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.