Stata missing values how to#

Mi register imputed age hlthplan2 medcost qstlang endocomp2 income checkup colonendo educa personaldoc mscode raceethn marstat2 For this variable, then, there are no actual missing observations, even though the pre-coded dataset allows for various categories of potential missings.I am trying to generate a multiply imputed data set using the mi chained command. Unsurprisingly, there are no instances of these values in the dataset. In this case, it would mean that respondents were not allocated to either A, B, or C.Д¬odebook information in Figure 9 shows, though, that no values are thus coded, meaning that everyone was assigned to one of the three versions of the questionnaire 8 and 9 indicate вЂњdonвЂ™t knowвЂќ and вЂњrefusals.вЂќ Although in the dataset these values are coded, they are not relevant for this variable because it is completed by the interviewer on giving the self-completion questionnaire to the respondent. In addition to the three versions, coded 1, 2, or 3, there are other values that could be regarded as missing. The variable ABCVer identifies which questionnaire version, A, B, or C, that each respondent received. To recode the variable so that these values will be treated as missing in analysis, we select the following Stata menu options:ДЇigure 8: Coding Information for ABCVer, ChildHh, and plnallow. Values 8 and 9 are not assigned as missing in the dataset, but there are 29 responses of вЂњcanвЂ™t chooseвЂќ and 25 of вЂњnot answered.вЂќ We therefore need to recode the variable before we can use it in analysis. There are also 542 respondents who failed to return their self-completion questionnaire (A, B, or C). In this case, there are 1,619 observations for which вЂњskip version off route,вЂќ meaning that the questions were not asked of the recipients of Versions B and C of the questionnaire. We do not know the opinion of these respondents or whether they have one at all. In the case of the variable plnallow, for example, values of в€’2, в€’1, 8, and 9 do not locate the respondent on a continuum of attitude to plane travel between вЂњagreeing stronglyвЂќ and вЂњdisagreeing stronglyвЂќ that people should be able to travel by plane as much as they like. Often вЂњmissingвЂќ responses are given numerical values which do not meet this need. When using variables as ordinal or quantitative, values should be on a continuum. This will provide a list of all available value labels whether or not there were applicable cases. Different statistical packages treat missings slightly differently for different computations and procedures, but the principles remain the same. Both are examples of the so-called вЂњcomplete case analysisвЂќ or вЂњlistwise deletionвЂќ where cases without data on the variable of interest are not used to derive estimates related to this variable. From the point of view of this introductory guide, the main options are, broadly, to discard any cases with missings on any of the variables used in a set of analyses that produce statistical estimates or alternatively, to use all possible cases available for each estimate, thereby using a different subset of cases for each analysis.

Stata missing values how to#

Is it planned, refusal, non-substantive? Thirdly, you will need to decide how to prepare the data for analysis. Secondly, you need to understand the reasons for each type of missingness.

The first task is to assess the amount of missing data and how it is distributed across the variables in your dataset. How you deal with missing data depends on the type of missingness, how much of the data are missing, and the reasons for the missingness. With less data, oneвЂ™s inferences are less powerful and if the data are missing in patterns that relate to what is being measured in the study, then inferences can be biased as a result.

Missing data can pose a problem for the analysis of the data and for drawing appropriate inferences. Simply put, data are missing when information that is present in variables for some cases is not present for others. Missing data are found in almost every dataset.