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Unit 8 Data Analysis and App

Unit 8 Data Analysis and Application

In this assignment, you will learn how to code dummy variables in a regression model. You will use the IBM SPSS Linear Regression procedure to accurately compute a dummy-coded multiple regression and an orthogonal-coded regression with the u08a1data.sav file in the Resources.

Suppose that a researcher conducts a study to see how level of anxiety ( A1 = low, A2 = medium, A3 = high) predicts exam performance ( Y). The performance ( Y) and anxiety ( A) data are already entered into u08a1data.sav. Your task is to correctly enter the dummy codes to run regression. First, for dummy-coded regression, assume that the researcher wants to compare the medium anxiety group to the low and high anxiety groups. Enter the dummy codes for the low anxiety group contrast ( D1) and the high anxiety group contrast ( D2). Next, generate orthogonal codes for a positive linear trend ( O1) and a quadratic (curvilinear) trend for an upside-down U ( O2).

Use the DAA Template located in the resources to write up your assignment. The deadline for submitting your work is 11:59 PM CST on Sunday of Week 8.

Step 1. Write Section 1 of the DAA. In Section 1 of the DAA, articulate your predictor variables, the outcome variable, and the scales of measurement for each variable. Specify the sample size of the data set.

Step 2. Write Section 2 of the DAA. Test the normality assumption of multiple regression with a visual interpretation of the Y histogram.

Step 3. Write Section 3 of the DAA. Specify a research question for dummy-coded regression. Articulate a null hypothesis and alternative hypothesis for the overall regression model. Articulate the null hypothesis and alternative hypothesis for each predictor. Next, articulate a research question for the orthogonal-coded regression. Articulate a null hypothesis and alternative hypothesis for the overall regression model. Articulate the null hypothesis and alternative hypothesis for each predictor. Specify the alpha level.

Step 4. Write Section 4 of the DAA.

Begin with a brief statement reviewing the normality assumption; state your codes for the dummy-coded regression and the orthogonal regression.
Next, paste the SPSS output of the Model Summary for the dummy-coded regression.
Report R, R2, and interpret this effect size.
Next, paste the ANOVA output.
Report the F test and state your conclusion regarding the null hypothesis.
Next, paste the Coefficients output.
Interpret the b coefficients (i.e., what do the b values represent?) For each b coefficient, report the t tests and p-values, and for D1 and D2, a statement regarding the null hypothesis. Report the squared semipartial correlations for D1 and D2 with an interpretation of effect size.
Next, paste the SPSS output of the Model Summary for the orthogonal-coded regression.
Report R, R2, and interpret the effect size.
Next, paste the ANOVA output.
Report the F test and state your conclusion regarding the null hypothesis.
Next, paste the Coefficients output.
Interpret the b coefficients (i.e., what do the b values represent?) For each b coefficient, report the t tests and p-values, and for O1 and O2, a statement regarding the null hypothesis. Report the squared semipartial correlations for O1 and O2 with an interpretation of effect size.

Step 5. Write Section 5 of the DAA. Discuss your conclusions of the both the dummy-coded multiple regression and the orthogonal-coded multiple regression as they relate to your stated research question and hypotheses for the overall regression model and the individual predictors. Conclude with an analysis of the strengths and limitations of dummy-coded and orthogonal-coded regression.

Unit 9 Data Analysis and Application

In this assignment, you will learn how to conduct and interpret a chi square analysis. You will use the IBM SPSS Crosstabs procedure with the u09a1data.sav file located in the Resources. Use the DAA Template located in the Resources to write up your assignment. The deadline for submitting your work is 11:59 PM CST on Sunday of Week 9.

Step 1. Write Section 1 of the DAA. Refer to Comprehension Question 7 on page 343 of your Warner text to provide a context for the chi square analysis. In Section 1 of the DAA, articulate the two variables and the scales of measurement for each variable. Specify the sample size of the data set.

Step 2. Write Section 2 of the DAA. Articulate the assumptions of chi square:

Paste the SPSS output showing expected frequencies ( E). Report E for each cell in the 2 x 2 table.
Determine if the expected frequencies assumption is met.
If the assumption is met, indicate which version of chi square should be reported.
If the assumption is not met, indicate which version of chi square should be reported.

Step 3. Write Section 3 of the DAA. Specify a research question for this chi square. Articulate a null hypothesis and alternative hypothesis for the chi square. Specify the alpha level.

Step 4. Write Section 4 of the DAA:

Begin by stating your conclusion regarding the expected frequencies assumption.
Next, create a table of observed frequencies for the two variables Class and Saved, referring to Table 8.4 of the Warner text as an example.
Report the observed number of passengers in first class and third class who were saved and not saved.
Next, paste the SPSS output for Symmetric Measures.
Report the phi coefficient and interpret the effect size.
Then paste the SPSS output for Chi-Square Tests.
Report the version of chi square that is appropriate given the E assumption. Include the symbol for chi square, the degree of freedom, the chi square value, the p-value, and your decision regarding the null hypothesis.
Next, calculate and report the odds of survival for the first class female group and odds of survival for the third class female group.
Finally, calculate the odds ratio of survival comparing the first class female group to the third class female group.

Step 5. Write Section 5 of the DAA. Discuss your conclusions of the chi square as it relates to your stated research question. Conclude with an analysis of the strengths and limitations of chi s

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