The PSPP Graphical User Interface
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This chapter provides a description of the PSPP graphical user interface.

Primary Windows
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The PSPP graphical user interface provides a set of windows through which you store, edit and analyze data. You can choose to perform tasks and operations through interactive drop down menus in the data editor window or through command line statements entered in the syntax editor.The three primary windows include the Data Editor, Syntax Editor, and the Output Viewer.

Data Editor

     The data editor window is central to PSPP and launches when you start a session. This window is similar in organization to a spreadsheet, where data is stored and edited in columns and rows of cells.

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Syntax Editor

     The syntax editor window is where you enter command line instructions to edit, import and analyze data. This has the advantage of enabling batch processing and code reuse. You can launch the syntax editor from the data editor window by selecting File->Open->Syntax.

Output Viewer

     This window pops up after the execution of an instruction set and displays the instructions executed and results of statistical analyses including, text, tables, and charts.

Data Editor
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The data editor has two views; data view shows the numerical value held by each datum and variable view displays the metadictionary data of each variable.  You can toggle between the two views by clicking on the Data View or Variable View tab at the bottom left hand corner of the data editor.

A modification to gamma that takes into account tied pairs tied on both the dependent and independent variable. This is a variant of Tau-B that can reach the maximum and minimum values of +1 and -1 for non-square tables.

Nominal by Interval

Eta

Use this gauge of association when the dependent variable is a quantitative measure and the independent variable is categorical. Eta ranges from 0 to 1, with increasing values indicating a higher degree of association.

Cohen's Kappa

Kappa is a measure of the degree to which two raters agree over their sorting of items into mutually exclusive categories, and takes into account agreement occurring by chance. Values range from -1 to +1 and represent the proportion of agreements between raters after adjusting for those that take place by chance. A value of  +1 indicates perfect agreement and -1 perfect disagreement. When kappa is zero, then there is no relationship between the raters and any agreements or disagreements are due to chance only.

Estimated Relative Risk

The observed risk of having the outcome of interest be greater due to a specific factor.  Use the odds ratio as an estimate or measure of risk if the occurrence of the factor is rare. A confidence interval greater than one indicates there is a greater chance of the event occurring in the presence of the factor. Note: this is only suitable for 2 x 2 tables.

     As an example, consider a medical study examining the relative risk of patients having cerebella toxicity who have taken a generic chemotherapeutic agent rather than a non-generic one.

       Toxicity+   Toxicity-
     Generic+   11   14
     Generic-   3    31

       * Enter the following data into PSPP as follows, where +1 represents positive (+) and 0 negative (-):

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       * Weight cases by selecting Data->Weight Cases, and then choose weight cases by freq.

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       * Select Analyze-> Descriptive Statistics -> Crosstabs to open the Crosstabs dialogue box.

       * Move the variable emph{toxicity} from the list on the left side of the dialogue box into Rows and emph{Generic} into Columns.

       * Click Statistics to bring up the Statistics dialogue box.

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       * Select the Risk check box and click Continue to close this dialogue box.

       * Click OK on the Crosstabs dialogue box.

     PSPP displays the results in the output viewer.

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Compare Means

One-Sample T-Test

Tests if the mean of a single variable differs from a specified constant.

       * Open the file LightBulbHours.sav.

       * Select Analyze-> Compare Means -> One Sample T Test.

       * Select the hour variable from the list on the left side of the dialogue box and then click the right arrow to move it into the Test Variables text box.

       * Enter the value 700 into the Test Value text box.

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       * Click OK.

     You can view the results in the output window.

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Independent Samples T Test

Compares means of the same variable from two independent samples. The test requires normally distributed data, although minor departures from this assumption are tolerable, and there is no requirement that the samples are the same size.

     Compare the mean height of 12-year-old boys and girls in centimeters measured in a class of ten girls and ten boys.

       * Enter the following height and group data into PSPP, where 0 represents female and 1 male.

          152.71    0
          142.57    0
          155.48    0
          148.79    0
          167.74    0
          149.51    0
          142.00    0
          150.21    0
          171.19    0
          141.99    0
          152.67    1
          139.65    1
          152.19    1
          156.94    1
          151.86    1
          157.79    1
          152.49    1
          157.52    1
          156.95    1
          141.61    1

       * Name the first variable emph{Height} and the second emph{Gender}.

       * Select Analyze-> Compare Means -> Independent Samples T Test.

       * Select Height from the list on the left side of the dialogue box and then click the upper right arrow to move it into the Test Variables box.

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       * Select Gender and click the lower right arrow to move the variable into Define Groups.

       * Click the Define Groups button at the bottom of the dialogue box.

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       * Enter 0 into the Group1 Value textbox of the Define Groups dialogue box.

       * Enter 1 into the Group2 Value textbox of the Define Groups dialogue box.

       * Select Continue.

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       * Click OK on the Independent-Samples T Test dialogue box.

       * The output viewer displays the results.

       * Examine the results in the output viewer. As you can see, in this instance, the girls are taller.

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Paired Samples T Test

Compares the mean of two sets of quantitative data where data sets are organized into pairs with a definite relationship, e.g., one set of data might represent a measurement taken before some event and the other a measurement after. Each pair is then the measurement before coupled with the measurement after.

     Use the paired samples t-test to compare the means of the following measurements from nine asthmatics of their peak expiratory flow rate before and after a walk on a cold winters day.

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       * Select Analyze->Compare Means-> Paired Samples T Test.

       * Highlight the emph{Before} variable with the mouse and then click the right arrow to place it in the Test Variables dialogue box under emph{Var1}.