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Factor Structure and Incremental Utility of the Multidimensional Cognitive Attentional Syndrome Scale (MCASS): A Bifactor Analysis

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Abstract

The Multidimensional Cognitive Attentional Syndrome Scale (MCASS) was developed to assess the seven maladaptive forms of self-regulation that make up the cognitive attentional syndrome (CAS). Both theory and empirical evidence highlight important distinctions among the seven forms of self-regulation underlying the CAS. The primary purpose of the present study was to determine whether the MCASS item scores are sufficiently multidimensional to warrant the use of subscale scores. A secondary aim was to examine the incremental utility of the MCASS domain-specific factors. A battery of self-report measures was administered to adults recruited through a crowd-sourcing website (N = 359). Bifactor analysis was used to examine the multidimensionality of MCASS item scores. This analytic approach allowed for the quantification of variance captured by each domain-specific item score independent of the general factor. Results from the bifactor analysis suggest that the MCASS is a multidimensional measure, consisting of a strong general factor and domain-specific factors that are sufficiently distinct. Additionally, the majority of domain-specific factors provided incremental utility in predicting two criterion variables (i.e., general distress, happiness emotion goals) after accounting for the general factor. Taken together, results support continued use of the MCASS total scale and subscale scores and suggest that researchers may want to consider using a bifactor model when examining structural models that include the MCASS.

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Data Availability

The dataset used in this study may be obtained from the corresponding author upon reasonable request.

References

  • Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological Assessment, 10(2), 176–181.

    Article  Google Scholar 

  • Bardeen, J. R., Fergus, T. A., & Wu, K. D. (2013a). The interactive effect of worry and intolerance of uncertainty on posttraumatic stress symptoms. Cognitive Therapy and Research, 37(4), 742–751.

    Article  Google Scholar 

  • Bardeen, J. R., Kumpula, M. J., & Orcutt, H. K. (2013b). Emotion regulation difficulties as a prospective predictor of posttraumatic stress symptoms following a mass shooting. Journal of Anxiety Disorders, 27(2), 188–196.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bonifay, W., Lane, S. P., & Reise, S. P. (2017). Three concerns with applying a bifactor model as a structure of psychopathology. Clinical Psychological Science, 5(1), 184–186.

    Article  Google Scholar 

  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.

    Google Scholar 

  • Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality data? Perspectives on Psychological Science, 6(1), 3–5.

    Article  PubMed  Google Scholar 

  • Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29(6), 2156–2160.

    Article  Google Scholar 

  • Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12, 53–81.

    Article  PubMed  Google Scholar 

  • Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255.

    Article  Google Scholar 

  • Conboy, N. E., Fergus, T. A., & Bardeen, J. R. (2021). Development and validation of the multidimensional cognitive attentional syndrome scale (MCASS). Psychological Assessment, 33(6), 489–498.

    Article  PubMed  Google Scholar 

  • Crump, M. J., McDonnell, J. V., & Gureckis, T. M. (2013). Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PLoS One, 8(3), 1–18.

    Article  Google Scholar 

  • Dueber, D. M. (2016). Bifactor Indices Calculator: A Microsoft Excel-based tool to calculate various indices relevant to bifactor CFA models. Available at http://sites.education.uky.edu/apslab/resources

  • Fergus, T. A., & Bardeen, J. R. (2017). Examining the incremental contribution of metacognitive beliefs beyond content-specific beliefs in relation to posttraumatic stress in a community sample. Psychological Trauma: Theory, Research, Practice, and Policy, 9(6), 723–730.

    Article  Google Scholar 

  • Fergus, T. A., & Bardeen, J. R. (2019). The Metacognitions Questionnaire-30: An examination of a bifactor model and measurement invariance among men and women in a community sample. Assessment, 26(2), 223–234.

    Article  PubMed  Google Scholar 

  • Fergus, T. A., Bardeen, J. R., & Orcutt, H. K. (2012). Attentional control moderates the relationship between activation of the cognitive attentional syndrome and symptoms of psychopathology. Personality and Individual Differences, 53(3), 213–217.

    Article  Google Scholar 

  • Fergus, T. A., Valentiner, D. P., McGrath, P. B., Gier-Lonsway, S., & Jencius, S. (2013). The cognitive attentional syndrome: Examining relations with mood and anxiety symptoms and distinctiveness from psychological inflexibility in a clinical sample. Psychiatry Research, 210(1), 215–219.

    Article  PubMed  Google Scholar 

  • Ford, B. Q., Shallcross, A. J., Mauss, I. B., Floerke, V. A., & Gruber, J. (2014). Desperately seeking happiness: Valuing happiness is associated with symptom and diagnosis of depression. Journal of Social and Clinical Psychology, 33(10), 890–905.

    Article  PubMed  PubMed Central  Google Scholar 

  • Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44(2), 227–239.

    Article  PubMed  Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

  • Kahriz, B. M., Bower, J. L., Glover, F. M., & Vogt, J. (2020). Wanting to be happy but not knowing how: Poor attentional control and emotion-regulation abilities mediate the association between valuing happiness and depression. Journal of Happiness Studies, 21(7), 2583–2601.

    Article  Google Scholar 

  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

    Google Scholar 

  • Kowalski, J., Wypych, M., Marchewka, A., & Dragan, M. (2019). Neural correlates of cognitive-attentional syndrome: An fMRI study on repetitive negative thinking induction and resting state functional connectivity. Frontiers in Psychology, 10, 648.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Psychology Foundation of Australia.

    Google Scholar 

  • Luciano, J. V., Sanabria‐Mazo, J. P., Feliu‐Soler, A., & Forero, C. G. (2020). The pros and cons of bifactor models for testing dimensionality and psychopathological models: A commentary on the manuscript “A systematic review and meta‐analytic factor analysis of the depression anxiety stress scales”. Clinical Psychology: Science and Practice, e12386.

  • Mauss, I. B., Tamir, M., Anderson, C. L., & Savino, N. S. (2011). Can seeking happiness make people unhappy? Paradoxical Effects of Valuing Happiness. Emotion, 11(4), 807–815.

    PubMed  Google Scholar 

  • McGuirk, L., Kuppens, P., Kingston, R., & Bastian, B. (2018). Does a culture of happiness increase rumination over failure? Emotion, 18(5), 755.

    Article  PubMed  Google Scholar 

  • Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52(3), 431–462.

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (2015). MPlus (Version 7.4) [Computer software]. Los Angeles, CA: Muthén & Muthén.

  • Nolen-Hoeksema, S. (2000). The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of Abnormal Psychology, 109(3), 504–511.

    Article  PubMed  Google Scholar 

  • Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184–188.

    Article  Google Scholar 

  • Papageorgiou, C., & Wells, A. (2000). Treatment of recurrent major depression with attention training. Cognitive and Behavioral Practice, 7(4), 407–413.

    Article  Google Scholar 

  • Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696.

    Article  PubMed  PubMed Central  Google Scholar 

  • Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95(2), 129–140.

    Article  PubMed  Google Scholar 

  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150.

    Article  PubMed  Google Scholar 

  • Rogers, T. A., Bardeen, J. R., Fergus, T. A., & Benfer, N. (2020). Factor structure and incremental utility of the Distress Tolerance Scale: A bifactor analysis. Assessment, 27(2), 297–308.

    Article  PubMed  Google Scholar 

  • Roussis, P., & Wells, A. (2008). Psychological factors predicting stress symptoms: Metacognition, thought control, and varieties of worry. Anxiety, Stress, & Coping, 21(3), 213–225.

    Article  Google Scholar 

  • Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514.

    Article  Google Scholar 

  • Seligowski, A. V., Lee, D. J., Bardeen, J. R., & Orcutt, H. K. (2015). Emotion regulation and posttraumatic stress symptoms: A meta-analysis. Cognitive Behaviour Therapy, 44(2), 87–102.

    Article  PubMed  Google Scholar 

  • Stucky, B. D., & Edelen, M. O. (2015). Using hierarchical IRT to create unidimensional measures from multidimensional data. In S. P. Reise & D. A. Revicki (Eds.), Handbook of item response theory modeling: Applications to typical performance assessment (pp. 183–206). New York, NY: Routledge.

  • Stucky, B. D., Thissen, D., & Edelen, M. O. (2013). Using logistic approximations of marginal trace lines to develop short assessments. Applied Psychological Measurement, 37(1), 41–57.

    Article  Google Scholar 

  • Wang, M., & Russell, S. S. (2005). Measurement equivalence of the job descriptive index across Chinese and American workers: Results from confirmatory factor analysis and item response theory. Educational and Psychological Measurement, 65(4), 709–732.

    Article  Google Scholar 

  • Wells, A. (2019). Breaking the cybernetic code: Understanding and treating the human metacognitive control system to enhance mental health. Frontiers in Psychology, 10, 2621.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wells, A. (2009). Metacognitive Therapy for Anxiety and Depression. Guilford Press.

    Google Scholar 

  • Wells, A., & Matthews, G. (1996). Modelling cognition in emotional disorder: The S-REF model. Behaviour Research and Therapy, 34(11–12), 881–888.

    Article  PubMed  Google Scholar 

  • Yarrish, C., Groshon, L., Mitchell, J. D., Appelbaum, A., Klock, S., Winternitz, T., & Friedman-Wheeler, D. G. (2019). Finding the signal in the noise: Minimizing responses from bots and inattentive humans in online research. The Behavior Therapist, 42(7), 235–242.

    Google Scholar 

  • Young, C. C., & Dietrich, M. S. (2015). Stressful life events, worry, and rumination predict depressive and anxiety symptoms in young adolescents. Journal of Child and Adolescent Psychiatric Nursing, 28(1), 35–42.

    Article  PubMed  Google Scholar 

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Correspondence to Joseph R. Bardeen.

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The authors complied with APA ethical standards in the treatment of study participants and the study protocol was approved by the Office of Research Compliance at Auburn University.

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Informed consent was obtained from all participants included in this study.

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Joseph R. Bardeen, Kate Clauss and Thomas A. Fergus have no conflict of interests to report.

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Bardeen, J.R., Clauss, K. & Fergus, T.A. Factor Structure and Incremental Utility of the Multidimensional Cognitive Attentional Syndrome Scale (MCASS): A Bifactor Analysis. J Psychopathol Behav Assess 44, 836–843 (2022). https://doi.org/10.1007/s10862-022-09955-x

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