Thesis Data Analysis in SPSS: A Step-by-Step Guide
How to run thesis data analysis in SPSS step by step: data entry and cleaning, normality checks, choosing the right test, and APA 7 reporting.
Practical writing on thesis and journal statistics, prepared with peer-review rigour.
How to run thesis data analysis in SPSS step by step: data entry and cleaning, normality checks, choosing the right test, and APA 7 reporting.
Not sure which statistical test to use? A decision guide that matches your research question, variable types and distribution to the correct test.
What is meta-analysis and how is it done? PRISMA 2020 flow, pooling effect sizes, heterogeneity (I²) and publication bias in one practical guide.
A practical APA 7 checklist for reporting t-tests, ANOVA, correlation and regression, worked examples, effect sizes and table rules.
Fixed or random effects in panel data analysis? The Hausman test decision rule, within and GLS estimators, key diagnostics and robust standard errors.
A practical unit root test guide: ADF, PP and KPSS hypotheses, lag selection, deterministic terms, spurious regression and a joint decision strategy.
A practical guide to cointegration analysis: Johansen trace and max-eigenvalue tests, VECM specification, error-correction terms and Granger causality.
When and how to use the ARDL bounds test: F-statistic decision rules, error-correction terms, long-run coefficients and CUSUM stability diagnostics.
How to fit a GARCH model: ARCH-LM testing, GARCH(1,1), persistence (α+β), EGARCH and GJR extensions, Student-t errors and volatility forecasting.
Sample size calculation made practical: a-priori power analysis in G*Power, effect size choice, required N for t-tests, ANOVA and regression, reporting.
Scale development from item pool to CFA: KMO and Bartlett, parallel analysis, rotation choice, item retention rules, fit indices, AVE and CR.
Structural equation modelling with AMOS or SmartPLS? CB-SEM vs PLS-SEM, sample size rules, fit index thresholds and reporting standards explained.
Mediation analysis and moderation with the PROCESS macro: bootstrap confidence intervals, Models 1 and 4, moderated mediation and reporting templates.
What does Cronbach's alpha assume, and when does it mislead? Why McDonald's omega is the modern default, with thresholds, software and reporting advice.
A practical missing data analysis guide: MCAR, MAR and MNAR mechanisms, Little's test, why deletion fails, EM, multiple imputation and reporting rules.
How to interpret odds ratios in logistic regression: odds vs probability, confidence intervals, model fit, ROC/AUC and an APA reporting template.
How to run a thematic analysis: the six-phase process, codebooks, Cohen's kappa, saturation, trustworthiness criteria and NVivo vs MAXQDA in one guide.
Practical likert scale analysis guide: the item-versus-scale distinction, when parametric tests are defensible, the 0.80 band formula, common mistakes.
How to interpret each effect size: Cohen's d, Hedges' g, η², ω² and r, with benchmark thresholds, confidence intervals and a test-by-test map.
How to write a reviewer response letter for statistical revisions: point-by-point format, three response strategies and handling common requests.