Los Angeles Pierce College Introductory Statistic One Way Independent ANOVA Worksheet

One Factor Between Subjects Analysis of Variance

The data are stored in the files “Lab #4 – Problem 1 data.sav” and “Lab #4 – Problem 2 data.sav”. The data file could be downloaded from “Canvas” and save it on the Desktop.

Formulating the statistical hypotheses (null H0 and alternative H1)

The null hypothesis H0 reflects the “all is same view of the world”. In other words, the means of all three conditions are predicted to be equal to one another.

The alternative hypothesis H1 reflects the means of all three conditions are predicted to not be equal to one another.

Setting the Decision Stage

Choosing a statistical test

Individual observations between the groups are not related. Therefore, we can model the data as independent. However, because we have to compare three groups as opposed to only two, we cannot use the t-test. We have to use the One Factor Between Subjects ANOVA. (One factor – because we have only one independent variable that serves to group the data. Between – because the observations in different groups are independent.).

Selecting a significance level (alpha)

Usually, we select alpha = .05. But we could be more conservative and use alpha = .01. For this lab, we are going to use alpha = .05.

Formulating the decision rules

The decision rule is:

We reject H0 if the p-value associated with the observed statistic is less than or equal to alpha.

We fail to reject H0 if the p-value associated with the observed statistic is greater than alpha.

Calculating the observed statistic

• Go to the data window.
• Click on “Analyze”, select “Compare Means”, and click on “One Way ANOVA”.
• Select the appropriate variable and move it into the “Dependent List” field.
• Select the appropriate variable and move it into the “Factor” field.
• Click on the POST HOC
• Select “Tukey” option, then click CONTINUE.

Making a decision

The rejection of H0 means that on average the groups differ from each other in their dependent variable. But how do they differ? This question is answered by the post hoc test, Tukey’s honestly significant difference.

The second part involves interpreting the results from the post hoc test.