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ERIC Number: EJ833388
Record Type: Journal
Publication Date: 2009-May
Pages: 18
Abstractor: As Provided
ISSN: ISSN-0023-9690
Mixed Effects Modeling of Morris Water Maze Data: Advantages and Cautionary Notes
Young, Michael E.; Clark, M. H.; Goffus, Andrea; Hoane, Michael R.
Learning and Motivation, v40 n2 p160-177 May 2009
Morris water maze data are most commonly analyzed using repeated measures analysis of variance in which daily test sessions are analyzed as an unordered categorical variable. This approach, however, may lack power, relies heavily on post hoc tests of daily performance that can complicate interpretation, and does not target the nonlinear trends evidenced in learning data. The present project used Monte Carlo simulation to compare the relative strengths of the traditional approach with both linear and nonlinear mixed effects modeling that identifies the learning function for each animal and condition. Both trend-based mixed effects modeling approaches showed much greater sensitivity to identifying real effects, and the nonlinear approach provided uniformly better fits of learning trends. The common practice of removing a rat from the maze after 90s, however, proved more problematic for the nonlinear approach and produced an underestimate of y-axis intercepts. (Contains 7 figures and 1 table.)
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A