Introduction:

This essayconsists of two parts. Part A is an analysis of quantitative data and Part B is an analysis of qualitative data. We will give you the data in both cases. Each part should consist of data analysis, commentary and interpretation. You should write well-structured report of between 750 and 1000 words for each part plus any diagrams and charts you produce and a list of references.

Part A

The report carries out the data analysis of employee data to answer some human resources related questions using the SPSS software. The screenshots of the entire data are presented in the Appendix 1 and Appendix 2. The report provides the findings for the HR (human resources) issues after carrying out the data analysis of employees data. The outcome of the analysis reveals that workers between 25 and 55 years of age form the largest percentage of employees in the organization where the mean age of all the entire workforce is 39.19 years. However, workers between 18 and 29 years of age consist of 30.4% of the workforce. However, workers between age of 30 and 40 consist of 23.2% of employees within the organization. Employees between 41 and 50 years of age consist of 23.2% of the workforce while employees between 51 and 63 of age consist of 21.7%.

The findings also reveal the proportion of the employees belonging to each ethnic group. The entire workforce is 70 in number and White ethnic group forms 51.4% (36) of all employees while Asians rank second with 25.7% (18). West Indians are 20% (14) while Africans are 2.9% (2). The average income of all workers is $7,819.12. The result of the regression analysis reveals that the number of salary increases with an increase in the number of years worked in the organization because the p-value is 8.6, which is more than 0.05 the significant level. Moreover, the average salary is statistically significant at different skill categories. The p-value is .074 which is more than 0.05 the significant level showing that as skills of workers increase, their salary also increases. Moreover, there is a significant difference between males and females who attended the firms meeting last month because 58.3% of males attended the meeting while 41.7% of female attended the meeting last month. The result of the "Pearson Chi-Square" shows that ?(1) = 0.206, p = .650 revealing that there a significant difference between the proportion of males and females who attended the firms meeting last month.

Following is the analysis of employee data:

1. What is the age distribution of the workforce? (Use, for example, Histogram)

FREQUENCIES VARIABLES=age

/STATISTICS=RANGE MEAN

/HISTOGRAM

/ORDER=ANALYSIS.

Frequencies

[DataSet1] C:UsersucerDesktop188375_jss.sav

Statistics

Age

N
Valid
69

Missing
1

Mean
39,19

Range
45



Age

Frequency
Percent
Valid Percent
Cumulative Percent

Valid
18
2
2,9
2,9
2,9

19
2
2,9
2,9
5,8

21
2
2,9
2,9
8,7

23
1
1,4
1,4
10,1

26
3
4,3
4,3
14,5

27
2
2,9
2,9
17,4

28
3
4,3
4,3
21,7

29
6
8,6
8,7
30,4

30
1
1,4
1,4
31,9

31
4
5,7
5,8
37,7

32
2
2,9
2,9
40,6

33
1
1,4
1,4
42,0

34
1
1,4
1,4
43,5

35
1
1,4
1,4
44,9

37
2
2,9
2,9
47,8

38
1
1,4
1,4
49,3

39
1
1,4
1,4
50,7

40
2
2,9
2,9
53,6

42
2
2,9
2,9
56,5

43
3
4,3
4,3
60,9

45
1
1,4
1,4
62,3

46
2
2,9
2,9
65,2

47
1
1,4
1,4
66,7

48
5
7,1
7,2
73,9

50
2
2,9
2,9
76,8

51
1
1,4
1,4
78,3

52
2
2,9
2,9
81,2

53
4
5,7
5,8
87,0

54
2
2,9
2,9
89,9

55
2
2,9
2,9
92,8

57
1
1,4
1,4
94,2

59
1
1,4
1,4
95,7

61
1
1,4
1,4
97,1

62
1
1,4
1,4
98,6

63
1
1,4
1,4
100,0

Total
69
98,6
100,0

Missing
0
1
1,4

Total
70
100,0

2.What proportion of employees belongs to each ethnic group? (Use, for example, Bar Graph)

FREQUENCIES VARIABLES=ethnicgp

/STATISTICS=RANGE MEAN

/GROUPED=ethnicgp

/BARCHART FREQ

/ORDER=ANALYSIS.

Frequencies

[DataSet1] C:UsersucerDesktop188375_jss.sav

Statistics

Ethnic Group

N
Valid
70

Missing
0

Mean
1,74

Range
3

Ethnic Group

Frequency
Percent
Valid Percent
Cumulative Percent

Valid
white
36
51,4
51,4
51,4

asian
18
25,7
25,7
77,1

west indian
14
20,0
20,0
97,1

african
2
2,9
2,9
100,0

Total
70
100,0
100,0



3.What is the average income? (Use, for example, Descriptive Statistics, Descriptives)

DESCRIPTIVES VARIABLES=income

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

[DataSet1] C:UsersucerDesktop188375_jss.sav

Descriptive Statistics

N
Minimum
Maximum
Mean
Std. Deviation

Income
68
5900
10500
7819,12
997,947

Valid N (listwise)
68

4.How is number of years worked related to salary, 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 Entered
Variables Removed
Method

1
Years Workedb
.
Enter

a. Dependent Variable: Income

b. All requested variables entered.

Model Summary

Model
R
R Square
Adjusted R Square
Std. Error of the Estimate

1
,340a
,115
,102
945,711

a. Predictors: (Constant), Years Worked

ANOVAa

Model
Sum of Squares
df
Mean Square
F
Sig.

1
Regression
7696787,937
1
7696787,937
8,606
,005b

Residual
59028359,122
66
894369,078

Total
66725147,059
67

a. Dependent Variable: Income

b. Predictors: (Constant), Years Worked

Coefficientsa

Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95,0% Confidence Interval for B

B
Std. Error
Beta
Lower Bound
Upper Bound

1
(Constant)
7410,810
180,346

41,092
,000
7050,737
7770,883

Years Worked
31,841
10,854
,340
2,934
,005
10,170
53,511

a. Dependent Variable: Income

5. How different are the average salaries of the different skill categories? (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

Income

N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Minimum
Maximum

Lower Bound
Upper... Error Sig.
95% Confidence Interval

Lower Bound
Upper Bound

unskilled
semi-skilled
339,683
335,804
,43
-546,12
1225,48

fairly skilled
-466,429
328,377
,491
-1332,63
399,78

highly skilled
-608,929
344,864
,299
-1518,62
300,77

semi-skilled
unskilled
-339,683
335,804
,743
-1225,48
546,12

fairly skilled
-806,111
306,163
,051
-1613,72
1,50

highly skilled
-948,611*
323,784
,024
-1802,70
-94,52

fairly skilled
unskilled
466,429
328,377
,491
-399,78
1332,63

semi-skilled
806,111
306,163
,051
-1,50
1613,72

highly skilled
-142,500
316,073
,969
-976,25
691,25

highly skilled
unskilled
608,929
344,864
,299
-300,77
1518,62

semi-skilled
948,611*
323,784
,024
94,52
1802,70

fairly skilled
142,500
316,073
,969
-691,25
976,25

*. The mean difference is significant at the 0.05 level.

Homogeneous Subsets

Income

Tukey HSDa,b

rated skill
N
Subset for alpha = 0.05

1
2

semi-skilled
18
7288,89

unskilled
14
7628,57
7628,57

fairly skilled
20
8095,00
8095,00

highly skilled
16

8237,50

Sig.

,074
,252

Means for groups in homogeneous subsets are displayed.

a. Uses Harmonic Mean Sample Size = 16,703.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

6. Is there a significant difference between the proportion of males and females who attended the firms meeting last month? (Use, for example, Chi-Squared)

CROSSTABS

/TABLES=gender BY attend

/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ PHI

/CELLS=COUNT ROW COLUMN TOTAL

/COUNT ROUND CELL.

Crosstabs

[DataSet1] C:UsersucerDesktop188375_jss.sav

Case Processing Summary

Cases

Valid
Missing
Total

N
Percent
N
Percent
N
Percent

Gender * attended meeting
70
100,0%
0
0,0%
70
100,0%

Gender * attended meeting Crosstabulation

attended meeting
Total

yes
no

Gender
male
Count
21
18
39

% within Gender
53,8%
46,2%
100,0%

% within attended meeting
58,3%
52,9%
55,7%

% of Total
30,0%
25,7%
55,7%

female
Count
15
16
31

% within Gender
48,4%
51,6%
100,0%

% within attended meeting
41,7%
47,1%
44,3%

% of Total
21,4%
22,9%
44,3%

Total
Count
36
34
70

% within Gender
51,4%
48,6%
100,0%

% within attended meeting
100,0%
100,0%
100,0%

% of Total
51,4%
48,6%
100,0%

Chi-Square Tests

Value
df
Asymp. Sig. (2-sided)
Exact Sig. (2-sided)
Exact Sig. (1-sided)

Pearson Chi-Square
,206a
1
,650

Continuity Correctionb
,045
1
,831

Likelihood Ratio
,206
1
,650

Fisher's Exact Test

,810
,416

Linear-by-Linear Association
,203
1
,652

N of Valid Cases
70

a. 0 cells (0,0%) have expected count less than 5. The minimum expected count is 15,06.

b. Computed only for a 2x2 table

Symmetric Measures

Value
Approx. Sig.

Nominal by Nominal
Phi
,054
,650

Cramer's V
,054
,650

N of Valid Cases
70

Part B

The empirical evidence shows that there is scanty of research that investigates the experience of women with reference to Pakistan context. The uniqueness of the Pakistan is their interplay between family structure religion, culture, and class, which affects their reconciliation of family roles and works among Pakistani women. The study uses the in-depth semi-structured interview to investigate the experience of Pakistani women with reference to work-family conflict. The study uses the qualitative data analysis to enhance data reliability and validity. The analysis is carried out by reading the texts to understand the entire context of the interviews.

The outcome of the analysis produces several themes. The first theme produced from the interview is the joint family. It is revealed from the interview that joint family is an essential part of the Pakistani culture. (Anthias, 2013). In Pakistan, all family member sees themselves as one. For example, if one of the family members gets sick, all the family members will rally round the person and take care of him or her. If a woman falls sick, it is a member of the family who will do all the caring while her husband does nothing. The shortcoming of the joint family is that it is impossible for a woman to buy something for herself alone, she is obliged to buy the item for all the member of the family. Moreover, a woman in the joint family cannot buy things for her children alone, she is obliged to buy the same item for all member of the family. (Ammons, . & Edgell, 2007).

Another theme identified in the study is a conflict between a woman role in the family and work. In the Pakistani context, woman expectation is to perform the family roles with regards to the family expectation. While a woman in Pakistan might have worked long hours, the office work responsibility should not affect the childcare and domestic responsibilities. (Anwar, & Shahzad, 2011). For example, the society will judge a woman based on the level of her family caring responsibility. Thus, Pakistani women are expected to fulfill all homemaking no matter her position in her place of work. (Ali et al., 2011). Since women are required to perform both the role of a good woman and ideal worker, inability to perform these roles will be considered a sign of deficiencies. (Arif, & Amir, 2008). For example, women are responsible take care of their husband such as making the breakfast ready, iron their clothes and get everything ready that the husband will need for the office hour the next day. A woman has to fulfill these roles even if she works. Based on the load of work that women are required to carry out in the matrimonial home, 95% of women are happy that they are not working.

Another theme in the study is that men in Pakistan women are not obliged to work and it is men who are to work and take care of the family. One of the participants confirmed this assertion when she was complaining that she had a backache because the office seats are not comfortable, and they are required working non-stop sitting for long hours on the same chair without any break. The…