SPSS Course

Lesson 1 • Navigating the SPSS Interface

Covers the main windows, menus, and toolbars in SPSS. Establishes spatial familiarity needed for all subsequent operations.

Lesson 2 • Saving and Managing SPSS Files

Explains SPSS file formats and best practices for file organisation. Reliable file management supports reproducibility across all course projects.

Lesson 3 • Setting Up the Variable View

Teaches variable definition including name, label, values, and missing-value codes. Proper setup ensures accurate output labelling throughout the course.

Lesson 4 • Understanding Data Types and Measurement

Defines nominal, ordinal, and scale measurement levels and their role in analysis. Correct classification prevents errors in later statistical procedures.

Lesson 1 • Detecting and Correcting Outliers

Uses boxplots and descriptive statistics to locate extreme values. Outlier decisions directly affect the accuracy of means and regression estimates.

Lesson 2 • Merging and Restructuring Datasets

Covers adding cases, adding variables, and transposing between wide and long formats. Restructuring enables longitudinal and multi-source data analysis.

Lesson 3 • Sorting, Selecting, and Filtering Cases

Demonstrates sorting rows and applying case filters for subgroup analysis. Selective analysis is essential for comparing groups and validating assumptions.

Lesson 4 • Identifying and Handling Missing Data

Covers detection of missing values and strategies for handling them. Addresses a universal data quality issue that affects every analysis type.

Lesson 1 • Exploring Distributions with Explore

Uses the Explore procedure to produce stem-and-leaf plots, boxplots, and normality tests. Normality assessment informs the choice between parametric and nonparametric tests.

Lesson 2 • Crosstabulation and Contingency Tables

Builds crosstabs with row, column, and total percentages for categorical relationships. Lays groundwork for chi-square testing introduced in the next chapter.

Lesson 3 • Creating Charts in SPSS Chart Builder

Introduces the Chart Builder for histograms, bar charts, and scatterplots. Visualisation skills support both exploration and professional reporting.

Lesson 4 • Frequencies and Descriptive Summaries

Generates frequency tables and central tendency measures for categorical and continuous variables. Provides the baseline summary every analysis report requires.

Lesson 1 • Nonparametric Alternatives

Introduces Mann-Whitney, Wilcoxon, and Kruskal-Wallis tests for non-normal data. Provides valid options when parametric assumptions cannot be met.

Lesson 2 • Chi-Square Tests for Categorical Data

Applies chi-square goodness-of-fit and independence tests to categorical variables. Connects to crosstabulation skills built in the previous chapter.

Lesson 3 • One-Way ANOVA and Post Hoc Tests

Compares means across three or more groups using one-way ANOVA with post hoc comparisons. Extends t-test logic to multi-group designs without inflating error rates.

Lesson 4 • Independent and Paired Samples t-Tests

Runs t-tests for two-group mean comparisons with independent and related samples. Covers Levene's test for equal variances as a required assumption check.

Lesson 1 • Scatterplots and Linear Relationships

Uses scatterplots with fit lines to visually assess linearity before regression. Visual inspection is the first assumption check for regression analysis.

Lesson 2 • Running Simple Linear Regression

Executes simple linear regression and interprets coefficients, R-squared, and the F-test. Establishes the regression workflow used in the multiple regression chapter.

Lesson 3 • Regression Assumption Checking

Examines residual plots, normality of errors, and homoscedasticity for simple regression. Assumption violations identified here guide remediation strategies in advanced chapters.

Lesson 1 • Variable Selection Methods

Compares Enter, Stepwise, Forward, and Backward selection methods and their trade-offs. Method choice affects model parsimony and generalisability.

Lesson 2 • Building Multiple Regression Models

Adds multiple predictors to the regression framework and interprets partial coefficients. Understanding partial effects is central to controlling for confounders.

Lesson 3 • Dummy Coding Categorical Predictors

Creates dummy variables to include nominal predictors in regression models. Proper coding allows categorical group effects to be estimated and interpreted.

Lesson 4 • Regression Diagnostics and Influential Cases

Identifies influential observations using Cook's D, leverage, and DFBeta statistics. Removing or adjusting influential cases improves model stability and validity.

Lesson 1 • Mixed ANOVA: Between and Within Factors

Combines between-subjects and within-subjects factors in a single model. Mixed designs are common in longitudinal and experimental research.

Lesson 2 • One-Way Repeated Measures ANOVA

Analyses within-subject designs where the same participants are measured multiple times. Sphericity testing and correction are unique requirements of repeated measures designs.

Lesson 3 • Two-Way Factorial ANOVA

Tests main effects and interactions of two independent factors simultaneously. Interaction interpretation is the key skill distinguishing factorial from one-way ANOVA.

Lesson 4 • MANOVA: Multiple Dependent Variables

Extends ANOVA to simultaneously test group differences on multiple outcomes. MANOVA controls familywise error when several dependent variables are theoretically related.

Lesson 1 • Binary Logistic Regression

Models the probability of a binary outcome using logistic regression with odds ratios. Extends regression skills to categorical dependent variables common in applied research.

Lesson 2 • Exploratory Factor Analysis

Reduces a large set of variables to underlying factors using principal components and common factor methods. Factor analysis is foundational for scale development and validation.

Lesson 3 • Reliability Analysis with Cronbach's Alpha

Assesses internal consistency of multi-item scales using Cronbach's alpha and item statistics. Reliability evidence is required before scale scores are used in further analysis.

Lesson 4 • Using SPSS Syntax for Reproducibility

Writes and runs SPSS syntax to automate and document analytical workflows. Syntax-based analysis ensures reproducibility and efficiency for large or repeated projects.