Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-b2xwp Total loading time: 0.429 Render date: 2022-09-29T02:21:07.935Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

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*
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: d.borsboom@uva.nl)

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.

Type
Review Article
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, 5465.CrossRefGoogle ScholarPubMed
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Publishing: Arlington, VA.Google ScholarPubMed
Arminger, G, Stein, P, Wittenberg, J (1999). Mixtures of conditional mean- and covariance-structure models. Psychometrika 64, 475494.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. 2751. 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, 858862.CrossRefGoogle ScholarPubMed
Bollen, KA (1989). Structural Equations with Latent Variables. Wiley: New York.CrossRefGoogle 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, 91121.CrossRefGoogle ScholarPubMed
Borsboom, D, Mellenbergh, GJ, Van Heerden, J (2003). The theoretical status of latent variables. Psychological Review 110, 203219.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, 747757.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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, 681703.CrossRefGoogle ScholarPubMed
Cooper, G, Humphry, SM (2012). The ontological distinction between units and entities. Synthese 187, 393401.CrossRefGoogle 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, 957965.CrossRefGoogle ScholarPubMed
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, 414431.CrossRefGoogle Scholar
Cramer, AOJ, Waldorp, LJ, van der Maas, HLJ, Borsboom, D (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences 33, 137193.CrossRefGoogle ScholarPubMed
Cronbach, LJ, Meehl, PE (1955). Construct validity in psychological tests. Psychological Bulletin 52, 281302.CrossRefGoogle ScholarPubMed
Croon, MA (1990). Latent class analysis with ordered latent classes. British Journal of Mathematical and Statistical Psychology 43, 171192.CrossRefGoogle Scholar
De Boeck, P, Wilson, M, Acton, GS (2005). A conceptual and psychometric framework for distinguishing categories and dimensions. Psychological Review 112, 129158.CrossRefGoogle ScholarPubMed
Dolan, CV, Van der Maas, HLJ (1998). Fitting multivariate normal finite mixtures subject to structural equation modeling. Psychometrika 63, 227253.CrossRefGoogle 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, 211250.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, 118.CrossRefGoogle Scholar
Erosheva, EA (2005). Comparing latent structures of the grade of membership, Rasch, and latent class models. Psychometrika 70, 619628.CrossRefGoogle 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, 647656.CrossRefGoogle ScholarPubMed
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, 564583.CrossRefGoogle ScholarPubMed
Hamaker, EL, Nesselroade, JR, Molenaar, CM (2007). The integrated trait–state model. Journal of Research in Personality 41, 295315.CrossRefGoogle 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, 164.Google Scholar
Hyland, ME (2011). The Origins of Health and Disease. Cambridge University Press: Cambridge, UK.CrossRefGoogle Scholar
Jablensky, A (2006). Subtyping schizophrenia: implications for genetic research. Molecular Psychiatry 11, 815836.CrossRefGoogle ScholarPubMed
Jablensky, A (2010). The diagnostic concept of schizophrenia: its history, evolution, and future prospects. Dialogues in Clinical Neuroscience 12, 271287.Google ScholarPubMed
Kendler, KS, Zachar, P, Craver, C (2011). What kinds of things are psychiatric disorders? Psychological Medicine 41, 11431150.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, 984991.CrossRefGoogle ScholarPubMed
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, 705715.CrossRefGoogle ScholarPubMed
Lubke, GH, Muthén, B (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods 10, 2139.CrossRefGoogle ScholarPubMed
Lubke, GH, Neale, M (2006). Distinguishing between latent classes and continuous factors: resolution by maximum likelihood. Multivariate Behavioral Research 41, 499532.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, 592620.CrossRefGoogle ScholarPubMed
MacCallum, RC, Zhang, S, Preacher, KJ, Rucker, DD (2002). On the practice of dichotomization of quantitative variables. Psychological Methods 7, 1940.CrossRefGoogle ScholarPubMed
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, 133144.CrossRefGoogle ScholarPubMed
Maraun, MD, Slaney, K (2005). An analysis of Meehl's MAXCOV-HITMAX procedure for continuous indicators. Multivariate Behavioral Research 40, 489518.CrossRefGoogle ScholarPubMed
Maraun, MD, Slaney, K, Goddyn, L (2003). An analysis of Meehl's MAXCOV-HITMAX procedure for dichotomous indicators. Multivariate Behavioral Research 38, 81112.CrossRefGoogle ScholarPubMed
Markus, KA, Borsboom, D (2012). The cat came back: evaluating arguments against psychological measurement. Theory and Psychology 22, 452466.CrossRefGoogle Scholar
Markus, KA, Borsboom, D (2013). Frontiers of Test Validity Theory: Measurement, Causation, and Meaning. Routledge: New York.Google 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, 470493.CrossRefGoogle ScholarPubMed
McCutcheon, AL (1987). Latent Class Analysis. Quantitative Applications in the Social Sciences Series no. 64. Sage: Thousand Oaks, CA.CrossRefGoogle Scholar
McGrath, RE, Walters, GD (2012). Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure. Psychological Methods 17, 284293.CrossRefGoogle ScholarPubMed
McLachlan, G, Peel, D (2000). Finite Mixture Models. Wiley: New York.CrossRefGoogle ScholarPubMed
Meehl, PE (1992). Factors and taxa, traits and types, difference of degree and differences in kind. Journal of Personality 60, 117174.CrossRefGoogle Scholar
Meehl, PE (1995). Bootstraps taxometrics: solving the classification problem in psychopathology. American Psychologist 50, 266275.CrossRefGoogle ScholarPubMed
Mellenbergh, GJ (1989). Item bias and item response theory. International Journal of Educational Research 13, 127143.CrossRefGoogle Scholar
Mellenbergh, GJ (1994). Generalized linear item response theory. Psychological Bulletin 115, 300307.CrossRefGoogle Scholar
Meredith, W (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika 58, 525543.CrossRefGoogle Scholar
Michell, J (1997). Quantitative science and the definition of measurement in psychology. British Journal of Psychology 88, 355383.CrossRefGoogle 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, 293317.CrossRefGoogle 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, 201218.Google Scholar
Molenaar, PCM, Campbell, CG (2009). The new person-specific paradigm in psychology. Current Directions in Psychology 18, 112117.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. 226242. 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, 631640.CrossRefGoogle ScholarPubMed
Muthén, B (2006). Should substance use disorders be considered as categorical or dimensional? Addiction 101 (Suppl. 1), 616.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. 124. 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, 6171.CrossRefGoogle ScholarPubMed
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, 292300.CrossRefGoogle Scholar
Pearl, J (2009). Causality: Models, Reasoning, and Inference, 2nd edn. Cambridge University Press: Cambridge, UK.CrossRefGoogle Scholar
Reise, SP, Waller, NG (2009). Item response theory and clinical measurement. Annual Review of Clinical Psychology 5, 2748.CrossRefGoogle ScholarPubMed
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, 349386.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, 5359.CrossRefGoogle ScholarPubMed
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, 344348.CrossRefGoogle ScholarPubMed
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, 427443.CrossRefGoogle ScholarPubMed
Stevens, SS (1946). On the theory of scales of measurement. Science 103, 667680.CrossRefGoogle Scholar
Suppes, P, Zinnes, JL (1963). Basic measurement theory. In Handbook of Mathematical Psychology (ed. Luce, R.D., Bush, R. and Galanter, E.), pp. 376. 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, 579599.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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, 8792.CrossRefGoogle Scholar
Van der Maas, HLJ, Molenaar, PCM (1992). Stagewise cognitive development: an application of catastrophe theory. Psychological Review 99, 395417.CrossRefGoogle ScholarPubMed
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, 23.CrossRefGoogle ScholarPubMed
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. 149176. MIT Press: Cambridge, MA.Google 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, 283294.CrossRefGoogle 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, 185208.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, 13491360.CrossRefGoogle ScholarPubMed
Woodbury, MA, Manton, KG (1989). Grade of membership analysis of depression-related psychiatric disorders. Sociological Methods and Research 18, 126163.CrossRefGoogle 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, 297330.CrossRefGoogle Scholar
Zeeman, EC (1977). Catastrophe Theory: Selected Papers. Addison-Wesley: Reading, MA.Google Scholar
73
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

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

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

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

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *