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.