Research Data Analysis Basics: Methodological Foundations for Scholars
Executive Abstract
An introduction to choosing appropriate statistical techniques, verifying normality and measurement models, and transparently reporting empirical findings.
1. Statistical Alignment with Research Design
Selecting statistical tests begins with research questions, not data packages. Scholars must categorize variables as categorical (nominal/ordinal) or continuous (interval/ratio). Parametric tests (t-tests, ANOVA, linear regression) demand strict adherence to assumptions of normality, homoscedasticity, and absence of multicollinearity.
2. Verifying Measurement Models in Survey Research
Prior to testing structural relationships between constructs, researchers must establish measurement validity. Cronbach's alpha and Composite Reliability (CR) verify internal consistency, while Average Variance Extracted (AVE) confirms convergent validity. For multi-item constructs, exploratory or confirmatory factor analysis (CFA) is indispensable.
3. Choosing Between CB-SEM and PLS-SEM
Covariance-Based SEM (e.g., AMOS, LISREL) is optimal for theory testing and confirmation requiring multivariate normal data. Partial Least Squares SEM (PLS-SEM, using SmartPLS) is ideal for exploratory models, complex hierarchical models, non-normal distributions, or research focusing on predictive relevance.
4. Transparent Reporting Standards
Always report effect sizes (Cohen's d, R², f²) alongside p-values. Disclose data cleaning protocols, outlier management strategies, and missing data imputation procedures in full alignment with APA guidelines.