For your chapter 4 and 5 thesis chapter. Experienced STATISTICIAN/RESEARCHER and PHD Finalist with 15 plus years’ experience in quantitative analysis.

See https://www.up.ac.za/news/post_1722144-up-student-wins-fellowship-to-study-womens-empowerment-index

My Linkdin profile: https://www.linkedin.com/in/christopher-manyamba-04490713/

I analyse, interpret and write up Chapter 4 (15-25 pages, or more), which you then discuss in Ch5.

Overall I provide the following quantitative analysis and modelling expertise;

1. Proposal development including study design and sampling;

2. Univariate analysis: Frequencies. Means and std deviations for continuous data, tables and graphs;

3. Bivariate analysis: pairwise correlation, Spearman’s for non-parametric and Pearson’s for normally distributed data;

4. For Likert scale data, reliability (Cronbach’s alpha) and normality tests (Wilk Shapiro tests, skewness, kurtosis);

5. Hypothesis testing -parametric tests e.g. paired t-tests, and non-parametric tests (e.g. Wilcoxon sign rank, Kruskal Wallis);

6. Multivariate-Regression analyses which cover ordinal, count, categorical and binary outcomes. Ordinary Least Squares (OLS), Partial/Pooled effects; Factor/Principal component analysis;

7. Pathway Analysis and Structural Equation Modelling (SEM), and its post estimation results

8. Time-series, Econometric Models: Autoregressive Conditional Heteroscedastic (ARCH) and Auto-Regressive Distributed Lag (ARDL) family models. The stepwise analysis includes setting data to time series, white noise detection (stationarity tests), Dick Fuller or P-Peroni unit root tests (VAR), Vector error correction models (VEC), Granger causality tests;

9. Survival analysis (epidemiological): Includes setting data to survival, run descriptive stats with survival analysis family of analysis. Survival analysis: Censoring, prevalence and incident rates, regression models e.g. Cox proportionate regression, interactions (log-rank, and Kaplan Meier survival functions graphs);

10. Checking multicollinearity/endogeneity-Variance Inflation Factor (VIF in STATA), or any pre-model estimation;

11. Software experience: Excel, SPSS, STATA, R, EViews (advanced/excellent), EPI Info (intermediate), R (intermediate), SAS (Intermediate). Turnaround time for analysis, interpretation and write up: In a space of 5 days;