Onderzoeksmethoden Research Methods

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  • Research Proposals (Chapter B6)

    Research proposals play a crucial role in the social sciences, serving as a roadmap for researchers and a tool for gaining approval or funding. Matthews and Ross (2010) emphasize the importance of research proposals in their textbook “Research Methods: A Practical Guide for the Social Sciences,” highlighting their role in outlining the scope, methodology, and significance of a research project.

    The choice of research method in social research is a critical decision that depends on various factors, including the research question, available resources, and ethical considerations. Matthews and Ross (2010) discuss several key research methods, including quantitative, qualitative, and mixed methods approaches.

    Quantitative methods involve collecting and analyzing numerical data, often using statistical techniques. These methods are particularly useful for testing hypotheses and identifying patterns across large populations. On the other hand, qualitative methods focus on in-depth exploration of phenomena, often using techniques such as interviews, focus groups, or participant observation (Creswell & Creswell, 2018).

    Mixed methods research, which combines both quantitative and qualitative approaches, has gained popularity in recent years. This approach allows researchers to leverage the strengths of both methodologies, providing a more comprehensive understanding of complex social phenomena (Tashakkori & Teddlie, 2010).

    When choosing a research method, researchers must consider the nature of their research question and the type of data required to answer it effectively. For example, a study exploring the prevalence of a particular behavior might be best suited to a quantitative approach, while an investigation into the lived experiences of individuals might benefit from a qualitative methodology.

    Ethical considerations also play a significant role in method selection. Researchers must ensure that their chosen method minimizes harm to participants and respects principles such as informed consent and confidentiality (Israel, 2014).

    Structure

    Introduction: This section sets the stage for your research by introducing the research problem or topic, clearly stating the research question(s), and outlining the objectives of your project3. It also establishes the context and significance of your research, highlighting its potential contributions and who might benefit from its findings

    Literature Review: This section demonstrates your understanding of the existing knowledge and research related to your topic4. It involves critically evaluating relevant literature and synthesizing key themes and findings, providing a foundation for your research questions and methodology.

    Methodology/Methods: This crucial section details how you plan to conduct your research4. It outlines the research design, the data collection methods you will employ, and the sampling strategy used to select participants or cases5. The methodology should align with your research questions and the type of data needed to address them.

    Dissemination: This section describes how you intend to share your research findings with relevant audiences. It may involve outlining plans for presentations, publications, or other forms of dissemination, ensuring the research reaches those who can benefit from it.

    Timetable: A clear timetable provides a realistic timeline for your research project, outlining key milestones and deadlines for each stage, including data collection, analysis, and writing6. It demonstrates your understanding of the time required to complete the research successfully.

    References:

    Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

    Israel, M. (2014). Research ethics and integrity for social scientists: Beyond regulatory compliance. Sage.

    Matthews, B., & Ross, L. (2010). Research methods: A practical guide for the social sciences. Pearson Education.

    Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage handbook of mixed methods in social & behavioral research. Sage.

    Citations:
    [1] https://www.bol.com/nl/nl/f/research-methods/39340982/
    [2] https://search.worldcat.org/title/Research-methods-:-a-practical-guide-for-the-social-sciences/oclc/867911596
    [3] https://www.pearson.com/en-gb/subject-catalog/p/research-methods-a-practical-guide-for-the-social-sciences/P200000004950/9781408226186


    [4] https://search.worldcat.org/title/Research-methods-:-a-practical-guide-for-the-social-sciences/oclc/780979587
    [5] https://www.studeersnel.nl/nl/document/tilburg-university/methodologie-4-ects/summary-research-methods-bob-matthews-liz-ross/109770
    [6] https://books.google.com/books/about/Research_Methods.html?id=g2mpBwAAQBAJ
    [7] https://books.google.com/books/about/Research_Methods.html?id=7s4ERAAACAAJ
    [8] https://academic.oup.com/bjc/article-abstract/52/5/1017/470134?login=false&redirectedFrom=fulltext

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  • Data Collection (Part C)

    Research Methods in Social Research: A Comprehensive Guide to Data Collection

    Part C of “Research Methods: A Practical Guide for the Social Sciences” by Matthews and Ross focuses on the critical aspect of data collection in social research. This section provides a comprehensive overview of various data collection methods, their applications, and practical considerations for researchers.

    The authors emphasize that data collection is a practical activity, building upon the concept of data as a representation of social reality (Matthews & Ross, 2010). They introduce three key continua to help researchers select appropriate tools for their studies:

    1. Structured/Semi-structured/Unstructured Data
    2. Present/Absent Researcher
    3. Active/Passive Researcher

    These continua highlight the complexity of choosing data collection methods, emphasizing that it’s not a simple binary decision but rather a nuanced process considering multiple factors[1].

    The text outlines essential data collection skills, including record-keeping, format creation, note-taking, communication skills, and technical proficiency. These skills are crucial for ensuring the quality and reliability of collected data[1].

    Chapters C3 through C10 explore specific data collection methods in detail:

    1. Questionnaires: Widely used for collecting structured data from large samples[1].
    2. Semi-structured Interviews: Offer flexibility for gathering in-depth data[1].
    3. Focus Groups: Leverage group dynamics to explore attitudes and opinions[1].
    4. Observation: Involves directly recording behaviors in natural settings[1].
    5. Narrative Data: Focuses on collecting and analyzing personal stories[1].
    6. Documents: Valuable sources for insights into past events and social norms[1].
    7. Secondary Sources of Data: Utilizes existing datasets and statistics[1].
    8. Computer-Mediated Communication (CMC): Explores new avenues for data collection in the digital age[1].

    Each method is presented with its advantages, disadvantages, and practical considerations, providing researchers with a comprehensive toolkit for data collection.

    The choice of research method in social research depends on various factors, including the research question, the nature of the data required, and the resources available. As Bryman (2016) notes in “Social Research Methods,” the selection of a research method should be guided by the research problem and the specific aims of the study[2].

    Creswell and Creswell (2018) in “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches” emphasize the importance of aligning the research method with the philosophical worldview of the researcher and the nature of the inquiry[3]. They argue that the choice between qualitative, quantitative, or mixed methods approaches should be informed by the research problem and the researcher’s personal experiences and worldviews.

    Part C of Matthews and Ross’s “Research Methods: A Practical Guide for the Social Sciences” provides a comprehensive foundation for understanding and implementing various data collection methods in social research. By considering the three key continua and exploring the range of available methods, researchers can make informed decisions about the most appropriate approaches for their specific research questions and contexts.

    References:

    Matthews, B., & Ross, L. (2010). Research methods: A practical guide for the social sciences. Pearson Education.

    Bryman, A. (2016). Social research methods. Oxford University Press.

    Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

    Citations:
    [1] https://www.bol.com/nl/nl/f/research-methods/39340982/
    [2] https://search.worldcat.org/title/Research-methods-:-a-practical-guide-for-the-social-sciences/oclc/867911596
    [3] https://www.pearson.com/en-gb/subject-catalog/p/research-methods-a-practical-guide-for-the-social-sciences/P200000004950/9781408226186
    [4] https://search.worldcat.org/title/Research-methods-:-a-practical-guide-for-the-social-sciences/oclc/780979587
    [5] https://www.studeersnel.nl/nl/document/tilburg-university/methodologie-4-ects/summary-research-methods-bob-matthews-liz-ross/109770
    [6] https://books.google.com/books/about/Research_Methods.html?id=g2mpBwAAQBAJ
    [7] https://books.google.com/books/about/Research_Methods.html?id=7s4ERAAACAAJ
    [8] https://academic.oup.com/bjc/article-abstract/52/5/1017/470134?login=false&redirectedFrom=fulltext

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  • Research Design (Chapter B3)

    Research Methods in Social Research: Choosing the Right Approach

    The choice of research method in social research is a critical decision that shapes the entire study. Matthews and Ross (2010) emphasize the importance of aligning the research method with the research questions and objectives. They discuss various research methods, including experimental designs, quasi-experimental designs, cross-sectional studies, longitudinal studies, and case studies.

    Experimental designs, while offering strong causal inferences, are often challenging to implement in social research due to the complexity of real-world situations[1]. Quasi-experimental designs provide a more practical alternative, allowing researchers to approximate experimental conditions in natural settings[1].

    Cross-sectional studies offer a snapshot of a phenomenon at a specific point in time, useful for describing situations or comparing groups[1]. In contrast, longitudinal studies track changes over time, providing insights into trends and potential causal relationships[1]. However, as Bryman (2016) notes, longitudinal studies can be resource-intensive and may face challenges with participant attrition over time[2].

    Case studies, as highlighted by Yin (2018), offer in-depth exploration of specific instances, providing rich, contextual data[3]. While case studies may lack broad generalizability, they can offer valuable insights into complex social phenomena[3].

    The choice of research method should be guided by several factors:

    1. Research questions and objectives
    2. Available resources and time constraints
    3. Ethical considerations
    4. Nature of the phenomenon being studied
    5. Desired level of generalizability

    Creswell and Creswell (2018) emphasize the growing importance of mixed methods research, which combines qualitative and quantitative approaches to provide a more comprehensive understanding of social phenomena[4].

    The selection of research method in social research is a nuanced decision that requires careful consideration of multiple factors. As Matthews and Ross (2010) stress, there is no one-size-fits-all approach, and researchers must critically evaluate the strengths and limitations of each method in relation to their specific research context[1].

    References:

    Matthews, B., & Ross, L. (2010). Research methods: A practical guide for the social sciences. Pearson Education.

    Bryman, A. (2016). Social research methods. Oxford University Press.

    Yin, R. K. (2018). Case study research and applications: Design and methods. Sage publications.

    Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

    Citations:
    [1] https://www.bol.com/nl/nl/f/research-methods/39340982/
    [2] https://search.worldcat.org/title/Research-methods-:-a-practical-guide-for-the-social-sciences/oclc/867911596
    [3] https://www.pearson.com/en-gb/subject-catalog/p/research-methods-a-practical-guide-for-the-social-sciences/P200000004950/9781408226186
    [4] https://search.worldcat.org/title/Research-methods-:-a-practical-guide-for-the-social-sciences/oclc/780979587
    [5] https://www.studeersnel.nl/nl/document/tilburg-university/methodologie-4-ects/summary-research-methods-bob-matthews-liz-ross/109770
    [6] https://books.google.com/books/about/Research_Methods.html?id=g2mpBwAAQBAJ
    [7] https://books.google.com/books/about/Research_Methods.html?id=7s4ERAAACAAJ
    [8] https://academic.oup.com/bjc/article-abstract/52/5/1017/470134?login=false&redirectedFrom=fulltext

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  • Choosing Method(Chapter B4)

    The choice of research method in social research is a critical decision that shapes the entire research process. Matthews and Ross (2010) emphasize the importance of aligning research methods with research questions and objectives. This alignment ensures that the chosen methods effectively address the research problem and yield meaningful results.

    Quantitative and qualitative research methods represent two distinct approaches to social inquiry. Quantitative research deals with numerical data and statistical analysis, aiming to test hypotheses and establish generalizable patterns[1]. It employs methods such as surveys, experiments, and statistical analysis of existing data[3]. Qualitative research, on the other hand, focuses on non-numerical data like words, images, and sounds to explore subjective experiences and attitudes[3]. It utilizes techniques such as interviews, focus groups, and observations to gain in-depth insights into social phenomena[1].

    The debate between quantitative and qualitative approaches has evolved into a recognition of their complementary nature. Mixed methods research, which combines both approaches, has gained prominence in social sciences. This approach allows researchers to leverage the strengths of both methodologies, providing a more comprehensive understanding of complex social issues[4]. For instance, a study might use surveys to gather quantitative data on trends, followed by in-depth interviews to explore the underlying reasons for these trends.

    When choosing research methods, several practical considerations come into play. Researchers must consider the type of data required, their skills and resources, and the specific research context[4]. The nature of the research question often guides the choice of method. For example, if the goal is to test a hypothesis or measure the prevalence of a phenomenon, quantitative methods may be more appropriate. Conversely, if the aim is to explore complex social processes or understand individual experiences, qualitative methods might be more suitable[2].

    It’s important to note that the choice of research method is not merely a technical decision but also reflects epistemological and ontological assumptions about the nature of social reality and how it can be studied[1]. Researchers should be aware of these philosophical underpinnings when selecting their methods.

    In conclusion, the choice of research method in social research is a crucial decision that requires careful consideration of research objectives, practical constraints, and philosophical assumptions. By thoughtfully selecting appropriate methods, researchers can ensure that their studies contribute meaningful insights to the field of social sciences.

    References:

    Matthews, B., & Ross, L. (2010). Research methods: A practical guide for the social sciences. Pearson Education.

    Scribbr. (n.d.). Qualitative vs. Quantitative Research | Differences, Examples & Methods.

    Simply Psychology. (2023). Qualitative vs Quantitative Research: What’s the Difference?

    National University. (2024). What Is Qualitative vs. Quantitative Study?

    Citations:
    [1] https://www.scribbr.com/methodology/qualitative-quantitative-research/
    [2] https://researcher.life/blog/article/qualitative-vs-quantitative-research/
    [3] https://www.simplypsychology.org/qualitative-quantitative.html
    [4] https://www.nu.edu/blog/qualitative-vs-quantitative-study/
    [5] https://pmc.ncbi.nlm.nih.gov/articles/PMC3327344/
    [6] https://www.thesoundhq.com/qualitative-vs-quantitative-research-better-together/
    [7] https://www.fullstory.com/blog/qualitative-vs-quantitative-data/
    [8] https://accelerate.uofuhealth.utah.edu/improvement/understanding-qualitative-and-quantitative-approac

  • Guide SPSS How to: Calculate ANOVA

    Here’s a step-by-step guide for 1st year students on how to calculate ANOVA in SPSS:

    Step 1: Prepare Your Data

    1. Open SPSS and enter your data into the Data View.
    2. Create two columns: one for your independent variable (factor) and one for your dependent variable (score)
    3. For the independent variable, use numbers to represent different groups (e.g., 1, 2, 3 for three different groups)

    Step 2: Run the ANOVA

    1. Click on “Analyze” in the top menu.
    2. Select “Compare Means” > “One-Way ANOVA”
    3. In the dialog box that appears:
    • Move your dependent variable (score) to the “Dependent List” box.
    • Move your independent variable (factor) to the “Factor” box

    Step 3: Additional Options

    1. Click on “Options” in the One-Way ANOVA dialog box.
    2. Select the following:
    • Descriptive statistics
    • Homogeneity of variance test
    • Means plot
    1. Click “Continue” to return to the main dialog box.

    Step 4: Post Hoc Tests

    1. Click on “Post Hoc” in the One-Way ANOVA dialog box
    2. Select “Tukey” for the post hoc test
    3. Ensure the significance level is set to 0.05 (unless your study requires a different level)
    4. Click “Continue” to return to the main dialog box.

    Step 5: Run the Analysis

    Click “OK” in the main One-Way ANOVA dialog box to run the analysis

    Step 6: Interpret the Results

    1. Check the “Test of Homogeneity of Variances” table. The significance value should be > 0.05 to meet this assumption
    2. Look at the ANOVA table:
    • If the significance value (p-value) is < 0.05, there are significant differences between groups
    1. If significant, examine the “Post Hoc Tests” table to see which specific groups differ
    2. Review the “Descriptives” table for means and standard deviations of each group

    Remember, ANOVA requires certain assumptions to be met, including normal distribution of the dependent variable and homogeneity of variances

    Always check these assumptions before interpreting your results.

  • Guide SPSS How to: Calculate the dependent t-test

    Here’s a guide for 1st year students on how to calculate the dependent t-test in SPSS:

    Step-by-Step Guide for Dependent t-test in SPSS

    1. Prepare Your Data

    • Ensure your data is in the correct format: two columns, one for each condition (e.g., before and after)
    • Each row should represent a single participant

    2. Open SPSS and Enter Data

    • Open SPSS and switch to the “Variable View”
    • Define your variables (e.g., “Before” and “After”)
    • Switch to “Data View” and enter your data

    3. Run the Test

    • Click on “Analyze” in the top menu
    • Select “Compare Means” > “Paired-Samples t Test”.
    • In the dialog box, move your two variables (e.g., Before and After) to the “Paired Variables” box
    • Click “OK” to run the test

    4. Interpret the Results

    • Look at the “Paired Samples Statistics” table for descriptive statistics
    • Check the “Paired Samples Test” table:
    • Find the t-value, degrees of freedom (df), and significance (p-value)
    • If p < 0.05, there’s a significant difference between the two conditions

    5. Report the Results

    • State whether there was a significant difference.
    • Report the t-value, degrees of freedom, and p-value.
    • Include means for both conditions.

    Tips:

    • Always check your data for accuracy before running the test.
    • Ensure your sample size is adequate for reliable results.
    • Consider the assumptions of the dependent t-test, such as normal distribution of differences between pairs.

    Remember, practice with sample datasets will help you become more comfortable with this process.

  • Guide SPSS How to: Calculate the independent t-test

    Step-by-Step Guide

    1. Open your SPSS data file.
    2. Click on “Analyze” in the top menu, then select “Compare Means” > “Independent-Samples T Test”
    3. In the dialog box that appears:
    • Move your dependent variable (continuous) into the “Test Variable(s)” box.
    • Move your independent variable (categorical with two groups) into the “Grouping Variable” box
    1. Click on the “Define Groups” button next to the Grouping Variable box
    2. In the new window, enter the values that represent your two groups (e.g., 0 for “No” and 1 for “Yes”)[1].
    3. Click “Continue” and then “OK” to run the test

    Interpreting the Results

    1. Check Levene’s Test for Equality of Variances:
    • If p > 0.05, use the “Equal variances assumed” row.
    • If p ≤ 0.05, use the “Equal variances not assumed” row
    1. Look at the “Sig. (2-tailed)” column:
    • If p ≤ 0.05, there is a significant difference between the groups.
    • If p > 0.05, there is no significant difference
    1. If significant, compare the means in the “Group Statistics” table to see which group has the higher score

    Tips

    • Ensure your data meets the assumptions for an independent t-test, including normal distribution and independence of observations
    • Consider calculating effect size, as SPSS doesn’t provide this automatically

  • Guide SPSS How to: Calculate Chi Square

    1. Open your data file in SPSS.
    2. Click on “Analyze” in the top menu, then select “Descriptive Statistics” > “Crosstabs”
    3. In the Crosstabs dialog box:
    • Move one categorical variable into the “Row(s)” box.
    • Move the other categorical variable into the “Column(s)” box.
    1. Click on the “Statistics” button and check the box for “Chi-square”
    2. Click on the “Cells” button and ensure “Observed” is checked under “Counts”
    3. Click “Continue” and then “OK” to run the analysis.

    Interpreting the Results

    1. Look for the “Chi-Square Tests” table in the output
    2. Find the “Pearson Chi-Square” row and check the significance value (p-value) in the “Asymptotic Significance (2-sided)” column
    3. If the p-value is less than your chosen significance level (typically 0.05), you can reject the null hypothesis and conclude there is a significant association between the variables

    Main Weakness of Chi-square Test

    The main weakness of the Chi-square test is its sensitivity to sample size[3]. Specifically:

    1. Assumption violation: The test assumes that the expected frequency in each cell should be 5 or more in at least 80% of the cells, and no cell should have an expected frequency of less than 1
    2. Sample size issues:
    • With small sample sizes, the test may not be valid as it’s more likely to violate the above assumption.
    • With very large sample sizes, even small, practically insignificant differences can appear statistically significant.

    To address this weakness, always check the “Expected Count” in your output to ensure the assumption is met. If not, consider combining categories or using alternative tests for small samples, such as Fisher’s Exact Test for 2×2 tables

  • Guide SPSS How to: Correlation

    Calculating Correlation in SPSS

    Step 1: Prepare Your Data

    • Enter your data into SPSS, with each variable in a separate column.
    • Ensure your variables are measured on an interval or ratio scale for Pearson’s r, or ordinal scale for Spearman’s rho

    Step 2: Access the Correlation Analysis Tool

    1. Click on “Analyze” in the top menu.
    2. Select “Correlate” from the dropdown menu.
    3. Choose “Bivariate” from the submenu

    Step 3: Select Variables

    • In the new window, move your variables of interest into the “Variables” box.
    • You can select multiple variables to create a correlation matrix

    Step 4: Choose Correlation Coefficient

    • For Pearson’s r: Ensure “Pearson” is checked (it’s usually the default).
    • For Spearman’s rho: Check the “Spearman” box

    Step 5: Additional Options

    • Under “Test of Significance,” select “Two-tailed” unless you have a specific directional hypothesis.
    • Check “Flag significant correlations” to highlight significant results

    Step 6: Run the Analysis

    • Click “OK” to generate the correlation output

    Interpreting the Results

    Correlation Coefficient

    • The value ranges from -1 to +1.
    • Positive values indicate a positive relationship, negative values indicate an inverse relationship[1].
    • Strength of correlation:
    • 0.00 to 0.29: Weak
    • 0.30 to 0.49: Moderate
    • 0.50 to 1.00: Strong

    Statistical Significance

    • Look for p-values less than 0.05 (or your chosen significance level) to determine if the correlation is statistically significant.

    Sample Size

    • The output will also show the sample size (n) for each correlation.

    Remember, correlation does not imply causation. Always interpret your results in the context of your research question and theoretical framework.

    To interpret the results of a Pearson correlation in SPSS, focus on these key elements:

    1. Correlation Coefficient (r): This value ranges from -1 to +1 and indicates the strength and direction of the relationship between variables
    • Positive values indicate a positive relationship, negative values indicate an inverse relationship.
    • Strength interpretation:
      • 0.00 to 0.29: Weak correlation
      • 0.30 to 0.49: Moderate correlation
      • 0.50 to 1.00: Strong correlation
    1. Statistical Significance: Look at the “Sig. (2-tailed)” value
    • If this value is less than your chosen significance level (typically 0.05), the correlation is statistically significant.
    • Significant correlations are often flagged with asterisks in the output.
    1. Sample Size (n): This indicates the number of cases used in the analysis

    Example Interpretation

    Let’s say you have a correlation coefficient of 0.228 with a significance value of 0.060:

    1. The correlation coefficient (0.228) indicates a weak positive relationship between the variables.
    2. The significance value (0.060) is greater than 0.05, meaning the correlation is not statistically significant
    3. This suggests that while a small positive correlation was observed in the sample, there’s not enough evidence to conclude that this relationship exists in the population
    4. Remember, correlation does not imply causation. Always interpret results in the context of your research question and theoretical framework.

  • Guide SPSS how to: Measures of Central Tendency and Measures of Dispersion

    Here’s a guide for 1st year students to calculate measures of central tendency and dispersion in SPSS:

    Calculating Measures of Central Tendency

    1. Open your dataset in SPSS.
    2. Click on “Analyze” in the top menu, then select “Descriptive Statistics” > “Frequencies”
    3. In the new window, move the variables you want to analyze into the “Variable(s)” box
    4. Click on the “Statistics” button
    5. In the “Frequencies: Statistics” window, check the boxes for:
    • Mean
    • Median
    • Mode
    1. Click “Continue” and then “OK” to run the analysis

    Calculating Measures of Dispersion

    1. Follow steps 1-4 from above.
    2. In the “Frequencies: Statistics” window, also check the boxes for:
    • Standard deviation
    • Range
    • Minimum
    • Maximum
    1. For interquartile range, check the box for “Quartiles”
    2. Click “Continue” and then “OK” to run the analysis.

    Interpreting the Results

    • Mean: The average of all values
    • Median: The middle value when data is ordered
    • Mode: The most frequently occurring value
    • Range: The difference between the highest and lowest values
    • Standard Deviation: Measures the spread of data from the mean
    • Interquartile Range: The range of the middle 50% of the data.

    Choosing the Appropriate Measure

    • For nominal data: Use mode only.
    • For ordinal data: Use median and mode.
    • For interval/ratio data: Use mean, median, and mode.

    Remember, if your distribution is skewed, the median may be more appropriate than the mean for interval/ratio data.