title: Windowed cross-correlation and peak picking for the analysis of variability in the association between behavioral time series.
abstract: Cross-correlation and most other longitudinal analyses assume that the association between 2 variables is stationary. Thus, a sample of occasions of measurement is expected to be representative of the association between variables regardless of the time of onset or number of occasions in the sample. The authors propose a method to analyze the association between 2 variables when the assumption of stationarity may not be warranted. The method results in estimates of both the strength of peak association and the time lag when the peak association occurred for a range of starting values of elapsed time from the beginning of an experiment.
authors.affiliation: Department of Psychology, University of Notre Dame, Indiana 46556, USA. sboker@nd.edu
authors.forename: Steven M
authors.initials: SM
authors.lastname: Boker
authors.forename: Minquan
authors.initials: M
authors.lastname: Xu
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Rotondo
authors.forename: Kadijah
authors.initials: K
authors.lastname: King
mesh_terms: D001519:Behavior; D006801:Humans; D011584:Psychology / Q000379:methods*