Research Questions and Results on Various Statistical Analyses | Desklib

This article presents research questions and results on various statistical analyses such as mean attitudinal scores, skewness, kurtosis, percentile rank, and more. The article includes interpretation and analysis of the results obtained from the statistical analyses. The subject matter covers demographics, anxiety scores, community population, and more. The article is relevant for students pursuing courses in statistics, research methodology, and related fields.

1
RESEARCH QUESTIONS

2
Table of Contents
WEEK 1...........................................................................................................................................4
WEEK 2...........................................................................................................................................9
WEEK 3.........................................................................................................................................10
WEEK 4- Lesson 24 Exercise File 1.............................................................................................19
Results for the test evaluating homogeneity of variance...........................................................19
3. Cohen’s d effect.....................................................................................................................21
5. Boxplot..................................................................................................................................22
Week 4 - Lesson 41 Exercise File 1..............................................................................................22
a, b & c.......................................................................................................................................22
d)................................................................................................................................................23
2. Clustered graph......................................................................................................................24
3. Results....................................................................................................................................24
WEEK-5........................................................................................................................................25
1. One way annova and post hoc test.........................................................................................25
2. Relationship between hair colour and extrovertedness.........................................................27
3. Boxplot..................................................................................................................................27
WEEK-6........................................................................................................................................28
Linear regression.......................................................................................................................28
Relationship between multiple R and bivariate correlation.......................................................31
Scatterplot..................................................................................................................................31
Result section.............................................................................................................................32
WEEK-7........................................................................................................................................32
Regression..................................................................................................................................32
1. Multiple regression................................................................................................................32
2. Regression equation...............................................................................................................32
3. and 4. Performance of the regression analysis.......................................................................33
Factor analysis...........................................................................................................................36
1. Factors underlying in SCVS on the basis of scree plot.........................................................38

3
2. Factor based on eigenvalue greater than 1 criterion..............................................................39
3. Result section.........................................................................................................................39
REFERENCES................................................................................................................................1

4
WEEK 1
Lesson 21, Exercise File 2
Mean Attitudinal scores:
For Republican respondents:
Descriptive Statistics
N Minimum Maximum Sum Mean
att1 21 1 5 68 3.24
att2 21 2 4 69 3.29
att3 21 2 5 73 3.48
att4 21 2 5 70 3.33
att5 21 1 4 49 2.33
Valid N (listwise) 21
Interpretation:
From the above table it has been found that the mean of att1 is 3.24, att2 is 3.29, att3 is 3.48,
att4 is 3.33 and att5 is 2.33. Thus it can be interpreted that on an average, respondents had
neutral views for att1, att2, att3, att4 while for att5, average number of respondents disagreed.
For Democrat respondents:
Descriptive Statistics
N Minimum Maximum Sum Mean
att1 19 1 4 53 2.79
att2 19 1 4 44 2.32
att3 19 1 3 41 2.16
att4 19 1 3 39 2.05

5
att5 19 1 4 33 1.74
Valid N (listwise) 19
Interpretation:
From the above table it can be understood that the mean of att1 is 2.79, att2 is 2.32, att3 is
2.16, att4 is 2.05 and att5 is 1.74. Hence, it can be interpreted that the average number of
respondents have disagree view for att1, att2, att3, att4. However, for att5 the view is highly
disagree.
Total attitude scores from the scores for the five attitudinal items:
The total attitude score of Republic party is 329 while that of Democrat party is 210.
Means on the total attitude scores for the two political parties:
The mean of Republic party is 15.66 while that of Democrat party is 11.05.
Boxplot:
For Republic party:
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent

6
att1 21 100.0% 0 0.0% 21 100.0%
att2 21 100.0% 0 0.0% 21 100.0%
att3 21 100.0% 0 0.0% 21 100.0%
att4 21 100.0% 0 0.0% 21 100.0%
att5 21 100.0% 0 0.0% 21 100.0%
Interpretation:
From the above boxplot it can be interpreted that tall boxplots for att1, att3 and att5 show
that different attitudes are held by students in republican party. Also, there is a striking difference
between the responses for att5 as compared to the other attitudes.

7
For Democrat party:
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
att1 19 100.0% 0 0.0% 19 100.0%
att2 19 100.0% 0 0.0% 19 100.0%
att3 19 100.0% 0 0.0% 19 100.0%
att4 19 100.0% 0 0.0% 19 100.0%
att5 19 100.0% 0 0.0% 19 100.0%

8
Interpretation:
From the above boxplot it would be interpreted that overall respondents have higher
agreement with att3. However, with regard to att1 respondents hold quite different opinion. In
the same way from the boxplot of att1 and att3 it would be said that there is a difference in
response and opinion. Likewise, with the existence of uneven size of box it would further be
interpreted that the response of respondents vary i.e. they may get agree while at other point they
hold different response.

9
WEEK 2
Lesson 21 Exercise File 1
Computation of Skewness, Mean, Standard Deviation, and Kurtosis of anxiety scores:
Descriptive Statistics
N Minimum Maximum Mean Std.
Deviation
Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Anxiety Scores 15 5 78 32.27 23.478 .416 .580 -1.124 1.121
Valid N
(listwise)
15
Result section:
As the ideal range of Skewness and Kurtosis for measuring its distribution is ± 1.0 (Hair
Jr, and et.al., 2021). And as per the above calculation it would be analyzed that the Skewness of
the respondent’s anxiety is 0.416 which is falling in the category of ± 1.0. This means it would
be right to interpreted that the respondent’s anxiety scores fall in the category of normality, i.e.
the distribution lies in normal category. However, while analysing the Kurtosis, it can be
observed that the value of Kurtosis is -1.124 which is certainly lower than the ideal range of ±
1.0. Thus, it can be interpreted that the distribution of Kurtosis is too flat.
In the same from the above table it is also observed that the mean of anxiety score is
32.27 while the standard deviation is 23.478 (Green, Salkind, 2017). Thus, it can be interpreted
that the standard deviation of the dataset i.e. respondent’s anxiety is high because the data point
is spread at a large range.

10
In the same it can also be interpreted that as the mean of students is 32.27 which depict that
the average number of students suffers from severe anxiety. This is because as per the range of
anxiety the score of 30-63 shows the anxiety of severe extent.
WEEK 3
Exercise 1 Lesson 20
Frequency analysis on the gender and marital status:
Percentage of men
Gender
Frequency Percent Valid Percent Cumulative Percent
Valid
Men 13 52.0 52.0 52.0
Women 12 48.0 48.0 100.0
Total 25 100.0 100.0
Results:
From the above table it can be evaluated that there is an unequal percent breakout. As
there are 52% are men while that of 48% are women. This mean that the percent of men are more
in comparison with women.
Mode of marital status
Statistics
Marital Status
N Valid 25

11
Missing 0
Mode 2
Interpretation:
From the above table it can be analyzed that the mode of marital status is 2 which means
that majority are carrying a status of Divorced.
Frequency of divorced people
Marital Status
Frequency Percent Valid Percent Cumulative Percent
Valid
Married 9 36.0 36.0 36.0
Divorced 11 44.0 44.0 80.0
Never Married 5 20.0 20.0 100.0
Total 25 100.0 100.0
Interpretation:
As per the above table it can be evaluated that the frequency of Divorced are 11 which is the
highest. This means that there is a majority of respondent who are Divorced.
Frequency table of educational level of respondents:
Education Level
Frequency Percent Valid Percent Cumulative Percent
Valid Did not graduate from
high school
6 24.0 24.0 24.0

12
High School Graduate 4 16.0 16.0 40.0
Bachelor's Degree 8 32.0 32.0 72.0
Post Graduate Degree 7 28.0 28.0 100.0
Total 25 100.0 100.0
Results:
As per the above table it can be interpreted that majority of respondents i.e. 8 holding the
status of Bachelor’s degree while on the other hand there are least respondents i.e. 4 holding an
educational level of high school graduates. In addition of this it is also interpreted that there are
certainly high respondents i.e. 7 which hold an educational level of post graduate degree.
However, the remaining i.e. 6 respondents did not graduate from high level.
Bar chart representing community population:

13
Results:
As per the above graph it can be interpreted that the majority of community population
lie in the range of 1001 to 5000 while the least belong to the range of more than 100000. In the
same way, the number of people belonging range of 50001 to 100000 and less than 500.
Participant’s section:
In the sample of Ann’s the participants are selected on the basis of demographics. It is a
sample of 25 participants. This demographic includes participant’s gender, educational level,
marital status and community population size.

14
Exercise 2 Lesson 21
Percentile rank on anxiety score assuming normal distribution:
Statistics
Anxiety Scores
N
Valid 15
Missing 0
Percentiles
12 5.92
25 10.00
27 10.64
38 16.16
50 25.00
73 49.04
75 50.00
88 61.44
Results:
As per the above table it can be seen that the percentile of 12 is 5.92 while that of 27 is
10.64. In the same way the percentile score of 38 is 16.16, 73 is 49.04 and 88 is 61.44. This
means that the anxiety score of the respondents are not marking up to the given percentile.
Percentile rank on anxiety score assuming non normal distribution:

15
Statistics
perrank
N
Valid 15
Missing 0
Mean 8.9630
Median 8.8889
Percentiles
12 2.1333
25 4.4444
27 4.8000
38 6.7556
50 8.8889
73 12.9778
75 13.3333
88 15.6444
Results:
From the above table it can be analyzed that for non-normal distribution the percentile of
12 is 2.13 while that of 27 is 4.8, and 38 is 6.75. Likewise, for 73 is 12.97 and 88 is 15. This
means that the anxiety level of respondents do not mark with the given percentile in case of non-
distribution also.
Histogram of anxiety scores:
Anxiety Scores
Frequency Percent Valid Percent Cumulative
Percent

16
Valid
5 1 6.7 6.7 6.7
6 1 6.7 6.7 13.3
9 1 6.7 6.7 20.0
10 1 6.7 6.7 26.7
12 1 6.7 6.7 33.3
16 1 6.7 6.7 40.0
18 1 6.7 6.7 46.7
25 1 6.7 6.7 53.3
46 1 6.7 6.7 60.0
47 2 13.3 13.3 73.3
50 1 6.7 6.7 80.0
55 1 6.7 6.7 86.7
60 1 6.7 6.7 93.3
78 1 6.7 6.7 100.0
Total 15 100.0 100.0

17
Results:
As per the above histogram it can be interpreted that majority of respondents’s anxiety
score lies in the range of 0-20 which means mild to moderate anxiety while that of lowest
number of respondents fall in the range of 20-40 i.e. possessing moderate to severe anxiety. In
the same way with the range of 40-60 the number of respondents are below the highest score i.e.
severe anxiety. Here the method that is being adopted is related with the consideration with
normality distribution. In the same way with the analysis of the normal curve it would be
interpreted that the curve is Right-skewed which means falling towards the right. This means that

18
the anxiety scores or level among the students are initially low which raises at later stage but
with a declining rate.
Use of Percentile rank method:
While making a comparison between histogram and descriptive statistics it would be
right to state that histogram would show better description and would be more effective in
comparison with descriptive statistics. This is because with the above presented histogram the
anxiety score of the student would be able to better evaluate along with its range and trend.

19
WEEK 4- Lesson 24 Exercise File 1
Mean eating time and standard deviation
Statistics
Weight
classification
Time spent
eating Big
Mac specials
in seconds
N Valid 40 40
Missing 0 0
Mean 1.75 671.05
Std. Deviation .439 88.527
From the above mean eating and time and stan deviation it can be said that average time
spend in eating Big Mac special in seconds is approximately 671.05. lower standard deviation
helps in interpretation that data is not spread out and is clustered.
T-Test
Group Statistics
Weight
classification
N Mean Std.
Deviation
Std. Error Mean
Time spent eating Big Mac
specials in seconds
0 0a . . .
overweight 10 589.00 42.615 13.476
a. t cannot be computed because at least one of the groups is empty.
Results for the test evaluating homogeneity of variance
H0 (Null hypothesis): There is no difference between the mean value of weight and time spent
for eating Big Mc Meal.
H1 (Alternative hypothesis): There is a difference between the mean value of weight and time
spent for eating Big Mc Meal.
Oneway

20
Descriptives
Time spent eating Big Mac specials in seconds
N Mean Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
overweight 10 589.00 42.615 13.476 558.52 619.48 535 660
normal
weight 30 698.40 82.949 15.144 667.43 729.37 478 889
Total 40 671.05 88.527 13.997 642.74 699.36 478 889
Test of Homogeneity of Variances
Time spent eating Big Mac specials in seconds
Levene
Statistic
df1 df2 Sig.
2.745 1 38 .106
ANOVA
Time spent eating Big Mac specials in seconds
Sum of
Squares
df Mean Square F Sig.
Between Groups 89762.700 1 89762.700 15.800 .000
Within Groups 215879.200 38 5681.032
Total 305641.900 39
Interpretation:

21
From the above analysis it has been interpreted that alternative hypothesis has been
accepted because significance value is less than 0.05. Therefore it can be said that difference
between the mean value of weight and time spent for eating Big Mc Meal.
From the above analysis it can further be interpreted that mean eating time of overweight
individuals is much less than mean eating time of individuals with normal weight.
3. Cohen’s d effect
In order to determine the effect between weight and speed of eating a Big Mac meal, the
following formula has been used which is as mentioned below:
D = M(1) – M(2) / S
Here, M1 and M2 denotes to sample mean for the group 1 and 2 whereas S denotes to
pooled estimated population.
Therefore, D = 38 – 1 / 88.527
D = 37 / 88.527
D = 0.41
Thus, it reflected that it indicates approx. medium effect because 0.41 is below 0.50.

22
5. Boxplot
Above box plat graph clearly helps in understanding that over weight individuals spend less time
in eating Big Mac specials in seconds but if it is compared with time sped in eating Big Mac
specials in seconds of normal weight individuals then it can be said that normal weight
individuals spend much more time then over weight children in eating big mac specials
Week 4 - Lesson 41 Exercise File 1
a, b & c
Math classes * Parents of female high school student Crosstabulation
Count
Parents of female high school
student
Total
Primarily
father
Father and
mother

23
Math classes No advanced math 21 92 113
Some advanced math 9 8 17
Total 30 100 130
a) percentage of females who took advance maths class are approximately 13 percent of
females.
b) Percentage of females who took no advance maths class and were raised by their fathers
is 70 percent of females.
c) Percent of female students raised by father only are approximately 23 percent.
d)
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Pearson Chi-Square 9.826a 1 .002
Continuity Correctionb 7.986 1 .005
Likelihood Ratio 8.434 1 .004
Fisher's Exact Test .004 .004
Linear-by-Linear
Association 9.751 1 .002
N of Valid Cases 130
a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.92.
b. Computed only for a 2x2 table
From the above Chi square test it can be clearly interpreted that p<0.05 which helps in
identifying that alternative hypothesis is correct i.e. there is relationship between maths class and
parents of female students of high school
Symmetric Measures
Value Asymp. Std.
Errora
Approx.
Tb
Approx.
Sig.
Nominal by
Nominal
Phi -.275 .002
Cramer's V .275 .002
Interval by Interval Pearson's R -.275 .102 -3.235 .002c
Ordinal by Ordinal Spearman Correlation -.275 .102 -3.235 .002c
N of Valid Cases 130
a. Not assuming the null hypothesis.

24
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
d) X2 value of Pearson is approximately 9.826
e) Strength of relationship between taking advance maths class and level of parenting is 9:8.
2. Clustered graph
Above bar graph clearly helps in understanding that maximum number of females with both
father and mother has no advance maths class as compared with females with father. But if it is
compared with females with advance math class and if both only father and both parents ration
of FEMAES with some advance maths class are compared then it can be said that this ration is
approximability same as compared to above graph.
3. Results
From the above analysis it can be interpreted that maximum number of females only
focused upon opting no advance maths class (including with both parents and with fathers). Even
in this females with both father and mother had selected non advance maths class. It has further

25
been analysed that percentage of females with bothy the parents who choose advance maths class
was lowest which is approximately 6 percent.
WEEK-5
1. One way annova and post hoc test
Hypothesis:
Null hypothesis: There is no significant relationship between hair colour and extrovertedness
Alternative hypothesis: There is a significant relationship between hair colour and
extrovertedness.
One way annova:
Post hoc test:
ANOVA
Social Extroversion
Sum of Squares df Mean Square F Sig.
Between Groups 24.111 2 12.056 3.511 .056
Within Groups 51.500 15 3.433
Total 75.611 17
Multiple Comparisons
Dependent Variable: Social Extroversion
Tukey HSD
(I) Hair Color (J) Hair Color Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval
Lower Bound Upper Bound
Blond
Brunet 1.500 1.070 .365 -1.28 4.28
Redhead 2.833* 1.070 .045 .05 5.61
Brunet
Blond -1.500 1.070 .365 -4.28 1.28
Redhead 1.333 1.070 .446 -1.45 4.11
Redhead Blond -2.833* 1.070 .045 -5.61 -.05

26
Brunet -1.333 1.070 .446 -4.11 1.45
*. The mean difference is significant at the 0.05 level.
Homogeneous Subsets
Social Extroversion
Tukey HSD
Hair Color N Subset for alpha = 0.05
1 2
Redhead 6 2.33
Brunet 6 3.67 3.67
Blond 6 5.17
Sig. .446 .365
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 6.000.
a) F ratio:
The F ratio with the above test is 3.5 which is more than the significant value. This means null
hypothesis is accepted and there is no relationship between hair colour and extrovertedness.
b) Sum of square:
From the above table it can be analysed that the sum of square is 12 between group and 3.4
within the group. Thus, with regard to this analysis it would be interpreted that there is an
existence of higher variation between the group while making it compared it with the within the
group.
c) Mean:

27
The mean value of redhead is 3 which means that on an average there are 3 responded which
have a redhead.
d) P value:
The p value of the above data is 0.056 which is higher than 0.05. This means that the p value is
higher than alpha value i.e. 0.05 which means that null hypothesis is accepted and evaluates that
there is no relationship between hair colour and extrovertedness.
2. Relationship between hair colour and extrovertedness
From the above test and annova it would be right to stat that there is no existence of relationship
between the hair colour and extrovertedness. As the p value of the test is higher than alpha value
of 0.05 which shows the acceptance of null hypothesis i.e. shows that there is no significant
relationship between the variables i.e. hair colour and extrovertedness.
3. Boxplot
Case Processing Summary
Hair Color Cases
Valid Missing Total
N Percent N Percent N Percent
Social Extroversion
Blond 6 100.0% 0 0.0% 6 100.0%
Brunet 6 100.0% 0 0.0% 6 100.0%
Redhead 6 100.0% 0 0.0% 6 100.0%
Social Extroversion

28
Interpretation:
From the above box plot it would be interpreted that the median of blond hair color is
high in comparison with brunet and redhead. In the same way it is also interpreted that majority
of responded with regard to blond color are of different social extroversion while making it
compared it with the redhead.
WEEK-6
Linear regression
Descriptive Statistics
Mean Std. Deviation N

29
Number of times hitting a bobo
doll 4.90 8.048 10
Number of times hitting a peer 11.60 20.095 10
Correlations
Number of times
hitting a bobo doll
Number of times
hitting a peer
Pearson Correlation
Number of times hitting a bobo
doll 1.000 .930
Number of times hitting a peer .930 1.000
Sig. (1-tailed)
Number of times hitting a bobo
doll . .000
Number of times hitting a peer .000 .
N
Number of times hitting a bobo
doll 10 10
Number of times hitting a peer 10 10
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 Number of times
hitting a peerb . Enter
a. Dependent Variable: Number of times hitting a bobo doll
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .930a .865 .848 3.138
a. Predictors: (Constant), Number of times hitting a peer
ANOVAa
Model Sum of Squares df Mean Square F Sig.

30
1
Regression 504.136 1 504.136 51.205 .000b
Residual 78.764 8 9.845
Total 582.900 9
a. Dependent Variable: Number of times hitting a bobo doll
b. Predictors: (Constant), Number of times hitting a peer
Coefficientsa
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig. 95.0% Confidence Interval
for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constant) .580 1.161 .499 .631 -2.099 3.258
Number of times
hitting a peer .372 .052 .930 7.156 .000 .252 .492
a. Dependent Variable: Number of times hitting a bobo doll
a) The slope that is associated with the predictor is 1 because the regression line is diagonal from
lower left to the direction of upper right. This means the slope is 1.
b) Additive constant for the regression equation is 0.580.
c) The mean number is 11.60 when they struck the classmate i.e. peers.
d) The correlation between the number of time they hit the bobo doll and number of time they
struck the peer is 0.93 i.e. 93%. This shows that they are highly correlated with each other which
means as the change in one variable would occur the same changes would also be incurred in
other variable.
e) The standard error of estimate is 3.138.

31
Relationship between multiple R and bivariate correlation
While making an analysis of the relationship between multiple R and bivariate correlation
it is analyzed that the value of multiple R and bivariate correlation is 0.93 i.e. 93%. This shows
that that there is an existence of high relationship between multiple R and bivariate correlation
i.e. between hitting of bobo doll and struck the peer.
Scatterplot
Interpretation:
As per the above scatter graph and it would be interpreted that there is an existence of
positive relationship between the X and Y variable i.e. hitting of bobo doll and hitting of pears.

32
As the graph is sloping upwards from left to right which means that with a rise in the hitting of
bobo doll the hitting up of peers would also increases and raises.
Result section
From the analysis of the above graph and table it can be interpreted that there is an
existence of high correlation and relationship between the variable i.e. hitting of bobo doll and
struck of peer. Likewise, the upward move of regression graph also states the existence of strong
relationship between the variables. Thus the result would be an existence of direct and high
relationship.
WEEK-7
Regression
1. Multiple regression
Test performance in statistics course would be considered as criterion variable.
Yes, the predictor variable must be divided into sets and so that the relationship would be more
critically analyzed between the criterion variable. The predictor sets would include the high
school test score and grade point average. Along with making a division of the predictor into sets
it is important and essential that they need to be ordered so that along with determining
relationship between the statistics performance and predictor the appropriate trend in association
with the research would be able to get analyzed and identified.
2. Regression equation
Test performance = a+ b High school test score and grade point average
or
Stateexam = a+ b mathtest, engtest, eng_gpa, math_gpa, othr_gpa

33
3. and 4. Performance of the regression analysis
Descriptive Statistics
Mean Std. Deviation N
Average percentage correct on
statistics exams 60.11 19.788 100
Math aptitude test score 460.60 77.366 100
English aptitude test score 478.20 71.653 100
High school English GPA 2.8183 .27633 100
High school math GPA 2.7763 .30234 100
GPA in other high school
classes 3.0236 .22220 100
Correlations
Average
percentage
correct on
statistics
exams
Math
aptitude
test score
English
aptitude
test score
High
school
English
GPA
High
school
math GPA
GPA in
other high
school
classes
Pearson
Correlation
Average percentage
correct on statistics
exams
1.000 .484 .202 .062 .229 .024
Math aptitude test
score .484 1.000 .121 .069 .369 .164
English aptitude test
score .202 .121 1.000 .414 .171 .244
High school English
GPA .062 .069 .414 1.000 .374 .253
High school math
GPA .229 .369 .171 .374 1.000 .319
GPA in other high
school classes .024 .164 .244 .253 .319 1.000
Sig. (1-tailed) Average percentage
correct on statistics
exams
. .000 .022 .270 .011 .407
Math aptitude test
score
.000 . .115 .247 .000 .051

34
English aptitude test
score .022 .115 . .000 .044 .007
High school English
GPA .270 .247 .000 . .000 .006
High school math
GPA .011 .000 .044 .000 . .001
GPA in other high
school classes .407 .051 .007 .006 .001 .
N
Average percentage
correct on statistics
exams
100 100 100 100 100 100
Math aptitude test
score 100 100 100 100 100 100
English aptitude test
score 100 100 100 100 100 100
High school English
GPA 100 100 100 100 100 100
High school math
GPA 100 100 100 100 100 100
GPA in other high
school classes 100 100 100 100 100 100
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1
GPA in other high
school classes,
Math aptitude test
score, High
school English
GPA, English
aptitude test
score, High
school math GPAb
. Enter
a. Dependent Variable: Average percentage correct on
statistics exams
b. All requested variables entered.

35
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .519a .269 .230 17.361
a. Predictors: (Constant), GPA in other high school classes, Math aptitude
test score, High school English GPA, English aptitude test score, High school
math GPA
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 10432.432 5 2086.486 6.922 .000b
Residual 28333.358 94 301.419
Total 38765.790 99
a. Dependent Variable: Average percentage correct on statistics exams
b. Predictors: (Constant), GPA in other high school classes, Math aptitude test score, High school English
GPA, English aptitude test score, High school math GPA
Coefficientsa
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig. 95.0% Confidence Interval
for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constant) 6.745 27.691 .244 .808 -48.236 61.726
Math aptitude test
score .116 .025 .453 4.726 .000 .067 .164
English aptitude test
score .049 .027 .179 1.816 .073 -.005 .103
High school English
GPA -3.365 7.446 -.047 -.452 .652 -18.150 11.420
High school math
GPA 5.478 6.865 .084 .798 .427 -8.153 19.109
GPA in other high
school classes -9.702 8.500 -.109 -1.141 .257 -26.578 7.175
a. Dependent Variable: Average percentage correct on statistics exams

36
As per the above table and analysis it would be right to interpret that there is an existence
of direct and strong relationship between the predictors and criterion. This is because, as the
regression analysis it is observed that that the significance value is 0.00 which shows the strong
relationship between the variables. Likewise, it is also observed that the R value is 0.51 which
shows that there is an existence of moderate relationship i.e. with the change in one variable
there would be a change of 51%.
Thus, as per the able table and analysis it would be interpreted that there would be direct
impact between the high school test score over and above the high school grade point averages
and vice versa. Since the regression equation shows the significance value of 0.00 which means
that there is an existence of direct and strong relationship between the variables. Also as the R
value is showing a percentage of 51 which means that with the change in one variable there
would be an occurrence of change in other variable too. This means there is an existence of the
direct or moderate association and relationship between the variables.
Factor analysis
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 6.031 50.261 50.261 6.031 50.261 50.261
2 .941 7.841 58.102
3 .745 6.205 64.307
4 .693 5.774 70.081
5 .653 5.443 75.524
6 .598 4.986 80.510
7 .504 4.197 84.707
8 .472 3.930 88.638
9 .423 3.522 92.160

37
10 .363 3.027 95.187
11 .295 2.462 97.649
12 .282 2.351 100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
I don’t need to have a career
to be fulfilled. .759
I would rather have a career
than a family. -.757

38
To me, marriage and family
are as important as having a
career.
.752
Planning for and succeeding
in a career is one of my
primary goals.
-.737
Having a career would
interfere with my family
responsibilities.
.726
I prefer to pursue my career
without the distractions of
marriage, children, or a
household.
-.714
I would feel unfulfilled
without a career. -.696
I consider myself to be very
career-minded. -.692
I could be happy without a
career. .692
I consider marriage and
having a family to be more
important than a career.
.671
I would leave my career to
raise my children. .654
I often think about what type
of job I’ll have 10 years from
now.
-.644
Extraction Method: Principal Component
Analysis.
a. 1 components extracted.
1. Factors underlying in SCVS on the basis of scree plot
As per the analysis of the scree plot it would be observed that there is only one factor that
would lead to have an impact on SCVS. This factor holds the value of 6. As it is being observed
that in the scree plot there is a presentation of all the values and factors but among those factors
there is only one factor that would be considered to be underlying on the SCVS.

39
2. Factor based on eigenvalue greater than 1 criterion
However as per the above analysis of the eigenvalues and above table it would be right to
interpret that there is only one factor with a eigenvalues of 6.031 that is underlying and
impacting the SCVS. In addition of this, it is also being analysed that this factor along with
showing the highest values also fulfil and justify the greater than 1 criterion because the value
that is being observed in the eigenvalues is 6.031 which is greater than 1.
3. Result section
From the above analysis it would be right to conclude that among the various factors there
is only one factor that would underlie the SCVS. Likewise, it would also be interpreted that the
consideration of marriage and family while comparison with the career is showing the highest
value. This means that the consideration and focus towards the family and marriage among the
women are higher while making compared it with the career. Also, as per the scaling if the value
would be higher than 3 then it falls under the category of agree and while making an above
analysis it would be right to state and interpret that as the value is higher than 1 i.e 6 then it
shows that the strong agree towards the SCVS i.e. relationship.

REFERENCES
Books and Journals
Hair Jr, and et.al., 2021. A primer on partial least squares structural equation modeling (PLS-
SEM). Sage publications.
Online references
Green, S.B., Salkind, N.J., 2017. Using SPSS for Windows and Macintosh. [Online]. Available
through <https://bookshelf.vitalsource.com/reader/books/9780134416410/pageid/3>
1