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Clinical and Personality Assessment: An Essay in the Honor of Scott O. Lilienfeld

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Abstract

This chapter was written in honor of the late Professor Scott O. Lilienfeld, who contributed substantially and impressively to the field of clinical personality assessment. We provide an overview of this field as well as a discussion of personality scale construction and evaluation. More specifically, we review the history of personality assessment including a brief overview of Sir Francis Galton’s work which set the stage for modern psychometrics. We further review contemporary personality assessment principles, which typically focus on the assessment of psychological constructs from either typological (e.g., categorical diagnosis) or dimensional, individual differences perspectives. We describe numerous potential sources of information should be considered in assessment practice, with a particular focus on evidence-based assessment principles. The second part of this chapter covers scale construction and evaluation. In terms of the former, we detail one deductive approach that places construct validity at the center of emphasis originally championed by Loevinger, and more recently by Clark and Watson, and a second more inductive approach that allows for the elaboration of theoretical constructs through scale construction; the latter was favored by Scott Lilienfeld throughout his career. We also discuss psychometric principles of reliability, internal structure, and validity, ending with examples derived from Lilienfeld’s published work.

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Notes

  1. 1.

    For instance, let’s assume a scale with three items. Each item has a latent factor loading of 0.75, which means that the proportion of variance unaccounted for in each item is.50 ((1–0.75)2). The formula for omega is the squared sum of factor loadings divided by the total variance (i.e., squared sum of factor loadings and sum of item residual variances). A three item scale would yield an omega coefficient of 0.77. A five-item scale with the same factor loadings (and thus, the same amount of measurement error associated with each item) would yield an omega coefficient of.85. See Revelle and Condon (2019) for a more sophisticated illustration.

  2. 2.

    Some scholars use principal components analysis (PCA) for this same purpose. PCA should not be confused with EFA, however, because it is not based on the common factor model as it does not parcel out shared and unique variances (Mulaik, 2010). Rather, PCA is a more simplistic procedure that attempts to maximize the amount of variance for which can be accounted in the indicators rather than making assumptions about causation. Some scholars nonetheless argue that PCA might be advantageous to EFA because it is more simplistic, is less prone to problematic solutions, and PCA and EFA often yield similar results. However, others (e.g., Brown., 2014; Fabrigar et al., 1999) have generally refuted these arguments as solutions are indeed dissimilar under various conditions (e.g., few indicators per factor, small communalities [i.e., amount of variance accounted for in an indicator by all factors]), and more generally, analyses should be applied based on the underlying theoretical assumptions made about associations among variables.

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Authors and Affiliations

  1. Department of Psychology, University of Otago, Dunedin, New Zealand

    Martin Sellbom

  2. Kent State University, Kent, OH, USA

    Yossef S. Ben-Porath

  3. Takeda Pharmaceuticals, Cambridge Massachusetts, Cambridge, USA

    Robert D. Latzman

Authors
  1. Martin Sellbom
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  2. Yossef S. Ben-Porath
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  3. Robert D. Latzman
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Correspondence to Martin Sellbom .

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Editors and Affiliations

  1. Department of Human Development and Family Science, Auburn University, Auburn, AL, USA

    Cory L. Cobb

  2. Department of Psychology, Binghamton University, Binghamton, NY, USA

    Steven Jay Lynn

  3. Department of Psychology, University of Nevada, Reno, NV, USA

    William O’Donohue

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Sellbom, M., Ben-Porath, Y.S., Latzman, R.D. (2022). Clinical and Personality Assessment: An Essay in the Honor of Scott O. Lilienfeld. In: Cobb, C.L., Lynn, S.J., O’Donohue, W. (eds) Toward a Science of Clinical Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-14332-8_8

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