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Chapter III Methodology Research Locale

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Chapter III METHODOLOGY Research Locale

The study was conducted at Marinduque National High School, a DepEd managed partially urban secondary public school and a mother school of all secondary public school in Marinduque. This school comprises Senior High School and offers two tracks which are Academic and Technical Vocational Tracks. Under Academic Track are General Academic Strand (GAS) and Science, Technology, Engineering and Mathematics Stand (STEM). It is located at Isok-1, Boac, Marinduque (Latitude: 13°26'43", Longitude: 121°50'22") and the write up was done at Pili, Boac, Marinduque.

Source: Google Maps and Nona Figure 2. Map of the Municipality of Boac, Marinduque

Research Design

The research study aims to identify the relationship between perceptions of STEM students on hands-on activities and their level of understanding in Chemistry lessons to determine student’s attitudes towards Chemistry subject.

Exploratory research inquiry was administered in order to explore beyond the students’ perceptions on hands-on activities and on how they deal with the concepts of the lessons. Cross-sectional research design was used to collect data from a cross-section of a population at one point in time. This research design was used to look for and determine the relationship between the two variables of the study and to test out ideas and hypotheses.

Instrumentation

The research instrument was used for data gathering consisting close and open-ended questions. It has 20 items to be answered specifically ten (10) for the perceptions of students on Chemistry Hands-on Activities and ten (10) for the students’ level of understanding in Chemistry lessons. For the level of understanding of students in Chemistry lesson they were asked to state their reasons why did they answer that letter. The research instrument adapted the student’s questionnaire of Mwangi’s research (2016) entitled “Effect of Chemistry Practicals on Students’ performance in Chemistry in Public Secondary Schools of Machakos and Nairobi counties in Kenya” and Mulford’s Chemical Concept Inventory. The research instrument was administered to measure student’s

collected three (3) days after. Revisions were made after the content validation considering the comments and suggestion of the teachers. Table 1. Mean Rating of Master Teachers in Science on the research instrument

Based from the table above, Mrs. Mingi (Expert 1) agree that the questionnaire is organized yet she commented that the researchers must check the grammar while Mr. Marmol (Expert 2) strongly agree that the research instrument is well constructed. On the other hand, Mrs. Solas (Expert 3) moderately agree that the research instrument is well made. She suggests that the questions should be simplified and modified by the researchers. The researchers considered these comments and suggestions and revised the research instrument. Thus, the master teachers in science agree that the questionnaire is valid and agree with each of the statement in the questionnaire for content validation of instrument based from the Questionnaire on Content Validity of Pili (2006) and Survey Rubic (2015).

Table 2. Reliability statistics of Master Teachers in Science’s rating in the research instrument

Experts Mean Rating 1 4. 2 5. 3 3. Overall Mean 4.

Total Items 12 Cronbach’s alpha 0.

Based from reliability statistics presented, the Cronbach's alpha computed is 0 or almost equivalent to 1. This indicates that 96% of the variance in the scores/ratings in the research instrument’s content validation of Master Teachers in Science is reliable or only 3% error variance. According to Lance et. al.(2006), 1 Cronbach’s alpha is a perfect consistency in measurement and values over .90 are generally considered to reflect adequate fit of the model to the data.

Construct Validation of the Research Instrument The Grade 11 Science Technology Engineering and Mathematics (STEM) students of Marinduque National High School SY 2017-2018 served as the validators in the test run of the instrument. The questionnaires were distributed to the Grade 11 STEM Students on September 25, 2017 and were collected after two (2) days. Table 3. Mean Rating of Grade 11 STEM Students on the research instrument

shown in table 3, the Grade 11 STEM students agree that hands-on activities is important but their level of understanding in Chemistry is correct/incomplete

Mean rating Perceptions on Chemistry Hands-on activities 3. Level of Understanding in Chemistry Lessons 2.

used to ensure a fairly equal selection on the population size and in accordance to the requirements of the research study.

Data Gathering Procedure

The questionnaires were distributed personally to the Grade 12 STEM Students and were then collected. The data gathered were analyzed, interpreted and tabulated.

Statistical Treatment of Data

The information was analyzed using descriptive and inferential statistics. Descriptive statistics (e. frequencies, mean) help to describe and understand the features of the specific data set by giving short summaries about the sample and measures of the data. Cronbach’s alpha was computed for the credibility and reliability testing of the instrument. Computing for the Pearson Product Moment Coefficient of Correlation (r) and P-value was applied to the data in measuring the significant relationship between the two variables. The results were then presented using figures and tables for easiness of understanding and analysis.

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Research Locale

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A research locale refers to the specific geographical area or location where a study or research is conducted. This locale is carefully chosen based on the study’s objectives, the population of interest, and the relevance of the location to the research questions. Selecting an appropriate research locale is crucial as it impacts the validity and generalizability of the study’s findings. The locale provides the context within which data is collected, analyzed, and interpreted, making it a fundamental aspect of the research action plan . In studies focusing on environmental or biological aspects, understanding the endemic species within the research locale is essential, as these species are native to the area and can significantly influence the research outcomes.

What is Research Locale?

Research locale refers to the specific geographical location or setting where a study is conducted. This area is chosen based on the objectives and requirements of the research, as it provides the necessary context and environment for gathering relevant data. The research locale can range from a small community or institution to a larger region or multiple sites, depending on the scope of the study.

Examples of Research Locale

• Schools: Conducting a study on the effectiveness of a new teaching method in elementary, middle, or high schools.
• Universities: Researching student behaviors, learning outcomes, or the impact of specific academic programs in higher education settings.
• Hospitals: Investigating patient recovery rates or the efficacy of new treatments in a hospital setting.
• Clinics: Studying the accessibility and quality of healthcare services in local clinics.
• Urban Areas: Examining the effects of urbanization on residents’ quality of life, health, or social interactions.
• Rural Areas: Researching agricultural practices, rural healthcare accessibility, or educational challenges in rural settings.
• Corporations: Studying employee satisfaction, productivity, or the impact of corporate policies in large companies.
• Small Businesses: Investigating the challenges and successes of small business operations in local communities.
• Parks: Researching the usage patterns and benefits of public parks for community health and well-being.
• Libraries: Examining the role of public libraries in community education and engagement.
• Countries: Conducting cross-national studies on economic development, public health, or educational systems.
• Regions: Researching environmental impacts, cultural practices, or regional policies in specific areas such as the Midwest, the Himalayas, or the Amazon Basin.
• Social Media Platforms: Studying user behavior, misinformation spread, or social interactions on platforms like Facebook, Twitter, or Instagram.
• Virtual Communities: Investigating the dynamics of online forums, gaming communities, or e-learning environments.

Research Locale Examples in School

• Classroom Dynamics: Investigating how seating arrangements affect student interaction and participation in a third-grade classroom.
• Reading Programs: Assessing the impact of a new phonics-based reading program on literacy rates among first graders.
• Bullying Prevention: Studying the effectiveness of anti-bullying programs and policies in reducing incidents of bullying among sixth to eighth graders.
• STEM Education: Evaluating the success of extracurricular STEM clubs in improving students’ interest and performance in science and math subjects.
• College Preparation: Analyzing how different college preparatory programs influence the readiness and success of students applying to universities.
• Sports Participation: Researching the correlation between participation in high school sports and academic performance, self-esteem, and social skills.
• Inclusive Practices: Investigating the effectiveness of inclusive education practices on the social integration and academic achievements of students with special needs.
• Assistive Technologies: Evaluating the impact of various assistive technologies on the learning outcomes of students with disabilities.
• Curriculum Impact: Assessing the impact of specialized curricula (e.g., arts, sciences, or technology-focused) on student engagement and academic performance.
• Student Diversity: Studying the effects of a diverse student body on cultural awareness and interpersonal skills among students.
• Innovative Teaching Methods: Examining the outcomes of innovative teaching methods and curricula implemented in charter schools compared to traditional public schools.
• Parental Involvement: Researching how parental involvement in charter schools affects student motivation and achievement.
• Residential Life: Investigating the effects of boarding school environments on student independence, social development, and academic performance.
• Extracurricular Activities: Studying the role of extracurricular activities in shaping the overall development and well-being of boarding school students.
• Multicultural Education: Examining the impact of multicultural education programs on students’ global awareness and acceptance of cultural diversity.
• Language Acquisition: Researching the effectiveness of bilingual education programs in international schools on students’ proficiency in multiple languages.

Examples of Research Locale Quantitative

• Measuring the effect of a new math curriculum on standardized test scores among fourth-grade students.
• Analyzing the relationship between breakfast programs and student attendance rates.
• Quantifying the impact of restorative justice practices on the frequency of disciplinary actions.
• Assessing the correlation between educational technology use in classrooms and student achievement in science.
• Investigating factors influencing graduation rates, including socio-economic status and teacher-student ratios.
• Evaluating the effectiveness of college preparatory programs by comparing college admission rates of participants versus non-participants.
• Measuring the progress of students with Individualized Education Plans (IEPs) in academic performance and behavioral improvements.
• Quantifying the impact of different assistive technologies on academic success.
• Comparing academic performance data between students in magnet schools and traditional public schools.
• Analyzing enrollment data to determine the diversity of student populations and its impact on academic outcomes.
• Assessing academic outcomes by comparing standardized test scores between charter school students and traditional public school students.
• Measuring teacher retention rates in charter schools versus public schools.
• Quantifying academic performance by analyzing GPA and standardized test scores of boarding school students.
• Conducting surveys to collect quantitative data on student well-being and correlating it with academic success.
• Measuring language proficiency levels in bilingual programs using standardized language tests.
• Using surveys to quantify students’ cultural competence and its relationship with academic performance.

Examples of Research Locale Qualitative

• Classroom Interaction: Observing and documenting student-teacher interactions to understand the dynamics of effective teaching strategies.
• Playground Behavior: Conducting interviews and focus groups with students to explore their social interactions and conflict resolution methods during recess.
• Peer Relationships: Exploring the nature of peer relationships and their impact on students’ emotional well-being through in-depth interviews.
• Curriculum Implementation: Gathering teacher narratives on the challenges and successes of implementing a new curriculum.
• Extracurricular Activities: Investigating students’ experiences and perceptions of participating in extracurricular activities through case studies and interviews.
• Career Aspirations: Conducting focus groups to understand how students’ backgrounds and school experiences shape their career aspirations.
• Parent Perspectives: Interviewing parents of students with special needs to gather insights into their experiences and satisfaction with the educational services provided.
• Teacher Experiences: Collecting narratives from special education teachers about their experiences, challenges, and strategies in teaching students with diverse needs.
• Student Motivation: Exploring the factors that motivate students to attend and succeed in magnet schools through in-depth interviews.
• Cultural Integration: Studying how students from diverse backgrounds integrate and interact within the specialized environment of magnet schools.
• Teacher Retention: Investigating the reasons behind teacher retention and turnover in charter schools through qualitative interviews with current and former teachers.
• Parent Involvement: Conducting case studies to understand the role and impact of parent involvement in charter school communities.
• Residential Life: Exploring students’ experiences of residential life, focusing on their personal growth and social development through narrative inquiry.
• Alumni Perspectives: Interviewing alumni to gather insights on how their boarding school experience has influenced their post-graduation life.
• Cultural Adaptation: Examining the experiences of expatriate students adapting to new cultural environments through ethnographic studies.
• Multilingual Education: Conducting interviews with teachers and students to explore the challenges and benefits of multilingual education in international schools.

Research locale Sample Paragraph

This study was conducted in three public high schools located in the urban district of Greenville, North Carolina. The selected schools—Greenville High School, Central High School, and Riverside High School—were chosen for their diverse student populations and varying levels of technological integration in the classroom. Each school enrolls approximately 1,200 students, offering a mix of Advanced Placement (AP) courses, vocational training, and special education programs. Greenville High School recently implemented a 1:1 laptop initiative, providing each student with a personal device for educational use. Central High School utilizes a blended learning model with shared computer labs and mobile tablet carts, while Riverside High School maintains a more traditional approach with limited use of digital tools. This study focuses on 11th-grade students enrolled in English and Mathematics courses, examining how different levels of technology integration impact student engagement and academic performance. Data was collected through a combination of student surveys, standardized test scores, classroom observations, and interviews with teachers and administrators, aiming to provide comprehensive insights into the effectiveness of technology-enhanced learning environments.

How to write Research Locale?

The research locale section of your study provides a detailed description of the location where the research will be conducted. This section is crucial for contextualizing your research and helping readers understand the setting and its potential influence on your study. Here are the steps to write an effective research locale:

1. Introduction to the Locale

• Name and Description : Start by naming the locale and providing a brief description. Include geographic, demographic, and cultural aspects.
• Relevance : Explain why this locale is suitable for your study.

2. Geographic Details

• Location : Provide precise details about the location, including the city, state, country, and any specific areas within these larger regions.
• Map and Boundaries : If possible, include a map to illustrate the locale and its boundaries.

3. Demographic Information

• Population : Describe the population size, density, and composition. Include information on age, gender, ethnicity, and socio-economic status.
• Community Characteristics : Mention any unique characteristics of the community that are relevant to your study.

4. Socio-Economic and Cultural Context

• Economic Activities : Outline the primary economic activities and employment sectors in the locale.
• Cultural Practices : Highlight cultural practices, traditions, and values that might influence the study.

5. Educational and Institutional Context

• Schools and Institutions : If relevant, describe the educational institutions, such as schools or universities, and their role in the community.
• Other Institutions : Mention any other institutions (e.g., healthcare, religious) that might be relevant.

6. Accessibility and Infrastructure

• Transportation : Explain the transportation infrastructure, including roads, public transit, and accessibility.
• Facilities : Mention key facilities like hospitals, libraries, and recreational centers.

7. Environmental Factors

• Climate and Geography : Describe the climate and any geographic features that could impact your research.
• Environmental Conditions : Note any environmental conditions, such as pollution or natural resources, relevant to your study.

FAQ’s

Why is the research locale important.

The research locale is crucial because it influences the study’s context, data collection, and findings’ applicability.

How do you select a research locale?

Selection involves considering relevance to the research question, accessibility, availability of data, and potential impact on results.

What factors influence the choice of a research locale?

Factors include geographical location, demographic characteristics, cultural context, and logistical feasibility.

Can a study have multiple research locales?

Yes, studies can include multiple locales to compare different environments or enhance the study’s generalizability.

How does the research locale affect data collection?

The locale can determine the methods used, participant availability, and types of data collected.

What is the difference between research locale and research setting?

The research locale is the broader geographical area, while the research setting refers to the specific place within that locale.

How do you describe a research locale in a study?

Include geographical details, demographic information, cultural characteristics, and any relevant historical or social context.

Why might a researcher choose an urban research locale?

Urban locales offer diverse populations, accessible resources, and varied social dynamics.

Why might a researcher choose a rural research locale?

Rural locales provide unique insights into less-studied populations, community dynamics, and environmental factors.

What role does the research locale play in qualitative research?

In qualitative research, the locale is integral to understanding participants’ lived experiences and contextual factors.

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Chapter III METHODOLOGY Research Locale

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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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McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 18 November 2024, from https://www.scribbr.co.uk/research-methods/research-design/

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