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Exhibit 14-1 to Comply with Recent Federal Legislation, School Districts

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Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global "composite" score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.)
y = growth in composite score (Soph. - Fresh. score)
x1 = last year's composite score
x2 = 1 if a student is African American, 0 if white
x3 = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not
The computer output from the regression analysis is shown below.
Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global composite score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.) y = growth in composite score (Soph. - Fresh. score) x<sub>1</sub> = last year's composite score x<sub>2</sub> = 1 if a student is African American, 0 if white x<sub>3</sub> = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not The computer output from the regression analysis is shown below. -The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x<sub>4</sub> = x<sub>2</sub>x<sub>3</sub>. The computer output is shown below: ​ ​ ​ a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output. b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch. c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output.
Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global composite score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.) y = growth in composite score (Soph. - Fresh. score) x<sub>1</sub> = last year's composite score x<sub>2</sub> = 1 if a student is African American, 0 if white x<sub>3</sub> = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not The computer output from the regression analysis is shown below. -The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x<sub>4</sub> = x<sub>2</sub>x<sub>3</sub>. The computer output is shown below: ​ ​ ​ a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output. b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch. c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output.
Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global composite score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.) y = growth in composite score (Soph. - Fresh. score) x<sub>1</sub> = last year's composite score x<sub>2</sub> = 1 if a student is African American, 0 if white x<sub>3</sub> = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not The computer output from the regression analysis is shown below. -The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x<sub>4</sub> = x<sub>2</sub>x<sub>3</sub>. The computer output is shown below: ​ ​ ​ a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output. b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch. c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output.
-The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x4 = x2x3. The computer output is shown below: Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global composite score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.) y = growth in composite score (Soph. - Fresh. score) x<sub>1</sub> = last year's composite score x<sub>2</sub> = 1 if a student is African American, 0 if white x<sub>3</sub> = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not The computer output from the regression analysis is shown below. -The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x<sub>4</sub> = x<sub>2</sub>x<sub>3</sub>. The computer output is shown below: ​ ​ ​ a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output. b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch. c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output. ​ Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global composite score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.) y = growth in composite score (Soph. - Fresh. score) x<sub>1</sub> = last year's composite score x<sub>2</sub> = 1 if a student is African American, 0 if white x<sub>3</sub> = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not The computer output from the regression analysis is shown below. -The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x<sub>4</sub> = x<sub>2</sub>x<sub>3</sub>. The computer output is shown below: ​ ​ ​ a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output. b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch. c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output. ​ Exhibit 14-1 To comply with recent Federal legislation, school districts must study their students' growth as a whole, as well as the achievement of various subgroups of students. Over a 3-day period, students are assessed on their reading achievement, science and math knowledge, and social studies skills and these results are combined into a global composite score. To analyze the increase in this global score from the Freshman year to the Sophomore year, the model y = α + β1x1 + β2x2 + e was fit to a sample of student data. (The actual data contained categorical variables for each ethnic subgroup. To simplify the analysis, only the African American / White categorical variable is included here.) y = growth in composite score (Soph. - Fresh. score) x<sub>1</sub> = last year's composite score x<sub>2</sub> = 1 if a student is African American, 0 if white x<sub>3</sub> = 1 if a student receives free or reduced price lunch (a measure of socio-economic status), 0 if not The computer output from the regression analysis is shown below. -The data in Exhibit 14-1 were reanalyzed after adding an interaction variable, AAFR, where x<sub>4</sub> = x<sub>2</sub>x<sub>3</sub>. The computer output is shown below: ​ ​ ​ a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output. b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch. c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output. ​
a) Is the Model Utility test significant at the .10 level? Explain your reasoning, referring to specific information in the computer output.
b) Calculate the expected mean growth in composite score for African-American students who scored 280 as Freshmen and are receiving free/reduced lunch.
c) One concern the Federal legislation is intended to address are differences in the school's impact on disadvantaged youngsters. Using the computer output from the interaction model, what, if any, differences in growth scores on the Composite Score from the Freshman to Sophomore year are statistically significant at the .05 level? Does it appear that the different subgroups have different amounts of growth? Justify your reasoning with appropriate references to the data analysis presented in the computer output.

Comprehend the importance of aligning counseling program objectives with outcome measures.
Recognize the ethical responsibility of program evaluators to represent diverse voices in the evaluation process.
Learn the phases and steps involved in conducting a focused program evaluation.
Understand the roles and responsibilities of program evaluators.

Definitions:

Level 0 Response

The most basic or initial reaction to a stimulus or situation, often without any significant processing or complexity.

Self-disclose

The act of sharing personal information, thoughts, or feelings with others, which can foster trust and facilitate deeper connections.

Marital Status

The legal standing of an individual in terms of marriage, such as single, married, divorced, or widowed.

Empathic Responding

The ability to understand and share the feelings of another, mirroring their emotions in a supportive manner.

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