One-Way ANOVA in SPSS

In this article, you’ll learn how to conduct a one-way analysis of variance (One-Way ANOVA) in SPSS – without writing a single line of code. This is especially useful if you prefer working with clicks rather than R scripts. You’ll get a complete workflow: from entering the data and testing the assumptions to conducting the post hoc test.

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Example: Study Methods and Exam Performance

We’ll stick with the familiar example:

Research question: Is study method (flashcards, videos, group work) related to test scores?

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1. Entering Data in SPSS

Create two variables:

• method (categorical, e.g., 1 = flashcards, 2 = videos, 3 = group work)
• score (scale, e.g., 78, 85, …)

Tip: In the Variable Editor, use “Values” (value labels) to name the group codes. For example: 1 = flashcards.

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2. Checking the Assumptions

Definition (Box):
ANOVA assumes:

• Normality within each group
• Homogeneity of variances (equal variances)
• Independent observations

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a) Normal distribution (Shapiro–Wilk test)

In SPSS:
Analyze → Descriptive Statistics → Explore

• dependent variable: “points”
• grouping variable: “method”
• Under “Plots”: select Normality plots with tests

Interpretation:

• If the Shapiro–Wilk test $p > 0.05$, the normality assumption is not violated.

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b) Homogeneity of variances (Levene’s test)

This is automatically included in the ANOVA output (see below). You don’t need to do anything extra!

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c) Independence

You must ensure this conceptually, e.g., through careful study design:
Each person belongs to only one group and is measured once.

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3. Conducting a one-way ANOVA

In SPSS:
Analyze → Compare Means → One-Way ANOVA

• dependent variable: “points”
• factor: “method”
• Click Options: activate “Descriptive statistics” + “Homogeneity of variance test”

Then click OK.

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Example output:

Descriptive statistics

You will get the means and standard deviations for the three groups.

Test of homogeneity of variances (Levene’s test):

$p > 0.05$ → equal variances → assumption met
$p < 0.05$ → it is better to activate Welch’s ANOVA (visible under “ANOVA → Robust tests”)

ANOVA table:

F-value and p-value show whether the group means are significantly different.

Definition (box):
F-value: Ratio of explained to unexplained variance
p-value: Indicates whether the differences are random or statistically significant

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4. Post-hoc test: Which groups differ?

In the same ANOVA window, click Post-hoc:

• Select Tukey (if the variances are equal)
• Or Games-Howell (if the Levene test is not passed)

Then click OK.

Interpretation:
In the table, you will see:

• Difference between the groups (e.g., group work – videos = +19 points)
• Whether this difference is significant ($p < 0{,}05$)

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5. Bonus: Create a chart

If you want to show the results visually:

Graphs → Mean Plot → Simple

• Category axis: “method”
• Dependent axis: “points”

→ Adding error bars (confidence interval) gives you a nice visual right away!

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Summary: ANOVA in SPSS — Step by step

Step	What you do
1.	Enter the data and label the variables
2.	Test the assumptions (Explore + Levene)
3.	Run the ANOVA (Analyze → One-Way ANOVA)
4.	Post hoc test (Tukey or Games-Howell)
5.	Create a chart for illustration

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Q&A – Test Your Knowledge

Question 1: What does the one-way ANOVA test?
Answer: Whether the means of several groups differ significantly.

Question 2: Why do you need a post hoc test?
Answer: To see which groups specifically differ after the ANOVA was significant.

Question 3: What does a significant Levene’s test mean?
Answer: That the assumption of equal variances has been violated—so it is better to use a robust test such as Games-Howell.

Question 4: Which setting in SPSS provides the test of normality?
Answer: Explore under “Descriptive Statistics,” with “Normality plots with tests.”

Question 5: Where can you find the F-value and p-value in SPSS?
Answer: In the main ANOVA table after clicking OK in the One-Way ANOVA dialog.