SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients

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1 SPSS Output Homework 1-1e ANOVA a Sum of Squares df Mean Square F Sig. 1 Regression b Residual Total a. Dependent Variable: Sexual Harassment Knowledge b. Predictors: (Constant), Number of Examples a 95.0% Confidence Interval for B B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) Number of Examples a. Dependent Variable: Sexual Harassment Knowledge Instructor note: The estimated y-intercept is labeled (Constant) and B is the symbol SPSS uses for the estimated unstandardized slope (β 1). SPSS calls the standardized slope "Beta" which is equal to the Pearson correlation in the case of one predictor variable. The Residual Mean Square (31.08) is the MS E. Homework 1-2e 1

2 Homework 1-2f a 95.0% Confidence Interval for B B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) parent a. Dependent Variable: child Homework 1-2g Correlations child parent child Pearson Correlation ** Sig. (2-tailed).000 N parent Pearson Correlation.521 ** 1 Sig. (2-tailed).000 N **. Correlation is significant at the 0.01 level (2-tailed). Instructor note: Use the sample correlation (.521) and sample size (100) from above table to compute a confidence interval for the population correlation using the R CIcorr function. Homework 1-2j Descriptive Statistics N Skewness Kurtosis Statistic Statistic Std. Error Statistic Std. Error child parent Valid N (listwise) 100 2

3 Homework 2-1c Correlations Control Variables Mathscore MSEscore Time Mathscore Correlation Significance (2-tailed)..044 df 0 9 MSEscore Correlation Significance (2-tailed).044. df 9 0 Instructor note: Use the sample partial correlation (.615) from above table and sample size (12) to compute a confidence interval for the population partial correlation using the R CIcorr function. The df is reported for the t-test that the population partial correlation is equal to 0. Homework 2-2c R R Square Summary Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), fatheraggr, hours Std. B Error Beta a t Sig. 95.0% Confidence Interval for B Lower Upper Bound Bound 1 (Constant) Correlations Zeroorder Partial Part hours fatheraggr a. Dependent Variable: sonaggr Instructor note: SPSS calls standardized slopes "Beta" which should not be confused with unstandardized regression coefficients. Homework 2-2e Use R Square value (.699) and the Part Correlation values (.450 and.582) from SPSS output to compute confidence intervals for the population semipartial correlations using the R CIsemipartcorr function. 3

4 Homework 2-3c a 95.0% Confidence Interval for B B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) diff spec inter a. Dependent Variable: hr Homework 2-4c ANOVA a Sum of Squares df Mean Square F Sig. 1 Regression b Residual Total a. Dependent Variable: score b. Predictors: (Constant), GroupxGPA, dummy, GPAcenter Instructor note: The Residual Mean Square (2.577) is the MS E needed for the sample size calculation. The Residual df is the df for the t-tests below. a 95.0% Confidence Interval for B B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) dummy GPAcenter GroupxGPA a. Dependent Variable: score 4

5 Homework 3-1b Estimates of Fixed Effects a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound Intercept DOW a. Dependent Variable: mood. Homework 3-1c Parameter Estimates of Covariance Parameters a Estimate Std. Error Wald Z Sig. Lower Upper Bound Bound Repeated Measures Variance Intercept + DOW [subject = employee] a. Dependent Variable: mood. UN (1,1) UN (2,1) UN (2,2) Instructor note: UN(1,1) is the variance of the random y-intercept, UN(2,2) is the variance of the random slope, and UN(2,1) is the covariance between the y- intercept and random slope. Take the square roots of the confidence interval endpoints to obtain confidence intervals for the random coefficient standard deviations. 5

6 Homework 3-2b Estimates of Fixed Effects a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound Intercept vocab sex grpsize week a. Dependent Variable: socskill. Estimates of Covariance Parameters a Parameter Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound Repeated Measures AR1 diagonal a. Dependent Variable: socskill. AR1 rho Instructor note: When using a first-order autoregressive covariance structure, the estimated autocorrelation (.515) could be informative. Homework 3-2c Estimates of Covariance Parameters a Parameter Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound Repeated Measures Variance Intercept + week [subject = child] a. Dependent Variable: socskill. UN (1,1) UN (2,1) UN (2,2) Instructor note: The hypothesis testing and confidence interval results are obtained using different methods and will not always agree. In this study, the null hypothesis of zero variance cannot be rejected for the random y-intercept or random slope but the lower limits are above zero. Furthermore, the confidence intervals are extremely wide given the values of the estimated y-intercept (31.36) and slope (0.814). A larger sample size is needed to obtain usefully narrow confidence intervals for the random coefficient variances. 6

7 Homework 3-3b Estimates of Fixed Effects a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound Intercept PhysAct Grade Program a. Dependent Variable: SelfEst. Homework 3-3c Estimates of Covariance Parameters a Parameter Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound Repeated Measures Variance Intercept + Grade UN (1,1) [subject = girl] UN (2,1) UN (2,2) a. Dependent Variable: SelfEst. Instructor note: The hypothesis testing and confidence interval results are obtained using different methods and will not always agree. In this study, the null hypothesis of zero variance cannot be rejected for the random y-intercept or random slope but the lower limits are above zero. Furthermore, the confidence intervals are extremely wide given the values of the estimated y-intercept (24.4) and slope (-2.85). A larger sample size is needed to obtain usefully narrow confidence intervals for the random coefficient variances. 7

8 Homework 3-4b Estimates of Fixed Effects a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound Intercept LvsH SvsR Interaction time a. Dependent Variable: score. 8

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