They summarize the evidence against older procedures and, with few exceptions. The performance of multiple imputation for likerttype items. These methods include listwise deletion, pairwise deletion, mean substitution, regression imputation, maximumlikelihood methods and multiple imputation. Instead of lling in a single value for each missing value, a multiple imputation procedure replaces each missing value with a set of plausible values that represent the. Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. Some of the most commonlyused software include r packages hmsic harrell 2011, function aregimpute, norm novo and schafer 2010, cat harding, tusell, and schafer 2011, mix schafer 2010 for a variety of techniques to create multiple imputations in continuous, categorical or mixture of continuous and categorical datasets. Several mi techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification fcsstandard and joint multivariate normal imputation jmmvn, which treat repeated measurements as distinct variables, and various extensions based on. In recent years, multiple imputation has emerged as a convenient and flexible.
Most popular statistical software packages have options for multiple imputation, which require little. Multiple imputation for missing data in epidemiological and. Missing data and multiple imputation columbia university. There is currently only a limited amount of software for generating multiple imputations under multivariate completedata models and for analyzing multiplyimputed data sets i. Joseph l schafer department of statistics, the pennsylvania state university. The treatment of missing data can be difficult in multilevel research because stateoftheart procedures such as multiple imputation mi may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. Either way, dealing with the multiple copies of the data is the bane of mi analysis. Many research studies have used multiple imputation e.
New computational algorithms and software described in a recent book schafer, 1997 allow us to create proper multiple imputations in complex multivariate settings. Smallsample degrees of freedom for multicomponent signi. Comparing joint and conditional approaches jonathan kropko university of virginia ben goodrich columbia university. Multiple imputation for missing data in epidemiological. Described in detail by schafer and graham 2002, the missing values are imputed based on the observed values for a given individual and the relations observed in the data for other participants, assuming the observed. The report ends with a summary of other software available for missing data and a list of the useful references that guided this report. Multiple imputation an overview sciencedirect topics.
The twolevel imputation algorithm is a combination of three existing multiple imputation algorithms. National center for education statistics working paper series comparison of proc impute and schafers multiple imputation software working paper no. The mi procedure in the sasstat software is a multi. Conceived by rubin and described further by little and rubin and schafer, multiple imputation imputes each missing value multiple times.
Ml and mi are now becoming standard because of implementations in free and commercial software. An overview of the state of the art center for statistical research and methodology cs rm united states census bureau may16, 2015 views expressed are those of the author and not necessarily those of the u. The multiple imputation process using sas software imputation mechanisms the sas multiple imputation procedures assume that the missing data are missing at random mar, that is, the probability that an observation is missing may depend on the observed values but not the missing values. Multiple imputation of incomplete multivariate data under a normal model.
A comparison of multiple imputation methods for missing. With a slight abuse of the terminology, we will use the term imputation to mean the data where missing values are replaced with one set of plausible values. Using multiple imputation to address missing values of. Missing data, multiple imputation and associated software. Among these procedures, multiple imputation mi, together with maximum likelihood estimation, is becoming one of the preferred techniques for dealing with. Rubin 1987 book on multiple imputation schafer 1997 book on mcmc and multiple imputation for missingdata problems more subjectoriented carpenter, j. To learn more about multiple imputation see rubin, 1987, 1996. In the missing data literature, pan has been recommended for mi of multilevel data. Features this paper describes the r package mice 2. Multiple imputationnuts and bolts mi can import already imputed data from nhanes or ice, or you can start with original data and form imputations yourself.
Joseph schafers list of multiple imputation software routines. Jan 01, 2010 multiple imputation for missing income data in the national health interview survey. Multiple imputation provides a useful strategy for dealing with data sets with missing values. Multiple imputation is a popular method for addressing data that are presumed to be missing at random. Comparison of proc impute and schafers multiple imputation. Multiple imputation involves filling in the missing values multiple times, creating multiple complete datasets. The validity of results from multiple imputation depends on such modelling being done carefully and appropriately.
Graham pennsylvania state university statistical procedures for missing data have vastly improved, yet miscon ception and unsound practice still abound. The following is the procedure for conducting the multiple imputation for missing data that was created by. Stata only the most recent version 12 has a builtin comprehensive and easy to use module for multiple imputation, including multivariate imputation using chained equations. Mathematical, physical and engineering sciences, 10. Jun 10, 2010 new computational algorithms and software described in a recent book schafer, 1997a allow us to create proper multiple imputations in complex multivariate settings. A comparison of multiple imputation methods for missing data. Mi has been adapted to a variety of different types of data for example, survival data. It also includes appendices showing splus functions for continuous variables, categorical variables, and mixed variables in schafers multiple imputation software. This report provides detailed evaluations of both software packages as well as comparing the packages. Multiple imputation in a largescale complex survey. The results from the m complete data sets are combined for the inference. However, programming ones own multiple imputation algorithm is considerably more challenging than the programming required to specify analysis models in most evaluations. The software on this page is available for free download, but is not supported by the methodology centers helpdesk.
In the last two decades, multiple imputation has evolved beyond the context of large sample survey nonresponse. New computational algorithms and software described in a recent book schafer, 1997a allow us to create proper multiple imputations in. Following the seminal books by rubin 1987 and schafer 1997, mi has. Multiple imputation for continuous and categorical data. Multiple imputation for missing data statistics solutions. Pdf statistical inference in missing data by mcmc and.
Multiple imputation mi is now widely used to handle missing data in longitudinal studies. What is the best statistical software to handling missing data. An imputation represents one set of plausible values for missing data, and so multiple imputations represent multiple sets of plausible values. Multiple imputation inference involves three distinct phases. Missing data takes many forms and can be attributed to many causes. Multiple imputation relies on regression models to predict the missingness and missing values, and incorporates uncertainty through an iterative approach. Against a common view, we demonstrate anew that the complete case estimator can be unbiased, even if data are not missing completely at random.
Comparison of proc impute and schafer s multiple imputation software. Schafer 1997, van buuren and oudshoom 2000 and raghunathan et al. Multiple imputation in multivariate problems when the imputation and analysis models differ joseph l. More precisely, we imputed missing variables contained in the student background datafile for tunisia one of the timss 2007 participating countries, by using van buuren, boshuizen, and knooks sm 18. The performance of multiple imputation in a variety of missing data situations has been. They clear up common misunderstandings regarding the missing at random mar concept. Nov 07, 2001 comparison of proc impute and schafer s multiple imputation software.
However, one of the big uncertainties about the practice of multiple imputation is how many imputed data sets are needed to get good results. Department of education office of educational research and improvement. Abstract multiple imputation provides a useful strategy for dealing with data sets that have missing values. It also includes appendices showing splus functions for continuous variables, categorical variables, and mixed variables in schafer s multiple imputation software. The m complete data sets are analyzed by using standard procedures. Multiple imputation is a powerful and flexible technique for dealing with missing data. Several mi techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification fcsstandard and joint multivariate normal imputation jmmvn, which treat repeated measurements as distinct variables, and various extensions based on generalized. See enders 2010 for a discussion of other statistical software packages that can perform multiple imputation and other modern missing data procedures. Therefore, specialized mi software may be useful for people who expect to conduct mi regularly. Multiple imputation has potential to improve the validity of medical research. See also joseph schafer s multiple imputation faq page for introductory explanations and further references. Multiple imputation in multivariate problems when the. Development of this software has been supported by grant 2r44ca6514702 from. Feb 24, 2011 multiple imputation involves filling in the missing values multiple times, creating multiple complete datasets.
In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the timss database. Individual researchers now routinely use multiple imputation for missing data in small samples, as evidenced by the development of multiple imputation procedures for mainstream software like sas, stata, and splus. Schafer, j l and olsen, m k 1 998 multiple imputation for multivariate missingdata problems. Multiple imputation using sas software yang yuan sas institute inc. To obtain accurate results, ones imputation model must be congenial to appropriate for ones intended analysis model. In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Multiple imputation mi is an approach for handling missing values in a dataset that allows researchers to use. Multiple imputation mi is often presented as an improvement over listwise deletion lwd for regression estimation in the presence of missing data. See also joseph schafers multiple imputation faq page for introductory explanations and further references. The top level of the data level 2 is imputed using an adaptation of the multiple imputation algorithm developed by tanner and wong 1987 and popularized by schafer 1997.
Multiple imputation can be used by researchers on many analytic levels. Multiple imputation mi is a way to deal with nonresponse bias missing research data that. Comparison of proc impute and schafers multiple imputation software. The development of diagnostic techniques for multiple imputation, though, has been retarded by the belief that the assumptions of the procedure are untestable from observed data. Multiple imputation mi is an approach for handling missing.
Accounting for missing data in statistical analyses. When can multiple imputation improve regression estimates. Jun 29, 2009 multiple imputation has potential to improve the validity of medical research. New computational algorithms and software described in a recent book schafer, 1997a allow us to create proper multiple imputations in complex multivariate settings. Finally, section 5 explains how to carry out multiple imputation and maximum likelihood using sas and stata. Adapted from schafer, jl 1997b, introduction to multiple imputations for missing data problems, viewed 6 may 2002. Both articles discuss various available software for multiple imputation and their utility for sem. Flexible, free software for multilevel multiple imputation. Computational routines used in norm are described by schafer, j. Why you probably need more imputations than you think. Recai m yucel, multiple imputation inference for multivariate multilevel continuous data with ignorable nonresponse, philosophical transactions of the royal society a.
Schafer department of statistics and the methodology center, the pennsylvania state university, 326 thomas building, university park, pa 16802, usa. Missing data analysis using multiple imputation circulation. Reweighting, long used by survey methodologists, has been proposed for handling missing values in regression models with missing covariates ibrahim, 1990. The traditional multiple imputation method used by most commercial statistical software packages such as sas, iveware, etc. Schafer and olsen 1989 suggest that a good starting point is a number.
Statas new mi command provides a full suite of multipleimputation methods for the analysis of incomplete data, data for which some values are missing. Some practical clarifications of multiple imputation theory. However, the multiple imputation procedure requires the user to model the distribution of each variable with missing values, in terms of the observed data. Nov 09, 2012 over the last decade, multiple imputation has rapidly become one of the most widelyused methods for handling missing data.
Compares solas, sas, mice, splus implementations of imputation. Reporting the results although the use of multiple imputation and other missing data procedures is increasing, however many modern missing data procedures are still largely misunderstood. I examine two approaches to multiple imputation that have been incorporated into widely available software. Clearly the method of imputation plays a key role in success of the multiple imputation methods. Inferences using the multiply imputed data thus account for the missing data and the uncertainty in the imputations. The performance of multiple imputation for likerttype.
Multiple imputation for missing data is an attractive method for handling missing data in multivariate analysis. Four studies investigated specialized situations for multiple imputation, such as smallsample degrees of freedom in da barnard and rubin 1999, likertscale data in da leite and beretvas 2010, nonparametric multiple imputation cranmer and gill 20, and variance estimators hughes, sterne, and tilling 2016. Columnwise speci cation of the imputation model section3. Norm users guide the methodology center penn state. Bayesian simulation methods and hotdeck imputation. Multiple imputation for multivariate missingdata problems. Key advantages over a complete case analysis are that it preserves n without introducing bias if data are mar, and provides corrects ses for uncertainty due to missing values.
1295 732 402 1188 41 1380 1141 1454 1246 1111 500 380 535 943 743 442 729 33 640 916 674 1496 398 375 158 425 1267 424 1416 1117 372 1181 804 367 892 1000 125 1301 356 857