General Linear Model Relationships (one-way ANOVA, t-test, r, simple regression)

Why? Understanding how general linear model analyses are related as part of the same analytical family helps us develop greater conceptual awareness of what our methods are doing for us and how they can be used. Regression, ANOVA, t-tests, etc. are all ultimately correlational-type analyses. It is important to realize how the correlation coefficient is connected to each of these.

Assignment:

Use the data set below to conduct the following analyses:

1. One-way ANOVA to examine mean differences between the two groups.
2. Independent samples t-test to examine mean differences between the two groups.
3. Pearson r between the group and DV variables.
4. Simple (one predictor) regression where the group membership variable predicts the DV.

Group DV

1.00 30.00

1.00 32.00

1.00 25.00

1.00 29.00

1.00 19.00

1.00 35.00

1.00 26.00

1.00 15.00

1.00 16.00

1.00 28.00

2.00 25.00

2.00 19.00

2.00 20.00

2.00 56.00

2.00 43.00

2.00 33.00

2.00 23.00

2.00 43.00

2.00 44.00

2.00 45.00

After conducting the analyses, be sure to examine (and compute them if necessary) the effect sizes for each analysis.

Consider and answer the following questions:

1. Are there any similarities between the p-values obtained for the various analyses? What is the explanation for similarities/differences?
2. Are there any similarities between the effect sizes obtained for the various analysis? Explanation?
3. What is the connection/relationship between the F statistic obtained in the ANOVA analysis and the t statistic obtained in the t-test? (Yes, there is one…)
4. What are you broad conclusions, if any, about the general linear model and relationships among some of our common statistical analyses?

Hand in your (a) answers to the questions and (b) syntax for the analyses conducted.

Price: $12.33

Solution: The downloadable solution consists of 7 pages, 533 words and 2 charts.
Deliverable: Word Document