SPSS Assessment: Program Evaluation, Gender and Activity Association, Mood and Stress Relationship, Impact of Mood, Motivation, and Stress on Cognitive Performance

This SPSS assessment includes program evaluation, gender and activity association, mood and stress relationship, and the impact of mood, motivation, and stress on cognitive performance. It provides descriptive analysis, findings, and conclusions. The assessment covers various subjects and courses related to cognitive learning and performance.

SPSS assessment
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Table of Contents
REFERENCES..............................................................................................................................11
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1. Does program work? Does it matter aspect of cognition are assessed?
Description
Here, for this descriptive analysis is used to find out whether program worked or not.
Also, for cognition aspects used in this relate to finding out mean, mode, SD etc. this enabled
in evaluating outcomes of program in effective way.
Findings
It is found that mean of training is 1.49 that states that program is being completed by
many people. Besides that, SD is .50 which means that there is no high variation in it (Barbieri
& et.al., 2020)
Descriptive analysis
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Training 235 1.00 2.00 1.4936 .50103
Gender 235 .00 1.00 .4936 .50103
Activity 235 1.00000000 3.00000000 1.8468085106 .77483140262
WM 235 1.00 8.00 5.5660 1.48745
Attention 235 1.00 8.00 5.9362 1.51353
Vocab 235 2.00 8.00 5.1660 1.07919
Stress 235 4.00 16.00 11.5830 2.53786
Mood 235 4.00 16.00 11.5660 2.81611
Motivation 235 .00 129.00 34.7745 22.37511
Valid N (listwise) 235
Conclusion
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Thus, it is stated that training program worked in efficient way and there was no such
impact of which cognition was assessed. It enabled in providing training to elderly through
various activity.
2. Is there association between gender and preferred activity of cognition are assessed?
Description of process
In order to analyse the data chi square test was used. It is a statistical method to test the
hypothesis. Basically, it is used to find out difference between observed and expected frequency
in one or more category. Similarly, in this as well gender and activity were having more than 1
variable that is why it was used.
Findings
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Activity * Gender 235 100.0% 0 0.0% 235 100.0%
Activity * Gender Crosstabulation
Count
Gender Total
Male Female
Activity
1.00000000 39 52 91
2.00000000 58 31 89
3.00000000 22 33 55
Total 119 116 235
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Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 12.212a 2 .002
Likelihood Ratio 12.362 2 .002
Linear-by-Linear Association .043 1 .836
N of Valid Cases 235
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 27.15.
Interpretation- it is analysed from table that P value obtained from it is P= .836 that is more
than P= 0.05. therefore, null hypothesis is accepted. It means that there is association between
gender and preferred activity in elderly. The preferred activity depends on gender.
Findings- it is evaluated that activity needs to be assessed on basis of gender. So that it is easy
to improve cognitive performance. Besides, learning ability also depends on activity performed.
This is because cognitive learning differs in both. Also, on activity it depends on what type of
activity is included in training (Hao & et.al., 2020)
Descriptive analysis
Descriptive Statistics
Mean Std. Deviation N
Gender .4936 .50103 235
Activity 1.8468085106 .77483140262 235
Conclusion
It is concluded that the cell count is not less than 5. However, there is association
between gender and preferred activity. This means that use of preferred activity depends on
gender. In training program there are 3 activity that is cross word, soduko and socialize. So, these
are decided on basis of preference of male and female.
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3. Is there association between mood and stress?
Description of process
Here, regression analysis process was used. Regression is used to examine relationship
between dependent and independent variable. Thus, here as well to find out impact of stress on
mood regression analysis was used. Thus, mood was taken as dependent variable and stress was
independent variable.
Findings
Correlations
Mood Stress
Pearson Correlation Mood 1.000 -.399
Stress -.399 1.000
Sig. (1-tailed) Mood . .000
Stress .000 .
N Mood 235 235
Stress 235 235
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .399a .159 .155 2.58831 .159 44.001 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 233a .000 1.240
a. Predictors: (Constant), Stress
b. Dependent Variable: Mood
ANOVAa
Model Sum of Squares df Mean Square F Sig.
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Regression 294.779 1 294.779 44.001 .000b
Residual 1560.949 233 6.699
Total 1855.728 234
a. Dependent Variable: Mood
b. Predictors: (Constant), Stress
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 16.689 .790 21.111 .000
Stress -.442 .067 -.399 -6.633 .000
a. Dependent Variable: Mood
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 9.6125 14.9196 11.5660 1.12238 235
Residual -7.38153 6.38749 .00000 2.58277 235
Std. Predicted Value -1.740 2.988 .000 1.000 235
Std. Residual -2.852 2.468 .000 .998 235
a. Dependent Variable: Mood
Interpretation- by analyzing table it is states that significant value obtained P= .000 that is less
than P = 0.05. thus, here, null hypothesis is rejected. It means that there is no association
between stress on mood. Furthermore, there are many reasons due to which mood is affected.
Findings- the mood of a person reflect about its stimuli and how it behave. As stress is related
to mental health but it does not affect mood. It is because there are other factors as well that
affect mood. (Rogaten, & et.al., 2019)
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Descriptive analysis
Descriptive Statistics
Mean Std. Deviation N
Mood 11.5660 2.81611 235
Stress 11.5830 2.53786 235
Conclusion
It is concluded that there is relationship between stress and mood. However. When rude
mood can led to more stress among people. Also, stress can occur due to various reasons such as
personal, professional, etc. high stress will not affect mood. A positive mood level will not led
to low stress level. Mood is not affected by stress and it depends on certain other factors as
well.
4. Do mood, motivation and stress levels contribute to cognitive performance
Description of process
Here, regression analysis process was used. Regression is used to examine relationship
between dependent and independent variable. Thus, here as well to find out impact of stress on
mood regression analysis was used. Thus, mood was taken as dependent variable and stress was
independent variable.
Findings
Correlations
WM Stress Mood Motivation
Pearson Correlation
WM 1.000 -.352 .494 .001
Stress -.352 1.000 -.399 .033
Mood .494 -.399 1.000 .146
Motivation .001 .033 .146 1.000
Sig. (1-tailed)
WM . .000 .000 .497
Stress .000 . .000 .305
Mood .000 .000 . .013
Motivation .497 .305 .013 .
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N
WM 235 235 235 235
Stress 235 235 235 235
Mood 235 235 235 235
Motivation 235 235 235 235
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .525a .275 .266 1.27458 .275 29.229 3
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 231a .000 2.035
a. Predictors: (Constant), Motivation, Stress, Mood
b. Dependent Variable: WM
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 142.454 3 47.485 29.229 .000b
Residual 375.274 231 1.625
Total 517.728 234
a. Dependent Variable: WM
b. Predictors: (Constant), Motivation, Stress, Mood
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
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(Constant) 4.273 .665 6.427 .000
Stress -.104 .036 -.178 -2.899 .004
Mood .228 .033 .431 6.947 .000
Motivation -.004 .004 -.057 -.993 .322
a. Dependent Variable: WM
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.4916 7.3838 5.5660 .78024 235
Residual -4.00926 3.90102 .00000 1.26639 235
Std. Predicted Value -2.659 2.330 .000 1.000 235
Std. Residual -3.146 3.061 .000 .994 235
a. Dependent Variable: WM
Interpretation- by interpreting the table it is states that significant value obtained P= .000 that
is less than P = 0.05. thus, here, null hypothesis is rejected. It means that there is no association
between mood, motivation and stress levels in contribution of cognitive performance. It is
because cognitive performance depends on learning, environment, activity, etc.
Findings- it is stated that motivation and mood levels does not impact cognitive performance.
As cognitive is related to internal learning so mood and stress level do not contribute in it. this is
because even if mood or stress is low then also cognitive learning is not affected (Siburian,
Corebima & SAPTASARI, 2019).
Descriptive analysis
Descriptive Statistics
Mean Std. Deviation N
WM 5.5660 1.48745 235
Stress 11.5830 2.53786 235
Mood 11.5660 2.81611 235
Motivation 34.7745 22.37511 235
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Conclusion
Thus, it is summarized that mood, motivation and stress does not led to attention, vocab
or WM.. this is because in elderly training is given to enhance their skills. So, mood and stress
level does not impact on working memory. Similarly, motivation also does not contribute in it.
apart from that it is found that stress level also do not contribute in cognitive performance. It is
because there has to be change in way of activity which will enable in attention. Likewise,
motivation does not contribute in it and improve vocab. There needs to have proper way of doing
activity by elderly.
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REFERENCES
Books and journals
Barbieri, C.A., & et.al., (2020). Improving fraction understanding in sixth graders with
mathematics difficulties: Effects of a number line approach combined with cognitive
learning strategies. Journal of Educational Psychology, 112(3), p.628.
Hao, Y & et.al., (2020). Learning for smart edge: cognitive learning-based computation
offloading. Mobile Networks and Applications, 25(3), 1016-1022.
Rogaten, J. & et.al., (2019). Reviewing affective, behavioural and cognitive learning gains in
higher education. Assessment & Evaluation in Higher Education, 44(3), pp.321-337.
Siburian, J., Corebima, A.D. & SAPTASARI, M., (2019). The correlation between critical and
creative thinking skills on cognitive learning results. Eurasian Journal of Educational
Research, 19(81), pp.99-114.
Barbieri & et.al., (2020
Hao & et.al., (2020
Rogaten, & et.al., (2019
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