In my part time I analyse, interpret and write up chapter 4/5 thesis, in 3-5 days. I am an experienced STATISTICIAN/RESEARCHER and PHD Finalist (University of Pretoria), with 15 plus years’ experience in quantitative analysis. My experience is specifically on cross sectional and longitudinal research (econometric models and survival models) processes. I have also supervised Masters thesis. My profiles online for more https://www.linkedin.com/in/christopher-manyamba-04490713/ http://oasis.col.org/handle/11599/3934?show=full https://www.up.ac.za/news/post_1722144-up-student-wins-fellowship-to-study-womens-empowerment-index https://researchspace.ukzn.ac.za/handle/10413/8822

1. Proposal development: Intro, Literature review, Methodology (study design, approach, sampling, reliability, ethical issues) 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 EViews (advanced/excellent), EPI Info (intermediate), R (intermediate), SAS (Intermediate). If you need to self-tutor STATA, you can get a STATA version 15-30-day trial