There has been ongoing debate about whether childhood disorders, in general, are better conceptualized dimensionally or as categories. Beauchaine (2003) noted that developmental psychopathologists generally prefer dimensional models for a variety of reasons, including that dimensional systems may better capture developmental processes (as opposed to categories that identify presumptively immutable outcomes) and because categories may minimize the importance of situational influences. It is, however, worth noting that some developmental processes, such as puberty, may be categorical, and situational influences can contribute to taxonic conditions (e.g., cancers resulting from a combination of a genetic diathesis and an environmental carcinogen). In terms of assessment, Achenbach and McConaughy (1997) advocated for classifying childhood disorders quantitatively and Achenbach’s (1991a) Child Behavior Checklist (CBCL) is a frequently used dimensional measure of child psychopathology. Consistent with a dimensional approach, Fergusson and Horwood (1995) found that continuous measures of externalizing behaviors were better predictors of subsequent substance abuse and delinquent behaviors, than were DSM-III-R diagnoses. However, Cantwell (1996) defended the value of categorical approaches both for identifying rare disorders and for their pragmatic value. Ultimately, whether a specific “disorder represents a discrete entity is an empirical question that cannot be settled through methodological convention or philosophical debate” (Beauchaine, 2003, p. 503), or as noted by Sonuga-Barke (1998), “particular categories of disorder should be seen as scientific hypotheses that can be tested using taxometric analysis” (p. 116).

Sonuga-Barke (2005) noted that knowing whether ADHD is a discrete category or a continuous trait is critical to identifying causal models for ADHD. Many experts believe that ADHD is best understood dimensionally (e.g., “the dimensional approach to ADHD seems most consistent with the available evidence” Barkley, 2006, p. 96). Additionally, multiple pathway models of ADHD (e.g., Nigg, Goldsmith, & Sachek, 2004; Sonuga-Barke, 2005) are likely to be more consistent with a dimensional latent structure than with a taxonic latent structure. A number of studies provide indirect support for a dimensional conceptualization. Delays and variability in processing speed are linearly distributed from typical controls, to children with borderline ADHD, to those with ADHD (Kalff et al., 2005). A meta-analysis on genetic and environmental influences on ADHD symptoms stated that “examination of behavioral dimensions as opposed to diagnostic subtype categories may provide a more clear and consistent answer as to potential etiological differences between” symptoms of inattention and hyperactivity (Nikolas & Burke, 2010, p. 2).

Nevertheless, these findings do not address the question of latent structure directly. Although it appears that various indicators of ADHD (e.g., processing speed, inattention, response inhibition) are continuously distributed, dimensional indicators can be symptoms of a taxonic condition (e.g., fever as an indicator of influenza). Furthermore, ADHD may have a taxonic latent structure even if there are varying levels of severity within the taxon (e.g., mild and severe cases of influenza may be caused by the same pathogen). In fact, there appears to be consensus that the question of whether ADHD is categorical or continuous is both important and unresolved (e.g., Levy, McStephen, & Hay, 2001; Nigg et al., 2002; Sonuga-Barke, 1998; Stevenson et al., 2005).

Some studies have used latent class analysis (LCA) to examine the structure of ADHD. The primary aim of these studies has been to clarify the subtypes of ADHD (e.g., is there a pure hyperactive-impulsive subtype?), and only a few of these studies have explicitly addressed the question of whether ADHD or its subtypes are categorical or dimensional. Hudziak et al. (1998) examined the latent structure of ADHD symptoms in a sample of female adolescent twins. They analyzed parent reports of DSM-IV ADHD symptoms and found evidence of three separate dimensions (inattention, hyperactivity-impulsivity, and combined). Each dimension was continuously distributed and there was no evidence to suggest that ADHD or any of these subtypes were categorical. In their LCA of parent interview data, Neuman et al. (1999) found evidence of inattentive and combined subtypes. Both subtypes were continuously distributed in their sample, again suggesting that ADHD is not taxonic. However, Todd et al. (2001) found greater heritability within than across latent class ADHD subtypes, which they took as possible evidence that the various latent classes may be categorical.

Ultimately, LCA is useful for identifying subgroups, but because researchers are left to interpret whether the latent classes are qualitatively or quantitatively distinct, LCA does not directly test whether a construct is taxonic or dimensional. In contrast, Meehl’s (e.g., Ruscio, Haslam, & Ruscio, 2006; Waller & Meehl, 1998) taxometric method appears to be a more accurate method for identifying a construct’s latent structure. There have been two prior taxometric analyses of ADHD. Haslam et al. (2006) examined the latent structure of ADHD in two large epidemiological samples, one of children and one of adolescents. They used two taxometric procedures to analyze indicators drawn from parent reports on the CBCL and the Diagnostic Interview Schedule for Children. A clear majority of the findings were more consistent with a dimensional than a taxonic structure, and overall, this study provided support that ADHD has a dimensional latent structure.

Frazier, Youngstrom, and Naugle (2007) examined the latent structure of ADHD symptoms in a clinical sample of individuals referred for ADHD evaluations, 72% of whom were subsequently diagnosed with ADHD. The taxometric analyses used a range of indicators including parent reports, intelligence and achievement test results, and results from the Connors’ Continuous Performance Task (CPT). Frazier et al.’s analytic strategy involved performing separate taxometric analyses for each domain of symptoms (e.g., one set of analyses for CPT performance and another set for IQ indexes), as well as analyses using a mixture of indicators across domains. Consistent with Haslam et al. (2006), Frazier and colleagues concluded that ADHD has a dimensional structure.

Despite the lack of evidence to fully support qualitatively distinct subgroups of individuals with or without ADHD, the conceptualization and treatment of ADHD as categorical is still held strongly within much current research—particularly in fields, such as genetic and psychiatric research (e.g., Acosta et al., 2008; Althoff et al., 2006). For example, Elia and colleagues (2009) identified six ADHD phenotypes via LCA that they described as homogenous groups that could be used in future genetic research. Similarly, a categorical conceptualization remains the way that ADHD is diagnosed clinically based on the current DSM-IV-TR and the proposal criteria for the DSM-V (American Psychiatric Association, 2010). Thus, based on extant research it may be that categorical views of ADHD have been disproportionately influential on shaping ADHD research and diagnosis and additional research on the latent structure of ADHD is needed to clarify existing ideas and practices.

The present study bridges the gap between the Haslam et al. and Frazier et al. studies by using (a) indicators drawn from a wide range of domains and methods with (b) a general population sample of individuals. We conducted a set of taxometric analyses using parent and teacher ADHD symptom reports and measures of cognitive and executive functions as indicators. These data were collected as part of a multi-site population-based study. In addition, we tested the validity of our taxometric findings by comparing the utility of dimensional versus categorical models of ADHD on their ability to predict features associated with ADHD (for the importance of validating taxometric findings see Waldman & Lilienfeld, 2001; Watson, 2006). More specifically, we examined whether continuous scoring of ADHD symptoms or dichotomous classification systems accounted for more variance in known correlates of ADHD.

The correlations among the primary and secondary indicators of ADHD are provided in Table 1. Because indicator skew can influence the shape of taxometric curves (e.g., Ruscio & Ruscio, 2002), the skew of each indicator is also provided in this table. All of the indicators were positively skewed except for the TOH and WJR, which were the only two variables in which higher scores indicated higher functioning. These two variables were reverse scored in the taxometric analyses.

Taxometric analyses require valid indicators and Meehl (1995) recommended that they should demonstrate at least 1.25 standard deviation units (SDU) of separation between taxon and complement groups. Unfortunately, the SECCYD data set did not include formal diagnoses of ADHD. More importantly, there is no reason to presuppose that the diagnosis of ADHD would be isomorphic with a presumptive taxon; therefore, we used two strategies to estimate the validity of the various indicator sets. First, we presumed a taxon base rate of 7%, which is consistent with the population base rate of ADHD and also consistent with the participants’ scores on the DBQ (see below). Second, the MAMBAC and MAXEIG procedures provide base rate estimates based on the shape of the taxometric curves and these base rate estimates were used to compute indicator validities, by combining the indicators and using this composite to divide the sample according to the base rate. With the exception of a few individual indicators (see below), all of the indicator sets yielded validity coefficients above 1.25 SDUs (Table 2).

Ideally, the indicators for a taxometric analysis are correlated with one another in the full sample, but there is smaller covariance among the indicators within the taxon and complement groups (i.e., low nuisance covariance). Table 2 also reports the average full sample and nuisance correlations for each set of indicators, once again presuming a 7% taxon base rate. The full sample correlations are .30 or greater for all but one of the reported sets of analyses. There was generally little evidence of nuisance covariance.¹

The indicators for the primary taxometric analyses for inattention, hyperactivity/impulsivity, and ADHD used the relevant DBQ items, because they map directly onto the DSM-IV-TR diagnostic criteria. The mother and teacher DBQs were analyzed separately and they were also combined across teacher and mother responses. If the teacher and mother both provided a report, these items were averaged; otherwise, the response provided was used. The rationale for averaging the mother and teacher responses was because children behave differently in different situations so these raters have access to separate types of information and because raters may interpret the same behavior differently (Pelham, Fabiano, Massetti, 2005). Although average ratings are not the same as having independent reports as is required for comprehensive ADHD assessments (Pelham et al., 2005), these average ratings were a way to use information from both the mother and teacher and can be considered a compromise between the “or” and the “and” rule for combining multi-informant data (Gizer et al., 2008). In the interest of succinctness and because there is no rationale for assigning the separate mother or teacher ratings primacy, we provide detailed reports of the results from the average of the mother and teacher responses (and the corresponding graphs), but also provide the results from the separate analyses of the mother and teacher reports in Table 2.

The results of the current study were consistent with the conclusions from the two prior studies that used Meehl’s taxometric method to examine ADHD (Frazier et al., 2007; Haslam et al., 2006). Furthermore, the current study complemented these previous studies in a number of ways. Unlike Haslam et al., which was based entirely on indicators that used a single method, the current set of taxometric analyses included analyses using a variety of raters and methods. Unlike Frazier et al., the current sample was drawn from the general population and was not clinic referred. Taken together, these results provide strong and consistent evidence that ADHD symptoms are best represented using a dimensional latent variable. ⁶

We also conducted secondary analyses to examine the practical consequences of these dimensional findings. Overall, scoring the ADHD measures on a continuum predicted theoretically relevant associated features of ADHD with greater accuracy than did group classification, despite our use of multiple cut-scores. Such findings are consistent with Achenbach’s (1991a) dimensional approach to assessing childhood psychopathology and with Fergusson and Horwood’s (1995) findings that dimensional measures of externalizing behavior are better predictors of subsequent problems than are categorical diagnoses.

Although the present study was not designed to test etiological models of ADHD, our dimensional findings are consistent with the underlying assumptions of most contemporary models of ADHD, such as those proposed by Nigg and colleagues (2004) and Sonuga-Barke (2005), both of which presuppose that ADHD has a dimensional structure. Furthermore, these dimensional findings are consistent with the results of a recent meta-analysis (Nikolas & Burt, 2010), which found that each component of ADHD could be best explained as arising from a multi-locus genetic basis with a mix of additive and non-additive effects, along with nonshared environmental influences. Such a genetic model is likely to be more consistent with a dimensional latent structure than with a taxonic one, because each influence may add to the severity of the disabilities along a continuum from subclinical to meeting diagnostic threshold. Of course these findings do not rule out the possibility that there are sub-groups of ADHD with alternative transmission patterns.