Quantitative Data Analysis Cohort 1 (MORNING)

Categories: Quantitative Analysis
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About Course

Research survey and Data analysis training (Quantitative and Qualitative) a professional practice-oriented hand on course in the capture (data collection), management, analysis, and reporting of data from questionnaire surveys.

This training workshop is based on survey analysis using Advanced Excel, STATA, and SPSS Software for analysis.

 

 

What is SPSS?

An integrated computer program that enable user to read data from the questionnaire survey and other sources e.g. excel) to manipulate them in various ways and to produce a wide range of statistical analysis and report together with documentation. The Statistical Package for Social Sciences (SPSS) offers broad range of capabilities for understanding and analyzing data. It is possible to generate decision-making information quickly using statistics that have rigor and power and effectively present results with high-quality graphical output.

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What Will You Learn?

  • Discuss the role of statistics in data analysis
  • Apply SPSS in data entry, analysis and interpretation
  • Generate graphics using SPSS
  • Consolidate SPSS outputs using Microsoft Office.
  • Report writing
  • Introduction to SPSS
  • Types of variables (Numerical, discrete variables, dummy variables,
  • Entering categorical and continuous data
  • Defining and labelling variables
  • Validation and Sorting variables
  • Transforming, recoding and computing variables
  • Tabulation and graphical presentation of data
  • Descriptive Statistics
  • Frequency tables
  • Tables for categorical data
  • Graphs and charts
  • Exercise: Data Management, Graphing and Tabulations
  • Hypothesis testing
  • Comparing Means.
  • Regression and Correlation analysis
  • Interpreting the data
  • Exercise: Data Analysis and interpretation

Course Content

Introduction To Stata
installation of Stata software

  • Quantitative Analysis Cohort 1

Data Entry using Stata
Introduction to coding Variable

Data Management Stata
Compute codebook

Descriptive Analysis
descriptive Statistics

Inferential Analysis
Chi-square Test Pearson Correlation independent Sample T test

Regression
OLS Regression

Introduction to Advanced EXCEL
Data Entry

Functions
AGGREGRATE functions

Conditional Statements
IF

Pivot Table
summary Report

Introduction to Statistical Package For Social Science
• Explain how IBM SPSS Statistics is used for basic analysis • Explain the basic steps in data analysis • Understand the primary windows in IBM SPSS Statistics • Understand the different components of dialog boxes

Data Entry Primary Sources
• Describe and define variable properties in the Variable View window • Use the Define Variable Properties dialog box • Save variable properties with data in an IBM SPSS Statistics data file • Use the Variables utility to view variable properties interactively • Use the Display Data Dictionary facility and the Codebook procedure to view variable properties

Reading Data
Import data from different types of file formats • Describe choices on the File menu for reading data • Read Microsoft Excel files • Read delimited text files

Data Management
Use Visual Binning to reclassify values of an ordinal or scale variable • Use Recode into a Different Variable to reclassify values of a nominal variable • Use Automatic Recode to create a numeric variable from a string variable Describe the features of Compute Variable • Create new variables with numeric expressions • Create new variables with conditional numeric expressions

Summarizing Individual Variables
Define levels of measurement • Use the Frequencies procedure to produce tables and charts appropriate for nominal variables • Use the Frequencies procedure to produce tables and charts appropriate for ordinal variables • Use the Frequencies and Descriptive procedure to produce tables and charts for scale variables

Relationship Between Variables
• Select the appropriate procedure to summarize the relationship between two variables • Use the Crosstabs procedure to summarize the relationship between categorical variables • Use the Means procedure to summarize the relationship between a scale and a categorical variable

Regression Analysis

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Find Us

Email: adm@drstc.co.ke

Phone: +254202020021

Map: Nairobi, Kenya