Replication data for: A Unified Approach To Measurement Error And Missing Data: Details And Extensions.
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'We extend a unified and easy-to-use approach to measurement error and missing data. Blackwell, Honaker, and King (2014a) gives an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details; more sophisticated measurement error model specifications and estimation procedures; and analyses to assess the approach's robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.Notes:This is the second of two articles to appear in the same issue of the same journal by the same authors.  The other one is  “A Unified Approach to Measurement Error and Miss' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["missing data, measurement error, imputation, multiple overimputation"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We extend a unified and easy-to-use approach to measurement error and missing data. Blackwell, Honaker, and King (2014a) gives an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details; more sophisticated measurement error model specifications and estimation procedures; and analyses to assess the approach's robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.Notes:This is the second of two articles to appear in the same issue of the same journal by the same authors.  The other one is  “A Unified Approach to Measurement Error and Miss' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We extend a unified and easy-to-use approach to measurement error and missing data. Blackwell, Honaker, and King (2014a) gives an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details; more sophisticated measurement error model specifications and estimation procedures; and analyses to assess the approach's robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.Notes:This is the second of two articles to appear in the same issue of the same journal by the same authors.  The other one is  “A Unified Approach to Measurement Error and Miss
missing data, measurement error, imputation, multiple overimputation