Do you think running a two-way ANOVA with an interaction effect is challenging? Then this is the tutorial for you. We'll run the analysis by following a simple flowchart and we'll explain each step in simple language. After reading it, you'll know what to do and you'll understand why.

We'll use depression.sav throughout. The screenshot below shows it in variable view.

## Research Questions

Very basically, 100 participants suffering from depression were divided into 4 groups of 25 each. Each group was given a different medicine. After 4 weeks, participants filled out the BDI, short for Beck's depression inventory. Our main research question is: did our different medicines result in different mean BDI scores? A secondary question is whether the BDI scores are related to gender in any way. In short we'll try to gain insight into 4 (medicines) x 2 (gender) = 8 mean BDI scores.

## Quick Check: Histogram over Scores

Before jumping blindly into statistical tests, let's first see if our BDI scores make any sense in the first place. Before analyzing any metric variable, I always first inspect its histogram. The fastest way to create it is running the syntax below.

***Inspect histogram, see if distribution for bdi scores looks plausible.**

frequencies bdi

/format notable

/histogram.

## Result

The scores look fine. We've perhaps one outlier who scores only 15 but we'll leave it in the data. The scores are roughly normally distributed and there's no need to specify any missing values.

## ANOVA Assumptions

When we compare more than 2 means, we usually do so by ANOVA -short for analysis of variance. Doing so requires our data to meet the following assumptions:

**Independent observations**(or independent and identically distributed variables). This often holds if each case contains a distinct person and the participants didn't interact.**Homogeneity**: the population variances are all equal over subpopulations. Violation of this assumption is less serious insofar as sample sizes are equal.**Normality**: the test variable must be normally distributed in each subpopulation. This assumption becomes less important insofar as the sample sizes are larger.

## ANOVA Flowchart

## Inspecting Means and Sample Sizes

The first question in our ANOVA flowchart is whether the sample sizes are roughly equal. I like to run a means table for inspecting this because I'm going to need this table anyway for my report. I'll create it with the syntax below.

***Run means table and inspect if sample sizes are roughly equal.**

means bdi by gender by medicine

/cells count mean stddev.

***Note: MEANS allows you to choose exactly which statistics you want.**

## Result

Note that this table shows the 8 means (2 genders * 4 medicines) that our analysis is all about. Each of these 8 means is based on 10 through 15 observations so the **sample sizes are roughly equal**.

This means that we don't need to bother about the homogeneity assumption. We can therefore skip Levene's test as shown in our flowchart.

## Running Two-Way ANOVA in SPSS

We'll now run our two-way ANOVA through

. We'll then follow the screenshots below.This results in the syntax below. Let's run it and see what happens.

***SPSS Two-Way ANOVA syntax as pasted from screenshots.**

UNIANOVA bdi BY gender medicine

/METHOD=SSTYPE(3)

/INTERCEPT=INCLUDE

/POSTHOC=medicine(TUKEY)

/PLOT=PROFILE(medicine*gender) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO

/PRINT ETASQ HOMOGENEITY

/CRITERIA=ALPHA(.05)

/DESIGN=gender medicine gender*medicine.

## ANOVA Output - Between Subjects Effects

Following our flowchart, we should now find out if the interaction effect is statistically significant. A -somewhat arbitrary- convention is that an effect is statistically significant if “Sig.” < 0.05. According to the table below, our 2 main effects and our interaction are all statistically significant.

The flowchart says we should now rerun our ANOVA with simple effects. For now, we'll **ignore the main effects** -even if they're statistically significant.
But why?!
Well, this will become clear if we understand what our interaction effect really means. So let's inspect our profile plots.

## ANOVA Output - Profile Plots

The profile plot shown below basically just shows the 8 means from our means table.If you ran the ANOVA like we just did, the “Estimated Marginal Means” are always the same as the observed means that we saw earlier. Interestingly, it also shows how medicine and gender affect these means.

An interaction effect means that the effect of one factor depends on the other factor and it's shown by the **lines in our profile plot not running parallel**.

In this case, the effect for medicine

*interacts with*gender. That is, medicine affects females differently than males. Roughly, we see the red line (females) descent quite steeply from “None” to “Pharmaceutical” whereas the blue line (males) is much more horizontal. Since it depends on gender, there's no such thing as

*the*effect of medicine. So that's why we ignore the main effect of medicine -even if it's statistically significant. This main effect “lumps together” the different effects for males and females and this obscures -rather than clarifies- how medicine really affects the BDI scores.

## Interaction Effect? Run Simple Effects.

So what *should* we do? Well, if medicine affects males and females differently, then we'll **analyze male and female participants separately**: we'll run a one-way ANOVA
for just medicine on either group. This is what's meant by “**simple effects**” in our flowchart.

## ANOVA with Simple Effects - Split File

How can we analyze 2 (or more) groups of cases separately? Well, SPSS has a terrific solution for this, known as SPLIT FILE. It requires that we first sort our cases so we'll do so as well.

***Interaction statistically significant? Go for simple effects: run one-way ANOVAs for male and female participants separately.**

sort cases by gender.

split file separate by gender.

Minor note: SPLIT FILE```
does not change your data in any way. It merely affects your output as we'll see in a minute. You can simply undo it by running
SPLIT FILE OFF.
but don't do so yet; we first want to run our one-way ANOVAs for inspecting our simple effects.
```

## ANOVA with Simple Effects in SPSS

Since we switched on our SPLIT FILE, we can now just run one-way ANOVAs. We'll use

. The screenshots below guide you through the next steps.This results in the syntax below. Let's run it.

***Simple effects: SPLIT FILE by gender and run one-way ANOVAs for males and females separately.**

UNIANOVA bdi BY medicine

/METHOD=SSTYPE(3)

/INTERCEPT=INCLUDE

/POSTHOC=medicine(TUKEY)

/PLOT=PROFILE(medicine) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO

/PRINT ETASQ HOMOGENEITY

/CRITERIA=ALPHA(.05)

/DESIGN=medicine.

## ANOVA Output - Between Subjects Effects

First off, note that the output window now contains all ANOVA results for male participants and then a similar set of results for females. According to our flowchart we should now inspect the main effect.

The effect for medicine is statistically significant. However, this just means it's probably not zero. But it's not very strong either as indicated by its partial eta squared of 0.208. This should't come as a surprise. The 4 medicines don't differ much for males as we saw in our profile plots.

## ANOVA Output - Post Hoc Tests

Our main effect suggests that our 4 medicines did not all perform similarly. But which one(s) really differ from which other one(s)? This question is addressed by our post hoc tests, in this case Tukey's HSD (“honestly significant differences”) comparisons.

This table compares each medicine with each other medicine (twice). The only comparison yielding p < 0.05 is “None” versus “Pharmaceutical”. Our profile plots show that these are the worst and best performing medicines for male participants. The means for all other medicines are too close to differ statistically significantly.

## Female Participants

According to our flowchart, we're now done -at least for males. Interpreting the results for female participants is left as an exercise for the reader. However, I do want to point out the following:

- our profile plots show a much steeper line for females than for males.
- the main effect of medicine has a much higher partial eta squared of 0.63 for females.
- the effect of medicine has p = 0.000 for females and p = 0.019 for males.
- for females, all post hoc comparisons are statistically significant except for “Homeopathic” versus “Placebo” (p = 0.997).

All these findings indicate a much stronger effect of medicine for females than for males; there's a substantial **interaction effect between medicine and gender** on BDI scores. And this -again- is the reason why we need to analyze these groups separately and rerun our ANOVA with simple effects -like we just did.

## Final Notes

Do you still think running a two-way ANOVA with an interaction effect is challenging? I hope this tutorial helped you understand the main line of thinking. And -hopefully!- things start to sink in for you -perhaps after a second reading.

## This tutorial has 15 comments

## By Ruben Geert van den Berg on November 17th, 2018

First off, I think "statistically significant" really is a poor criterion in the first place.

Sure, the interaction must be statistically significant. However, its effect size -partial eta squared- must also be substantial. The lines in a profile plot must have very

convincingly different slopes. Ideally, those make some theoretical sense as well.In this case it just doesn't make sense to proceed with anything else than

simple effects-forget about any interaction or main effects. If you know any reputable source that disagrees with that, I'd love to hear it.And perhaps that's why there aren't any post hoc tests for interaction effects. They don't make sense. Well, not to me anyway.

## By saba on November 17th, 2018

Hi, again.

Some researchers believe that we should not interpret main effects when the interaction effect is statistically significant.

For example, suppose a high level of the dependent variable is desirable. When interaction is significant, then it is possible that the highest level of dependent variable occurs only for a specific combination of two levels of factors. And this may not necessary observed if we analyze the main effects, separately.

As a hypothetical example:

Suppose we want to analyze the effect 3 types of diets (d1-d3) and 4 types of exercise (e1-e4) of weight loss.

The interaction is statistically significant and the highest level of weight loss occurs for d2*e3. But if we analyze main effects separately, we observe the highest level of weight loss occurs for d3 and e4, respectively. This has not been explained in your guide.

To the best of my knowledge, SPSS only do the post hoc for main effects and not interactions. However, as I explained above, if interactions are significant existence of post hoc comparison for them is essential. Do you know any way to do that?

## By Ruben Geert van den Berg on November 17th, 2018

Hi Saba, nice to hear from you again!

Indeed, you could have a massive violation of homogeneity if sample sizes are equal. However, with (roughly) equal sample sizes, ANOVA is robust with regard to such a violation. So there's no need to bother about it. You can safely ignore Levene's test altogether.

Second, if there's a substantial interaction, you shouldn't interpret the main effects at all. They're misleading. Interpret simple effects instead. I think this tutorial makes this point very clear.

Have a great weekend!

SPSS tutorials

## By saba on November 17th, 2018

Hi,

Thank you for useful post.

1- Note that a serious non-homogenity of variance can still exists even if the sample sizes is exactly equal for all level of sex and medicine.

2- How do you interpret the results if the interaction is statistically significant but one of the main effects not?

## By Ruben Geert van den Berg on May 2nd, 2018

Hi Cameron!

To what extent the normality and homogeneity assumptions are required depends on your sample sizes. Are they reasonably large and roughly equal? Then don't bother.

I totally see that 1-way models aren't overly useful. Perhaps just run your 2-way ANOVA and be honest about breaking assumptions. Or -since age is a ratio variable- see if you can formulate your questions as a multiple regression model with a sex by age interaction term.

Last, p < 0.000 for Levene's test indicates statistical significance but that's different from effect size. It indicates that the population variances being

exactly equalis very unlikely but it does not indicate to what extent they may differ. If this extent is very small, it may not be a real problem.Hope that helps!