Research, Data management and Statistical Analysis using Stata

INTRODUCTION

Stata is a complete statistical software package for data management and statistical data analysis. Results from its analysis can easily be presented in both tables and graphs. It comprises a collection of data functions that range from simple to advanced application. The software is very popular with data manipulation, cross sectional data analysis, panel data analysis, forecasting, time series analysis, survival analysis, longitudinal survey methods, structural equation modeling and simulations. The software can be used via a graphical user interface (where users interact with menus, icons and dialog boxes) or a command-line interface (using its very intuitive programming language). The software is supported by a very active user community that not only offer dedicated support but also develops new packages that continuously improves the software capabilities. This five days course is designed to enhance participants’ abilities of using the software. By the end of the training sessions, participants would become intermediate and advanced level users of the software.

DURATION

5 Days

LEARNING OBJECTIVES

By the end of the training, you will be able to:

• Understand both descriptive and inferential statistics
• Understand various data collection techniques and data processing methods and use mobile phones for data collection(Open data Kit)
• Use important functionalities in Stata that relate to data manipulation and management.
• Create and manipulate graphs and figures in Stata
• Use Stata effectively for analyzing quantitative data descriptively.
• Use Stata effectively for analyzing quantitative data using inferential statistics.
• Keep records of your work and create reproducible analyses.
• Export the results of analyses into word processing programs such as Microsoft Word.

TOPICS TO BE COVERED

Module 1: Introduction

Introduction to Stata

• Introduction to Statistics Concepts
• Concepts and Software and technologies for Data Collection and Processing
• Stata Basics: Program window features, Data Structures and Types of Variables
• Data Management using Stata
• Output Management
• Basics of stata programming

Introduction to Mobile Data gathering

• Benefits of Mobile Applications
• Data and types of Data
• Introduction to  common mobile based data collection platforms
• Managing devices
• Challenges of Data Collection
• Data aggregation, storage and dissemination
• Questionnaire Design

Survey Authoring and Preparation of mobile phone for data collection

Survey Authoring

• Design forms using a web interface: ODK Build, Koboforms, PurcForms

Preparing the mobile phone for data collection

• Installing applications: ODK Collect (using Google play)

Designing forms and advanced survey authoring

Designing forms manually: Using XLS Forms

• Introduction to XLS forms syntax
• New data types
• Notes and dates
• Multiple choice Questions
• Multiple Language Support
• Hints and Metadata
• Hands-on Exercise

Advanced survey Authoring

• Conditional Survey Branching

• Required questions
• Constraining responses
• Skip: Asking Relevant questions
• The specify other
• Skipping many questions at once (Skipping a section)

• Grouping questions

• Repeating a set of questions
• Special formatting
• Making dynamic calculations

Module 2: Descriptive Statistics

Measures of Variability and Central Tendency  

• Describing quantitative data

• Describing qualitative data (Tabulating data with Stata)

Stata Graphics

• Graphing quantitative data
• Graphing qualitative data

Module 3: Correlation, Chi-square and mean comparison analysis

Correlation  

• Correlation
• Subgroup Correlations
• Scatterplots of Data by Subgroups
• Overlay Scatterplots

Comparing Means

• One Sample t-tests
• Paired Sample t-tests
• Independent Samples t-tests
•  Comparing Means Using One-Way ANOVA 

Comparing Means Using Factorial ANOVA 

• Factorial ANOVA Using GLM Univariate
• Simple Effects

Module 4: Regression Analysis and Nonparametric Statistics 

Regression Analysis

• Assumptions of selected types of regression
• Linear regression; Binary logistic regression; ordered logistic regression; multinomial logistic regression and Poisson regression

Nonparametric Statistics 

• Mann-Whitney Test
• Wilcoxon’s Matched Pairs Signed-Ranks Test
• Kruskal-Wallis One-Way ANOVA
• Friedman’s Rank Test for k Related Samples