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Ravalier PhD Theis.pdf - Anglia Ruskin Research Online

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186<br />

y-<br />

General<br />

Survey<br />

Cynicism<br />

Professio<br />

nal<br />

Efficacy<br />

Control -.291 -2.67 .<br />

00<br />

9<br />

Role -.980 -3.94 .<br />

00<br />

1<br />

Change -.772 -4.99 .<br />

00<br />

1<br />

Managerial<br />

Support<br />

.655 4.85 .<br />

00<br />

1<br />

Role .467 2.42 .<br />

01<br />

7<br />

Control .361 3.41 .<br />

00<br />

1<br />

Demands .307 3.23 .<br />

00<br />

2<br />

.<br />

41<br />

5<br />

.<br />

43<br />

1<br />

.406 .703<br />

.413<br />

1.42<br />

3<br />

.651 1.53<br />

6<br />

.678 1.47<br />

5<br />

.861 1.16<br />

2<br />

.836 1.19<br />

6<br />

Table 13 above therefore shows the results of Linear Regression analyses with psychosocial hazards as<br />

measured by the MSIT (IV) acting as the variables which may associate with stress-related outcomes as measured<br />

by the MBI-GS (DV). An assumption of multiple regression analyses is that the factors being assessed are not cocorrelated<br />

with one another, thus indicating that they are not measuring the same thing. In this respect VIF<br />

(Variance Inflation Factor) values (which check for colinearity) for variables should be fewer than 5 (Denis, 2011)<br />

and therefore Tolerance above 0.2 (as Tolerance = 1/VIF) in order to be clear that there are differences between the<br />

predictors. There is some scepticism as to the use of clear cut off data for VIF (and therefore Tolerance), with

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