The Data Generation Mechanism
Relationship Between Constructs and Their Indicators
DOI :
https://doi.org/10.1590/0102.3772e39nspe08.enMots-clés :
Psychometrics, Data analysis, Data generation mechanism, Reflective models, Formative models, Network analysisRésumé
The choice of statistical data analysis should be guided by a critical analysis that supports the theoretical relationship between the construct and its indicators. This theoretical article reviews the three main existing psychometric paradigms and their proposals for explaining the relationship between indicators and their constructs. The discussion begins with the standard paradigm that guides the construction and analysis of data in psychology, reflective model. Then, a description of the formative models is performed and finally the Network Analysis as an alternative. The definitions, consequences, and limitations of the use of each measurement model are presented such as a reflection on making decisions about which data generation mechanisms are more appropriate.
Téléchargements
Références
Barzel, B., & Biham, O. (2009). Quantifying the connectivity of a network: The network correlation function method. Physical Review E, 80(4), 046104-046104. https://doi.org/10.1103/PhysRevE.80.046104
Benassi, M., Garofalo, S, Ambrosini, F., Sant'Angelo, R.P., Raggini, R., De Paoli, G., Ravani, C., Giovagnoli, S., Orsoni, M., Piraccini, G. (2020). Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients. Frontiers in Psychology, 10(11), 1085. https://doi.org/10.3389/fpsyg.2020.01085
Bollen, K. A. (1989). Structural equations with latent variables (Vol. 210). John Wiley & Sons. https://doi.org/10.1002/9781118619179
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605-634. https://doi.org/10.1146/annurev.psych.53.100901.135239
Bollen, K. A., & Bauldry, S. (2011). Three Cs in measurement models: causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265-265. https://doi.org/10.1037/a0024448
Bollen, K. A., & Davis, W. R. (2009). Causal indicator models: Identification, estimation, and testing. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 498- 522. https://doi.org/10.1080/10705510903008253
Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581-581. https://doi.org/10.1037/met0000056
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305-305. https://doi.org/10.1037/0033-2909.110.2.305
Borsboom, D. (2005). Measuring the Mind: Conceptual Issues in Contemporary Psychometrics Cambridge University Press. https://doi.org/10.1017/CBO9780511490026
Borsboom, D. (2008). Latent Variable Theory. Measurement: Interdisciplinary Research and Perspectives, 6, 25-35. https://doi.org/10.1080/15366360802035497
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5-13. https://doi.org/10.1002/wps.20375
Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G. et al. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(58). https://doi.org/10.1038/s43586-021-00055-w
Brennan, R. L. (1992). Generalizability theory. Educational Measurement: Issues and Practice, 11(4), 27-34. https://doi.org/10.1111/j.1745-3992.1992.tb00260.x
Chang, W., Franke, G. R., & Lee, N. (2016). Comparing reflective and formative measures: New insights from relevant simulations. Journal of Business Research, 69(8), 3177-3185. https://doi.org/10.1016/j.jbusres.2015.12.006
Christensen, A. P., Golino, H., & Silvia, P. J. (2020). A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality, 34(6), 1095-1108. https://doi.org/10.1002/per.2265
Cole, D. A., & Preacher, K. J. (2014). Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19(2), 300-315. https://doi.org/10.1037/a0033805
Cramer, A. O., & Borsboom, D. (2015). Problems attract problems: A network perspective on mental disorders. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource, 1-15. https://doi.org/10.1002/9781118900772.etrds0264
Dalege, J., Borsboom, D., van Harreveld, F., & van der Maas, H. L. (2019). A network perspective on attitude strength: Testing the connectivity hypothesis. Social Psychological and Personality Science, 10(6), 746-756. https://doi.org/10.1177/1948550618781062
Darrow, J. B., & Behrend, T. S. (2017). Person-environment fit is a formative construct. Journal of Vocational Behavior, 103, 117-131. https://doi.org/10.1016/j.jvb.2017.08.007
De Beurs, D., Fried, E. I., Wetherall, K., Cleare, S., O' Connor, D. B., Ferguson, E., … O' Connor, R. C. (2019). Exploring the psychology of suicidal ideation: A theory driven network analysis. Behaviour Research and Therapy, 120, 103419. https://doi.org/10.1016/j.brat.2019.103419
Diamantopoulos, A., & Temme, D. (2013). MIMIC models, formative indicators and the joys of research. AMS Review, 3(3), 160-170. https://doi.org/10.1007/s13162-013-0050-0
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269-277. https://doi.org/10.1509/jmkr.38.2.269.18845
Edwards, J. R. (2011). The fallacy of formative measurement. Organizational Research Methods, 14(2), 370-388. https://doi.org/10.1177/1094428110378369
Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155-155. https://doi.org/10.1037/1082-989X.5.2.155
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212. https://doi.org/10.3758/s13428-017-0862-1
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617-617. https://doi.org/10.1037/met0000167
Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), 1-34. https://doi.org/10.31234/osf.io/8ha93
Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12, 999-1020. https://doi.org/10.1177/1745691617705892
Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS One, 12(6), e0174035-e0174035. https://doi.org/10.1371/journal.pone.0174035
Hanafiah, M.H. (2020). Formative Vs. Reflective Measurement Model: Guidelines for Structural Equation Modeling Research. International Journal of Analysis and Applications, 18, 876-889. https://doi.org/10.28924/2291-8639-18-2020-876
Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12(2), 205-218. https://doi.org/10.1037/1082-989X.12.2.205
Ikanga, J., Hill, E. M., & MacDonald, D. A. (2017). The conceptualization and measurement of cognitive reserve using common proxy indicators: Testing some tenable reflective and formative models. Journal of Clinical and Experimental Neuropsychology, 39(1), 72-83. https://doi.org/10.1080/13803395.2016.1201462
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199-218. https://doi.org/10.1086/376806
Jonas, K. G., & Markon, K. E. (2016). A descriptivist approach to trait conceptualization and inference. Psychol Rev ., 123(1), 90-96. https://doi.org/10.1037/a0039542
Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409-426. https://doi.org/10.1007/BF02291366
Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70(351a), 631-639. https://doi.org/10.2307/2285946
Kalisch, R., Cramer, A. O., Binder, H., Fritz, J., Leertouwer, I., Lunansky, G., & van Harmelen, A. L. (2019). Deconstructing and reconstructing resilience: a dynamic network approach. Perspectives on Psychological Science, 14(5), 765-777. https://doi.org/10.1177/1745691619855637
Kruis, J., & Maris, G. (2016). Three representations of the Ising model. Scientific Reports, 6(1), 1-11. https://doi.org/10.1038/srep34175
Lange, J., Dalege, J., Borsboom, D., van Kleef, G. A., & Fischer, A. H. (2020). Toward an Integrative Psychometric Model of Emotions. Perspectives on Psychological Science, 15(2), 444-468. https://doi.org/10.1177/1745691619895057
Law, K. S., & Wong, C.-S. (1999). Multidimensional Constructs M Structural Equation Analysis: An Illustration Using the Job Perception and Job Satisfaction Constructs. Journal of Management, 25(2), 143-160. https://doi.org/10.1177/014920639902500202
Lord, F. M., Novick, M. R., & Birnbaum, A. (1968). The axioms and principal results of classical test theory Addison-Wesley. https://doi.org/10.1002/j.2333-8504.1965.tb00132.x
MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: some practical issues. Psychological Bulletin, 12, 413-429. https://doi.org/10.1037/0033-2909.114.3.533
MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710-710. https://doi.org/10.1037/0021-9010.90.4.710
McDonald, R. P. (1996). Latent traits and the possibility of motion. Multivariate Behavioral Research, 31(4), 593-601. https://doi.org/10.1207/s15327906mbr3104_12
McNally, r. j. (2016). Can network analysis transform psychopathology?, Behaviour Research and Therapy, 86, 95-104. https://doi.org/10.1016/j.brat.2016.06.006
Mellenbergh, G. J. (1994). Generalized linear item response theory. Psychological Bulletin, 115(2), 300-307. https://doi.org/10.1037/0033-2909.115.2.300
Molenaar, P. C. M. (2010). Latent variable models are net- work models. Behavioral and Brain Sciences, 33(2-3), 166-166. doi:10.1017/S0140525X10000798
Neal, Z. P., Forbes, M. K., Neal, J. W., Brusco, M., Krueger, R., Markon, K., Steinley, D., Wasserman, S., & Wright, A. G. C. Critiques of network analysis of multivariate data in psychological science. (2022). Nat Rev Methods Primers, 2, 90. https://doi.org/10.1038/s43586-022-00177-9
Novick, M. R. (1966). The axioms and principal results of classical test theory. Journal of Mathematical Psychology, 3(1), 1-18. https://doi.org/10.1016/0022-2496(66)90002-2
Pearl, J. (2000). Causal inference without counterfactuals: Comment. Journal of the American Statistical Association, 95(450), 428-431. https://doi.org/10.2307/2669380
Peterson, C. H., Gischlar, K. L., & Peterson, N. A. (2017). Item construction using reflective, formative, or rasch measurement models: Implications for group work. Journal for Specialists in Group Work, 42(1), 17-32. https://doi.org/10.1080/01933922.2016.1264523
Petter, S., Rai, A., & Straub, D. (2012). The Critical Importance of Construct Measurement Specification: A Response to Aguirre-urreta and Marakas. MIS Quarterly, 36(1), 147-156. https://doi.org/10.2307/41410411
Primi, R. (2003). Inteligência: avanços nos modelos teóricos e nos instrumentos de medida. Avaliação Psicológica, 2(1), 67-77.
Rasch, G. (1960). Studies in mathematical psychology: I. Probabilistic models for some intelligence and attainment tests Nielsen & Lydiche. Available in: https://psycnet.apa.org/record/1962-07791-000
Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 25(1), 30-30. https://doi.org/10.1037/met0000220
Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125(6), 747-747. https://doi.org/10.1037/abn0000181
Schmittmann, V. D., Cramer, A. O., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: a network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43-53. https://doi.org/10.1016/j.newideapsych.2011.02.007
Spearman, C. (1904). "General Intelligence", objectively determined and measured. American Journal of Psychology, 15, 201-293. https://doi.org/10.2307/1412107
Spector, P. E. (1992). Summated Rating Scale Construction: An Introduction Sage CA. https://doi.org/10.4135/9781412986038
Thompson, B. (2003). Guidelines for authors reporting score reliability estimates. In B. Thompson (Ed.), Reliability: Contemporary thinking on reliability issues(91-102). Thousand Oaks, CA: Sage. https://dx.doi.org/10.4135/9781412985789
van der Maas, H. L., Kan, K.-J., & Borsboom, D. (2014). Intelligence Is What the Intelligence Test Measures. Seriously. Journal of Intelligence, 2(1), 12-15. https://doi.org/10.3390/jintelligence2010012
van der Maas, H. L., Dolan, C. v., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: the positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842-842. https://doi.org/10.1037/0033-295X.113.4.842
van Rooij, A. J., van Looy, J., & Billieux, J. (2017). Internet Gaming Disorder as a formative construct: Implications for conceptualization and measurement. Psychiatry and Clinical Neurosciences, 71(7), 445-458. https://doi.org/10.1111/pcn.12404
Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications Cambridge University Press. https://doi.org/10.1017/CBO9780511815478
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world'networks. Nature, 393(6684), 440-442. https://doi.org/10.1038/30918
Willoughby, M. T., & Blair, C. B. (2016). Measuring executive function in early childhood: A case for formative measurement. Psychological Assessment, 28(3), 319-330. https://doi.org/10.1037/pas0000152
Zagenczyk, T. J., Scott, K. D., Gibney, R., Murrell, A. J., & Thatcher, J. B. (2010). Social influence and perceived organizational support: A social networks analysis.Organizational Behavior and Human Decision Processes,111(2), 127-138. https://doi.org/10.1016/j.obhdp.2009.11.004
Téléchargements
Publié-e
Comment citer
Numéro
Rubrique
Licence
(c) Tous droits réservés Raissa Damasceno Cunha, Cristiane Faiad, Makilim Nunes Baptista, Hugo Ferrari Cardoso 2023
Cette œuvre est sous licence Creative Commons Attribution 4.0 International.
