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Article

Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches?

by
Péter Czine
1,
Péter Balogh
2,3,*,
Zsanett Blága
4,5,
Zoltán Szabó
6,
Réka Szekeres
5,
Stephane Hess
7 and
Béla Juhász
5
1
Coordination Center for Research in Social Sciences, Faculty of Economics and Business, University of Debrecen, 4032 Debrecen, Hungary
2
Institute of Methodology and Business Digitalization, Faculty of Economics and Business, University of Debrecen, 4032 Debrecen, Hungary
3
HUN-REN-DE High-Tech Technologies for Sustainable Management Research Group, University of Debrecen, Boszormenyi Street 138, 4032 Debrecen, Hungary
4
University Pharmacy, Clinical Centre, University of Debrecen, 4032 Debrecen, Hungary
5
Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
6
Department of Emergency Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
7
Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Econometrics 2024, 12(3), 22; https://doi.org/10.3390/econometrics12030022
Submission received: 21 May 2024 / Revised: 30 June 2024 / Accepted: 30 July 2024 / Published: 8 August 2024

Abstract

Heterogeneity in preferences can be addressed through various discrete choice modeling approaches. The random-parameter latent class (RLC) approach offers a desirable alternative for analysts due to its advantageous properties of separating classes with different preferences and capturing the remaining heterogeneity within classes by including random parameters. For latent class specifications, however, more empirical evidence on the optimal number of classes to consider is needed in order to develop a more objective set of criteria. To investigate this question, we tested cases with different class numbers (for both fixed- and random-parameter latent class modeling) by analyzing data from a discrete choice experiment conducted in 2021 (examined preferences regarding COVID-19 vaccines). We compared models using commonly used indicators such as the Bayesian information criterion, and we took into account, among others, a seemingly simple but often overlooked indicator such as the ratio of significant parameter estimates. Based on our results, it is not sufficient to decide on the optimal number of classes in the latent class modeling based on only information criteria. We considered aspects such as the ratio of significant parameter estimates (it may be interesting to examine this both between and within specifications to find out which model type and class number has the most balanced ratio); the validity of the coefficients obtained (focusing on whether the conclusions are consistent with our theoretical model); whether including random parameters is justified (finding a balance between the complexity of the model and its information content, i.e., to examine when (and to what extent) the introduction of within-class heterogeneity is relevant); and the distributions of MRS calculations (since they often function as a direct measure of preferences, it is necessary to test how consistent the distributions of specifications with different class numbers are (if they are highly, i.e., relatively stable in explaining consumer preferences, it is probably worth putting more emphasis on the aspects mentioned above when choosing a model)). The results of this research raise further questions that should be addressed by further model testing in the future.
Keywords: heterogeneity in preferences; latent class modeling; optimal class number; information criteria heterogeneity in preferences; latent class modeling; optimal class number; information criteria

Share and Cite

MDPI and ACS Style

Czine, P.; Balogh, P.; Blága, Z.; Szabó, Z.; Szekeres, R.; Hess, S.; Juhász, B. Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches? Econometrics 2024, 12, 22. https://doi.org/10.3390/econometrics12030022

AMA Style

Czine P, Balogh P, Blága Z, Szabó Z, Szekeres R, Hess S, Juhász B. Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches? Econometrics. 2024; 12(3):22. https://doi.org/10.3390/econometrics12030022

Chicago/Turabian Style

Czine, Péter, Péter Balogh, Zsanett Blága, Zoltán Szabó, Réka Szekeres, Stephane Hess, and Béla Juhász. 2024. "Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches?" Econometrics 12, no. 3: 22. https://doi.org/10.3390/econometrics12030022

APA Style

Czine, P., Balogh, P., Blága, Z., Szabó, Z., Szekeres, R., Hess, S., & Juhász, B. (2024). Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches? Econometrics, 12(3), 22. https://doi.org/10.3390/econometrics12030022

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