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Cognitive Therapy and Research

, Volume 20, Issue 2, pp 115–133 | Cite as

A factor-analytic study of the Social Problem-Solving Inventory: An integration of theory and data

  • Albert Maydeu-Olivares
  • Thomas J. D'Zurilla
Article

Abstract

Restricted (confirmatory) and unrestricted (exploratory) factor analyses were used to investigate the factor structure of the Social Problem-Solving Inventory (SPSI; D'Zurilla & Nezu, 1990). The SPSI is based on a theoretical model consisting of two general components (problem orientation and problem-solving skills) which are further divided into seven primary subcomponents (cognitive, emotional, and behavioral aspects of problem orientation and four specific problem-solving skills). Thus, both a two-factor model and a hierarchical model with seven first-order factors and two second-order factors were tested. The results provided only modest support for the two-factor model, and the hierarchical model failed to show substantial improvement over this model. Further analyses using exploratory as well as confirmatory methods found that an alternative five-factor model was best for the SPSI in the sense of goodness of fit, parsimony, and cross-validation. The implications of these results for social problem-solving theory and assessment are discussed.

Key words

everyday personal interpersonal problem-solving assessment cognitive-behavioral assessment 

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References

  1. Bentler, P. M. (1990). Comparative fit indexes in structural models.Psychological Bulletin, 107, 238–246.PubMedGoogle Scholar
  2. Bollen, K. A. (1989).Structural equations with latent variables. New York: Wiley.Google Scholar
  3. Bollen, K. A., & Long, J. S. (Eds.) (1993).Testing structural equation models. Newbury Park, CA: Sage.Google Scholar
  4. Bozdogan, H. (1987). Model selection and Akaike's information criteria (AIC).Psychometrika, 52, 345–370.Google Scholar
  5. Browne, M. W., & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures.Multivariate Behavioral Research, 24, 445–455.Google Scholar
  6. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.)Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.Google Scholar
  7. Chang, E. C., D'Zurilla, T. J., & Maydeu-Olivares, A. (1994). Assessing the dimensionality of optimism and pessimism using a multimeasure approach.Cognitive Therapy and Research, 18, 143–160.Google Scholar
  8. D'Zurilla, T. J. (1986).Problem-solving therapy: A social competence approach to clinical intervention. New York: Springer.Google Scholar
  9. D'Zurilla, T. J., & Goldfried, M. (1971). Problem-solving and behavior modification.Journal of Abnormal Psychology, 78, 104–126.Google Scholar
  10. D'Zurilla, T. J., & Maschka, G. (1988, November).Outcome of a problem-solving approach to stress management: I. Comparison with social support. Paper presented at the Association for the Advancement of Behavior Therapy Convention, New York.Google Scholar
  11. D'Zurilla, T. J., & Maydeu-Olivares, A. (1995). Conceptual and methodological issues in social problem-solving assessment.Behavior Therapy, 26, 409–432.Google Scholar
  12. D'Zurilla, T. J., & Nezu, A. M. (1982). Social problem-solving in adults. In P. C. Kendall (Ed.), Advances in cognitive-behavioral research and therapy (Vol. 1, pp. 201–274). New York: Academic Press.Google Scholar
  13. D'Zurilla, T. J., & Nezu, A. M. (1990). Development and preliminary evaluation of the Social Problem-Solving Inventory.Psychological Assessment: A Journal of Consulting and Clinical Psychology, 2, 156–163.Google Scholar
  14. D'Zurilla, T. J., Nezu, A. M., & Maydeu-Olivares, A. (1995).Manual for the Social Problem-Solving Inventory-Revised. Unpublished manuscript, State University at New York at Stony Brook.Google Scholar
  15. D'Zurilla, T. J., & Sheedy, C. F. (1991). The relation between social problem-solving ability and subsequent level of psychological stress in college students.Journal of Personality and Social Psychology, 61, 841–846.PubMedGoogle Scholar
  16. D'Zurilla, T. J., & Sheedy, C. F. (1992). The relation between social problem-solving ability and subsequent level of academic competence in college students.Cognitive Therapy and Research, 16, 589–599.Google Scholar
  17. Faccini, L. (1992). The relationship between stress, problem solving, and suicide.Dissertation Abstracts International, 53, 03B. (University Microfilms No. 9219142)Google Scholar
  18. Heppner, P. P. (Ed.). (1990). Problem solving and cognitive therapy. [Special issue].Journal of Cognitive Psychotherapy: An International Quarterly, 4(3).Google Scholar
  19. Janis, I. L., & Mann, I. (1977).Decision making: A psychological analysis of conflict, choice, and commitment. New York: Free Press.Google Scholar
  20. Jöreskog, K. G., & Sörbom, D. (1993).LISREL 8. User's reference guide. Chicago: Scientific Software.Google Scholar
  21. Kant, G. L. (1992). Problem solving as a moderator of stress-related depression and anxiety in older and middle-aged adults.Dissertion Abstracts International, 1100, 5402 B. (University Microfilms No. 9309980).Google Scholar
  22. Marshall, G. N., Wortman, C. B., Kusulas, J. W., Hervig, L. K., & Vickers, R. R. (1992). Distinguishing optimism from pessimism: Relations to fundamental dimensions of mood and personality.Journal of Personality and Social Psychology, 62, 1067–1074.Google Scholar
  23. McDonald, R. P. (1981). The dimensionality of tests and items.British Journal of Mathematical and Statistical Psychology, 34, 100–117.Google Scholar
  24. McDonald, R. P., & Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and goodness of fit.Psychological Bulletin, 107, 247–255.Google Scholar
  25. Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators.Psychometrika, 49, 115–132.Google Scholar
  26. Muthén, B., & Kaplan, D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model.British Journal of Mathematical and Statistical Psychology, 45, 19–30.Google Scholar
  27. Nezu, A. M., & D'Zurilla, T. J. (1989). Social problem solving and negative affective conditions. In P. C. Kendall & D. Watson (Eds),Anxiety and depression: Distinctive and overlapping features (pp. 285–315). New York: Academic Press.Google Scholar
  28. Nezu, A. M., Nezu, C. M., & Perri, M. G. (1989).Problem-solving therapy for depression: Theory, research, and clinical guidelines. New York: Wiley.Google Scholar
  29. Sadowski, C., & Kelley, M. L. (1993). Social problem solving in suicidal adolescents.Journal of Consulting and Clinical Psychology, 61, 121–127.PubMedGoogle Scholar
  30. Sadowski, C., Moore, L. A., & Kelley, M. L. (1994). Psychometric properties of the Social Problem Solving Inventory (SPSI) with normal and emotionally disturbed adolescents.Journal of Abnormal Child Psychology, 22, 487–500.PubMedGoogle Scholar
  31. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach.Multivariate Behavioral Research, 25, 173–180.Google Scholar
  32. Tanaka, J. S. (1993). Multifaceted conceptions of fit in structural equation models. In K. A. Bollen & J. S. Long (Eds.),Testing structural equation models (pp. 10–39). Newbury Park, CA: Sage.Google Scholar

Copyright information

© Plenum Publishing Corporation 1996

Authors and Affiliations

  • Albert Maydeu-Olivares
    • 1
  • Thomas J. D'Zurilla
    • 2
  1. 1.University of Illinois at Urbana-ChampaignUSA
  2. 2.Department of PsychologyState University of New York at Stony BrookStony Brook

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