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Analyzing Data

crosstabs |
Crosstabulations |

t-test |
t-tests |

glm |
General linear models |

regression |
OLS regressions |

pplot |
Normal probability plot |

logistic |
Logistic regressions |

npar |
Non-parametric tests |

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

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

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

* chi-square test.crosstabs /tables prgtype by ses /statistic = chisq.

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

t-test /testval=50 /variables=write.

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

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**.

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

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

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.

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**.

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**.

glm math with science write /design= science write.

This is plain old OLS regression.

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.

regression /dependent math /method=enter socst write ses /save residual (res_1) /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.

*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.

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

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!

* creating a dichotomous variable.compute honcomp = (write > 60). execute.* logistic regression.logistic regression var=honcomp /method=enter read socst.

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

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

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

* 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.

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

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

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

**Choosing the Correct Statistical Test in SPSS**Includes guidelines for choosing the correct non-parametric test

**SPSS Frequently Asked Questions**

Covers many different topics including: ANOVA, Generalized Linear Models (GLM) and linear regression**SPSS Regression Webbook**Includes such topics as diagnostics, categorical predictors, testing interactions and testing contrasts**SPSS Data Analysis Examples**Includes examples of common data analysis techniques

**SPSS Annotated Output****SPSS Library**Topics in ANOVA and other subjects

Includes annotated output for descriptive statistics, correlation, regression and logistic regression

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