Assignment A Elaboration Chi Square (Please pay attention to numbers marked in color) Please examine the following tables and answer questions on page 2 Table 1 Chi Square Test between Gender (I V ) and Care Accidents (D V ) without Using Control Variable (Bivariate Relationship, ) Gender (I V ) Total Female Male Car Accidents (D V ) Few accidents Count (N) 200 85 285 66 7 34 0 51 8 Many accidents Count (N) 100 165 265 33 3 66 0 48 2 Total Count (N) 300 250 550 100 0 100 0 100 0 Table 2 Chi Square Tests (Bivariate Relationship, Sig ) Value df Asymptotic Significance (2 sided) Pearson Chi Square 58 283 a 1 000 Continuity Correction b 56 982 1 000 Likelihood Ratio 59 308 1 000 Fisher's Exact Test Linear by Linear Association 58 177 1 000 N of Valid Cases 550 The following tables demonstrated the results ofelaboration with miles driven being added as the control variable After introducing the control variable, two partial tables were produced, one for Had Driven Few Miles and another for Had Driven Many Miles Table 3 Partial Table for the Few Miles Driven Group ( ) Miles Driven Gender Total Female Male Driven Few Miles Car Accidents Few accidents Count (N) 180 45 225 90 0 90 0 90 0 Many accidents Count (N) 20 5 25 10 0 10 0 10 0 Total Count Count (N) 50 250 100 0 100 0 Table 4 Chi Square Tests (Partial Table for the Few Miles Driven Group, Sig ) Miles Driven Value df Asymptotic Significance (2 sided) Driven Few Miles Pearson Chi Square 000 c 1 1 000 Continuity Correction b 000 1 1 000 Likelihood Ratio 000 1 1 000 Fisher's Exact Test Linear by Linear Association 000 1 1 000 N of Valid Cases 250 Table 5 Partial Table for Many Miles Driven Group ( ) Miles Driven Gender Total Female Male Driven Many Miles Car Accidents Few accidents Count (N) 20 40 60 20 0 20 0 20 0 Many accidents Count (N) 80 160 240 80 0 80 0 80 0 Total Count Count 200 300 100 0 100 0 Table 6 Chi Square Tests (Partial Table for Many Miles Driven Group, Sig ) Miles Driven Value Value df Asymptotic Significance (2 sided) Driven Many Mile Pearson Chi Square 000 c 1 1 000 Continuity Correction b 000 1 1 000 Likelihood Ratio 000 1 1 000 Fisher's Exact Test Linear by Linear Association 000 1 1 000 N of Valid Cases 300 Please answer the following questions Explain what Elaboration is and why this procedure is necessary in research (please refer to the lecture and the text) Based on your computing results, explain the bivariate relationship between Gender and Car Accidents if Gender affects the number of accidents (between few and many) Use percentages and statistical significance to explain the Chi Square result After introducing the control variable Miles driven , did the original relationship between Gender and Car Accidents change Explain what type of change that the control variable has Please use the findings ( percentages and statistical significance ) in your explanation Please continue to answer questions on Assignment B Assignment B Multiple Regression (Please pay attention to numbers marked in color) Please examine the following tables of the ways 5 independent variables may have affected students' overall satisfaction with YSU Please answer questions in page 4 In Table 1, Variables Entered are the independent variables that affect the dependent variable Overall Satisfaction with YSU simultaneously Table 1 Variables Entered Removed Model Variables Entered Method 1 Independent Variables tuition value, sense of belonging, faculty help, academic programs, staff's ability Enter a Dependent Variable var 103 overall satisfaction b All requested variables entered In Table 2 please pay attention to R Square, 430 means 43 of the reasons of why or why not YSU students are satisfied with YSU were explained by this model (or all independent variables included in the model) Table 2 Model Summary Model R R Square Adjusted R Square Std Error of the Estimate 1 656 a 430 427 615 a Predictors (Independent Variables), tuition value, sense of belonging, faculty help, academic programs, staff's ability The ANOVA table (Table 3) shows the model is statistically significant at 001 level Table 3 ANOVA a Model Sum of Squares df Mean Square F Sig 1 Regression 238 468 5 47 694 126 150 000 b Residual 316 065 836 378 Total 554 533 841 a Dependent Variable var 103 overall satisfaction b Predictors (Independent Variables), tuition value, sense of belonging, faculty help, academic programs, staff's ability In Table 4 please pay attention to the beta values, the higher the beta, the greater the impact on the dependent variable Table 4 Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig B Std Error Beta 1 Independent Variables 763 120 6 343 000 academic programs 289 029 311 10 064 000 faculty help 118 025 146 4 773 000 staff's ability 090 026 106 3 423 001 sense of belonging 129 024 150 5 350 000 tuition value 169 024 215 6 923 000 a Dependent Variable var 103 overall satisfaction Please answer the following questions Explain what multiple regression is, the meaning of R and Beta, and under what circumstances multiple regression is used Please make reference to the lecture notes and the text when you answer this question Explain the meaning resulting R Please refer to Table 2 Write down two hypotheses about which independent variable has the most effect on var103 and which variable has the least effect on var103 overall satisfaction with YSU H1 H2 Based on the results of multiple regression (Beta and Sig ), do you accept or reject your hypotheses Write accepted or rejected after each of your hypothesis and briefly explain why Please refer to Table 4 H1 H2

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Assignment A: Elaboration - Chi Square (Please pay attention to numbers marked in color) Please examine the following tables and answer questions on page 2

Assignment A: Elaboration - Chi Square (Please pay attention to numbers marked in color)

Please examine the following tables and answer questions on page 2

Table 1: Chi Square Test between Gender (I.V.) and Care Accidents (D.V.) without Using Control Variable (Bivariate Relationship, %)
	Gender (I.V.)	Total
Female	Male
Car Accidents (D.V.)	Few accidents	Count (N)	200	85	285
%	66.7%	34.0%	51.8%
Many accidents	Count (N)	100	165	265
%	33.3%	66.0%	48.2%
Total	Count (N)	300	250	550
%	100.0%	100.0%	100.0%

Table 2: Chi-Square Tests (Bivariate Relationship, Sig.)

	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	58.283^a	1	.000
Continuity Correction^b	56.982	1	.000
Likelihood Ratio	59.308	1	.000
Fisher's Exact Test
Linear-by-Linear Association	58.177	1	.000
N of Valid Cases	550

The following tables demonstrated the results ofelaboration with "miles driven" being added as the control variable.After introducing the control variable, two partial tables were produced, one for "Had Driven Few Miles" and another for "Had Driven Many Miles"

Table 3: Partial Table for the "Few Miles Driven" Group (%)
Miles Driven	Gender	Total
Female	Male
Driven Few Miles	Car Accidents	Few accidents	Count (N)	180	45	225
%	90.0%	90.0%	90.0%
Many accidents	Count (N)	20	5	25
%	10.0%	10.0%	10.0%
Total	Count	Count (N)	50	250
%	%	100.0%	100.0%

Table 4: Chi-Square Tests (Partial Table for the "Few Miles Driven" Group, Sig.)

Miles Driven	Value	df	Asymptotic Significance (2-sided)
Driven Few Miles	Pearson Chi-Square	.000^c	1	1.000
Continuity Correction^b	.000	1	1.000
Likelihood Ratio	.000	1	1.000
Fisher's Exact Test
Linear-by-Linear Association	.000	1	1.000
N of Valid Cases	250

Table 5: Partial Table for "Many Miles Driven" Group (%)

Miles Driven	Gender	Total
Female	Male
Driven Many Miles	Car Accidents	Few accidents	Count (N)	20	40	60
%	20.0%	20.0%	20.0%
Many accidents	Count (N)	80	160	240
%	80.0%	80.0%	80.0%
Total	Count	Count	200	300
%	%	100.0%	100.0%

Table 6: Chi-Square Tests (Partial Table for "Many Miles Driven" Group, Sig.)

Miles Driven	Value	Value	df	Asymptotic Significance (2-sided)
Driven Many Mile	Pearson Chi-Square	.000^c	1	1.000
Continuity Correction^b	.000	1	1.000
Likelihood Ratio	.000	1	1.000
Fisher's Exact Test
Linear-by-Linear Association	.000	1	1.000
N of Valid Cases	300

Please answer the following questions:

Explain what Elaboration is and why this procedure is necessary in research (please refer to the lecture and the text).

Based on your computing results, explain the bivariate relationship between Gender and Car Accidents - if Gender affects the number of accidents (between few and many)? Use percentages and statistical significance to explain the Chi Square result.

After introducing the control variable "Miles driven", did the original relationship between Gender and Car Accidents change? Explain what type of change that the control variable has. Please use the findings (percentagesand statistical significance) in your explanation.

Please continue to answer questions on Assignment B.

Assignment B: Multiple Regression (Please pay attention to numbers marked in color)

Please examine the following tables of the ways 5 independent variables may have affected students' overall satisfaction with YSU. Please answer questions in page 4

In Table 1, "Variables Entered" are the independent variables that affect the dependent variable Overall Satisfaction with YSU simultaneously.

Table 1: Variables Entered/Removed
Model	Variables Entered		Method
1	Independent Variables: tuition value, sense of belonging, faculty help, academic programs, staff's ability .	Enter
a. Dependent Variable: var 103 overall satisfaction
b. All requested variables entered.

In Table 2 please pay attention to R Square, .430 means 43% of the reasons of why or why not YSU students are satisfied with YSU were explained by this model (or all independent variables included in the model).

Table 2: Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.656^a	.430	.427	.615
a. Predictors: (Independent Variables), tuition value, sense of belonging, faculty help, academic programs, staff's ability

The ANOVA table (Table 3) shows the model is statistically significant at .001 level.

Table 3: ANOVA^a
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	238.468	5	47.694	126.150	.000^b
Residual	316.065	836	.378
Total	554.533	841
a. Dependent Variable: var 103 overall satisfaction
b. Predictors: (Independent Variables), tuition value, sense of belonging, faculty help, academic programs, staff's ability

In Table 4 please pay attention to the beta values, the higher the beta, the greater the impact on the dependent variable.

Table 4: Coefficients^a
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	Independent Variables	.763	.120		6.343	.000
academic programs	.289	.029	.311	10.064	.000
faculty help	.118	.025	.146	4.773	.000
staff's ability	.090	.026	.106	3.423	.001
sense of belonging	.129	.024	.150	5.350	.000
tuition value	.169	.024	.215	6.923	.000
a. Dependent Variable: var 103 overall satisfaction

Please answer the following questions:

Explain what multiple regression is, the meaning of R and Beta, and under what circumstances multiple regression is used. Please make reference to the lecture notes and the text when you answer this question.

Explain the meaning resulting R. Please refer to Table 2.

Write down two hypotheses about which independent variable has the most effect on var103 and which variable has the least effect on var103 overall satisfaction with YSU.

H1:

H2:

Based on the results of multiple regression (Beta and Sig.), do you accept or reject your hypotheses? Write "accepted" or "rejected" after each of your hypothesis and briefly explain why. Please refer to Table 4.

H1:

H2: