Data analysis is a crucial component of academic research that can make or break your thesis or journal submission. Following best practices ensures that your analysis is statistically rigorous, transparent, and reproducible by external peer reviewers.
1. Data Cleaning & Normality Screening
Before testing hypotheses, researchers must conduct thorough diagnostic checks on raw survey and experimental datasets. Common pitfalls include ignored outliers and skewness, which invalidate parametric tests like ANOVA and multiple regression.
2. Choosing Between CB-SEM and PLS-SEM
A frequent dilemma for business and social science scholars is selecting between Covariance-Based Structural Equation Modeling (AMOS) and Partial Least Squares (SmartPLS). The decision should be grounded in research objectives:
- AMOS (CB-SEM): Optimal for confirming established theories with large samples (>250) and normally distributed data.
- SmartPLS (PLS-SEM): Ideal for exploratory structural models, complex mediator/moderator paths, and small-to-medium sample sizes.
3. Clear APA Presentation of Empirical Tables
Academic committees require descriptive statistics, Pearson correlations, reliability metrics (Cronbach's Alpha and Composite Reliability), and discriminant validity (Fornell-Larcker and HTMT criteria) displayed strictly according to APA 7th style conventions.
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