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.

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;

3. File 1

4. File 2

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

12. Regression Output

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;

15. Thank you