Student Decision-Making and Distance

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.

SPSS Programme

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
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

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

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.

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

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

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.

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

[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

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

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

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

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,

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.

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.