Activity #9
You will submit one Word document for this activity. In the first part your activity #9 document, provide short answers to the following questions (250 words or less).

Part A . Questions about non-parametric procedures

1. What are the most common reasons you would select a non-parametric test over the parametric alternative?
2. Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?
3. For each of the following parametric tests, identify the appropriate non-parametric counterpart:
   1. Dependent t-test
   2. Independent samples t-test
   3. Repeated measures ANOVA (one-variable)
   4. One-way ANOVA (independent)
   5. Pearson Correlation
      
      Part B . SPSS Activity
      In this part of Activity #9, you will perform the non-parametric version of the tests you used in Activities 6, 7, and 8. In each case, assume that you opted to use the non-parametric equivalent rather than the parametric test. Using the data files from earlier activities, complete the following tests and paste your results into the assignment Word document:
      1. Activity 6A: non-parametric version of the dependent t-test
      2. Activity 6B: non-parametric version of the independent t-test
      3. Activity 6C: non-parametric version of the single factor ANOVA
4. Activity 7: non-parametric version of the factorial ANOVA

Part C . Contingency tables
Sometimes a researcher is only interested in the following: Whether or not two variables are dependent on one another, (e.g. are death and smoking dependent variables; are SAT scores and high school grades independent variables?)

To test this type of claim a contingency table could be used, with the null hypothesis being that the variables are independent. Setting up a contingency table is easy; the rows are one variable the columns another. In contingency table analysis (also called two-way ANOVA) the researcher determines how closely the amount in each cell coincides with the expected value of each cell if the two variables were independent.

The following contingency table lists the response to a bill pertaining to gun control.

Notice that cell 1 indicates that 10 people in the Northeast were in favor of the bill.

Example: In the previous contingency table, 40 out of 160 (1/4) of those surveyed were from the Northeast. If the two variables were independent, you would expect 1/2 of that amount (20) to be in favor of the amendment since there were only two choices. We would be checking to see if the observed value of 10 was significantly different from the expected value of 20.

To determine how close the expected values are to the actual values, the test statistic chi-square is determined. Small values of chi-square support the claim of independence between the two variables. That is, chi-square will be small when observed and expected frequencies are close. Large values of chi-square would cause the null hypothesis to be rejected and reflect significant differences between observed and expected frequencies. This part of the activity is not included in the text book. See the tutorial Chi-square pdf file in the additional resources section of the course room for details on how to perform a chi-square test in SPSS.

For part C, download the gss.sav file, and following the steps described in the Chi-Square tutorial. Examine the relationship between education (degree) and perception of life (life). Can you reject the null that education and perception of life are independent? Make a bar chart that graphically summarizes your findings. Be sure to include the relevant portions of the chi-square test output in your explanation.

Price: $16.37

Solution: The downloadable solution consists of 7 pages, 937 words.
Deliverable: Word Document