SPSS Tutorials

SPSS stands for Statistical Package for the Social Sciences and was first introduced in 1968. After its acquisition by IBM in 2009, the software became officially known as IBM SPSS Statistics, though it is still commonly referred to simply as “SPSS.”

SPSS is used to analyze, manage, and visualize data, especially in research and social science settings. It’s popular because it allows users to perform complex analyses with a relatively user-friendly interface.

SPSS Introduction

These tutorials are designed to help beginners build a strong foundation in using SPSS for data analysis. The first tutorial introduces the SPSS interface by explaining Data View vs. Variable View and demonstrates how to perform basic descriptive statistics. The second tutorial focuses on selecting cases in SPSS, showing how to filter data, analyze specific groups, and conduct statistical tests using only the selected cases. Together, these tutorials provide essential skills for managing and analyzing data effectively in SPSS.

SPSS Basics

These tutorials cover a broad range of essential topics in data measurement, statistical reasoning, and applied data analysis using SPSS and R. They begin by introducing different levels of measurement—nominal, ordinal, and scale—and addressing common methodological questions such as whether Likert-scale data should be treated as ordinal or interval, as well as clarifying the difference between interval and ratio data. Building on this conceptual foundation, the tutorials then provide practical, step-by-step guidance on creating scatter plots, visualizing interaction effects involving categorical variables, and managing datasets in SPSS, including selecting variables and saving them as a new file. Finally, the series extends beyond SPSS by demonstrating how to read SPSS files in R, helping learners integrate statistical software tools for more flexible and advanced data analysis.

SPSS Data Analytics

These tutorials focus on core inferential statistical analyses commonly used in research and data analytics with SPSS. They guide learners through essential techniques for comparing group means, examining relationships between variables, and analyzing categorical and count data, using clear explanations and step-by-step procedures.

The first set of lectures introduces mean comparison methods, including the independent t-test, paired t-test, one-sample t-test, and both one-way and two-way ANOVA, helping learners understand how to test differences within and between groups. The next section covers correlation and regression, demonstrating how to assess associations between variables and build predictive models using correlation analysis, simple linear regression, and multiple linear regression. The final section focuses on count and categorical data analysis, including logistic regression, chi-square tests, and McNemar’s test, which are widely used for analyzing frequencies, proportions, and binary outcomes. Together, these tutorials provide a practical foundation for conducting and interpreting statistical analyses in SPSS.

Section 1: Mean Comparisons:

Section 2: Correlation and Regression

Section 3: Count Data

SPSS: Advanced Tutorials

These advanced SPSS tutorials are designed for learners who already have a solid understanding of basic statistical analyses and want to deepen their analytical skills. The tutorials focus on more complex modeling, interpretation, and decision-making techniques that are commonly used in applied research.

Topics include examining interactions between categorical and continuous variables, using the Johnson–Neyman technique to interpret moderation effects, and understanding how dummy coding and contrast coding influence model estimates and p-values. The series also introduces linear mixed models for analyzing data with hierarchical or repeated-measures structures, as well as Levene’s test for assessing the assumption of homogeneity of variance. Together, these tutorials equip users with advanced tools to perform more nuanced and rigorous analyses in SPSS.

SPSS Syntax