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The factor structure and composite reliability of the Profile of Emotional Distress

Published online by Cambridge University Press:  27 November 2013

Philip Hyland*
Affiliation:
School of Psychology, University of Ulster, Londonderry, UK
Mark Shevlin
Affiliation:
School of Psychology, University of Ulster, Londonderry, UK
Gary Adamson
Affiliation:
School of Psychology, University of Ulster, Londonderry, UK
Daniel Boduszek
Affiliation:
Department of Behavioural and Social Sciences, University of Huddersfield, Huddersfield, UK
*
*Author for correspondence: Mr P. Hyland, School of Psychology, University of Ulster, Magee Campus, Northland Road, Lodonderry BT48 7JL, UK (email: [email protected])

Abstract

This study provides the first assessment of the latent structure of the Profile of Emotional Distress (PED). The PED is a self-report measure of emotional distress (ED) associated strongly with its links to Rational Emotive Behaviour Therapy (REBT). To date, the PED has been weakly conceptualized using both unitary and binary models of ED. In this study, the dimensionality of the PED was examined within an alternative models’ framework using confirmatory factor analysis and bifactor modelling techniques. A total of 313 law enforcement, military, and related emergency-service personnel completed the PED. Results indicated that a bifactor model conceptualization was the best fit of the data. The bifactor model included a single general factor (ED) and four grouping factors (Concern, Anxiety, Sadness, Depression). Model parameter estimates indicated that the ED factor accounts for the majority of covariance among the observable indicators. Low factor loadings were observed on each of the grouping factors, thus subscale construction is not recommended. Composite reliability results demonstrated that the ED factor possesses excellent internal reliability. The PED was found to be a reliable and valid measure of emotional distress.

Type
Original Research
Copyright
Copyright © British Association for Behavioural and Cognitive Psychotherapies 2013 

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References

Akaike, H (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716723.CrossRefGoogle Scholar
Bagozzi RP, Yi Y (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16, 7494.CrossRefGoogle Scholar
Bentler, P (1990). Comparative fit indexes in structural models. Psychological Bulletin 107, 238246.CrossRefGoogle ScholarPubMed
Bollen, KA (1989). Structural Equations with Latent Variables. New York: Wiley.CrossRefGoogle Scholar
Chen, FF, West, SG, Sousa, KH (2006). A comparison of bifactor and second-order models of quality-of-life. Multivariate Behavioral Research 41, 189225.CrossRefGoogle ScholarPubMed
David, D, Montgomery, GH, Macavei, B, Bovbjerg, DH (2005 b). An empirical investigation of Albert Ellis's binary model of distress. Journal of Clinical Psychology 61, 499516.CrossRefGoogle ScholarPubMed
David, D, Schnur, J, Belloiu, A (2002). Another search for the ‘hot’ cognitions: appraisal, irrational beliefs, attributions, and their relation to emotion. Journal of Rational-Emotive and Cognitive-Behavior Therapy 15, 93131.CrossRefGoogle Scholar
David, D, Schnur, J, Birk, J (2004). Functional and dysfunctional feelings in Ellis’ cognitive theory of emotion: an empirical analysis. Cognition and Emotion 18, 869880.CrossRefGoogle Scholar
David, D, Szentagotai, A, Kallay, E, Macavei, B (2005 a). A synopsis of rational-emotive behavior therapy (REBT): fundamental and applied research. Journal of Rational-Emotive and Cognitive-Behavior Therapy 23, 175221.CrossRefGoogle Scholar
Diamantopoulos, A, Winklhofer, HM (2001). Index construction with formative indicators: an alternative to scale development. Journal of Marketing Research 38, 269277.CrossRefGoogle Scholar
DiLorenzo, TA, David, D, Montgomery, GH (2011). The impact of general and specific rational and irrational beliefs on exam distress; a further investigation of the binary model of emotional distress as an emotional regulation model. Journal of Cognitive and Behavioural Psychotherapies 11, 121142.Google Scholar
Dryden, W, Neenan, M (2004). Rational Emotive Behavioural Counselling in Action, 3rd edn.London: Sage.CrossRefGoogle Scholar
Ellis, A (1994). Reason and Emotion in Psychotherapy (revised edn). Secaucus, NJ: Birch Lane.Google Scholar
Ellis, A (2001). Overcoming Destructive Beliefs, Feelings, and Behaviours: New Directions for Rational Emotive Behaviour Therapy. Amherst, New York: Prometheus Books.Google Scholar
Ellis, A, DiGiuseppe, R (1993). Are inappropriate or dysfunctional feelings in rational-emotive therapy qualitative or quantitative? Cognitive Therapy and Research 5, 471477.CrossRefGoogle Scholar
Hair, Jr. JF, Anderson, RE, Tatham, RL, Black, WC (1998). Multivariate Data Analysis with Readings, 5th edn. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Hu, L, Bentler, PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 155.CrossRefGoogle Scholar
Jöreskog, K, Sörbom, D (1981). LISREL V: Analysis of Linear Structural Relationships by the Method of Maximum Likelihood. Chicago: National Educational Resources.Google Scholar
Jöreskog, K, Sörbom, D (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Chicago, IL: Scientific Software International Inc.Google Scholar
Kline, P (1994). An Easy Guide to Factor Analysis. London: Routledge.Google Scholar
Muthen, LK, Muthen, BO (1998–2010). Mplus – Statistical Analysis with Latent Variables. User's Guide, 6th edn.Los Angeles: Muthen and Muthen.Google Scholar
Opris, D, Macavei, B (2007). The profile of emotional distress: norms for the Romanian population. Journal of Cognitive and Behavioral Psychotherapies 7, 139158.Google Scholar
Raykov, T (1998). Coefficient alpha and composite reliability with interrelated nonhomogeneous items. Applied Psychological Measurement 22, 375385.CrossRefGoogle Scholar
Reise, SP, Moore, TM, Haviland, MG (2010). Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment 92, 544559.CrossRefGoogle ScholarPubMed
Reise, SP, Morizot, J, Hays, RD (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research 16, 1931.CrossRefGoogle ScholarPubMed
Shevlin, ME, Miles, JNV, Davies, MNO, Walker, S (2000). Coefficient alpha: a useful indicator of reliability? Personality and Individual Differences 28, 229238.CrossRefGoogle Scholar
Steiger, JH (1990). Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research 25, 173180.CrossRefGoogle ScholarPubMed
Tanaka, JS (1987). ‘How big is big enough?’ Sample size and goodness of fit in structural equation models with latent variables. Child Development 58, 134146.CrossRefGoogle Scholar
Tucker, LR, Lewis, C (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika 38, 110.CrossRefGoogle Scholar
Wessler, RL (1996). Idiosyncratic definitions and unsupported hypotheses: rational-emotive behavior therapy as pseudoscience. Journal of Rational-Emotive & Cognitive-Behavior Therapy 14, 4161.CrossRefGoogle Scholar
Yung, Y, Thissen, D, McLeod, LD (1999). On the relationship between the higher order factor model and the hierarchical factor model. Psychometrika 64, 113128.CrossRefGoogle Scholar
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