Choose and run SPSS analysis procedures for comparing means of interval data

Created for students taking AL 8250, AL 8520, AL 9300, AL 9371 at GSU and other SPSS beginners

Nan Jiang, Ph.D.

Assistant Professor
Department of Applied Linguistics
Georgia State University



Choose The Right Procedure Based On The Design Of Your Study

 One factor two levels

One factor 2/more levels

 2/more factors

Between-subject
design

 Independent-Sample T Test

 One-Way ANOVA

GLM Univariate

Within-subject
design

 Paired-Samples T Test

 GLM Repeated Measures

GLM Repeated Measures

Mixed design

 GLM Repeated Measures

       

Run The Procedures (by Submenus under Analysis Menu)

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.
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
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