aspr 2009 presentation (tony machin)

Predicting job satisfaction and Predicting job satisfaction and depression at work: How important depression at work: How important are work-related factors?are work-related factors?

M. Anthony Machin

Associate Professor

University of Southern Queensland

Presented at the 2009 Australasian Society for Psychiatric Research (ASPR) Annual Conference, Canberra

Mental health at workMental health at work

Depression is only one of a number of mental health problems that present in the workplace. Others include anxiety disorders, substance abuse disorders, chronic fatigue syndrome, schizophrenia and organic states.

The Australian Bureau of Statistics (1998) found that 6% of the population had experienced depression during the 12 months prior to the 1997 survey.

Burden of disease and injuryBurden of disease and injury

The first burden of disease and injury study carried out in Australia found that depression was found to be the top-ranking cause of non-fatal disease burden in Australia, causing 8% of the total years lost due to disability in 1996. Mental disorders overall were responsible for nearly 30% of the non-fatal disease burden (Mathers, Vos, Stevenson & Begg, 2000)

Workplace well-beingWorkplace well-being

Conceptual models of subjective well-being typically include three components: Positive affect (PA) Negative affect (NA) and Evaluations of satisfaction with one’s life

Page and Vella-Brodrick (2009) suggested that workplace well-being (WWB) has a similar structure

Figure 1 from Page and Vella-Brodrick Figure 1 from Page and Vella-Brodrick (2009) (2009)

Page, K. M., & Vella-Broderick, D. A. (2009). The ‘What’, ‘Why’ and ‘How’ of employee well-being: A new model. Social Indicators Research, 90, 441-458.

What predicts WWB?What predicts WWB?

Judge and Kammeyer-Mueller (2008) proposed that a combination of situational and dispositional factors would explain employees’ affective states and (through these), employees’ satisfaction and performance

Figure 8.1 from Judge and Kammeyer-Figure 8.1 from Judge and Kammeyer-Mueller (2008)Mueller (2008)

Judge, T. A., & Kammeyer-Mueller, J. D. (2008). Affect, satisfaction, and performance. In N. M. Ashkanasy & C. L. Cooper (Eds.), Research companion to emotion in organizations (Ch. 8, pp. 136-151). Cheltenham, UK: Edward Elgar Press.

Which work characteristics to use?Which work characteristics to use?

The Demand-Control-Support (D-C-S) model provides a theoretical means of understanding the relationship between psychosocial work factors and subjective well-being, and has accordingly dominated research in this area (Van der Doef & Maes, 1999; Van Veldhoven, Taris, Jonge, & Broersen, 2005)

Main effects or interaction effects?Main effects or interaction effects?

The D-C-S model specifically looks at the interaction effects of combinations of these variables (also includes terms such as moderation and buffering)

These interactions are also not well-supported in the literature and the actual magnitude of interaction effects is usually quite small (after controlling for main effects)

Importance of PA and NAImportance of PA and NA

Thoresen et al. (2003) showed that both PA and NA are important predictors of job satisfaction, organisational commitment, dimensions of burnout, and turnover intentions

Therefore, PA and NA should be included in models that attempt to explain how job-related variables are related to employee well-being and mental health.

Aim of the current studyAim of the current study

This study examined the the unique contribution of various work characteristics to the prediction of job satisfaction and depression, after controlling for measures of personality and affectivity (both positive and negative)

Method & Data AnalysesMethod & Data Analyses

N = 416 employed Australians over 18 years of age, were invited to participate through an email providing a link to a web-based survey.

Males = 23% 31.4% of participants were <25 years of

age, while 23% were >39 years of age.

Study VariablesStudy Variables

Psychological demands, control, and social support variables were measured using the DCSQ (Theorell & Karasek, 1996) (α’s = .74, .75, .92 and .91)

Depression was assessed using the DASS-21 (Lovibond & Lovibond, 1995) (α = .91)

PA and NA were measured using the Job-Related Affective Well-being Scale (JAWS; van Katwyk, Fox, Spector, & Kelloway, 2000) (α’s = .93 and .85)

Measures of Neuroticism and Extraversion were obtained from the IPIP website (http://ipip.ori.org) (α’s = .88 and .89)

Four item were used to assess job satisfaction (α = .93) Overall, I am satisfied with the kind of work I do Overall, I am satisfied with the workplace in which I work Overall, I am satisfied with my job I would recommend this workplace as a place to work to others

Results – CorrelationsResults – Correlations

Dep. PA NA Extra. Neuro.Work dems. Control

Sup. Supprt

Co-work

Supprt Job Sat.

Depression 1.00 -.46** .54** -.37** .62** .07 -.27** -.30** -.35** -.50**

Positive Affect 1.00 -.43** .32** -.35** .01 .62** .50** .45** .75**

Negative Affect 1.00 -.26** .37** .22** -.23** -.41** -.44** -.56**

Extraversion 1.00 -.50** .07 .30** .18** .24** .25**

Neuroticism 1.00 .04 -.29** -.23** -.28** -.30**

Work demands 1.00 .13** -.09* -.13** -.09*

Control 1.00 .35** .33** .53**

Supervisor support 1.00 .68** .54**

Co-worker support 1.00 .54**

Job Satisfaction 1.00

*p < .05**p < .01

Results – Hierarchical regressionsResults – Hierarchical regressions

When predicting Job Satisfaction At Step 1, PA and NA both contributed

uniquely as predictors (R2 = .63, p > .001) At Step 2, neither Extraversion nor

Neuroticism contributed as predictors (R2 = .00, ns)

At Step 3, both Control and Co-worker support were significant unique predictors (R2 = .04, p > .001)

Results – Hierarchical regressionsResults – Hierarchical regressions

When predicting Depression At Step 1, PA and NA both contributed

uniquely as predictors (R2 = .35, p > .001) At Step 2, only Neuroticism contributed

uniquely as a predictor (R2 = .17, p > .001)

At Step 3, none of the D-C-S variables were significant unique predictors (R2 = .00, ns)

Results - Structural equation modelling Results - Structural equation modelling

In order to examine the direct and indirect impact of the D-C-S variables on Job Satisfaction, Model #1 was specified so that it captured the indirect impact of the D-C-S variables via PA and NA

Model #1 omitted Extraversion and Neuroticism as they did not uniquely contribute to Job satisfaction

Structural equation model #1Structural equation model #1

.48

PA

.25

NA

.66

Job Satisfaction

Work demands

Control

Supervisor support

e1

e2

e3

.50

Co-worker support

-.09

.68

.33

.35

.13

.12

-.27

.17

.50

-.25

-.26

.11

.17

.24-.13

-.18

-.10

This model is a good fit to the data. χ2 = 5.09 df = 3, p = .165 CFI = 1.00, AGFI = .968, RMSEA = .04

The combination of predictors is explaining 66% of the variance in Job Satisfaction, 48% of the variance in PA and 25% of the variance in NA.

Model #2 omitted the D-C-S variables as they did not uniquely contribute to predicting Depression.

Structural equation model #2Structural equation model #2

.15

PA

.14

NA

.52

Depression

e1

e2

e3

-.17

.30

Neuroticism

Extraversion

.37

.16

-.01

.45

-.50

-.33

-.28

This model is a reasonable fit to the data. χ2 = 3.28 df = 1, p = .07 CFI = .996, AGFI = .953, RMSEA = .07

The combination of predictors is explaining 52% of the variance in Depression, 15% of the variance in PA and 14% of the variance in NA.

Model #3 is an attempt to capture the effects of Extraversion, Neurocitism, PA and NA on Job Satisfaction and Depression simultaneously

.15

PA

.14

NA

.54

Depression

e1

e2

e3

.23

Neuroticism

Extraversion

.37

.16

.47

-.50

-.33

.63

Job Satisfaction

.62

-.30

-.23

e4

-.28

This model is a very good fit to the data. χ2 = 5.59 df = 5, p = .348 CFI = 1.00, AGFI = .981, RMSEA = .02

The D-C-S variables are not included as they will only add slightly to the prediction of Job Satisfaction. However, they do add substantially to the prediction of PA and NA.

ConclusionsConclusions

The D-C-S variables explain considerable variation in PA and NA (two aspects of WWB) but very little variation in Job Satisfaction (the third aspect of WWB)

The D-C-S variables explain no additional variance in Depression after controlling for PA, NA and personality

The focus of work-related interventions designed to improve satisfaction or alleviate depression should give priority to the determinants of PA and NA

These include several workplace characteristics (work demands, control, supervisor support and co-worker support)

However, there are other possibilities!

What’s missing?What’s missing?

Psychological Well-Being (PWB) has been suggested as an important component of employee well-being.

Page and Vella-Brodrick (2009) suggested that PWB is an important component of employee mental health.

Research conducted by Dr Richard Burns (CMHR at ANU) for his PhD focused on moving beyond a focus on SWB to include PWB.

Just published!Just published!

Burns and Machin (2010) showed that, after controlling for personality, aspects of PWB were a significant predictor of SWB.

Fava and Tomba (2009) describe an approach called “Well-being therapy” which focuses on developing PWB and resilience.

Burns, R. A., & Machin, M. A. (2010). Identifying gender differences in the independent effects of personality and psychological well-being on two broad affect components of subjective well-being. Personality and Individual Differences, 48, 22-27. DOI: 10.1016/j.paid.2009.08.007

Contact me if you have any questionsContact me if you have any questions

Associate Professor Tony Machin, Department of Psychology, University of Southern Queensland, Toowoomba, 4350. Australia. Telephone +61 7 46312587. Fax +61 7 46312721. Email: machin@usq.edu.au