Statistical Analysis using SPSS

• Basic understanding of mathematical concepts
• No prior experience with SPSS required
• Familiarity with basic statistics is beneficial but not mandatory

Audience

• Data analysts
• Researchers
• Business professionals working with statistical data

SPSS (Statistical Package for the Social Sciences) is powerful software used for editing, managing, and analyzing statistical data across various fields such as social sciences, healthcare, business, and research.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to perform statistical analysis using SPSS to interpret data accurately, run complex statistical tests, and generate meaningful insights.

By the end of this training, participants will be able to:

• Navigate the SPSS interface and manage datasets efficiently.
• Perform descriptive and inferential statistical analyses.
• Conduct t-tests, ANOVA, MANOVA, regression, and correlation analyses.
• Apply non-parametric tests, principal component analysis, and factor analysis for advanced data interpretation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

Getting Started with SPSS

• Introduction to SPSS interface and functionalities
• Importing and exporting data files
• Basic data entry and management

Obtaining, Editing, and Saving Statistical Output

• Generating statistical reports
• Customizing output tables and charts
• Saving and exporting analysis results

Manipulating Data

• Data transformation techniques
• Re-coding variables and computing new ones
• Managing missing data

Descriptive Statistics Procedures

• Calculating measures of central tendency and variability
• Frequency distributions and cross-tabulations
• Visualizing data with charts and graphs

Evaluating Score Distribution Assumptions

• Normality tests and graphical assessments
• Assessing skewness and kurtosis
• Checking for outliers

t-Tests

• Independent samples t-test
• Paired samples t-test
• Interpreting t-test results

Univariate Group Differences: ANOVA and ANCOVA

• One-way ANOVA and post-hoc comparisons
• Factorial ANOVA for multiple variables
• Introduction to ANCOVA and its applications

Multivariate Group Differences: MANOVA

• Understanding MANOVA concepts
• Running MANOVA tests in SPSS
• Interpreting MANOVA output

Nonparametric Procedures for Analyzing Frequency Data

• Chi-square tests of independence
• Mann-Whitney U test and Wilcoxon signed-rank test
• Kruskal-Wallis H test for non-parametric ANOVA

Correlations

• Pearson correlation coefficient
• Spearman rank correlation
• Partial and point-biserial correlation

Regression with Quantitative Variables

• Simple linear regression analysis
• Multiple regression models
• Interpreting regression coefficients and diagnostics

Regression with Categorical Variables

• Dummy variable coding for categorical data
• Logistic regression analysis
• Interpreting odds ratios and logistic model fit

Principal Components Analysis and Factor Analysis

• Exploratory factor analysis (EFA)
• Principal components analysis (PCA) techniques
• Factor rotation methods and interpretation of results

Summary and Next Steps