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How SEM Bridges Theory and Measurement in Social Science Research How SEM Bridges Theory and Measurement in Social Science Research As a seasoned researcher, I have seen firsthand how Structural Equation Modeling, or SEM, transforms the way we approach studies in the social sciences. It brings together measurement and theory in a unified statistical framework that truly goes beyond what traditional regressions or factor analyses can offer. What Makes SEM Unique? You can think of SEM as a combination of two powerful tools. First, there is confirmatory factor analysis, which helps us measure hidden constructs. Then, there is path analysis, which allows us to map out relationships between variables. By blending these approaches, SEM helps us understand not just what we measure, but how those concepts connect and interact in real life. More details Overview on ResearchGate Why Do Social Scientists Prefer SEM? Modeling...

Case-Control Sample Size Calculator Proportion exposed in controls ( p 0 ): Expected Odds Ratio ( OR ): Significance level (α): 0.05 (Z α/2 = 1.96) 0.10 (Z α/2 = 1.64) Power (1 − β): 80% (Z β = 0.84) 90% (Z β = 1.28) Control-to-Case Ratio ( r ): Calculate Estimated sample size: Cases ( n 1 ): Controls ( n 0 ): 📘 About This Calculator This calculator estimates the required sample size for a case-control study , based on the expected odds ratio (OR) , control exposure proportion ( p 0 ), significance level (α), desired power (1−β), and case:control ratio ( r ). Formula used: n 1 = [(Z α/2 + Z β )² × (r + 1) × p̄(1 − p̄)] / [r × (ln(OR))²] n 1 = number of cases n 0 = number of controls = r × n 1 p̄ = (p 0 + p 1 ) / 2 p 1 = (OR × p 0 ) / [1 + p 0 (OR − 1)] 📚 Reference Kelsey JL, Whittemore AS, Evans AS, Thompson WD. Methods in Observational Epidemiology . 2nd ed. New York: Oxford University Press; 1996. See Chap...

Krejcie & Morgan Sample Size Calculator Enter Population Size (N): Calculate Sample Size Recommended Sample Size (n): 📘 About This Calculator This calculator uses the Krejcie & Morgan (1970) formula to estimate the minimum sample size required when the total population size is known. It is commonly used in social sciences, education, and health research. The formula is: n = (X² × N × P × (1 − P)) / (d² × (N − 1) + X² × P × (1 − P)) X² = 3.841 (for 95% confidence level) P = 0.5 (maximum variability) d = 0.05 (±5% precision) 📚 Citation Krejcie, R.V., & Morgan, D.W. (1970). Determining Sample Size for Research Activities . Educational and Psychological Measurement, 30 (3), 607–610. https://doi.org/10.1177/001316447003000308