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I have two datasets that describe reports of caregivers and parents from 8 daycares. The two data sets are matched by daycare's name. We want to merge the data into a single dataset that will contain parents responses (their children go to a specific daycare) and caregivers' responses. At the end of the day, we want to compare the daycares (N = 8). We have 164 parents, and 68 caregivers. We can average responses per daycare and make a N = 8 dataset. It should be noted that caregivers and parents answered different questionnaires. I wonder whether there is a method that weights the number of observations before/during aggregating data and averaging. Any ideas to handle these datasets to be able to compare daycares?

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    $\begingroup$ You need to do a repeated measures MANOVA. $\endgroup$ – gung Nov 6 '14 at 15:56
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You can use some metrics for internal consistency, a measure based on the correlations between different items on the same test. These measurements methods are commonly used for surveys.

A common metric use is Cronbach's alpha and Cohen kappa

See here for information on different methods.

http://pareonline.net/getvn.asp?v=9&n=4

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