The log file will be stored in the folder under the current directory. This module will explore missing data in stata, focusing on numeric missing data. You should know what each variable is, how its coded, how missing values are. Using mvdecode and mvencode for treatment of missing values basics. Option mi treats missing values as a separate category otherwise observations withi missing values are ignored. Regressionmodelsforcategorical dependentvariablesusingstata thirdedition j. Deal with missing data use what you know about why data is missing distribution of missing data decide on the best analysis strategy to yield the least biased estimates deletion methods listwise deletion, pairwise deletion single imputation methods meanmode substitution, dummy variable method, single regression. Helpful if you have many variables and want to focus on a subset of them. Chained equations and more in multiple imputation in stata 12. That is, age has 12 missing values in the data, and in every observation in which age is missing, so is bmi, although bmi has another 16 missing. Panel data refers to data that follows a cross section over timefor example, a sample of individuals surveyed repeatedly for a number of years or data for all 50 states for all census years. If string make sure the categories have the same spelling i. An imputation represents one set of plausible values for missing data, and so. Nis4 analyses sample stata syntax this syntax was tested using stata version 14 with the survey replication based standard errors package.
To see this, simply view the data using browse or list. Missing data mechanisms missing completely at random mcar missing value y neither depends on x nor y example. A comparison of sas, stata, iveware, and r patricia a. Imputation of sf12 health scores for respondents with.
Missing data create a number of potential challenges for statistical analysis. Several commands in stata can provide help in dealing with missing values. How to do statistical analysis when data are missing. However, there is a special procedure in stata that makes dealing with missing values safer. In a small dataset, like the one below, it is very easy to look at the raw data and see where values are missing. Sem requires the wide structure for the data, and withinwave and wholewave missing data are treated in the same way. Test to see if the missing data are biased or if they are randomly distributed along each of the other ivs and dvs of interest.
Introduction features of good data work managing a big data project is hard. Statistical analysis with missing data wiley series in. Handling missing data in stata a whirlwind tour 2012 italian stata. That is, when data is missing for either or both variables for a subject, the case is excluded from the computation of rij. Several excellent books and articles on missing data methods, many of which are cited in this report, explain the theory and. Software for the handling and imputation of missing data an overview.
Reading data in stata using and saving stata data files inputting raw data files into stata. Lets say you have data on the health habits of canadians aged 12 and up. Introduction multiple imputation full information maximum. Jun 03, 2017 if there are missing observations in your data it can really get you into trouble if youre not careful. A practical introduction to stata harvard university. Respondents in service occupations less likely to report income missing not at random nmar. In the tables that display individual cases, the following symbols are used. Handling missing values in longitudinal panel data with. Cases are listed in the order they appear in the data file, unless a variable is specified in sort by. Create dummy variables representing cases that are missing data. Berglund, university of michiganinstitute for social research abstract this presentation emphasizes use of sas 9.
The first block of columns in the output shows the patterns of missing data. Before we begin, we need some data with missing values, the code below inputs a small dataset into stata, and then displays that data. Make sure to map where the using data is located in this case mydata2. Stata is available for windows, unix, and mac computers. To create an efficient imputation algorithm for imputing the sf 12 physical component summary pcs and mental component summary mcs scores when patients have one to eleven sf 12 items missing. Part 2 implementing multiple imputation in stata and spss carol b. Missing data also referred to as missingness may cause bias and will always cause a reduction in efficiency. Features of good data work managing a big data project is hard. Because of the complex sample design and use of paired jackknife replicate weighting jk2.
Imputation and likelihoodbased approaches rose medeiros statacorp lp. The following code will come in handy for this tutorial. However, the way that missing values are omitted is not always consistent across commands, so lets take a look at some examples. Merging two datasets require that both have at least one variable in common either string or numeric. Microeconometrics using stata, revised edition, by a. Multiple imputation using the fully conditional specification method. Filling gap of missing data statalist the stata forum. The dependent variable for this example is attack, coded 0 if the subject did not have a heart attack and 1 if he or she did. Multiple imputation of missing values the stata journal. Opens a data browser for variables make, mpg, and price. Variables are registered as imputed, passive, or regular, or they are left unregistered.
As a general rule, stata commands that perform computations of any type handle missing data by omitting the row with the missing values. Handling missing data in stata nick huntingtonklein. Primary data collection was performed between 1996 and 1998. Type \help import stata documentation is extensive and outstanding. Data file use the example syntax below to set up your dataset. Missing data using stata basics for further reading many methods assumptions assumptions ignorability assumptions listwise deletion complete case listwise deletion continued listwise deletion continued pairwise deletion available case dummy variable adjustment imputation maximum likelihood properties of maximum likelihood ml with ignorable. If there are missing observations in your data it can really get you into trouble if youre not careful.
Introduction multiple imputation full information maximum likelihood. Overview of statistical tests in stata overview of stata syntax missing values in stata 3. In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. You must use the weights when working with nis4 data. Mcgovern harvard center for population and development studies geary institute and school of economics, university college dublin august 2012 abstract this document provides an introduction to the use of stata. Chained equations and more in multiple imputation in stata 12 multiple imputation using chained equations examples. Scott long departments of sociology and statistics indiana university bloomington, indiana. Microeconometrics using stata, revised edition stata press. Another way to bring excel data into stata is by saving the excel file as. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. These methods assumes that the missing data are mar and yield parameter estimates for similarly structured models that are essentially equivalent to those obtained with mi graham, 2009. Graham pennsylvania state university statistical procedures for missing data have vastly improved, yet miscon ception and unsound practice still abound.
Such a matrix is computed by using for each pair of variables xi, xj as many cases as have values for both variables. Software for the handling and imputation of missing data an. The proportion of missing data should not be used to guide. Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. Each case is tabulated, and missing and extreme values are indicated for each variable. This example is adapted from pages 114 of the stata 12 multiple imputation manual which i highly recommend reading and also quotes directly. Values in a data set are missing completely at random mcar if the events that lead to any particular data item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random.
Multiple imputation of missing data using stata data and statistical. Stata tutorial university of california, san diego. Graphics introduction to graphics overview of graph twoway plots twoway scatterplots combining twoway scatterplots common graph options 4. Missing data in stata centre for multilevel modelling, 20 4 the output from misstable patterns shows, for the specified variables, each pattern of missing data which occurs, ordered according to the frequency with which they occur. Most commands work the same in stata versions 12, 11, 10, and 9.
Data imputation involves representing missing values in a dataset. Hcup methods report 12 1114 3 missing data methods 1. Missing data in stata centre for multilevel modelling, 20 2. You must close the data editor before you can run any further commands. Software for the handling and imputation of missing data.
Stata has a built in feature that allows you to access the user manual as well as. If more than 6 hourly values are consecutively missing in 24, the data for that specific date is set to missing. Introduction this report is not a general tutorial on missing data methods. Particularly if the missing data is limited to a small number of observations, you may just opt to eliminate those cases from the analysis. It will describe how to indicate missing data in your raw data files, as well as how missing data are handled in stata logical commands and assignment statements. Listwise listwise deletion completecase analysis removes all data for an observation that has one or more missing values. You can access the pdf documentation by clicking on the blue text for example, d import. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using stata. Chi square test for categorical variables ttest for continuous variables. Data are missing on some variables for some observations problem. That is, when data is missing for either or both variables for a subject, the case is. Missing data mechanisms there are different assumptions about missing data mechanisms.
Creating running mean with missing values in stata sas. The first edition of statistical analysis with missing data has been a standard reference on missingdata methods. Make sure one dataset is loaded into stata in this case mydata1, then use merge. Importing data and syntax structure all homeworks and examples will use data in stata format extension. The second table contains the information on the pattern of missing values. Aimed at students and researchers, this book covers topics left out of microeconometrics textbooks and omitted from basic introductions to stata. Do pairs of variables tend to have values missing in multiple cases. Minimize bias maximize use of available information get good estimates of uncertainty. Missing data is a common problem in epidemiology, and participant drop out can substantially reduce the sample size available for analysis even in initially large cohorts.
The ratio of information lost due to the missing data to the total information that would be present if there were no missing data. We will say that these values are mcar if the probability of missing data on y is unrelated to the value of y itself or to the values of any other variable in the data set. This tutorial was created using the windows version, but most of the contents applies to the other platforms as. The gcse score is formed by assigning numerical scores to the grades obtained by a child at gcse aa7 through to grade g1, truncated at 12 grade aas giving a maximum score of 84. This can be achieved with the help of stata commands for data transformations, i. While stata permits this, when you come back to this in a few months or if you show your results to somebody else, you they will. How stata handles missing data in stata procedures. I focus explicitly on the foundations of using such software and ignore statistical procedures. Multiple imputation using the fully conditional specification. Hohl k 2012 software for the handling and imputation of missing data an overview. A short guide to stata 14 1 introduction 2 2 the stata environment 2 3 where to get help 3 4 additions to stata 3 5 opening and saving data 4 6 importing data 5 7 data manipulation 6 8 descriptive statistics 9 9 graphs 10 10 ols regression 11 11 log files 12 dofiles 14 important functions and operators 16 version. Respondents in service occupations less likely to report income.
725 797 1356 363 1570 1539 1339 1001 1158 1246 647 860 353 1210 1522 259 663 1364 414 111 437 164 959 1414 373 333 1259 29 708 1198 79 1132 1153 1424 1550 815 816 1313 361 245 313 798 1007