SPSS On-Line Training Workshop

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In this Tutorial:

univariate

General Linear Model gives us univariate procedures, multivariate procedures and repeated measures.

In this on-line workshop, you will find many movie clips. Each movie clip will demonstrate some specific usage of SPSS.

Helps us to run an Analysis of Variance for experiments with two or more factors

	Model: Here you decide on the model. The default is a full factorial. You can customize this to only include the interactions that you want. Sum of Squares is also set here. If there are no missing cells, Type III is most commonly used. If you have missing cells, you must use Type IV.
	Contrasts - These are used to test for differences among the levels of a factor. They are like Post Hoc, but are specified prior to the experiment.
	Plots - This is chosen if you want a profile (line) plot of the marginal means.
	Post Hoc - Here you can choose a test and use it to determine which means differ. Tukeys is generally used if you have a large number of comparisons. For a small number of comparisons, Bonferroni can also be used.
	Save - If you want to save any of your output variables, (i.e. predicted values, residuals, diagnostics), you must choose this. You can save these to your data editor window or save them to a new file.
	Options - Options lets you select the factors for estimates of marginal means. We can also choose additional tests here (such as the Homogeneity test to confirm the assumption of equal variance) and set the significance level.

This choice can also be used for a regression analysis if there is multiple dependent variables

	Model: Here you decide on the model. The default is a full factorial. You can customize this to only include the interactions that you want. You may want to customize if you want covariate interaction included, as this is not included in the full factorial. Sum of Squares is also set here. If there are no missing cells, Type III is most commonly used. If you have missing cells, you must use Type IV.
	Contrasts - These are used to test for differences among the levels of a factor. They are like Post Hoc, but are specified prior to the experiment. This is only used if you have data with more than two levels.
	Plots - This is chosen if you want a profile (line) plot of the marginal means.
	Post Hoc - Here you can choose a test and use it to determine which means differ. Tukey is generally used if you have a large number of comparisons. For a small number of comparisons, Bonferroni can also be used. This is only used if you have more than two levels for the variable.
	Save - If you want to save any of your output variables: (i.e. predicted values, residuals, diagnostics), you must choose this. You can save these to your data editor window or save them to a new file.
	Options - Options let you select the factors for estimates of marginal means. You can also choose additional tests here (such as the Homogeneity test to confirm the assumption of equal variance) and set the significance level.

This test would help you to analyze a variable when the same measurement is made several times on the same subject.

	Model: Here you decide on the model. The default is a full factorial. You can customize this to only include the interactions that you want. Sum of Squares is also set here. If there are no missing cells, Type III is most commonly used. If you have missing cells, you must use Type IV.
	Contrasts - These are used to test for differences among the levels of a factor. They are like Post Hoc, but are specified prior to the experiment. This is only used if you have data with more than two levels.
	Plots - This is chosen if you want a profile (line) plot of the marginal means.
	Post Hoc - Here you can choose a test and use it to determine which means differ. Tukey is generally used if you have a large number of comparisons. For a small number of comparisons, Bonferroni can also be used. This is only used if you have more than two levels for the variable.
	Save - If you want to save any of your output variables: (i.e. predicted values, residuals, diagnostics), you must choose this. You can save these to your data editor window or save them to a new file.
	Options - Options let you select the factors for estimates of marginal means. We can also choose additional tests here (such as the Homogeneity test to confirm the assumption of equal variance) and set the significance level.

Variance Components: This procedure estimates the contribution of each random effect to the variance of the dependent variable.