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I am want to assess whether successful implementation of newly developed policies are dependent on involvement of all relevant stakeholders and middle managers.

I have a 5-point Likert scale with ten questions (items). I collected data on 85 respondents. I assigned values ('Strongly Agree'=1, 'Agree'=2, 'Undecided'=3, 'Disagree'=4, 'Strongly Disagree'=5). I have calculated percents of valid responses, and mean and standard deviation for each item. I have summed up total agree, total disagree and total undecided (neither agree or disagree). I have put these infos into graphs.

What do I do next?

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In order to help you, we would need to know how the first (which shows what hypothesis you are interested in) and second (which shows what tool you used to collect data) paragraph are connected. Do you perhaps have an external variable that says whether respondents belong to stakeholders or middle managers class, or do all items target the same kind of respondents (unrelated to the above distinction)? Does it make sense to use all items (i.e., do they refer to the same unidimensional construct)? etc. – chl May 1 '12 at 10:27
This is probably too much for the original poster, but others might find this interesting: – Jessica May 1 '12 at 12:31

If you designed your questionnaire correctly you have form hypotheses between relations of constructs. For example I think you have implementation success as an Y variable dependent on manager involvement. Now, you most probably have questions in your questionnaire which measure those variables somehow.

When you have the results of your questions you can make a scale (or index for that matter) to measure your construct. You can do this by using a few methods (be sure to invert negative questions in terms of scale)

When for example you measure implementation success with 5 questions. You may take the mean of those scores (again, rescale negative questions!) assuming they have equal weights for determination of the construct. Also, you could just give them weights by guesstimates.

What's more, you could extract factor scores to have a less arbitrary weighting. All of these methods have their (dis)advantages over the others.

Checking whether questions measure the same thing can be done by using reliability (Cronbach's) alpha. But be sure to know what it does.And also be sure to use other metrics, measures, tests and a healthy dose of face validity and common sense.

Finally you investigate the relationships between your constructs (measured by your developed scales) with for example correlations or regressions.

I hope this helps, good luck!

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Hi C. Pieters. Thanks. wld try out ur suggestion. Never used Cronbach's alpha but wld gv a go. Thanks again – Lolus May 1 '12 at 22:39

Adding to C. Pieters' answer: Along with or instead of Cronbach's alpha, you can use factor analysis to validate a group of questions. If a single factor explains almost all of the variation in the group of questions, you can be more confident that the group of questions measures the same thing. I would recommend factor analysis because it is more easily interpreted.

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Exactly, the weights become less arbitrary and you can look at the factor loadings which question explains a lot of variation. – C. Pieters May 2 '12 at 11:30

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