Theoretical Framework

Lecture 10 focuses on the development of the theoretical framework as the foundational backbone of empirical research. A theoretical framework is a conceptual model of how a researcher theorizes or logically makes sense of the relationships among the several factors identified as important to the problem. Students explore the core components of a theoretical framework: identifying the relevant variables, explaining why those variables are expected to relate to each other, specifying the directional nature of the relationships based on prior findings, and delineating the theoretical rationale that underpins these conjectures. The lecture demonstrates schematic model diagramming, visualizing how independent, moderating, intervening, and dependent variables interact. This structural model directly guides subsequent hypothesis formulation, instrumentation design, and statistical data analysis in university research projects. Psychometric assessment criteria, encompassing construct validity, content validity, criterion validity, and test-retest reliability, are evaluated. Survey methodology principles are explored, contrasting face-to-face personal interviews, intercept surveys, and self-administered questionnaires. The curriculum examines sampling design fundamentals, distinguishing probability sampling methods from non-probability sampling approaches. Detailed discussions of probability sampling techniques, including simple random, systematic, stratified, and cluster sampling, explain sampling error mitigation. Non-probability sampling strategies, such as convenience sampling, purposive judgment sampling, quota sampling, and snowball sampling, are critically evaluated. Experimental research designs are analyzed with focus on internal validity control, external validity generalization, and manipulation of independent variables. True experimental designs, quasi-experimental designs, and pre-experimental arrangements are contrasted regarding treatment randomization and confounding control. Students explore non-reactive research methods, examining unobtrusive measures, physical traces, content analysis, and secondary archival data utilization. Field research and observational studies are investigated to capture naturalistic human behavior, participant observation dynamics, and qualitative field notes. Historical comparative research methodologies illustrate how archival primary sources, oral histories, and comparative historical analyses generate deep contextual understanding. Focus group discussions and qualitative interviewing techniques are evaluated for exploratory depth, group synergy, and moderator facilitation dynamics. Data transformation workflows, including categorical coding, data editing, missing value imputation, and scale index construction, are systematically outlined.