潛在類別模式插補法下各影響因子之探討

A Comparison of Imputation Methods for Incomplete Categorical Data Using Latent Class Model

吳丞根(Cheng-Ken Wu)

指導教授 ： 林定香

國立臺北大學/商學院/統計學系/碩士(2008年)

摘要

無資料

關鍵字

潛在類別模式 ； EM演算法 ； 插補方法 ； 缺失資料

並列摘要

Survey is a popular research tool, but often causes missing values for some reasons. When the proportion of the missing value is high, it can seriously affect the conclusion. Imputation is an alternative is to handle missing data.

For categorical missing data, both model-based and non- model based imputation methods have been proposed, for example, hot deck imputation and loglinear models. However, there are still some problems for these methods. Latent class model (LCM) is a popularly used method for categorical variable. We extended the research of Vermunt al (2007) to study what are the important factors on accuracy rate of imputation for categorical data. Four imputation methods and 6 other independent variables were examined for their effects on accuracy of imputation.

The imputation methods were evaluated in terms of accuracy rates. The result shows the significant factors are conditional probability, latent class proportions, number of manifest variables, imputation method, sample size, missing data mechanism. The accuracy rate of imputation is higher with substantially different conditional probability and latent class proportions, more manifest variables, method2 or method3, larger sample sizes, MCAR, and lower missing rate.

並列關鍵字

latent class model ； EM algorithm ； imputation ； missing data

參考文獻

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Hang, H.C. (2005). Latent Class Model with Two Hierarchical Latent Variables.