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This assignment examines how distance influences students' educational journey, decision-making processes, and the role of family income. It requires students to analyze data using SPSS software and conduct statistical tests such as independent samples t-tests and chi-square tests to understand the relationship between these variables. The findings will be used to provide insights into student behavior and inform decisions regarding college policies, particularly concerning transportation subsidies.

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SPSS Programme

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Table of Contents

INTRODUCTION...........................................................................................................................3

a. Presenting the estimated effect of distance on years of education...........................................3

b. Explaining the extent to which effect might differ according to the racial aspects.................5

3. Presenting the extent to whichfamily incomeinfluences the level of distance........................5

4. Identifying alternative technique which can be used for analyzing the data set......................6

CONCLUSION................................................................................................................................8

REFERENCES................................................................................................................................9

Table..............................................................................................................................................11

INTRODUCTION...........................................................................................................................3

a. Presenting the estimated effect of distance on years of education...........................................3

b. Explaining the extent to which effect might differ according to the racial aspects.................5

3. Presenting the extent to whichfamily incomeinfluences the level of distance........................5

4. Identifying alternative technique which can be used for analyzing the data set......................6

CONCLUSION................................................................................................................................8

REFERENCES................................................................................................................................9

Table..............................................................................................................................................11

INTRODUCTION

SPSS may be served as a data analysis technique which is undertaken by the researcher to

evaluate and analyze the quantitative facts and figures. It is the most effectual techniques which

in turn help in presenting the fair view and solution of issue to the large extent. The present

report is based on the case situation which presents that department of XYZ is planning to make

decision whether to offer the transport subsidies to the students or not. In this regard, the present

report will shed light on the action that higher management of university needs to undertake for

offering better facilities to the students.

a. Presenting the estimated effect of distance on years of education

H0: There is no significant difference between the effects of distance on the years of education.

H1: There is a significant difference between the effects of distance on the years of education.

Regression

Notes

Output Created 09-JAN-2017 11:58:19

Comments

Input

Data C:\Users\karen\Downloads\

transport_1483781068.sav

Active Dataset DataSet2

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File 3796

Missing Value Handling

Definition of Missing User-defined missing values are

treated as missing.

Cases Used

Statistics are based on cases

with no missing values for any

variable used.

SPSS may be served as a data analysis technique which is undertaken by the researcher to

evaluate and analyze the quantitative facts and figures. It is the most effectual techniques which

in turn help in presenting the fair view and solution of issue to the large extent. The present

report is based on the case situation which presents that department of XYZ is planning to make

decision whether to offer the transport subsidies to the students or not. In this regard, the present

report will shed light on the action that higher management of university needs to undertake for

offering better facilities to the students.

a. Presenting the estimated effect of distance on years of education

H0: There is no significant difference between the effects of distance on the years of education.

H1: There is a significant difference between the effects of distance on the years of education.

Regression

Notes

Output Created 09-JAN-2017 11:58:19

Comments

Input

Data C:\Users\karen\Downloads\

transport_1483781068.sav

Active Dataset DataSet2

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File 3796

Missing Value Handling

Definition of Missing User-defined missing values are

treated as missing.

Cases Used

Statistics are based on cases

with no missing values for any

variable used.

Syntax

REGRESSION

/DESCRIPTIVES MEAN

STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS R

ANOVA CHANGE

/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN

/DEPENDENT ed

/METHOD=ENTER dist.

Resources

Processor Time 00:00:00.02

Elapsed Time 00:00:00.59

Memory Required 1500 bytes

Additional Memory Required for

Residual Plots 0 bytes

[DataSet2] C:\Users\karen\Downloads\transport_1483781068.sav

Descriptive Statistics

Mean Std. Deviation N

ed 13.83 1.814 3796

dist 1.7249 2.13384 3796

Correlations

ed dist

Pearson Correlation ed 1.000 -.086

dist -.086 1.000

Sig. (1-tailed) ed . .000

dist .000 .

N

ed 3796 3796

dist 3796 3796

REGRESSION

/DESCRIPTIVES MEAN

STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS R

ANOVA CHANGE

/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN

/DEPENDENT ed

/METHOD=ENTER dist.

Resources

Processor Time 00:00:00.02

Elapsed Time 00:00:00.59

Memory Required 1500 bytes

Additional Memory Required for

Residual Plots 0 bytes

[DataSet2] C:\Users\karen\Downloads\transport_1483781068.sav

Descriptive Statistics

Mean Std. Deviation N

ed 13.83 1.814 3796

dist 1.7249 2.13384 3796

Correlations

ed dist

Pearson Correlation ed 1.000 -.086

dist -.086 1.000

Sig. (1-tailed) ed . .000

dist .000 .

N

ed 3796 3796

dist 3796 3796

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Variables Entered/Removeda

Model Variables Entered Variables

Removed

Method

1 distb . Enter

a. Dependent Variable: ed

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square Change F Change df1 df2

1 .086a .007 .007 1.807 .007 28.476 1 3

a. Predictors: (Constant), dist

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 93.026 1 93.026 28.476 .000b

Residual 12394.357 3794 3.267

Total 12487.383 3795

a. Dependent Variable: ed

b. Predictors: (Constant), dist

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 13.956 .038 369.945 .000

dist -.073 .014 -.086 -5.336 .000

a. Dependent Variable: ed

Model Variables Entered Variables

Removed

Method

1 distb . Enter

a. Dependent Variable: ed

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square Change F Change df1 df2

1 .086a .007 .007 1.807 .007 28.476 1 3

a. Predictors: (Constant), dist

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 93.026 1 93.026 28.476 .000b

Residual 12394.357 3794 3.267

Total 12487.383 3795

a. Dependent Variable: ed

b. Predictors: (Constant), dist

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 13.956 .038 369.945 .000

dist -.073 .014 -.086 -5.336 .000

a. Dependent Variable: ed

Findings and discussion

By doing investigation on 3796 young adults it has been assessed that mean value of

education years and distance is 13.83 &1.72 respectively. This aspect shows that average

distance level is 1.72sthathascompleted education in the year of14.Standard deviation of both

such variables is 1.81 &2.13. By considering this, it can be said that in the near future distance

level of the students will not deviate to the large extent. Along with this, R of education years

and distance level is0.08 which presents that there is no high level of relationship takes place

between such two variables. R square of both the variables is.007which entails that if changes

are taken place in the years of education then it does not place more impact on the students who

completed education by 2010.

By doing investigation on 3796 young adults it has been assessed that mean value of

education years and distance is 13.83 &1.72 respectively. This aspect shows that average

distance level is 1.72sthathascompleted education in the year of14.Standard deviation of both

such variables is 1.81 &2.13. By considering this, it can be said that in the near future distance

level of the students will not deviate to the large extent. Along with this, R of education years

and distance level is0.08 which presents that there is no high level of relationship takes place

between such two variables. R square of both the variables is.007which entails that if changes

are taken place in the years of education then it does not place more impact on the students who

completed education by 2010.

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Further, level of significance is 0.00 which is under the acceptable criteria. Thus, by

taking into account all such aspects it can be said that alternative hypothesis is accepted. On the

basis of this aspect, distance level had significantly impacted the students who completed study

by 2010.Hence, it is recommended to the higher management of XYZ University to offer

transport subsidies to the students. By doing this, college can attract the large number of students

and thereby maximizes the profitability aspects.

Descriptive Statistics

N Range Minimu

m

Maximu

m

Mean Std.

Deviatio

n

Varianc

e

Skewness Kurtosis

Statisti

c

Statisti

c

Statistic Statistic Statisti

c

Std.

Error

Statistic Statistic Statisti

c

Std.

Erro

r

Statisti

c

Std.

Erro

r

ed 3796 6 12 18 13.83 .029 1.814 3.290 .413 .040 -1.280 .079

female 3796 1 0 1 .55 .008 .498 .248 -.182 .040 -1.968 .079

bytest 3796 42.41 28.95 71.36 51.001

9

.1431

4 8.81925 77.779 -.055 .040 -.905 .079

dadcoll 3796 1 0 1 .20 .007 .402 .161 1.485 .040 .204 .079

momcoll 3796 1 0 1 .14 .006 .346 .120 2.084 .040 2.342 .079

tuition 3796 .97 .43 1.40 .9122 .0046

2 .28460 .081 -.167 .040 -.989 .079

incomeh

i 3796 1 0 1 .29 .007 .452 .204 .946 .040 -1.106 .079

dist 3796 16.00 .00 16.00 1.7249 .0346

3 2.13384 4.553 2.906 .040 12.499 .079

Valid N

(listwise

)

3796

By applying the tools of descriptive statistics, it has been assessed that most of the

respondents are female who have presented their views. Along with this, descriptive statics

results show that there is no university degree has taken by father and mothers of students.

Further, it has been assessed that there are large number of students whose family income is less

than $ 50000. It shows that there are several students who belong from the middle income level

taking into account all such aspects it can be said that alternative hypothesis is accepted. On the

basis of this aspect, distance level had significantly impacted the students who completed study

by 2010.Hence, it is recommended to the higher management of XYZ University to offer

transport subsidies to the students. By doing this, college can attract the large number of students

and thereby maximizes the profitability aspects.

Descriptive Statistics

N Range Minimu

m

Maximu

m

Mean Std.

Deviatio

n

Varianc

e

Skewness Kurtosis

Statisti

c

Statisti

c

Statistic Statistic Statisti

c

Std.

Error

Statistic Statistic Statisti

c

Std.

Erro

r

Statisti

c

Std.

Erro

r

ed 3796 6 12 18 13.83 .029 1.814 3.290 .413 .040 -1.280 .079

female 3796 1 0 1 .55 .008 .498 .248 -.182 .040 -1.968 .079

bytest 3796 42.41 28.95 71.36 51.001

9

.1431

4 8.81925 77.779 -.055 .040 -.905 .079

dadcoll 3796 1 0 1 .20 .007 .402 .161 1.485 .040 .204 .079

momcoll 3796 1 0 1 .14 .006 .346 .120 2.084 .040 2.342 .079

tuition 3796 .97 .43 1.40 .9122 .0046

2 .28460 .081 -.167 .040 -.989 .079

incomeh

i 3796 1 0 1 .29 .007 .452 .204 .946 .040 -1.106 .079

dist 3796 16.00 .00 16.00 1.7249 .0346

3 2.13384 4.553 2.906 .040 12.499 .079

Valid N

(listwise

)

3796

By applying the tools of descriptive statistics, it has been assessed that most of the

respondents are female who have presented their views. Along with this, descriptive statics

results show that there is no university degree has taken by father and mothers of students.

Further, it has been assessed that there are large number of students whose family income is less

than $ 50000. It shows that there are several students who belong from the middle income level

group. On the basis of this aspect, higher management of XYZ college institution needs to make

focus on offering transport subsidies to the student’s whose residential area is very far from

college. Moreover, due to the higher transportation charges sometimes it is not possible for

students to afford the services provided by XYZ institution.

b. Explaining the extent to which effect might differ according to the racial aspects

H0: There is no difference between the racial aspect and distance level.

H1: There is a difference between the racial aspect and distance level.

ANOVA

dist

Sum of Squares df Mean Square F Sig.

Between Groups 172.435 2 86.218 19.116 .000

Within Groups 17107.167 3793 4.510

Total 17279.602 3795

Post Hoc Tests

Multiple Comparisons

Dependent Variable: dist

Tukey HSD

(I) race (J) race Mean Difference

(I-J)

Std. Error Sig. 95% Confidence Interval

Lower Bound Upper Bound

1 2 -.00409 .09866 .999 -.2354 .2272

3 .53974* .08931 .000 .3303 .7491

2 1 .00409 .09866 .999 -.2272 .2354

3 .54383* .11873 .000 .2655 .8222

3 1 -.53974* .08931 .000 -.7491 -.3303

2 -.54383* .11873 .000 -.8222 -.2655

*. The mean difference is significant at the 0.05 level.

dist

Tukey HSD

race N Subset for alpha = 0.05

1 2

focus on offering transport subsidies to the student’s whose residential area is very far from

college. Moreover, due to the higher transportation charges sometimes it is not possible for

students to afford the services provided by XYZ institution.

b. Explaining the extent to which effect might differ according to the racial aspects

H0: There is no difference between the racial aspect and distance level.

H1: There is a difference between the racial aspect and distance level.

ANOVA

dist

Sum of Squares df Mean Square F Sig.

Between Groups 172.435 2 86.218 19.116 .000

Within Groups 17107.167 3793 4.510

Total 17279.602 3795

Post Hoc Tests

Multiple Comparisons

Dependent Variable: dist

Tukey HSD

(I) race (J) race Mean Difference

(I-J)

Std. Error Sig. 95% Confidence Interval

Lower Bound Upper Bound

1 2 -.00409 .09866 .999 -.2354 .2272

3 .53974* .08931 .000 .3303 .7491

2 1 .00409 .09866 .999 -.2272 .2354

3 .54383* .11873 .000 .2655 .8222

3 1 -.53974* .08931 .000 -.7491 -.3303

2 -.54383* .11873 .000 -.8222 -.2655

*. The mean difference is significant at the 0.05 level.

dist

Tukey HSD

race N Subset for alpha = 0.05

1 2

3 731 1.2885

1 2496 1.8282

2 569 1.8323

Sig. 1.000 .999

Means for groups in homogeneous subsets are

displayed.

a. Uses Harmonic Mean Sample Size =

850.798.

b. The group sizes are unequal. The harmonic

mean of the group sizes is used. Type I error

levels are not guaranteed.

Findings and discussion

From quantitative investigation, it has been analyzed that significance value is 0.00 that is

less than 0.05. Thus, according to the standard criteria null hypothesis is rejected to the great

extent. By taking into consideration such aspect it can be said that distance level differs

according to the racial aspects. Further, outcome derived from Tukey test also presents that

distance level of white and Hispanic people insignificant differs from others. Along with this, in

1 and 2 level of combination significance value of black people is 0.00.From investigation, it has

been assessed that significance value of the combination between the Hispanic and black people

is .999. On the other side, significance outcome is .99 between the white and black people. Thus,

by taking into account all such aspects it can be said that effect of differ according to the racial

basis such as white, black and Hispanic. All these aspect can clearly be seen in the Post hoc test

table where different combination of racial aspects has been identified. By making thorough

analysis of secondary data it has been identified that institution can build its distinct image by

treating all the students equally. Thus, at the time of the formulation of competent strategic and

policy framework XYZ needs to consider such aspects which in turn help it in gaining

competitive edge over others.

3. Presenting the extent to which family income influences the level of distance

H0:There is no significant difference between the mean impacts of the family income on distance

level.

1 2496 1.8282

2 569 1.8323

Sig. 1.000 .999

Means for groups in homogeneous subsets are

displayed.

a. Uses Harmonic Mean Sample Size =

850.798.

b. The group sizes are unequal. The harmonic

mean of the group sizes is used. Type I error

levels are not guaranteed.

Findings and discussion

From quantitative investigation, it has been analyzed that significance value is 0.00 that is

less than 0.05. Thus, according to the standard criteria null hypothesis is rejected to the great

extent. By taking into consideration such aspect it can be said that distance level differs

according to the racial aspects. Further, outcome derived from Tukey test also presents that

distance level of white and Hispanic people insignificant differs from others. Along with this, in

1 and 2 level of combination significance value of black people is 0.00.From investigation, it has

been assessed that significance value of the combination between the Hispanic and black people

is .999. On the other side, significance outcome is .99 between the white and black people. Thus,

by taking into account all such aspects it can be said that effect of differ according to the racial

basis such as white, black and Hispanic. All these aspect can clearly be seen in the Post hoc test

table where different combination of racial aspects has been identified. By making thorough

analysis of secondary data it has been identified that institution can build its distinct image by

treating all the students equally. Thus, at the time of the formulation of competent strategic and

policy framework XYZ needs to consider such aspects which in turn help it in gaining

competitive edge over others.

3. Presenting the extent to which family income influences the level of distance

H0:There is no significant difference between the mean impacts of the family income on distance

level.

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H1:There is a significant difference between the mean impacts of the family income on distance

level.

Regression

Notes

Output Created 09-JAN-2017 12:29:48

Comments

Input

Data C:\Users\karen\Downloads\

transport_1483781068.sav

Active Dataset DataSet2

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data

File 3796

Missing Value Handling

Definition of Missing User-defined missing values

are treated as missing.

Cases Used

Statistics are based on cases

with no missing values for any

variable used.

Syntax

REGRESSION

/DESCRIPTIVES MEAN

STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS

CI(95) R ANOVA CHANGE

/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN

/DEPENDENT dist

/METHOD=ENTER incomehi.

Resources

Processor Time 00:00:00.02

Elapsed Time 00:00:00.02

Memory Required 1500 bytes

Additional Memory Required

for Residual Plots 0 bytes

level.

Regression

Notes

Output Created 09-JAN-2017 12:29:48

Comments

Input

Data C:\Users\karen\Downloads\

transport_1483781068.sav

Active Dataset DataSet2

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data

File 3796

Missing Value Handling

Definition of Missing User-defined missing values

are treated as missing.

Cases Used

Statistics are based on cases

with no missing values for any

variable used.

Syntax

REGRESSION

/DESCRIPTIVES MEAN

STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS

CI(95) R ANOVA CHANGE

/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN

/DEPENDENT dist

/METHOD=ENTER incomehi.

Resources

Processor Time 00:00:00.02

Elapsed Time 00:00:00.02

Memory Required 1500 bytes

Additional Memory Required

for Residual Plots 0 bytes

[DataSet2] C:\Users\karen\Downloads\transport_1483781068.sav

Descriptive Statistics

Mean Std. Deviation N

dist 1.7249 2.13384 3796

incomehi .29 .452 3796

Correlations

dist incomehi

Pearson Correlation dist 1.000 -.085

incomehi -.085 1.000

Sig. (1-tailed) dist . .000

incomehi .000 .

N dist 3796 3796

incomehi 3796 3796

Variables Entered/Removeda

Model Variables

Entered

Variables

Removed

Method

1 incomehib . Enter

a. Dependent Variable: dist

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square Change F Change df1 df2

1 .085a .007 .007 2.12648 .007 27.306 1 379

a. Predictors: (Constant), incomehi

ANOVAa

Descriptive Statistics

Mean Std. Deviation N

dist 1.7249 2.13384 3796

incomehi .29 .452 3796

Correlations

dist incomehi

Pearson Correlation dist 1.000 -.085

incomehi -.085 1.000

Sig. (1-tailed) dist . .000

incomehi .000 .

N dist 3796 3796

incomehi 3796 3796

Variables Entered/Removeda

Model Variables

Entered

Variables

Removed

Method

1 incomehib . Enter

a. Dependent Variable: dist

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square Change F Change df1 df2

1 .085a .007 .007 2.12648 .007 27.306 1 379

a. Predictors: (Constant), incomehi

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 123.475 1 123.475 27.306 .000b

Residual 17156.128 3794 4.522

Total 17279.602 3795

a. Dependent Variable: dist

b. Predictors: (Constant), incomehi

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig. 95.0% Confidence Interv

B Std. Error Beta Lower Bound Upper

1 (Constant) 1.839 .041 45.016 .000 1.759

incomehi -.399 .076 -.085 -5.226 .000 -.549

a. Dependent Variable: dist

1

Regression 123.475 1 123.475 27.306 .000b

Residual 17156.128 3794 4.522

Total 17279.602 3795

a. Dependent Variable: dist

b. Predictors: (Constant), incomehi

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig. 95.0% Confidence Interv

B Std. Error Beta Lower Bound Upper

1 (Constant) 1.839 .041 45.016 .000 1.759

incomehi -.399 .076 -.085 -5.226 .000 -.549

a. Dependent Variable: dist

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Findings and discussion

By applying the tools and techniques of SPSS it has been identified that mean value of

distance and family income is 1.72 &.29. In this, output derived from the tools present that

average people come from the category where income level of family is less than 50000. Along

with this, results show that 1.72s are the average miles from which home of the students are

away from the college. R and r square of such two variables are .08 &.01which is not higher. In

addition to this, level of significance is 0.00 that shows effect of distance is highly influenced

from the level of family income. This aspect can be supported from the secondary data analysis

which clearly shows that incomes one of the main factors that affects the decision making of

By applying the tools and techniques of SPSS it has been identified that mean value of

distance and family income is 1.72 &.29. In this, output derived from the tools present that

average people come from the category where income level of family is less than 50000. Along

with this, results show that 1.72s are the average miles from which home of the students are

away from the college. R and r square of such two variables are .08 &.01which is not higher. In

addition to this, level of significance is 0.00 that shows effect of distance is highly influenced

from the level of family income. This aspect can be supported from the secondary data analysis

which clearly shows that incomes one of the main factors that affects the decision making of

both students and their parents. Moreover, students are encouraged to go far for better education

if it is affordable to them. The rationale behind this, along with tution fees, transportation

expenses are the main elements which impose fixed financial burden in front of students and

their family as well. Thus, distance level is also considered by the students at the time of making

selection of college. By considering all the above aspects it can be stated that individuals with

sound income do not consider because it is affordable to them.

4. Identifying alternative technique which can be used for analyzing the data set

In this, regression model has been undertaken by the scholar to assess the impact of one

variable on another. This technique is highly effective which in turn helps in identifying the

dependency level of one variable on another. Thus, by keeping all such factors in mind

researcher has employed regression tool (Giménez-Toledo, Tejada-Artigas and Mañana-

Rodríguez, 2013). Through this, effect of distance level on the years of education has been

assessed by the scholar. In addition to this, researcher has also been used such tool to determine

the influence of family income on distance level. Thus, tool which has been applied by the

scholar to analyze the dependency level is appropriate to the large extent. Moreover, by

evaluating mean, standard deviation, R and R square researcher is in condition to present the fair

view of study (Fraley and Hudson, 2014). In this way, on the basis of level of significance

dependency level has been measured by the scholar.

Along with this, in order to analyze the effects of independent variables one way

ANOVA’s has been applied by the scholar. With the help Tukey test researcher has assessed the

extent to which variables are related to each other. Moreover, such test develops number of

combinations according to the variables (Dimaggio, 2013). On the basis of significance values of

such combinations decision has been made by the researcher in relation to acceptance or

rejection of hypothesis. In this way, by making evaluation of each and every aspect researcher

has identified that distance level of white and Hispanic people are highly differs to the great

extent. Thus, both the techniques which are employed by the scholar are highly effectual which

in turn helps in presenting the fair view of study.

In order to strengthen the findings researcher is required to employ t-test to assess the

impact of distance level on the educational decision of students. By applying such tool

difference between the mean of two groups can be assessed in the best possible way. Along with

if it is affordable to them. The rationale behind this, along with tution fees, transportation

expenses are the main elements which impose fixed financial burden in front of students and

their family as well. Thus, distance level is also considered by the students at the time of making

selection of college. By considering all the above aspects it can be stated that individuals with

sound income do not consider because it is affordable to them.

4. Identifying alternative technique which can be used for analyzing the data set

In this, regression model has been undertaken by the scholar to assess the impact of one

variable on another. This technique is highly effective which in turn helps in identifying the

dependency level of one variable on another. Thus, by keeping all such factors in mind

researcher has employed regression tool (Giménez-Toledo, Tejada-Artigas and Mañana-

Rodríguez, 2013). Through this, effect of distance level on the years of education has been

assessed by the scholar. In addition to this, researcher has also been used such tool to determine

the influence of family income on distance level. Thus, tool which has been applied by the

scholar to analyze the dependency level is appropriate to the large extent. Moreover, by

evaluating mean, standard deviation, R and R square researcher is in condition to present the fair

view of study (Fraley and Hudson, 2014). In this way, on the basis of level of significance

dependency level has been measured by the scholar.

Along with this, in order to analyze the effects of independent variables one way

ANOVA’s has been applied by the scholar. With the help Tukey test researcher has assessed the

extent to which variables are related to each other. Moreover, such test develops number of

combinations according to the variables (Dimaggio, 2013). On the basis of significance values of

such combinations decision has been made by the researcher in relation to acceptance or

rejection of hypothesis. In this way, by making evaluation of each and every aspect researcher

has identified that distance level of white and Hispanic people are highly differs to the great

extent. Thus, both the techniques which are employed by the scholar are highly effectual which

in turn helps in presenting the fair view of study.

In order to strengthen the findings researcher is required to employ t-test to assess the

impact of distance level on the educational decision of students. By applying such tool

difference between the mean of two groups can be assessed in the best possible way. Along with

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this, one way Annova tool also provides assistance in identifying the difference between the

mean score of two interventions and change score more effectively (Hribar, Kravet and Wilson,

2014). Thus, by employing such technique researcher can determine the effect of distance level

within the different racial groups. In this, independent sample t-test offers opportunity to make

comparison of the means of two groups which are unrelated to each other. For instance: In the

present case situation researcher has asked to assess to extent to which distance level differs

according to theracial aspect. In this, variable such as race has three subgroups such as white,

Hispanic and black. Hence, in such situation by considering the independent sample t-test

researcher can find out the suitable solution of issue.

Along with this, by applying the chi-square test researcher can identify the fact that there

is significant relationship takes place between the two variables. Hence, by using such measures

researcher can identify the fact that two variable are related to each other due to having any

significant reason or not (Agrawal and Cooper, 2015). For instance: by applying chi-square test

researcher can determine that family income level having significant impact on the decision

making of students or not. In this way, by identifying such kind of relationship researcher can

provide XYZ college institution with the competent framework for decision making. Hence, by

applying the chi-square and independent sample t-test technique researchers can determine the

suitable solution of issue.All such tools and analysis based on it will make the output more

effective (Adibah Wan Ismail and et.al., 2013). Hence, by making use of such other SPSS tools

and techniques researcher can identify the factors that have impact on the decision-making

aspect of students. By this, higher authority of college can decide whether they need to offer

transport subsidy to the students or not. In this, by making use of the outcome of SPSS

researcher can determine the aspect which will prove to be more beneficial for college

institution.

CONCLUSION

From the above report, it has been concluded that distance level have impact on the years

of education to the large extent. Moreover, there are several variables which highly differ from

one year to another. Besides this, it can be revealed from the report that family income level is

the significant factors which have high level of impact on the decision making of students. Thus,

mean score of two interventions and change score more effectively (Hribar, Kravet and Wilson,

2014). Thus, by employing such technique researcher can determine the effect of distance level

within the different racial groups. In this, independent sample t-test offers opportunity to make

comparison of the means of two groups which are unrelated to each other. For instance: In the

present case situation researcher has asked to assess to extent to which distance level differs

according to theracial aspect. In this, variable such as race has three subgroups such as white,

Hispanic and black. Hence, in such situation by considering the independent sample t-test

researcher can find out the suitable solution of issue.

Along with this, by applying the chi-square test researcher can identify the fact that there

is significant relationship takes place between the two variables. Hence, by using such measures

researcher can identify the fact that two variable are related to each other due to having any

significant reason or not (Agrawal and Cooper, 2015). For instance: by applying chi-square test

researcher can determine that family income level having significant impact on the decision

making of students or not. In this way, by identifying such kind of relationship researcher can

provide XYZ college institution with the competent framework for decision making. Hence, by

applying the chi-square and independent sample t-test technique researchers can determine the

suitable solution of issue.All such tools and analysis based on it will make the output more

effective (Adibah Wan Ismail and et.al., 2013). Hence, by making use of such other SPSS tools

and techniques researcher can identify the factors that have impact on the decision-making

aspect of students. By this, higher authority of college can decide whether they need to offer

transport subsidy to the students or not. In this, by making use of the outcome of SPSS

researcher can determine the aspect which will prove to be more beneficial for college

institution.

CONCLUSION

From the above report, it has been concluded that distance level have impact on the years

of education to the large extent. Moreover, there are several variables which highly differ from

one year to another. Besides this, it can be revealed from the report that family income level is

the significant factors which have high level of impact on the decision making of students. Thus,

XYZ college institution needs to offer subsidies to the students. Moreover, due to the high

transportation charges there are several students who do not prefer to take admission in such

college.

transportation charges there are several students who do not prefer to take admission in such

college.

REFERENCES

Books and Journals

Adibah Wan Ismail and et.al., 2013. Earnings quality and the adoption of IFRS-based

accounting standards: Evidence from an emerging market. Asian Review of Accounting.

21(1). pp.53-73.

Agrawal, A. and Cooper, T., 2015. Insider trading before accounting scandals. Journal of

Corporate Finance. 34. pp.169-190.

Dimaggio, C., 2013. Introduction. In SAS for Epidemiologists (pp. 1-5). Springer New York.

Fraley, R.C. and Hudson, N.W., 2014. Review of Intensive Longitudinal Methods: An

Introduction to Diary and Experience Sampling Research. The Journal of Social Psychology.

154(1). pp.89-91.

Giménez-Toledo, E., Tejada-Artigas, C. and Mañana-Rodríguez, J., 2013. Evaluation of

scientific books’ publishers in social sciences and humanities: Results of a survey. Research

Evaluation. 22(1). pp.64-77.

Hribar, P., Kravet, T. and Wilson, R., 2014. A new measure of accounting quality. Review of

Accounting Studies. 19(1). pp.506-538.

Books and Journals

Adibah Wan Ismail and et.al., 2013. Earnings quality and the adoption of IFRS-based

accounting standards: Evidence from an emerging market. Asian Review of Accounting.

21(1). pp.53-73.

Agrawal, A. and Cooper, T., 2015. Insider trading before accounting scandals. Journal of

Corporate Finance. 34. pp.169-190.

Dimaggio, C., 2013. Introduction. In SAS for Epidemiologists (pp. 1-5). Springer New York.

Fraley, R.C. and Hudson, N.W., 2014. Review of Intensive Longitudinal Methods: An

Introduction to Diary and Experience Sampling Research. The Journal of Social Psychology.

154(1). pp.89-91.

Giménez-Toledo, E., Tejada-Artigas, C. and Mañana-Rodríguez, J., 2013. Evaluation of

scientific books’ publishers in social sciences and humanities: Results of a survey. Research

Evaluation. 22(1). pp.64-77.

Hribar, P., Kravet, T. and Wilson, R., 2014. A new measure of accounting quality. Review of

Accounting Studies. 19(1). pp.506-538.

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