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Context:

  • I am running a large survey and am trying to figure out what statistical analyses to perform.

  • The survey asks questions in every format known to man (e.g., likert scales, multi-choice, dichotomous) and is about psychosocial health and work stress.

  • I plan to run a factor analysis to see what demographic questions factor together to effect quality of life and my supervisor has suggested then doing Pearson correlations on the factors that get thrown up. Then running n-way MANOVA to determine % of variance explained. Despite having aced stats up til now, I'm finding my old brain is confused by all this and somehow the order seems backwards.

Question:

  • If you had a huge data set and you expected lots of the questions to come together to be influential factors on a dependent variable (quality of life in this case) what would you do and in what order?
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  • $\begingroup$ can you qualify huge? Different fields have different definitions of this term. $\endgroup$ – richiemorrisroe Sep 29 '11 at 14:36
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    $\begingroup$ IMHO that really matters is why do you need either of them? And your factors are constructed not to be orthogonal to each other combinations? Are you trying to construct just a some sort of index for separation purposes or do you need to implement rotation of the factor space to have a nice interpretation? $\endgroup$ – Dmitrij Celov Sep 29 '11 at 22:06
  • $\begingroup$ @richmorrisroe Huge = more than 100 variables altogether $\endgroup$ – Pundy Sep 30 '11 at 1:50
  • $\begingroup$ @DmitrijCelov I think I need a nice interpretation. I am really lost about this. $\endgroup$ – Pundy Sep 30 '11 at 1:53
  • $\begingroup$ @rolando2 I'll make two groups from the upper and lower qunitles scorers on my quality of life and psychsocial health measures. Was thinking to see if the factors were different for these two - risk vs protective maybe. $\endgroup$ – Pundy Sep 30 '11 at 1:53
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I don't see the applicability of MANOVA, since it is designed to identify group differences, and I haven't heard you say that you have any groups to compare.

Correlations among factors would be useful as a side analysis to help prepare for the next step. What I mean is, if the factors are essentially uncorrelated with one another (perhaps with *r*s <.3) they could be built into a regression to explain the outcome. If they are more strongly correlated, then you would want to avoid the collinearity problem by simply seeing how strongly the outcome correlates with each factor: no regression, no partial control for one another, because the results would be too muddied.

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