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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bayesian estimation of a flexible bifactor generalized partial credit model to survey data

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da Silva, Marcelo A. [1, 2] ; Huggins-Manley, Anne C. [3] ; Mazzon, Jose A. [4] ; Bazan, Jorge L. [5]
Total Authors: 4
[1] Univ Fed Sao Carlos, Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Interinst Grad Program Stat, Sao Carlos, SP - Brazil
[3] Univ Florida, Coll Educ, Gainesville, FL - USA
[4] Univ Sao Paulo, Fac Econ Adm & Contabilidade, Sao Paulo - Brazil
[5] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Paulo - Brazil
Total Affiliations: 5
Document type: Journal article
Source: Journal of Applied Statistics; v. 46, n. 13, p. 1-16, MAR 2019.
Web of Science Citations: 0

Item response theory (IRT) models provide an important contribution in the analysis of polytomous items, such as Likert scale items in survey data. We propose a bifactor generalized partial credit model (bifac-GPC model) with flexible link functions - probit, logit and complementary log-log - for use in analysis of ordered polytomous item scale data. In order to estimate the parameters of the proposed model, we use a Bayesian approach through the NUTS algorithm and show the advantages of implementing IRT models through the Stan language. We present an application to marketing scale data. Specifically, we apply the model to a dataset of non-users of a mobile banking service in order to highlight the advantages of this model. The results show important managerial implications resulting from consumer perceptions. We provide a discussion of the methodology for this type of data and extensions. Codes are available for practitioners and researchers to replicate the application. (AU)

FAPESP's process: 17/15452-5 - New regression models to data set with binary and/or bounded response
Grantee:Jorge Luis Bazan Guzman
Support type: Scholarships abroad - Research