![]() But is there an evidence-based/peer-reviewed method to determine the cutoff? What is this SPSS-permitted method even called? I am so tempted to use it, but afraid that without adequate backing, I will get slated when I submit for publication (I am aiming for a top journal). Therefore, I was thinking of using 10% as my cutoff. The interaction odds ratio can be simply. Bennett (2001) maintained that statistical analysis is likely to be biased when more than 10% of data are missing. We should not pay much attention to the main effects, given that the interaction is powerful. Schafer (1999) asserted that a missing rate of 5% or less is inconsequential. In addition I am working on the factor analysis to determine how many items can be loaded together, etc. I am looking deeper into how I can determine whether my data is Missing Completely At Random (MCAR), Missing At Random (MAR), etc. ![]() As someone above has already asked, I wanted to determine the criteria to use as my cutoff mark for missing data. I understand this method is available in SPSS, and it is very useful indeed. This works the same way in the syntax or in the Transform–>Compute menu dialog. A better approach is to calculate the mean, then multiply by 5). (This same distinction holds for the SUM function in SPSS, but the scale changes based on how many are being averaged. You can specify any number of variables that need to be observed. If fewer than 4 of the variables are observed, Newvar will be system missing. Running it the following way will only calculate the mean if any 4 of the 5 variables is observed. SPSS has an option for dealing with this situation. If only one or two variables are present, the mean may not be a reasonable estimate of the mean of all 5 variables. While this seems great at first, the researcher may wish to limit how many of the 5 variables need to be observed in order to calculate the mean. In the second method, if any of the variables is missing, it will still calculate the mean. In the first method, if any of the variables are missing, due to SPSS’s default of listwise deletion, Newvar will also be missing. There are two ways to do this in SPSS syntax. ![]() It allows you to add or average variables, while specifying how many are allowed to be missing.įor example, a very common situation is a researcher needs to average the values of the 5 variables on a scale, each of which is measured on the same Likert scale. SPSS has a nice little feature for adding and averaging variables with missing data that many people don’t know about.
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