SPSS What Does Combined Effect Mean in a Mixed ANOVA (Using the

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Introduction

A mixed ANOVA is a statistical test that is used to compare the means of two or more groups when the data are collected from a combination of fixed and random effects. The fixed effects are factors that are controlled by the researcher, such as the treatment condition. The random effects are factors that are not controlled by the researcher, such as the subject's gender or age.

The combined effect in a mixed ANOVA is the overall effect of all of the factors in the model, both fixed and random. This effect is tested by comparing the mean of the group that received the treatment to the mean of the group that did not receive the treatment.

Interpretation

The combined effect in a mixed ANOVA can be interpreted in the same way as the main effect in a one-way ANOVA. If the combined effect is significant, then it means that there is a significant difference between the means of the two groups.

The size of the combined effect can be measured by the eta squared statistic. This statistic represents the proportion of variance in the dependent variable that is explained by the combined effect.

Example

The following table shows the results of a mixed ANOVA that was conducted to compare the mean scores of two groups on a test. The fixed effect in this ANOVA was the treatment condition, and the random effect was the subject's gender.

As you can see from the table, the combined effect of the treatment and gender is significant (p = 0). This means that there is a significant difference between the means of the two groups on the test.

The eta squared statistic for the combined effect is 0, which means that the combined effect explains 1% of the variance in the dependent variable.

Applications

Mixed ANOVAs are used in a wide variety of applications, including:

• Comparing the means of two or more groups when the data are collected from a combination of fixed and random effects
• Testing the effect of a treatment when the subjects are nested within groups
• Analyzing data from repeated measures experiments

Benefits

Mixed ANOVAs offer several benefits over other statistical tests, including:

• They can be used to analyze data from a variety of designs
• They are relatively easy to interpret
• They can be used to test a variety of hypotheses

FAQs

Q: What is the difference between a fixed effect and a random effect?

A: A fixed effect is a factor that is controlled by the researcher, such as the treatment condition. A random effect is a factor that is not controlled by the researcher, such as the subject's gender or age.

Q: What is the combined effect in a mixed ANOVA?

A: The combined effect in a mixed ANOVA is the overall effect of all of the factors in the model, both fixed and random.

Q: How is the combined effect tested?

A: The combined effect is tested by comparing the mean of the group that received the treatment to the mean of the group that did not receive the treatment.

Q: How is the size of the combined effect measured?

A: The size of the combined effect is measured by the eta squared statistic. This statistic represents the proportion of variance in the dependent variable that is explained by the combined effect.

Q: What are the benefits of using mixed ANOVAs?

A: Mixed ANOVAs offer several benefits over other statistical tests, including:

• They can be used to analyze data from a variety of designs
• They are relatively easy to interpret
• They can be used to test a variety of hypotheses

Conclusion

Mixed ANOVAs are a powerful statistical tool that can be used to analyze data from a variety of designs. They are relatively easy to interpret and can be used to test a variety of hypotheses.