From ANOVA to regression: 10 key statistical analysis methods explained
Last updated
24 October 2024
Reviewed by
Miroslav Damyanov
Every action we take generates data. When you stream a video, browse a website, or even make a purchase, valuable data is created. However, without statistical analysis, the potential of this information remains untapped.
Understanding how different statistical analysis methods work can help you make the right choice. Each is applicable to a certain situation, data type, and goal.
- What is statistical analysis?
Statistical analysis is the process of collecting, organizing, and interpreting data. The goal is to identify trends and relationships. These insights help analysts forecast outcomes and make strategic business decisions.
This type of analysis can apply to multiple business functions and industries, including the following:
Finance : helps companies assess investment risks and performance
Marketing : enables marketers to identify customer behavior patterns, segment markets, and measure the effectiveness of advertising campaigns
Operations: helps streamline process optimization and reduce waste
Human resources : helps track employee performance trends or analyze turnover rates
Product development : helps with feature prioritization, evaluating A/B test results, and improving product iterations based on user data
Scientific research: supports hypothesis testing, experiment validation, and the identification of significant relations in data
Government: informs public policy decisions, such as understanding population demographics or analyzing inflation
With high-quality statistical analysis, businesses can base their decisions on data-driven insights rather than assumptions. This helps build more effective strategies and ultimately improves the bottom line.
- Importance of statistical analysis
Statistical analysis is an integral part of working with data. Implementing it at different stages of operations or research helps you gain insights that prevent costly errors.
Here are the key benefits of statistical analysis:
Informed decision-making
Statistical analysis allows businesses to base their decisions on solid data rather than assumptions.
By collecting and interpreting data, decision-makers can evaluate the potential outcomes of their strategies before they implement them. This approach reduces risks and increases the chances of success.
Understanding relationships and trends
In many complex environments, the key to insights is understanding relationships between different variables. Statistical methods such as regression or factor analysis help uncover these relationships.
Uncovering correlations through statistical methods can pave the way for breakthroughs in fields like medicine, but the true impact lies in identifying and validating cause-effect relationships. By distinguishing between simple associations and meaningful patterns, statistical analysis helps guide critical decisions, such as developing potentially life-saving treatments.
Predicting future outcomes
Statistical analysis, particularly predictive analysis and time series analysis, provides businesses with tools to forecast events based on historical data.
These forecasts help organizations prepare for future challenges (such as fluctuations in demand, market trends, or operational bottlenecks). Being able to predict outcomes allows for better resource allocation and risk mitigation.
Improving efficiency and reducing waste
Using statistical analysis can lead to improved efficiency in areas where waste occurs. In operations, this can result in streamlining processes.
For example, manufacturers can use causal analysis to identify the factors contributing to defective products and then implement targeted improvements to eliminate the causes.
Enhancing accuracy in research
In scientific research, statistical methods ensure accurate results by validating hypotheses and analyzing experimental data.
Methods such as regression analysis and ANOVA (analysis of variance) allow researchers to draw conclusions from experiments by examining relationships between variables and identifying key factors that influence outcomes.
Without statistical analysis, research findings may not be reliable. This could result in teams drawing incorrect conclusions and forming strategies that cost more than they’re worth.
Validating business assumptions
When businesses make assumptions about customer preferences, market conditions, or operational outcomes, statistical analysis can validate them.
For example, hypothesis testing can provide a framework to either confirm or reject an assumption. With these results at hand, businesses reduce the likelihood of pursuing incorrect strategies and improve their overall performance.
- Types of statistical analysis
The two main types of statistical analysis are descriptive and inferential. However, there are also other types. Here’s a short breakdown:
Descriptive analysis
Descriptive analysis focuses on summarizing and presenting data in a clear and understandable way. You can do this with simple tools like graphs and charts.
This type of statistical analysis helps break down large datasets into smaller, digestible pieces. This is usually done by calculating averages, frequencies, and ranges. The goal is to present the data in an orderly fashion and answer the question, “What happened?”
Businesses can use descriptive analysis to evaluate customer demographics or sales trends. A visual breakdown of complex data is often useful enough for people to come to useful conclusions.
Diagnostic statistics
This analysis is used to determine the cause of a particular outcome or behavior by examining relationships between variables. It answers the question, “Why did this happen?”
This approach often involves identifying anomalies or trends in data to understand underlying issues.
Inferential analysis
Inferential analysis involves drawing conclusions about a larger population based on a sample of data. It helps predict trends and test hypotheses by accounting for uncertainty and potential errors in the data.
For example, a marketing team can arrive at a conclusion about their potential audience’s demographics by analyzing their existing customer base. Another example is vaccine trials, which allow researchers to come to conclusions about side effects based on how the trial group reacts.
Predictive analysis
Predictive analysis uses historical data to forecast future outcomes. It answers the question, “What might happen in the future?”
For example, a business owner can predict future customer behavior by analyzing their past interactions with the company. Meanwhile, marketers can anticipate which products are likely to succeed based on past sales data.
This type of analysis requires the implementation of complex techniques to ensure the expected results. These results are still educated guesses—not error-free conclusions.
Prescriptive analysis
Prescriptive analysis goes beyond predicting outcomes. It suggests actionable steps to achieve desired results.
This type of statistical analysis combines data, algorithms, and business rules to recommend actual strategies. It often uses optimization techniques to suggest the best course of action in a given scenario, answering the question, “What should we do next?”
For example, in supply chain management, prescriptive analysis helps optimize inventory levels by providing specific recommendations based on forecasts. A bank can use this analysis to predict loan defaults based on economic trends and adjust lending policies accordingly.
Exploratory data analysis
Exploratory data analysis (EDA) allows you to investigate datasets to discover patterns or anomalies without predefined hypotheses. This approach can summarize a dataset’s main characteristics, often using visual methods.
EDA is particularly useful for uncovering new insights that weren’t anticipated during initial data collection .
Causal analysis
Causal analysis seeks to identify cause-and-effect relationships between variables. It helps determine why certain events happen, often employing techniques such as experiments or quasi-experimental designs to establish causality.
Understanding the “why” of specific events can help design accurate proactive and reactive strategies.
For example, in marketing, causal analysis can be applied to understand the impact of a new advertising campaign on sales.
Bayesian statistics
This approach incorporates prior knowledge or beliefs into the statistical analysis. It involves updating the probability of a hypothesis as more evidence becomes available.
- Statistical analysis methods
Depending on your industry, needs, and budget, you can implement different statistical analysis methods. Here are some of the most common techniques:
A t-test helps determine if there’s a significant difference between the means of two groups. It works well when you want to compare the average performance of two groups under different conditions.
There are different types of t-tests, including independent or dependent.
T-tests are often used in research experiments and quality control processes. For example, they work well in drug testing when one group receives a real drug and another receives a placebo. If the group that received a real drug shows significant improvements, a t-test helps determine if the improvement is real or chance-related.
2. Chi-square tests
Chi-square tests examine the relationship between categorical variables. They compare observed results with expected results. The goal is to understand if the difference between the two is due to chance or the relationship between the variables.
For instance, a company might use a chi-square test to analyze whether customer preferences for a product differ by region.
It’s particularly useful in market research , where businesses analyze responses to surveys .
ANOVA, which stands for analysis of variance, compares the means of three or more groups to determine if there are statistically significant differences among them.
Unlike t-tests, which are limited to two groups, ANOVA is ideal when comparing multiple groups at once.
One-way ANOVA: analysis with one independent variable and one dependent variable
Two-way ANOVA: analysis with two independent variables
Multivariate ANOVA (MANOVA): analysis with more than two independent variables
Businesses often use ANOVA to compare product performance across different markets and evaluate customer satisfaction across various demographics. The method is also common in experimental research, where multiple groups are exposed to different conditions.
4. Regression analysis
Regression analysis examines the relationship between one dependent variable and one or more independent variables. It helps businesses and researchers predict outcomes and understand which factors influence results the most.
This method determines a best-fit line and allows the researcher to observe how the data is distributed around this line.
It helps economists with asset valuations and predictions. It can also help marketers determine how variables like advertising affect sales.
A company might use regression analysis to forecast future sales based on marketing spend, product price, and customer demographics.
6. Time series analysis
Time series analysis evaluates data points collected over time to identify trends. An analyst records data points at equal intervals over a certain period instead of doing it randomly.
This method can help businesses and researchers forecast future outcomes based on historical data. For example, retailers might use time series analysis to plan inventory around holiday shopping trends, while financial institutions rely on it to track stock market trends. An energy company can use it to evaluate consumption trends and streamline the production schedule.
7. Survival analysis
Survival analysis focuses on time-to-event data, such as the time it takes for a machine to break down or for a customer to churn. It looks at a variable with a start time and end time. The time between them is the focus of the analysis.
This method is highly useful in medical research—for example, when studying the time between the beginning of a patient’s cancer remission and relapse. It can help doctors understand which treatments have desired or unexpected effects.
This analysis also has important applications in business. For example, companies use survival analysis to predict customer retention , product lifespan, or time until product failure.
8. Factor analysis
Factor analysis (FA) reduces large sets of variables into fewer components. It’s useful when dealing with complex datasets because it helps identify underlying structures and simplify data interpretation. This analysis is great for extracting maximum common variance from all necessary variables and turning them into a single score.
For example, in market research, businesses use factor analysis to group customer responses into broad categories. This helps reveal hidden patterns in consumer behavior .
It’s also helpful in product development, where it can use survey data to identify which product features are most important to customers.
9. Cluster analysis
Cluster analysis groups objects or individuals based on their similarities. This technique works great for customer segmentation, where businesses group customers based on common factors (such as purchasing behavior, demographics, and location).
Distinct clusters help companies tailor marketing strategies and develop personalized services. In education, this analysis can help identify groups of students who require additional assistance based on their achievement data. In medicine, it can help identify patients with similar symptoms to create targeted treatment plans.
10. Principal component analysis
Principal component analysis (PCA) is a dimensionality-reduction technique that simplifies large datasets by converting them into fewer components. It helps remove similar data from the line of comparison without affecting the data’s quality.
PCA is widely used in fields like finance, marketing, and genetics because it helps handle large datasets with many variables. For example, marketers can use PCA to identify which factors most influence customer buying decisions.
- How to choose the right statistical analysis method
Since numerous statistical analysis methods exist, choosing the right one for your needs may be complicated. While all of them can be applicable to the same situation, understanding where to start can save time and money.
Define your objective
Before choosing any statistical method, clearly define the objective of your analysis. What do you want to find out? Are you looking to compare groups, predict outcomes, or identify relationships between variables?
For example, if your goal is to compare averages between two groups, you can use a t-test. If you want to understand the effect of multiple factors on a single outcome, regression analysis could be the right choice for you.
Identify your data type
Data can be categorical (like yes/no or product types) or numerical (like sales figures or temperature readings).
For example, if you’re analyzing the relationship between two categorical variables, you may need a chi-square test. If you’re working with numerical data and need to predict future outcomes, you could use a time series analysis.
Evaluate the number of variables
The number of variables involved in your analysis influences the method you should choose. If you’re working with one dependent variable and one or more independent variables, regression analysis or ANOVA may be appropriate.
If you’re handling multiple variables, factor analysis or PCA can help simplify your dataset.
Determine sample size and data availability
Consider the assumptions of each method.
Each statistical method has its own set of assumptions, such as the distribution of the data or the relationship between variables.
For example, ANOVA assumes that the groups being compared have similar variances, while regression assumes a linear relationship between independent and dependent variables .
Understand if observations are paired or unpaired
When choosing a statistical test, you need to figure out if the data is paired or unpaired.
Paired data : the same subjects are measured more than once, like before and after a treatment or when using different methods.
Unpaired data: each group has different subjects.
For example, if you’re comparing the average scores of two groups, use a paired t-test for paired data and an independent t-test for unpaired data.
- Making the most of key statistical analysis methods
Each statistical analysis method is designed to simplify the process of gaining insights from a specific dataset. Understanding which data you need to analyze and which results you want to see can help you choose the right method.
With a comprehensive approach to analytics, you can maximize the benefits of insights and streamline decision-making. This isn’t just applicable in research and science. Businesses across multiple industries can reap significant benefits from well-structured statistical analysis.
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15 Types of Research Methods
Chris Drew (PhD)
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]
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Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).
Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:
- Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
- Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.
Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.
Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .
Types of Research Methods
Research methods can be broadly categorized into two types: quantitative and qualitative.
- Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
- Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.
These can be further broken down into a range of specific research methods and designs:
Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:
- Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
- Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.
Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.
Qualitative Research Methods
Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).
These methods are useful when a detailed understanding of a phenomenon is sought.
1. Ethnographic Research
Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.
Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).
In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .
The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.
However, it can be time-consuming and may reflect researcher biases due to the immersion approach.
Example of Ethnography
Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.
2. Phenomenological Research
Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).
It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).
This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.
Example of Phenomenological Research
A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.
3. Historical Research
Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).
As you might expect, it’s common in the research branches of history departments in universities.
This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.
Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.
Example of Historical Research
A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.
4. Content Analysis
Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).
A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.
However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.
Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .
Example of Content Analysis
How is Islam Portrayed in Western Media? by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.
5. Grounded Theory Research
Grounded theory involves developing a theory during and after data collection rather than beforehand.
This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).
Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).
Grounded Theory Example
Developing a Leadership Identity by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.
6. Action Research
Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).
This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.
Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.
Action Research Example
Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.
7. Natural Observational Research
Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.
This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.
While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.
Observational Research Example
A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.
8. Case Study Research
Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).
Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).
However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).
See More: Case Study Advantages and Disadvantages
Example of a Case Study
Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.
Quantitative Research Methods
Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
9. Experimental Research
Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).
This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.
This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.
Example of Experimental Research
A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).
10. Surveys and Questionnaires
Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).
Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.
They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).
Example of a Survey Study
A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).
11. Longitudinal Studies
Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.
With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.
Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.
While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.
Example of a Longitudinal Study
A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.
12. Cross-Sectional Studies
Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.
This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.
The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.
However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.
Example of a Cross-Sectional Study
Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.
13. Correlational Research
Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).
This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.
While correlational research can reveal relationships between variables, it cannot establish causality.
Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.
Example of Correlational Research
A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.
14. Quasi-Experimental Design Research
Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.
Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.
The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.
Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.
Example of Quasi-Experimental Design
A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.
Related: Examples and Types of Random Assignment in Research
15. Meta-Analysis Research
Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .
Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.
Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.
However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.
Example of a Meta-Analysis
The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.
Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.
Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.
Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.
Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.
Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage
Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.
Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.
Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.
Walliman, N. (2021). Research methods: The basics. London: Routledge.
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