Missing Data: Our View of the State of the Art
Joseph L. Schafer and John W. Graham
Pennsylvania State University
Statistical procedures for missing data have vastly improved, yet misconception and
unsound practice still abound. The authors frame the missing-data problem, review
methods, offer advice, and raise issues that remain unresolved. They clear up
common misunderstandings regarding the missing at random (MAR) concept. They
summarize the evidence against older procedures and, with few exceptions, discourage
their use. They present, in both technical and practical language, 2 general
approaches that come highly recommended: maximum likelihood (ML) and Bayesian
multiple imputation (MI). Newer developments are discussed, including some
for dealing with missing data that are not MAR. Although not yet in the mainstream,
these procedures may eventually extend the ML and MI methods that
currently represent the state of the art.
Why do missing data create such difficulty in scientific
research? Because most data analysis procedures
were not designed for them. Missingness is usually
a nuisance, not the main focus of inquiry, but
handling it in a principled manner raises conceptual
difficulties and computational challenges. Lacking resources
or even a theoretical framework, researchers,
methodologists, and software developers resort to editing
the data to lend an appearance of completeness.
Unfortunately, ad hoc edits may do more harm than
good, producing answers that are biased, inefficient
(lacking in power), and unreliable.
Purposes of This Article
This article’s intended audience and purposes are
varied. For the novice, we review the available methods
and describe their strengths and limitations. For
those already familiar with missing-data issues, we
seek to fill gaps in understanding and highlight recent
developments in this rapidly changing field. For
methodologists, we hope to stimulate thoughtful discussion
and point to important areas for new research.
One of our main reasons for writing this article is to
familiarize researchers with these newer techniques
and encourage them to apply these methods in their
own work.
In the remainder of this article, we describe criteria
by which missing-data procedures should be evaluated.
Fundamental concepts, such as the distribution
of missingness and the notion of missing at random
(MAR), are presented in nontechnical fashion. Older
procedures, including case deletion and single imputation,
are reviewed and assessed. We then review and
compare modern procedures of maximum likelihood
(ML) and multiple imputation (MI), describing their
strengths and limitations. Finally, we describe new
techniques that attempt to relax distributional assumptions
and methods that do not assume that missing
data are MAR.
In general, we emphasize and recommend two approaches.
The first is ML estimation based on all
available data; the second is Bayesian MI. Readers
who are not yet familiar with these techniques may
wish to see step-by-step illustrations of how to apply
them to real data. Such examples have already been
published, and space limitations do not allow us to
repeat them here. Rather, we focus on the underlying
motivation and principles and provide references so
that interested readers may learn the specifics of applying
them later. Many software products (both free
and commercial) that implement ML and MI are listed
here, but in this rapidly changing field others will
Joseph L. Schafer, Department of Statistics and the Methodology
Center, Pennsylvania State University; John W.
Graham, Department of Biobehavioral Health, Pennsylvania
State University.
This research was supported by National Institute on
Drug Abuse Grant 1-P50-DA10075.
Correspondence concerning this article should be addressed
to Joseph L. Schafer, The Methodology Center,
Pennsylvania State University, Henderson S-159, University
Park, Pennsylvania 16802. E-mail: jls@stat.psu.edu
Psychological Methods Copyright 2002 by the American Psychological Association, Inc.
2002, Vol. 7, No. 2, 147–177 1082-989X/02/$5.00 DOI: 10.1037//1082-989X.7.2.147
147
undoubtedly become available soon. A resource Web
page maintained by John W. Graham (http://
methodology.psu.edu/resources.html) will provide
timely updates on missing-data applications and utilities
as they evolve, along with step-by-step instructions
on MI with NORM (Schafer, 1999b).
Fundamentals
What Is a Missing Value?
Data contain various codes to indicate lack of response:
“Don’t know,” “Refused,” “Unintelligible,”
and so on. Before applying a missing-data procedure,
one should consider whether an underlying “true”
value exists and, if so, whether that value is unknown.
The answers may not be obvious. Consider a questionnaire
for adolescents with a section on marijuana
use. The first item is “Have you ever tried marijuana?”
If the response is “No,” the participant is
directed to skip items on recent and lifetime use and
proceed to the next section. Many researchers would
not consider the skipped items to be missing, because
never having used marijuana logically implies no recent
or lifetime use. In the presence of response error,
however, it might be a mistake to presume that all
skipped items are zero, because some answers to the
initial question may be incorrect.
Interesting issues arise in longitudinal studies in
which unfortunate events preclude measurement. If
participants die, should we consider their characteristics
(e.g., mental functioning) at subsequent occasions
to be missing? Some might balk at the idea, but in
some contexts it is quite reasonable. If deaths are rare
and largely unrelated to the phenomena of interest,
then we may want to estimate parameters for an ideal
scenario in which no one dies during the study. At
other times, we may want to describe the characteristics
of live participants only and perhaps perform
additional analyses with mortality itself as the outcome.
Even then, however, it is sometimes convenient
to posit the existence of missing values for the deceased
purely as a computational device, to permit the
use of missing-data algorithms. These issues are clarified
later, after we review the notion of MAR.
Missing values are part of the more general concept
of coarsened data, which includes numbers that have
been grouped, aggregated, rounded, censored, or truncated,
resulting in partial loss of information (Heitjan
& Rubin, 1991). Latent variables, a concept familiar
to psychologists, are also closely related to missing
data. Latent variables are unobservable quantities
(e.g., intelligence, assertiveness) that are only imperfectly
measured by test or questionnaire items. Computational
methods for missing data may simplify parameter
estimation in latent-variable models; a good
example is the expectation-maximization (EM) algorithm
for latent class analysis (Clogg & Goodman,
1984).
Psychologists have sometimes made a distinction
between missing values on independent variables
(predictors) and missing values on dependent variables
(outcomes). From our perspective, these two do
not fundamentally differ. It is true that, under certain
assumptions, missing values on a dependent variable
may be efficiently handled by a very simple method
such as case deletion, whereas good missing-data procedures
for independent variables can be more difficult
to implement. We discuss these matters later as
we review specific classes of missing-data procedures.
However, we caution our readers not to believe
general statements such as, “Missing values on a dependent
variable can be safely ignored,” because such
statements are imprecise and generally false.
Historical Development
Until the 1970s, missing values were handled primarily
by editing. Rubin (1976) developed a framework
of inference from incomplete data that remains
in use today. The formulation of the EM algorithm
(Dempster, Laird, & Rubin, 1977) made it feasible to
compute ML estimates in many missing-data problems.
Rather than deleting or filling in incomplete
cases, ML treats the missing data as random variables
to be removed from (i.e., integrated out of) the likelihood
function as if they were never sampled. We
elaborate on this point later after introducing the notion
of MAR. Many examples of EM were described
by Little and Rubin (1987). Their book also documented
the shortcomings of case deletion and single
imputation, arguing for explicit models over informal
procedures. About the same time, Rubin (1987) introduced
the idea of MI, in which each missing value is
replaced with m > 1 simulated values prior to analysis.
Creation of MIs was facilitated by computer technology
and new methods for Bayesian simulation discovered
in the late 1980s (Schafer, 1997). ML and MI
are now becoming standard because of implementations
in free and commercial software.
The 1990s have seen many new developments. Reweighting,
long used by survey methodologists, has
been proposed for handling missing values in regression
models with missing covariates (Ibrahim, 1990).
SCHAFER AND GRAHAM148
New lines of research focus on how to handle missing
values while avoiding the specification of a full parametric
model for the population (Robins, Rotnitzky,
& Zhao, 1994). New methods for nonignorable modeling,
in which the probabilities of nonresponse are
allowed to depend on the missing values themselves,
are proliferating in biostatistics and public health. The
primary focus of these nonignorable models is dropout
in clinical trials, in which participants may be
leaving the study for reasons closely related to the
outcomes being measured (Little, 1995). Researchers
are now beginning to assess the sensitivity of results
to alternative hypotheses about the distribution of
missingness (Verbeke & Molenberghs, 2000).
Goals and Criteria
With or without missing data, the goal of a statistical
procedure should be to make valid and efficient
inferences about a population of interest—not to estimate,
predict, or recover missing observations nor to
obtain the same results that we would have seen with
complete data. Attempts to recover missing values
may impair inference. For example, the common
practice of mean substitution—replacing each missing
value for a variable with the average of the observed
values—may accurately predict missing data but distort
estimated variances and correlations. A missingvalue
treatment cannot be properly evaluated apart
from the modeling, estimation, or testing procedure in
which it is embedded.
Basic criteria for evaluating statistical procedures
have been established by Neyman and Pearson (1933)
and Neyman (1937). Let Q denote a generic population
quantity to be estimated, and let Qˆ denote an
estimate of Q based on a sample of data. If the sample
contains missing values, then the method for handling
them should be considered part of the overall procedure
for calculating Qˆ . If the procedure works well,
then Qˆ will be close to Q, both on average over repeated
samples and for any particular sample. That is,
we want the bias—the difference between the average
value of Qˆ and the true Q—to be small, and we also
want the variance or standard deviation of Qˆ to be
small. Bias and variance are often combined into a
single measure called mean square error, which is the
average value of the squared distance (Qˆ − Q)2
over
repeated samples. The mean square error is equal to
the squared bias plus the variance.
Bias, variance, and mean square error describe the
behavior of an estimate, but we also want honesty in
the measures of uncertainty that we report. A reported
standard error SE(Qˆ ) should be close to the true standard
deviation of Qˆ . A procedure for confidence intervals—for
example, Qˆ ± 2SE(Qˆ ) for a 95% interval—should
cover the true Q with probability close to
the nominal rate. If the coverage rate is accurate, the
probability of Type I error (wrongly rejecting a true
null hypothesis) will also be accurate. Subject to correct
coverage, we also want the intervals to be narrow,
because shorter intervals will reduce the rate of Type
II error (failure to accept a true alternative hypothesis)
and increase power.
When missing values occur for reasons beyond our
control, we must make assumptions about the processes
that create them. These assumptions are usually
untestable. Good science suggests that assumptions
be made explicit and the sensitivity of results to
departures be investigated. One hopes that similar
conclusions will follow from a variety of realistic alternative
assumptions; when that does not happen, the
sensitivity should be reported.
Finally, one should avoid tricks that apparently
solve the missing-data problem but actually redefine
the parameters or the population. For example, consider
a linear regression of Y on X, where the predictor
X is sometimes missing. Suppose we replace the missing
values by an arbitrary number (say, zero) and
introduce a dummy indicator Z that is one if X is
missing and zero if X is observed. This procedure
merely redefines the coefficients. In the original
model E(Y) ‫ס‬ ␤0 + ␤1X, where E represents expected
value, ␤0 and ␤1 represent the intercept and slope for
the full population; in the expanded model E(Y) ‫ס‬ ␤0
+ ␤1X + ␤2Z, ␤0 and ␤1 represent the intercept and
slope for respondents, and ␤0 + ␤2 represents the
mean of Y among nonrespondents. For another example,
suppose that missing values occur on a nominal
outcome with response categories 1, 2, . . . , k.
One could treat the missing value as category k + 1,
but that merely redefines categories 1, . . . , k to apply
only to respondents.
Types and Patterns of Nonresponse
Survey methodologists have historically distinguished
unit nonresponse, which occurs when the entire
data collection procedure fails (because the
sampled person is not at home, refuses to participate,
etc.), from item nonresponse, which means that partial
data are available (i.e., the person participates but
does not respond to certain individual items). Survey
statisticians have traditionally handled unit nonresponse
by reweighting and item nonresponse by
MISSING DATA 149
single imputation. These older methods, which are
reviewed in this article, may perform reasonably well
in some situations, but more modern procedures (e.g.,
ML and MI) exhibit good behavior more generally.
In longitudinal studies, participants may be present
for some waves of data collection and missing for
others. This kind of missingness may be called wave
nonresponse. Attrition, or dropout, which is a special
case of wave nonresponse, occurs when one leaves
the study and does not return. Overall, dropout or
attrition may be the most common type of wave nonresponse.
However, it is not uncommon for participants
to be absent from one wave and subsequently
reappear. Because repeated measurements on an individual
tend to be correlated, we recommend procedures
that use all the available data for each participant,
because missing information can then be
partially recovered from earlier or later waves. Longitudinal
modeling by ML can be a highly efficient
way to use the available data. MI of missing responses
is also effective if we impute under a longitudinal
model that borrows information across waves.
Many data sets can be arranged in a rectangular or
matrix form, where the rows correspond to observational
units or participants and the columns correspond
to items or variables. With rectangular data,
there are several important classes of overall missingdata
patterns. Consider Figure 1a, in which missing
values occur on an item Y but a set of p other items
X1, . . . , Xp is completely observed; we call this a
univariate pattern. The univariate pattern is also
meant to include situations in which Y represents a
group of items that is either entirely observed or entirely
missing for each unit. In Figure 1b, items or
item groups Y1, . . . , Yp may be ordered in such a way
that if Yj is missing for a unit, then Yj+1, . . . , Yp are
missing as well; this is called a monotone pattern.
Monotone patterns may arise in longitudinal studies
with attrition, with Yj representing variables collected
at the jth occasion. Figure 1c shows an arbitrary pattern
in which any set of variables may be missing for
any unit.
The Distribution of Missingness
For any data set, one can define indicator variables
R that identify what is known and what is missing. We
refer to R as the missingness. The form of the missingness
depends on the complexity of the pattern. In
Figure 1a, R can be a single binary item for each unit
indicating whether Y is observed (R ‫ס‬ 1) or missing
(R ‫ס‬ 0). In Figure 1b, R can be a integer variable (1,
2, . . . , p) indicating the highest j for which Yj is observed.
In Figure 1c, R can be a matrix of binary
indicators of the same dimension as the data matrix,
with elements of R set to 1 or 0 according to whether
the corresponding data values are observed or miss-
ing.
In modern missing-data procedures missingness is
regarded as a probabilistic phenomenon (Rubin,
1976). We treat R as a set of random variables having
a joint probability distribution. We may not have to
specify a particular distribution for R, but we must
agree that it has a distribution. In statistical literature,
the distribution of R is sometimes called the response
mechanism or missingness mechanism, which may be
confusing because mechanism suggests a real-world
process by which some data are recorded and others
are missed. To describe accurately all potential causes
or reasons for missingness is not realistic. The distribution
of R is best regarded as a mathematical device
to describe the rates and patterns of missing values
and to capture roughly possible relationships between
the missingness and the values of the missing items
themselves. To avoid suggestions of causality, we
Figure 1. Patterns of nonresponse in rectangular data sets: (a) univariate pattern, (b) monotone
pattern, and (c) arbitrary pattern. In each case, rows correspond to observational units
and columns correspond to variables.
SCHAFER AND GRAHAM150
therefore refer to the probability distribution for R as
the distribution of missingness or the probabilities of
missingness.
Missing at Random
Because missingness may be related to the data, we
classify distributions for R according to the nature of
that relationship. Rubin (1976) developed a typology
for these distributions that is widely cited but less
widely understood. Adopting a generic notation, let us
denote the complete data as Ycom and partition it as
Ycom ‫ס‬ (Yobs, Ymis), where Yobs and Ymis are the observed
and missing parts, respectively. Rubin (1976)
defined missing data to be MAR if the distribution of
missingness does not depend on Ymis,
P(R|Ycom) ‫ס‬ P(R|Yobs). (1)
In other words, MAR allows the probabilities of missingness
to depend on observed data but not on missing
data.1
An important special case of MAR, called missing
completely at random (MCAR), occurs when the
distribution does not depend on Yobs either,
P(R|Ycom) ‫ס‬ P(R).
When Equation 1 is violated and the distribution depends
on Ymis, the missing data are said to be missing
not at random (MNAR). MAR is also called ignorable
nonresponse, and MNAR is called nonignorable.
For intuition, it helps to relate these definitions to
the patterns in Figure 1. Consider the univariate pattern
of Figure 1a, where variables X ‫ס‬ (X1, . . . , Xp)
are known for all participants but Y is missing for
some. If participants are independently sampled from
the population, then MCAR, MAR, and MNAR have
simple interpretations in terms of X and Y: MCAR
means that the probability that Y is missing for a
participant does not depend on his or her own values
of X or Y (and, by independence, does not depend on
the X or Y of other participants either), MAR means
that the probability that Y is missing may depend on X
but not Y, and MNAR means that the probability of
missingness depends on Y. Notice that under MAR,
there could be a relationship between missingness and
Y induced by their mutual relationships to X, but there
must be no residual relationship between them once X
is taken into account. Under MNAR, some residual
dependence between missingness and Y remains after
accounting for X.
Notice that Rubin’s (1976) definitions describe statistical
relationships between the data and the missingness,
not causal relationships. Because we often
consider real-world reasons why data become missing,
let us imagine that one could code all the myriad
reasons for missingness into a set of variables. This
set might include variables that explain why some
participants were physically unable to show up (age,
health status), variables that explain the tendency to
say “I don’t know” or “I’m not sure” (cognitive functioning),
variables that explain outright refusal (concerns
about privacy), and so on. These causes of missingness
are not likely to be present in the data set, but
some of them are possibly related to X and Y and thus
by omission may induce relationships between X or Y
and R. Other causes may be entirely unrelated to X
and Y and may be viewed as external noise. If we let
Z denote the component of cause that is unrelated to
X and Y, then MCAR, MAR, and MNAR may be
represented by the graphical relationships in Figure 2.
MCAR requires that the causes of missingness be
entirely contained within the unrelated part Z, MAR
allows some causes to be related to X, and MNAR
requires some causes to be residually related to Y after
relationships between X and R are taken into account.
If we move from the univariate pattern of Figure 1a
to the monotone pattern of Figure 1b, MCAR means
that Yj is missing with probability unrelated to any
variables in the system; MAR means that it may be
related only to Y1, . . . , Yj−1; and MNAR means that it
is related to Yj, . . . , Yp. If Y1, . . . , Yp are repeated
measurements of an outcome variable in a longitudinal
study, and missing data arise only from attrition,
then MCAR requires dropout to be independent of
responses at every occasion, MAR allows dropout to
depend on responses at any or all occasions prior to
dropout, and MNAR means that it depends on the
unseen responses after the participant drops out. In
this specialized setting, MAR has been called noninformative
or ignorable dropout, whereas MNAR is
called informative (Diggle & Kenward, 1994).
1
In Rubin’s (1976) definition, Equation 1 is not required
to hold for all possible values of R, but only for the R that
actually appeared in the sample. This technical point clarifies
certain issues. For example, suppose that an experiment
produced no missing values even though it could have. In
that case, Equation 1 would hold because Ymis is empty, and
Rubin’s (1976) results indicate that one should simply analyze
the complete data without worrying about the fact that
missing values could have arisen in hypothetical repetitions
of the experiment.
MISSING DATA 151
With the arbitrary pattern of Figure 1c, MCAR still
requires independence between missingness and
Y1, . . . , Yp. However, MAR is now more difficult to
grasp. MAR means that a participant’s probabilities of
response may be related only to his or her own set of
observed items, a set that may change from one participant
to another. One could argue that this assumption
is odd or unnatural, and in many cases we are
inclined to agree. However, the apparent awkwardness
of MAR does not imply that it is far from true.
Indeed, in many situations, we believe that MAR is
quite plausible, and the analytic simplifications that
result from making this assumption are highly benefi-
cial.
In discussions with researchers, we have found that
most misunderstandings of MCAR, MAR, and
MNAR arise from common notions about the meaning
of random. To a statistician, random suggests a
process that is probabilistic rather than deterministic.
In that sense, MCAR, MAR, and MNAR are all random,
because they all posit probability distributions
for R. To a psychologist, random may suggest a process
that is unpredictable and extraneous to variables
in the current study (e.g., tossing a coin or rolling a
die), a notion that agrees more closely with MCAR
than with MAR. In retrospect, Rubin’s (1976) choice
of terminology seems a bit unfortunate, but these
terms are now firmly established in the statistical literature
and are unlikely to change.
The Plausibility of MAR
In certain settings, MAR is known to hold. These
include planned missingness in which the missing
data were never intended to be collected in the first
place: cohort-sequential designs for longitudinal studies
(McArdle & Hamagami, 1991; Nesselroade &
Baltes, 1979) and the use of multiple questionnaire
forms containing different subsets of items (Graham,
Hofer, & Piccinin, 1994; Graham, Hofer, & MacKinnon,
1996). Planned missingness in a study may have
important advantages in terms of efficiency and cost
(Graham, Taylor, & Cumsille, 2001). Planned missing
values are usually MCAR, but MAR situations sometimes
arise—for example, if participants are included
in a follow-up measure only if their pretest scores
exceed a cutoff value. Latent variables are missing
with probability one and are therefore also known to
be MAR.
When missingness is beyond the researcher’s control,
its distribution is unknown and MAR is only an
assumption. In general, there is no way to test whether
MAR holds in a data set, except by obtaining followup
data from nonrespondents (Glynn, Laird, & Rubin,
1993; Graham & Donaldson, 1993) or by imposing an
unverifiable model (Little & Rubin, 1987, chapter
11). In most cases we should expect departures from
MAR, but whether these departures are serious
enough to cause the performance of MAR-based
methods to be seriously degraded is another issue entirely
(Graham, Hofer, Donaldson, MacKinnon, &
Schafer, 1997). Recently, Collins, Schafer, and Kam
(2001) demonstrated that in many realistic cases, an
erroneous assumption of MAR (e.g., failing to take
into account a cause or correlate of missingness) may
often have only a minor impact on estimates and standard
errors.
Example
Suppose that systolic blood pressures of N participants
are recorded in January (X). Some of them have
a second reading in February (Y), but others do not.
Table 1 shows simulated data for N ‫ס‬ 30 participants
drawn from a bivariate normal population with means
␮x ‫ס‬ ␮y ‫ס‬ 125, standard deviations ␴x ‫ס‬ ␴y ‫ס‬ 25,
and correlation ␳ ‫.06.ס‬ The first two columns of the
Figure 2. Graphical representations of (a) missing completely at random (MCAR), (b) missing at random (MAR), and (c)
missing not at random (MNAR) in a univariate missing-data pattern. X represents variables that are completely observed, Y
represents a variable that is partly missing, Z represents the component of the causes of missingness unrelated to X and Y, and
R represents the missingness.
SCHAFER AND GRAHAM152
table show the complete data for X and Y. The other
columns show the values of Y that remain after imposing
missingness by three methods. In the first
method, the 7 measured in February were randomly
selected from those measured in January; this mechanism
is MCAR. In the second method, those who
returned in February did so because their January
measurements exceeded 140 (X > 140), a level used
for diagnosing hypertension; this is MAR but not
MCAR. In the third method, those recorded in February
were those whose February measurements exceeded
140 (Y > 140). This could happen, for example,
if all individuals returned in February, but the
staff person in charge decided to record the February
value only if it was in the hypertensive range. This
third mechanism is an example of MNAR. (Other
MNAR mechanisms are possible; e.g., the February
measurement may be recorded only if it is substantially
different from the January reading.) Notice that
as we move from MCAR to MAR to MNAR, the
observed Y values become an increasingly select and
unusual group relative to the population; the sample
mean increases, and the standard deviation decreases.
This phenomenon is not a universal feature of MCAR,
MAR, and MNAR, but it does happen in many realistic
examples.
We frequently return to this example throughout
this article to illustrate the performance of various
methods. Because the rate of missing values is high,
the chosen method will exert a high degree of influence
over the results, and differences among competing
methods will be magnified. Effects will also be
large because of the unusually strong nature of the
MAR and MNAR processes. In psychological studies,
one would rarely expect a datum to be missing if and
only if its value exceeds a sharp cutoff. More commonly,
one might expect a gradual increasing or decreasing
or perhaps curvilinear relationship between X
or Y and the probability of missingness. Also, in most
cases, the reasons or causes of missingness are not X
and Y themselves but external factors that are merely
related to X and Y, perhaps not strongly. Differences
in results due to varying missing-data treatments in
this example should therefore be regarded as more
extreme than what we usually see in practice.
We would ideally like to have a single procedure
for estimating all the parameters (␮X, ␮Y, ␴X, ␴Y, ␳)
from the observed data that performs well over repeated
samples regardless of how missingness is distributed.
Technically speaking, this is not possible.
We see below that some data editing methods perform
well for some parameters under MCAR and occasionally
MAR. Using likelihood or Bayesian procedures,
one may achieve good performance for all parameters
without knowing the distribution of missingness, provided
that it is MAR. Under MNAR data the task
becomes more difficult; one must specify a model for
the missingness that is at least approximately correct,
Table 1
Simulated Blood Pressure Measurements (N = 30
Participants) in January (X) and February (Y) With
Missing Values Imposed by Three Different Methods
X
Y
Complete MCAR MAR MNAR
Data for individual participants
169 148 148 148 148
126 123 — — —
132 149 — — 149
160 169 — 169 169
105 138 — — —
116 102 — — —
125 88 — — —
112 100 — — —
133 150 — — 150
94 113 — — —
109 96 — — —
109 78 — — —
106 148 — — 148
176 137 — 137 —
128 155 — — 155
131 131 — — —
130 101 101 — —
145 155 — 155 155
136 140 — — —
146 134 — 134 —
111 129 — — —
97 85 85 — —
134 124 124 — —
153 112 — 112 —
118 118 — — —
137 122 122 — —
101 119 — — —
103 106 106 — —
78 74 74 — —
151 113 — 113 —
Summary data: Mean (with standard deviation
in parentheses)
125.7 121.9 108.6 138.3 153.4
(23.0) (24.7) (25.1) (21.1) (7.5)
Note. Dashes indicate missing values. MCAR ‫ס‬ missing completely
at random; MAR ‫ס‬ missing at random; MNAR ‫ס‬ missing
not at random.
MISSING DATA 153
and even then the performance may be poor unless the
sample is very large. However, even though there is
no single procedure that is technically correct in every
situation, all is not lost. We do believe that good
performance is often achievable through likelihood or
Bayesian methods without specifically modeling the
probabilities of missingness, because in many psychological
research settings the departures from MAR are
probably not serious.
Implications of MAR, MCAR, and MNAR
With a complete-data Ycom, statistical methods are
usually motivated by an assumption that the data
are randomly sampled from a population distribution
P(Ycom; ␪), where ␪ represents unknown parameters.
To a statistician, P(Ycom; ␪) has two very different
interpretations. First, it is regarded as the repeatedsampling
distribution for Ycom; it describes the probability
of obtaining any specific data set among all the
possible data sets that could arise over hypothetical
repetitions of the sampling procedure and data collection.
Second, it is regarded as a likelihood function for
␪. In the likelihood interpretation, we substitute the
realized value of Ycom into P(Ycom; ␪), and the resulting
function for ␪ summarizes the data’s evidence
about parameters. Certain classes of modern statistical
procedures, including all ML methods, are motivated
by viewing P(Ycom; ␪) as a likelihood function. Other
procedures, including nonparametric and semiparametric
methods, are justified only from the repeatedsampling
standpoint.
When portions of Ycom are missing, it is tempting to
base all statistical procedures on P(Yobs; ␪), the distribution
of the observed part only. This distribution is
obtained by calculus as the definite integral of P(Ycom;
␪) with respect to Ymis,
P͑Yobs; ␪) = ͐P͑Ycom; ␪͒ dYmis. (2)
Details and examples of integrating probability distributions
are given in texts on probability theory (e.g.,
Hogg & Tanis, 1997). In missing-data problems, however,
it is not automatically true that Equation 2 is the
correct sampling distribution for Yobs and the correct
likelihood for ␪ based on Yobs. Rubin (1976) first identified
the conditions under which it is a proper sampling
distribution and a proper likelihood; interestingly,
the conditions are not identical. For Equation 2
to be a correct sampling distribution, the missing data
should be MCAR. For Equation 2 to be a correct
likelihood, we need only MAR. The weaker conditions
for the latter suggest that missing-data procedures
based on likelihood principles are generally
more useful than those derived from repeatedsampling
arguments only. We believe that to be true.
Many of the older data-editing procedures bear no
relationship to likelihood and may be valid only under
MCAR. Even when MCAR does hold, these methods
may be inefficient. Methods motivated by treating
Equation 2 as a likelihood tend to be more powerful
and better suited to real-world applications in which
MCAR is often violated.
Finally, we note that the attractive properties of
likelihood carry over to the Bayesian method of MI,
because in the Bayesian paradigm we combine a likelihood
function with a prior distribution for the parameters.
As the sample size grows, the likelihood
dominates the prior, and Bayesian and likelihood answers
become similar (Gelman, Rubin, Carlin, &
Stern, 1995).
Missing Values That Are Not MAR
What happens when the missing data are not MAR?
It is then not appropriate to use Equation 2 either as a
sampling distribution or as a likelihood. From a likelihood
standpoint, the correct way to proceed is to
choose an explicit model for the missingness,
P(R|Ycom; ␰), where ␰ denotes unknown parameters of
the missingness distribution. For example, if missingness
is confined to a single variable, then we may
suppose that the binary indicators in R are described
by a logistic regression on the variable in question,
and ␰ would consist of an intercept and slope. The
joint model for the data and missingness becomes the
product of P(Ycom; ␪) and P(R|Ycom; ␰). The correct
likelihood function is then given by the integral of this
product over the unseen missing values,
P͑Yobs, R; ␪, ␰͒ = ͐P͑Ycom; ␪͒ P͑R|Ycom; ␰͒ dYmis,
(3)
where d is the calculus differential. The practical implication
of MNAR is that the likelihood for ␪ now
depends on an explicit model for R. In most cases, this
missingness model is a nuisance; questions of substantive
interest usually pertain to the distribution of
Ycom, not the distribution of R. Nevertheless, under
MNAR the model for R contributes information about
␪, and the evidence about ␪ from Equation 3 may
present a very different picture from that given by
Equation 2. Likelihoods for MNAR models are often
difficult to handle from a computational standpoint,
but some interesting work in this area has been pubSCHAFER
AND GRAHAM154
lished recently. Methods for MNAR data are reviewed
at the end of this article.
Missing Values That Are Out of Scope
In addition to MAR, there is another situation in
which Equation 2 is an appropriate likelihood: when
the fact that an observation is missing causes it to
leave the universe of interest. Consider a questionnaire
with an item, “How well do you get along with
your siblings?” Responses for some participants are
missing because they have no siblings. Literally
speaking, there are no missing data in this problem at
all. However, if the intended analysis could be carried
out more simply if the data were balanced (i.e., if
responses to this item were available for each participant),
then for computational reasons it may be worthwhile
to write the likelihood P(Yobs; ␪) in the form of
Equation 2, where Yobs denotes responses for those
who have siblings and Ymis represents hypothetical
responses for those who do not. In this case, the hypothetical
missing data could be regarded as MAR
and ␪ would be the parameters of the distribution of
responses for the population of those with siblings.
We need not worry about whether missingness depends
on the characteristics of nonexistent siblings;
these missing values are introduced merely as a mathematical
device to simplify the computations.
To a certain extent, this discussion also applies to
longitudinal studies in which some participants die.
Outcome measures of physical or mental status (e.g.,
cognitive functioning) have meaning for live persons
only. One who dies automatically leaves the universe
of interest, so values that are “missing” because of
death may often be regarded as MAR. However, if the
measurements of these outcomes are spaced far apart
in time—for example, if they are taken annually—
then the death of a participant may provide indirect
evidence of an unmeasured steep decline in the outcome
prior to death, and response trajectories estimated
under an MAR assumption may be somewhat
optimistic. In those cases, joint modeling of the outcome
and death events may be warranted (Hogan &
Laird, 1997).
Older Methods
Case Deletion
Among older methods for missing data, the most
popular is to discard units whose information is incomplete.
Case deletion, also known commonly as
listwise deletion (LD) and complete-case analysis, is
used by default in many statistical programs, but details
of its implementation vary. LD confines attention
to units that have observed values for all variables
under consideration. For example, suppose we are
computing a sample covariance matrix for items
X1, . . . , Xp. LD omits from consideration any case
that has a missing value on any of the variables
X1, . . . , Xp.
Available-case (AC) analysis, in contrast to LD,
uses different sets of sample units for different parameters.
For estimating covariances, this is sometimes
called pairwise deletion or pairwise inclusion.
For example, we may use every observed value of Xj
to estimate the standard deviation of Xj, and every
observed pair of values (Xj, Xk) to estimate the covariance
of Xj and Xk. For the correlation between Xj
and Xk, we might compute the sample correlation coefficient
using the same set of units that we used to
estimate the covariance. On the other hand, we could
also divide our estimated covariance by the estimated
standard deviations. The latter seems more efficient,
but it could conceivably yield a correlation outside of
the interval [−1, 1], causing one or more eigenvalues
to be negative. We believe that the underlying principle
of AC analysis—to make use of all the available
data—is eminently sensible, but deleting cases is a
poor way to operationalize it. Another limitation of
AC analysis is that, because parameters are estimated
from different sets of units, it is difficult to compute
standard errors or other measures of uncertainty; analytic
methods are troublesome, and other procedures
(e.g., bootstrapping) are at least as tedious as they
would be for other estimates with better properties.
Properties of case deletion. Case deletion can be
motivated by viewing Equation 2 as a sampling distribution
for Yobs and is generally valid only under
MCAR. In a few circumstances, it produces inferences
that are optimal under MAR. For example, under
the univariate missingness pattern of Figure 1a,
the parameters of the regression of Y on any subset of
X1, . . . , Xp can be estimated from the complete cases
and the estimates are both valid and efficient under
MAR (e.g., see Graham & Donaldson, 1993). However,
this result does not extend to other measures of
association between Y and X such as correlation coefficients,
nor does it extend to parameters of the marginal
distribution of Y. When the missing data are not
MCAR, results from case deletion may be biased,
because the complete cases can be unrepresentative of
the full population. If the departures from MCAR are
not serious, then the impact of this bias might be
MISSING DATA 155
unimportant, but in practice it can be difficult to judge
how large the biases might be.
When MCAR holds, case deletion can still be inefficient.
Consider again the univariate pattern in Figure
1a, and suppose that we want to estimate aspects
of the marginal distribution of Y (e.g., the population
mean). Furthermore, suppose that Y and the Xs are
highly related, so that the missing values of Y can be
predicted from X with near certainty. Case deletion
bases estimates on the reduced sample of Y values,
ignoring the strong predictive information contained
in the Xs. Researchers become acutely aware of the
inefficiency of case deletion in multivariate analyses
involving many items, in which mild rates of missing
values on each item may cause large portions of the
sample to be discarded.
The main virtue of case deletion is simplicity. If a
missing-data problem can be resolved by discarding
only a small part of the sample, then the method can
be quite effective. However, even in that situation,
one should explore the data to make sure that the
discarded cases are not unduly influential. In a study
of a rare disease or condition, for example, one should
verify that the small group being discarded does not
contain a large proportion of the participants possessing
that condition. For more discussion on the properties
of case deletion and further references, see
chapter 3 of Little and Rubin (1987).
Example. For the systolic blood pressure data
shown in Table 1, LD removes all participants whose
Y values are missing. To demonstrate the properties of
LD over repeated samples, we performed a simulation
experiment. One thousand samples were drawn from
the bivariate normal population, and missing values
were imposed on each sample by each of the three
mechanisms. For MAR and MNAR, Y was made
missing if X Յ 140 and Y Յ 140, respectively, producing
an average rate of 73% missing observations
for Y. For MCAR, each Y value was made missing
with probability .73. The total number of participants
in each sample was increased to 50 to ensure that,
after case deletion, a sufficient number remained to
support the estimation of correlation and regression
coefficients. After deletion, standard techniques were
used to calculate estimates and 95% intervals for five
population parameters: the mean of Y (␮Y ‫ס‬ 125), the
standard deviation of Y (␴Y ‫ס‬ 25), the correlation
coefficient (␳XY ‫ס‬ .60), the slope for the regression of
Y on X (␤Y|X ‫ס‬ .60), and the slope for the regression
of X on Y (␤X|Y ‫ס‬ .60). For ␴Y, the confidence interval
consisted of the square roots of the endpoints for
the classical interval for the variance of a normal
population. For ␳, the interval was calculated by applying
Fisher’s transformation z ‫ס‬ tanh−1
(r) to the
sample correlation r, adding and subtracting 1.96 (N −
3)−1/2
, and applying the inverse transformation r ‫ס‬
tanh (z) to the endpoints.
Results of the simulation are summarized in Table
2. The top panel of the table reports the average values
of the parameter estimates under each mechanism.
A discrepancy between this average and the true paTable
2
Performance of Listwise Deletion for Parameter
Estimates and Confidence Intervals Over 1,000 Samples
(N = 50 Participants)
Parameter MCAR MAR MNAR
Average parameter estimate (with RMSE in parentheses)
␮Y ‫ס‬ 125.0 125.0 143.3 155.5
(6.95) (19.3) (30.7)
␴Y ‫ס‬ 25.0 24.6 20.9 12.2
(5.26) (5.84) (13.2)
␳ ‫ס‬ .60 .59 .33 .34
(.19) (.37) (.36)
␤Y|X ‫ס‬ .60 .61 .60 .21
(.27) (.51) (.43)
␤X|Y ‫ס‬ .60 .60 .20 .60
(.25) (.44) (.52)
Coverage (with average interval width in parentheses)
␮Y 94.3 18.8 0.0
(30.0) (25.0) (14.7)
␴Y 94.3 90.7 17.4
(23.3) (19.4) (11.4)
␳ 95.4 82.5 82.7
(0.76) (0.93) (0.94)
␤Y|X 94.6 95.9 40.0
(1.10) (2.20) (0.73)
␤X|Y 95.3 37.7 96.6
(1.08) (0.71) (2.23)
Note. Parameters: ␮ is the population mean; ␴ is the population
standard deviation; ␳ is the population correlation; ␤ is the population
regression slope. Coverage represents the percentage of confidence
intervals that include the parameter value; values near 95
represent adequate coverage. Use of boldface type in the top panel
indicates problematic levels of bias (i.e., bias whose absolute size is
greater than about one half of the estimate’s standard error); use of
boldface in the bottom panel indicates seriously low levels of coverage
(i.e., coverage that falls below 90%, which corresponds to a
doubling of the nominal rate of error). MCAR ‫ס‬ missing completely
at random; MAR ‫ס‬ missing at random; MNAR ‫ס‬ missing
not at random; RMSE ‫ס‬ root-mean-square error.
SCHAFER AND GRAHAM156
rameter indicates bias. A rule of thumb that we have
found useful is that bias becomes problematic if its
absolute size is greater than about one half of the
estimate’s standard error; above this threshold, bias
begins to noticeably degrade the coverage of 95%
confidence intervals. Biases that are practically important
according to this rule are indicated by boldface
type. Some may disagree with this rule, because
the standard error of the estimate depends on the
sample size (N ‫ס‬ 50), which was chosen arbitrarily;
those readers may compare the average of the parameter
estimate to the true value of the parameter and
judge for themselves whether the bias seems practically
important. The top panel of Table 2 also displays
the root-mean-square error (RMSE), which measures
the typical distance between the estimate and the true
value. Small values of RMSE are desirable. Examining
these results, we find that LD is unbiased under
MCAR. Under MAR and MNAR, the complete cases
are unrepresentative of the population, and biases are
substantial, with two exceptions: The estimate of ␤Y|X
is unbiased under MAR, and the estimate of ␤X|Y is
unbiased under MNAR.
The bottom panel of Table 2 summarizes the performance
of confidence intervals. For each parameter
and each mechanism, we report the coverage—the
percentage of intervals that covered the true parameter—and
the average interval width. Narrow intervals
are desirable provided that their coverage is near
95%. As a rough rule of thumb, we consider the coverage
to be seriously low if it falls below 90%, which
corresponds to a doubling of the nominal rate of error;
these values are indicated by boldface type. Examining
the bottom panel of Table 2, we see that coverage
is acceptable for all parameters under MCAR but low
for most parameters under MAR and MNAR. Undercoverage
has two possible sources: bias, which causes
the interval on average to be centered to the left or to
the right of the target, and underestimation of the
estimate’s true variability, which causes the interval
to be narrower than it should be. Under MAR and
MNAR, both phenomena are occurring; the observed
Y values tend to be higher and less variable than those
of the full population, which biases both the parameter
estimates and their standard errors.
This simulation clearly illustrates that case detection
may produce bias under non-MCAR conditions.
Some might argue that these results are unrealistic
because a missingness rate of 73% for Y is too high.
However, it is not difficult to find published analyses
in which 73% or more of the sample cases were omitted
because of incomplete information. On the basis
of a survey of articles in major political science journals,
King, Honaker, Joseph, and Scheve (2001) reported
alarmingly high rates of case deletion with
serious implications for parameter bias and inefficiency.
Other simulations revealing the shortcomings
of case deletion have been reported by Brown (1994),
Graham et al. (1996), and Wothke (2000).
Reweighting
In some non-MCAR situations, it is possible to reduce
biases from case deletion by the judicious application
of weights. After incomplete cases are removed,
the remaining complete cases are weighted so
that their distribution more closely resembles that of
the full sample or population with respect to auxiliary
variables. Weights are derived from the probabilities
of response, which must be estimated from the data
(e.g., by a logistic or probit regression). Weighting
can eliminate bias due to differential response related
to the variables used to model the response probabilities,
but it cannot correct for biases related to variables
that are unused or unmeasured. For a review of
weighting in the context of sample surveys, see Little
and Rubin (1987, section 4.4).
Weighting is nonparametric, requiring no model for
the distribution of the data values in the population. It
does, however, require some model for the probabilities
of response. Weights are easy to apply for univariate
and monotone missing-data patterns. For the
arbitrary pattern of missing values shown in Figure
1c, weighting becomes unattractive because one must
potentially compute a different set of weights for each
variable. Recent years have seen a resurgence of interest
in weighting, with new methods for parametric
and semiparametric regression appearing in biostatistics;
some of these newer methods are reviewed near
the end of this article.
Averaging the Available Items
Many characteristics of interest to psychologists—
for example, self-esteem, depression, anxiety, quality
of life—cannot be reliably measured by a single item,
so researchers may create a scale by averaging the
responses to multiple items. An average can be motivated
by the idea that the items are exchangeable,
equally reliable measures of a unidimensional trait.
The items are typically standardized to have a mean of
zero and a standard deviation of one before averaging.
If a participant has missing values for one or more
items, it seems more reasonable to average the items
MISSING DATA 157
that remain rather than report a missing value for the
entire scale. This practice is widespread, but its properties
remain largely unstudied; it does not even have
a well-recognized name. Undoubtedly, some researchers
may have used this method without realizing
that it is a missing-data technique, choosing instead
to regard it as part of the scale definition. Some
might call it case-by-case item deletion. A colleague
has suggested the term ipsative mean imputation, because
it is equivalent to substituting the mean of a
participant’s own observed items for each of his or her
missing items (H. B. Bosworth, personal communication,
February 2001).
Averaging the available items is difficult to justify
theoretically either from a sampling or likelihood perspective.
Unlike case deletion, it may introduce bias
under MCAR. For example, suppose that a scale is
defined as the average of six items, and we compute
an average for each participant who responds to at
least three. With missing data the variance of the scale
tends to increase, because it becomes a mixture of the
averages of three, four, five, or six items rather than
the average of all six. The scale also becomes less
reliable, because reliability decreases as the number
of items drops. The method also raises fundamental
conceptual difficulties. The scale has been redefined
from the average of a given set of items to the average
of the available items, a definition that now depends
on the particular rates and patterns of nonresponse in
the current sample and that also varies from one participant
to another. This violates the principle that an
estimand be a well-defined aspect of a population, not
an artifact of a specific data set.
Despite these theoretical problems, preliminary investigations
suggest that the method can be reasonably
well behaved. As an illustration, suppose that a
scale is defined as the average of standardized items
Y1, . . . , Y4, but these items are not equally correlated
with each other or with other items. In particular,
suppose that the covariance matrix for Y1, . . . , Y4 is
∑ =
΄
1.0 0.5 0.2 0.2
0.5 1.0 0.2 0.2
0.2 0.2 1.0 0.5
0.2 0.2 0.5 1.0
΅,
and suppose that the correlation between these and
another standardized item X is .50 for Y1 and Y2 and
.10 for Y3 and Y4. Under these circumstances, the true
slope for the regression of Y ‫ס‬ (Y1 + Y2 + Y3 + Y4)/4
on X is ␤ ‫ס‬ .30. Now suppose that Y1 and Y2 are
missing fairly often, whereas Y3 and Y4 are nearly
always observed. We imposed missing values in an
MCAR fashion at a rate of 30% for Y1 and Y2 and 5%
for Y3 and Y4. We then averaged the items provided
that at least two of the four were available (participants
with fewer than two were deleted) and regressed
the scale on X. Over 1,000 samples of N ‫ס‬ 100 cases
each, the average value of the estimated slope was
.26, and 903 of the nominal 95% confidence intervals
covered the population value ␤ ‫ס‬ .30. The bias is
noticeable but not dramatic. Modifying this example,
we found that bias tends to decrease as the Yjs become
more equally correlated with each other and with X
and as the intercorrelations among the Yjs increase
even if they are unequal.
Newer methods of MI provide a more principled
solution to this problem. With MI, one imputes missing
items prior to forming scales. Using modern software,
it is now routinely possible to impute 100 or so
items simultaneously and preserve the intercorrelations
between the items, provided that enough sample
cases are available to estimate the joint covariance
structure. If MI is not feasible, then averaging the
available items may be a reasonable choice, especially
if the reliability is high (say, ␣ > .70) and each group
of items to be averaged seems to form a single, welldefined
domain.
Single Imputation
When a unit provides partial information, it is
tempting to replace the missing items with plausible
values and proceed with the desired analysis rather
than discard the unit entirely. Imputation, the practice
of filling in missing items, has several desirable features.
It is potentially more efficient than case deletion,
because no units are sacrificed; retaining the full
sample helps to prevent loss of power resulting from
a diminshed sample size. Moreover, if the observed
data contain useful information for predicting the
missing values, an imputation procedure can make
use of this information and maintain high precision.
Imputation also produces an apparently complete data
set that may be analyzed by standard methods and
software. To a data user, the practical value of being
able to apply a favorite technique or software product
can be immense. Finally, when data are to be analyzed
by multiple persons or entities, imputing once, prior to
all analyses, helps to ensure that the same set of units
is being considered by each entity, facilitating the
comparison of results. On the negative side, imputaSCHAFER
AND GRAHAM158
tion can be difficult to implement well, particularly in
multivariate settings. Some ad hoc imputation methods
can distort data distributions and relationships.
The shortcomings of single imputation have been
documented by Little and Rubin (1987) and others.
Here we briefly classify and review some popular
single-imputation methods.
Imputing unconditional means. Consider the
popular practice of mean substitution, in which missing
values are replaced by the average of the observed
values for that item. The average of the variable is
preserved, but other aspects of its distribution—
variance, quantiles, and so forth—are altered with potentially
serious ramifications. Consider a largesample
95% confidence interval for the population
mean,
y ‫ע‬ 1.96 ͱS2
N
,
where y and S2
are the sample mean and variance and
N is the sample size. Mean substitution narrows this
interval in two ways: by introducing a downward bias
into S2
and by overstating N. Under MCAR, the coverage
probability after mean substitution is approximately
2⌽(1.96r) − 1, where ⌽ is the standard normal
cumulative distribution function and r is the response
rate. With 25% missing values (r ‫ס‬ .75) the coverage
drops to 86%, and the error rate is nearly three times
as high as it should be. In addition to reducing variances,
the method also distorts covariances and intercorrelations
between variables.
Imputing from unconditional distributions. The
idea underlying mean substitution—to predict the
missing data values—is somewhat misguided; it is
generally more desirable to preserve a variable’s distribution.
Survey methodologists, who have long been
aware of this, have developed a wide array of singleimputation
methods that more effectively preserve
distributional shape (Madow, Nisselson, & Olkin,
1983). One popular class of procedures known as hot
deck imputation fills in nonrespondents’ data with
values from actual respondents. In a simple univariate
hot deck, we replace each missing value by a random
draw from the observed values. Hot-deck imputation
has no parametric model. It partially solves the problem
of understating uncertainty, because the variability
of the item is not distorted. However, without further
refinements, the method still distorts correlations
and other measures of association.
Imputing conditional means. In the univariate
situation of Figure 1a, a regression model for predicting
Y from X ‫ס‬ (X1, . . . , Xp) may provide a basis for
imputation. The model is first fit to the cases for
which Y is known. Then, plugging values of X for the
nonrespondents into the regression equation, we obtain
predictions Yˆ for the missing values of Y. Replacing
Y with Yˆ is called conditional mean imputation,
because Yˆ estimates the conditional mean of Y given
X. Conditional mean imputation is nearly optimal for
a limited class of estimation problems if special corrections
are made to standard errors (Schafer &
Schenker, 2000). The method is not recommended for
analyses of covariances or correlations, because it
overstates the strength of the relationship between Y
and the X variables; the multiple regression R2
among
the imputed values is 1.00. If there is no association
between Y and the covariates X, then the method reduces
to ordinary mean substitution.
Imputing from a conditional distribution. Distortion
of covariances can be eliminated if each missing
value of Y is replaced not by a regression prediction
but by a random draw from the conditional or predictive
distribution of Y given X. With a standard linear
model, we may add to Yˆ a residual error drawn from
a normal distribution with mean zero and variance
estimated by the residual mean square. In a logistic
regression for a dichotomous Y, one may calculate the
fitted probability pˆ for each case, draw a random uniform
variate u, and set Y ‫ס‬ 1 if u Յ pˆ and Y ‫ס‬ 0 if
u > pˆ.
More generally, suppose that we have data Ycom ‫ס‬
(Yobs, Ymis) from distribution P (Yobs, Ymis; ␪). Imputing
from the conditional distribution means simulating
a draw from
P͑Ymis|Yobs; ␪͒ =
P͑Yobs, Ymis; ␪͒
P͑Yobs; ␪͒
, (4)
where the denominator is given by Equation 2. In
practice, the parameters are unknown and must be
estimated, in which case we would draw from
P(Ymis|Yobs; ␪ˆ), where ␪ˆ is an estimate of ␪ obtained
from Yobs. Imputing from Equation 4 assumes MAR.
The method produces nearly unbiased estimates for
many population quantities under MAR if the model
(Equation 4) is correctly specified. Formulating the
conditional distribution and drawing from it tends to
be easiest for univariate missing-data patterns. With a
monotone pattern, the conditional distribution can be
expressed as a sequence of regressions for Yj given
Y1, . . . , Yj−1 for j ‫ס‬ 1, . . . , p, which is not too difficult.
With the arbitrary patterns, the conditional disMISSING
DATA 159
tribution can be quite complicated, and drawing from
it may require nearly as much effort as full MI, which
has superior properties.
Example. Consider the problem of imputing the
missing blood pressure readings in Table 1. For the
MAR condition, we imputed the missing values of Y
by four methods: (a) performing mean substitution,
(b) using a simple hot deck, (c) performing conditional
mean imputation based on the linear regression
of Y on X, and (d) drawing from the estimated predictive
distribution of Y given X based on the same
regression. Bivariate scatter plots of Y versus X for the
four imputation methods are displayed in Figure 3.
These plots clearly reveal the shortcomings of the first
three methods. Because the variables are jointly normal
with correlation .60, a plot of complete data
should resemble an elliptical cloud with a moderate
positive slope. Mean substitution (see Figure 3a)
causes all the imputed values of Y to fall on a horizontal
line, whereas conditional mean imputation (see
Figure 3c) causes them to fall on a regression line.
The hot deck (see Figure 3b) produces an elliptical
cloud with too little correlation. The only method that
produces a reasonable point cloud is (see Figure 3d)
imputation from the conditional distribution of Y and X.
To illustrate the operating characteristics of these
methods, we performed a simulation similar to the
one we did for case deletion. One thousand samples of
N ‫ס‬ 50 were drawn from the bivariate normal population,
and missing values were imposed on each
sample by the MCAR, MAR, and MNAR methods.
Each incomplete data set was then imputed by the
four methods shown in Figure 3. The results are summarized
in Table 3. As before, the top panel of the
table reports the average of the parameter estimates
and the RMSE for each mechanism–imputation
method combination; estimates with substantial bias
are displayed in boldface. Coverage and average
width of the nominal 95% confidence intervals are
shown in the bottom panel, with seriously low coverages
in boldface. On the basis of these results in the
top panel, we make the following general observations.
Mean substitution and the hot deck produce
biased estimates for many parameters under any type
of missingness. Conditional mean imputation performs
slightly better but still may introduce bias. ImFigure
3. Example scatter plots of blood pressure measured at two occasions, with missing
at random missing values imputed by four methods.
SCHAFER AND GRAHAM160
puting from a conditional distribution is essentially
unbiased under MCAR or MAR but potentially biased
under MNAR.
The problem of undercoverage. Examining the
bottom panel of Table 3, we find that the performance
of estimated intervals is a disaster. In nearly all cases,
the actual coverage is much lower than 95%. (The
only exceptions to this rule occur for ␤X|Y under mean
substitution, which causes the intervals to be exceptionally
wide). This shortcoming of single imputation
is well documented (Rubin, 1987). Even if it preserves
marginal and joint distributions, it still tends to
understate levels of uncertainty, because conventional
uncertainty measures ignore the fact the imputed values
are only guesses. With single imputation, there is
no simple way to reflect missing-data uncertainty. In
this example, the implications are very serious because
the amount of missing information is unusually
high, but even in less extreme situations undercoverage
is still an issue. The problem of undercoverage is
solved by MI, to be described later.
When single imputation is reasonable. Despite
the discouraging news in the bottom panel of Table 3,
there are situations in which single imputation is reasonable
and better than case deletion. Imagine a data
set with 25 variables in which 3% of all the data
values are missing. If missing values are spread uniformly
across the data matrix, then LD discards more
than half of the participants (1 − .9725
‫ס‬ .53). On the
other hand, imputing once from a conditional distriTable
3
Performance of Single-Imputation Methods for Parameter Estimates and Confidence Intervals Over 1,000 Samples
(N = 50 Participants)
Parameter
MCAR MAR MNAR
MS HD CM PD MS HD CM PD MS HD CM PD
Average parameter estimate (with RMSE in parentheses)
␮Y ‫ס‬ 125.0 125.1 125.2 125.2 125.1 143.5 143.5 124.9 124.8 155.5 155.5 151.6 151.6
(7.18) (7.89) (6.26) (6.57) (19.4) (19.5) (18.1) (18.3) (30.7) (30.73) (26.9) (26.9)
␴Y ‫ס‬ 25.0 12.3 23.4 18.2 24.7 10.6 20.0 20.4 27.0 6.20 11.7 8.42 12.9
(13.0) (5.40) (8.57) (5.37) (14.6) (6.68) (10.7) (8.77) (18.9) (13.7) (16.9) (12.7)
␳ ‫ס‬ .60 .30 .16 .79 .59 .08 .04 .64 .50 .15 .08 .55 .38
(.32) (.46) (.27) (.20) (.52) (.57) (.48) (.40) (.47) (.53) (.40) (.37)
␤Y|X ‫ס‬ .60 .16 .16 .61 .60 .04 .04 .61 .62 .04 .04 .21 .21
(.45) (.47) (.25) (.27) (.56) (.57) (.57) (.57) (.56) (.56) (.43) (.43)
␤X|Y ‫ס‬ .60 .61 .17 1.12 .60 .20 .06 .78 .45 .61 .19 1.63 .76
(.26) (.46) (.64) (.24) (.44) (.56) (.75) (.40) (.55) (.53) (1.72) (.68)
Coverage (with average interval width in parentheses)
␮Y 39.2 60.0 58.5 71.0 0.2 2.4 25.7 32.3 0.0 0.0 0.0 0.0
(7.0) (13.3) (10.4) (14.1) (6.0) (11.4) (11.6) (15.3) (3.5) (6.7) (4.8) (7.3)
␴Y 0.7 63.7 31.3 65.4 0.1 45.3 30.0 49.4 0.0 1.7 0.7 4.4
(5.1) (9.6) (7.5) (10.2) (4.4) (8.2) (8.4) (11.1) (2.5) (4.8) (3.5) (5.3)
␳ 25.5 5.5 21.7 65.0 0.0 0.0 19.6 40.7 2.2 0.5 37.6 50.0
(0.50) (0.53) (0.19) (0.35) (0.55) (0.55) (0.21) (0.34) (0.54) (0.54) (0.31) (0.43)
␤Y|X 1.2 16.5 38.6 63.5 0.0 0.8 17.2 33.5 0.0 0.1 3.1 7.4
(0.27) (0.54) (0.22) (0.44) (0.25) (0.47) (0.22) (0.45) (0.14) (0.27) (0.13) (0.26)
␤X|Y 98.1 23.9 8.9 71.1 91.2 14.5 18.6 60.0 97.4 71.3 19.1 56.2
(1.18) (0.63) (0.50) (0.47) (1.43) (0.75) (0.56) (0.46) (2.50) (1.30) (1.46) (1.05)
Note. Parameters: ␮ is the population mean; ␴ is the population standard deviation; ␳ is the population correlation; ␤ is the population
regression slope. Coverage represents the percentage of confidence intervals that include the parameter value; values near 95 represent
adequate coverage. Use of boldface type in the top panel indicates problematic levels of bias (i.e., bias whose absolute size is greater than about
one half of the estimate’s standard error); use of boldface in the bottom panel indicates seriously low levels of coverage (i.e., coverage that
falls below 90%, which corresponds to a doubling of the nominal rate of error). MCAR ‫ס‬ missing completely at random; MAR ‫ס‬ missing
at random; MNAR ‫ס‬ missing not at random; MS ‫ס‬ mean substitution; HD ‫ס‬ hot deck; CM ‫ס‬ conditional mean imputation; PD ‫ס‬
predictive distribution imputation; RMSE ‫ס‬ root-mean-square error.
MISSING DATA 161
bution permits the use of all participants with only a
minor negative impact on estimates and uncertainty
measures.
ML Estimation
The principle of drawing inferences from a likelihood
function is widely accepted. Under MAR, the
marginal distribution of the observed data (Equation
2) provides the correct likelihood for the unknown
parameters ␪, provided that the model for the complete
data is realistic. Little and Rubin (1987, p. 89)
referred to this function as “the likelihood ignoring
the missing-data mechanism,” but for brevity we simply
call it the observed-data likelihood. The logarithm
of this function,
l(␪; Yobs) ‫ס‬ log L (␪; Yobs), (5)
plays a crucial role in estimation. The ML estimate ␪ˆ,
the value of ␪ for which Equation 5 is highest, has
attractive theoretical properties just as it does in complete-data
problems. Under rather general regularity
conditions, it tends to be approximately unbiased in
large samples. It is also highly efficient; as the sample
size grows, its variance approaches the theoretical
lower bound of what is achievable by any unbiased
estimator (e.g., Cox & Hinkley, 1974).
Confidence intervals and regions are often computed
by appealing to the fact that, in regular problems
with large samples, ␪ˆ is approximately normally
distributed about the true parameter ␪ with approximate
covariance matrix
V(␪ˆ) ≈ [−lЉ(␪ˆ)]−1
, (6)
where lЉ(␪ˆ) is the matrix of second partial derivatives
of Equation 5 with respect to the elements of ␪. The
matrix −lЉ(␪ˆ), which is often called observed information,
describes how quickly the log-likelihood
function drops as we move away from the ML estimate;
a steep decline indicates that the ML estimate is
apparently precise, whereas a gradual decline implies
there is considerable uncertainty about where the true
parameter lies. This matrix is sometimes replaced by
its expected value, which is called expected information
or Fisher information, because the expected
value is sometimes easier to compute. In completedata
problems, the approximation (Equation 6) is still
valid when the observed information is replaced by
the expected information. However, as recently
pointed out by Kenward and Molenberghs (1998), this
is not necessarily true with missing data. Expected
information implicitly uses Equation 5 as a sampling
distribution for Yobs, which is valid only if the missing
data are MCAR. In missing-data problems, therefore,
if we want to obtain standard errors and confidence
intervals that are valid under the general MAR condition,
we should base them on an observed rather
than an expected information matrix. If a software
package that performs ML estimation with missing
data offers the choice of computing standard errors
from the observed or the expected information, the
user should opt for the former.
Log likelihood also provides a method for testing
hypotheses about elements or functions of ␪. Suppose
that we wish to test the null hypothesis that ␪ lies in
a certain area or region of the parameter space versus
the alternative that it does not. Under suitable regularity
conditions, this test may be performed by comparing
a difference in log likelihoods to a chisquare
distribution. More specifically, let ␪ˆ denote the
maximizer of the log likelihood over the full parameter
space, and let ␪˜ be the maximizer over the region
defined by the null hypothesis. We would reject
the null at the designated alpha level if 2 [l(␪ˆ; Yobs) −
l(␪ˆ; Yobs)] exceeds the 100 (1 − ␣) percentile of the
chi-square distribution. The degrees of freedom are
given by the difference in the number of free parameters
under the null and alternative hypotheses—that
is, the number of restrictions that must be placed on
the elements of ␪ to ensure that it lies in the null
region. These likelihood-ratio tests are quite attractive
for missing-data problems, because they only require
that we be able to compute the maximizers ␪ˆ and ␪˜; no
second derivatives are needed.
Computing ML Estimates
In a few problems, the maximizer of the log likelihood
(Equation 5) can be computed directly. One
famous example is the bivariate normal monotone
problem presented by Anderson (1957). Suppose that
a portion of sample units (Part A) has values recorded
for variables X and Y, and the remainder of sample
units (Part B) has values only for X. The basic idea is
as follows: Use the full sample (A + B) to estimate the
mean and variance of X, use the reduced sample (A
only) to estimate the parameters of the linear regression
of Y and X, and then combine the two sets of
estimates to obtain the parameters for the full joint
distribution. For details on this procedure, see Little
and Rubin (1987, section 6.2).
Except for these special cases, expressions for ML
estimates cannot in general be written down in closed
SCHAFER AND GRAHAM162
form, and computing them requires iteration. A general
method for ML in missing-data problems was
described by Dempster et al. (1977) in their influential
article on the EM algorithm. The key idea of EM is to
solve a difficult incomplete-data estimation problem
by iteratively solving an easier complete-data problem.
Intuitively, we “fill in the missing data” with a
best guess at what it might be under the current estimate
of the unknown parameters, then reestimate the
parameters from the observed and filled-in data. To
obtain the correct answer, we must clarify what it
means to “fill in the missing data.” Dempster et al.
showed that, rather than filling in the missing data
values per se, we must fill in the complete-data sufficient
statistics. The form of these statistics depends
on the model under consideration. Overviews of EM
have been given by Little and Rubin (1987), Schafer
(1997), and McLachlan and Krishnan (1996).
Little and Rubin (1987) catalogued EM algorithms
for any missing-data problems. EM has also been applied
to many situations that are not necessarily
thought of as missing-data problems but can be formulated
as such: multilevel linear models for unbalanced
repeated measures data, where not all participants
are measured at all time points (Jennrich &
Schluchter, 1986; Laird & Ware, 1982); latent class
analysis (Clogg & Goodman, 1984) and other finitemixture
models (Titterington, Smith, & Makov,
1985); and factor analysis (Rubin & Thayer, 1983).
For some of these problems, non-EM methods are
also available. Newton–Raphson and Fisher scoring
are now considered by many to be the preferred
method for fitting multilevel linear models (Lindstrom
& Bates, 1988). However, in certain classes of
models—finite mixtures, for example—EM is still the
method of choice (McLachlan & Peel, 2000).
Software for ML Estimation in
Missing-Data Problems
An EM algorithm for ML estimation of an unstructured
covariance matrix is available in several programs.
The first commercial implementation was released
by BMDP (BMDP Statistical Software, 1992),
now incorporated into the missing-data module of
SPSS (Version 10.0). The procedure is also found in
EMCOV (Graham & Hofer, 1991), NORM, SAS
(Y. C. Yuan, 2000), Amelia (King et al., 2001), SPLUS
(Schimert, Schafer, Hesterberg, Fraley, &
Clarkson, 2001), LISREL (Jo¨reskog & So¨rbom,
2001), and Mplus (L. K. Muthe´n & Muthe´n, 1998).
ML is also available for normal models with structured
covariance matrices. Multilevel linear models
can be fit with HLM (Bryk, Raudenbush, & Congdon,
1996), MLWin (Multilevel Models Project, 1996), the
SAS procedure PROC MIXED (Littell, Milliken,
Stroup, & Wolfinger, 1996), Stata (Stata, 2001), and
the lme function in S-PLUS (Insightful, 2001). Any of
these may be used for repeated measures data. In
some cases, the documentation and accompanying literature
do not mention missing values specifically but
describe “unbalanced” data sets, in which participants
are not measured at a common set of time points. We
must emphasize that if the imbalance occurs not by
design but as a result of uncontrolled nonresponse
(e.g., attrition), all of these programs will assume
MAR. ML estimates for structural equation models
with incomplete data are available in Mx (Neale,
Boker, Xie, & Maes, 1999), AMOS (Arbuckle &
Wothke, 1999), LISREL, and Mplus, which also assume
MAR. These programs provide standard errors
based on expected or observed information. If offered
a choice, the user should opt for observed rather than
expected, because the latter is appropriate only under
MCAR. The producers of EQS (Bentler, in press)
have also announced plans for a new version with
missing-data capabilities, but as of this writing it has
not yet been released.
Latent class analysis (LCA) is a missing-data problem
in the sense that the latent classification is missing
for all participants. A variety of software packages
for LCA are available; one of the most popular is
LEM (Vermunt, 1997). Latent transition analysis
(LTA), an extension of LCA to longitudinal studies
with a modest number of time points, is available in
WinLTA (Collins, Flaherty, Hyatt, & Schafer, 1999).
The EM algorithm in the most recent version of LTA
allows missing values to occur on the manifest variables
in an arbitrary pattern. ML estimates for a wider
class of latent-variable models with incomplete data,
including finite mixtures and models with categorical
responses, are available in Mplus.
Example
To illustrate the properties of likelihood methods,
we conducted another simulation using the blood
pressure example. One thousand samples of size N ‫ס‬
50 were generated, and missing values were imposed
on each sample by the MCAR, MAR, and MNAR
methods. From each incomplete data set, ML estimates
were computed by the technique of Anderson
(1957). Standard errors were obtained by inverting the
observed information matrix, and approximate 95%
MISSING DATA 163
confidence intervals for each parameter were computed
by the normal approximation (estimate ±1.96
SEs).
Results from this simulation are summarized in
Table 4. The top panel of Table 4 reports the average
and RMSE of the estimates, and the bottom panel
reports the coverage and average width of the intervals.
Once again, estimates whose bias exceeds one
half of a standard error and coverage values below
90% are displayed in boldface. Examining the top
panel, we find that the ML estimates are not substantially
biased under MCAR or MAR but are quite biased
under MNAR. This agrees with the theoretical
result that likelihood inferences are appropriate under
any MAR situation. In the bottom panel, however,
notice that the coverage for some intervals under
MAR is quite poor. The reason for this is that a
sample of N ‫ס‬ 50 with a high rate of missing values
is not large enough for the normal approximation to
work well. Another simulation was performed with
the sample size increased to 250. Results from that
simulation, which we omit, show that the intervals for
all parameters achieve coverage near 95% under
MCAR and MAR but have seriously low coverage
under MNAR.
General Comments on ML for
Missing-Data Problems
In theory, likelihood methods are more attractive
than ad hoc techniques of case deletion and single
imputation. However, they still rest on a few crucial
assumptions. First, they assume that the sample is
large enough for the ML estimates to be approximately
unbiased and normally distributed. In missingdata
problems the sample may have to be larger than
usual, because missing values effectively reduce the
sample size. Second, the likelihood function comes
from an assumed parametric model for the complete
data P (Yobs, Ymis; ␪). Depending on the particular
application, likelihood methods may or may not be
robust to departures from model assumptions. Sometimes
(e.g., in structural equation models) departures
might not have a serious effect on estimates but could
cause standard errors and test statistics to be very
misleading (Satorra & Bentler, 1994). If one dispenses
with the full parametric model, estimation procedures
with incomplete data are still possible, but
they typically require the missing values to be MCAR
rather than MAR (K. H. Yuan & Bentler, 2000; Zeger,
Liang, & Albert, 1988). For an evaluation of these
new procedures for structural equation models, see
the recent article by Enders (2001).
Finally, the likelihood methods described in this
section assume MAR. When missingness is not controlled
by the researcher, it is unlikely that MAR is
precisely satisfied. In many realistic applications,
however, we believe that departures from MAR are
not large enough to effectively invalidate the results
of an MAR-based analysis (Collins et al., 2001).
When the reasons for missingness seem strongly related
to the data, one can formulate a likelihood or
Table 4
Performance of Maximum Likelihood for Parameter
Estimates and Confidence Intervals Over 1,000 Samples
(N = 50 Participants)
Parameter MCAR MAR MNAR
Average parameter estimate (with RMSE in parentheses)
␮Y ‫ס‬ 125.0 124.8 125.2 151.6
(6.52) (16.9) (26.9)
␴Y ‫ס‬ 25.0 24.2 25.5 12.3
(5.73) (7.45) (13.2)
␳ ‫ס‬ .60 .61 .52 .39
(.19) (.38) (.36)
␤Y|X ‫ס‬ .60 .61 .60 .21
(.27) (.51) (.43)
␤X|Y ‫ס‬ .60 .63 .49 .79
(.23) (.38) (.68)
Coverage (with average interval width in parentheses)
␮Y 91.2 91.6 0.9
(22.2) (58.2) (16.4)
␴Y 86.1 90.2 7.4
(17.8) (28.6) (9.94)
␳ 84.2 76.7 89.2
(0.65) (1.20) (0.99)
␤Y|X 90.3 90.7 28.2
(0.88) (1.78) (0.59)
␤X|Y 91.6 93.0 80.5
(0.80) (1.26) (2.18)
Note. Parameters: ␮ is the population mean; ␴ is the population
standard deviation; ␳ is the population correlation; ␤ is the population
regression slope. Coverage represents the percentage of confidence
intervals that include the parameter value; values near 95
represent adequate coverage. Use of boldface type in the top panel
indicates problematic levels of bias (i.e., bias whose absolute size is
greater than about one half of the estimate’s standard error); use of
boldface in the bottom panel indicates seriously low levels of coverage
(i.e., coverage that falls below 90%, which corresponds to a
doubling of the nominal rate of error). MCAR ‫ס‬ missing completely
at random; MAR ‫ס‬ missing at random; MNAR ‫ס‬ missing
not at random; RMSE ‫ס‬ root-mean-square error.
SCHAFER AND GRAHAM164
Bayesian solution to take this into account. However,
these methods—which are reviewed in the last section
of this article—are not a panacea, because they still
rest on unverifiable assumptions and may be sensitive
to departures from the assumed model.
Multiple Imputation
MI, proposed by Rubin (1987), has emerged as a
flexible alternative to likelihood methods for a wide
variety of missing-data problems. MI retains much of
the attractiveness of single imputation from a conditional
distribution but solves the problem of understating
uncertainty. In MI, each missing value is replaced
by a list of m > 1 simulated values as shown in
Figure 4. Substituting the jth element of each list for
the corresponding missing value, j ‫ס‬ 1, . . . , m, produces
m plausible alternative versions of the complete
data. Each of the m data sets is analyzed in the same
fashion by a complete-data method. The results,
which may vary, are then combined by simple arithmetic
to obtain overall estimates and standard errors
that reflect missing-data uncertainty as well as finitesample
variation. Reviews of MI have been published
by Rubin (1996) and Schafer (1997, 1999a). Graham,
Cumsille, and Elek-Fisk (in press) and Sinharay,
Stern, and Russell (2001) have provided less technical
presentations for researchers in psychology.
MI has many attractive features. Like single imputation,
it allows the analyst to proceed with familiar
complete-data techniques and software. One good set
of m imputations may effectively solve the missingdata
problems in many analyses; one does not necessarily
need to re-impute for every new analysis. Unlike
other Monte Carle methods, with MI we do not
need a large number of repetitions for precise estimates.
Rubin (1987) showed that the efficiency of an
estimate based on m imputations, relative to one based
on an infinite number, is (1 + ␭/m)−1
, where ␭ is the
rate of missing information.2
For example, with 50%
missing information, m ‫ס‬ 10 imputations is 100/(1 +
.05) ‫ס‬ 95% efficient; additional imputations do little
to remove noise from the estimate itself. In some
cases, researchers also like to remove noise from
other statistical summaries (e.g., significance levels or
probability values); in many practical applications, we
have found that m ‫ס‬ 20 imputations can effectively
do this. Once a procedure for managing multiple versions
of the data has been established, the additional
time and effort required to handle m ‫ס‬ 20 versions
rather than m ‫ס‬ 10 is often of little consequence.
Rubin’s Rules for Combining Estimates and
Standard Errors
The simplest method for combining the results of m
analyses is Rubin’s (1987) method for a scalar (onedimensional)
parameter. Suppose that Q represents a
population quantity (e.g., a regression coefficient) to
be estimated. Let Qˆ and √U denote the estimate of Q
and the standard error that one would use if no data
were missing. The method assumes that the sample is
large enough so that √U(Qˆ − Q) has approximately a
standard normal distribution, so that Qˆ ± 1.96 √U has
about 95% coverage. Of course, we cannot compute Qˆ
and U; rather, we have m different versions of them,
[Qˆ ( j)
, U( j)
], j ‫ס‬ 1, . . . , m. Rubin’s (1987) overall
estimate is simply the average of the m estimates,
Q = m−1
͚j=1
m
Qˆ ͑ j͒
.
The uncertainy in Q has two parts: the average withinimputation
variance,
U = m−1
͚j=1
m
U͑ j͒
,
and the between-imputations variance,
2
The rate of missing information, as distinct from the
rate of missing observations, measures the increase in the
large-sample variance of a parameter estimate (Equation 6)
due to missing values. It may be greater or smaller than the
rate of missing values in any given problem.
Figure 4. Schematic representation of multiple imputation,
where m is the number of imputations.
MISSING DATA 165
B = ͑m − 1͒−1
͚j=1
m
͓Qˆ ͑ j͒
− Q͔2
.
The total variance is a modified sum of the two com-
ponents,
T = U + ͑1 + m−1
͒B,
and the square root of T is the overall standard error.
For confidence intervals and tests, Rubin (1987) recommended
the use of a Student’s t approximation
T −1/2
(Q¯ − Q) ‫ف‬ tv, where the degrees of freedom are
given by
v = ͑m − 1͒ ͫ1 +
U
͑1 + m−1
͒B
ͬ2
.
The degree of freedom may vary from m − 1 to ϱ
depending on the rate of missing information. When
the degrees of freedom are large, the t distribution is
essentially normal, the total variance is well estimated,
and there is little to be gained by increasing m.
The estimated rate of missing information for Q is
approximately ␶/(␶ + 1), where ␶ ‫ס‬ (1 + m−1
)B/U¯ is
the relative increase in variance due to nonresponse.
Additional methods for combining multidimensional
parameter estimates, likelihood-ratio test statistics,
and probability values from hypothesis tests were reviewed
by Schafer (1997, chapter 4).
Proper MI
The validity of MI rests on how the imputations are
created and how that procedure relates to the model
used to subsequently analyze the data. Creating MIs
often requires special algorithms (Schafer, 1997). In
general, they should be drawn from a distribution for
the missing data that reflects uncertainty about the
parameters of the data model. Recall that with single
imputation, it is desirable to impute from the conditional
distribution P(Ymis|Yobs; ␪ˆ), where ␪ˆ is an estimate
derived from the observed data. MI extends this
by first simulating m independent plausible values for
the parameters, ␪(1)
, . . . , ␪(m)
, and then drawing the
missing data Y (t)
mis from P[Ymis|Yobs; ␪(t)
] for t ‫ס‬
1, . . . , m.
Treating parameters as random rather than fixed is
an essential part of MI. For this reason, it is natural
(but not essential) to motivate MI from the Bayesian
perspective, in which the state of knowledge about
parameters is represented through a posterior distribution.
Bayesian methods have become increasingly
popular in recent years; a good modern overview was
provided by Gelman et al. (1995). For our purposes, it
is not necessary to delve into the details of the Bayesian
perspective to explain how MI works; we simply
note a few principles of Bayesian analysis.
First, in a Bayesian analysis, all of the data’s evidence
about parameters is summarized with a likelihood
function. As with ML, the assumed parametric
form of the model may be crucial; if the model is
inaccurate, then the posterior distribution may provide
an unrealistic view of the state of knowledge about ␪.
Second, Bayesian analysis requires a prior distribution
for the unknown parameters. Critics may regard
the use of a prior distribution as subjective, artificial,
or unscientific. We tend to regard it as a “necessary
evil.” In some problems, prior distributions can be
formulated to reflect a state of relative ignorance
about the parameters, mitigating the effect of the subjective
inputs. Finally, in a Bayesian analysis, the influence
of the prior diminishes as the sample size
increases. Indeed, one often finds that a range of plausible
alternative priors leads to similar posterior distributions.
Because MI already relies on large-sample
approximations for the complete-data distribution, the
prior rarely exerts a major influence on the results.
Creating MIs Under a Normal Model
To understand what is happening within an MI algorithm,
consider a hypothetical data set with three
variables Y1, Y2, and Y3, which we assume to be
jointly normally distributed. Suppose that one group
of participants (Group A) has measurements for all
three variables, another group (Group B) has measurements
for Y1 and Y2 but missing values for Y3, and
a third group (Group C) has measurements for Y3 but
missing values for Y1 and Y2. The parameters of the
trivariate normal model—three means, three variances
and three correlations—are not known and
should be estimated from all three groups. If the parameters
were known, MIs could be drawn in the
following way. Group A requires no imputation. For
Group B, we would need to compute the linear regression
of Y3 on Y1 and Y2. Then, for each participant
in Group B, we would use his or her own values of Y1
and Y2 to predict the unknown value of Y3 and impute
the predicted value Yˆ
3 plus random noise drawn from
a normal distribution with the appropriate residual
variance. For Group C, we would compute the bivariate
regression of Y1 and Y2 on Y3, obtain the joint
prediction (Yˆ
1, Yˆ
2) for each participant, and add random
noise drawn from a bivariate normal distribution
SCHAFER AND GRAHAM166
with the appropriate residual variances and covari-
ance.
A crucial feature of MI is that the missing values
for each participant are predicted from his or her own
observed values, with random noise added to preserve
a correct amount of variability in the imputed data.
Another feature is that the joint relationships among
the variables Y1, Y2, and Y3 must be estimated from all
available data in Groups A, B, and C. ML estimates of
the parameters could be computed using an EM algorithm,
but proper MI requires that we reflect uncertainty
about these parameters from one imputation to
the next. Therefore, instead of using ML estimates,
we need to draw random values of the parameters
from a posterior distribution based on the observeddata
likelihood and a prior. The form of this posterior
distribution is not easy to describe, but it can be
sampled from by a variety of techniques; data augmentation
(Schafer, 1997) is straightforward, but one
could also use importance resampling (King et al.,
2001). The effect of drawing parameters from a posterior
distribution, rather than using ML estimates,
means that for Group B the regression of Y3 on Y1 and
Y2 will be randomly perturbed from one set of imputations
to the next; similarly, in Group C the joint
regression of (Y1, Y2) on Y3 will also be perturbed.
A review of MI computations is beyond the scope
of this article. Rubin (1987) described how to create
MIs for some univariate and monotone situations
shown in Figure 1, a and b. Algorithms for multivariate
data with arbitrary patterns were given by Schafer
(1997), along with detailed guidance and data examples.
Proper application of these procedures requires
some understanding of the properties of data
augmentation, especially its convergence behavior. A
gentle introduction and tutorial on the use of MI under
a multivariate normal model was provided by Schafer
and Olsen (1998; also see Graham et al., in press;
Sinharay et al., 2001).
Choosing the Imputation Model
Notice that the MI procedure described above is
based on a joint normality assumption for Y1, Y2, and
Y3. This model makes no distinctions between response
(dependent) or predictor (independent) variables
but treats all three as a multivariate response.
The imputation model is not intended to provide a
parsimonious description of the data, nor does it represent
structural or causal relationships among variables.
The model is merely a device to preserve important
features of the joint distribution (means,
variances, and correlations) in the imputed values. A
procedure that preserves the joint distribution of Y1,
Y2, and Y3 will automatically preserve the linear regression
of any of these variables on the others.
Therefore, in a subsequent analysis of the imputed
data, any variable could be treated as a response or as
a predictor. For example, we may regress Y2 on Y1 in
each imputed data set and combine the estimated intercepts
and slopes by Rubin’s (1987) rules. Distinctions
between dependent and independent variables
and substantive interpretation of relationships should
be left to postimputation analyses.
Although it is not necessary to have a scientific
theory underlying an imputation model, it is crucial
for that model to be general enough to preserve effects
of interest in later analyses. Suppose that we will examine
differences in mean response between an experimental
and control group. For differences to be
preserved in the imputed values, some indicator of
group membership should enter the imputation model.
For example, a dummy variable (0‫ס‬control, 1‫ס‬experimental)
could be included in the normal model,
which will preserve the main effect of group membership
on any other variable. To preserve interaction
between group membership and other variables, one
could split the data set and apply a separate imputation
model to each group.
Real data rarely conform to normality. Some might
hesitate to use a normal imputation model, fearing
that it may distort the distributions of nonnormal variables.
Many tricks are available to help preserve distributional
shape (Schafer, 1997). Binary or ordinal
variables may be imputed under a normality assumption
and then rounded off to discrete values. If a variable
is right skewed, it may be modeled on a logarithmic
(or some other power-transformed) scale and
transformed back to the original scale after imputation.
Transformations and rounding help to make the
imputed values aesthetically appealing; for example, a
log transformation for a positive variable will guarantee
that the imputed values are always positive. Depending
on the analysis applied to the imputed data,
however, these procedures may be superfluous. Graham
and Schafer (1999) presented a simulation in
which highly nonnormal variables were imputed under
normality assumptions with no transformations or
rounding and reported excellent performance for linear
regression even with relatively small samples. Although
joint normality is rarely realistic, we have
found the model to be useful in a surprisingly wide
variety of problems. Analyses involving normalMISSING
DATA 167
based MI with real data have been published by Graham
and Hofer (2000), Graham et al. (in press), and
others.
Of course, there are situations in which the normal
model should be avoided—to impute variables that
are nominal (unordered categories), for example.
Schafer (1997) presented imputation methods for
multivariate categorical data and for data sets containing
both continuous and categorical variables. With
these models, one can specify and preserve higher
order associations among the variables, provided that
the data are rich enough to estimate these associations.
These models assume that the rows or observational
units in the data set have been independently
sampled and thus do not automatically take into account
longitudinal or clustered structure. Imputation
models specifically designed for longitudinal and
clustered data have been described by Liu, Taylor,
and Belin (2000) and Schafer (2001).
For a small class of problems, it is possible to create
MIs without a data model. Rubin (1987) described
a method called the approximate Bayesian bootstrap
(ABB), which involves two cycles of hot-deck imputation.
Rubin’s ABB applies to univariate missing
data without covariates. Lavori, Dawson, and Shera
(1995) generalized the ABB to include fully observed
covariates as in Figure 1a. This method has been
implemented in a program called SOLAS (Statistical
Solutions, 1998), where it is called the propensityscore
option. This procedure was designed to provide
unbiased estimates of the distribution of a single outcome,
not to preserve the relationship between the
outcome and other items. Imputations created by this
method may seriously distort covariance structure
(Allison, 2000). An alternative procedure in SOLAS,
called the model-based method, is described below; it
is more appropriate for situations in which postimputation
analyses will involve covariances and correla-
tions.
MI Software
Many computer programs for MI are now available.
NORM, a free program for Windows, creates
MIs for incomplete data with arbitrary patterns of
missing values under an unstructured normal model.
Algorithms used in NORM were described by Schafer
(1997). NORM includes utilities for automatic preand
postimputation transformations and rounding
of imputed values. A new SAS procedure, PROC MI
(Y. C. Yuan, 2000), brings the method used by
NORM into the popular SAS environment. Other versions
of the same algorithm have also been implemented
in a newly released missing-data library in
S-PLUS and in LISREL. Amelia, a free program created
by King et al. (2001), relies on the normal model
but uses a different computational technique.
With any of the programs mentioned above, a sufficient
number of cases must be available to estimate
an unstructured covariance matrix for all variables.
This may be problematic. For example, in psychological
research it is not uncommon to collect about p ‫ס‬
100 or more items for only N ‫ס‬ 100 subjects, with the
intention of later averaging the items into a few scales
or subscales. Without imposing additional prior information
or structure, we cannot fit a normal model to
all items at once. Song (1999) simplified the covariance
structure by assuming a few common factors,
generating MIs under this restricted model; however,
a software implementation of this new method is not
yet available. Longitudinal structure arising from repeated
measurements over time has been implemented
in the S-PLUS function PAN (Schafer, 2001;
Schafer & Yucel, in press).
For nonnormal imputation models, the software
choices are more limited. Methods described by Schafer
(1997) for multivariate categorical data, and for
mixed data sets containing both continuous and categorical
variables, are commercially available in the
new S-PLUS missing-data module; this supersedes
the older CAT and MIX libraries for S-PLUS written
by Schafer. The propensity score method of SOLAS
does not assume a parametric model for the data but,
as previously mentioned, can seriously distort intervariable
relationships and is potentially dangerous.
Rather than describing the joint distribution of all
variables by a multivariate model, some prefer to
implement MI with a sequence of single-response regressions.
With the monotone pattern of Figure 1b, the
information about missing values can indeed be captured
by a sequence of regressions for Yj given
Y1, . . . , Yj−1 for j ‫ס‬ 1, . . . , p. The so-called modelbased
method in SOLAS (Version 2.0 or later) creates
proper MIs for univariate or monotone patterns by
such a regression sequence. The method can also be
applied to nonmonotone patterns, but cases that do not
conform to the monotone pattern are ignored in the
model fitting. For data sets that deviate substantially
from a monotonicity, the joint modeling procedures
used in S-PLUS seem preferable.
In a large survey application, Kennickell (1991)
performed approximate MI for nonmonotone data by
specifying a single-response regression model for
SCHAFER AND GRAHAM168
each variable given the others and repeatedly fitting
the models in an iterated sequence. A general implementation
of this method using SAS macros is found
in the IVEware library (Raghunathan, Solenberger, &
Van Hoewyk, 2000). A similar library for S-PLUS,
called MICE, was written by Van Buuren and Oudshoorn
(1999). From a theoretical standpoint, this
technique is problematic, because the sequence of regression
models might not be consistent with a true
joint distribution. Technically speaking, these iterative
algorithms may never “converge” because the
joint distribution to which they may converge does
not exist. Nevertheless, simulation work (Brand,
1999) suggests that in some practical applications the
method can indeed work well despite the theoretical
problems.
Some utilities are also available to simplify the task
of analyzing imputed data sets. NORM combines parameter
estimates and standard errors from multiple
analyses using Rubin’s (1987) rules; it can also combine
groups of coefficients and their covariance matrices
for multiparameter inference. A new SAS procedure
called PROC MIANALYZE does essentially
the same thing. The new S-PLUS missing-data library
includes the functions miApply and miEval, which
automatically carry out a data analysis procedure
(e.g., fitting a regression model) for all the imputed
data sets, storing the results together in a convenient
format; four additional functions are available to consolidate
the results.
This list of software is not exhaustive and may be
outdated when this article appears in print. For up-todate
information on MI software, readers should refer
to on-line resources; a good starting point is the Web
site at http://www.multiple-imputation.com. Some of
the programs listed above were recently reviewed by
Horton and Lipsitz (2001).
Example
Returning to the blood pressure example, we simulated
one thousand samples of N ‫ס‬ 50 participants
and imposed missing values by the MCAR, MAR,
and MNAR methods. Using NORM, we multiply imputed
the missing values for each incomplete data set
20 times. We chose m ‫ס‬ 20 because of the unusually
high rate of missing values in this example (nearly
80%); with more moderate rates fewer would suffice.
After imputation, we computed estimates and information-based
standard errors for the five parameters
from each imputed data set, then combined the results
across each set of m ‫ס‬ 20 imputations by Rubin’s
(1987) rules.
The results are shown in Table 5. Comparing the
top panel of Table 5 with the likelihood-based results
in the top panel of Table 4, we see that MI and ML
estimates are very similar. NORM’s imputation
method is theoretically appropriate for MAR missingness,
but some bias is evident under the MAR condition
because of the small sample size; if the sample
size is increased, the bias disappears. The behavior of
Table 5
Performance of Multiple Imputation (m = 20) Under a
Normal Model for Parameter Estimates and Confidence
Intervals Over 1,000 Samples (N = 50 Participants)
Parameter MCAR MAR MNAR
Average parameter estimate (with RMSE in parentheses)
␮Y ‫ס‬ 125.0 124.9 125.3 151.6
(6.53) (17.2) (26.9)
␴Y ‫ס‬ 25.0 25.9 28.7 13.6
(5.93) (8.24) (12.1)
␳ ‫ס‬ .60 .57 .45 .35
(.19) (.37) (.36)
␤Y|X ‫ס‬ .60 .61 .59 .21
(.27) (.52) (.43)
␤X|Y ‫ס‬ .60 .56 .39 .66
(.22) (.38) (.56)
Coverage (with average interval width in parentheses)
␮Y 93.5 94.5 1.8
(26.1) (71.5) (19.9)
␴Y 93.2 96.1 18.0
(22.1) (35.1) (12.8)
␳ 90.8 86.9 90.4
(0.75) (1.35) (1.06)
␤Y|X 93.6 94.5 39.4
(1.05) (2.18) (0.71)
␤X|Y 94.9 95.0 91.4
(0.87) (1.29) (2.18)
Note. m is the number of imputations. Parameters: ␮ is the population
mean; ␴ is the population standard deviation; ␳ is the population
correlation; ␤ is the population regression slope. Coverage
represents the percentage of confidence intervals that include the parameter
value; values near 95 represent adequate coverage. Use of
boldface type in the top panel indicates problematic levels of bias (i.e.,
bias whose absolute size is greater than about one half of the estimate’s
standard error); use of boldface in the bottom panel indicates seriously
low levels of coverage (i.e., coverage that falls below 90%, which
corresponds to a doubling of the nominal rate of error). MCAR ‫ס‬
missing completely at random; MAR ‫ס‬ missing at random; MNAR
‫ס‬ missing not at random; RMSE ‫ס‬ root-mean-square error.
MISSING DATA 169
the MI intervals reported in the bottom panel of Table
5, however, shows some improvement over the likelihood-based
intervals in the bottom panel of Table 4;
in most cases the coverage is closer to 95%. Good
performance of MI intervals in small samples has
been previously noted (Graham & Schafer, 1999). Increasing
the sample size to 250, we found that the
results from MI became nearly indistinguishable from
the ML results with the same sample size.
General Comments on MI
MI is a relative newcomer, but its theoretical properties
are fairly well understood. Because MI relies on
Bayesian arguments, its performance is similar to that
of a likelihood method that makes similar assumptions.
Like ML, MI does rely on large-sample approximations,
but some limited experience (e.g., the
simulation reported above) suggests that with small to
moderate sample sizes these approximations may
work better for MI than they do for ML. Of course,
MI also requires assumptions about the distribution of
missingness. Nearly all MI analyses to date have assumed
that the missing data are MAR, but a few
MNAR applications have been published (Glynn et
al., 1993; Verbeke & Molenberghs, 2000). Nothing in
the theory of MI requires us to keep the MAR assumption,
and new methods for generating MIs under
MNAR models will certainly arrive in the future.
MI uses a model at the imputation phase, so robustness
to departures from the model is a concern. In
many situations we expect MI to be fairly robust,
because the model is effectively applied not to the
entire data set but only to the conditional distribution
of the missing part. It is also possible to combine a
fully parametric MI procedure with postimputation
analysis by a robust method, and unless the imputation
model is grossly misspecified the performance
should be quite good (Meng, 1999). In structural
equation modeling, for example, one could multiply
impute the missing values under a normality assumption
and then fit the structural model to the imputed
data using the robust techniques of Satorra and
Bentler (1994). Although further study of this is
needed, we conjecture that the properties of these hybrid
procedures will be excellent, perhaps better
than the normality-based likelihood approach of
AMOS or Mx.
One important difference between MI and likelihood
methods is that with likelihood the missing values
are dealt with during the model-fitting procedure,
whereas in MI they are dealt with prior to the analysis.
When the same model is used for imputation and
analysis, MI produces answers similar to those of a
likelihood analysis under that same model. Much of
the strength and flexibility (and, perhaps, the danger)
of MI, however, stems from the interesting possibility
of using different models for imputation and analysis.
Differences in these two models do not necessarily
invalidate the method but may actually strengthen it.
With MI the imputer is free to make use of additional
data (e.g., extra variables) that do not appear in the
analysis, and if those data are useful for predicting
missing values, then MI increases power. Properties
of MI when the imputer’s and analyst’s models differ
have been explored theoretically by Meng (1994) and
Rubin (1996) and from a practical standpoint by Collins,
Schafer, and Kam (2001).
Recent Developments
Methods Based on Weighting
The notion of reducing bias due to non-MCAR
missingness by reweighting has a long history in the
survey literature (Little & Rubin, 1987, chapter 4).
Recently, biostatisticians have begun to apply this
idea in regression modeling with incomplete covariates.
Robins et al. (1994) developed weighted regression
that requires an explicit model for the missingness
but relaxes some of the parametric assumptions
in the data model. Their method is an extension of
generalized estimating equations (GEE), a popular
technique for modeling marginal or populationaveraged
relationships between a response variable
and predictors (Zeger et al., 1988). These models are
called semiparametric, because they require the regression
equation to have a specific form (e.g., linear
or log-linear) but beyond that do not specify any particular
probability distribution for the response variable
itself. The same estimation procedure can be applied
whether the response is continuous or discrete.
Older GEE methods can accommodate missing values
only if they are MCAR; newer methods allow them to
be MAR or even MNAR, provided that a model for
the missingness is correctly specified. Further results
and extensions have been given by Robins and Rotnitzky
(1995) and Robins, Rotnitzky, and Scharfstein
(1998).
A primary motivation of these weighting methods
is to achieve robustness, good performance over more
general classes of population distributions. However,
extra generality does not come for free. Semiparametric
estimators can be less efficient and less powerful
SCHAFER AND GRAHAM170
than ML or Bayesian estimators under a wellspecified
parametric model. With missing data, Rubin’s
(1976) results show that ML or Bayesian methods
perform uniformly well over any MAR
missingness distribution, and the user does not need to
specify that distribution. However, semiparametric
methods that relax assumptions about the data must in
turn assume a specific form for the distribution of
missingness. We acknowledge that these weighting
techniques may be useful in some circumstances.
However, as a general principle, we also believe that
a researcher’s time and effort are probably better
spent building an intelligent model for the data rather
than building a good model for the missingness, especially
if departures from MAR are not a serious
concern. In one simulated example, Meng (1999)
noted that, for these semiparametric methods to gain a
substantial advantage over Bayesian MI, the parametric
model had to be so grossly misspecified that only
the most “statistically challenged” researcher would
use it.
Principles of weighting have also been used to
compute MI estimates for fully parametric regression
models with missing covariates. Ibrahim (1990) developed
a weighted estimation method for generalized
linear models, a class that encompasses traditional
linear regression, logistic regression, and log-linear
modeling (McCullagh & Nelder, 1989). Ibrahim’s
method is an EM algorithm under a generic model for
the joint distribution of the predictors. This method
requires the predictors to be discrete and does not take
relationships among them into account. With a normal
response, Ibrahim’s algorithm is a special case of EM
for mixed continuous and categorical data considered
by Little and Rubin (1987) and Schafer (1997). The
method has also been used for survival analysis
(Schluchter & Jackson, 1989; Lipsitz & Ibrahim,
1996, 1998). Weighting methods for ML regression
with missing covariates were reviewed by Horton and
Laird (1999). Although formal comparisons have not
yet been made, we expect that, in many cases, these
weighting techniques produce answers similar to MI
under a suitable joint model for the response and co-
variates.
Methods That Do Not Assume MAR
Many recent publications focus on MNAR missingness.
MNAR is a potentially serious concern in
clinical trials, in which participants may be dropping
out for reasons closely related to the response being
measured. For example, Hedeker and Gibbons (1997)
described an experiment in which patients were
treated for depression. The response variable was a
standardized depression score. Patients who were doing
well (i.e., experiencing lower levels of depression)
appeared to have higher rates of attrition, perhaps because
they believed treatment was no longer necessary.
Patients who were not improving also appeared
to have higher attrition rates, perhaps because they
decided to seek alternative treatment. In these situations,
it seems useful to allow the probability of dropout
at any occasion to depend on the participant’s
response at that occasion.
Without the MAR assumption, one must explicitly
specify a distribution for the missingness in addition
to the model for the complete data. There are two
fundamentally different ways to do this: selection
models and pattern-mixture models.
Selection models. Selection models were first
used by econometricians to describe how the probability
of response to a sensitive questionnaire item
(e.g., personal income) may depend on that item
(Amemiya, 1984; Heckman, 1976). In a selection
model, we first specify a distribution for the complete
data, then propose a manner in which the probability
of missingness depends on the data. For example, we
could assume that the logarithm of income is normally
distributed in the population and that each individual’s
probability of responding is related to his or her
log-income by logistic or probit regression. Mathematically,
a selection model builds a joint distribution
for the complete data Ycom and the missingness R
by specifying a marginal distribution for Ycom ‫ס‬
(Yobs, Ymis) and a conditional distribution for R given
Ycom,
P(Ycom, R; ␪, ␰) ‫ס‬ P(Ycom; ␪) P(R|Ycom; ␰), (7)
where ␪ represents unknown parameters of the complete-data
population and ␰ represents unknown parameters
of the conditional distribution of missingness.
The likelihood function is obtained by
collapsing this joint distribution over the unknown
Ymis, as shown in Equation 3.
Selection models for longitudinal studies with
dropout have been reviewed by Little (1995) and Verbeke
and Molenberghs (2000). In typical applications
(e.g., Diggle & Kenward, 1994), researchers assume
that measurements over time (Y1, . . . , YT) follow a
well-recognized distribution such as a multivariate
normal and allow the probability of dropout at occasion
t to follow a logistic regression on the previous
and current responses (Y1, . . . , Yt) but not on future
MISSING DATA 171
responses. The procedures required to obtain ML estimates
are not trivial, because the likelihood (Equation
3) often cannot be computed directly and must be
approximated. The likelihood for these models can be
oddly shaped, with large, flat regions indicating that
parameters are poorly identified.
Selection models for dropout have intuitive appeal.
It is natural to think about how a participant’s data
values may influence his or her probability of dropping
out, a notion that corresponds directly to Equation
7. Moreover, the same factorization is used in the
definitions of MCAR, MAR, and MNAR. If we alter
the dropout model by setting the logistic coefficients
for (Y1, . . . , Yt) or Yt to zero, we obtain MCAR and
MAR versions as special cases. This raises the interesting
possibility of “testing” the MAR hypothesis, by
seeing whether the confidence interval for the coefficient
of Yt includes zero. As pointed out by Kenward
(1998), Little and Rubin (1987, chapter 11), and many
others (see, e.g., the discussions following Diggle &
Kenward, 1994), results of such tests rest heavily on
untestable assumptions about the population distribution,
and minor changes in the assumed shape of this
distribution may drastically alter the conclusions
(Kenward, 1998). Many consider these models to be
too unstable for scientific applications and to be more
useful for raising questions than generating answers
(Laird, 1994).
Pattern-mixture models. As an alternative to the
selection model, Little (1993) described an alternative
class of MNAR methods based on a pattern-mixture
formulation. Pattern-mixture models do not describe
individuals’ propensities to respond. Rather, they
classify individuals by their missingness and describe
the observed data within each missingness group. A
generic pattern-mixture model can be written as
P(Ycom, R; ␪, ␰) ‫ס‬ P(R; ␩) P(Ycom|R; ␯), (8)
where ␩ denotes the proportions of the population
falling into the various missingness groups and ␯ represents
the parameters of the conditional distributions
of the data within groups. Estimation of ␯ always
requires some unverifiable assumptions, because a
portion of Ycom is hidden for every group having missing
data; Little (1993) called these assumptions identifying
restrictions. For a review of pattern-mixture
models for longitudinal studies with dropout, see
Little (1995) or Verbeke and Molenberghs (2000).
Pattern-mixture models are closely related to multiple-group
procedures for missing data in structural
equation modeling (Allison, 1987; Duncan & Duncan,
1994; Muthe´n, Kaplan, & Hollis, 1987). In the multiple-groups
approach, observational units are sorted
by missingness pattern and each pattern is assumed to
provide information about a subset of the model parameters.
A model is fit to each pattern, and parameters
from different patterns with equivalent meaning
are constrained to be equal. Because of the particular
form of the constraints used in these published articles,
the estimation procedures yielded ML parameter
estimates appropriate under MAR. Other types of
constraints that produce MNAR models are possible;
for a simple example, see Little (1994).
By nature, pattern-mixture models do not posit
strong theories about the mechanisms of missingness;
rather, they describe the observed responses in each
missingness group and extrapolate aspects of this behavior
to unseen portions of the data. The likelihood
function for a pattern-mixture model, obtained by collapsing
Equation 8 over the missing data Ymis, tends to
be more convenient to maximize than the likelihood
for a selection model. In some classes of patternmixture
models—the so-called random coefficients
models used by Hedeker and Gibbons (1997)—ML
estimates can be computed by conventional longitudinal
modeling software. One inconvenient feature of
these models, however, is that the parameters appearing
in the formulation (Equation 8) are rarely the parameters
of scientific interest. In most cases, we want
to describe some aspect of the distribution of Ycom
(e.g., a treatment effect) in the population of all missingness
groups combined. To estimate those parameters,
one usually needs to compute a weighted average
of group-specific estimates, with weights
determined by the relative sizes of the groups in the
sample. Alternatively, one may carry out this averaging
through MI.
Pattern-mixture models may not suffer from the
extreme sensitivity to distributional shape exhibited
by selection models, but their assumptions are no less
strong. Estimation of population effects is possible
only through identifying restrictions, and the observed
data provide no evidence whatsoever to support or
contradict these assumptions. Proponents of patternmixture
models (e.g., Little, 1993) have suggested
using these methods for sensitivity analysis, varying
the identifying restrictions to see how the results
change. Detailed examples of pattern-mixture modeling
were given by Verbeke and Molenberghs (2000).
Discussion of MNAR methods. MNAR modeling
seems worthwhile for clinical studies in which reasons
for dropout may be closely related to the outSCHAFER
AND GRAHAM172
comes being measured. In other situations, psychologists
should perhaps resist the urge to apply these
methods routinely. Simulations by Collins et al.
(2001) show that when the true cause of missingness
is the response variable itself, failure to account for
the MNAR aspect may indeed introduce a sizable bias
into parameter estimates. In other situations in which
the true cause is not the response but an unmeasured
variable that is only moderately correlated with the
response (with, say, a correlation of .40 or less), failure
to account for the cause seems capable of introducing
only minor bias. For many social science applications,
we suspect that the former is the exception
and the latter is the rule. For example, Graham et al.
(1997) described a longitudinal study of drug and alcohol
use among middle and high school students.
Attrition greatly reduced the sample size over time.
Common notions about substance use might cast
doubt on results of an MAR-based analysis, because
users could be dropping out of school at higher rates
than nonusers. Subsequent debriefing of the data collectors
revealed, however, that in most cases attrition
could be explained by students’ moving away or
transferring to other schools that did not participate in
the study. It was relatively infrequent that dropout
could be plausibly related to substance use (Graham et
al., 1997). Even if one argues that mobility and substance
use are related, it stretches the imagination to
believe that the correlation between them could be
much greater than .40. Therefore, we are inclined to
trust the results of an MAR-based analysis in this
example.
When data are collected by self-report questionnaire,
it is again natural to speculate whether missingness
is caused by the phenomena being measured,
especially if the items are of a personal or sensitive
nature. If an item pertains to nonnormative behavior,
some participants exhibiting that behavior may indeed
leave it blank in order to mask their true values, despite
repeated assurances of confidentiality. On the
other hand, some who do not exhibit that behavior
may also skip the item, thinking that the question
cannot possibly apply to them. Reasons for nonresponse
vary from one person to another. Many of
these reasons could be correlated with the item itself,
but probably not to the same degree, and perhaps not
even in the same direction. Are we to believe that the
correlation between the best aggregate measure of
“cause” is correlated with our item to a degree of .40
or more, even after accounting for its relationship to
other observed covariates? Some methodologists may
be afraid to answer this question, choosing instead to
leave it blank. Perhaps we are too bold, but in most
cases we are inclined to say, “No.”
If MNAR attrition is anticipated, researchers may
be able to mitigate its effects by simple changes in the
study design. For example, at each occasion of measurement,
we could ask each participant, “How likely
are you to drop out of this study before the next session?”
Collecting this additional covariate and including
it in analyses may effectively convert an MNAR
situation to MAR.
Concluding Remarks
Although other procedures are occasionally useful,
we recommend that researchers apply likelihoodbased
procedures, where available, or the parametric
MI methods described in this article, which are appropriate
under general MAR conditions. As MNAR
methods are incorporated into mainstream software,
they, too, become attractive in certain circumstances,
particularly for sensitivity analysis. Until then, ML
and MI under the MAR assumption represent the
practical state of the art.
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Received July 23, 2001
Revision received January 15, 2002
Accepted January16, 2002 I
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