SPSS Class Notes
Analyzing Data


1.0  SPSS commands used in this unit

crosstabs Crosstabulations
t-test t-tests
glm General linear models
regression OLS regressions
pplot Normal probability plot
logistic Logistic regressions
npar Non-parametric tests

2.0 Demonstration and explanation

For this section we will be using the hs1.sav data set that we worked with in previous sections.

  • File
     Open
      Data
       select C:\spss_data\hs1.sav
get file "c:\spss_data\hs1.sav".

2.1  Chi-square

The chi-square test is used to determine if there is a relationship between two categorical variables.

  • Analyze
     Descriptive Statistics
      Crosstabs...
       select prgtype for the rows and ses for the columns
        click on "Statistics"
         check the chi-square box
* chi-square test.
crosstabs
  /tables prgtype by ses
  /statistic = chisq.

2.2  t-tests

This is the one-sample t-test, testing whether the sample of writing scores was drawn from a population with a mean of 50.

  • Analyze
     Compare Means
      One Sample t-test
       select write and compare it to 50
t-test
  /testval=50
  /variables=write.

This is the two-sample independent t-test with separate (unequal) variances.

  • Analyze
     Compare Means
      Independent Samples t-test
       select write as the dependent variable and female as the independent 
       variable
t-test
  groups=female(0 1)
  /variables=write.

This is the paired t-test, testing whether or not the mean of write equals the mean of science.

  • Analyze
     Compare Means
      Paired Samples t-test
       select write and science
t-test
  pairs= write with science (paired).

2.2  ANOVA

In this example the glm command is used to perform a one-way analysis of variance (ANOVA).

  • Analyze
     General Linear Models
      Univariate
       select write as the dependent variable and prog as the fixed factor
glm
  write  by prog
  /design = prog.

In this example the glm command is used to perform a two-way analysis of variance (ANOVA).  The plot option creates plots of the means, which can be a great visual aid to understanding the data.

  • Analyze
     General Linear Models
      Univariate
       select write as the dependent variable and prog and ses as fixed factors
        Plots
         select prog to be the X axis and ses to be the separate lines
          Add
           Continue
glm write by prog ses
 /design = prog, ses, prog*ses
 /plot = profile(prog*ses).

The Tukey test is used to test all the pair-wise comparisons of the levels of prog.

  • Repeat the above analysis (dialogue recall)
     Post Hoc
      select prog and choose Tukey test
glm write  by prog ses
 /design = prog, ses, prog*ses
 /posthoc = prog(tukey).

Here the glm command performs an analysis of covariance (ANCOVA). Note that the results are exactly the same as in the regression where write and science are regressed on math.

  • Analyze
     General Linear Models
      Univariate
       select math as the dependent variable and science and write as covariates
        model 
         select custom
          choose main effect in the build terms field and select every variable
          in the Factors & Covariates field and move them to the Model field.
glm math with science write
 /design= science write.

2.3  Regression

This is plain old OLS regression.

  • Analyze
     Regression
      Linear
       select math as the dependent variable and write and science as independent 
       variables
regression
 /dependent math
 /method=enter write science.

It is often very useful to look at the standardized residual versus standardized predicted plot in order to look for outliers and to check for homogeneity of variance.  The ideal situation is to see no observations beyond the reference lines, which means that there are no outliers.  Also, we would like the points on the plot to be distributed randomly, which means that all the systematic variance has been explained by the model.

  • Analyze
     Regression
      Linear
       select math as the dependent variable and female, write and socst
       as independent variables
        Plots...
         select Zresid for the Y axis and ZPred for the X axis
          Continue
           OK
            Double click on the plot
             Options
              Y Axis Reference Line
               add a line at Y = -2.5
                Apply
                 add a line at Y = 2.5
                  Apply
regression
 /dependent math
 /method=enter socst write ses
 /scatterplot=(*zresid ,*zpred).
* The reference lines are added
* via the point-and-click
* interface in the Chart Editor.

The P-P plots command produces a normal probability plot.  It is a method of testing if the residuals from the regression are normally distributed.

  • Analyze
     Descriptives
      P-P plots
       select res_1 and the test distribution to be "normal"
*residual plots.
pplot
 /variables=res_1
 /type=p-p
 /dist=normal.

The Q-Q plots produces a normal quantile plot. It is another method for testing if the residuals are normally distributed. The normal quantile plot is more sensitive to deviances from normality in the tails of the distribution, whereas the normal probability plot is more sensitive to deviances near the mean of the distribution.

  • Analyze
     Descriptives
      Q-Q plots
       Select res_1 and the test distribution to be "normal"
pplot
 /variables=res_1
 /type=q-q
 /dist=normal.

2.4  Logistic regression

Logistic regression requires a dependent variable that is dichotomous (i.e., has only two values).  As we do not have such a variable in our data set, we will create one called honcomp (honors composition). This is purely for illustrative purposes only!

  • Transform
     Compute
      select honcomp for the "target variable" and for numeric expression enter 
      "write >= 60".
  • Analyze
     Regression
      Binary Logistic
       select honcomp as the dependent variable, and select read and socst as 
       covariates
* creating a dichotomous variable.
compute honcomp = (write > 60).
execute.


* logistic regression.
logistic regression var=honcomp
 /method=enter read socst.

2.5  Non-parametric tests

The binomial test is the nonparametric analog of the single-sample two-sided t-test.

  • Analyze
     Nonparametric Tests
      Binomial 
       select write and define the cut point to be 50
* binomial test.
npar test
 /binomial (.50)= write (50).

The signrank test is the nonparametric analog of the paired t-test.

  • Analyze
     Nonparametric Tests
      2 Related Samples 
       select write and read as the test pair list and select Wilcoxon 
       as the test type
* sign test.
npar test
 /sign= read with write (paired).

The Mann Whitney U test is the nonparametric analog of the independent two-sample t-test.

  • Analyze
     Nonparametric Tests
      2 Independent Samples 
       select write as the test variable list, 
       select female as the group variable
        click on Define Groups and enter 0 and 1
         Continue
          select Mann Whitney U as the test type
*signrank test.
npar tests
 /m-w= write by female(1 0).

The Kruskal Wallis test is the nonparametric analog of the one-way ANOVA.

  • Analyze
     Nonparametric Tests
      K Independent Samples 
       select write as the test variable list and select prog as the group variable
        click on Define Range and enter 1 for the Minimum and 3 for the Maximum
         Continue
* kruskal-wallis test.
npar tests
 /k-w=write by prog(1 3).

3.0 Syntax version

get file "c:\spss_data\hs1.sav".

* chi-square test.
crosstabs
  /tables prgtype by ses
  /statistic = chisq.
* t-tests.
t-test
  /testval=50
  /variables=write.

t-test
  groups=female(0 1)
  /variables=write.

t-test
  pairs= write with science (paired).

* anova.
glm
  write  by prog
  /design = prog.

glm
  write  by prog ses
  /design = prog, ses, prog*ses
  /plot = profile(prog*ses).

glm
  write  by prog ses
  /design = prog, ses, prog*ses
  /posthoc = prog(tukey).
  
* ancova.
glm
 math with science write
 /design= science write.

* regression.
regression
  /dependent math
  /method=enter write science.

regression
  /dependent math
  /method=enter socst write ses
  /scatterplot=(*zresid ,*zpred ).

*residual plots.
pplot
  /variables=res_1
  /type=p-p
  /dist=normal.

pplot
  /variables=res_1
  /type=q-q
  /dist=normal.

* creating a dichotomous variable.
compute honcomp = (write > 60).
execute.

* logistic regression.
logistic regression var=honcomp
  /method=enter read socst.

* non-parametric tests.

* binomial test.
npar test
  /binomial (.50)= write (50).

* sign test.
npar test
  /sign= read with write (paired).

*signrank test.
npar tests
  /m-w= write by female(1 0).

* kruskal-wallis test.
npar tests
  /k-w=write by prog(1 3).

4.0 For more information

How to cite this page

Report an error on this page or leave a comment

The content of this web site should not be construed as an endorsement of any particular web site, book, or software product by the University of California.