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Nanova is a new procedure I developed for analyzing nominal data collected using factorial experimental designs.

Nominal responses are the natural way for people to report actions or opinions. Because nominal responses are not numerical values, they have been under-utilized in behavioral research. On those occasions where nominal responses are elicited, the responses are customarily aggregated over people or trials so that large-sample statistics can be employed. The new analysis directly associates responses with sources in factorial designs. A pair of nominal responses either matches or does not; when responses do not match, they vary. That analog to variance is incorporated in the Nominal Analysis of “Variance” (Nanova) procedure, wherein the proportion of non-matches associated with a source plays the same role as sum of squares does in analysis of variance. Because there are no distributional assumptions associated with nominal data, the significance levels of the N-ratios formed by comparing proportions are determined by resampling. The Nanova table has the same structure as an analysis of variance table.

The NANOVA computer program I wrote to carry out this analysis is available for free download. The program employs the same user-friendly interface as the CALSTAT suite that accompanies my Analysis of Variance (without quotes) textbook. Screen shots of the NANOVA program are shown here.

A paper that describes the theory underlying Nanova, and that presents illustrative applications, is available here. The paper has been submitted for publication in a journal. Examples of Nanova analyses, including some not included in the paper, are shown here.

The development of NANOVA was was supported by the United States Department of Homeland Security through the Center for Risk and Economic Analysis of Terrorism Events (CREATE) under grant number 2007-ST-061-000001. However, the program is the responsibility of the author and does not necessarily reflect views of the United States Department of Homeland Security.