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Research Design and data analysis for Experimental Studies


Data analysis is the application of one or more statistical techniques to a set of data as collected. In designed experiments, some form of treatment is applied to experimental units and responses are observed. This course is designed to transform participants into professional data analysts. It is designed for participants without or with very little experience using statistical software. Some basic knowledge on statistics is required. During the course, the instructors will interchangeably use Stata, Excel, SAS and SPSS to demonstrate relevant techniques in each topic.


10 Days


Module 1

The Big Picture

  • The importance of careful experimental design
  • What you should learn here

 Variable Classification

  • What makes a “good” variable?
  • Classification by role
  • Classification by statistical type
  • Tricky cases

 Overview of statistical analysis

 Inferential Statistics

  • Covariance and Correlation
  • From Descriptions to Inferences
  • The Role of Probability Theory
  • The Null and Alternative Hypothesis
  • The Sampling Distribution and Statistical Decision Making
  • Type I Errors, Type II Errors, and Statistical Power
  • Effect Size
  • Meta-analysis
  • Parametric Versus Nonparametric Analyses
  • Selecting the Appropriate Analysis: Using a Decision Tree

 Module 2

Review of Probability

  • Definition(s) of probability
  • Probability mass functions and density functions
  • Probability calculations
  • Populations and samples
  • Parameters describing distributions
  • Central tendency: mean and median
  • Spread: variance and standard deviation
  • Skewness and kurtosis
  • Multivariate distributions: joint, conditional, and marginal
  • Covariance and correlation
  • The Importance of Variability
  • Tables and Graphs
  • Thinking Critically About Everyday Information
  • Central limit theorem

 Common distributions

  • Binomial distribution
  • Multinomial distribution
  • Poisson distribution
  • Gaussian distribution
  • t-distribution
  • Chi-square distribution
  • F-distribution

 Module 3

Exploratory Data Analysis

  • Typical data format and the types of Exploratory Data Analysis
  • Univariate non-graphical Exploratory Data Analysis
  • Categorical data
  • Characteristics of quantitative data
  • Central tendency
  • Spread
  • Skewness and kurtosis

 Univariate graphical Exploratory Data Analysis

  • Histograms
  • Stem-and-leaf plots
  • Boxplots
  • Quantile-normal plots

 Multivariate non-graphical Exploratory Data Analysis

  • Cross-tabulation
  • Correlation for categorical data
  • Univariate statistics by category
  • Correlation and covariance
  • Covariance and correlation matrices

 Multivariate graphical Exploratory Data Analysis

  • Univariate graphs by category
  • Scatterplots

 A note on degrees of freedom

 Module 4 and 5

Learning Stata, Excel, SAS and SPSS: Data and Exploratory Data Analysis

  • Overview of software
  • Starting the programs
  • Typing in data
  • Loading data
  • Creating new variables
    • Recoding
    • Automatic recoding
    • Visual binning
  • Non-graphical Exploratory Data Analysis
  • Graphical Exploratory Data Analysis
    • Overview of programs Graph
    • Histogram
    • Boxplot
    • Scatterplot
  • SPSS convenience item: Explore

 Module 6


  • How classical statistical inference works
    • The steps of statistical analysis
    • Model and parameter definition
    • Null and alternative hypotheses
    • Choosing a statistic
    • Computing the null sampling distribution
    • Finding the p-value
    • Confidence intervals
    • Assumption checking
    • Subject matter conclusions
    • Power
  • t-test in Stata, Excel, SAS and SPSS

One-way ANOVA 

  • How one-way ANOVA works
    • The model and statistical hypotheses
    • The F statistic (ratio)
    • Null sampling distribution of the F statistic
    • Inference: hypothesis testing
    • Inference: confidence intervals
  • One-way ANOVA in Stata, Excel, SAS and SPSS
  • Reading the ANOVA table
  • Assumption checking
  • Results interpretation and reporting

Threats to Your Experiment 

  • Internal validity
  • Construct validity
  • External validity
  • Maintaining Type 1 error
  • Power
  • Missing explanatory variables
  • Practicality and cost
  • Threat summary

 Module 7

Simple Linear Regression

  • The model behind linear regression
  • Statistical hypotheses
  • Simple linear regression example
  • Regression calculations
  • Interpreting regression coefficients
  • Residual checking
  • Robustness of simple linear regression
  • Additional interpretation of regression output
  • Using transformations
  • How to perform simple linear regression in Stata, Excel, SAS and SPSS

 Analysis of Covariance

  • Multiple regression
  • Interaction
  • Categorical variables in multiple regression
    • ANCOVA with no interaction
    • ANCOVA with interaction
  • Analysis of Covariance in Stata, Excel, SAS and SPSS

 Two-Way ANOVA

  • Application areas of Two-Way ANOVA
  • Interpreting the two-way ANOVA results
  • Examples
  • More on profile plots, main effects and interactions
  • Two-Way ANOVA in Stata, Excel, SAS and SPSS

 Module 8

Statistical Power 

  • The concept
  • Improving power
  • Specific researchers’ lifetime experiences
  • Expected Mean Square
  • Power Calculations
  • Choosing effect sizes
  • Using n.c.p. to calculate power
  • A power applet
  • Overview
  • One-way ANOVA
  • Two-way ANOVA without interaction
  • Two-way ANOVA with interaction
  • Linear Regression

 Module 9

Contrasts and Custom Hypotheses

  • Contrasts, in general
  • Planned comparisons
  • Unplanned or post-hoc contrasts
  • Contrasts and Custom Hypotheses in Stata, Excel, SAS and SPSS
    • Contrasts in one-way ANOVA
    • Contrasts for Two-way ANOVA

 Within-Subjects Designs

  • Overview of within-subjects designs
  • Multivariate distributions
  • Example and alternate approaches
  • Paired t-test
  • One-way Repeated Measures Analysis
  • Mixed between/within-subjects designs in Stata, Excel, SAS and SPSS

 Mixed Models

  • Overview
  • Mixed model approach
  • Setting up a model in Stata, Excel, SAS and SPSS
  • Interpreting the results for Mixed models
  • Model selection for Mixed models
    • Penalized likelihood methods for model selection
    • Comparing models with individual p-values

 Module 10

Categorical Outcomes

  • Contingency tables and chi-square analysis
    • Why ANOVA and regression don’t work
  • Testing independence in contingency tables
    • Contingency and independence
    • Contingency tables
    • Chi-square test of Independence
  • Logistic regression
    • Introduction
    • Example and EDA for logistic regression
    • Fitting a logistic regression model
    • Tests in a logistic regression model
    • Predictions in a logistic regression model
    • Logistic regression in Stata, Excel, SAS and SPSS


Participants should be reasonably proficient in English. Applicants must live up to Indepth Research Services (IRES) admission criteria.


The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.


Upon successful completion of this training, participants will be issued with an Indepth Research Services (IRES) certificate.


The training will be held at the IRES Training Centre in in Kigali, Rwanda . The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.


Accommodation is arranged upon request. For reservations contact the Training Officer.

Email: This email address is being protected from spambots. You need JavaScript enabled to view it. 

Mob: +250 789 621 067


This training can also be customized for your institution upon request to a minimum of 4 participants. You can have it delivered in our training centre or at a convenient location.


Payment should be transferred to IRES account through bank on or before C.O.B 15th July 2019.

Send proof of payment to This email address is being protected from spambots. You need JavaScript enabled to view it.


Payment for the all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

1.    Participants may cancel attendance 14 days or more prior to the training commencement date.

2.    No refunds will be made 14 days or less to the training commencement date. However, participants who are unable to attend may opt to attend a similar training at a later date, or send a substitute participant provided the participation criteria have been met.

Please Note: The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.


Event Properties

Event Duration 10 Days
Event Date 22-07-2019
Event End Date 02-08-2019
Cut off date 15-07-2019
Individual Price(rwanda franc) R₣ 1,112,000
Individual Price (International) EUR 1,771
Individual Price(International in Dollars) USD 1,999
Location Kigali,Rwanda
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