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SAS coding for analysis-Using SAIL company's (HR) data

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Dhanusuya Raman
Dhanusuya Raman

This study analyzed the quality of work life of employees at Steel Authority of India Limited's Salem Steel Plant using SAS software. Two datasets on various factors affecting quality of work life were imported and merged into one dataset of 79 observations. Descriptive statistics, frequency analysis, correlation, regression, and ANOVA were conducted on variables like welfare, employee compensation, motivation, career development, human resource practices, and safety. The results provided insights into the relationship between these factors and quality of work life categories.

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A Study on QUALITY OF WORK LIFE OF EMPLOYEES At STEEL AUTHORITY OF INDIA LIMTED, SALEM STEEL PLANT [SSP] USING SAS By Monica GS
Importing two different data and merging Proc import datafile="E:trim 5James Sir - SAPSailQWL1.xls" out=work.QWL1; run; Proc import datafile="E:trim 5James Sir - SAPSailQWL2.xls" out=work.QWL2; run; Proc sql; create table work.model as select * from work.QWL1, work.QWL2 where QWL1.CATEGORY=QWL2.CATEGORY order by QWL1.SLNO; quit;
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The FREQ Procedure CATEGORY CATEGORY Frequency Percent Cumulative Frequency Cumulative Percent 1 28 35.44 28 35.44 2 51 64.56 79 100.00 proc freq data = work.model; tables CATEGORY; run; frequency
proc means data =work.model; var WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY; run; he MEANS Procedure Variable Label N Mean Std Dev Minimum Maximum WELFARE INDUSTRIA LRELATION SHIP EMPLOYEE COMPENSA TION EMPLOYEE MOTIVATIO N CAREERDE VELOPMEN T HRP SAFETY WELFARE INDUSTRIA LRELATION SHIP EMPLOYEE COMPENSA TION EMPLOYEE MOTIVATIO N CAREERDE VELOPMEN T HRP SAFETY 79 79 79 79 79 79 79 3.9240506 3.4683544 3.6708861 3.4810127 3.8481013 3.7468354 4.0506329 0.6154455 0.7980014 0.6349141 0.7313626 0.7176995 0.6692582 0.7662552 3.0000000 2.0000000 2.0000000 2.0000000 3.0000000 2.0000000 3.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 DESCRIPTIVE STATISTICS
proc freq data =work.model; tables WELFARE * CATEGORY/chisq; run; proc freq data =work.model; tables INDUSTRIALRELATIONSHIP * CATEGORY/chisq; run; proc freq data =work.model; tables EMPLOYEECOMPENSATION * CATEGORY/chisq; run; proc freq data =work.model; tables EMPLOYEEMOTIVATION * CATEGORY/chisq; run; proc freq data =work.model; tables CAREERDEVELOPMENT * CATEGORY/chisq; run; proc freq data =work.model; tables HRP * CATEGORY/chisq; run; proc freq data =work.model; tables SAFETY * CATEGORY/chisq; run; Chisq
Table of CAREERDEVELOPMENT by CATEGORY CAREERDEVEL OPMENT(CAR EERDEVELOP MENT) CATEGORY(CATEGORY) Total 1 2 3 12 15.19 44.44 42.86 15 18.99 55.56 29.41 27 34.18 ? ? 4 10 12.66 27.03 35.71 27 34.18 72.97 52.94 37 46.84 ? ? 5 6 7.59 40.00 21.43 9 11.39 60.00 17.65 15 18.99 ? ? Total 28 35.44 51 64.56 79 100.00
Statistic DF Value Prob Chi-Square 2 2.2376 0.3267 Likelihood Ratio Chi- Square 2 2.2558 0.3237 Mantel- Haenszel Chi- Square 1 0.3277 0.5670 Phi Coefficient ? 0.1683 ? Contingency Coefficient ? 0.1660 ? Cramer's V ? 0.1683
proc corr data =work.model; var WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY; run; proc reg data =work.model; model CATEGORY = WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY; run; proc anova data =work.model; class CATEGORY; model WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY = CATEGORY; run; Correlation/regression/anova
Correlation Output Pearson Correlation Coefficients, N = 79 Prob > |r| under H0: Rho=0 WELFARE INDUSTRIALRELATION SHIP EMPLOYEECOMPENSAT ION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY WELFARE WELFARE 1.00000 ? 0.54323 <.0001 0.29612 0.0081 0.45248 <.0001 0.32185 0.0038 0.54411 <.0001 0.41605 0.0001 INDUSTRIALRELATION SHIP INDUSTRIALRELATION SHIP 0.54323 <.0001 1.00000 ? 0.56117 <.0001 0.64149 <.0001 0.43920 <.0001 0.58494 <.0001 0.38005 0.0006 EMPLOYEECOMPENSAT ION EMPLOYEECOMPENSAT ION 0.29612 0.0081 0.56117 <.0001 1.00000 ? 0.53856 <.0001 0.50786 <.0001 0.37466 0.0007 0.45633 <.0001 EMPLOYEEMOTIVATIO N EMPLOYEEMOTIVATIO N 0.45248 <.0001 0.64149 <.0001 0.53856 <.0001 1.00000 ? 0.67833 <.0001 0.64487 <.0001 0.57366 <.0001 CAREERDEVELOPMENT CAREERDEVELOPMENT 0.32185 0.0038 0.43920 <.0001 0.50786 <.0001 0.67833 <.0001 1.00000 ? 0.66627 <.0001 0.64360 <.0001 HRP HRP 0.54411 <.0001 0.58494 <.0001 0.37466 0.0007 0.64487 <.0001 0.66627 <.0001 1.00000 ? 0.60031 <.0001 SAFETY SAFETY 0.41605 0.0001 0.38005 0.0006 0.45633 <.0001
Regression Output
ANOVA Output Source DF Sum of Squares Mean Square F Value Pr > F Model 1 1.504662 62 1.504662 62 3.87 0.0528 Error 77 29.93837 535 0.388810 07 ? ? Correcte d Total 78 31.44303 797 ? ? ? R-Square Coeff Var Root MSE EMPLOYEECO MPENSATION Mean 0.047854 16.98626 0.623546 3.670886 Source DF Anova SS Mean Square F Value Pr > F CATEGOR Y 1 1.504662 62 1.504662 62 3.87 0.0528
Webpage and PDF Coding ods pdf file= 'E:trim 5James Sir - SAPSailSas assignment.pdf'; startpage = 1; ods pdf text = "SAS ASSIGNMENT-13MBA1042"; ods pdf close; run; quit; ods html file= 'E:trim 5James Sir - SAPSailSas assignment.html'; ::::::::: ::::::::: ::::::::: ods html close; run; quit;
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SAS coding for analysis-Using SAIL company's (HR) data

  • 1. A Study on QUALITY OF WORK LIFE OF EMPLOYEES At STEEL AUTHORITY OF INDIA LIMTED, SALEM STEEL PLANT [SSP] USING SAS By Monica GS
  • 2. Importing two different data and merging Proc import datafile="E:trim 5James Sir - SAPSailQWL1.xls" out=work.QWL1; run; Proc import datafile="E:trim 5James Sir - SAPSailQWL2.xls" out=work.QWL2; run; Proc sql; create table work.model as select * from work.QWL1, work.QWL2 where QWL1.CATEGORY=QWL2.CATEGORY order by QWL1.SLNO; quit;
  • 5. The FREQ Procedure CATEGORY CATEGORY Frequency Percent Cumulative Frequency Cumulative Percent 1 28 35.44 28 35.44 2 51 64.56 79 100.00 proc freq data = work.model; tables CATEGORY; run; frequency
  • 6. proc means data =work.model; var WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY; run; he MEANS Procedure Variable Label N Mean Std Dev Minimum Maximum WELFARE INDUSTRIA LRELATION SHIP EMPLOYEE COMPENSA TION EMPLOYEE MOTIVATIO N CAREERDE VELOPMEN T HRP SAFETY WELFARE INDUSTRIA LRELATION SHIP EMPLOYEE COMPENSA TION EMPLOYEE MOTIVATIO N CAREERDE VELOPMEN T HRP SAFETY 79 79 79 79 79 79 79 3.9240506 3.4683544 3.6708861 3.4810127 3.8481013 3.7468354 4.0506329 0.6154455 0.7980014 0.6349141 0.7313626 0.7176995 0.6692582 0.7662552 3.0000000 2.0000000 2.0000000 2.0000000 3.0000000 2.0000000 3.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 DESCRIPTIVE STATISTICS
  • 7. proc freq data =work.model; tables WELFARE * CATEGORY/chisq; run; proc freq data =work.model; tables INDUSTRIALRELATIONSHIP * CATEGORY/chisq; run; proc freq data =work.model; tables EMPLOYEECOMPENSATION * CATEGORY/chisq; run; proc freq data =work.model; tables EMPLOYEEMOTIVATION * CATEGORY/chisq; run; proc freq data =work.model; tables CAREERDEVELOPMENT * CATEGORY/chisq; run; proc freq data =work.model; tables HRP * CATEGORY/chisq; run; proc freq data =work.model; tables SAFETY * CATEGORY/chisq; run; Chisq
  • 8. Table of CAREERDEVELOPMENT by CATEGORY CAREERDEVEL OPMENT(CAR EERDEVELOP MENT) CATEGORY(CATEGORY) Total 1 2 3 12 15.19 44.44 42.86 15 18.99 55.56 29.41 27 34.18 ? ? 4 10 12.66 27.03 35.71 27 34.18 72.97 52.94 37 46.84 ? ? 5 6 7.59 40.00 21.43 9 11.39 60.00 17.65 15 18.99 ? ? Total 28 35.44 51 64.56 79 100.00
  • 9. Statistic DF Value Prob Chi-Square 2 2.2376 0.3267 Likelihood Ratio Chi- Square 2 2.2558 0.3237 Mantel- Haenszel Chi- Square 1 0.3277 0.5670 Phi Coefficient ? 0.1683 ? Contingency Coefficient ? 0.1660 ? Cramer's V ? 0.1683
  • 10. proc corr data =work.model; var WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY; run; proc reg data =work.model; model CATEGORY = WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY; run; proc anova data =work.model; class CATEGORY; model WELFARE INDUSTRIALRELATIONSHIP EMPLOYEECOMPENSATION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY = CATEGORY; run; Correlation/regression/anova
  • 11. Correlation Output Pearson Correlation Coefficients, N = 79 Prob > |r| under H0: Rho=0 WELFARE INDUSTRIALRELATION SHIP EMPLOYEECOMPENSAT ION EMPLOYEEMOTIVATION CAREERDEVELOPMENT HRP SAFETY WELFARE WELFARE 1.00000 ? 0.54323 <.0001 0.29612 0.0081 0.45248 <.0001 0.32185 0.0038 0.54411 <.0001 0.41605 0.0001 INDUSTRIALRELATION SHIP INDUSTRIALRELATION SHIP 0.54323 <.0001 1.00000 ? 0.56117 <.0001 0.64149 <.0001 0.43920 <.0001 0.58494 <.0001 0.38005 0.0006 EMPLOYEECOMPENSAT ION EMPLOYEECOMPENSAT ION 0.29612 0.0081 0.56117 <.0001 1.00000 ? 0.53856 <.0001 0.50786 <.0001 0.37466 0.0007 0.45633 <.0001 EMPLOYEEMOTIVATIO N EMPLOYEEMOTIVATIO N 0.45248 <.0001 0.64149 <.0001 0.53856 <.0001 1.00000 ? 0.67833 <.0001 0.64487 <.0001 0.57366 <.0001 CAREERDEVELOPMENT CAREERDEVELOPMENT 0.32185 0.0038 0.43920 <.0001 0.50786 <.0001 0.67833 <.0001 1.00000 ? 0.66627 <.0001 0.64360 <.0001 HRP HRP 0.54411 <.0001 0.58494 <.0001 0.37466 0.0007 0.64487 <.0001 0.66627 <.0001 1.00000 ? 0.60031 <.0001 SAFETY SAFETY 0.41605 0.0001 0.38005 0.0006 0.45633 <.0001
  • 13. ANOVA Output Source DF Sum of Squares Mean Square F Value Pr > F Model 1 1.504662 62 1.504662 62 3.87 0.0528 Error 77 29.93837 535 0.388810 07 ? ? Correcte d Total 78 31.44303 797 ? ? ? R-Square Coeff Var Root MSE EMPLOYEECO MPENSATION Mean 0.047854 16.98626 0.623546 3.670886 Source DF Anova SS Mean Square F Value Pr > F CATEGOR Y 1 1.504662 62 1.504662 62 3.87 0.0528
  • 14. Webpage and PDF Coding ods pdf file= 'E:trim 5James Sir - SAPSailSas assignment.pdf'; startpage = 1; ods pdf text = "SAS ASSIGNMENT-13MBA1042"; ods pdf close; run; quit; ods html file= 'E:trim 5James Sir - SAPSailSas assignment.html'; ::::::::: ::::::::: ::::::::: ods html close; run; quit;