Thanks for chatting with us! We have attached the data file.
It contains six variables.
LM is the dependent variable (continuous).
Each score on LM is a rating. Each rating was made by a Rater and was made on a Subject. The variable Rid gives the Rater. The variable Sid gives the Subject. Both of these variables are categorical. Each numerical ID refers to a specific person. Some Raters and Subjects appear multiple times.
Some of the participants were both subjects and raters. That is, the same ID number will be in Sid and in Rid.
There are two independent variables: persinf and updates. Each is binary (0/1) and corresponds to experimental conditions the subjects were placed in. We are primarily interested in updates.
To reiterate: persinf and updates both are on the SUBJECT (Sid) level.
Finally, CM is a covariate on the observation level.
In general, each observation (LM) is grouped in two ways: by subject (Sid) and by rater (Rid). We are interested in the effects of subject-level predictors (updtes and persinf) above and beyond the effects of the covariate CM.
The first thing we need is a model which appropriately groups each observation by both rater and subject. We want to look at the effects of updates above and beyond the variance explained by rater and subject grouping and the variance explained by the covariate CM.
Once that is done, we want to add persinf into the model. We are interested in both the main effects explained by updates and persinf as well as the interaction between them.
In addition to the results from the two models, please also provide the syntax for the analysis. If you have any questions, let us know. Thank you!
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