Categorical and Dimensional Definitions and Evaluations of Symptoms of ADHD: History of the SNAP and the SWAN Rating Scales.

An earlier version of this article was originally submitted for publication in early 2000 to introduce a new dimensional of concept of Attention Deficit Hyperactivity Disorder (ADHD) provided by the Strengths and Weaknesses of ADHD-symptoms and Normal-behavior (SWAN) rating scale. The SWAN was developed to correct some obvious deficiencies of the Swanson, Nolan and Pelham (SNAP) rating scale that was based on the categorical concept of ADHD. The first submission was not accepted for publication, so a draft of the article was posted on a website (www.ADHD.net). The SWAN scale was published as a table in a review article (Swanson et al, 2001) to make it available to those interested in this dimensional approach to assessment of ADHD. Despite its relative inaccessibility, the SWAN has been used in several genetic studies of ADHD (e.g., Hay, Bennett, Levy, Sergeant, & Swanson, 2005; Cornish et al, 2005) and has been translated into several languages for European studies of ADHD (e.g., Lubke et al, 2006; Polderman et al, 2010) and into Spanish for studies in the United States (e.g., Lakes, Swanson, & Riggs, 2011; Kudo et al., this issue). Recently, invitations to include the SWAN in the PhenX Toolkit (www.phenx.org) for genomic studies (Hamilton et al, 2011) and to describe thedimensional approach of the SWAN for discussion of diagnostic (Swanson, Wigal, & Lakes, 2009) and ethical (Swanson, Wigal, Lakes, &Volkow, 2011) issues has convinced us that the unpublished article is still relevant after more than a decade, so it is presented here with some minor updates. We use examples (a) to document some consequences (e.g., over-identification of extreme cases) of using statistical cutoffs based on the assumption for a distribution of SNAP ratings that is highly skewed and (b) to show how the SWAN corrects the skewness of the SNAP by rewording the items on the scale and using a wider range of rating alternatives, which corrects the tendency to over-identify extreme cases.