Submenu	Procedure	Function	Example
Compare Means	Means	calculates subgroup means and related univariate statistics for dependent variables within categories of one or more independent variables. you can obtain a one-way analysis of variance
	One-Sample T Test	tests whether the mean of a single variable differs from a specified constant, e.g., whether the average IQ score for a group of students differs from 100.	compare the results of a class to a national norm
	Independent- Samples T Test	compares means for two groups of cases. The subjects should be randomly assigned to two groups. between-subj design;	compare two methods
	Paired-Samples T Test	compares the means of two variables/ measurements for a single group; or the means from two matched groups; within-subj design; repeated measures; or paired samples;	compare L1-L2 and L2-L1 translation latencies.
	One-Way ANOVA	produces an analysis of variance for one treatment factor to test the hypothesis that several means are equal. In addition to determining that differences exist among the means, you may want to know which means differ by running a priori contrasts or post hoc tests. Used with no repeated measures; between-subj design; when the independent variable has more than 2 levels (same as Indep-S T Test with 2 levels).	three groups tested on three presentation conditions.
General Linear Model	Univariate	provides regression analysis and analysis of variance for one dependent variable by one or more factors and/or variables. The factor variables divide the population into groups; investigate interactions between factors as well as the effects of individual factors; used for factorial ANOVA with between-subject design;	High and low proficiency subjects tested on translation, semantic, and unrelated pairs. 6 cells all with different subjts.
	Multivariate	provides regression analysis and analysis of variance for multiple dependent variables by one or more factor variables or covariates. The factor variables divide the population into groups. investigate interactions between factors as well as the effects of individual factors.
	Repeated Measures	provides analysis of variance when the same measurement is made several times on each subject or case. If between- subjects factors are specified, they divide the population into groups; test null hypotheses about the effects of both the between- subjects factors and the within-subjects factors; investigate interactions between factors as well as the effects of individual factors.
	Variance Components	for mixed-effects models, estimates the contribution of each random effect to the variance of the dependent variable; particularly interesting for analysis of mixed models such as split plot, univariate repeated measures, and random block designs. By calculating variance components, you can determine where to focus attention in order to reduce the variance.
Non-parametric Tests	1-Sample K-S
	2 Independent Samples
	K Independent Samples
	2 Related Samples
	K Related Samples