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Files have been sent to joy@topassignmentexperts. Two additional assignment will be provided with the files on Saturday with one (Exploratory Factor Analysis) having at least a 5-day deadline and the other (Path Analysis Techniques) 8-day deadline.Assignment and Exercises Write a 1,050- to 1,400-word paper that includes the following: · Assess the major concepts and components for hierarchical linear regression and multilevel modeling. See Exercise 8B – pages 2 through 20 · Replicate the major tables for hierarchical regression and after each of the primary tables explain (from your own understanding) the primary results that are shown in certain columns that lead to the interpretation of test results and the decisions to reject or failure to reject (i.e., retain or accept) the null hypotheses. Evaluate the strengths and weaknesses or limitations of the model. See Exercise 9B – pages 21 – 38 · Replicate the major tables for multilevel modeling and after each of the primary tables explain (from your own understanding) the primary results that are shown in certain table columns that lead to the interpretation of the test results and the decision to reject or failure to reject (i.e., retain or accept) the null hypotheses. Evaluate the strengths and weaknesses or limitations of the model. · Draw conclusions about how hierarchical analysis is related to the multilevel analysis and the interaction effects of variables in both the models. Exercise 8B Multiple Regression: Beyond Statistical Regression Using IBM SPSS 8B.1 Hierarchical Linear Regression We use the example of hierarchical linear regression that was described in Section 8A.2.2. In that example, we were interested in predicting the level of awareness of cultural barriers believed to be faced by their clientele as reported by mental health practitioners. The key predictors we used were a subscale assessing institutional discrimination, a subscale assessing ethnic identity exploration, and collectivism orientation. Control variables were social desirability and two sample characteristics variables dealing with the length of time spent as a provider in mental health and whether or not the practitioners had received specialized training in multicultural counseling. The data set we use here is Victoria. 8B.1.1 Hierarchical Regression Analysis Setup Selecting the path Analyze  Regression  Linear from the main menu brings us to the Linear Regression main dialog window displayed in Figure 8b.1. From the variables list panel, we click over CBMCSACB (California Brief Multicultural Competence Scale Awareness of Cultural Barriers subscale) to the Dependent panel and MCSDS (Marlowe–Crowne Social Desirability scale) to the Independent(s) panel. This will be the first “block” or “set” of variables in the hierarchical analysis. The Method drop-down menu will be left at its default setting of Enter, which specifies that the variables we have in the panel will be entered simultaneously in that block (with only one variable, it does not matter anyway). Click the Next pushbutton to reset the window so that the second block of variables can be specified. Figure 8b.1   The First Block of the Hierarchical Analysis Will Enter Social Desirability Into the Model With the main dialog window reset and the Independent(s) panel now empty, place YearMenHlth (years of mental health experience) and MCWorkshp (yes coded as 1 and no coded as 0 for having participated in a multicultural workshop for mental health practitioners) into the Independent(s) panel (see Figure 8b.2). This will be the second block or set of variables in the hierarchical analysis. The Method drop-down menu will be left at its default setting of Enter, which specifies that the variables we have in the panel will be entered simultaneously in that block (although we have not done so, we could have specified one of the step methods). Click the Next pushbutton to reset the window so that the third and final block of variables can be specified. Once again we are presented with an empty Independent(s) panel into which we now place COBRASID (Color-Blind Racial Attitudes Scale Institutional Discrimination sub-scale), MEIMEIE (Multigroup Ethnic Identity Measure Ethnic Identity Exploration sub-scale), and INDCOLC (Individual/Collectivism scale Collectivism subscale) as shown in Figure 8b.3. The Method drop-down menu will be left at its default setting of Enter, which specifies that these variables will be entered simultaneously. Set up the Statistics window as we have done in the previous chapters (see Figure 8b.4), click Continue to return to the main dialog window, and select OK to perform the analysis. Figure 8b.2   The Second Block of the Hierarchical Analysis Will Enter the Two Experience Variables Into the Model Figure 8b.3   The Third and Final Block of the Hierarchical Analysis Will Enter Institutional Discrimination, Ethnic Identity Exploration, and Collectivism Into the Model Figure 8b.4   The Statistics Window 8B.1.2 Hierarchical Regression Analysis Output The correlation matrix of the variables is shown in Figure 8b.5. As we have already seen, the dependent variable is placed in the first row and first column. The correlations of the other predictors are quite modest, with the highest being associated with institutional discrimination. As can be seen, awareness of cultural barriers is not significantly (p = .176) correlated with social desirability (r = −.049), and thus, its effect as a covariate is likely to be minimal. Figure 8b.6 presents the results of the significance testing of the models. This output and the output that follows is structured akin to the way in which the output from a step analysis is structured. Here, each model corresponds to each block of variables that we entered in succession. The results of the first block confirm what we saw from the correlation matrix: Social desirability did not significantly increase our predictive ability. The second block, composed of the experience variables, and the third block, composed of our predictors of interest, each were statistically significant, that is, a statistically significant amount of prediction was obtained for each of these blocks. The main results are contained in the two tables shown in Figure 8b.7. We can see in the Model Summary that the R2 value went from .002 to .030 to .276 over the three hierarchical blocks of variables, with the R2 change being statistically significant for the second and third blocks. Figure 8b.5   Correlations of the Variables Figure 8b.6   Statistical Significance of the Models Figure 8b.7   The Main Regression Results The Coefficients table tracks the changes in the model as variables were entered. After the second block, not having participated in a multicultural workshop predicts greater awareness when social desirability and years of experience are statistically controlled, which may be of some theoretical and practical interest to the researchers. But the dynamics evolve. After the third and final block where only the primary three variables—institutional discrimination, ethnic identity exploration, and collectivism—emerge as the statistically significant predictors in the model controlling for the other variables. The workshop variable is no longer statistically significant at the .05 level and social desirability is very close to statistical significance, and these trends might provide the impetus to further research. 8B.1.3 Reporting Hierarchical Multiple Regression Results A three-stage hierarchical linear regression analysis was used to predict the level of awareness of cultural barriers believed to be faced by their clientele as reported by mental health practitioners. In the first block, social desirability was entered as a covariate; in the second block, the number of years spent as a provider in mental health and whether or not the practitioners had received specialized training in multicultural counseling were simultaneously entered; in the third block, institutional discrimination, ethnic identity exploration, and collectivism orientation were entered simultaneously as the primary variables of interest. The correlations of the variables are shown in Table 8b.1. As can be seen, the awareness of barriers variable correlated most strongly with institutional discrimination and ethnic identity exploration. Results of the hierarchical regression analysis are shown in Table 8b.2. Social desirability, entered on the first block, was not a significant covariate, F(1, 366) = 0.870, p = .352. When the two experience variables were added on the second block, the prediction model was statistically significant, F(3, 364) = 3.804, p = .010, R2 = .030, Adjusted R2 = .022. In the second block, not having participated in a multicultural workshop modestly predicted greater awareness of cultural barriers. For the final block, the model increased substantially in its predictive power, F(6, 361) = 22.902, p

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Multiple Regression: Beyond Statistical Regression Using IBM SPSS
first block, social desirability entered as a covariate; in the second block,

Assignment and Exercises Write a 1,050- to 1,400-word paper that includes the following: · Assess the major concepts and components for hierarchical linear regression and multilevel modeling. See...

Answer To: Assignment and Exercises Write a 1,050- to 1,400-word paper that includes the following: · Assess...

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