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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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.
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.
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.
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.
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.
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.
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.
Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12, 53–81.
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
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.
Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Psychology Foundation of Australia.
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.
McGuirk, L., Kuppens, P., Kingston, R., & Bastian, B. (2018). Does a culture of happiness increase rumination over failure? Emotion, 18(5), 755.
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.
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.
Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184–188.
Papageorgiou, C., & Wells, A. (2000). Treatment of recurrent major depression with attention training. Cognitive and Behavioral Practice, 7(4), 407–413.
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696.
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.
Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150.
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.
Roussis, P., & Wells, A. (2008). Psychological factors predicting stress symptoms: Metacognition, thought control, and varieties of worry. Anxiety, Stress, & Coping, 21(3), 213–225.
Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514.
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.
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.
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.
Wells, A. (2019). Breaking the cybernetic code: Understanding and treating the human metacognitive control system to enhance mental health. Frontiers in Psychology, 10, 2621.
Wells, A. (2009). Metacognitive Therapy for Anxiety and Depression. Guilford Press.
Wells, A., & Matthews, G. (1996). Modelling cognition in emotional disorder: The S-REF model. Behaviour Research and Therapy, 34(11–12), 881–888.
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.
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.
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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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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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DOI: https://doi.org/10.1007/s10862-022-09955-x