chi square goodness of fit test (expected ratio) (procedure and output)
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The document provides a step-by-step guide for performing a chi square goodness of fit test using SPSS, including instructions on data weighting, analysis selection, and expected value entry. It outlines how to view and analyze observed and expected frequencies, as well as discusses the interpretation of the Pearson chi square statistics. Additionally, it includes contact information for the developer of the guide.
chi square goodness of fit test (expected ratio) (procedure and output)
- 1. Chi Square Goodness of Fit Test (Expected Ratio) (Procedure and Output in SPSS)
- 2. Data View Data view represents the cases/data points for different variables.
- 3. Variable View Variable view represents the properties of all variables. One can toggle between data and variable view with the shortcut ctrl + t.
- 4. Step 1 Weighting Data: Go to the Data tab in Menu bar, click on Weight cases
- 5. Step 2 Click on the variable that has frequencies and drag it to Frequency Variable box then Click Ok to run the process.
- 6. Step 3 Click on Analyze tab of Menu bar, click Nonparametric Tests, then Legacy dialogs and select Chi square to run the Goodness of fit test
- 7. Step 4 In this dialog box you select variable for which you want to run Goodness of Fit Test. Select the Variable and drag it in the Test Variable list box.
- 8. Step 5 Select “values” in the expected value box and add expected values by manually typing and clicking on Add button. Info: Our expected value is given in the “Exp_Num_Pref” variable. We need to manually enter the values
- 9. Step 6 Click on Options button to select statistics to calculate.
- 10. Step 7 Select Descriptive and Quartiles in the statistics box and click on Continue button
- 11. Step 8 Click on Ok button to run the Goodness of Fit Test.
- 12. Frequencies In this Table Observed and Expected Frequencies are given with residuals.
- 13. Goodness of Fit Test This table displays Pearson chi square statistics with it’s associated degree of freedom and significance value. It tests the hypothesis that the distribution of cases in a single categorical variable follows a known or hypothesized distribution
- 14. Developed and designed by: Address: Mau - Chitrakoot Contact: unexplordsolutions@gmail.com