Data/hora: 09/06/2024 13:59:43

Provedor de dados: 	Research in Agricultural & Applied Economics (AgEcon Searh)	
País: 	United States	
Título: 	Implications of Missing Data Imputation for Agricultural Household Surveys: An Application to Technology Adoption	
Autores: 	Gedikoglu,   Haluk; Parcell,   Joseph L.	
Data: 	2012-05-31	
Ano: 	2012	
Palavras-chave: 	Missing Data; Multiple Imputation; Bayesian Inference; Household Surveys; Research Methods/ Statistical Methods.	
Resumo: 	Missing data is a problem that occurs frequently in survey data. Missing data results in biased estimates and reduced efficiency for regression estimates. The objective of the current study is to analyze the impact of missing-data imputation, using multiple-imputation methods, on regression estimates for agricultural household surveys. The current study also analyzes the impact of multiple-imputation on regression results, when all the variables in the regression have missing observations. Finally, the current study compares the impact of univariate multiple imputation with multivariate normal multiple imputation, when some of the missing variables have discrete distribution. The results of the current study show that multivariate-normal multiple imputation performs better than univariate multiple imputation model, and overall both methods improve the efficiency of regression estimates.	
Tipo: 	Presentation	
Idioma: 	Inglês	
Identificador: 	http://purl.umn.edu/124333	
Relação: 	Agricultural and Applied Economics Association&gt;2012 Annual Meeting, August 12-14, 2012, Seattle, Washington

Selected Paper

239	
Formato: 	28