Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T16:07:15.116Z Has data issue: false hasContentIssue false
  • English
  • Français

Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs

Published online by Cambridge University Press:  21 March 2016

D. Borsboom ,
M. Rhemtulla ,
A. O. J. Cramer ,
H. L. J. van der Maas ,
M. Scheffer  and
C. V. Dolan
D. Borsboom*
Affiliation:
Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands
M. Rhemtulla
Affiliation:
Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands
A. O. J. Cramer
Affiliation:
Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands
H. L. J. van der Maas
Affiliation:
Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands
M. Scheffer
Affiliation:
Department of Aquatic Ecology and Water Quality Management, Wageningen University, 6700 AA Wageningen, The Netherlands
C. V. Dolan
Affiliation:
Department of Biological Psychology, VU University, 1081 BT Amsterdam, The Netherlands
*
*Address for correspondence: D. Borsboom, Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The question of whether psychopathology constructs are discrete kinds or continuous dimensions represents an important issue in clinical psychology and psychiatry. The present paper reviews psychometric modelling approaches that can be used to investigate this question through the application of statistical models. The relation between constructs and indicator variables in models with categorical and continuous latent variables is discussed, as are techniques specifically designed to address the distinction between latent categories as opposed to continua (taxometrics). In addition, we examine latent variable models that allow latent structures to have both continuous and categorical characteristics, such as factor mixture models and grade-of-membership models. Finally, we discuss recent alternative approaches based on network analysis and dynamical systems theory, which entail that the structure of constructs may be continuous for some individuals but categorical for others. Our evaluation of the psychometric literature shows that the kinds–continua distinction is considerably more subtle than is often presupposed in research; in particular, the hypotheses of kinds and continua are not mutually exclusive or exhaustive. We discuss opportunities to go beyond current research on the issue by using dynamical systems models, intra-individual time series and experimental manipulations.

Keywords

Dynamical systemslatent variable modelsnetwork modelspsychometricstaximetrics
Type
Review Article
Information
Psychological Medicine , Volume 46 , Issue 8 , June 2016 , pp. 1567 - 1579
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adolf, J, Schuurman, NK, Borkenau, P, Borsboom, D, Dolan, CV (2014). Measurement invariance within and between individuals: a distinct problem in testing the equivalence of intra- and inter-individual model structures. Frontiers in Quantitative Psychology and Measurement 5, 883.Google ScholarPubMed
Agresti, A (2013). Categorical Data Analysis. Wiley: New York.Google Scholar
Ahmed, AO, Buckley, PF, Mabe, PA (2012). Latent structure of psychotic experiences in the general population. Acta Psychiatrica Scandinavia 125, 54–65.Google Scholar
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Publishing: Arlington, VA.Google Scholar
Arminger, G, Stein, P, Wittenberg, J (1999). Mixtures of conditional mean- and covariance-structure models. Psychometrika 64, 475–494.CrossRefGoogle Scholar
Asparouhov, T, Muthén, B (2008). Multilevel mixture models. In Advances in Latent Variable Mixture Models (ed. Hancock, G.R. and Samuelsen, K.M.), pp. 27–51. Information Age Publishing, Inc.: Charlotte, NC.Google Scholar
Bartholomew, DJ (1987). Latent Variable Models and Factor Analysis. Griffin: London.Google Scholar
Boker, SM, Molenaar, PCM, Nesselroade, JR (2009). Issues in intraindividual variability: individual differences in equilibria and dynamics over multiple time scales. Psychology and Aging 24, 858–862.CrossRefGoogle ScholarPubMed
Bollen, KA (1989). Structural Equations with Latent Variables. Wiley: New York.Google Scholar
Borsboom, D (2005). Measuring the Mind: Conceptual Issues in Contemporary Psychometrics. Cambridge University Press: Cambridge.CrossRefGoogle Scholar
Borsboom, D, Cramer, AOJ (2013). Networks: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology 9, 91–121.Google Scholar
Borsboom, D, Mellenbergh, GJ, Van Heerden, J (2003). The theoretical status of latent variables. Psychological Review 110, 203–219.CrossRefGoogle ScholarPubMed
Bringmann, LF, Lemmens, LHJM, Huibers, MJH, Borsboom, D, Tuerlinckx, F (2015). Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychological Medicine 45, 747–757.Google Scholar
Bringmann, LF, Vissers, N, Wichers, M, Geschwind, N, Kuppens, P, Peeters, F, Borsboom, D, Tuerlinckx, F (2013). A network approach to psychopathology: new insights into clinical longitudinal data. PLOS ONE 8, e60188.Google Scholar
Clark, SL, Muthén, B, Kaprio, J, D'Onofrio, BM, Viken, R, Rose, RJ (2013). Models and strategies for factor mixture analysis: an example concerning the structure underlying psychological disorders. Structural Equation Modeling: A Multidisciplinary Journal 20, 681–703.Google Scholar
Cooper, G, Humphry, SM (2012). The ontological distinction between units and entities. Synthese 187, 393–401.Google Scholar
Cramer, AOJ (2013). The glue of (ab)normal mental life: networks of interacting thoughts, feelings and behaviors. Ph.D. Thesis (http://dare.uva.nl/record/452479). Accessed September 2015.Google Scholar
Cramer, AOJ, Borsboom, D, Aggen, SH, Kendler, KS (2012 a). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the patterns of depressive symptom inter-correlations. Psychological Medicine 42, 957–965.Google Scholar
Cramer, AOJ, van der Sluis, S, Noordhof, A, Wichers, M, Geschwind, N, Aggen, SH, Kendler, KS, Borsboom, D (2012 b). Dimensions of normal personality as networks in search of equilibrium: you can't like parties if you don't like people. European Journal of Personality 26, 414–431.CrossRefGoogle Scholar
Cramer, AOJ, Waldorp, LJ, van der Maas, HLJ, Borsboom, D (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences 33, 137–193.Google Scholar
Cronbach, LJ, Meehl, PE (1955). Construct validity in psychological tests. Psychological Bulletin 52, 281–302.CrossRefGoogle ScholarPubMed
Croon, MA (1990). Latent class analysis with ordered latent classes. British Journal of Mathematical and Statistical Psychology 43, 171–192.CrossRefGoogle Scholar
De Boeck, P, Wilson, M, Acton, GS (2005). A conceptual and psychometric framework for distinguishing categories and dimensions. Psychological Review 112, 129–158.CrossRefGoogle ScholarPubMed
Dolan, CV, Van der Maas, HLJ (1998). Fitting multivariate normal finite mixtures subject to structural equation modeling. Psychometrika 63, 227–253.Google Scholar
Dutilh, G, Wagenmakers, EJ, Visser, I, Van der Maas, HLJ (2010). A phase transition model for the speed–accuracy trade-off in response time experiments. Cognitive Science 34, 211–250.Google Scholar
Epskamp, S, Cramer, AOJ, Waldorp, LJ, Schmittmann, VD, Borsboom, D (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software 48, 1–18.Google Scholar
Erosheva, EA (2005). Comparing latent structures of the grade of membership, Rasch, and latent class models. Psychometrika 70, 619–628.Google Scholar
Forbes, D, Haslam, N, Williams, BJ, Creamer, M (2005). Testing the latent structure of posttraumatic stress disorder: a taxometric study of combat veterans. Journal of Traumatic Stress 18, 647–656.Google Scholar
Halpin, PF, Dolan, CV, Grasman, RPPP, De Boeck, P (2011). On the relation between the linear factor model and the latent profile model. Psychometrika 76, 564–583.Google Scholar
Hamaker, EL, Nesselroade, JR, Molenaar, CM (2007). The integrated trait–state model. Journal of Research in Personality 41, 295–315.Google Scholar
Haslam, N, Holland, E, Kuppens, P (2012). Categories versus dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychological Medicine 42, 903.CrossRefGoogle ScholarPubMed
Hölder, O (1901). Die Axiome der Quantität und die Lehre vom Mass (The axioms of quantity and the doctrine of weight). Ber. Verh. Kgl. Sächsis. Ges. Wiss. Leipzig, Math.-Phys. Classe 53, 1–64.Google Scholar
Hyland, ME (2011). The Origins of Health and Disease. Cambridge University Press: Cambridge, UK.Google Scholar
Jablensky, A (2006). Subtyping schizophrenia: implications for genetic research. Molecular Psychiatry 11, 815–836.CrossRefGoogle ScholarPubMed
Jablensky, A (2010). The diagnostic concept of schizophrenia: its history, evolution, and future prospects. Dialogues in Clinical Neuroscience 12, 271–287.Google Scholar
Kendler, KS, Zachar, P, Craver, C (2011). What kinds of things are psychiatric disorders? Psychological Medicine 41, 1143–1150.CrossRefGoogle ScholarPubMed
Kindermann, R, Snell, JL (1980). Markov Random Fields and their Applications. American Mathematical Society: Providence, RI.CrossRefGoogle Scholar
Kraepelin, E, Dierendorf, AR (1915). Clinical Psychiatry. A Textbook for Students and Physicians. The MacMillan Company: New York.Google Scholar
Krantz, DH, Luce, RD, Suppes, P, Tversky, A (1971). Foundations of Measurement, vol. I. Academic Press: New York.Google Scholar
Kuppens, P, Allen, NB, Sheeber, LB (2010). Emotional inertia and psychological maladjustment. Psychological Science 21, 984–991.Google Scholar
Lazarsfeld, PF, Henry, NW (1968). Latent Structure Analysis. Houghton-Mifflin: Boston, MA.Google Scholar
Lord, FM, Novick, MR (1968). Statistical Theories of Mental Test Scores. Addison-Welsley: Reading, MA.Google Scholar
Lubke, GH, Miller, PJ (2015). Does nature have joints worth carving? A discussion of taxometrics, model-based clustering and latent variable mixture modeling. Psychological Medicine 45, 705–715.Google Scholar
Lubke, GH, Muthén, B (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods 10, 21–39.Google Scholar
Lubke, GH, Neale, M (2006). Distinguishing between latent classes and continuous factors: resolution by maximum likelihood. Multivariate Behavioral Research 41, 499–532.CrossRefGoogle ScholarPubMed
Lubke, GH, Neale, M (2008). Distinguishing between latent classes and continuous factors with categorical outcomes: class invariance of parameters of factor mixture models. Multivariate Behavioral Research 43, 592–620.Google Scholar
MacCallum, RC, Zhang, S, Preacher, KJ, Rucker, DD (2002). On the practice of dichotomization of quantitative variables. Psychological Methods 7, 19–40.Google Scholar
Manton, KG, Korten, A, Woodbury, MA, Anker, M, Jablensky, A (1994). Symptom profiles of psychiatric disorders based on graded disease classes: an illustration using data from the WHO International Pilot Study of Schizophrenia. Psychological Medicine 24, 133–144.CrossRefGoogle ScholarPubMed
Maraun, MD, Slaney, K (2005). An analysis of Meehl's MAXCOV-HITMAX procedure for continuous indicators. Multivariate Behavioral Research 40, 489–518.Google Scholar
Maraun, MD, Slaney, K, Goddyn, L (2003). An analysis of Meehl's MAXCOV-HITMAX procedure for dichotomous indicators. Multivariate Behavioral Research 38, 81–112.Google Scholar
Markus, KA, Borsboom, D (2012). The cat came back: evaluating arguments against psychological measurement. Theory and Psychology 22, 452–466.Google Scholar
Markus, KA, Borsboom, D (2013). Frontiers of Test Validity Theory: Measurement, Causation, and Meaning. Routledge: New York.CrossRefGoogle Scholar
Masyn, K, Henderson, C, Greenbaum, P (2010). Exploring the latent structures of psychological constructs in social development using the dimensional–categorical spectrum. Social Development 19, 470–493.Google Scholar
McCutcheon, AL (1987). Latent Class Analysis. Quantitative Applications in the Social Sciences Series no. 64. Sage: Thousand Oaks, CA.Google Scholar
McGrath, RE, Walters, GD (2012). Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure. Psychological Methods 17, 284–293.Google Scholar
McLachlan, G, Peel, D (2000). Finite Mixture Models. Wiley: New York.Google Scholar
Meehl, PE (1992). Factors and taxa, traits and types, difference of degree and differences in kind. Journal of Personality 60, 117–174.Google Scholar
Meehl, PE (1995). Bootstraps taxometrics: solving the classification problem in psychopathology. American Psychologist 50, 266–275.Google Scholar
Mellenbergh, GJ (1989). Item bias and item response theory. International Journal of Educational Research 13, 127–143.Google Scholar
Mellenbergh, GJ (1994). Generalized linear item response theory. Psychological Bulletin 115, 300–307.CrossRefGoogle Scholar
Meredith, W (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika 58, 525–543.CrossRefGoogle Scholar
Michell, J (1997). Quantitative science and the definition of measurement in psychology. British Journal of Psychology 88, 355–383.Google Scholar
Michell, J (1999). Measurement in Psychology: A Critical History of a Methodological Concept. Cambridge University Press: Cambridge, UK.CrossRefGoogle Scholar
Molenaar, D, Dolan, CV, Verhelst, ND (2010). Testing and modeling non-normality within the one factor model. British Journal of Mathematical and Statistical Psychology 63, 293–317.Google Scholar
Molenaar, PCM (2004). A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives 2, 201–218.Google Scholar
Molenaar, PCM, Campbell, CG (2009). The new person-specific paradigm in psychology. Current Directions in Psychology 18, 112–117.CrossRefGoogle Scholar
Molenaar, PCM, Lerner, RM, Newell, KM (2013). Handbook of Developmental Systems. Guilford: New York.Google Scholar
Molenaar, PCM, Von Eye, A (1994). On the arbitrary nature of latent variables. In Latent Variables Analysis (ed. Von Eye, A. and Clogg, C.C.), pp. 226–242. Sage Publications: Thousand Oaks, CA.Google Scholar
Montpetit, MA, Bergeman, CS, Deboeck, PR, Tiberio, SS, Boker, SM (2010). Resilience-as-process: negative affect, stress, and coupled dynamical systems. Psychology and Aging 25, 631–640.Google Scholar
Muthén, B (2006). Should substance use disorders be considered as categorical or dimensional? Addiction 101 (Suppl. 1), 6–16.CrossRefGoogle ScholarPubMed
Muthén, B (2008). Latent variable hybrids: overview of old and new models. In Advances in Latent Variable Mixture Models (ed. Hancock, G. R. and Samuelsen, K. M.), pp. 1–24. Information Age: Charlotte, NC.Google Scholar
Nurnberg, HG, Woodbury, MA, Bogenschutz, MP (1999). A mathematical typology analysis of DSM-III-R personality disorder. Comprehensive Psychiatry 40, 61–71.Google Scholar
Pe, ML, Kircanski, K, Thompson, RJ, Bringmann, LF, Tuerlinckx, F, Mestdagh, M, Mata, J, Jaeggi, SM, Buschkuehl, M, Jonides, J, Kuppens, P, Gotlib, IH (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science 3, 292–300.Google Scholar
Pearl, J (2009). Causality: Models, Reasoning, and Inference, 2nd edn. Cambridge University Press: Cambridge, UK.Google Scholar
Reise, SP, Waller, NG (2009). Item response theory and clinical measurement. Annual Review of Clinical Psychology 5, 27–48.Google Scholar
Robitzsch, A (2014). sirt: Supplementary Item Response Theory Models. R package version 0.47–36 (http://cran.r-project.org/web/packages/sirt/). Accessed September 2015.Google Scholar
Ruscio, J, Haslam, N, Ruscio, AM (2006). Introduction to the Taxometric Method: A Practical Guide. Lawrence Erlbaum Associates: Mahwah, NJ.Google Scholar
Ruscio, J, Ruscio, AM, Meron, M (2007). Applying the bootstrap to taxometric analysis: generating empirical sampling distributions to help interpret results. Multivariate Behavioral Research 42, 349–386.CrossRefGoogle ScholarPubMed
Scheffer, M, Bascompte, J, Brock, WA, Brovkin, V, Carpenter, SR, Dakos, V, Held, H, van Nes, EH, Rietkerk, M, Sugihara, G (2009). Early-warning signals for critical transitions. Nature 461, 53–59.Google Scholar
Scheffer, M, Carpenter, SR, Lenton, TM, Bascompte, J, Brock, W, Dakos, V, van de Koppel, J, van de Leemput, IA, Levin, SA, van Nes, EH, Pascual, M, Vandermeer, J (2012). Anticipating critical transitions. Science 338, 344–348.Google Scholar
Schmitt, JE, Aggen, SH, Mehta, PD, Kubarych, TS, Neale, MC (2006). Semi-nonparametric methods for detecting latent non-normality: a fusion of latent trait and ordered latent class modeling. Multivariate Behavioral Research 41, 427–443.Google Scholar
Stevens, SS (1946). On the theory of scales of measurement. Science 103, 667–680.Google Scholar
Suppes, P, Zinnes, JL (1963). Basic measurement theory. In Handbook of Mathematical Psychology (ed. Luce, R.D., Bush, R. and Galanter, E.), pp. 3–76. Wiley: New York.Google Scholar
Tao, T (2011). An Introduction to Measure Theory. American Mathematical Society: Providence, RI.CrossRefGoogle Scholar
Thom, R (1975). Structural Stability and Morphogenesis. Benjamin Press: Reading, MA.Google Scholar
Trendler, G (2009). Measurement theory, psychology, and the revolution that cannot happen. Theory and Psychology 19, 579–599.Google Scholar
Van Borkulo, CD, Borsboom, D, Epskamp, S, Blanken, TF, Boschloo, L, Schoevers, RA, Waldorp, LJ (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918.Google Scholar
Van de Leemput, IA, Wichers, M, Cramer, AOJ, Borsboom, D, Tuerlinckx, F, Kuppens, P, Van Nes, EH, Viechtbauer, W, Giltay, EJ, Aggen, SH, Derom, C, Jacobs, N, Kendler, KS, Van der Maas, HLJ, Neale, MC, Peeters, F, Thiery, E, Zachar, P, Scheffer, M (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences USA 111, 87–92.Google Scholar
Van der Maas, HLJ, Molenaar, PCM (1992). Stagewise cognitive development: an application of catastrophe theory. Psychological Review 99, 395–417.Google Scholar
Van der Sluis, S, Posthuma, D, Nivard, MG, Verhage, M, Dolan, CV (2013). Power in GWAS: lifting the curse of the clinical cut-off. Molecular Psychiatry 18, 2–3.Google Scholar
Verkuilen, J, Kievit, RA, Zand Scholten, A (2011). Representing concepts by fuzzy sets. In Concepts and Fuzzy Logic (ed. Belohavek, R. and Klir, G.J.), pp. 149–176. MIT Press: Cambridge, MA.CrossRefGoogle Scholar
Vermunt, JK (2001). The use of restricted latent class models for defining and testing nonparametric and parametric item response theory models. Applied Psychological Measurement 25, 283–294.Google Scholar
Von Davier, M, Naemi, B, Roberts, RD (2012). Factorial versus typological models: a comparison of methods for personality data. Measurement: Interdisciplinary Research and Perspectives 10, 185–208.Google Scholar
Waller, NG, Meehl, PE (1998). Multivariate Taxometric Procedures: Distinguishing Types from Continua. Sage: Thousand Oaks, CA.Google Scholar
Wichers, M (2014). The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges. Psychological Medicine 44, 1349–1360.Google Scholar
Woodbury, MA, Manton, KG (1989). Grade of membership analysis of depression-related psychiatric disorders. Sociological Methods and Research 18, 126–163.Google Scholar
World Health Organization (1992). International Statistical Classification of Diseases, Injuries, and Causes of Death. Sixth Revision of the International List of Diseases and Causes of Death. World Health Organization: Geneva.Google Scholar
Yung, YF (1997). Finite mixtures in confirmatory factor-analysis models. Psychometrika 62, 297–330.Google Scholar
Zeeman, EC (1977). Catastrophe Theory: Selected Papers. Addison-Wesley: Reading, MA.Google Scholar