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What if interaction wipes out my direct effects in regression?

I did hierarchical regression analysis on my data due to having interaction effects in my research model. All the variables are continuous.

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R2 increased from .695 in model1 (main effect only) to .734 in model2 (main &interaction effects)(sig. F change = .000). All the assumptions for the regression analysis have been met. I have two problems with the "coefficients" table, though:

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  1. As u can see in the table, the insignificant beta value of ZSC in model1 became significant in model2! Is it ok? I'm confused! Which value should i consider to reject/accept the related hypothesis? B of model1 (which rejects the hypothesis) or 2 (which confirms it!!)?

  2. Although the Beta value for ZSC_X_CS is significant, its positive sign is against the hypothesis! it's supposed to have a negative sign according to the literature & also logic! How should i treat this hypothesis? Accept? Reject? Partially accept?!!!

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Cyrus
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  • @whuber: I think this question is asking the opposite of the other one (significance "appearing" instead of "disappearing"), and the context is sufficiently different to warrant a response. – Aniko Jul 20 '12 at 15:55
  • @ whuber: I read the similar post b4 posting this question. The context of my question is different & also mine includes two different questions the 2nd of which has not been asked in that post. Kindly please pay more attention to the context and specs of the question b4 closing it, TQ. – Cyrus Jul 20 '12 at 18:21
  • @Cyrus, although this wasn't the thread cited by, this question is almost an exact, verbatim duplicate of another question you ask [here](http://stats.stackexchange.com/questions/32653/how-to-interpret-inconsistent-beta-values-in-different-steps-of-hierarchical-reg). Please consider editing your question if it does not get a response - that has a much better chance of success than reposting the same thing and hoping for different results :) – Macro Jul 21 '12 at 01:15

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