Latent Class Modeling: I'm pretty sure this is the future. It's a much more powerful, higher resolution method than linear correlation and its derivatives. The whole issue of frog jumping I talked about in an earlier post is eliminated by attending to different profiles of participants, which LCM does.
Non-narcissistic self-esteem: It would be extremely cool if someone built a reliable self-esteem measure that doesn't also carry narcissism. RSE is going to carry both high self-esteem and high narcissism (or narcissistic self-esteem and non-narcissistic self-esteem.) So you have to use supplemental measures, like NPI-16, to tease them apart, or cross explicit and implicit self-esteem (which isn't as valid, probably.)
Kernis was heading in that general direction before he passed away. Implicit self-esteem is tough. I know that Diener and colleagues used initial letter liking as an implicit measure. I think it might have done some work in predicting life satisfaction. (Letter liking is presenting people with their own initials and capturing their attitudes, like J for me, or D. I'm not sure how much validation we have on that.)
Rosenberg also bothers me a bit because is wording is dated, and probably comes off as awkward to a lot of contemporary participants. This is likely to be another source of noise.
I'll probably try out some new measures. I also love Simine Vazire's method of repeating questions on personality measures, basically Are you sure this is the right answer? It correlates better with peer ratings, thus seems more accurate.
José L. Duarte
Social Psychology, Scientific Validity, and Research Methods.