AfroQuant
Quantitative Research
Quantitative research and statistical analysis support for postgraduate researchers and academics.
Applied · Rigorous · Research-focused
Independent quantitative research, statistical analysis and methodological support for postgraduate researchers, academics and research teams in business, management, finance, economics and related social sciences. The emphasis goes beyond generating statistical output: we help you develop the capability to choose appropriate analytical methods, understand their methodological basis, implement them correctly, and interpret and report results in relation to your research questions, objectives and hypotheses.
Quantitative & Statistical Analysis
- Foundations: descriptive statistics, exploratory data analysis, correlation analysis, hypothesis testing, t-tests, ANOVA and related inferential procedures.
- Regression analysis: simple and multiple linear regression and logistic regression; diagnostics and assumption testing; mediation and moderation analysis; bootstrapping; and direct and indirect effect estimation.
- Data preparation: data screening and preparation, missing-data analysis, outlier assessment, sample-size and statistical-power considerations, and exploratory factor analysis.
Structural Equation Modelling (SEM)
- Measurement models: confirmatory factor analysis (CFA), factor loadings, model-fit assessment, composite reliability, average variance extracted, and convergent and discriminant validity assessment.
- Structural models: path analysis; direct, indirect and total effects; mediation and moderation; bootstrapping; standardised estimates; model-fit assessment; modification indices; and theoretically defensible model refinement.
Data Analysis & Computing Tools
- R/RStudio: statistical modelling, SEM with lavaan, data analysis and visualisation with ggplot2.
- IBM SPSS: data management, descriptive and inferential statistics, reliability analysis, regression, diagnostics and exploratory factor analysis.
- IBM AMOS: CFA, measurement models, structural models, path analysis and SEM.
- Python: data preparation, cleaning, manipulation, exploratory analysis and reproducible data-analysis workflows.
- QualCoder: coding and organising qualitative data, such as interviews and open-ended survey responses, for mixed-methods studies.
Research & Methodological Support
- Construct identification, operationalisation and measurement
- Conceptual framework and hypothesised relationship review
- Alignment of research objectives, questions, hypotheses, constructs and analytical methods
- Questionnaire, measurement instrument and quantitative methodology review
- Statistical analysis planning, diagnostics, interpretation and reporting
- Guided development of researchers' practical quantitative analysis skills
How Support Works
Guided Analysis: Work through analytical decisions, implementation, diagnostics, interpretation and reporting with guidance while developing the ability to explain and defend each decision.
Analysis and Review: Review completed quantitative analyses for methodological alignment, statistical appropriateness, diagnostics, interpretation and reporting, with feedback identifying issues and areas requiring further attention.
Academic integrity
Guidance is provided for legitimate quantitative analysis, methodological development, analysis review and researcher capability development. It complements, and does not replace, formal academic supervision. The research remains the researcher's own work, and researchers remain responsible for understanding, explaining and defending their methodological and analytical decisions.