Logistic growth curve analysis in associative learning data

We propose an alternative statistical method, logistic growth curve analysis, for the analysis of associative learning data with two or more comparison groups. Logistic growth curve analysis is more sensitive and easier to interpret than previously published methods such as χ^sup 2^ or ANOVA, which require the data to be collapsed into individual total scores or proportion of responses over time. Additionally, this type of analysis better fits the typical graphical representation of associative learning data. An analysis is presented where associative learning data from honeybees are analyzed using the three techniques, and the accessibility and power of the logistic growth curve analysis is highlighted.[PUBLICATION ABSTRACT]