The Performance of Multiple Imputation for Likert-type Items with Missing Data
نویسندگان
چکیده
منابع مشابه
On the Imputation of Missing Data in Surveys with Likert-Type Scales
The aim of this paper is two-fold: to propose the imputation procedure named ABBN for replacing missing data in likert-type scales and to compare its performance with some well-known imputation methods. ABBN is a hot-deck imputation procedure which modifies the Approximate Bayesian Bootstrap method by sampling the donor in the neighbourhood of the nonrespondent. The comparison among the imputat...
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Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...
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Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
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This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of predicting one scale score from another. The examined strategies include imputing using normal data models with naïve rounding/without rounding, using latent va...
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ژورنال
عنوان ژورنال: Journal of Modern Applied Statistical Methods
سال: 2010
ISSN: 1538-9472
DOI: 10.22237/jmasm/1272686820