DATA ANALYSIS FOR ACADEMIC RESEARCH (SURVEY ANALYSIS)

About the course

Statistical Package for Social Sciences (SPSS) is one of the most user-friendly statistical software for researchers providing visualization and data analytical tools. This course provides the participants with a practical application of the statistical component of IBM® SPSS® Statistics. Participants will review several statistical techniques, gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.

Course duration

Online

2 Days

What you will learn

• Understand and appropriately use statistical terms and concepts
• Perform data analysis tasks with SPSS
• Perform simple to complex data management tasks using SPSS
• Statistical tests using SPSS

Course Outline

Introduction to Statistical Analysis

• Explain the basic steps of the research process
• Explain differences between populations and samples
• Explain differences between experimental and non-experimental research designs
• Explain differences between independent and dependent variables

Introduction to SPSS statistical software

• SPSS interface and features
• Key terminologies used in SPSS
• Views: Variable, Data views, Syntax editor
• Data file preparation
• Data entry into SPSS
• Data manipulation: merge files, spit files, sorting files, missing values

Basic Statistics using SPSS

• Descriptive statistics for numeric variables
• Frequency tables
• Distribution and relationship of variables
• Cross tabulations of categorical variables
• Stub and Banner Tables

Graphics using SPSS

• Introduction to graphs in SPSS
• Graph commands in SPSS
• Different types of Graphs in SPSS

Statistical Tests using SPSS

• One Sample T Test
• Independent Samples T Test
• Paired Samples T Test
• One-Way ANOVA

Statistical Associations in SPSS

• Chi-Square test
• Pearson's Correlation
• Spearman's Rank-Order Correlation
• Bivariate Plots and Correlations for Scale Variables

Predictive Models using SPSS

• Linear Regression
• Multiple Regression
• Logistic Regression
• Ordinal Regression

Training Approach

T