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Model-based analysis of concept maps
Journal article   Open access

Model-based analysis of concept maps

Sam K Hui, Yanliu Huang and Edward I George
Bayesian Anal, v 3(no. 3), pp 479-512
2008
DOI: https://doi.org/10.1214/08-BA319
url
https://doi.org/10.1214/08-ba319View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open
url
https://doi.org/10.1214/08-BA319View
Published, Version of Record (VoR) Open

Abstract

Bayesian hypothesis testing concept maps network analysis
A concept map is a data collection tool developed in psychology and education to obtain information about mental representations of concept associations. This methodology has recently been introduced to marketing to study consumers' brand perceptions (John et al. (2006); Joiner (1998)) and attitudes towards health risk (e.g., Huang (1997)). In conjunction with other more established methods (e.g., Multidimensional scaling), concept maps provide an additional valuable tool for researchers to understand consumers' structural knowledge about different important marketing concepts. ¶ Building on the introduction by John et al. (2006), we propose a descriptive probability model of concept map formation, along with concept map analyses based on parameter estimates. In particular, we demonstrate how to test hypotheses about differences between two groups of maps, and how to aggregate across individual concept maps to form a "consensus map." To demonstrate our methodology, we apply our model to a dataset that uses concept maps to study college students' perceptions of Sexually Transmitted Diseases (STDs), an important topic of growing interest in health marketing (e.g., Hill (1988); LaTour and Pitts (1989); Raghubir and Menon (1998); Treise and Weigold (2001)). Though parsimonious in nature, our model adequately recovers map-level, concept-level, and link-level summary statistics commonly considered by other researchers, yet rarely modeled directly.

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