Exercises

  1. Examine the GDS data for the two types of outliers. Conduct frequency distributions of the variables (they should all be 0 or 1) and visually scan the data for specific response patterns that might be problematic (e.g., all 0 or all 1). Conduct an EFA using ULS extraction and direct oblimin rotation, extracting five variables. Determine whether there are any variables that might be outliers.
  2. Replicate our analysis to impute missing data for a subsample of the SDQ data. Use the syntax presented at the bottom of this section to select the subsample of N=300 and generate a nonrandom missing sample by changing all responses of 6 on Eng1 to missing. Then use the code presented in the chapter to impute the missing data and run the EFA from the covariance matrix, using ULS extraction and direct oblimin rotation.
  3. Impute missing data for the GDS data set, using a similar process as we used for the subsample of SDQ data. Conduct an EFA on the original data and the imputed version of the data using ULS extraction and direct oblimin rotation, this time extracting three variables. Compare the results from the model with imputed data to those without data imputed. Is there a difference in the results? Why or why not?
Syntax for exercise 2 above: 
*Replicate the selection of the 300;
proc surveyselect data=sdqdata method=srs n=300 out=sdqdata_ss1 
      seed=39302;
run;
*Generate nonrandom missing sample;
data nonrandom_miss;
   set sdqdata_ss1;
   if Eng1 =6 then Eng1=.;
run;
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset