communicate directly with your writer, allowing for updates, revisions, and clarifications to ensure the final product meets your expectations. Once completed, your research paper will undergo a thorough quality check before being delivered to you via your chosen method.
• Free Revisions : Your satisfaction is our priority. Upon receiving your research paper, you’ll have the opportunity to review the work and request any necessary revisions. iResearchNet offers free revisions within a specified period, ensuring that your final paper perfectly aligns with your academic requirements and expectations.

Our process is designed to provide you with a stress-free experience and a research paper that reflects your academic goals. From placing your order to enjoying the success of a well-written paper, iResearchNet is here to support you every step of the way.

Our Extras: Enhancing Your iResearchNet Experience

At iResearchNet, we are committed to offering more than just standard research paper writing services. We understand the importance of providing a comprehensive and personalized experience for each of our clients. That’s why we offer a range of additional services designed to enhance your experience and ensure your academic success. Here are the exclusive extras you can benefit from:

• VIP Service : Elevate your iResearchNet experience with our VIP service, offering you priority treatment from the moment you place your order. This service ensures your projects are given first priority, with immediate attention from our team, and direct access to our top-tier writers and editors. VIP clients also benefit from our highest level of customer support, available to address any inquiries or needs with utmost urgency and personalized care.
• Plagiarism Report : Integrity and originality are paramount in academic writing. To provide you with peace of mind, we offer a detailed plagiarism report with every research paper. This report is generated using advanced plagiarism detection software, ensuring that your work is unique and adheres to the highest standards of academic honesty.
• Text Messages : Stay informed about your order’s progress with real-time updates sent directly to your phone. This service ensures you’re always in the loop, providing immediate notifications about key milestones, writer assignments, and any changes to your order status. With this added layer of communication, you can relax knowing that you’ll never miss an important update about your research paper.
• Table of Contents : A well-organized research paper is key to guiding readers through your work. Our service includes the creation of a detailed table of contents, meticulously structured to reflect the main sections and subsections of your paper. This not only enhances the navigability of your document but also presents your research in a professional and academically appropriate format.
• Abstract Page : The abstract page is your research paper’s first impression, summarizing the essential points of your study and its conclusions. Crafting a compelling abstract is an art, and our experts are skilled in highlighting the significance, methodology, results, and implications of your research succinctly and effectively. This service ensures that your paper makes a strong impact from the very beginning.
• Editor’s Check : Before your research paper reaches you, it undergoes a final review by our team of experienced editors. This editor’s check is a comprehensive process that includes proofreading for grammar, punctuation, and spelling errors, as well as ensuring that the paper meets all your specifications and academic standards. This meticulous attention to detail guarantees that your paper is polished, professional, and ready for submission.

To ensure your research paper is of the highest quality and ready for submission, it undergoes a rigorous editor’s check. This final review process includes a thorough examination for any grammatical, punctuation, or spelling errors, as well as a verification that the paper meets all your specified requirements and academic standards. Our editors’ meticulous approach guarantees that your paper is polished, accurate, and exemplary.

By choosing iResearchNet and leveraging our extras, you can elevate the quality of your research paper and enjoy a customized, worry-free academic support experience.

A research paper is an academic piece of writing, so you need to follow all the requirements and standards. Otherwise, it will be impossible to get the high results. To make it easier for you, we have analyzed the structure and peculiarities of a sample research paper on the topic ‘Child Abuse’.

The paper includes 7300+ words, a detailed outline, citations are in APA formatting style, and bibliography with 28 sources.

To write any paper you need to write a great outline. This is the key to a perfect paper. When you organize your paper, it is easier for you to present the ideas logically, without jumping from one thought to another.

In the outline, you need to name all the parts of your paper. That is to say, an introduction, main body, conclusion, bibliography, some papers require abstract and proposal as well.

A good outline will serve as a guide through your paper making it easier for the reader to follow your ideas.

I. Introduction

Ii. estimates of child abuse: methodological limitations, iii. child abuse and neglect: the legalities, iv. corporal punishment versus child abuse, v. child abuse victims: the patterns, vi. child abuse perpetrators: the patterns, vii. explanations for child abuse, viii. consequences of child abuse and neglect, ix. determining abuse: how to tell whether a child is abused or neglected, x. determining abuse: interviewing children, xi. how can society help abused children and abusive families, introduction.

An introduction should include a thesis statement and the main points that you will discuss in the paper.

A thesis statement is one sentence in which you need to show your point of view. You will then develop this point of view through the whole piece of work:

‘The impact of child abuse affects more than one’s childhood, as the psychological and physical injuries often extend well into adulthood.’

Child abuse is a very real and prominent social problem today. The impact of child abuse affects more than one’s childhood, as the psychological and physical injuries often extend well into adulthood. Most children are defenseless against abuse, are dependent on their caretakers, and are unable to protect themselves from these acts.

Childhood serves as the basis for growth, development, and socialization. Throughout adolescence, children are taught how to become productive and positive, functioning members of society. Much of the socializing of children, particularly in their very earliest years, comes at the hands of family members. Unfortunately, the messages conveyed to and the actions against children by their families are not always the positive building blocks for which one would hope.

In 2008, the Children’s Defense Fund reported that each day in America, 2,421 children are confirmed as abused or neglected, 4 children are killed by abuse or neglect, and 78 babies die before their first birthday. These daily estimates translate into tremendous national figures. In 2006, caseworkers substantiated an estimated 905,000 reports of child abuse or neglect. Of these, 64% suffered neglect, 16% were physically abused, 9% were sexually abused, 7% were emotionally or psychologically maltreated, and 2% were medically neglected. In addition, 15% of the victims experienced “other” types of maltreatment such as abandonment, threats of harm to the child, and congenital drug addiction (National Child Abuse and Neglect Data System, 2006). Obviously, this problem is a substantial one.

In the main body, you dwell upon the topic of your paper. You provide your ideas and support them with evidence. The evidence include all the data and material you have found, analyzed and systematized. You can support your point of view with different statistical data, with surveys, and the results of different experiments. Your task is to show that your idea is right, and make the reader interested in the topic.

In this example, a writer analyzes the issue of child abuse: different statistical data, controversies regarding the topic, examples of the problem and the consequences.

Several issues arise when considering the amount of child abuse that occurs annually in the United States. Child abuse is very hard to estimate because much (or most) of it is not reported. Children who are abused are unlikely to report their victimization because they may not know any better, they still love their abusers and do not want to see them taken away (or do not themselves want to be taken away from their abusers), they have been threatened into not reporting, or they do not know to whom they should report their victimizations. Still further, children may report their abuse only to find the person to whom they report does not believe them or take any action on their behalf. Continuing to muddy the waters, child abuse can be disguised as legitimate injury, particularly because young children are often somewhat uncoordinated and are still learning to accomplish physical tasks, may not know their physical limitations, and are often legitimately injured during regular play. In the end, children rarely report child abuse; most often it is an adult who makes a report based on suspicion (e.g., teacher, counselor, doctor, etc.).

Even when child abuse is reported, social service agents and investigators may not follow up or substantiate reports for a variety of reasons. Parents can pretend, lie, or cover up injuries or stories of how injuries occurred when social service agents come to investigate. Further, there is not always agreement about what should be counted as abuse by service providers and researchers. In addition, social service agencies/agents have huge caseloads and may only be able to deal with the most serious forms of child abuse, leaving the more “minor” forms of abuse unsupervised and unmanaged (and uncounted in the statistical totals).

While most laws about child abuse and neglect fall at the state levels, federal legislation provides a foundation for states by identifying a minimum set of acts and behaviors that define child abuse and neglect. The Federal Child Abuse Prevention and Treatment Act (CAPTA), which stems from the Keeping Children and Families Safe Act of 2003, defines child abuse and neglect as, at minimum, “(1) any recent act or failure to act on the part of a parent or caretaker which results in death, serious physical or emotional harm, sexual abuse, or exploitation; or (2) an act or failure to act which presents an imminent risk or serious harm.”

Using these minimum standards, each state is responsible for providing its own definition of maltreatment within civil and criminal statutes. When defining types of child abuse, many states incorporate similar elements and definitions into their legal statutes. For example, neglect is often defined as failure to provide for a child’s basic needs. Neglect can encompass physical elements (e.g., failure to provide necessary food or shelter, or lack of appropriate supervision), medical elements (e.g., failure to provide necessary medical or mental health treatment), educational elements (e.g., failure to educate a child or attend to special educational needs), and emotional elements (e.g., inattention to a child’s emotional needs, failure to provide psychological care, or permitting the child to use alcohol or other drugs). Failure to meet needs does not always mean a child is neglected, as situations such as poverty, cultural values, and community standards can influence the application of legal statutes. In addition, several states distinguish between failure to provide based on financial inability and failure to provide for no apparent financial reason.

Statutes on physical abuse typically include elements of physical injury (ranging from minor bruises to severe fractures or death) as a result of punching, beating, kicking, biting, shaking, throwing, stabbing, choking, hitting (with a hand, stick, strap, or other object), burning, or otherwise harming a child. Such injury is considered abuse regardless of the intention of the caretaker. In addition, many state statutes include allowing or encouraging another person to physically harm a child (such as noted above) as another form of physical abuse in and of itself. Sexual abuse usually includes activities by a parent or caretaker such as fondling a child’s genitals, penetration, incest, rape, sodomy, indecent exposure, and exploitation through prostitution or the production of pornographic materials.

Finally, emotional or psychological abuse typically is defined as a pattern of behavior that impairs a child’s emotional development or sense of self-worth. This may include constant criticism, threats, or rejection, as well as withholding love, support, or guidance. Emotional abuse is often the most difficult to prove and, therefore, child protective services may not be able to intervene without evidence of harm to the child. Some states suggest that harm may be evidenced by an observable or substantial change in behavior, emotional response, or cognition, or by anxiety, depression, withdrawal, or aggressive behavior. At a practical level, emotional abuse is almost always present when other types of abuse are identified.

Some states include an element of substance abuse in their statutes on child abuse. Circumstances that can be considered substance abuse include (a) the manufacture of a controlled substance in the presence of a child or on the premises occupied by a child (Colorado, Indiana, Iowa, Montana, South Dakota, Tennessee, and Virginia); (b) allowing a child to be present where the chemicals or equipment for the manufacture of controlled substances are used (Arizona, New Mexico); (c) selling, distributing, or giving drugs or alcohol to a child (Florida, Hawaii, Illinois, Minnesota, and Texas); (d) use of a controlled substance by a caregiver that impairs the caregiver’s ability to adequately care for the child (Kentucky, New York, Rhode Island, and Texas); and (e) exposure of the child to drug paraphernalia (North Dakota), the criminal sale or distribution of drugs (Montana, Virginia), or drug-related activity (District of Columbia).

One of the most difficult issues with which the U.S. legal system must contend is that of allowing parents the right to use corporal punishment when disciplining a child, while not letting them cross over the line into the realm of child abuse. Some parents may abuse their children under the guise of discipline, and many instances of child abuse arise from angry parents who go too far when disciplining their children with physical punishment. Generally, state statutes use terms such as “reasonable discipline of a minor,” “causes only temporary, short-term pain,” and may cause “the potential for bruising” but not “permanent damage, disability, disfigurement or injury” to the child as ways of indicating the types of discipline behaviors that are legal. However, corporal punishment that is “excessive,” “malicious,” “endangers the bodily safety of,” or is “an intentional infliction of injury” is not allowed under most state statutes (e.g., state of Florida child abuse statute).

Most research finds that the use of physical punishment (most often spanking) is not an effective method of discipline. The literature on this issue tends to find that spanking stops misbehavior, but no more effectively than other firm measures. Further, it seems to hinder rather than improve general compliance/obedience (particularly when the child is not in the presence of the punisher). Researchers have also explained why physical punishment is not any more effective at gaining child compliance than nonviolent forms of discipline. Some of the problems that arise when parents use spanking or other forms of physical punishment include the fact that spanking does not teach what children should do, nor does it provide them with alternative behavior options should the circumstance arise again. Spanking also undermines reasoning, explanation, or other forms of parental instruction because children cannot learn, reason, or problem solve well while experiencing threat, pain, fear, or anger. Further, the use of physical punishment is inconsistent with nonviolent principles, or parental modeling. In addition, the use of spanking chips away at the bonds of affection between parents and children, and tends to induce resentment and fear. Finally, it hinders the development of empathy and compassion in children, and they do not learn to take responsibility for their own behavior (Pitzer, 1997).

One of the biggest problems with the use of corporal punishment is that it can escalate into much more severe forms of violence. Usually, parents spank because they are angry (and somewhat out of control) and they can’t think of other ways to discipline. When parents are acting as a result of emotional triggers, the notion of discipline is lost while punishment and pain become the foci.

In 2006, of the children who were found to be victims of child abuse, nearly 75% of them were first-time victims (or had not come to the attention of authorities prior). A slight majority of child abuse victims were girls—51.5%, compared to 48% of abuse victims being boys. The younger the child, the more at risk he or she is for child abuse and neglect victimization. Specifically, the rate for infants (birth to 1 year old) was approximately 24 per 1,000 children of the same age group. The victimization rate for children 1–3 years old was 14 per 1,000 children of the same age group. The abuse rate for children aged 4– 7 years old declined further to 13 per 1,000 children of the same age group. African American, American Indian, and Alaska Native children, as well as children of multiple races, had the highest rates of victimization. White and Latino children had lower rates, and Asian children had the lowest rates of child abuse and neglect victimization. Regarding living arrangements, nearly 27% of victims were living with a single mother, 20% were living with married parents, while 22% were living with both parents but the marital status was unknown. (This reporting element had nearly 40% missing data, however.) Regarding disability, nearly 8% of child abuse victims had some degree of mental retardation, emotional disturbance, visual or hearing impairment, learning disability, physical disability, behavioral problems, or other medical problems. Unfortunately, data indicate that for many victims, the efforts of the child protection services system were not successful in preventing subsequent victimization. Children who had been prior victims of maltreatment were 96% more likely to experience another occurrence than those who were not prior victims. Further, child victims who were reported to have a disability were 52% more likely to experience recurrence than children without a disability. Finally, the oldest victims (16–21 years of age) were the least likely to experience a recurrence, and were 51% less likely to be victimized again than were infants (younger than age 1) (National Child Abuse and Neglect Data System, 2006).

Child fatalities are the most tragic consequence of maltreatment. Yet, each year, children die from abuse and neglect. In 2006, an estimated 1,530 children in the United States died due to abuse or neglect. The overall rate of child fatalities was 2 deaths per 100,000 children. More than 40% of child fatalities were attributed to neglect, but physical abuse also was a major contributor. Approximately 78% of the children who died due to child abuse and neglect were younger than 4 years old, and infant boys (younger than 1) had the highest rate of fatalities at 18.5 deaths per 100,000 boys of the same age in the national population. Infant girls had a rate of 14.7 deaths per 100,000 girls of the same age (National Child Abuse and Neglect Data System, 2006).

One question to be addressed regarding child fatalities is why infants have such a high rate of death when compared to toddlers and adolescents. Children under 1 year old pose an immense amount of responsibility for their caretakers: they are completely dependent and need constant attention. Children this age are needy, impulsive, and not amenable to verbal control or effective communication. This can easily overwhelm vulnerable parents. Another difficulty associated with infants is that they are physically weak and small. Injuries to infants can be fatal, while similar injuries to older children might not be. The most common cause of death in children less than 1 year is cerebral trauma (often the result of shaken-baby syndrome). Exasperated parents can deliver shakes or blows without realizing how little it takes to cause irreparable or fatal damage to an infant. Research informs us that two of the most common triggers for fatal child abuse are crying that will not cease and toileting accidents. Both of these circumstances are common in infants and toddlers whose only means of communication often is crying, and who are limited in mobility and cannot use the toilet. Finally, very young children cannot assist in injury diagnoses. Children who have been injured due to abuse or neglect often cannot communicate to medical professionals about where it hurts, how it hurts, and so forth. Also, nonfatal injuries can turn fatal in the absence of care by neglectful parents or parents who do not want medical professionals to possibly identify an injury as being the result of abuse.

Estimates reveal that nearly 80% of perpetrators of child abuse were parents of the victim. Other relatives accounted for nearly 7%, and unmarried partners of parents made up 4% of perpetrators. Of those perpetrators that were parents, over 90% were biological parents, 4% were stepparents, and 0.7% were adoptive parents. Of this group, approximately 58% of perpetrators were women and 42% were men. Women perpetrators are typically younger than men. The average age for women abusers was 31 years old, while for men the average was 34 years old. Forty percent of women who abused were younger than 30 years of age, compared with 33% of men being under 30. The racial distribution of perpetrators is similar to that of victims. Fifty-four percent were white, 21% were African American, and 20% were Hispanic/Latino (National Child Abuse and Neglect Data System, 2006).

There are many factors that are associated with child abuse. Some of the more common/well-accepted explanations are individual pathology, parent–child interaction, past abuse in the family (or social learning), situational factors, and cultural support for physical punishment along with a lack of cultural support for helping parents here in the United States.

The first explanation centers on the individual pathology of a parent or caretaker who is abusive. This theory focuses on the idea that people who abuse their children have something wrong with their individual personality or biological makeup. Such psychological pathologies may include having anger control problems; being depressed or having post-partum depression; having a low tolerance for frustration (e.g., children can be extremely frustrating: they don’t always listen; they constantly push the line of how far they can go; and once the line has been established, they are constantly treading on it to make sure it hasn’t moved. They are dependent and self-centered, so caretakers have very little privacy or time to themselves); being rigid (e.g., having no tolerance for differences—for example, what if your son wanted to play with dolls? A rigid father would not let him, laugh at him for wanting to, punish him when he does, etc.); having deficits in empathy (parents who cannot put themselves in the shoes of their children cannot fully understand what their children need emotionally); or being disorganized, inefficient, and ineffectual. (Parents who are unable to manage their own lives are unlikely to be successful at managing the lives of their children, and since many children want and need limits, these parents are unable to set them or adhere to them.)

Biological pathologies that may increase the likelihood of someone becoming a child abuser include having substance abuse or dependence problems, or having persistent or reoccurring physical health problems (especially health problems that can be extremely painful and can cause a person to become more self-absorbed, both qualities that can give rise to a lack of patience, lower frustration tolerance, and increased stress).

The second explanation for child abuse centers on the interaction between the parent and the child, noting that certain types of parents are more likely to abuse, and certain types of children are more likely to be abused, and when these less-skilled parents are coupled with these more difficult children, child abuse is the most likely to occur. Discussion here focuses on what makes a parent less skilled, and what makes a child more difficult. Characteristics of unskilled parents are likely to include such traits as only pointing out what children do wrong and never giving any encouragement for good behavior, and failing to be sensitive to the emotional needs of children. Less skilled parents tend to have unrealistic expectations of children. They may engage in role reversal— where the parents make the child take care of them—and view the parent’s happiness and well-being as the responsibility of the child. Some parents view the parental role as extremely stressful and experience little enjoyment from being a parent. Finally, less-skilled parents tend to have more negative perceptions regarding their child(ren). For example, perhaps the child has a different shade of skin than they expected and this may disappoint or anger them, they may feel the child is being manipulative (long before children have this capability), or they may view the child as the scapegoat for all the parents’ or family’s problems. Theoretically, parents with these characteristics would be more likely to abuse their children, but if they are coupled with having a difficult child, they would be especially likely to be abusive. So, what makes a child more difficult? Certainly, through no fault of their own, children may have characteristics that are associated with child care that is more demanding and difficult than in the “normal” or “average” situation. Such characteristics can include having physical and mental disabilities (autism, attention deficit hyperactivity disorder [ADHD], hyperactivity, etc.); the child may be colicky, frequently sick, be particularly needy, or cry more often. In addition, some babies are simply unhappier than other babies for reasons that cannot be known. Further, infants are difficult even in the best of circumstances. They are unable to communicate effectively, and they are completely dependent on their caretakers for everything, including eating, diaper changing, moving around, entertainment, and emotional bonding. Again, these types of children, being more difficult, are more likely to be victims of child abuse.

Nonetheless, each of these types of parents and children alone cannot explain the abuse of children, but it is the interaction between them that becomes the key. Unskilled parents may produce children that are happy and not as needy, and even though they are unskilled, they do not abuse because the child takes less effort. At the same time, children who are more difficult may have parents who are skilled and are able to handle and manage the extra effort these children take with aplomb. However, risks for child abuse increase when unskilled parents must contend with difficult children.

Social learning or past abuse in the family is a third common explanation for child abuse. Here, the theory concentrates not only on what children learn when they see or experience violence in their homes, but additionally on what they do not learn as a result of these experiences. Social learning theory in the context of family violence stresses that if children are abused or see abuse (toward siblings or a parent), those interactions and violent family members become the representations and role models for their future familial interactions. In this way, what children learn is just as important as what they do not learn. Children who witness or experience violence may learn that this is the way parents deal with children, or that violence is an acceptable method of child rearing and discipline. They may think when they become parents that “violence worked on me when I was a child, and I turned out fine.” They may learn unhealthy relationship interaction patterns; children may witness the negative interactions of parents and they may learn the maladaptive or violent methods of expressing anger, reacting to stress, or coping with conflict.

What is equally as important, though, is that they are unlikely to learn more acceptable and nonviolent ways of rearing children, interacting with family members, and working out conflict. Here it may happen that an adult who was abused as a child would like to be nonviolent toward his or her own children, but when the chips are down and the child is misbehaving, this abused-child-turned-adult does not have a repertoire of nonviolent strategies to try. This parent is more likely to fall back on what he or she knows as methods of discipline.

Something important to note here is that not all abused children grow up to become abusive adults. Children who break the cycle were often able to establish and maintain one healthy emotional relationship with someone during their childhoods (or period of young adulthood). For instance, they may have received emotional support from a nonabusing parent, or they received social support and had a positive relationship with another adult during their childhood (e.g., teacher, coach, minister, neighbor, etc.). Abused children who participate in therapy during some period of their lives can often break the cycle of violence. In addition, adults who were abused but are able to form an emotionally supportive and satisfying relationship with a mate can make the transition to being nonviolent in their family interactions.

Moving on to a fourth familiar explanation for child abuse, there are some common situational factors that influence families and parents and increase the risks for child abuse. Typically, these are factors that increase family stress or social isolation. Specifically, such factors may include receiving public assistance or having low socioeconomic status (a combination of low income and low education). Other factors include having family members who are unemployed, underemployed (working in a job that requires lower qualifications than an individual possesses), or employed only part time. These financial difficulties cause great stress for families in meeting the needs of the individual members. Other stress-inducing familial characteristics are single-parent households and larger family size. Finally, social isolation can be devastating for families and family members. Having friends to talk to, who can be relied upon, and with whom kids can be dropped off occasionally is tremendously important for personal growth and satisfaction in life. In addition, social isolation and stress can cause individuals to be quick to lose their tempers, as well as cause people to be less rational in their decision making and to make mountains out of mole hills. These situations can lead families to be at greater risk for child abuse.

Finally, cultural views and supports (or lack thereof) can lead to greater amounts of child abuse in a society such as the United States. One such cultural view is that of societal support for physical punishment. This is problematic because there are similarities between the way criminals are dealt with and the way errant children are handled. The use of capital punishment is advocated for seriously violent criminals, and people are quick to use such idioms as “spare the rod and spoil the child” when it comes to the discipline or punishment of children. In fact, it was not until quite recently that parenting books began to encourage parents to use other strategies than spanking or other forms of corporal punishment in the discipline of their children. Only recently, the American Academy of Pediatrics has come out and recommended that parents do not spank or use other forms of violence on their children because of the deleterious effects such methods have on youngsters and their bonds with their parents. Nevertheless, regardless of recommendations, the culture of corporal punishment persists.

Another cultural view in the United States that can give rise to greater incidents of child abuse is the belief that after getting married, couples of course should want and have children. Culturally, Americans consider that children are a blessing, raising kids is the most wonderful thing a person can do, and everyone should have children. Along with this notion is the idea that motherhood is always wonderful; it is the most fulfilling thing a woman can do; and the bond between a mother and her child is strong, glorious, and automatic—all women love being mothers. Thus, culturally (and theoretically), society nearly insists that married couples have children and that they will love having children. But, after children are born, there is not much support for couples who have trouble adjusting to parenthood, or who do not absolutely love their new roles as parents. People look askance at parents who need help, and cannot believe parents who say anything negative about parenthood. As such, theoretically, society has set up a situation where couples are strongly encouraged to have kids, are told they will love kids, but then society turns a blind or disdainful eye when these same parents need emotional, financial, or other forms of help or support. It is these types of cultural viewpoints that increase the risks for child abuse in society.

The consequences of child abuse are tremendous and long lasting. Research has shown that the traumatic experience of childhood abuse is life changing. These costs may surface during adolescence, or they may not become evident until abused children have grown up and become abusing parents or abused spouses. Early identification and treatment is important to minimize these potential long-term effects. Whenever children say they have been abused, it is imperative that they be taken seriously and their abuse be reported. Suspicions of child abuse must be reported as well. If there is a possibility that a child is or has been abused, an investigation must be conducted.

Children who have been abused may exhibit traits such as the inability to love or have faith in others. This often translates into adults who are unable to establish lasting and stable personal relationships. These individuals have trouble with physical closeness and touching as well as emotional intimacy and trust. Further, these qualities tend to cause a fear of entering into new relationships, as well as the sabotaging of any current ones.

Psychologically, children who have been abused tend to have poor self-images or are passive, withdrawn, or clingy. They may be angry individuals who are filled with rage, anxiety, and a variety of fears. They are often aggressive, disruptive, and depressed. Many abused children have flashbacks and nightmares about the abuse they have experienced, and this may cause sleep problems as well as drug and alcohol problems. Posttraumatic stress disorder (PTSD) and antisocial personality disorder are both typical among maltreated children. Research has also shown that most abused children fail to reach “successful psychosocial functioning,” and are thus not resilient and do not resume a “normal life” after the abuse has ended.

Socially (and likely because of these psychological injuries), abused children have trouble in school, will have difficulty getting and remaining employed, and may commit a variety of illegal or socially inappropriate behaviors. Many studies have shown that victims of child abuse are likely to participate in high-risk behaviors such as alcohol or drug abuse, the use of tobacco, and high-risk sexual behaviors (e.g., unprotected sex, large numbers of sexual partners). Later in life, abused children are more likely to have been arrested and homeless. They are also less able to defend themselves in conflict situations and guard themselves against repeated victimizations.

Medically, abused children likely will experience health problems due to the high frequency of physical injuries they receive. In addition, abused children experience a great deal of emotional turmoil and stress, which can also have a significant impact on their physical condition. These health problems are likely to continue occurring into adulthood. Some of these longer-lasting health problems include headaches; eating problems; problems with toileting; and chronic pain in the back, stomach, chest, and genital areas. Some researchers have noted that abused children may experience neurological impairment and problems with intellectual functioning, while others have found a correlation between abuse and heart, lung, and liver disease, as well as cancer (Thomas, 2004).

Victims of sexual abuse show an alarming number of disturbances as adults. Some dislike and avoid sex, or experience sexual problems or disorders, while other victims appear to enjoy sexual activities that are self-defeating or maladaptive—normally called “dysfunctional sexual behavior”—and have many sexual partners.

Abused children also experience a wide variety of developmental delays. Many do not reach physical, cognitive, or emotional developmental milestones at the typical time, and some never accomplish what they are supposed to during childhood socialization. In the next section, these developmental delays are discussed as a means of identifying children who may be abused.

There are two primary ways of identifying children who are abused: spotting and evaluating physical injuries, and detecting and appraising developmental delays. Distinguishing physical injuries due to abuse can be difficult, particularly among younger children who are likely to get hurt or receive injuries while they are playing and learning to become ambulatory. Nonetheless, there are several types of wounds that children are unlikely to give themselves during their normal course of play and exploration. These less likely injuries may signal instances of child abuse.

While it is true that children are likely to get bruises, particularly when they are learning to walk or crawl, bruises on infants are not normal. Also, the back of the legs, upper arms, or on the chest, neck, head, or genitals are also locations where bruises are unlikely to occur during normal childhood activity. Further, bruises with clean patterns, like hand prints, buckle prints, or hangers (to name a few), are good examples of the types of bruises children do not give themselves.

Another area of physical injury where the source of the injury can be difficult to detect is fractures. Again, children fall out of trees, or crash their bikes, and can break limbs. These can be normal parts of growing up. However, fractures in infants less than 12 months old are particularly suspect, as infants are unlikely to be able to accomplish the types of movement necessary to actually break a leg or an arm. Further, multiple fractures, particularly more than one on a bone, should be examined more closely. Spiral or torsion fractures (when the bone is broken by twisting) are suspect because when children break their bones due to play injuries, the fractures are usually some other type (e.g., linear, oblique, compacted). In addition, when parents don’t know about the fracture(s) or how it occurred, abuse should be considered, because when children get these types of injuries, they need comfort and attention.

Head and internal injuries are also those that may signal abuse. Serious blows to the head cause internal head injuries, and this is very different from the injuries that result from bumping into things. Abused children are also likely to experience internal injuries like those to the abdomen, liver, kidney, and bladder. They may suffer a ruptured spleen, or intestinal perforation. These types of damages rarely happen by accident.

Burns are another type of physical injury that can happen by accident or by abuse. Nevertheless, there are ways to tell these types of burn injuries apart. The types of burns that should be examined and investigated are those where the burns are in particular locations. Burns to the bottom of the feet, genitals, abdomen, or other inaccessible spots should be closely considered. Burns of the whole hand or those to the buttocks are also unlikely to happen as a result of an accident.

Turning to the detection and appraisal of developmental delays, one can more readily assess possible abuse by considering what children of various ages should be able to accomplish, than by noting when children are delayed and how many milestones on which they are behind schedule. Importantly, a few delays in reaching milestones can be expected, since children develop individually and not always according to the norm. Nonetheless, when children are abused, their development is likely to be delayed in numerous areas and across many milestones.

As children develop and grow, they should be able to crawl, walk, run, talk, control going to the bathroom, write, set priorities, plan ahead, trust others, make friends, develop a good self-image, differentiate between feeling and behavior, and get their needs met in appropriate ways. As such, when children do not accomplish these feats, their circumstances should be examined.

Infants who are abused or neglected typically develop what is termed failure to thrive syndrome. This syndrome is characterized by slow, inadequate growth, or not “filling out” physically. They have a pale, colorless complexion and dull eyes. They are not likely to spend much time looking around, and nothing catches their eyes. They may show other signs of lack of nutrition such as cuts, bruises that do not heal in a timely way, and discolored fingernails. They are also not trusting and may not cry much, as they are not expecting to have their needs met. Older infants may not have developed any language skills, or these developments are quite slow. This includes both verbal and nonverbal means of communication.

Toddlers who are abused often become hypervigilant about their environments and others’ moods. They are more outwardly focused than a typical toddler (who is quite self-centered) and may be unable to separate themselves as individuals, or consider themselves as distinct beings. In this way, abused toddlers cannot focus on tasks at hand because they are too concerned about others’ reactions. They don’t play with toys, have no interest in exploration, and seem unable to enjoy life. They are likely to accept losses with little reaction, and may have age-inappropriate knowledge of sex and sexual relations. Finally, toddlers, whether they are abused or not, begin to mirror their parents’ behaviors. Thus, toddlers who are abused may mimic the abuse when they are playing with dolls or “playing house.”

Developmental delays can also be detected among abused young adolescents. Some signs include the failure to learn cause and effect, since their parents are so inconsistent. They have no energy for learning and have not developed beyond one- or two-word commands. They probably cannot follow complicated directions (such as two to three tasks per instruction), and they are unlikely to be able to think for themselves. Typically, they have learned that failure is totally unacceptable, but they are more concerned with the teacher’s mood than with learning and listening to instruction. Finally, they are apt to have been inadequately toilet trained and thus may be unable to control their bladders.

Older adolescents, because they are likely to have been abused for a longer period of time, continue to get further and further behind in their developmental achievements. Abused children this age become family nurturers. They take care of their parents and cater to their parents’ needs, rather than the other way around. In addition, they probably take care of any younger siblings and do the household chores. Because of these default responsibilities, they usually do not participate in school activities; they frequently miss days at school; and they have few, if any, friends. Because they have become so hypervigilant and have increasingly delayed development, they lose interest in and become disillusioned with education. They develop low self-esteem and little confidence, but seem old for their years. Children this age who are abused are still likely to be unable to control their bladders and may have frequent toileting accidents.

Other developmental delays can occur and be observed in abused and neglected children of any age. For example, malnutrition and withdrawal can be noticed in infants through teenagers. Maltreated children frequently have persistent or untreated illnesses, and these can become permanent disabilities if medical conditions go untreated for a long enough time. Another example can be the consequences of neurological damage. Beyond being a medical issue, this type of damage can cause problems with social behavior and impulse control, which, again, can be discerned in various ages of children.

Once child abuse is suspected, law enforcement officers, child protection workers, or various other practitioners may need to interview the child about the abuse or neglect he or she may have suffered. Interviewing children can be extremely difficult because children at various stages of development can remember only certain parts or aspects of the events in their lives. Also, interviewers must be careful that they do not put ideas or answers into the heads of the children they are interviewing. There are several general recommendations when interviewing children about the abuse they may have experienced. First, interviewers must acknowledge that even when children are abused, they likely still love their parents. They do not want to be taken away from their parents, nor do they want to see their parents get into trouble. Interviewers must not blame the parents or be judgmental about them or the child’s family. Beyond that, interviews should take place in a safe, neutral location. Interviewers can use dolls and role-play to help children express the types of abuse of which they may be victims.

Finally, interviewers must ask age-appropriate questions. For example, 3-year-olds can probably only answer questions about what happened and who was involved. Four- to five-year-olds can also discuss where the incidents occurred. Along with what, who, and where, 6- to 8-year-olds can talk about the element of time, or when the abuse occurred. Nine- to 10-year-olds are able to add commentary about the number of times the abuse occurred. Finally, 11-year-olds and older children can additionally inform interviewers about the circumstances of abusive instances.

A conclusion is not a summary of what a writer has already mentioned. On the contrary, it is the last point made. Taking every detail of the investigation, the researcher makes the concluding point. In this part of a paper, you need to put a full stop in your research. You need to persuade the reader in your opinion.

Never add any new information in the conclusion. You can present solutions to the problem and you dwell upon the results, but only if this information has been already mentioned in the main body.

Child advocates recommend a variety of strategies to aid families and children experiencing abuse. These recommendations tend to focus on societal efforts as well as more individual efforts. One common strategy advocated is the use of public service announcements that encourage individuals to report any suspected child abuse. Currently, many mandatory reporters (those required by law to report abuse such as teachers, doctors, and social service agency employees) and members of communities feel that child abuse should not be reported unless there is substantial evidence that abuse is indeed occurring. Child advocates stress that this notion should be changed, and that people should report child abuse even if it is only suspected. Public service announcements should stress that if people report suspected child abuse, the worst that can happen is that they might be wrong, but in the grander scheme of things that is really not so bad.

Child advocates also stress that greater interagency cooperation is needed. This cooperation should be evident between women’s shelters, child protection agencies, programs for at-risk children, medical agencies, and law enforcement officers. These agencies typically do not share information, and if they did, more instances of child abuse would come to the attention of various authorities and could be investigated and managed. Along these lines, child protection agencies and programs should receive more funding. When budgets are cut, social services are often the first things to go or to get less financial support. Child advocates insist that with more resources, child protection agencies could hire more workers, handle more cases, conduct more investigations, and follow up with more children and families.

Continuing, more educational efforts must be initiated about issues such as punishment and discipline styles and strategies; having greater respect for children; as well as informing the community about what child abuse is, and how to recognize it. In addition, Americans must alter the cultural orientation about child bearing and child rearing. Couples who wish to remain child-free must be allowed to do so without disdain. And, it must be acknowledged that raising children is very difficult, is not always gloriously wonderful, and that parents who seek help should be lauded and not criticized. These kinds of efforts can help more children to be raised in nonviolent, emotionally satisfying families, and thus become better adults.

Bibliography

When you write a paper, make sure you are aware of all the formatting requirements. Incorrect formatting can lower your mark, so do not underestimate the importance of this part.

Organizing your bibliography is quite a tedious and time-consuming task. Still, you need to do it flawlessly. For this reason, analyze all the standards you need to meet or ask professionals to help you with it. All the comas, colons, brackets etc. matter. They truly do.

Bibliography:

• American Academy of Pediatrics: https://www.aap.org/
• Bancroft, L., & Silverman, J. G. (2002). The batterer as parent. Thousand Oaks, CA: Sage.
• Child Abuse Prevention and Treatment Act, 42 U.S.C.A. § 5106g (1998).
• Childhelp: Child Abuse Statistics: https://www.childhelp.org/child-abuse-statistics/
• Children’s Defense Fund: https://www.childrensdefense.org/
• Child Stats.gov: https://www.childstats.gov/
• Child Welfare League of America: https://www.cwla.org/
• Crosson-Tower, C. (2008). Understanding child abuse and neglect (7th ed.). Boston: Allyn & Bacon.
• DeBecker, G. (1999). Protecting the gift: Keeping children and teenagers safe (and parents sane). New York: Bantam Dell.
• Family Research Laboratory at the University of New Hampshire: https://cola.unh.edu/family-research-laboratory
• Guterman, N. B. (2001). Stopping child maltreatment before it starts: Emerging horizons in early home visitation services. Thousand Oaks, CA: Sage.
• Herman, J. L. (2000). Father-daughter incest. Cambridge, MA: Harvard University Press.
• Medline Plus, Child Abuse: https://medlineplus.gov/childabuse.html
• Myers, J. E. B. (Ed.). (1994). The backlash: Child protection under fire. Newbury Park, CA: Sage.
• National Center for Missing and Exploited Children: https://www.missingkids.org/home
• National Child Abuse and Neglect Data System. (2006). Child maltreatment 2006: Reports from the states to the National Child Abuse and Neglect Data System. Washington, DC: U.S. Department of Health and Human Services, Administration for Children and Families.
• New York University Silver School of Social Work: https://socialwork.nyu.edu/
• Pitzer, R. L. (1997). Corporal punishment in the discipline of children in the home: Research update for practitioners. Paper presented at the National Council on Family Relations Annual Conference, Washington, DC.
• RAND, Child Abuse and Neglect: https://www.rand.org/topics/child-abuse-and-neglect.html
• Richards, C. E. (2001). The loss of innocents: Child killers and their victims. Wilmington, DE: Scholarly Resources.
• Straus, M. A. (2001). Beating the devil out of them: Corporal punishment in American families and its effects on children. Edison, NJ: Transaction.
• Thomas, P. M. (2004). Protection, dissociation, and internal roles: Modeling and treating the effects of child abuse. Review of General Psychology, 7(15).
• U.S. Department of Health and Human Services, Administration for Children and Families: https://www.acf.hhs.gov/

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Simple random sampling: definition, examples, and how to do it.

9 min read How can you pick a sample that’s truly random and representative of the participant population? Simple random sampling is the sampling method that makes this easy. Learn how it works in our ultimate guide.

Definition — what is simple random sampling?

Simple random sampling selects a smaller group (the sample) from a larger group of the total number of participants (the population). It’s one of the simplest systematic sampling methods used to gain a random sample.

The technique relies on using a selection method that provides each participant with an equal chance of being selected, giving each participant the same probability of being selected.

Since the selection process is based on probability and random selection, the end smaller sample is more likely to be representative of the total population and free from researcher bias . This method is also called a method of chances.

Simple random sampling is one of the four probability sampling techniques: Simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

The process of simple random sampling

• Define the population size you’re working with. This could be based on the population of a city. For this exercise, we will assume a population size of 1000.
• Assign a random sequential number to each participant in the population, which acts as an ID number – e.g. 1, 2, 3, 4, 5, and so on to 1000.
• Decide the sample size number needed. Not sure about what the right sample size should be? Try our Sample Size Calculator . For this exercise, let’s use 100 as the sample size.
• Select your sample by running a random number generator to provide 100 randomly generated numbers from between 1 and 1000.

Why do we use simple random sampling?

Simple random sampling is normally used where there is little known about the population of participants. Researchers also need to make sure they have a method for getting in touch with each participant to enable a true population size to work from. This leads to a number of advantages and disadvantages to consider.

Advantages of simple random sampling

This sampling technique can provide some great benefits.

• Participants have an equal and fair chance of being selected. As the selection method used gives every participant a fair chance, the resulting sample is unbiased and unaffected by the research team. It is perfect for blind experiments.
• This technique also provides randomised results from a larger pool. The resulting smaller sample should be representative of the entire population of participants, meaning no further segmenting is needed to refine groups down.
• Lastly, this method is cheap, quick, and easy to carry out – great when you want to get your research project started quickly.

Disadvantages of simple random sampling

• There may be cases where the random selection does not result in a truly random sample. Sampling errors may result in similar participants being selected, where the end sample does not reflect the total population.
• This provides no control for the researcher to influence the results without adding bias. In these cases, repeating the selection process is the fairest way to resolve the issue.

What selection methods can you use?

A lottery is a good example of simple random sampling at work. You select your set of numbers, buy a ticket, and hope your numbers match the randomly selected lotto balls. The players with matching numbers are the winners, who represent a small proportion of winning participants from the total number of players.

Other selection methods used include anonymising the population – e.g. by assigning each item or person in the population a number – and then picking numbers at random.

Researchers can use a simpler version of this by placing all the participants’ names in a hat and selecting names to form the smaller sample.

Comparing simple random sampling with the three other probability sampling methods

The three other types of probability sampling techniques have some clear similarities and differences to simple random sampling:

Systematic sampling

Systematic sampling, or systematic clustering, is a sampling method based on interval sampling – selecting participants at fixed intervals.

All participants are assigned a number. A random starting point is decided to choose the first participant. A defined interval number is chosen based on the total sample size needed from the population, which is applied to every nth participant after the first participant.

For example, the researcher randomly selects the 5th person in the population. An interval number of 3 is chosen, so the sample is populated with the 8th, 11th, 14th, 17th, 20th, (and so on) participants after the first selection.

Since the starting point of the first participant is random, the selection of the rest of the sample is considered to be random.

Simple random sampling differs from systematic sampling as there is no defined starting point. This means that selections could be from anywhere across the population and possible clusters may arise.

Stratified sampling

Stratified sampling splits a population into predefined groups, or strata, based on differences between shared characteristics – e.g. race, gender, nationality. Random sampling occurs within each of these groups.

This sampling technique is often used when researchers are aware of subdivisions within a population that need to be accounted for in the research – e.g. research on gender split in wages requires a distinction between female and male participants in the samples.

Simple random sampling differs from stratified sampling as the selection occurs from the total population, regardless of shared characteristics. Where researchers apply their own reasoning for stratifying the population, leading to potential bias, there is no input from researchers in simple random sampling.

Cluster sampling

There are two forms of cluster sampling: one-stage and two-stage.

One-stage cluster sampling first creates groups, or clusters, from the population of participants that represent the total population. These groups are based on comparable groupings that exist  – e.g. zip codes, schools, or cities .

The clusters are randomly selected, and then sampling occurs within these selected clusters. There can be many clusters and these are mutually exclusive, so participants don’t overlap between the groups.

Two-stage cluster sampling first randomly selects the cluster, then the participants are randomly selected from within that cluster.

Simple random sampling differs from both cluster sampling types as the selection of the sample occurs from the total population, not the randomly selected cluster that represents the total population.

In this way, simple random sampling can provide a wider representation of the population, while cluster sampling can only provide a snapshot of the population from within a cluster.

Frequently asked questions (FAQs) about simple random sampling

What if i’m working with a large population.

Where sample sizes and the participant population are large, manual methods for selection aren’t feasible with the available time and resources.

This is where computer-aided methods are needed to help to carry out a random selection process – e.g. using a spreadsheet’s random number function, using random number tables, or a random number generator.

What is the probability formula for being selected in the sample?

Let’s take an example in practice. A company wants to sell its bread brand in a new market area. They know little about the population. The population is made up of 15,000 people and a sample size of 10% (1,500) is required. Using this example, here is how this looks as a formula:

Sample size (S) = 1,500

The total population (P) = 15,000

The probability of being included in the sample is: (S ÷ P) x 100%

E.g. = (1,500 ÷ 15,000) x 100% = 10%

What are random number tables?

One way of randomly selecting numbers is to use a random number table (visual below). This places the total population’s sequential numbers from left to right in a table of N number of rows and columns.

To randomly select numbers, researchers will select certain rows or columns for the sample group.

As sourced from Statistical Aid

How do i generate random numbers in an excel spreadsheet.

Microsoft Office’s Excel spreadsheet application has a formula that can help you generate a random number. This is:

It provides a random number between 1 and 0.

For random numbers from the total population (for example, a population of 1000 participants), the formula is updated to:

=INT( 1000 *RAND())+1

Simply copy and paste the formula into cells until you get to the desired sample size – if you need a sample size of 25, you must paste this formula into 25 cells. The returned numbers between 1 and 1000 will indicate the participant’s ID numbers that make up the sample.

Conclusion: Where to go next to learn more?

What sample size should you go for? Try our online calculator to see how many people you should be selecting: Calculate the perfect sample size

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Related resources

Sampling methods 15 min read, sampling and non-sampling errors 10 min read, determining sample size 12 min read, selection bias: how to avoid errors in research 11 min read, systematic random sampling 12 min read, convenience sampling 18 min read, non-probability sampling 17 min read, request demo.

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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

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Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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