To further understand the similarities between continuousdiscrete interval and ratio variables, consider measurement precision. The chained equation approach to multiple imputation. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any. The ice program was written for stata 9 and above to perform imputation via. I am trying to impute two variables simultaneously in stata. Now, lets try reading the data and tell stata the names of the variables on the insheet command. Nevertheless i am left with one last issue on imputation. A simulation study of a linear regression with a response y and two predictors x1 and x 2 was performed on data with n 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80. Data imputation in r with nas in only one variable. Imputing a missing variable based on common variables with. Compared with standard methods based on linear regression and the normal distribution, pmm produces. I need to deal with missing data for noncontinuous variables. Multiple imputation mi was developed as a method to enable valid inferences to be obtained in the presence of missing data rather than to recreate the missing values.
This study examines the performance of these methods when data are missing at random on unordered categorical variables treated as predictors in the. For a list of topics covered by this series, see the introduction. This method has been implemented as userwritten software in stata. Statas new mi command provides a full suite of multipleimputation methods for the analysis of incomplete data, data for which some values are missing. Imputing instrumenting for missing variables in a casecontrol study. In many cases you can avoid managing multiply imputed data completely. Variables can have an arbitrary missingdata pattern. In some imputation software such as ice for stata or iveware for sas the regression model used to impute x m is specified explicitly, while in other imputation software such as the mi procedure in sas the regression model is implicit in the assumption that x,y are multivariate normal with mean. This preserves relationships among variables involved in the imputation model, but not variability around predicted values. After logarithimc transformation and back the results of imputation with ice seem fine. I also want to impute a discrete variable, namely the age of companies in years integers with a maximum of 37 years age has only been measured as of 1967. If working with multiple discrete groups of observations, consider imputing separately and combine afterward.
By default, stata provides summaries and averages of these values but the individual estimates can be obtained using the vartable. How to impute interactions, squares and other transformed variables. Stata s mi command provides a full suite of multipleimputation methods for the analysis of incomplete data, data for which some values are missing. I would like to replace the missing values by information on the relation between gross and net income. However, i realised the imputed values do not replace the missing values in the original variables. Most multiple imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical variables.
Missing data takes many forms and can be attributed to many causes. Which statistical program was used to conduct the imputation. Stata does not have a set of specialist commands for estimating the discrete time proportional odds or proportional hazards models. For each approach, we assess 1 the accuracy of the imputed values. Auxiliary variables in multiple imputation in regression. Alternative techniques for imputing values for missing items will be discussed.
Also, in addition to all the variables that may be used in the analysis model, you should include any auxiliary variables that may contain information about missing data. Many researchers prefer using indicator variables directly when running their analysis. Multiple imputation for continuous and categorical data. This is part four of the multiple imputation in stata series.
Multiple imputation of missing values the stata journal. In our workshops we show how to write the code to do this in stata, spss, and r. The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. Out of all variables only 1 categorical variable with 52 factors has nas no of factors in the categorical. But, as i explain below, its also easy to do it the wrong way. The aim of this work was to compare methods for imputing limitedrange variables, with a focus on those that restrict the range of the imputed values.
Multiple imputation of discrete and continuous data by fully conditional specification. Regression imputation imputing for missing items coursera. This is part five of the multiple imputation in stata series. Paul allison, one of my favorite authors of statistical information for researchers, did a study that showed that the most common method actually gives worse results that listwise deletion. Wherever possible, do any needed data cleaning, recoding, restructuring, variable creation, or other data management tasks before imputing. Multiplying variables generating new variables after mi. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit.
Missing data using stata basics for further reading many methods assumptions assumptions ignorability. In the output from mi estimate you will see several metrics in the upper right hand corner that you may find unfamilar these parameters are estimated as part of the imputation and allow the user to assess how well the imputation performed. One variable type for which mi may lead to implausible values is a limitedrange variable. In this case, a prior such as beta1,1 may be used for the stratumspecific probability. Additionally, while it is the case that single imputation and complete case are easier to implement, multiple imputation is not very difficult to implement. Inputting your data into stata stata learning modules. These new variables will be used by stata to track the imputed datasets and values. The joint modeling approach simply treats all functional terms as separate variables and imputes them together with the underlying imputation variables using a multivariate model, often a multivariate normal model. Theoretically, i could use logit and multinomial logit models, with the predict command, to obtain predicted values for missing cases. This is one of the best methods to impute missing values in. The former assumes a normal distribution of the variables in the imputation model and the latter fills in missing values taking into account the distributional form of the variables to be imputed.
Learn how to use the expectationmaximization em technique in spss to estimate missing values. The stata impute command uses ols to estimate missing values, appropriate only for continuous variables. Missing data could be in categorical, ordinal, discrete or continuous variables. The predicted value from a regression plus a random residual value. We use simulations to examine the implications of these assumptions. I am trying to understand the definition of a control variable in statistics. Choose from univariate and multivariate methods to impute missing values in continuous, censored, truncated, binary, ordinal, categorical, and count variables. The second procedure runs the analytic model of interest here it is a linear regression using proc glm within each of the imputed datasets. Accordingly, the outcome variable should always be present in the imputation model. Much of the literature concerns the problem of imputing a binary or other discrete incomplete variable within strata defined by one or more other discrete variables rubin and schenker, 1986. Methods using data from a study of adolescent health, we consider three variables based on responses to the general health questionnaire ghq, a tool for detecting minor psychiatric illness.
Finding an appropriate joint model with noncontinuous variables, for example binary or categorical variables, is more challenging. This post demonstrates how to create new variables, recode existing variables and label variables and values of variables. Stata s new mi command provides a full suite of multiple imputation methods for the analysis of incomplete data, data for which some values are missing. If you are using stata, there is user written function called psmatch2. Cross validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Comparison of methods for imputing limitedrange variables. Turning categorical variables into indicator variables and vice versa can be done using any statistical software package. Here we use the generate command to create a new variable representing population younger than 18 years. We consider the relative performance of two common approaches to multiple imputation mi. It is also advocated for data including categorical variables schafer, 1997, but a normal. My dataset has the variables net income and gross income.
The variable by variable specification of ice allows you to impute variables of different types by choosing from several univariate imputation methods the appropriate one for each variable. Avoiding bias due to perfect prediction in multiple. Pdf avoiding bias due to perfect prediction in multiple. Spssx discussion imputation of categorical missing values.
Avoiding bias due to perfect prediction in multiple imputation of. Imputing clustered data in stata imputation with cluster dummies imputation in wide form. Mice is a particular multiple imputation technique raghunathan et al. If a passive variable is determined by regular variables, then it can be treated as a regular variable since no imputation is needed. A bunch of variables are categorical some nominal, some ordered.
We use a probit model to create binary variables for the second case, an ordered probit model to create ordinal variables for the third case, and a multinomial probit model to create unorderedcategorical variables for the fourth case. The discrete choice models already noted are the natural platforms for anfor alyzing these variables. I did not need to create dummy variables, interaction terms, or polynomials. Multiple imputation of discrete and continuous data by fully conditional.
Ice is a flexible imputation technique for imputing various types of data. As we will see below, convenience is not the only reason to use factorvariable notation. A continuous variable can only be measured to a certain level of precision, and as such, in reality, can only take a discrete set of values. Predictive mean matching pmm is an attractive way to do multiple imputation for missing data, especially for imputing quantitative variables that are not normally distributed. The first is proc mi where the user specifies the imputation model to be used and the number of imputed datasets to be created. There are missing values for the variable net income coded. Mice operates under the assumption that given the variables used in the imputation procedure, the missing data are missing at random mar, which means that the probability that a value is missing depends only on observed values and. This will require us to create dummy variables for our categorical predictor prog. By imputing multiple times, multiple imputation certainly accounts for the uncertainty and range of values that the true value could have taken. Replace missing values expectationmaximization spss. Multiple imputation by chained equations journal of statistical.
Normally, you should go to multiple imputation impute missing data values, custom mcmc and then select pmm. Factorvariable notation allows stata to identify interactions and to distinguish between discrete and continuous variables to obtain correct marginal effects. For example, if i am creating a multivariate equation with an independent variable and a dependent variable, and wish to introduce a third variable as a control variable, would it be correct to use. This has all the advantages of regression imputation but adds in the advantages of the random component. And then i want to perform a linear regression for them.
780 1263 1606 322 1026 2 1535 1508 875 782 1666 504 227 494 1557 1234 93 761 1593 16 267 1594 574 1343 1387 212 86 1037 400 1413 457 286 1464 913 657 1215 12 255 1409 416 918 1416 509 222