What to Do if You Find a Difference in Anova Before Testing Again
In this tutorial, nosotros'll wait at how to perform a 1-way analysis of variance (ANOVA) for independent groups in SPSS, and how to interpret the result using Tukey's HSD.
Quick Steps
- Click on Analyze -> Compare Ways -> Ane-Manner ANOVA
- Drag and drib your contained variable into the Factor box and dependent variable into the Dependent List box
- Click on Post Hoc, select Tukey, and press Keep
- Click on Options, select Homogeneity of variance test, and press Keep
- Press the OK button, and your outcome will pop upwards in the Output viewer
The Information
Nosotros're starting from the assumption that you've already got your information into SPSS, and you're looking at a Data View screen that looks a flake similar this.
Our fictitious dataset contains a number of different variables. For the purposes of this tutorial, nosotros're interested in whether level of education has an issue on the ability of a person to throw a frisbee. Our independent variable, therefore, is Education, which has three levels – High Schoolhouse, Graduate and PostGrad – and our dependent variable is Frisbee Throwing Distance (i.e., the altitude a subject field throws a frisbee).
The i-way ANOVA test allows us to determine whether there is a significant difference in the mean distances thrown by each of the groups.
One-Manner Analysis of Variance (ANOVA)
To commencement, click on Clarify -> Compare Means -> Ane-Way ANOVA.
This will bring up the One-Way ANOVA dialog box.
To prepare the test, you've got to get your independent variable into the Cistron box (Instruction in this example, see above) and dependent variable into the Dependent Listing box. You can do this by dragging and dropping, or by highlighting a variable, and and so clicking on the appropriate pointer in the centre of the dialog.
Later you've moved the variables over, you should click the Mail Hoc push button, which volition allow you to specify the mail service hoc examination(southward) y'all wish to run.
The ANOVA examination will tell you lot whether there is a significant deviation between the means of two or more levels of a variable. However, if you've got more than than ii levels it'southward not going to tell you between which of the various pairs of means the difference is significant. You need to do a post hoc examination to find this out.
Postal service Hoc Tests
The Mail service Hoc dialog box looks similar this.
You should select Tukey, as shown above, and ensure that your significance level is set to 0.05 (or whatever alpha level is right for your written report).
Now press Continue to return to the previous dialog box.
Options
You should be looking at this dialog box over again.
Click Options to bring up the Options dialog box.
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 as well useful.
In one case you lot've made your selections, click Continue.
At this point, yous're ready to run the test.
Review your options, and click the OK button. You'll see the result pop upwards in the Output Viewer.
The Result
SPSS produces a lot of data for the i-way ANOVA exam. Let's deal with the important bits in turn.
Descriptives
Information technology's worth having a quick glance at the descriptive statistics generated by SPSS.
If y'all look above, you'll encounter that our sample data produces a difference in the mean scores of the three levels of our education variable. In particular, the information 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 form, is whether the difference in mean scores reaches significance.
Homogeneity of Variances
A requirement for the ANOVA exam is that the variances of each comparing group are equal. We have tested this using the Levene statistic. What yous're looking for hither is a significance value that is greater than .05. You don't want a significant effect, since a significant result would suggest a real difference between variances.
In our example, as you tin come across in a higher place, the significance value of the Levene statistic based on a comparison of medians is .155. This isnot a meaning result, which means the requirement of homogeneity of variance has been met, and the ANOVA exam can exist considered to be robust.
F Statistic (ANOVA Result)
Now that nosotros know we have equal variances, we can look at the issue of the ANOVA test.
The ANOVA result is easy to read. Yous're looking for the value of F that appears in the Betwixt Groups row (come across to a higher place) and whether this reaches significance (side by side column along).
In our example, we have a significant result. The value of F is 3.five, which reaches significance with ap-value of .038 (which is less than the .05 alpha level). This means there is a statistically significant difference betwixt the means of the different levels of the pedagogy variable.
However, equally all the same nosotros don't know between which of the various pairs of means the difference is significant. For this we need to expect at the event of the post hoc Tukey HSD examination.
Tukey HSD
If yous take a look at the Multiple Comparisons table above you'll see that significance values have been generated for the mean differences between pairs of the various levels of the education variable (Graduate – High School; Graduate – PostGrad; and High Schoolhouse – PostGrad).
In our case, the Tukey HSD (Honest Significant Difference) shows that information technology is only the mean difference between the High Schoolhouse and PostGrad groups that reaches significance (see the Sig. column, above). Thep-value is .034, which is less than the standard .05 alpha level.
Report the Result
When reporting the effect it'south normal to reference both the ANOVA test and the post hoc Tukey HSD test.
Thus, given our example here, y'all could write something like:
At that place was a statistically significant difference betwixt groups as demonstrated by one-fashion ANOVA (F(2,47) = 3.5, p = .038). A Tukey post hoc test showed that the PostGrad group was able to throw the frisbee statistically significantly further than the High Schoolhouse group (p = .034). There was no statistically significant difference between the Graduate and Loftier School groups (p = . 691) or between the Graduate and PostGrad groups (p = .099).
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Right, that's it for this tutorial. You should now be able to perform a one-fashion ANOVA exam in SPSS, check the homogeneity of variance assumption has been met, run a post hoc examination, and interpret and study your result.
Source: https://ezspss.com/one-way-anova-in-spss-including-interpretation/
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