AMELIA II: A Program for Missing Data - Harvard University

1 AMELIA II: A Program for Missing DataJames Honaker, Gary King, and Matthew BlackwellVersion 2, 2012 Contents1 Introduction32 WhatAmelia Assumptions .. Algorithm .. Analysis ..53 Versions Installation and Updates from R .. Installation in Windows ofAmeliaView as a Standalone Program .. Linux (local installation) ..74 A User s data and Initial Results .. Multiple Imputation .. Saving imputed datasets .. Combining MultipleAmelia Runs .. Screen Output .. Imputation-improving Transformations .. Ordinal .. Nominal .. Natural Log .. Square Root .. Logistic.

2 Identification Variables .. Time Series, or Time Series Cross Sectional data .. Lags and Leads .. Including Prior Information .. Ridge Priors for High Missingness, Smalln s, or Large Corre-lations .. Observation-level priors .. Logical bounds .. Diagnostics .. Comparing Densities .. Overimpute .. Overdispersed Starting Values .. Time-series plots .. Missingness maps .. Post-imputation Transformations .. Analysis Models .. Theameliaclass ..445 AmeliaView Menu LoadingAmeliaView .. Loading a data set intoAmeliaView .. Variable dashboard .. Options.

3 Numerical Options .. Add Distribution Prior .. Add Range Prior .. Imputing and checking diagnostics .. Diagnostics Dialog .. Sessions ..556 Reference to AMELIA s Functions5621 IntroductionMissing data is a ubiquitous problem in social science data . Respondents do notanswer every question, countries do not collect statistics every year, archives areincomplete, subjects drop out of panels. Most statistical analysis methods, however,assume the absence of Missing data , and are only able to include observations forwhich every variable is users to impute ( fill in or rectan-gularize) incomplete data sets so that analyses which require complete observationscan appropriately use all the information present in a dataset with missingness, andavoid the biases, inefficiencies, and incorrect uncertainty estimates that can resultfrom dropping all partially observed observations from the imputation, a general-purpose approach to data withmissing values.

4 Multiple imputation has been shown to reduce bias and increase ef-ficiency compared to listwise deletion. Furthermore, ad-hoc methods of imputation,such as mean imputation, can lead to serious biases in variances and , creating multiple imputations can be a burdensome process due tothe technical nature of algorithms provides users with a simpleway to create and implement an imputation model, generate imputed datasets, andcheck its fit using goes several significant steps beyond the capabilities ofthe first version ofAmelia (Honaker, Joseph, King, Scheve and Singh., 1998-2002).For one, the bootstrap-based EMB algorithm included inAmeliaIIcan imputemany more variables, with many more observations, in much less time.

5 The greatsimplicity and power of the EMB algorithm made it possible to writeAmeliaIIsothat it virtually never crashes which to our knowledge makes it unique amongall existing multiple imputation software and is much faster than the has features to make valid and much more accurate imputationsfor cross-sectional, time-series, and time-series-cross-section data , and allows theincorporation of observation and data -matrix-cell level prior information. In additionto all of this,AmeliaIIprovides many diagnostic functions that help users check thevalidity of their imputation model. This software implements the ideas developed inHonaker and King (2010).

6 2 WhatAmelia DoesMultiple imputation involves imputingmvalues for each Missing cell in your datamatrix and creatingm completed data sets. Across these completed data sets, theobserved values are the same, but the Missing values are filled in with a distribution ofimputations that reflect the uncertainty about the Missing data . After imputationwithAmeliaII s EMB algorithm, you can apply whatever statistical method youwould have used if there had been no Missing values to each of themdata sets,and use a simple procedure, described below, to combine the results1. Under normalcircumstances, you only need to impute once and can then analyze themimputed1 You can combine the results automatically by doing your data analyses within Zelig for R, orwithin Clarify for Stata; sets as many times and for as many purposes as you wish.

7 The advantage ofAmeliaIIis that it combines the comparative speed and ease-of-use of our algorithmwith the power of multiple imputation, to let you focus on your substantive researchquestions rather than spending time developing complex application-specific modelsfor nonresponse in each new data set. Unless the rate of missingness is very high,m= 5 (the Program default) is probably AssumptionsThe imputation model inAmeliaIIassumes that the complete data (that is, bothobserved and unobserved) are multivariate normal. If we denote the (n k) datasetasD(with observed partDobsand unobserved partDmis), then this assumption isD Nk( , ),(1)which states thatDhas a multivariate normal distribution with mean vector andcovariance matrix.

8 The multivariate normal distribution is often a crude approx-imation to the true distribution of the data , yet there is evidence that this modelworks as well as other, more complicated models even in the face of categorical ormixed data (see Schafer, 1997; Schafer and Olsen, 1998). Furthermore, transforma-tions of many types of variables can often make this normality assumption moreplausible (see for more information on how to implement this inAmelia).The essential problem of imputation is that we only observeDobs, not the entiretyofD. In order to gain traction, we need to make the usual assumption in multipleimputation that the data aremissing at random(MAR).

9 This assumption meansthat the pattern of missingness only depends on the observed dataDobs, not theunobserved dataDmis. LetMto be the missingness matrix, with cellsmij= 1 ifdij Dmisandmij= 0 otherwise. Put simply,Mis a matrix that indicates whetheror not a cell is Missing in the data . With this, we can define the MAR assumptionasp(M|D) =p(M|Dobs).(2)Note that MAR includes the case when Missing values are created randomly by, say,coin flips, but it also includes many more sophisticated missingness models. Whenmissingness is not dependent on the data at all, we say that the data aremissingcompletely at random(MCAR).

10 AMELIA requires both the multivariate normalityand the MAR assumption (or the simpler special case of MCAR). Note that theMAR assumption can be made more plausible by including additional variables inthe datasetDin the imputation dataset than just those eventually envisioned to beused in the analysis AlgorithmIn multiple imputation, we are concerned with the complete- data parameters, =( , ). When writing down a model of the data , it is clear that our observed data isactuallyDobsandM, the missingness matrix. Thus, the likelihood of our observed4data isp(Dobs, M| ). Using the MAR assumption2, we can break this up,p(Dobs, M| ) =p(M|Dobs)p(Dobs| ).