Essential SPSS Normality Test Worksheet | College Math

This practical worksheet provides a step-by-step guide for college-level statistics students on how to conduct and interpret essential assumption tests in SPSS. It focuses specifically on the Shapiro-Wilk test for normality and Levene's test for homogeneity of variances, foundational skills for accurate parametric data analysis.

At a Glance

• Grade: College · Subject: Math (Statistics)
• Standard: HSS-IC.A.1 — Understand statistics for making inferences about population parameters.
• Skill Focus: Normality & Homogeneity Testing in SPSS
• Format: 4 pages · 2 guided tutorials · No answer key needed · PDF
• Best For: Guided practice for introductory statistics labs.
• Time: 25–40 minutes

What's Inside

This four-page resource walks students through two critical statistical procedures using SPSS. The first section details how to perform a normality test, and the second covers the homogeneity of variance test. Each part includes clear screenshots of the SPSS interface, from menu navigation to output tables, ensuring students can follow the process on their own computers. The document is structured as a self-contained tutorial.

Skill Progression

The worksheet follows a clear gradual-release model to build student confidence and independence in data analysis.

• Guided practice: The initial steps for the normality test are clearly laid out with explicit instructions and corresponding SPSS menu screenshots, functioning as a direct "I Do" demonstration.
• Supported practice: As students move to interpreting the output tables for both the normality and homogeneity tests, they are provided with guiding explanations. This "We Do" phase helps them connect statistical values to their meaning.
• Independent practice: After completing the tutorial, students will have the procedural knowledge to apply the same tests to their own datasets, serving as the "You Do" component of the lesson.

Standards Alignment

This worksheet directly supports the development of skills aligned with standard HSS-IC.A.1, which requires students to understand statistics as a process for making inferences about population parameters. By learning to verify the assumptions of normality and homogeneity, students engage with the foundational logic that underpins valid statistical inference and hypothesis testing. Both standard codes can be copied directly into lesson plans, IEP goals, or district curriculum mapping tools.

How to Use It

This resource is ideal for use during a computer lab session after an introductory lecture on statistical assumptions. Students can work through the tutorial individually or in pairs, applying the steps to a sample dataset provided by the instructor. Formatively, an instructor can observe students' ability to correctly navigate SPSS menus and locate the p-value in the output tables. The tutorial is designed for completion within a single 40-minute class period.

Who It's For

This worksheet is designed for undergraduate students in introductory statistics, research methods, or quantitative analysis courses. It is particularly helpful for students who are new to SPSS or need a concrete refresher on assumption testing procedures. To extend the learning, pair this tutorial with a raw dataset and a research question that requires students to run a t-test or ANOVA after verifying the assumptions.

Executing prerequisite checks like normality and homogeneity tests is a cornerstone of rigorous quantitative analysis, a principle reinforced by Fisher & Frey (2014). This worksheet provides a procedural guide for standard HSS-IC.A.1, helping students understand the process of making valid statistical inferences. By mastering these SPSS procedures, students learn to ensure their chosen parametric tests (like t-tests or ANOVA) are appropriate for their data, preventing erroneous conclusions. This skill is critical for producing reliable and defensible research findings in academic and professional settings. The ability to verify statistical assumptions is a key differentiator in data literacy, a finding echoed in broader educational research on quantitative reasoning skills.