Quantitative and qualitative data analysis
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Introduction:
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This kind of essayconsists of two parts. Part A is an analysis of quantitative data and Portion B is usually an analysis of qualitative data. We will give you the information in both equally cases. Each part should consist of data analysis, commentary and model. You should write well-structured report of among 750 and 1000 words for each part plus any kind of diagrams and charts you produce and a list of references.
Part A
The report does the data analysis of worker data to reply to some human resources related questions using the SPSS software. The screenshots with the entire data are provided in the Appendix 1 and Appendix installment payments on your The statement provides the results for the HR (human resources) problems after performing the data research of employees data. The outcome of the research reveals that workers among 25 and 55 years old form the major percentage of employees inside the organization where the mean regarding all the complete workforce is 39. 19 years. Nevertheless , workers among 18 and 29 years old consist of 31. 4% with the workforce. However , workers among age of 40 and forty consist of twenty-three. 2% of employees within the organization. Personnel between forty one and 50 years of age contain 23. 2% of the workforce while workers between 51 and 63 of age consist of 21. seven percent.
The studies also uncover the portion of the workers belonging to each ethnic group. The entire staff is 70 in quantity and White colored ethnic group forms fifty-one. 4% (36) of all employees while Asians rank second with twenty-five. 7% (18). West Indians are twenty percent (14) although Africans happen to be 2 . 9% (2). The regular income of all workers is usually $7, 819. 12. A result of the regression analysis reveals that the range of salary raises with an increase in the number of years performed in the organization because the p-value is almost 8. 6, which is more than 0. 05 the significant level. In addition, the average salary is statistically significant in different skill categories. The p-value is usually. 074 which is more than zero. 05 the numerous level exhibiting that as skills of workers maximize, their income also raises. Moreover, there is a significant difference among males and females who also attended the firms getting together with last month since 58. 3% of guys attended the meeting while 41. seven percent of woman attended the meeting a month ago. The result of the “Pearson Chi-Square” shows that? (1) = zero. 206, l =. 600 revealing that there an important difference between your proportion of males and females who have attended the firms appointment last month.
Following is the evaluation of employee data:
1 . What is age distribution of the workforce? (Use, for example , Histogram)
EQ VARIABLES=age
/STATISTICS=RANGE MEAN
/HISTOGRAM
/ORDER=ANALYSIS.
Frequencies
[DataSet1] C: UsersucerDesktop188375_jss. sav
Statistics
Age
N
Valid
69
Missing
you
Mean
39, 19
Selection
45
Age
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
18
2
2, 9
2, 9
a couple of, 9
nineteen
2
2, 9
a couple of, 9
your five, 8
21
2
a couple of, 9
2, 9
8, 7
23
1
you, 4
one particular, 4
twelve, 1
twenty six
3
5, 3
5, 3
13, 5
twenty seven
2
a couple of, 9
2, 9
18, 4
twenty-eight
3
5, 3
4, 3
21, 7
29
6
almost eight, 6
eight, 7
35, 4
35
1
one particular, 4
one particular, 4
31, 9
thirty-one
4
five, 7
a few, 8
37, 7
thirty-two
2
2, 9
2, 9
forty five, 6
33
1
one particular, 4
you, 4
42, 0
34
1
you, 4
1, 4
43, 5
35
1
you, 4
one particular, 4
44, 9
37
2
two, 9
2, 9
forty seven, 8
38
1
you, 4
you, 4
49, 3
39
1
one particular, 4
one particular, 4
55, 7
40
2
two, 9
a couple of, 9
53, 6
forty two
2
two, 9
2, 9
56, 5
43
3
some, 3
four, 3
70, 9
forty-five
1
you, 4
one particular, 4
sixty two, 3
46
2
a couple of, 9
2, 9
66, 2
47
1
1, 4
1, 4
66, 7
forty-eight
5
several, 1
several, 2
73, 9
50
2
a couple of, 9
2, 9
76, 8
fifty-one
1
1, 4
one particular, 4
78, 3
52
2
two, 9
two, 9
seventy eight, 2
53
4
a few, 7
a few, 8
87, 0
54
2
a couple of, 9
two, 9
fifth 89, 9
fifty five
2
a couple of, 9
two, 9
92, 8
57
1
1, 4
you, 4
94, 2
59
1
you, 4
one particular, 4
96, 7
sixty one
1
you, 4
you, 4
97, 1
62
1
one particular, 4
one particular, 4
98, 6
63
1
1, 4
1, 4
75, 0
Total
69
98, 6
75, 0
Absent
0
one particular
1, 4
Total
70
100, 0
2 . What proportion of employees is owned by each ethnic group? (Use, for example , Club Graph)
FREQUENCIES VARIABLES=ethnicgp
/STATISTICS=RANGE MEAN
/GROUPED=ethnicgp
/BARCHART FREQ
/ORDER=ANALYSIS.
Eq
[DataSet1] C: UsersucerDesktop188375_jss. sav
Stats
Ethnic Group
N
Valid
70
Absent
0
Indicate
1, seventy four
Range
3
Ethnic Group
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
light
36
fifty-one, 4
51, 4
51, 4
oriental
18
25, 7
25, 7
77, 1
west indian
16
20, zero
20, zero
97, you
african
two
2, on the lookout for
2, 9
100, zero
Total
75
100, zero
100, 0
3. What is the average salary? (Use, for example , Descriptive Stats, Descriptives)
DESCRIPTIVES VARIABLES=income
/STATISTICS=MEAN STDDEV MIN MAX.
Descriptives
[DataSet1] C: UsersucerDesktop188375_jss. sav
Descriptive Statistics
N
Minimum
Optimum
Mean
Std. Deviation
Profits
68
5900
10500
7819, 12
997, 947
Valid N (listwise)
68
four. How is definitely number of years proved helpful related to earnings, if at all? (Use, for example , Linear Regression)
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R ANOVA
/CRITERIA=PIN(. 05) POUT(. 10)
/NOORIGIN
/DEPENDENT income
/METHOD=ENTER years.
Regression
[DataSet1] C: UsersucerDesktop188375_jss. sav
Variables Entered/Removeda
Model
Variables Moved into
Variables Taken off
Method
one particular
Years Workedb
.
Enter
a. Dependent Varying: Income
n. All requested variables joined.
Model Overview
Model
Ur
R Rectangular
Adjusted L Square
An std. Error of the Estimate
one particular
, 340a
, 116
, 102
945, 711
a. Predictors: (Constant), Years Performed
ANOVAa
Unit
Sum of Squares
df
Mean Sq .
F
Sig.
1
Regression
7696787, 937
1
7696787, 937
almost eight, 606
, 005b
Residual
59028359, 122
sixty six
894369, 078
Total
66725147, 059
67
a. Based mostly Variable: Salary
b. Predictors: (Constant), Years Worked
Coefficientsa
Model
Unstandardized Coefficients
Standardised Coefficients
to
Sig.
ninety five, 0% Confidence Interval pertaining to B
W
Std. Error
Beta
Decrease Bound
Top Bound
you
(Constant)
7410, 810
180, 346
41, 092
, 500
7050, 737
7770, 883
Years Worked
31, 841
10, 854
, 340
a couple of, 934
, 005
10, 169
53, 511
a. Reliant Variable: Salary
5. Just how different would be the average incomes of the different skill classes? (Use, for example , One-way ANOVA)
ONEWAY income BY skill
/POLYNOMIAL=1
/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS
/POSTHOC=TUKEY ALPHA(0. 05).
Oneway
[DataSet1] C: UsersucerDesktop188375_jss. sav
Descriptives
Profits
N
Mean
Std. Deviation
Std. Problem
95% Self confidence Interval intended for Mean
Minimum
Maximum
Lower Bound
Upper Bound
unskilled
14
7628, 57
730, 046
195, 113
7207, 06
8050, 09
6500
8800
semi-skilled
18
7288, 89
741, 135
174, 687
6920, 33
7657, 45
5900
8800
reasonably skilled
twenty
8095, 00
931, 029
208, 185
7659, 21
8530, seventy four
6200
9500
highly skilled
of sixteen
8237, 55
1267, 478
316, 869
7562, eleven
8912, fifth there’s 89
6400
10500
Total
sixty-eight
7819, doze
997, 947
121, 019
7577, 56
8060, 67
5900
10500
ANOVA
Salary
Sum of Squares
df
Mean Sq .
F
Sej.
Between Organizations
(Combined)
9891797, 852
several
3297265, 951
3, 713
, 016
Thready Term
Unweighted
5288030, 013
1
5288030, 013
your five, 955
, 017
Weighted
6062905, 680
1
6062905, 680
6, 827
, 011
Change
3828892, 73
2
1914446, 086
a couple of, 156
, 124
Within Organizations
56833349, 206
64
888021, 081
Total
66725147, 059
67
Post Hoc Checks
Multiple Evaluations
Dependent Variable: Income
Tukey HSD
(I) rated skill
(J) rated skill
Suggest Difference (I-J)
Std. Mistake
Sig.
95% Confidence Time period
Lower Destined
Upper Sure
unskilled
semi-skilled
339, 683
335, 804
, 743
-546, 12
1225, 48
quite skilled
-466, 429
328, 377
, 491
-1332, 63
399, 78
highly skilled
-608, 929
344, 864
, 299
-1518, 62
300, 77
semi-skilled
not skilled
-339, 683
335, 804
, 743
-1225, 48
546, 12
fairly skilled
-806, 111
306, 163
, 051
-1613, seventy two
1, 50
highly skilled
-948, 611*
323, 784
, 024
