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Data management, analysis and visualization for project managers

Participants will learn the principles of statistics and gain skills in using statistical tools to describe, study and investigate the various variables in survey data sets using Stata. The statistical background required to conduct research, describe, summarize, develop hypothesis, assess associations, analyze data, interpret and communicate results will be studied comprehensively.

The course targets professionals in research (or research related) organizations / institutions who wish to acquire or increase their computational skills in the Stata software. The course is developed to benefit data managers, research officers, project managers, statisticians, data analysts, students (undergraduate and postgraduate), trainers/facilitators, research organizations, non-governmental organizations, policy makers, among others.

Learning objectives:

  • Perform basic and advanced data management routines on survey data sets.
  • Perform explanatory data analysis tasks with easy.
  • Create univariate and bivariate graphs / charts.
  • Carry out classical tests of hypothesis and draw clear inferences and conclusion to them.
  • Perform non-parametric tests and draw conclusions.
  • Come up with linear, poisson and logistic regression models, tests their validity, make conclusions and use such models to make prediction about the general population.
Dates Monthly: Starts every First Monday
Duration: 2 weeks (10 days)
Time 0830Hrs - 1630Hrs EAT (Mon - Fri)
Delivery Classroom (online also available)
Charges KES 125,000 ($1,250)
The course can be customized to fit your specific needs in terms of course content, date, time and venue (including online sessions). Please talk to us for the customization.
  1. Data management and manipulation
    • Introduction to Stata
      • Starting Stata
      • Setting layout
      • Directory management commands
      • Data types in Stata
      • Using Stata as a calculator
      • Stata command and options
      • Stata do-files
      • Creating data sets directly in Stata
      • Display and viewing data sets
      • Interrupting computations
    • Working with data sets, dates and help
      • Rename of variables
      • Managing variables and/or variable properties
      • Importing data from other software
      • Exporting data to other software
      • Count number of observations
      • Generate sequential numbers
      • Change order of variables
      • Saving data sets
      • Loading data into the memory
      • The in and if qualifiers
      • The by prefix
      • Create subsets (keep and drop)
      • Create random variables (from distributions)
      • Sort variables
      • Random sampling
      • Working with dates
      • Obtaining help in Stata
    • Creating and changing variables
      • Create new variables
      • Extended generate command
      • Duplicate an existing variable
      • Replace contents of a variable
      • Convert numeric to string
      • Convert string numbers to numeric
      • Convert numeric values to missing and vice versa
      • Recode string variables
      • Decode numerically coded variables
      • Transforming a continuous variable to categorical
      • Reduce number of categories of a categorical variable
      • Managing duplicates
    • Transforming variables
      • Split variables
      • Extract parts of variables
      • Standardize variables
      • Create dummy variables
      • Create separate variables
      • Transpose variables
      • Stack variables
      • Unstack variables
      • Convert datasets from wide to long
      • Convert datasets from long to wide
    • Appending and merging data sets
      • Appending data sets by rows
      • Appending data sets by columns
      • One-to-one merging
      • One-to-many merging
      • Many-to-one merging
      • Many-to-many merging
      • Using merge to update data sets
  2. Descriptive statistics
    • Introduction to statistical concepts
      • Review of research process
      • Research designs
      • Sampling techniques
      • Types of data
      • Descriptive statistics
      • Graphs for descriptive statistics
    • Explanatory data analysis in Stata
      • One way frequency tables
      • Crosstabutions
      • Tables of descriptive statistics: tabstat command
      • Tables of descriptive statistics: table command
      • Exporting Stata tables to Excel with tabout command
      • Multiple responses with mrtab command
  3. Data visualization (charts)
    • Introduction to graphing
      • The graphics dialog windows
      • Graph elements (x and y labels, titles, legends)
      • Graph appearance (marker symbol, color, size, line width, pattern, e.t.c)
      • Multiple graphs (by option)
      • Graphics syntax
      • Adding text and annotations to graphs
      • Saving and printing graphs
      • Combining active graphs into one figure
      • Graphics window (interactive plotting)
    • Common graphs and charts
      • Pie chart
      • Simple bar graph
      • Grouped/clustered bar graph
      • Stacked bar graph
      • Confidence interval bands
      • Line graph
      • Combined graph
      • Area bar graph
      • Scatter plot
      • Box plot
  4. Hypothesis testing
    • Hypothesis testing background
      • Definitions
      • Statistical inference
      • Generalizability
      • Confidence intervals in clinical research
      • P-values in clinical research
      • Hypothesis testing
      • Interpreting hypothesis test results
    • Tests of differences in population means
      • One sample z tests
      • One sample t tests
      • Two sample independent z tests
      • Two sample independent t tests
      • Two sample paired t test
      • One way analysis of variance
      • Two way analysis of variance
      • Analysis of covariance
    • Analysis of contingency tables
      • Proportion test
      • Chi-square goodness of fit test
      • Chi-square test of independence
      • Chi-square test of homogeneity
      • Proportion test
      • Fisher’s exact test
      • McNemar matched pairs for binary response
    • Non-parametric methods
      • Sign test
      • Wilcoxon signed-rank test
      • Median test
      • Wilcoxon signed-sum (Mann-Whitney) test
      • Kolmogorov-Sminorv goodness-of-fit test
      • Kruskal-Wallis one way analysis of variance
      • Friedman two-way analysis of variance
      • Spearman's rank order correlation
      • Kendall's correlation
  5. Regression analysis and correlation
    • Pearson correlation coefficient
    • Linear regression for continuous response
      • Overview of linear regression analysis
      • Simple linear regression
      • Multiple linear regression
      • Prediction in linear regression
      • Testing for assumptions of linear regression
      • Regression diagonostics for linear regression
    • Logistic regression for categorical response
      • Overview of logistic regression analysis
      • Simple logistic regression
      • Multiple logistic regression
      • Tests for logistic regression
      • Ordinal logistic regression
      • Multinomial logistic regression
      • Conditional logistic regression
    • Poisson regression for count response
      • Overview of Poison regression analysis
      • Simple Poison regression
      • Multiple Poison regression
      • Ordinal Poison regression

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