Factorvariable notation allows stata to identify interactions and to distinguish between discrete and continuous variables to obtain correct marginal effects. 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. 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. 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 will require us to create dummy variables for our categorical predictor prog. I would like to replace the missing values by information on the relation between gross and net income. My dataset has the variables net income and gross income. If a passive variable is determined by regular variables, then it can be treated as a regular variable since no imputation is needed. 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. This preserves relationships among variables involved in the imputation model, but not variability around predicted values. As we will see below, convenience is not the only reason to use factorvariable notation. Independent variable are you prone to binge drinking 1yes, 2no dependent variable drinking and driving 1. For each approach, we assess 1 the accuracy of the imputed values. Theoretically, i could use logit and multinomial logit models, with the predict command, to obtain predicted values for missing cases.
The first is proc mi where the user specifies the imputation model to be used and the number of imputed datasets to be created. Now, lets try reading the data and tell stata the names of the variables on the insheet command. 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. And then i want to perform a linear regression for them. 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. Out of all variables only 1 categorical variable with 52 factors has nas no of factors in the categorical. Multiple imputation by chained equations journal of statistical. 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. 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. Data imputation in r with nas in only one variable. Avoiding bias due to perfect prediction in multiple. Normally, you should go to multiple imputation impute missing data values, custom mcmc and then select pmm. This is part four of the multiple imputation in stata series. It is also advocated for data including categorical variables schafer, 1997, but a normal.
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. In many cases you can avoid managing multiply imputed data completely. Finding an appropriate joint model with noncontinuous variables, for example binary or categorical variables, is more challenging. A bunch of variables are categorical some nominal, some ordered.
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. Nevertheless i am left with one last issue on imputation. Replace missing values expectationmaximization spss. Create some variables before imputation example, mutually exclusive binary variables for one construct race. 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. The ice program was written for stata 9 and above to perform imputation via. Multiple imputation is becoming increasingly popular. Within the applied setting, it remains unclear how important it is that imputed values should be plausible for individual observations. One variable type for which mi may lead to implausible values is a limitedrange variable. By default, stata provides summaries and averages of these values but the individual estimates can be obtained using the vartable. After logarithimc transformation and back the results of imputation with ice seem fine.
Cross validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Inputting your data into stata stata learning modules. Multiple imputation of discrete and continuous data by. There are missing values for the variable net income coded. But i have some experience in pmm predictive mean matching and for those who have both categoricalbinary and continuous data, i would never recommend multiple regression method. Statas new mi command provides a full suite of multipleimputation methods for the analysis of incomplete data, data for which some values are missing. We use simulations to examine the implications of these assumptions. All one has to do is reorganise the data set, define some new variables to specify the baseline. Stata s mi command provides a full suite of multipleimputation methods for the analysis of incomplete data, data for which some values are missing. The discrete choice models already noted are the natural platforms for anfor alyzing these variables.
Comparison of methods for imputing limitedrange variables. Variables can have an arbitrary missingdata pattern. Learn how to use the expectationmaximization em technique in spss to estimate missing values. Additionally, while it is the case that single imputation and complete case are easier to implement, multiple imputation is not very difficult to implement.
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. For a list of topics covered by this series, see the introduction. Accordingly, the outcome variable should always be present in the imputation model. 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. By imputing multiple times, multiple imputation certainly accounts for the uncertainty and range of values that the true value could have taken. If you are using stata, there is user written function called psmatch2. I did not need to create dummy variables, interaction terms, or polynomials. This has all the advantages of regression imputation but adds in the advantages of the random component. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any. This study examines the performance of these methods when data are missing at random on unordered categorical variables treated as predictors in the. The stata impute command uses ols to estimate missing values, appropriate only for continuous variables. Ice is a flexible imputation technique for imputing various types of data. In this case, a prior such as beta1,1 may be used for the stratumspecific probability. Multiple imputation of missing values the stata journal.
Compared with standard methods based on linear regression and the normal distribution, pmm produces. The chained equation approach to multiple imputation. Multiple imputation of discrete and continuous data by fully conditional specification. Wherever possible, do any needed data cleaning, recoding, restructuring, variable creation, or other data management tasks before imputing. We consider the relative performance of two common approaches to multiple imputation mi. 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. Mice is a particular multiple imputation technique raghunathan et al. The second procedure runs the analytic model of interest here it is a linear regression using proc glm within each of the imputed datasets. This method has been implemented as userwritten software in stata. This post demonstrates how to create new variables, recode existing variables and label variables and values of variables. 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. Missing data using stata basics for further reading many methods assumptions assumptions ignorability. The predicted value from a regression plus a random residual value.
But, as i explain below, its also easy to do it the wrong way. Alternative techniques for imputing values for missing items will be discussed. 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. Auxiliary variables in multiple imputation in regression. 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.
Pdf avoiding bias due to perfect prediction in multiple. How to impute interactions, squares and other transformed variables. Missing data takes many forms and can be attributed to many causes. However, i realised the imputed values do not replace the missing values in the original variables. Avoiding bias due to perfect prediction in multiple imputation of. Multiple imputation of discrete and continuous data by fully conditional. Missing data could be in categorical, ordinal, discrete or continuous variables. 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. Stata does not have a set of specialist commands for estimating the discrete time proportional odds or proportional hazards models. These new variables will be used by stata to track the imputed datasets and values.
Multiple imputation for continuous and categorical data. Turning categorical variables into indicator variables and vice versa can be done using any statistical software package. Multiplying variables generating new variables after mi. In our workshops we show how to write the code to do this in stata, spss, and r. Regression imputation imputing for missing items coursera. This is part five of the multiple imputation in stata series. The multivariate normal model implemented in mi impute mvn assumes all variables follow a multivariate normal distribution.
The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To further understand the similarities between continuousdiscrete interval and ratio variables, consider measurement precision. Most multiple imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical variables. I need to deal with missing data for noncontinuous variables. Choose from univariate and multivariate methods to impute missing values in continuous, censored, truncated, binary, ordinal, categorical, and count variables. Imputing instrumenting for missing variables in a casecontrol study. Here we use the generate command to create a new variable representing population younger than 18 years. Many researchers prefer using indicator variables directly when running their analysis. This is one of the best methods to impute missing values in.
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