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Running head: HAPPINESS ENGAGEMENT
1
HAPPINESS ENGAGEMENT
2
Happiness Engagement
Nasser Y Miranda
University of Phoenix
August 26th, 2018
According to (Berk and Carey 2009), to perform correlation in
excel we use a formula CORREL(array1,array2).
There was no relationship between the independent variables
age and relationship with coworkers. The correlation coefficient
0.00 indicates no relationship (Tutorialspoint 2016).
There was a moderate positive correlation (0.61) (Tutorialspoint
2016)between independent variable ‘relationship with direct
supervisor’ and dependent variable ‘workplace happiness
rating’. This implies that as relationship with direct supervisor
increased, workplace happiness increased as well. As
relationship with supervisor increases, workplace happiness
rating increased as well.
There was a weak positive correlation (0.03) (Tutorialspoint
2016)between the independent variable age and dependent
variable overall combined rating (combined workplace
happiness rating and workplace engagement rating). This mean
that as age increase, rating at the workplace increases too but at
a very low rate.
There was a moderate correlation (0.60) (Tutorialspoint 2016)
between both dependent variables workplace happiness rating
and engagement happiness rating. This implies that both
workplace happiness rating and engagement happiness rating
increase together.
References
Berk, K. N. and P. M. Carey (2009). Data analysis with
Microsoft excel: updated for office 2007, Cengage Learning.
Tutorialspoint (2016). Data Analysis with Excel.
Running Head: Statistics Project Part 3
1
Statistics Project Part 3
3
Statistics Project Part 3
Nasser Y Miranda
University of Phoenix
August 18th, 2018
Anova: Single Factor
Anova: Single Factor
SUMMARY
SUMMARY
Groups
Groups
Count
Sum
Average
Variance
Relationship
Supervisor
50
125
2.5
1.030612
Happiness
Happiness
50
370
7.4
2
ANOVA
ANOVA
Source of Variation
Source of Variation
SS
df
MS
F
P-value
F crit
Between Groups
Between Groups
600.25
1
600.25
396.1246
3.34E-36
3.938111
Within Groups
Within Groups
148.5
98
1.515306
Total
Total
748.75
99
Tukey's HSD
Difference
n (Relationship)
n (Happiness)
SE
q statistic
Relationship
4.9
50
50
1
4.9
Happiness
4.9
50
50
1
4.9
The Single factor one-way ANOVA is basically used to test
whether the population means of observations of more than one
treatment effects are equal (Brady, 2015). Since F > F crit the
null hypothesis has to be rejected in this case. In this case,
396.1246 > 3.938111 thus we reject the null hypothesis in the
sense that means for Happiness and Relationship are not equal.
Post Hoc testing is useful in identifying the differences that are
significant (Kuznetsova, Brockhoff, & Christensen, 2017). As
opposed to the t-test, the ANOVA is useful in comparing means
of more than two groups to test the hypothesis (Maurya, 2015).
Moreover, since ANOVA goes to the extent of showing the
statistical significance between the groups, it also reduces type
I error (Keith, 2014).
References
Brady, S. M., Burow, M., Busch, W., Carlborg, Ö., Denby, K.
J., Glazebrook, J., ... & Springer, N. M. (2015). Reassess the t
test: interact with all your data via ANOVA. The Plant Cell,
tpc-15.
Keith, T. Z. (2014). Multiple regression and beyond: An
introduction to multiple regression and structural equation
modeling. Routledge.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B.
(2017). lmerTest package: tests in linear mixed effects
models. Journal of Statistical Software, 82(13).
Maurya, V. N., Jaggi, C. K., Vashist, S., Ogubazghi, G.,
Varshney, D. K., Maurya, A. K., & Arora, D. K. (2015). Impact
of some significant factors for intern’s job satisfaction and
performance using t-test and ANOVA method. American
Journal of Biological and Environmental Statistics, Science
Publishing Group, USA, 1(1), 19-26.
Running Head: Statistics Project Part 2
1
Statistics Project Part 2
3
Statistics Project Part 2
Hypothesis Testing and Two-Group t Tests
Nasser Y Miranda
University of Phoenix
August 4th, 2018
Hypothesis for the Happiness and Engagement Dataset
Hypothesis: Are individuals who have a great relationship with
their supervisors likely to be completely happy at their
workplace?
Null hypothesis (H0): Individuals having a great relationship
with their supervisors are likely to be completely happy at their
workplace.
Alternative hypothesis (H1): Individuals having a great
relationship with their supervisors are not likely to be
completely happy at their workplace.
Justification from the Statistical Analysis
In this case, an independent samples t-test would be of great
importance since it compares two mean showing their
differences. Moreover, this statistical analysis shows the
measure of significance these differences have. The findings
enable one to be in a position of knowing whether or not these
differences could have resulted from chance (Anderson, 2011).
Independent samples T-Test
t-Test: Paired Two Sample for Means
Relationship
Happiness
Mean
2.5
7.4
Variance
1.030612245
2
Observations
50
50
Pearson Correlation
0.611237131
Hypothesized Mean Difference
0
df
49
t Stat
-30.67885265
P(T<=t) one-tail
6.0097E-34
t Critical one-tail
1.676550893
P(T<=t) two-tail
1.20194E-33
t Critical two-tail
2.009575237
Interpretation for the results
For a two-tailed test, the null hypothesis is rejected when t Stat
> t Critical (D'Agostino, 2017). In this case -30.67885265 <
2.009575237 indicating that we should accept the null
hypothesis. This shows that it is correct to say that Individuals
having a great relationship with their supervisors are likely to
be completely happy in their workplace. In cases like this,
where there is a negative t-value, there is a reversal in the
effect’s directionality. However, this does not have bearing on
the significance of the group’s differences (Macisaac et al.,
2015). From the dataset, it is clearly seen that the individuals
who have a great relationship with their supervisors have a high
score of happiness. Organizations should consider embracing
and emphasizing teamwork which not only keeps the employees
happy but also give them a high retention rate.
Gender
Age
Supervisor
Telecommute
Coworkers
Happiness
Engagement
Overall Rating
2
32
4
1
3
9
10
19
2
29
4
1
2
8
9
17
1
26
4
1
2
8
8
16
1
39
4
1
3
9
8
17
2
35
4
1
2
8
8
16
1
32
4
2
2
8
8
16
1
40
4
1
2
9
8
17
2
27
4
1
1
8
7
15
2
34
4
1
3
9
9
18
2
33
3
1
2
7
8
15
2
36
3
1
2
8
7
15
1
28
3
1
3
9
8
17
1
34
3
2
3
8
8
16
2
27
3
1
2
7
9
16
2
37
3
1
2
9
7
16
1
29
3
2
2
7
8
15
1
29
3
1
2
9
7
16
2
36
3
1
3
9
9
18
1
30
3
1
2
7
7
14
1
27
3
2
3
9
9
18
2
38
3
1
2
8
8
16
2
28
3
1
2
9
9
18
1
35
3
1
2
7
7
14
2
29
3
1
2
8
8
16
1
30
3
1
2
9
9
18
1
30
3
1
2
7
8
15
1
25
2
1
2
5
8
13
2
32
2
1
2
6
8
14
2
37
2
1
1
7
9
16
1
30
2
1
1
6
7
13
2
30
2
1
2
8
7
15
2
31
2
1
3
8
8
16
1
34
2
1
1
5
6
11
1
29
2
2
2
7
8
15
1
35
2
2
3
9
9
18
2
37
2
1
2
8
9
17
1
27
2
1
1
4
6
10
2
25
2
2
2
9
8
17
2
29
2
1
2
8
8
16
2
35
2
2
1
6
6
12
1
29
1
1
2
7
8
15
1
39
1
1
1
4
5
8
1
27
1
1
1
5
4
9
2
31
1
1
1
7
5
12
2
38
1
1
1
6
8
14
1
40
1
1
1
6
8
14
2
38
1
1
2
6
8
14
1
34
1
1
2
8
7
15
2
34
1
2
1
7
5
12
2
25
1
1
1
5
6
11
References
Anderson, T. W. (2011). The statistical analysis of time
series(Vol. 19). John Wiley & Sons.
D'Agostino, R. (2017). Goodness-of-fit-techniques. Routledge.
Macisaac, R. L., Khatri, P., Bendszus, M., Bracard, S.,
Broderick, J., Campbell, B., ... & Diener, H. C. (2015). A
collaborative sequential meta-analysis of individual patient data
from randomized trials of endovascular therapy and tPA vs. tPA
alone for acute ischemic stroke: T h R omb E ctomy A nd t PA
(TREAT) analysis: statistical analysis plan for a sequential
meta-analysis performed within the VISTA-Endovascular
collaboration. International Journal of Stroke, 10(SA100), 136-
144.
Running Head: Descriptive Statistics 1
Descriptive Statistics 2
Statistics Project, Part 1:Opening Data in Microsoft®
Excel®and Running Descriptive Statistics
Nasser Y Miranda
University of Phoenix
August 4th, 2018
Gender
The dataset consists of 50 individuals where 22 are males
and 28 females. Below is the pie chart graph that graphically
represent the gender composition. In terms of percentage, the
males 44% are whereas the females are 56%. This is an
indication that the data sample used was relatively balanced in
terms of gender. Atkinson-Bonasio (2017) asserts that in
research, fostering diversity achieved by gender equality
assures innovation. She further states that bias and gender
disparity should be examined so as to ensure a data-informed
approach especially to implementing policies and interventions
related to gender inequality.
Variable
Mean
Median
Mode
Gender
2
The above table shows that the mode of the gender is 2. In this
case, it implies that the females are frequently occurring in the
data when compared to the males which is proved clearly by
their percentage.
Age
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
Age
32.02
31.5
29
4.340083701
18.83633
15
The individuals used in this case age has an average of 32
years where those of 29 years of age are the frequently
occurring. The range of the populations is 15 years which shows
the difference of years between the youngest individual and the
oldest individual in the data set. The age has a high deviation
showing the high variance of the data from the mean which is
confirmed by the high variance of 18.83633.
Relationship with Direct Supervisor
This variable is data is further labelled into 4 categories
namely: 1 = negative relationship, 2 = neutral relationship, 3 =
positive relationship, 4 = great relationship.
Variable
Mean
Median
Mode
Standard Deviation
Variance
Supervisor
2.5
3
3
1.015190743
1.030612
The above table indicates that the average relationship for
all the 50 individuals with their direct supervisor is 2.5 which is
between neutral relationship and positive relationship. Most of
the individuals have a positive relationship with their direct
supervisor. The mode of 3 shows that majority of the
individuals have a positive relationship with their direct
supervisor. The relationship categories does not exhibit great
variance from what is expected thus the low case of 11
individuals out of 50 who have a negative relationship with
their direct supervisor.
Telecommute Schedule
The telecommute schedule variable is categorized as follows:
1= no ability to telecommute, 2 = able to telecommute at least 2
days per week.
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
Telecommute
1.18
1
1
0.388087934
0.150612
The table above shows that majority of the individuals have no
ability to telecommute as opposed to those who those who have
the ability to do so at least 2 day every week.
Telecommute Percentage
No ability
82
Able to
18
82% have no ability to telecommute while on the other hand
only 18% are able to. This is a clear indication that majority of
the individuals have no access to Internet access, email and
telephone from their homes. It is therefore not necessary to
consider giving them tasks that will need them to be
telecommunicating since most of them will not be able to
deliver. Jafroodi, Salajeghe & Kiani (2015) in their paper found
out that telecommuting is one of the factors that lead to
increased productivity and employee satisfaction scores among
others is telecommuting.
Relationship with Coworkers
This variable is categorized into the following: 1 = negative
relationship, 2 = no relationship, 3 = positive relationship
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
Coworkers
1.92
2
2
0.665168384
0.442449
The above table shows the relationship these individuals in the
dataset had with their fellow coworkers. From the table, the
average relationship is closer to being neutral in the sense that
most people have no relationship with their coworkers. This is
clearly seen in the mode where majority of these individuals
who frequently occur in the dataset, 28 to be precise, have no
relationship with their coworkers whatsoever. Only 9 out of the
50 have a positive relationship with their coworkers. This calls
for the organization to strive and make it their goal to increase
the number of individuals who have a positive relationship with
their workers. Positive relationships between colleagues are
very beneficial to both the individuals and organization in terms
of improved teamwork, increased productivity, high rates of
employee retention, and so on (Dutton & Ragins, 2017).
Workplace Happiness Rating
This variable is categorized as follows: Scale 0-10, 0 = no
happiness, 10 = completely happy
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
Happiness
7.4
8
8
1.414213562
2
5
From the above table, the average rate of happiness is
relatively high showing that most of the individuals are happy
in their workplace. The table further indicates that the
happiness score frequently occurring is 8 out of 10 which
suggests that most of the people are happy. However, it seems
like the company has to go an extra mile since there more room
for improvement. There is a need to identify the reason why
there are still other who are not that much happy in order to
know which areas the company needs to work on. One of the
most important things companies are striving to have is keeping
retaining employees while at the same time keeping them happy
and productive (Hsiao, 2015). Loyal employees perform better,
meet their deadlines, and most importantly are very supportive
and open to new ideas and changes which means a lot to
companies.
Workplace Engagement Rating
This variable is categorized into the following: Scale 0-10, 1 =
no engagement, 10 = highly engaged
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
Engagement
7.64
8
8
1.241460628
1.541224
6
The table above shows that the individuals have an average
score of 7.64 out of 10 level of engagement in the workplace.
Many people are actively engaged since the most frequent
occurring score is 8 out of 10. The range is relatively higher
indicating that the dataset contains a significant difference
between those actively engaged and those not that much
engaged. Sorting the workplace engagement rating shows that
only a few of the individuals are not engaged much. Companies
that gain higher profits have employees who are highly engaged,
motivated and valued. The passively engaged can be encouraged
to be engaged by being inspired, recognized, being given
flexible working hours as well as being given a fair pay
structure.
Overall Rating
This variable is categorized into the following: Scale 0-20, 0 =
not happy and not engaged, 20 = completely happy and highly
engaged.
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
Overall Rating
15.02
15.5
16
2.428487394
5.897551
11
The table above show that the mean score is 15.02 out of
20 implying that majority of the individuals are happy and
highly engaged. This however, shows that there is more the
company has to do in order to raise overall employee rating
score. The range of 11 shows that the level of happiness and
commitment in the department is varying in the sense that there
is a high variance between those that are completely happy and
highly engaged and those that are not. This calls for diversity in
the department which would bring diverse people with regard to
culture, religion, talent, background and exposure which bring
many benefits through the diverse pool of people brought
together. Team work is also enhanced in the sense that people
are given tasks according to their areas of strengths.
References
Atkinson-Bonasio, A. (2017). Gender balance in research: new
analytical report reveals uneven progress. Retrieved from
https://www.elsevier.com/connect/gender-balance-in-research-
new-analytical-report-reveals-uneven-progress
Dutton, J. E., & Ragins, B. R. (2017). Positive relationships at
work: An introduction and invitation. In Exploring positive
relationships at work (pp. 2-24). Psychology Press.
Hsiao, W. J. (2015). Happy Workers Work Happy? The
Perspective of Frontline Service Workers. In Industrial
Engineering, Management Science and Applications 2015 (pp.
473-476). Springer, Berlin, Heidelberg.
Jafroodi, N. R., Salajeghe, S., & Kiani, M. P. (2015).
Comparative analysis of the effect of organizational culture
characteristics on telecommuting system strategy through
inferential statistics and rough set theory.
Supervisor
Telecommute
Coworkers
Happiness
Engagement
Overall Rating
Variable
Mean
Median
Mode
Standard Deviation
Variance
Range
1
1
1
5
4
9
Gender
2
0.254693878
1
1
1
4
5
8
Age
32.02
31.5
29
4.340083701
18.83632653
15
1
1
1
7
5
12
Supervisor
2.5
3
3
1.015190743
1.030612245
3
1
2
1
7
5
12
Telecommute
1.18
1
1
0.388087934
0.150612245
2
1
1
4
6
10
Coworkers
1.92
2
2
0.665168384
0.44244898
1
1
1
5
6
11
Happiness
7.4
8
8
1.414213562
2
5
2
1
1
5
6
11
Engagement
7.64
8
8
1.241460628
1.54122449
6
2
2
1
6
6
12
Overall Rating
15.02
15.5
16
2.428487394
5.89755102
11
2
1
1
6
7
13
3
1
2
7
7
14
Gender Percentage
3
1
2
7
7
14
Male
44
4
1
1
8
7
15
Female
56
1
1
2
8
7
15
Telecommute Percentage
2
1
2
8
7
15
No ability
82
3
1
2
8
7
15
Able to
18
3
1
2
9
7
16
3
1
2
9
7
16
2
1
2
5
8
13
1
1
1
6
8
14
1
1
1
6
8
14
1
1
2
6
8
14
2
1
2
6
8
14
1
1
2
7
8
15
3
1
2
7
8
15
3
1
2
7
8
15
2
2
2
7
8
15
3
2
2
7
8
15
2
1
2
8
8
16
3
1
2
8
8
16
3
1
2
8
8
16
4
1
2
8
8
16
4
1
2
8
8
16
4
2
2
8
8
16
2
1
3
8
8
16
3
2
3
8
8
16
4
1
2
9
8
17
2
2
2
9
8
17
3
1
3
9
8
17
4
1
3
9
8
17
2
1
1
7
9
16
3
1
2
7
9
16
2
1
2
8
9
17
4
1
2
8
9
17
3
1
2
9
9
18
3
1
2
9
9
18
3
1
3
9
9
18
4
1
3
9
9
18
2
2
3
9
9
18
3
2
3
9
9
18
4
1
3
9
10
19
Overall Rating 9.0 8.0 12.0 12.0 10.0 11.0 11.0 12.0 13.0
14.0 14.0 15.0 15.0 15.0 15.0 16.0 16.0 13.0 14.0 14.0
14.0 14.0 15.0 15.0 15.0 15.0 15.0 16.0 16.0 16.0 16.0
16.0 16.0 16.0 16.0 17.0 17.0 17.0 17.0 16.0 16.0 17.0
17.0 18.0 18.0 18.0 18.0 18.0 18.0 19.0
Gender
Male Female 44.0 56.00000000000001
Telecommute
No ability Able to 82.0 18.0
Overall Rating 9.0 15.0 15.0 8.0 14.0 11.0 12.0 12.0 14.0
14.0 13.0 10.0 15.0 13.0 11.0 18.0 17.0 16.0 15.0 16.0
14.0 12.0 16.0 17.0 18.0 17.0 15.0 16.0 14.0 18.0 15.0
16.0 14.0 16.0 18.0 16.0 15.0 15.0 18.0 16.0 16.0 16.0
16.0 17.0 17.0 15.0 17.0 19.0 18.0 16.0
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  • 1. Running head: HAPPINESS ENGAGEMENT 1 HAPPINESS ENGAGEMENT 2 Happiness Engagement Nasser Y Miranda University of Phoenix August 26th, 2018 According to (Berk and Carey 2009), to perform correlation in excel we use a formula CORREL(array1,array2). There was no relationship between the independent variables age and relationship with coworkers. The correlation coefficient 0.00 indicates no relationship (Tutorialspoint 2016). There was a moderate positive correlation (0.61) (Tutorialspoint 2016)between independent variable ‘relationship with direct supervisor’ and dependent variable ‘workplace happiness rating’. This implies that as relationship with direct supervisor increased, workplace happiness increased as well. As relationship with supervisor increases, workplace happiness rating increased as well. There was a weak positive correlation (0.03) (Tutorialspoint 2016)between the independent variable age and dependent variable overall combined rating (combined workplace happiness rating and workplace engagement rating). This mean that as age increase, rating at the workplace increases too but at a very low rate. There was a moderate correlation (0.60) (Tutorialspoint 2016) between both dependent variables workplace happiness rating
  • 2. and engagement happiness rating. This implies that both workplace happiness rating and engagement happiness rating increase together. References Berk, K. N. and P. M. Carey (2009). Data analysis with Microsoft excel: updated for office 2007, Cengage Learning. Tutorialspoint (2016). Data Analysis with Excel. Running Head: Statistics Project Part 3 1 Statistics Project Part 3 3 Statistics Project Part 3 Nasser Y Miranda University of Phoenix August 18th, 2018 Anova: Single Factor Anova: Single Factor
  • 4. ANOVA ANOVA Source of Variation Source of Variation SS df MS F P-value F crit Between Groups Between Groups 600.25 1 600.25 396.1246 3.34E-36
  • 5. 3.938111 Within Groups Within Groups 148.5 98 1.515306 Total Total 748.75 99 Tukey's HSD Difference n (Relationship) n (Happiness) SE q statistic
  • 6. Relationship 4.9 50 50 1 4.9 Happiness 4.9 50 50 1 4.9 The Single factor one-way ANOVA is basically used to test whether the population means of observations of more than one treatment effects are equal (Brady, 2015). Since F > F crit the null hypothesis has to be rejected in this case. In this case, 396.1246 > 3.938111 thus we reject the null hypothesis in the sense that means for Happiness and Relationship are not equal. Post Hoc testing is useful in identifying the differences that are significant (Kuznetsova, Brockhoff, & Christensen, 2017). As opposed to the t-test, the ANOVA is useful in comparing means of more than two groups to test the hypothesis (Maurya, 2015). Moreover, since ANOVA goes to the extent of showing the statistical significance between the groups, it also reduces type I error (Keith, 2014). References Brady, S. M., Burow, M., Busch, W., Carlborg, Ö., Denby, K. J., Glazebrook, J., ... & Springer, N. M. (2015). Reassess the t test: interact with all your data via ANOVA. The Plant Cell, tpc-15.
  • 7. Keith, T. Z. (2014). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. Routledge. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: tests in linear mixed effects models. Journal of Statistical Software, 82(13). Maurya, V. N., Jaggi, C. K., Vashist, S., Ogubazghi, G., Varshney, D. K., Maurya, A. K., & Arora, D. K. (2015). Impact of some significant factors for intern’s job satisfaction and performance using t-test and ANOVA method. American Journal of Biological and Environmental Statistics, Science Publishing Group, USA, 1(1), 19-26. Running Head: Statistics Project Part 2 1 Statistics Project Part 2 3 Statistics Project Part 2 Hypothesis Testing and Two-Group t Tests Nasser Y Miranda University of Phoenix August 4th, 2018 Hypothesis for the Happiness and Engagement Dataset Hypothesis: Are individuals who have a great relationship with their supervisors likely to be completely happy at their workplace? Null hypothesis (H0): Individuals having a great relationship
  • 8. with their supervisors are likely to be completely happy at their workplace. Alternative hypothesis (H1): Individuals having a great relationship with their supervisors are not likely to be completely happy at their workplace. Justification from the Statistical Analysis In this case, an independent samples t-test would be of great importance since it compares two mean showing their differences. Moreover, this statistical analysis shows the measure of significance these differences have. The findings enable one to be in a position of knowing whether or not these differences could have resulted from chance (Anderson, 2011). Independent samples T-Test t-Test: Paired Two Sample for Means Relationship Happiness Mean 2.5 7.4 Variance 1.030612245 2 Observations 50 50 Pearson Correlation
  • 9. 0.611237131 Hypothesized Mean Difference 0 df 49 t Stat -30.67885265 P(T<=t) one-tail 6.0097E-34 t Critical one-tail 1.676550893 P(T<=t) two-tail 1.20194E-33 t Critical two-tail 2.009575237 Interpretation for the results For a two-tailed test, the null hypothesis is rejected when t Stat > t Critical (D'Agostino, 2017). In this case -30.67885265 < 2.009575237 indicating that we should accept the null hypothesis. This shows that it is correct to say that Individuals having a great relationship with their supervisors are likely to be completely happy in their workplace. In cases like this, where there is a negative t-value, there is a reversal in the effect’s directionality. However, this does not have bearing on the significance of the group’s differences (Macisaac et al., 2015). From the dataset, it is clearly seen that the individuals who have a great relationship with their supervisors have a high
  • 10. score of happiness. Organizations should consider embracing and emphasizing teamwork which not only keeps the employees happy but also give them a high retention rate. Gender Age Supervisor Telecommute Coworkers Happiness Engagement Overall Rating 2 32 4 1 3 9 10 19 2 29 4 1 2 8 9 17 1 26 4 1 2 8 8 16 1
  • 21. 34 1 2 1 7 5 12 2 25 1 1 1 5 6 11 References Anderson, T. W. (2011). The statistical analysis of time series(Vol. 19). John Wiley & Sons. D'Agostino, R. (2017). Goodness-of-fit-techniques. Routledge. Macisaac, R. L., Khatri, P., Bendszus, M., Bracard, S., Broderick, J., Campbell, B., ... & Diener, H. C. (2015). A collaborative sequential meta-analysis of individual patient data from randomized trials of endovascular therapy and tPA vs. tPA alone for acute ischemic stroke: T h R omb E ctomy A nd t PA (TREAT) analysis: statistical analysis plan for a sequential meta-analysis performed within the VISTA-Endovascular collaboration. International Journal of Stroke, 10(SA100), 136- 144. Running Head: Descriptive Statistics 1 Descriptive Statistics 2
  • 22. Statistics Project, Part 1:Opening Data in Microsoft® Excel®and Running Descriptive Statistics Nasser Y Miranda University of Phoenix August 4th, 2018 Gender The dataset consists of 50 individuals where 22 are males and 28 females. Below is the pie chart graph that graphically represent the gender composition. In terms of percentage, the males 44% are whereas the females are 56%. This is an indication that the data sample used was relatively balanced in terms of gender. Atkinson-Bonasio (2017) asserts that in research, fostering diversity achieved by gender equality assures innovation. She further states that bias and gender disparity should be examined so as to ensure a data-informed approach especially to implementing policies and interventions related to gender inequality. Variable Mean Median Mode Gender
  • 23. 2 The above table shows that the mode of the gender is 2. In this case, it implies that the females are frequently occurring in the data when compared to the males which is proved clearly by their percentage. Age Variable Mean Median Mode Standard Deviation Variance Range Age 32.02 31.5 29 4.340083701 18.83633 15 The individuals used in this case age has an average of 32 years where those of 29 years of age are the frequently occurring. The range of the populations is 15 years which shows the difference of years between the youngest individual and the oldest individual in the data set. The age has a high deviation showing the high variance of the data from the mean which is confirmed by the high variance of 18.83633. Relationship with Direct Supervisor This variable is data is further labelled into 4 categories namely: 1 = negative relationship, 2 = neutral relationship, 3 = positive relationship, 4 = great relationship. Variable Mean Median Mode
  • 24. Standard Deviation Variance Supervisor 2.5 3 3 1.015190743 1.030612 The above table indicates that the average relationship for all the 50 individuals with their direct supervisor is 2.5 which is between neutral relationship and positive relationship. Most of the individuals have a positive relationship with their direct supervisor. The mode of 3 shows that majority of the individuals have a positive relationship with their direct supervisor. The relationship categories does not exhibit great variance from what is expected thus the low case of 11 individuals out of 50 who have a negative relationship with their direct supervisor. Telecommute Schedule The telecommute schedule variable is categorized as follows: 1= no ability to telecommute, 2 = able to telecommute at least 2 days per week. Variable Mean Median Mode Standard Deviation Variance Range Telecommute 1.18 1 1
  • 25. 0.388087934 0.150612 The table above shows that majority of the individuals have no ability to telecommute as opposed to those who those who have the ability to do so at least 2 day every week. Telecommute Percentage No ability 82 Able to 18 82% have no ability to telecommute while on the other hand only 18% are able to. This is a clear indication that majority of the individuals have no access to Internet access, email and telephone from their homes. It is therefore not necessary to consider giving them tasks that will need them to be telecommunicating since most of them will not be able to deliver. Jafroodi, Salajeghe & Kiani (2015) in their paper found out that telecommuting is one of the factors that lead to increased productivity and employee satisfaction scores among others is telecommuting. Relationship with Coworkers This variable is categorized into the following: 1 = negative relationship, 2 = no relationship, 3 = positive relationship Variable Mean Median Mode Standard Deviation Variance Range Coworkers 1.92
  • 26. 2 2 0.665168384 0.442449 The above table shows the relationship these individuals in the dataset had with their fellow coworkers. From the table, the average relationship is closer to being neutral in the sense that most people have no relationship with their coworkers. This is clearly seen in the mode where majority of these individuals who frequently occur in the dataset, 28 to be precise, have no relationship with their coworkers whatsoever. Only 9 out of the 50 have a positive relationship with their coworkers. This calls for the organization to strive and make it their goal to increase the number of individuals who have a positive relationship with their workers. Positive relationships between colleagues are very beneficial to both the individuals and organization in terms of improved teamwork, increased productivity, high rates of employee retention, and so on (Dutton & Ragins, 2017). Workplace Happiness Rating This variable is categorized as follows: Scale 0-10, 0 = no happiness, 10 = completely happy Variable Mean Median Mode Standard Deviation Variance Range Happiness 7.4 8 8 1.414213562 2 5
  • 27. From the above table, the average rate of happiness is relatively high showing that most of the individuals are happy in their workplace. The table further indicates that the happiness score frequently occurring is 8 out of 10 which suggests that most of the people are happy. However, it seems like the company has to go an extra mile since there more room for improvement. There is a need to identify the reason why there are still other who are not that much happy in order to know which areas the company needs to work on. One of the most important things companies are striving to have is keeping retaining employees while at the same time keeping them happy and productive (Hsiao, 2015). Loyal employees perform better, meet their deadlines, and most importantly are very supportive and open to new ideas and changes which means a lot to companies. Workplace Engagement Rating This variable is categorized into the following: Scale 0-10, 1 = no engagement, 10 = highly engaged Variable Mean Median Mode Standard Deviation Variance Range Engagement 7.64 8 8 1.241460628 1.541224 6 The table above shows that the individuals have an average score of 7.64 out of 10 level of engagement in the workplace.
  • 28. Many people are actively engaged since the most frequent occurring score is 8 out of 10. The range is relatively higher indicating that the dataset contains a significant difference between those actively engaged and those not that much engaged. Sorting the workplace engagement rating shows that only a few of the individuals are not engaged much. Companies that gain higher profits have employees who are highly engaged, motivated and valued. The passively engaged can be encouraged to be engaged by being inspired, recognized, being given flexible working hours as well as being given a fair pay structure. Overall Rating This variable is categorized into the following: Scale 0-20, 0 = not happy and not engaged, 20 = completely happy and highly engaged. Variable Mean Median Mode Standard Deviation Variance Range Overall Rating 15.02 15.5 16 2.428487394 5.897551 11
  • 29. The table above show that the mean score is 15.02 out of 20 implying that majority of the individuals are happy and highly engaged. This however, shows that there is more the company has to do in order to raise overall employee rating score. The range of 11 shows that the level of happiness and commitment in the department is varying in the sense that there is a high variance between those that are completely happy and highly engaged and those that are not. This calls for diversity in the department which would bring diverse people with regard to culture, religion, talent, background and exposure which bring many benefits through the diverse pool of people brought together. Team work is also enhanced in the sense that people are given tasks according to their areas of strengths. References Atkinson-Bonasio, A. (2017). Gender balance in research: new analytical report reveals uneven progress. Retrieved from https://www.elsevier.com/connect/gender-balance-in-research- new-analytical-report-reveals-uneven-progress Dutton, J. E., & Ragins, B. R. (2017). Positive relationships at work: An introduction and invitation. In Exploring positive relationships at work (pp. 2-24). Psychology Press. Hsiao, W. J. (2015). Happy Workers Work Happy? The Perspective of Frontline Service Workers. In Industrial Engineering, Management Science and Applications 2015 (pp. 473-476). Springer, Berlin, Heidelberg. Jafroodi, N. R., Salajeghe, S., & Kiani, M. P. (2015). Comparative analysis of the effect of organizational culture characteristics on telecommuting system strategy through inferential statistics and rough set theory.
  • 46.
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  • 48. Overall Rating 9.0 8.0 12.0 12.0 10.0 11.0 11.0 12.0 13.0 14.0 14.0 15.0 15.0 15.0 15.0 16.0 16.0 13.0 14.0 14.0 14.0 14.0 15.0 15.0 15.0 15.0 15.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 17.0 17.0 17.0 17.0 16.0 16.0 17.0 17.0 18.0 18.0 18.0 18.0 18.0 18.0 19.0 Gender Male Female 44.0 56.00000000000001 Telecommute No ability Able to 82.0 18.0 Overall Rating 9.0 15.0 15.0 8.0 14.0 11.0 12.0 12.0 14.0 14.0 13.0 10.0 15.0 13.0 11.0 18.0 17.0 16.0 15.0 16.0 14.0 12.0 16.0 17.0 18.0 17.0 15.0 16.0 14.0 18.0 15.0 16.0 14.0 16.0 18.0 16.0 15.0 15.0 18.0 16.0 16.0 16.0 16.0 17.0 17.0 15.0 17.0 19.0 18.0 16.0