Question 4. Flashcards
Participant Inclusion Criteria
Definition: Criteria used to determine which individuals are eligible to participate in the study.
Example: Age range (e.g., 18-65 years old), diagnosis of a specific medical condition, absence of certain exclusion criteria (e.g., history of psychiatric disorders).
Importance: Ensures that participants meet specific characteristics necessary for the study’s objectives and helps maintain consistency in participant selection.
Participant Recruitment
Definition: Strategies and methods used to identify and enrol eligible participants in the study.
Example: Recruitment through community organisations, online advertisements, and referrals from healthcare providers.
Importance: Ensures an adequate and representative sample, enhances generalisability of findings, and facilitates the successful completion of the study.
Methods of Data Collection
Definition: Techniques or tools used to gather information from study participants.
Examples: Surveys/questionnaires, interviews, observations, physiological measurements (e.g., blood pressure, heart rate), and standardised assessments.
Importance: Provides relevant data to address research questions, allows for the collection of quantitative information, and facilitates the analysis of study outcomes.
Method of analysis
Definition: Statistical or analytical techniques used to analyze collected data and draw conclusions.
Example: Descriptive statistics (e.g., means, frequencies), inferential statistics (e.g., t-tests, ANOVA, regression analysis), multivariate analysis (e.g., factor analysis, structural equation modeling).
Importance: Helps identify patterns, relationships, and associations in the data, allows for hypothesis testing, and provides insights into the study’s findings.
Follow up: Participant Inclusion
Consider expanding the inclusion criteria to include a broader age range of adolescents, such as those up to 25 years old…
Include participants with different characteristics of the DV…
Ensure that participants are representative of the population of interest by including individuals from various socioeconomic backgrounds and geographical locations.
Follow up: Participant Recruitment
Utilize additional recruitment strategies, such as social media campaigns or community outreach events, to reach a more diverse pool of potential participants.
Offer incentives or compensation to participants to increase participation rates and reduce attrition over time.
Follow up: Data Collection
Incorporate additional measures of psychological well-being, such as measures of resilience or post-traumatic growth…
Consider using mixed-methods approaches, combining quantitative surveys with qualitative interviews, to gain a deeper understanding of survivors’ experiences and perspectives.
Follow up: Methods of Analysis
Conduct longitudinal analysis techniques, such as growth curve modeling or latent growth curve analysis…
Explore potential mediators or moderators of the relationship between IV and DV, such as coping strategies, social support, or treatment adherence…
Utilize advanced statistical methods, such as propensity score matching or structural equation modeling, to control for confounding variables and assess the robustness of the findings.
Experimental research and analysis
Analysis of Variance (ANOVA) or t-tests.
Explanation: Experimental research involves manipulating an independent variable to observe its effect on a dependent variable while controlling for extraneous variables. ANOVA or t-tests are used to compare means between experimental groups and determine if there are significant differences
Quasi-experimental and analysis
Similar to experimental research (ANOVA or t-tests), but with additional techniques to control for confounding variables.
Explanation: Quasi-experimental research lacks random assignment of participants to groups, so statistical techniques e.g. matching, are used to control for confounding variables that may influence the relationship between the independent and dependent variables
Longitudinal Research and analysis
Repeated measures ANOVA, growth curve modeling, or mixed-effects models.
Explanation: Longitudinal research involves collecting data from the same participants over time to track changes. Repeated measures ANOVA examines within-subject changes over time, while growth curve modeling and mixed-effects models can model individual growth trajectories and account for nested data structures.
Cross-Sectional Research and analysis
Independent samples t-tests, chi-square tests, or logistic regression.
Explanation: Cross-sectional research collects data from different individuals at a single point in time to compare groups or examine relationships between variables. Statistical analyses compare means or proportions between groups or examine associations between variables.
