This course is aimed at both new Stata users and data analysts who wish to learn techniques for efficient day-to-day use of Stata. Upon completion of the course, you will be able to use Stata efficiently for basic analyses and graphics. You will be able to do this in a reproducible manner, making collaborative changes and follow-up analyses much simpler. Finally, you will be able to make your datasets self-explanatory to your co-workers and yourself when using them in the future.
This course is aimed at quantitative researchers who want to simplify their approach in quantitative data management and analysis.
Its Fast, Accurate, Easy to use, Stata is a complete, integrated software package that provides all your data science needs— visualization, Data manipulation, statistics, and reproducible reporting.
No its not a promming language. If you want to learn programming used in Analysis check out our course R and Python.
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Background in Statistics is an added advantage but any one with passion to be a top quantitative researcher is encouraged to apply.
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This course aims to build the capacity of quantitative research in quantitative data management and analysis using STATA software. This software has many features that make it the most popular quantitative data analysis software available, given its processing speed and the and its pragmatic aspects of being stable and light
We shall be loiking at how to open packages, load different data sets into STATA and most importantly Perform Data cleaning
1. Basic Introduction to STATA.
2. STATA Installation.
3. Creating Directory.
4. The participant should be able to understand how to open data in STATA format as well as to import data from foreign format and to save the data in the working directory.
5. The user should be able to learn to rename variables, label variables, label categories (values) in a categorical variable.
6. categorize/split a continuous variable and convert variable types
7. handle missing values/Imputation
8. handle outliers (continuous variables)
9. handle inconsistencies (categorical variables)
The user will be subjected to various data managament skills in the next two training sessions.
10. The participant will learn to print conditional tables/filtering, sort data, append.
11. The participant will taken through data sorting techniques in STATA
12. The participant will learn appending techniques and will be required to append various datasets.
13. Merge and reshape datasets
14. Concatenate and order (reposition) variables
15. The user will be taught to declare string date variables as to be of type date, among other type conversions
Descriptive statistics, Inferential statistics
The learner will be taken through STATA’s powerful commands for calculation of measures of central tendency (mean, mode and median) and their confidence intervals; measures of dispersion (variance, standard deviation, interquartile range, range, coefficient of variation); skewness and kurtosis, proportions and frequencies and interpretations of the same.
The learner will be introduced to parametric and non-parametric methods of data analysis and the concepts underlying each. This will be accompanied by a rigorous hands-on insight into the various forms of regressions (linear, logistic, and probit) and their related concepts including model goodness of fit, R square, marginal effects and sensitivity and specificity, odds ratios for the case of binary logistic regression; correlations (Pearson’s r, Spearman’s rho, Kendall’s tau, Cramer’s v and phi),, tests of associations (Chi-square, Fisher’s exact, McNemar); and tests of hypothesis (ANOVA and its post hoc tests, t-tests, Kruskal-Wallis, Mann-Whitney U, and Cochrane’s Q)
learn to analyze longitudinal data using survival analysis approaches and be able to visualize the analysed data using various charts
The participants will learn to analyze longitudinal data using survival analysis approaches that include non-parametric (Kaplan-Meier, Nelson-Aalen); semi-parametric (cox-proportional hazards) and its assumptions; parametric (lognormal, exponential, Weibull, gaussian) and interpretations of Hazard ratios and risk ratios obtained from generalized estimating equations (GEE).
The participant will be able to visualize – continuous variables in histograms, box plots, scatter plots, density plots and line plots; categorical variables in the form of bar graphs and pie-charts; sensitivity and specificity in the form of Receiver Operating Characteristic (ROC) curves.
This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies. Training manuals and additional reference materials are provided to the participants.
Accommodation will be arranged for our participants upon request.
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