Multivariate analysis of multiple response data
Multivariate analysis of multiple response data
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DOI：
10.1509/jmkr.40.3.321.19233
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发表时间：
2003-08-01
影响因子：
6.1
通讯作者：
Allenby, GM
中科院分区：
文献类型：
--
作者：
Edwards, YD;Allenby, GM
多个回答问题，也称为pick any/J格式，在调查数据分析中经常遇到。当响应选项的数量J很大时，很难探索响应之间的关系。作者提出了一个多变量二项概率单位模型来分析多个响应数据，并使用标准的多变量分析技术进行探索性分析的潜在的多变量正态分布。估计概率单位模型的挑战是解决导致用单位对角元素指定的协方差矩阵的识别限制（即，相关矩阵）。作者提出了一种通用的方法来处理识别的限制，并制定具体的算法，多变量二项式概率模型。该估计算法是有效的，可以很容易地适应许多响应选项，经常遇到的营销数据的分析。作者说明了多变量分析的多个响应数据在三个应用程序。
Multiple response questions, also known as a pick any/J format, are frequently encountered in the analysis of survey data. The relationship among the responses is difficult to explore when the number of response options, J, is large. The authors propose a multivariate binomial probit model for analyzing multiple response data and use standard multivariate analysis techniques to conduct exploratory analysis on the latent multivariate normal distribution. A challenge of estimating the probit model is addressing identifying restrictions that lead to the covariance matrix specified with unit-diagonal elements (i.e., a correlation matrix). The authors propose a general approach to handling identifying restrictions and develop specific algorithms for the multivariate binomial probit model. The estimation algorithm is efficient and can easily accommodate many response options that are frequently encountered in the analysis of marketing data. The authors illustrate multivariate analysis of multiple response data in three applications.