SPSS ANOVA Tutorials – Overview
SPSS Two-Way ANOVA with Interaction Tutorial. In ANOVA and regression, an interaction effect means that some effect depends on another variable. Example: women become happier but men become un happier if they have children. So the effect of having children depends on sex. One Way ANOVA in SPSS Including Interpretation Click on Analyze -> Compare Means -> One-Way ANOVA. Drag and drop your independent variable into the Factor box and dependent variable into the Dependent List box. Click on Post Hoc, select Tukey, and press Continue. Click on Options, select Homogeneity of variance test, and press Continue.
In this section, we show you the main tables required to understand your results from the two-way ANOVA, including descriptives, between-subjects effects, Tukey post hoc tests multiple comparisonsa plot of the results, and how to write up these results. For a complete explanation of the output you have to interpret when checking your data for the six assumptions required to carry out a two-way ANOVA, see our enhanced spds.
This includes relevant boxplots, and output from your Shapiro-Wilk test for intsrpret and test inetrpret homogeneity of variances. Finally, if you have a statistically significant interaction, you will also need to report simple main effects. Alternately, if you do not have a statistically significant interaction, there are other procedures you will have to follow. Below, we take you through each interprte the main tables required to understand your results from the two-way ANOVA.
You can ingerpret appropriate descriptive statistics for when you report the results of your two-way ANOVA in the aptly named " Descriptive Statistics " table, as shown below:. This table is very useful because it provides the mean and standard deviation for each combination of the groups of the independent variables what is sometimes referred to as each "cell" of the design.
In addition, the table provides "Total" rows, which allows means and standard deviations for groups only split by one independent variable, or none at all, to be known. This might be more useful if you do not have a statistically significant interaction. Although this graph is probably not of lnterpret quality to present in your reports you can edit its look in SPSS Statisticsit does tend to provide a good graphical illustration of your results. An interaction effect can usually be seen as a set of non-parallel lines.
You can see from this graph that the lines do not appear to be parallel with the lines actually crossing. You might expect there to be a statistically significant interaction, which we can confirm in the spsz section. The actual result of the two-way ANOVA — namely, whether either of the two independent variables or their interaction are statistically significant — is inerpret in the Qnova of Between-Subjects Effects table, as shown below:.
You can see from the " Sig. When you have a statistically significant interactionreporting the main effects can be misleading.
Therefore, you will need to report the simple main effects. In our example, this would involve determining the mean difference in interest in politics between genders at each educational level, as well as between educational level for each gender.
Ihterpret, SPSS Statistics does not allow you to do this using the graphical interface you will be familiar with, but requires you to use syntax. Therefore, in our enhanced two-way ANOVA guide, we show you the procedure for doing this in SPSS Statistics, as well as explaining how to interpret and write up the output from your simple main effects. When you do not have a statistically spws interaction, we explain two options you have, as ti as a procedure you can use in SPSS Statistics to deal with this issue.
Anoca you do not have a statistically significant interaction, you might interpret the Tukey post hoc test results for the different levels of education, which can be found in the Multiple Intfrpret table, as shown xnova.
You can see from the table above that there is some repetition of the results, but regardless of which row we choose to read from, we are interested in the differences between 1 School and College, 2 School and University, and 3 College and University. You should emphasize the results from the interaction first before you mention the main effects. For example, you might report the result as:. A two-way ANOVA was conducted that examined the effect of gender and education level on interest in politics.
If you had a statistically significant interaction term and carried out the procedure for simple main effects in SPSS Statistics, you would also report these results.
You can learn more about our enhanced content on our Features: Overview page. Join the 10,s of students, academics and professionals who rely on Laerd Statistics. For example, how to remove embroidery from clothing might report the result as: General A two-way ANOVA was conducted that examined the effect of gender and education level on interest in politics.
SPSS Statistics Output of the Two-way ANOVA
We need ANOVA to make a conclusion about whether the IV (sugar amount) had an effect on the DV (number of words remembered). But looking at the means can give us a head start in interpretation. ANOVA Box. This is the next box you will look at. It shows the results of the 1 Way Between Subjects ANOVA that you conducted.
Our fictitious dataset contains a number of different variables. Our independent variable, therefore, is Education, which has three levels — High School, Graduate and PostGrad — and our dependent variable is Frisbee Throwing Distance i. The one-way ANOVA test allows us to determine whether there is a significant difference in the mean distances thrown by each of the groups. You can do this by dragging and dropping, or by highlighting a variable, and then clicking on the appropriate arrow in the middle of the dialog.
The ANOVA test will tell you whether there is a significant difference between the means of two or more levels of a variable. You need to do a post hoc test to find this out. You should select Tukey, as shown above, and ensure that your significance level is set to 0. At the very least, you should select the Homogeneity of variance test option since homogeneity of variance is required for the ANOVA test. Descriptive statistics and a Means plot are also useful. Review your options, and click the OK button.
In particular, the data analysis shows that the subjects in the PostGrad group throw the frisbee quite a bit further than subjects in the other two groups. The key question, of course, is whether the difference in mean scores reaches significance. We have tested this using the Levene statistic. In our example, as you can see above, the significance value of the Levene statistic based on a comparison of medians is.
This is not a significant result, which means the requirement of homogeneity of variance has been met, and the ANOVA test can be considered to be robust. In our example, we have a significant result. The value of F is 3. This means there is a statistically significant difference between the means of the different levels of the education variable.
For this we need to look at the result of the post hoc Tukey HSD test. The p -value is. You should now be able to perform a one-way ANOVA test in SPSS, check the homogeneity of variance assumption has been met, run a post hoc test, and interpret and report your result.