SPSS project: Correlation & Prediction

This SPSS project explores the correlation and prediction between the number of hours spent on social media and college GPA. Descriptive statistics, correlation test, and regression analysis are conducted to support the research hypotheses.

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SPSS project : Correlation & Prediction

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SPSS project : Correlation & Prediction
PART A
Null & research hypotheses1
Null hypothesis:
There is no significant relationship between the number of hours spent by students on
social media and their college GPA.
Research hypotheses:
There is a significant relationship between the number of hours spent by students on
social media and their college GPA.
Appropriate measure of central tendency
Statistics
Number of hours spent on social
media
N Valid 100
Missing 0
Mean 3.83
Median 4.00
Mode 4
Sum 383
Statistics
College GPA
N Valid 100
Missing 0
Mean 3.009
Median 3.000
Mode 3.0
Sum 300.9

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Additional statistics
One-Sample Statistics
N M
ean
Std.
Deviation
Std.
Error Mean
Number of hours
spent on social media
1
00
3.
83 1.477 .148
College GPA 1
00
3.
009 .5891 .0589
One-Sample Test
Test Value = 0

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t df Sig.
(2-tailed)
Mean
Difference
95% Confidence
Interval of the Difference
Lower Upper
Number
of hours spent on
social media
25.923 99 .000 3.830 3.54 4.12
College
GPA 51.074 99 .000 3.0090 2.892 3.126
Effect size using Cohen’s D = Mean difference / Standard deviation
Number of hours spent on social media = 3.830/1.477 = 2.59
College GPA = 3.0090/.5891 = 5.10
If the calculated Cohen’s D is more than .80 then the effect size is large which lowest
possibility of errors.
Correlation test
Correlations
Number of hours spent
on social media
College
GPA
Number of hours spent
on social media
Pearson
Correlation 1 -.809**
Sig. (2-tailed) .000
N 100 100
College GPA
Pearson
Correlation -.809** 1
Sig. (2-tailed) .000
N 100 100
**. Correlation is significant at the 0.01 level (2-tailed).
The reason behind selecting Bivariate correlation test is to compute whether the
two variables (scale) has positive or negative relationship with each other. Both the variables i.e.,
Number of hours spend on social media and GPA are scalable variables for which this test is
most appropriate.

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Procedure & results
Procedure:
The given SPSS data has two variables which is taken from 100 hypothetical
students. These variables are the Number of hours spent on social media by students and their
GPA. A research hypothesis is developed in this report which state that there is a significant
relationship between these two variables. To test this hypothesis, correlation test has been
conducted which is also supported by their effect size and descriptive statistics.
Results:
From the correlation test conducted, it has been seen that significance level is
computed from the software of SPSS as -.809. It is considered that if the significance is less than
-0.1 then there is negative relationship between these variables. In simpler worlds, if the number
of hours spend on social media increases, the GPA of those students will decrease and vice versa.
This implies that research hypothesis is true and there is a relationship between these two
variables.
PART B
Null & research hypotheses
Null hypothesis:
Number of hours spent on social media by students cannot help in predicting their GPA.
Research hypotheses:
Number of hours spent on social media by students can help in predicting their GPA
Appropriate measure of central tendency
Statistics
Number of hours spent on social
media
N Valid 100
Missing 0

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Mean 3.83
Median 4.00
Mode 4
Sum 383
Statistics
College GPA
N Valid 100
Missing 0
Mean 3.009
Median 3.000
Mode 3.0
Sum 300.9

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Additional statistics
One-Sample Statistics
N M
ean
Std.
Deviation
Std.
Error Mean
Number of hours
spent on social media
1
00
3.
83 1.477 .148
College GPA 1
00
3.
009 .5891 .0589
One-Sample Test
Test Value = 0
t df Sig.
(2-tailed)
Mean
Difference
95% Confidence
Interval of the Difference
Lower Upper
Number
of hours spent on
social media
25.923 99 .000 3.830 3.54 4.12
College
GPA 51.074 99 .000 3.0090 2.892 3.126
Regression
Coefficientsa
Model Unstandardized
Coefficients
Standa
rdized
Coefficients
t Si
g.
B Std.
Error
Beta
1
(Constant) 4.245 .097 4
3.742
.0
00
Number of hours
spent on social media -.323 .024 -.809 -
13.643
.0
00
a. Dependent Variable: College GPA

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The rationale behind conducting this regression analysis is to analyses whether prediction
using one variable of another variable can be done or not. In other words, this analysis is
conducted to determine whether GPA can be predicted by looking over number of hours spend
on social media.
Procedure & results
Procedure:
A research hypothesis is developed in this report stating GPA of a student can be
predicted using number of hours spend by that student at social media. For this a regression
analysis is conducted which is supported by effect size and scatter plot with regression equation.
Results:
From the performed analysis, it has been analyzed that prediction can be done
using the prediction equation of “y = mx + c”. In this case
y = GPA
c = constant
So, in the case where number of hours spend on social media is 8 then its GPA score will
be:
GPA score = -.323 * 8 + 4.245 = 1.661
By the above computation, it can be said that prediction can be done. So, the research
hypothesis is true that GPA score of students can be predicted using the information of number of
hours spend by them on social media.