![]() If it makes sense, impute the missing values.There are several ways to deal with problematic variables. The threshold for missing data is flexible, but generally, if you are missing more than 10% of the responses on a particular variable, or from a particular respondent, that variable or respondent may be problematic. The table in the output will show the number of missing values for each variable. Enter the variables in the variables list. To find out how many missing values each variable has, in SPSS go to Analyze, then Descriptive Statistics, then Frequencies. If you use gender in your causal models, then you will be heavily biased toward males, because you will not end up using the unreported responses. Perhaps only 50% of the females reported their gender, but 95% of the males reported gender. For example, if you asked about gender, and females are less likely to report their gender than males, then you will have male-biased data. Some people may not have answered particular questions in your survey because of some common issue. If you are missing several values in your data, the analysis just won't run.Īdditionally, missing data might represent bias issues. This number increases with the complexity of your model. The EFA, CFA, and path models require a certain number of data points in order to compute estimates. The most apparent problem is that there simply won't be enough data points to run your analyses. If you are missing much of your data, this can cause several problems.
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