Question on hierarchical nature and data format using lmer
Dear Bernard, The typical format is one row of data per observation. If you have one measurement per student, then you need to have a column per clinical placement (with a TRUE or FALSE value). Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// 2018-06-25 17:09 GMT+02:00 Bernard Liew <B.Liew at bham.ac.uk>:
Dear Community,
Thank you first for the help. My question pertains to a research design as follow:
200 students in total from 4 schools, undergoing different clinical placements in a semester. There are 5 different plausible clinical placements. This means some students have zero placements, others can have a maximum of three, with any placement combinations. Two out of three clinical placements are restricted to some schools. So some clinical placements are nested within schools, others are crossed across schools.
The response variable is an ordinal measure Likert scale of "sharing". The predictors are school and placement.
Qn to be answered: Does different school and clinical placement alter a student's degree of sharing?
Problem 1: data format
The traditional way to format the data is the long "tidy" method. However, because placements are not unique to an individual, how best should one format the data?
Solution 1 ( I think): make the placement variable into a wide format, so instead of one placement predictor, I now have five different placement predictors. This then appears to change the research question? Is there another solution?
Kind regards,
Bernard
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