Qualitative and Quantitative Data Analysis

.....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 employee's 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 firm's 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 χ(1) = 0.206, p = .650 revealing that "there a significant difference between the proportion of males and females who attended the firm's 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

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 Bound

unskilled

14

7628,57

730,046

195,113

7207,06

8050,09

semi-skilled

18

7288,89

741,135

174,687

6920,33

7657,45

fairly skilled

20

8095,00

931,029

208,185

7659,26

8530,74

highly skilled

16

8237,50

1267,478

316,869

7562,11

8912,89

10500

Total

68

7819,12

997,947

121,019

7577,56

8060,67

10500

ANOVA

Income

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

(Combined)

9891797,852

3

3297265,951

3,713

,016

Linear Term

Unweighted

5288030,013

1

5288030,013

5,955

,017

Weighted

6062905,680

1

6062905,680

6,827

,011

Deviation

3828892,173

2

1914446,086

2,156

,124

Within Groups

56833349,206

64

888021,081

Total

66725147,059

67

Post Hoc Tests

Multiple Comparisons

Dependent Variable: Income

Tukey HSD

(I) rated skill

(J) rated skill

Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

unskilled semi-skilled

339,683

335,804

,743

-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 firm's 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 husband replies: "Leave the job and rest at home. I am not demanding. I never demanded anything from him at all." The participant also confirms that women need to seek permission from her husband before being allowed to spend their earning. (Ansari, 2011).

A relationship between the Pakistani women and their husband's relatives is another theme identified in the study because a woman is required to become obstinate for the relative of her husband. In short, women consider men to be the head of the family in Pakistan and women are required to seek for permissions for everything from their husbands. Moreover, the mentality of the husband is to be the only person who is to contribute financially and women are not allowed to contribute financially. One of the participants confirms this assertion by stating:

"When I was at my home, at the time of emergency, birthdays, anniversaries, I used to contribute financially and bought gifts to please my family members. Even I cannot buy a gift for him. My husband tells me to buy things for him from his money. He is not developing a habit of taking money from me.. If a husband starts to take money from his wife, then the wife eventually ends up surrendering before him."

Reference

Ali, T. S. (2011). Living with violence in the home: Exposure and experiences among married women, residing in urban Karachi, Pakistan. Ph.D. Dissertation Department Of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.

Ammons, S. K. & Edgell, P. (2007). Religious Influences on Work -- Family Trade-Offs. Journal of Family Issues, 28(6). 794-826.

Ansari, S. A. (2011). Gender difference: Work and family conflicts and family-work conflicts. Pakistan Business Review, 13(2). 315-331.

Anthias, F. (2013). Intersectional what? Social divisions, intersectionality and levels of analysis. Ethnicities, 13(1). 3-19.

Anwar, M. & Shahzad, K. (2011). Impact of Work-Life Conflict on Perceived Employee Performance: Evidence from Pakistan. European Journal of Economics, Finance and Administrative Sciences, 13(2011). 82-86.

Arif, N. & Amir, T. (2008). Adjustment of Married Working Women in Joint Family System. MA Gender Studies Dissertation Department of Gender Studies, University of the Punjab, Pakistan.

Appendices

Appendix 1: Data Presentation

Appendix 2: Variable Presentation