impact of demographic and professional characteristics on job satisfaction

General
1

I want to analyze how demographic and professional characteristics impact job satisfaction.

I have a continuous variable for job satisfaction and 6 continuous variables for demographic and professional characteristics.

What would be a good method to analyze this? Regression?

I wrote this code. But I am not sure why it is not working

lm.out <- lm(pv1~AGE+EDU+SIZE+SCOPE+MGMT+SCOPE+TENURE, data=data)
summary(lm.out)

2

Some of the variables may not be meaningfully continuous, such as EDU. E.g., someone with two master's degrees may have the same number of years of post-graduate education as someone with a single Ph.D.

In addition, AGE, which comes closest to being a continuous variable may not be a linear parameter if, for example, y decays as a function of age between 35-40, rises from 25-30, is stable from 40-45 and decays again from 55-60.

3

True. But why is the code not working?

4

Hard to tell without a reprex

5

How do you know its not working?

1 Like
6

Small error. This is how the output looks like

lm.out <- lm(pv1~AGE+EDU+SIZE+SCOPE+MGMT+SCOPE+TENURE, data=data)
summary(lm.out)

Call:
lm(formula = pv1 ~ AGE + EDU + SIZE + SCOPE + MGMT + SCOPE +
TENURE, data = data)

Residuals:
Min 1Q Median 3Q Max
-2.9333 -0.5773 0.1871 0.4139 1.6265

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.03108 0.54933 7.338 1.19e-11 ***
AGE -0.01440 0.04570 -0.315 0.753
EDU -0.11773 0.11807 -0.997 0.320
SIZE -0.01542 0.04173 -0.370 0.712
SCOPE -0.09350 0.11778 -0.794 0.429
MGMT 0.21649 0.18681 1.159 0.248
TENURE 0.01434 0.06109 0.235 0.815

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.99 on 153 degrees of freedom
Multiple R-squared: 0.03106, Adjusted R-squared: -0.006937
F-statistic: 0.8174 on 6 and 153 DF, p-value: 0.558

7

The model is performing as specified and, therefore, works. It may not be the result desired, but that is a matter of variable selection, data selection, model selection or a combination.

8

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.