Consultation on choosing the right analysis method for your research design
SPSS — descriptive, inferential, ANOVA, regression, factor analysis, reliability (Cronbach's α)
SmartPLS (PLS-SEM) — measurement & structural model, bootstrapping, mediation/moderation
AMOS / Stata / LISREL — covariance-based SEM
NVivo — coding, thematic analysis, word frequency, matrix queries
Manual thematic and content analysis (Braun & Clarke six-step framework)
R programming — statistical modeling, tidyverse, ggplot2, machine learning basics
Python (Pandas, NumPy, scikit-learn, Matplotlib/Seaborn) for data science
SQL — querying, joins, aggregations, database-driven reporting
Excel advanced analytics — pivot tables, Power Query, regression, dashboards
Power BI / Tableau dashboards and data visualisation
STATA, for econometric and statistical analysis
Data cleaning, transformation, missing-value and outlier handling
Hypothesis testing, model validation, assumption checks
Publication-ready tables and figures
Output report with step-by-step explanation (so you can defend the analysis yourself)
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