The most commonly used imputation methods for survey data replace the missing values for the nonrespondent units by the observed values from one or multiple respondent units. Iveware developed by the researchers at the survey methodology program, survey research center, institute for social research, university of michigan performs. Our data contain missing values, however, and standard casewise deletion would result in a 40% reduction in sample size. Missing data, multiple imputation and associated software. We want to study the linear relationship between y and predictors x1 and x2. When information exists on the same record from which missing information can logically be inferred, that information is used to replace the missing information. Examples of missing data can be found in surveys where. When and how should multiple imputation be used for handling. The fourth step of multiple imputation for missing data is to average the values of the parameter estimates across the missing value samples in order to obtain a single point estimate. Imputation and variance estimation software wikipedia. Dear weighting, this is a very interesting question. However i will also provide the script that results from what i do.
During the 1980s, major federal survey programs in the united states and canada took the lead. Imputation and variance estimation software iveware is a collection of routines written under various platforms and packaged to perform multiple imputations, variance estimation or standard error and, in general, draw inferences from incomplete data. Getting started with multiple imputation in r statlab articles. Multiple imputation for missing data statistics solutions. However, when imputing weighted data, the currently most popular method is hotdeck. Multiple imputation for missing income data in the national health interview survey schenker, raghunathan, chiu, makuc, zhang, and cohen 2006, jasa national health interview survey nhis principal source of information on the health of the civilian noninstitutionalized population.
Reasons for the missingness might be respondent attrition, survey structure where some questions are asked only of a subset of respondents. Multiple imputation for missing data is an attractive method for handling missing data in multivariate analysis. For example, when you create a test or questionnaire for depression, you. Missingdata imputation department of statistics columbia. Multiple imputation of family income and personal earnings in. When multiple imputation works properly, it fills in data in such a way as to not change any relationships in the data but which enables the inclusion of all the observed data in the partially missing rows. Use features like bookmarks, note taking and highlighting while reading multiple imputation of. How will you deal with dont know and missing data in. Im analyzing data from a survey and would like to handle the missing values by multiple imputation. For example, in data derived from surveys, item missing data occurs when a respondent elects not to answer certain questions, resulting in only a dont know or refused. Missing data imputation methods are nowadays implemented in almost all statistical software. Common reasons for missing data include survey structure that deliberately results in missing data questions asked only of women, refusal to answer. If done well, it leads to unbiased parameter estimates and accurate standard errors.
Despite the popularity of multiple imputation of missing data, its acceptance and application still lag in largescale studies with complicated datasets such as cancors. Oct 14, 2019 multiple imputation was a huge breakthrough in statistics about 20 years ago because it solved a lot of these problems with missing data though, unfortunately not all. Oct 07, 2011 imputation is one of the key strategies that researchers use to fill in missing data in a dataset. Regardless of the nature of the postimputation phase, mi inference treats missing data as an explicit source of random variability and the uncertainty induced by this is explicitly incorporated. As arnold zellner remarked at a session on multiple imputation at the 1997 joint statistical meetings, one should always try to get the actual data one needs rather than trying to create a proxy later.
We use sequential regression multiple imputation, implemented in publicavailable software, to deal with nonresponse in the cancors surveys and construct a centralized. Regardless of the nature of the post imputation phase, mi inference treats missing data as an explicit source of random variability and the uncertainty induced by this is explicitly incorporated. How can i perform multiple imputation on longitudinal data using ice. The following is the procedure for conducting the multiple imputation for missing data that was created by rubin in 1987. Approaches to imputing missing data in complex survey data christine wells, ph. With nonweighted data, the currently most commonly method used to impute missing data is multiple imputation. Iveware developed by the researchers at the survey methodology program, survey research center, institute for social research, university of michigan performs imputations of missing values using the sequential regression also known as chained equations method. The performance of multiple imputation for likerttype. Statas new mi command provides a full suite of multipleimputation methods for the analysis of incomplete data, data for which some values are missing. Multiple imputation in the survey of consumer finances. Enhance the use of all available information for the creation of public use datasets design implications and future directions other two talks provide several applications and software related issues. Imputation methods for missing categorical questionnaire data. Multiple imputation and survey weights the methodology center.
Missing information usually reflects failures in the information collection process, and we should not lose sight of this fact. Approaches to imputing missing data in complex survey data. Imputing longitudinal or panel data poses special problems. This example uses the nhanes iii multiple imputation data sets. Multiple imputation does this by creating several say, five imputed values for. When and how should multiple imputation be used for. The third step of multiple imputation for missing data is to perform the desired analysis on each data set by using standard, complete data methods. Imputation and variance estimation software survey. Making the most of what you know, organizational research methods, 63, pp.
Differences across the datasets capture our uncertainty about the missing values. Studies of governments and local organizations using survey data have. The survey package works with the mitools package to analyze multiplyimputed data. The more missing data you have, the more you are relying on your imputation algorithm to be valid. In survey data, income might have 10% missing or more, while none of the other survey questions have more than 2% missing, and the missing cases on any one of those variables overlap a lot with. This session will discuss the drawbacks of traditional methods for dealing with missing data and describe why newer methods, such as multiple imputation, are preferable. Software survey research center imputation and variance. This special volume aims to provide exactly this, and it is my hope to see updates to this special volume to provide statistical and substantive.
Amelia ii is a new program, and follows in the spirit with the same purpose as the first version of amelia by james honaker, anne joseph. By using various calculations to find the most probable answer, imputed data is used in place of actual data in order to allow for more accurate analyses. The idea of multiple imputation for missing data was first proposed by rubin 1977. However, the free statistical computing environment r does not allow computation of r2 effect sizes after using multiple imputation procedures for missing data analysis. Should the survey weights be used as a covariate in the imputation model. Weighting and imputation as a general purpose solutions for missing data why we need multiple imputation. Missing data is a problem in almost every research study, and standard ways of dealing with missing values, such as complete case analysis, are generally inappropriate.
We use sequential regression multiple imputation, implemented in publicavailable software, to deal with nonresponse in the cancors surveys and construct a centralized completed database that can be easily used by investigators from multiple sites. Multiple imputation of family income and personal earnings. Below, i will show an example for the software rstudio. Due to high prevalence of missing data in research problems relying on empirical evidence, it is critical for the statistical community to provide objective and open source for missing data software. In survey data, income might have 10% missing or more, while none of the other survey questions have more than 2% missing, and the missing cases on any one of. Given the continuously rising cost of conducting censuses and sample surveys, imputation and other missingdata compensation methods aided by administrative records may come to argument actual data collection, in the future. When to use single imputation or multiple imputation. Multiple imputation for missing data in epidemiological and. In summary, multiple imputation works well when the missing data are. Multiple imputation of missing data using sas sas support. Because spss works primarily through a gui, it is easiest to present it that way. These imputation techniques are known as hotdeck imputation. Jonathan sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them missing data are unavoidable in epidemiological and clinical research but their potential to undermine the validity of research results has often been overlooked in the medical literature. Explicit methods include bayesian multiple imputation, propensity score matching and direct substitution of information extracted from administrative records.
How should i deal with missing data from my online survey. Multiple imputation of missing data using sas kindle edition by berglund, patricia, heeringa, steven g download it once and read it on your kindle device, pc, phones or tablets. Di erent approaches to imputing missing complex survey data stata. On the imputation of missing data in surveys with likert. Imputation is one of the key strategies that researchers use to fill in missing data in a dataset. We will fit the model using multiple imputation mi. If the data are in long form, each case has multiple rows in the dataset, so this needs to be accounted for in the estimation of any analytic model. When using multiple imputation, missing values are identified and are replaced by a random sample of plausible values imputations completed datasets. Multiple imputation and survey weights the methodology. Imputation and variance estimation software, version 0. Imputing missing data in complex survey data 25 28.
With the introduction of easytouse software to generate imputations and. It also leads to methods to adjust the variance to reflect the additional uncertainty created by the missing data. Neither package performs multiple imputation creating the imputations is only useful when it incorporates situationspecific knowledge. To handle the problem of missing data on family income and personal earnings in the nhis, multiple imputation of these items was performed for the survey years 1997 2007, with five. Software exists to fit such models automatically, so that one can conceivably. Data editing is generally preferred over statistical imputation, and it is used whenever a missing item can be logically inferred from other data that have been provided. Missing data and multiple imputation missing data is a pervasive and persistent problem in many data sets.
With 30% of missing data, mar conditions resulted in negatively biased correlations. There are three main problems that missing data causes. Using spss to handle missing data university of vermont. Implicit methods revolve around donorbased techniques such as hotdeck imputation and predictive mean. This article introduced an easytoapply algorithm, making multiple imputation within reach of practicing social scientists. Jun 29, 2015 multiple imputation using spss david c. How can i perform multiple imputation on longitudinal data.
Missing data takes many forms and can be attributed to many causes. See enders 2010 for a discussion of other statistical software packages that can perform multiple imputation and other modern missing data procedures. Multiple imputation t1 survey of consumer finances in summary the survey contains very large numimportant role in the survey in the scfs before 1989 ber of variables there is substantial missing or partially missing data were singly imputed using variety of tech. In statistics, imputation is the process of replacing missing data with substituted values. An imputation generally represents one set of plausible values for missing data multiple imputation represents multiple sets of plausible values. Multiple imputation for missing data in epidemiological. It, and the related software, has been widely used. Abb is a hotdeck procedure that imputes missing data by sampling from the complete data. Use features like bookmarks, note taking and highlighting while reading multiple imputation of missing data using sas. When substituting for a data point, it is known as unit imputation. Mi was robust to violations of continuity and normality. These runs incorporate survey designbased variance estimation andor multiple imputation analysis for missing data.
The performance of multiple imputation mi for missing data in likerttype items assuming multivariate normality was assessed using simulation methods. Multiple imputation of missing data using sas, berglund. The example data i will use is a data set about air. The performance of multiple imputation for likerttype items. For all observations that are nonmissing, calculate the mean, median or mode of the observed values for that variable, and fill in the missing values with it. Multiple imputation is a procedure that produces several data sets often in the range of 5, 10, or 30, with slightly different imputed values for the missing observations in each data set.
It can also be used to perform analysis without any missing data. Multiple imputation in a largescale complex survey. Multiple imputation t1 survey of consumer finances in summary the survey contains very large numimportant role in the survey in the scfs before 1989 ber of variables there is substantial missing or partially missing data were singly imputed using variety of tech missing range information the patterns of missing inniques including randomized regressions hot deck. Multiple imputation is a simulationbased statistical technique for handling missing data. Multiple imputation for missing data had long been recognized as theoretical appropriate, but algorithms to use it were difficult, and applications were rare. You can see part of that data file below, showing the last few lines of the original data and the first few lines of the data from imputation 1. This can be done in stata with weighted data in two ways. Now let is discuss what is different about handling missing data in a weighted dataset. However, you could apply imputation methods based on many other software such as spss, stata or sas. The areas shaded in yellow are imputed values where the. Section 1overview of missing data and multiple imputation missing data in longitudinal data sets missing data is especially common in longitudinal data sets. Most popular statistical software packages have options for multiple 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. Imputations of missing values using the sequential regression also known as chained equations method.
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