Qualitative and Quantitative Research Methods PDF

Week 9 - Qualitative Research 1. Definition Involves collecting, analyzing, interpreting non-numeric data (e.g., language). Applied across disciplines like anthropology, psychology, sociology, and kinesiology. Also known as ethnographic, grounded theory, phenomenological, etc. 2...

Week 9 - Qualitative Research 1. Definition Involves collecting, analyzing, interpreting non-numeric data (e.g., language). Applied across disciplines like anthropology, psychology, sociology, and kinesiology. Also known as ethnographic, grounded theory, phenomenological, etc. 2. Procedures Define the Problem: Establish the research focus. Formulate Questions & Framework: Develop theoretical basis. Data Collection: Includes training, participant selection, and entering the research setting. 3. Data Collection Methods Interviews: Formal/informal, individual/group, usually with a pilot-tested protocol. Focus Groups: Discussions with groups to gather insights. Observations: Detailed field notes. 4. Trustworthiness in Research Ensures the applicability, consistency, and neutrality of data. Components: ○ Credibility: Accurate representation of subjects and context. ○ Transferability: Applicability to similar contexts. ○ Dependability: Adaptability to changes during research. ○ Confirmability: Validity of findings when verified by others. 5. Evidence Towards Trustworthiness Data Quality: Prolonged engagement, audit trail, thick descriptions. Bias Management: Understanding and minimizing researcher bias. Triangulation: Multiple sources for supporting conclusions. Member Checking: Validating findings with participants. 6. Data Analysis to Conclusion Steps include transcribing interviews, sorting data, and merging insights. Techniques: Analytic narrative, narrative vignette, use of direct quotes. Week 9 - Experimental & Quasi-Experimental Research 1. Cause & Effect  Good theoretical framework, experimental design, proper variables, and statistical analysis are crucial. Criteria: ○ Cause must precede effect. ○ Cause and effect must correlate. ○ No other variable should explain the correlation. 2. Types of Validity Internal Validity: Ensures the treatment caused observed changes. External Validity: Generalizability to other contexts. 3. Threats to Validity Internal: History, maturation, testing effects, selection biases. External: Interaction of testing with treatment, biases in participant selection. 4. Design Types Pre-experimental: One-shot studies, one-group pretest-posttest. Experimental: Randomized groups, varying treatment levels. Quasi-Experimental: Single participant tracked over time. Week 8 - Epidemiology 1. Observational vs. Experimental Research Observational: Examines natural differences without intervention. Experimental: Tests effects of treatments (often ethically limited). 2. Key Epidemiological Concepts Distribution: Frequency (prevalence/incidence), patterns (person, place, time). Determinants: Characteristics affecting health (risk factors). 3. Study Designs Cross-sectional: Compares contrasting participants simultaneously. Cohort: Longitudinal follow-up on a large group. Case-Controlled: Matches participants with and without a condition. Week 5 - Correlation & Regression 1. Correlation Purpose: Examines relationships between two or more variables.  Types: Positive (direct relationship) or negative (inverse). Significance: Larger samples improve significance detection. 2. Regression & Prediction Simple Regression: Predicts outcomes from a single variable. Multiple Regression: Uses several predictors for a more complex outcome. Logistic Regression: Predicts binary outcomes (e.g., yes/no). Week 7 covers Research Synthesis, Systematic Reviews, and Meta-Analysis. Here’s a summary based on your notes: Week 7 - Research Synthesis and Meta-Analysis 1. Purpose of Research Synthesis Research Synthesis: Clearly defined purpose before starting the literature search; goal-oriented. Literature Review: Often exploratory, used to find a research purpose. 2. Meta-Analysis Process Objective: Answer research questions by analyzing data from multiple studies using a standard metric. Steps: 1. Identify the Problem: Define research objectives. 2. Literature Search: Use specified databases and criteria. 3. Study Review: Select studies based on inclusion/exclusion criteria. 4. Evaluate & Code: Extract important details (participants, validity, etc.). 5. Calculate Effect Sizes: These serve as data points. 6. Statistical Analysis: Treat results like an experimental study. 7. Report Findings: Publish the process and results. 3. Systematic Review Process Objective: Summarize research on a specific topic, often without the quantitative focus of meta-analysis. Steps: Form a team, register (e.g., PROSPERO), screen for eligibility, and include relevant studies in the review. Flowchart: A visual tool to track the steps from identification to inclusion of studies. 4. Considerations in Meta-Analysis Effect Size Coding: Choose appropriate statistics and account for group differences. Homogeneity Testing: Ensures consistency among studies included in the meta-analysis. Bias Control: Transparently document the selection and exclusion process. 5. Importance of Systematic Reviews Essential for evidence-based practice, as they bring together findings from multiple studies to support well-rounded conclusions.