### 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 female socst write /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

* non-parametric tests.

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

* sign test.
npar test

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

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