## The performance and anxiety statistics relationship

The performance and anxiety statistics relationship

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*). The performance and anxiety statistics relationship

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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.

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