To better learn cluster analysis I'm playing around with some Likert scale (agree-disagree) data I have, but the results I'm getting are basically useless. As the title suggests, rather than grouping respondents in any meaningful way, I'm just getting clusters of people who scored high on all questions, low on all questions, and somewhere in the middle. (That's an oversimplification, but you get the point). I've factor analyzed the questions and found a pretty clean factor solution with five factors, so there's reason to believe the questions should indeed be capable of dividing up the respondents.
Nonetheless, I've tried a variety of things and the solution always exhibits the same problem (e.g. I've tried different cluster methods, I've standardized the scores, I've run it with just a handful of variables I figured should work well, and I've run it with the regressed factor scores even though I know that isn't recommended).
My assumption is just that some people tended to agree more strongly across the board and it's confusing the cluster analysis. Indeed, the one thing I did that produced a more logical result was standardizing the scores by case rather than by variable, even though this approach seems far less common.
Is there any other explanation or any other way to deal with this issue?