• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
ASPR 2009 Presentation on 02.12.09 (Tony Machin)
 

ASPR 2009 Presentation on 02.12.09 (Tony Machin)

on

  • 409 views

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

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

Statistics

Views

Total Views
409
Views on SlideShare
407
Embed Views
2

Actions

Likes
0
Downloads
0
Comments
0

1 Embed 2

http://www.linkedin.com 2

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • The ABS 1997 National Survey of Mental Health and Wellbeing which used internationally recognised diagnostic interview schedules to assess the prevalence of mental disorder through the measurement of symptoms. This survey found that about 700,000 Australian adults aged 18 years and over, 6% of the population, had experienced depression during the 12 months prior to the survey.
  • This is an overview of the first burden of disease and injury studies carried out in Australia. Methods developed for the World Bank and World Health Organization Global Burden of Disease Study were adapted and applied to Australian population health data. 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.
  • Although the confounding effects of NA are alluded to in the literature (Dollard & Winefield, 1998; Vermeulen & Mustard, 2000), a recent meta-analysis of the literature relating positive and negative affectivity to job-related attitudes found that both PA and NA are important predictors of job satisfaction, organisational commitment, dimensions of burnout, and turnover intentions (Thoresen, Kaplan, Barsky, de Chermont & Warren, 2003). The implication of this research is that 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. This meta-analysis also found that the estimated mean population correlation between PA and NA was -.36 based on a k of 76 with N = 24,361 (95% CI = -.39 to -.32). The current study includes both NA and PA as predictors of depression as well as allowing them to mediate the effects of D-C-S variables on depression.

ASPR 2009 Presentation on 02.12.09 (Tony Machin) ASPR 2009 Presentation on 02.12.09 (Tony Machin) Presentation Transcript

  • Predicting job satisfaction and depression at work: How important 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 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 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-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 (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?
    • 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-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?
    • 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?
    • 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 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 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 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 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 – Correlations * p < .05 ** p < .01 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
  • Results – Hierarchical regressions
    • When predicting Job Satisfaction
      • At Step 1, PA and NA both contributed uniquely as predictors (  R 2 = .63, p > .001)
      • At Step 2, neither Extraversion nor Neuroticism contributed as predictors (  R 2 = .00, ns)
      • At Step 3, both Control and Co-worker support were significant unique predictors (  R 2 = .04, p > .001)
  • Results – Hierarchical regressions
    • When predicting Depression
      • At Step 1, PA and NA both contributed uniquely as predictors (  R 2 = .35, p > .001)
      • At Step 2, only Neuroticism contributed uniquely as a predictor (  R 2 = .17, p > .001)
      • At Step 3, none of the D-C-S variables were significant unique predictors (  R 2 = .00, ns)
  • 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 #1
    • This model is a good fit to the data.
      • χ 2 = 5.09 d f = 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 #2
    • This model is a reasonable fit to the data.
      • χ 2 = 3.28 d f = 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
  •  
    • This model is a very good fit to the data.
      • χ 2 = 5.59 d f = 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.
  • Conclusions
    • 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?
    • 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!
    • 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 questions
    • Associate Professor Tony Machin,
    • Department of Psychology,
    • University of Southern Queensland,
    • Toowoomba, 4350. Australia.
    • Telephone +61 7 46312587.
    • Fax +61 7 46312721.
    • Email: [email_address]