Premium
An examination of methods used to generate daily group scores from single‐item‐per‐subject data collected in intensive time‐series designs
Author(s) -
Monk John S.
Publication year - 1984
Publication title -
journal of research in science teaching
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.067
H-Index - 131
eISSN - 1098-2736
pISSN - 0022-4308
DOI - 10.1002/tea.3660210308
Subject(s) - pooling , rasch model , logistic regression , statistics , raw score , item response theory , psychology , computer science , mathematics , artificial intelligence , raw data , psychometrics
This study examines three methods which can be used to pool single‐item‐per‐subject data collected in intensive time‐series studies, to determine if method of pooling has an effect on subsequent data analysis. The methods examined were based on simple averaging, difficulty weightings of averages, and the application of the Rasch logistic model. Analyses were conducted which examined regression results obtained when the pooled scores of groups of students were regressed by day. Results indicate that the three methods of pooling do not significantly alter subsequent analysis, though the case is made that a pooling procedure based on the Rasch logistic model is the most heuristically sound.