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 

Betweensubject
design 

Withinsubject
design 

Mixed design 



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 oneway analysis of variance  
OneSample 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. betweensubj design;  compare two methods  
PairedSamples T Test 
compares the means of two variables/ measurements for a single group; or the means from two matched groups; withinsubj design; repeated measures; or paired samples;  compare L1L2 and L2L1 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;
betweensubj design; when the independent variable has more than 2 levels
(same as IndepS 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 betweensubject 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 withinsubjects factors; investigate interactions between factors as well as the effects of individual factors.  
Variance Components  for mixedeffects 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.  
Nonparametric Tests 
1Sample KS  
2 Independent Samples  
K Independent Samples  
2 Related Samples  
K Related Samples  