Onderzoeksmethoden Research Methods

Categorie: Qualitative Research

  • Thematic Analysis (Chapter D4)

    Chapter D4, Matthews and Ross

    Here is a guide on how to conduct a thematic analysis:

    What is Thematic Analysis?

    Thematic analysis is a qualitative research method used to identify, analyze, and report patterns or themes within data. It allows you to systematically examine a set of texts, such as interview transcripts, and extract meaningful themes that address your research question.

    Steps for Conducting a Thematic Analysis

    1. Familiarize yourself with the data

    Immerse yourself in the data by reading and re-reading the texts. Take initial notes on potential themes or patterns you notice.

    2. Generate initial codes

    Go through the data and code interesting features in a systematic way. Codes identify a feature of the data that appears interesting to the analyst. Some examples of codes could be:

    • “Feelings of anxiety”
    • “Financial stress”
    • “Family support”

    3. Search for themes

    Sort the different codes into potential themes. Look for broader patterns across the codes and group related codes together. At this stage, you may have a collection of candidate themes and sub-themes.

    4. Review themes

    Refine your candidate themes. Some themes may collapse into each other, while others may need to be broken down into separate themes. Check if the themes work in relation to the coded extracts and the entire data set.

    5. Define and name themes

    Identify the essence of what each theme is about and determine what aspect of the data each theme captures. Come up with clear definitions and names for each theme.

    6. Produce the report

    Select vivid, compelling extract examples, relate back to the research question and literature, and produce a scholarly report of the analysis.

    Tips for Effective Thematic Analysis

    • Be thorough and systematic in working through the entire data set
    • Ensure your themes are distinct but related
    • Use quotes from the data to support your themes
    • Look for both similarities and differences across the data set
    • Consider how themes relate to each other
    • Avoid simply paraphrasing the content – interpret the data

    Example

    Let’s say you were analyzing interview data about people’s experiences with online dating. Some potential themes that could emerge:

    • Feelings of anxiety and vulnerability
    • Importance of authenticity
    • Challenges of self-presentation
    • Impact on self-esteem
    • Changing nature of relationships

    For each theme, you would provide supporting quotes from the interviews and explain how they illustrate that theme.

    By following these steps and tips, you can conduct a rigorous thematic analysis that provides meaningful insights into your data. The key is to be systematic, thorough, and reflective throughout the process.

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  • Longitudinal Quantitative Research

    Observing Change Over Time

    Longitudinal research is a powerful research design that involves repeatedly collecting data from the same individuals or groups over a period of time, allowing researchers to observe how phenomena change and develop. Unlike cross-sectional studies, which capture a snapshot of a population at a single point in time, longitudinal research captures the dynamic nature of social life, providing a deeper understanding of cause-and-effect relationships, trends, and patterns.

    Longitudinal studies can take on various forms, depending on the research question, timeframe, and resources available. Two common types are:

    Prospective longitudinal studies: Researchers establish the study from the beginning and follow the participants forward in time. This approach allows researchers to plan data collection points and track changes as they unfold.

    Retrospective longitudinal studies: Researchers utilize existing data from the past, such as medical records or historical documents, to construct a timeline and analyze trends over time. This approach can be valuable when studying events that have already occurred or when prospective data collection is not feasible.

    Longitudinal research offers several advantages, including:

    • Tracking individual changes: By following the same individuals over time, researchers can observe how their attitudes, behaviors, or circumstances evolve, providing insights into individual growth and development.2
    • Identifying causal relationships: Longitudinal data can help establish the temporal order of events, strengthening the evidence for causal relationships.1 For example, a study that tracks individuals’ smoking habits and health outcomes over time can provide stronger evidence for the link between smoking and disease than a cross-sectional study.
    • Studying rare events or long-term processes: Longitudinal research is well-suited for investigating events that occur infrequently or phenomena that unfold over extended periods, such as the development of chronic diseases or the impact of social policies on communities.

      However, longitudinal research also presents challenges:
    • Cost and time commitment: Longitudinal studies require significant resources and time investments, particularly for large-scale projects that span many years.
    • Data management: Collecting, storing, and analyzing data over time can be complex and require specialized expertise.
    • Attrition: Participants may drop out of the study over time due to various reasons, such as relocation, loss of interest, or death. Attrition can bias the results if those who drop out differ systematically from those who remain in the study.

    Researchers utilize a variety of data collection methods in longitudinal studies, including surveys, interviews, observations, and document analysis. The choice of methods depends on the research question and the nature of the data being collected.

    A key aspect of longitudinal research design is the selection of an appropriate sample. Researchers may use probability sampling techniques, such as stratified sampling, to ensure a representative sample of the population of interest. Alternatively, they may employ purposive sampling techniques to select individuals with specific characteristics or experiences relevant to the research question.

    • Millennium Cohort Study: This large-scale prospective study tracks the development of children born in the UK in the year 2000, collecting data on their health, education, and well-being at regular intervals.
    • Study on children’s experiences with smoking: This study employed both longitudinal and cross-sectional designs to examine how children’s exposure to smoking and their own smoking habits change over time.
    • Study on the experiences of individuals participating in an employment program: This qualitative study used longitudinal interviews to track participants’ progress and understand their experiences with the program over time.

    Longitudinal research plays a crucial role in advancing our understanding of human behavior and social processes. By capturing change over time, these studies can provide valuable insights into complex phenomena and inform policy decisions, interventions, and theoretical development.

    EXAMPLE SETUP

    Research Question: Does exposure to social media impact the mental health of media students over time? 

    Hypothesis: Media students who spend more time on social media will experience a decline in mental health over time compared to those who spend less time on social media. 

    Methodology: 

    Participants: The study will recruit 100 media students, aged 18-25, who are currently enrolled in a media program at a university. 

    Data Collection: The study will collect data through online surveys administered at three time points: at the beginning of the study (Time 1), six months later (Time 2), and 12 months later (Time 3). The survey will consist of a series of questions about social media use (e.g., hours per day, types of social media used), as well as standardized measures of mental health (e.g., the Patient Health Questionnaire-9 for depression and the Generalized Anxiety Disorder-7 for anxiety). 

    Data Analysis: The study will use linear mixed-effects models to analyze the data, examining the effect of social media use on mental health outcomes over time while controlling for potential confounding variables (e.g., age, gender, prior mental health history). 

    Example Findings: After analyzing the data, the study finds that media students who spend more time on social media experience a significant decline in mental health over time compared to those who spend less time on social media. Specifically, students who spent more than 2 hours per day on social media at Time 1 experienced a 10% increase in depression symptoms and a 12% increase in anxiety symptoms at Time 3 compared to those who spent less than 1 hour per day on social media. These findings suggest that media students should be mindful of their social media use to protect their mental health 

  • Cohort Study

    A cohort study is a specific type of longitudinal research design that focuses on a group of individuals who share a common characteristic, often their age or birth year, referred to as a cohort. Researchers track these individuals over time, collecting data at predetermined intervals to observe how their experiences, behaviors, and outcomes evolve. This approach enables researchers to investigate how various factors influence the cohort’s development and identify potential trends or patterns within the group12.

    Cohort studies stand out for their ability to reveal changes within individuals’ lives, offering insights into cause-and-effect relationships that other research designs may miss. For example, a cohort study might track a group of students throughout their university experience to examine how alcohol consumption patterns change over time and relate those changes to academic performance, social interactions, or health outcomes3.

    Researchers can design cohort studies on various scales and timeframes. Large-scale studies, such as the Millennium Cohort Study, often involve thousands of participants and continue for many years, requiring significant resources and a team of researchers2. Smaller cohort studies can focus on more specific events or shorter time periods. For instance, researchers could interview a group of people before, during, and after a significant life event, like a job loss or a natural disaster, to understand its impact on their well-being and coping mechanisms2.

    There are two primary types of cohort studies:

    Prospective cohort studies are established from the outset with the intention of tracking the cohort forward in time.

    Retrospective cohort studies rely on existing data from the past, such as medical records or survey responses, to reconstruct the cohort’s history and analyze trends.

    While cohort studies commonly employ quantitative data collection methods like surveys and statistical analysis, researchers can also incorporate qualitative methods, such as in-depth interviews, to gain a richer understanding of the cohort’s experiences. For example, in a study examining the effectiveness of a new employment program for individuals receiving disability benefits, researchers conducted initial in-depth interviews with participants and followed up with telephone interviews after three and six months to track their progress and gather detailed feedback4.

    To ensure a representative and meaningful sample, researchers employ various sampling techniques in cohort studies. In large-scale studies, stratified sampling is often used to ensure adequate representation of different subgroups within the population25. For smaller studies or when specific characteristics are of interest, purposive sampling can be used to select individuals who meet certain criteria6.

    Researchers must carefully consider the ethical implications of cohort studies, especially when working with vulnerable populations or sensitive topics. Ensuring informed consent, maintaining confidentiality, and minimizing potential harm to participants are paramount throughout the study7.

    Cohort studies are a powerful tool for examining change over time and gaining insights into complex social phenomena. By meticulously tracking a cohort of individuals, researchers can uncover trends, identify potential causal relationships, and contribute valuable knowledge to various fields of study. However, researchers must carefully consider the challenges and ethical considerations associated with these studies to ensure their rigor and validity.

    1. Research question: Start by defining a clear research question for each cohort, such as “What is the effect of social media use on the academic performance of first-year media students compared to third-year media students over a two-year period?” 
    1. Sampling: Decide on the population of interest for each cohort, such as first-year media students and third-year media students at a particular university, and then select a representative sample for each cohort. This can be done through a random sampling method or by selecting participants who meet specific criteria (e.g., enrolled in a particular media program and in their first or third year). 
    1. Data collection: Collect data from the participants in each cohort at the beginning of the study, and then at regular intervals over the two-year period (e.g., every six months). The data can be collected through surveys, interviews, or observation. 
    1. Variables: Identify the dependent and independent variables for each cohort. In this case, the independent variable would be social media use and the dependent variable would be academic performance (measured by GPA, test scores, or other academic indicators). For the second cohort, the time in the media program might also be a variable of interest. 
    1. Analysis: Analyze the data for each cohort separately using appropriate statistical methods to determine if there is a significant relationship between social media use and academic performance. This can include correlation analysis, regression analysis, or other statistical techniques. 
    1. Results and conclusions: Draw conclusions based on the analysis for each cohort and compare the results between the two cohorts. Determine if the results support or refute the research hypotheses for each cohort and make recommendations for future research or practical applications based on the findings.
    2. Ethical considerations: Ensure that the study is conducted ethically for each cohort, with appropriate informed consent and confidentiality measures in place. Obtain necessary approvals from ethics committees or institutional review boards as required for each cohort