1: What about the omitted Satisfaction trust item? Having left it out of the aggregated Satisfaction scale, we may leave it out permanently. However, if it reflects something uniquely important, the mere fact it does not want to “play with” other items (in the sense that they do not want to group together) does not necessarily mean it should be left out of further analysis. You might include such “lone rider” items as sole variables that - by virtue of their lack of ”willingness” to group with other variables - are unique. [return]

2:Having said this, the use of this technique for ordinal variables is debated. Some people prefer keeping ordinal variables as numerical data, although it is more common to use the dummy variable technique. There also exist some more complex, although not widely-used techniques that adjust the regression results for ordinal variables. The beginner will probably be safe with using dummy variables. [return]

3: For instance, say you have a single Likert scale item of the form 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree as an independent variable. You could choose “neutral” as the reference variable, and then have two dummy variables (one to represent the “disagree” options, i.e. the “1” or “2”, and the other to represent the “agree” options, i.e. the “4” or “5”). [return]

4: The suggested cut-off for Leverage scores is ±2 p/n = ±2 (IVs + 1)/n where p is the number of independent variables + 1 and n is the sample size (Belsley, Kuh & Welsch, 1980). [return]

5: In SAS 9 go to Help > SAS Help and Documentation > SAS Products > SAS Procedures and choose ROBUSTREG, or reference SAS/STAT 13.2 User’s Guide. [return]

6: An example of a methodological design that can mitigate missing data include setting online survey tools to force answers (although this has the downside of reducing response rates.. [return]

7: For instance, Allison (2012); Dong & Peng (2013); Graham, Cumsille & Elek-Fisk (2003); Graham & Hofer (2000); Little & Rubin (2002); Rubin (1987); Schafer (1997); Schafer & Graham (2002); Wayman (2003). [return]

8: SAS has a great multiple imputation offering in the combination of PROC MI and PROC MIANALYZE [return]