Taxometric and Factor Analytic Models of Anxiety Sensitivity Among Youth: Exploring the Latent Structure of Anxiety Psychopathology Vulnerability

Behavior Therapy 38 (2007) 269 – 283

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Taxometric and Factor Analytic Models of Anxiety Sensitivity Among Youth: Exploring the Latent Structure of Anxiety Psychopathology Vulnerability Amit Bernstein, Michael J. Zvolensky, University of Vermont Sherry Stewart, Nancy Comeau, Dalhousie University

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), a wellestablished affect-sensitivity individual difference factor, among youth by employing taxometric and factor analytic approaches in an integrative manner. Taxometric analyses indicated that AS, as indexed by the Child Anxiety Sensitivity Index (CASI; Silverman, Flesig, Rabian, & Peterson, 1991), demonstrates taxonic latent class structure in a large sample of youth from North America (N = 4,462; Mage = 15.6 years; SD = 1.3). Subsequent confirmatory factor analyses indicated that the latent continuous, multidimensional, 4-factor model of AS among youth (Silverman, Goedhart, Barrett, & Turner, 2003) provided good fit for the CASI data among the complement class (“normative form” of AS), but not among the taxon class (“high-risk form” of AS). EFAs supported the prediction that the AS taxon demonstrates a unique, heretofore unexplored latent continuous, unidimensional factor structure among youth. Findings are discussed in relation to refining our understanding of the

This paper was supported by National Institute on Drug Abuse research grants (1 R01 DA018734-01A1, R03 DA16307-01, and 1 R21 DA016227-01) awarded to Dr. Zvolensky. This work also was supported by a National Research Service Award (F31 MH07320501) awarded to Amit Bernstein. Address correspondence to either Amit Bernstein, Ph.D., or Michael J. Zvolensky, Ph.D. Dr. Zvolensky can be contacted at The University of Vermont, Department of Psychology, 2 Colchester Avenue, John Dewey Hall, Burlington, VT 05405-0134, USA; email: [email protected]. Dr. Bernstein can be contacted at The University of Vermont, Department of Psychology, 2 Colchester Avenue, John Dewey Hall, Burlington, VT 05405-0134, USA; e-mail: [email protected]. 0005-7894/07/0269–0283$1.00/0 © 2007 Association for Behavioral and Cognitive Therapies. Published by Elsevier Ltd. All rights reserved.

latent structure of AS and the clinical implications that arise from it.

A N X I E T Y S E N S I T I V I T Y ( A S ) I S one of the most well-established cognitive risk factors for panic and related anxiety disorders (McNally, 2002). AS reflects the fear of arousal-related bodily sensations (Reiss & McNally, 1985). Various lines of research indicate that AS increases the risk for anxiety symptoms, panic attacks, and certain anxiety disorders (Hayward, Killen, Kraemer, & Taylor, 2000; Maller & Reiss, 1992; Schmidt, Lerew, & Jackson, 1999). Other work indicates that AS is related to maladaptive forms of avoidant coping (Zvolensky & Forsyth, 2002) and emotional processing (Stewart, Conrod, Gignac, & Pihl, 1998). To the extent that affect sensitivity and tolerance may be related emotional regulatory processes, others also have conceptualized AS as a cognitive-based index of emotional tolerance for anxiety states (Brown, Lejuez, Kahler, & Strong, 2002). Moreover, emerging work alludes to the potential theoretical importance of explicitly testing and developing AS theory in relation to specific affect regulatory processes (Zvolensky et al., 2004). With the established importance of AS, a concerted effort has been made to better understand the latent structure of the construct. The basic premise to this work is that by matching the measurement model of AS to the construct’s latent structure, theoretical models can be refined and research as well clinical strategies advanced (Ruscio & Ruscio, 2002). There have thus far been two principal approaches to the study of the latent structure of AS. The first approach has involved exploratory factor

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analytic (EFA) and confirmatory factor analytic (CFA) research (Silverman, Goedhart, Barrett, & Turner, 2003). Such work focuses on understanding latent dimensional structure, and specifically, whether it is a unitary or multidimensional construct. Among adults, AS, indexed by both the 16item Anxiety Sensitivity Index (ASI; Reiss, Peterson, Gursky, & McNally, 1986) and Anxiety Sensitivity Index–Revised (ASI-R; Taylor & Cox, 1998), is multidimensional and hierarchical in nature, comprised of a higher-order factor with a number of specific lower-order factors (Zinbarg, Mohlman, & Hong, 1999). Other studies focused on youth have produced conceptually similar results using the Childhood Anxiety Sensitivity Index (CASI; Silverman, Fleisig, Rabian, & Peterson, 1991), with threeand four-factor lower order solutions (Silverman et al., 2003; van Widenfelt, Siebelink, Goedhart, & Treffers, 2002). A second approach to study the latent structure of AS has focused on whether this construct is continuous or discontinuous (i.e., taxonic) in nature. It is important to understand empirically whether AS is latently dimensional or taxonic because of the considerable theoretical and clinical implications of such knowledge (Beauchaine, 2003). Clinically, for example, failure to match the measurement model of AS to its latent structure limits the development of assessment tools capable of premorbidly identifying individuals at “high risk” for anxiety psychopathology via the empirical delineation of nonarbitrary cutoffs of emotional intolerance. Such advances in measurement are key to the development and efficacious delivery of vulnerability-targeted therapeutic interventions (e.g., preventive-interventions strategies) to individuals varying qualitatively as a function of this specific emotional intolerance vulnerability trait. It is natural to ask why one would expect AS to vary as a function of a latent class. First, foremost in the theoretically based hypothesis of AS taxonicity is the important consideration that AS, at its core, is an adaptive factor and process (Barlow, 2002). A sensitivity to anxiety-related cues may serve to alert the organism to threatening internal or external cues. It is theoretically unclear how an adaptive process, normally distributed in the population, could therefore confer vulnerability for anxiety psychopathology such as panic. It is, however, theoretically possible that certain aversive emotional learning histories or gene-environmental interactions may qualitatively change the adaptive nature of AS to become maladaptive and thereby facilitate the developmental bifurcation of AS into unique classes and taxonic trajectories. Indeed, basic research on associative learning with anxiety suggests that once

such maladaptive emotional associative learning occurs, it is often highly resistant to extinction (Bouton, 2004). This lawful resistance to extinction may thereby facilitate and maintain the taxonic split between adaptive and maladaptive latent forms of AS. Based on predictions derived from this type of theoretical perspective, researchers have applied taxometric methods to examine the continuous/ discontinuous nature of AS. Taxometrics is a term used to denote a family of statistical procedures that can be used to investigate the latent class structure of constructs by evaluating whether or not they are taxa (i.e., discontinuous latent class variables; Meehl, 1999; Schmidt, Kotov, & Joiner, 2004). Using the ASI among adults, studies suggest AS is taxonic (i.e., dichotomous latent class structure) rather than dimensional (i.e., single latent continuous structure; Bernstein, Zvolensky, Feldner, Lewis, & Leen-Feldner, 2005; Schmidt, Kotov, Lerew, Joiner, & Ialongo, 2005; Zvolensky, Forsyth, Bernstein, & Leen-Feldner, 2007). Other work using the ASI-R (Taylor & Cox, 1998) among adults (Bernstein, Zvolensky, Kotov et al., 2006) and CASI among youth (Bernstein, Zvolensky, Stewart, Comeau, & Leen-Feldner, 2006; Bernstein, Zvolensky, Weems, Stickle, & Leen-Feldner, 2005) similarly indicates AS is taxonic. Moreover, taxonic indices of AS have demonstrated convergent, discriminant, predictive, as well as incremental validity in the prediction of panic (Bernstein, LeenFeldner, et al., 2006; Schmidt et al., 2005; Zvolensky et al., 2007) as well as posttraumatic stress symptoms (Bernstein, Zvolensky, Feldner, Lewis, Fauber et al., 2005; Bernstein, Zvolensky, et al., 2005). These data collectively suggest a systematically important AS taxon related to anxiety and a number of its disorders. Although important insights by factor analytic and taxometric research are refining our understanding of the latent structure of AS, as discussed by Waller and Meehl (1998), the existence of a taxon does not necessarily imply the absence of latent within-class quantitative gradations (see also Pickles & Angold, 2003). Thus, although it may be tempting to impose a nonarbitrary qualitative difference on within-class quantitative variability when taxonicity is detected, this exclusively categorical approach may fail to recognize systematically meaningful within-class variability within a complete latent structural model of AS. Indeed, there may be systematically important distinctions both between and within AS taxa (Schmidt et al., 2005; Waller & Meehl, 1998). The idea of taxonicity is sometimes simpler to grasp than that of within-class continuity among taxa. However, the concept is a

anxiety sensitivity and latent structure simple, logical one. To illustrate the concept of within-class dimensionality among taxa, it may be useful to consider what this type of within-class dimensionality would mean for AS in the most basic terms. For instance, within-class dimensionality within the AS taxon class would suggest that different levels across the distribution of AS within the taxon class (i.e., within the population) would be related to differential levels of risk for theoretically related anxiety vulnerability outcomes. In other words, in addition to qualitative variability in level of risk-associated differences between taxonic populations, individuals belonging to a single class or population (e.g., AS taxon) would demonstrate quantitative or continuous differences between one another, within that population. Thus, to develop a more complete latent structural model of AS that recognizes both discontinuous between-class and continuous within-class forms of variability, it is necessary to use taxometric and factor analytic methods in an integrative fashion. This integrative and novel approach is essential because factor analysis treats AS as a latent continuous variable exclusively and taxometrics treats AS as a latent class variable exclusively—so neither approach alone is capable of fully elucidating the latent structure of AS. In other words, factor analysis is limited to testing the latent continuous structure of a dimensional construct and so is not capable of testing the taxonicity of that construct, and taxometrics is limited to testing latent discontinuity of a construct and so is not capable of concurrently evaluating its within-class structure should taxonicity be observed. There are at least three reasons to theorize that the AS taxon and complement class may be expected to demonstrate distinct factor structures. First, as the base rate of the complement class is significantly larger than the taxon class (approximately 6:1 base rate ratio of complement class to taxon class; Bernstein, Zvolensky, Kotov, et al., 2006; Schmidt et al., 2005), the observed factor structure of AS may, by statistical artifact, reflect the much larger complement class when the latent classes are mixed. That is, there may be a disproportionate weighting of the complement relative to the taxon class. Thus, it may be the case that after separating the latent AS classes from one another, the well-replicated multidimensional factor structure may be indicative only of the larger base rate class (i.e., complement class or normative form of AS) but not necessarily the smaller base rate class (i.e., the taxon or high-risk form of AS). Second, if the AS taxon and complement classes are systematically meaningful in terms of their differential nomological networks (Bernstein, LeenFeldner, et al., 2006; Bernstein, Zvolensky, Feldner, Lewis, Fauber, et al., 2005; Zvolensky et al., 2007),

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then, it is not highly plausible that these discrete forms of AS will demonstrate the same underlying structural forms and therefore factor structures. Indeed, if both classes demonstrated the same factor solution as one another and as observed in previous mixed-class samples (i.e., hierarchical, multidimensional), then, by definition, each class would likely have the identical mean vector and covariance matrix (i.e., population homogeneity). Yet, if this was the case, taxometric analyses would not likely detect latent taxonic AS classes (i.e., population heterogeneity) in the first place. In other words, for a similar reason that taxometric procedures detect qualitative discrete latent classes, it is reasonable to hypothesize that each taxonic form of AS may demonstrate a unique, within-class dimensional structure. Finally, for a similar conceptual reason that taxometric analyses may be theoretically expected to demonstrate taxonic structure, the AS taxon and complement classes could be expected to demonstrate distinct factor structures. Adverse learning experiences and biological predispositions play formative roles in shaping individual susceptibility to anxiety problems (Barlow, 2002; Bouton, Mineka, & Barlow, 2001). Here, maladaptive associative learning experiences may be an important factor in contributing to differing levels of AS (Reiss & Havercamp, 1996; Stewart et al., 2001). Such experiences could thereby be related to the putative bifurcation of qualitatively distinct developmental trajectories with respect to AS and anxiety psychopathology vulnerability (Beauchaine, 2003). To the extent that negative life experiences, and perhaps gene-environmental interactions (Schmidt, Lerew, & Joiner, 2000), shape “clinically significant” levels of AS, it is possible that such sensitivity will be a distinguishing cognitive-based feature associated with the pathogenesis and maintenance of a taxonic, anxiety vulnerability-conferring, developmental trajectory. Thus, it is possible that for AS, an adaptive cognitive process at its etiological core, to become maladaptive and thereby confer risk for anxiety psychopathology, it needs change qualitatively or categorically over time. If this type of perspective were accurate, the AS complement and taxon class members could be expected to show different latent natures, including factor structures on instruments, such as the CASI, designed to tap individual differences in sensitivity to anxiety-related sensations. Together, the overarching aim of the present investigation was to employ taxometric and factor analytic approaches in an integrative fashion to study the latent structure of AS among youth. It was predicted that: (a) taxometric analyses would replicate existing findings that AS is taxonic among a large sample of youth; (b) CFA would

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demonstrate that the replicated preexisting hierarchical four-factor model of AS (Silverman et al., 2003) would fit the CASI data for the complement class (normative form of AS); and (c) this same factor-solution would not fit the CASI data for the taxon class (high-risk form of AS). Rather, we expected (d) that an EFA would identify a distinct factor structure for the taxon class. As the latter hypothesis is novel to the AS literature, no specific theory was available to guide hypotheses regarding the precise nature of the AS factor structure among the taxon class.

Method participants Participants were 4,484 adolescents (2,189 female; Mage = 15.6 years, SD = 1.3) from six secondary (junior and senior high) public schools in the Annapolis Valley School Board in the province of Nova Scotia, Canada1 . Data were collected over a 4-year period between 1999 to 2002 (see Procedure section for details). The average level of education at the time of testing was grade 9.7 (range = grades 7 to 12). Participants were primarily Caucasian; all participants spoke English fluently. measures Childhood anxiety sensitivity index (CASI; Silverman et al., 1991). The 18-item CASI, a modification of the adult 16-item ASI (Reiss et al., 1986), is designed to index a propensity to experience fear in response to one’s bodily sensations. Youth rate each item by selecting one of three choices: none, some, or a lot. The CASI has been shown to have satisfactory reliability (FullanaRivas, Servera, Weems, Tortella-Feliu, & Caseras, 2003) and validity estimates (Ginsburg & Drake, 2002; Lau, Calamari, & Waraczynski, 1996). procedure Study information was distributed to parents/ guardians of students in grades 7 to 12 in participating schools between the years of 1999 to 2002. Parental authorization was provided through a “negative consent” procedure, i.e., parents were asked to contact the researcher or the school guidance counselor for any further information about the study and were asked to let the researcher/ guidance counselor know if they did not consent to having their child participate. Parents/guardians 1 These data have been examined in previous taxometric analyses (Bernstein, Zvolensky, Stewart, Comeau, & Leen-Feldner, 2006). However, in those taxometric analyses, differences in the latent structure of AS were tested among gender groups separately, and factor analyses were not conducted in this previous investigation.

were provided with a toll-free number to contact the researcher. There were no parents/guardians who declined consent for their children to participate. In total, there were four separate data collections (Comeau, Stewart, & Loba, 2001; Conrod, Stewart, Comeau, & MacLean, 2006; Stewart, Comeau, Loba, & Theakston, 2002; Stewart, Comeau, Watt, et al., 2002). Prior to testing, students were informed about the nature of the study, and willing students provided written informed consent at the time of the survey. Across all data collections, the general questionnaire completion period was conducted in an identical fashion. Specifically, student consent forms indicated that the student’s participation was voluntary and that the youth’s confidentiality would be maintained. Data collection was conducted on a class-by-class basis. During questionnaire completion, students were permitted to ask questions of the researchers.

analytic approach: taxometric analyses Indicator selection. Theoretically and quantitatively based CASI items were identified as candidate manifest indicators of AS. Items 3, 6, 9, 11 represent disease concerns, items 4, 8, 10 represent unsteady concerns, items 2, 12, 15 represent mental illness concerns factors, and items 1, 5, 17 represent publicly observable anxiety reactions or social concerns. These indicators are based on the item composition of a recent factor structure of the CASI (Silverman et al., 2003). Prior to the final selection and retention of the described CASI item indicators in the subsequent taxometric analyses, the validity of each of the candidate indicators to discriminate between the putative latent classes was evaluated. Only candidate indicators (i.e., CASI items) that demonstrated an effect size of at least 1.00 SD in preliminary MAXEIG screening analyses conducted to estimate validity were retained (Meehl & Golden, 1982). MAXEIG-HITMAX. First, a short-scale MAXEIG procedure was conducted using the single-item manifest indicators that demonstrated adequate levels of validity (Ruscio, 2004). MAXEIG, which stands for maximum eigenvalue analyses, were conducted in line with recent recommended guidelines (Ruscio & Ruscio, 2004). MAXEIG derives the maximum eigenvalues of the covariance matrix of a set of output indicators at varying levels of an input indicator. Because each single-item manifest indicator had a limited scale length, a short-scale analytic approach was used to generate composite input indicators that were of a sufficient length to facilitate MAXEIG. In so doing, on a rotating basis, each pair of single-item indicators served as output indicators and all remaining variables were summed to form a composite input

anxiety sensitivity and latent structure variable. Furthermore, MAXEIG divides the composite input variables with overlapping intervals. Overlapping windows (i.e., conventional 90% overlap) enable investigators to detect low base rate taxa such as expected in the present youth samples. MAXEIG also affords a powerful and unique internal consistency test. Consistency tests, which characterize the taxometric approach, are intended to rule out the detection of a pseudotaxon by requiring convergence of data on a common conclusion and specific values that are unlikely to result in the absence of a taxon. This “inchworm” test is achieved by repeatedly conducting the MAXEIG analysis while systematically increasing the number of overlapping windows, thereby systematically decreasing the size of the subsamples used to calculate maximum eigenvalues in each overlapping window. Plots of taxonic data yield an increasingly better defined unimodal peak, as the number of windows is increased and the size of each window is decreased. In the instance of dimensional data, in contrast, the systematic increase of the number of overlapping windows will produce flat or cusped plots that do not systematically peak (Waller & Meehl, 1998). We followed guidelines provided by Waller and Meehl to interpret the plots. In addition, we conducted the MAXEIG analyses with internal replications to divide cases into overlapping windows by repeatedly resorting cases along the composite input indicators at random. In the present investigation, we conducted the “inchworm” test by generating 5 MAXEIG plots, from 100 windows to 500 windows, in 100-window increments. The specific number of windows were chosen a priori to achieve the assignment of between 50 to 100 cases per window to (a) limit sampling error; and (b) ensure the reliability of the results while simultaneously ensuring that cases (i.e., complement and taxon class members) are not compressed in the last intervals of the input indicators to artifactually prevent the detection of what was expected to be a relatively low base rate taxon. After taxonicity was tested, the General Covariance Mixture Theorem (Waller & Meehl, 1998) was used to estimate the taxon base rate for each MAXEIG plot. If the MAXEIG analyses indicated a taxonic latent structure, then individual participants were assigned to the taxon and complement classes using the grand base rate. Next, the validity of the manifest indicators to discriminate between taxon and complement class members was estimated based on how well the indicators discriminated between the latent classes (i.e., effect size difference between classes for each mean indicator score). Lastly, the level of “nuisance correlation” (i.e., intra-class covariance within the complement and taxon classes) was used to gauge the

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adequacy of the estimated parameters (Schmidt et al., 2004). MAMBAC. A “nose-count” of MAMBAC plots (Ruscio, 2004) was used as an external consistency test of the MAXEIG procedure. MAMBAC stands for mean above minus below a cut; in other words, MAMBAC d (i.e., difference) plots are constructed by plotting the difference between the mean of each output indicator above and below each cut score evenly spaced along each input indicator. MAMBAC d plots were generated, one for each possible combination of indicators until all combinations are exhausted. The MAMBAC procedure was conservatively used as an external consistency test of the latent structure of AS exclusively and was not used to derive parameter estimates. Parameter-matched Monte Carlo simulations. Following recently recommended guidelines for taxometric analyses (Ruscio, 2004), parameter-matched Monte Carlo simulated dimensional and taxonic data were derived. Simulated data were reproduced via the bootstrap and thereby matched to the number of indicators, sample size, the observed indicator correlation matrix, and the distributions of all indicators including their skew, kurtosis, as well as number of levels. Ten separate sets of simulated dimensional and taxonic data were derived to bolster the reliability and precision of the simulated data and the taxometric analyses conducted with these simulated data (Ruscio, 2004).

analytic approach: cfa and efa If taxometric analyses indicated taxonic latent structure, then CFA analyses of the ASI items were conducted. CFA analyses would test whether the hierarchical four-factor model of AS (Silverman et al., 2003) fit the data for complement and taxon classes separately. Although no definitive criteria for evaluating fit indices have been established conclusively, current conventional guidelines provide a conservative set of criteria that a fit is good if (a) Goodness-offit index (GFI) is .95 or greater, (b) the Bentler-Bonett normed fit index (NFI) and the Tucker-Lewis index (TLI) are .80 or greater, (c) Root Mean Square Error of Approximation (RMSEA) is less than .06, and (d) Standardized Root Mean Square Residual (SRMR) is less than .08 (e.g., Hu & Bentler, 1999). If, in contrast and as predicted, the four-factor model did not fit the CASI data among the taxon class, then, EFA would be employed.

Results taxometric analyses Indicator validity screening. Prior to the final selection and retention of the described CASI item

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indicators in the subsequent taxometric analyses, the validity of each of the candidate indicators to discriminate between the putative latent classes was evaluated. All three social concerns or fears of publicly observable anxiety reaction item-indicators (items 1, 5, 17) failed to sufficiently discriminate between the putative latent AS classes and were thereby omitted from subsequent taxometric analyses (Bernstein, Zvolensky, Weems, et al., 2005; Zvolensky et al., 2007). Subsequently, taxometric analyses were conducted with the remaining 10 valid single-item indicators. Suitability testing. Prior to the interpretation of taxometric analyses of the research data, suitability of the data for MAXEIG and MAMBAC analyses was tested using the parameter-matched simulated dimensional and taxonic data (see Figures 1 and 2, Simulated Plots). First, the MAXEIG inchworm consistency test of the simulated dimensional data failed to yield unimodally peaked plots as the number of overlapping windows was increased but instead yielded cusped plots, whereas the taxonic data yielded unimodally peaked plots resembling the head of an inchworm (Waller & Meehl, 1998). Second, MAMBAC plots of the simulated data were not as conspicuously distinguishable as the MAXEIG plots of the simulated data; nevertheless, MAMBAC plots of the simulated dimensional data yielded cusped plots and the taxonic data demonstrated unimodally peaked plots and no evidence of cusps (Meehl & Yonce, 1994). Because MAXEIG and MAMBAC analyses of the parameter-matched simulated data each passed suitability tests, MAXEIG and MAMBAC analyses of the research data are capable of distinguishing between dimensional and taxonic latent structures of AS. AS taxonicity and parameters. Table 1 provides a summary of the taxometric analyses according to number of plots observed, variability, and interrater reliability. MAXEIG. Consistent with prediction and evidentiary of latent taxonicity of AS, cusped plots became peaked as the number of overlapping windows was increased (see Figure 1; Ruscio & Ruscio, 2004; Waller & Meehl, 1998). Thus, the short-scale MAXEIG analysis and the “inchworm” consistency test of the research data support the taxonic conjecture as a majority of plots were characteristic of latent taxonicity (Schmidt et al., 2004; Waller & Meehl, 1998). In addition, the MAXEIG plots of the research data were dissimilar to the matched simulated dimensional data, and moreover, the majority were unimodally peaked and so were more similar to the parameter-matched simulated taxonic data (see Figure 1).

The base rate estimates were similar across the 45 MAXEIG plots with a mean base rate estimate of .09 (SD = .05). The intra-class nuisance correlations (i.e., within-class indicator correlations) were well within tolerable limits (Waller & Meehl, 1998). Thus, the estimated parameters of the latent distributions likely are reliable approximations. All 10 indicators discriminated between latent classes and were thus valid indicators for taxometric analyses, as observed in the MAXEIG analysis (range of indicator validity was 1.3 SDs to 2.0 SDs; average indicator validity was 1.7 SD). Class assignment for the purpose of the planned factor analyses was based on the MAXEIG analysis. MAMBAC. MAMBAC analyses of the 10 singleitem indicators, used as an external consistency test, produced a total of 90 d plots, the large majority of which were peaked, consistent with a low base rate taxon (see Figure 2; Meehl & Yonce, 1994; Schmidt et al., 2004). Furthermore, the MAMBAC plots of the research data were dissimilar to the matched simulated dimensional data, and moreover, the majority of plots unimodally peaked and so were more similar to the parameter-matched simulated taxonic data (e.g., see Figure 2). MAMBAC “nose count” results were therefore consistent with the MAXEIG results. Thus, convergence of evidence from MAXEIG and MAMBAC analyses of the research and Monte Carlo simulated data strongly supports the taxonic conjecture. MAXEIG & MAMBAC plot interrater reliability. First, two investigators trained in taxometric methodology rated the MAXEIG and MAMBAC plots as nontaxonic, taxonic, or ambiguous. Any (although there were very few) disagreements regarding ratings were discussed and a rating was assigned by consensus between the raters. Although this is likely sufficient to guard against biased plot ratings, we also asked an independent rater who is blind to the investigation and who is familiar with taxometrics to rate MAXEIG and MAMBAC plots using the same criteria applied by the investigators. On 94% of the MAXEIG and MAMBAC plots, we agreed about whether the plot reflected latent taxonicity or not. That is, discrepant ratings of plots between the investigators and the blind rater were observed on only 6% of plots. This level of interrater agreement is more than sufficient to provide confidence in the reported plot ratings (Waller & Meehl, 1998).

factor analyses of taxonic forms of as CFAs: complement class (n = 4099). Suggesting good estimation of all four variables consistent with

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FIGURE 1

Random selection of Short-Scale MAXEIG-HITMAX plots of AS manifest single-item indicators among youth: Matched research, simulated taxonic and simulated dimensional data.

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FIGURE 2

Random selection of MAMBAC plots of AS manifest single-item indicators among youth: Matched research, simulated taxonic and simulated dimensional data.

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anxiety sensitivity and latent structure Table 1 Summary of taxometric analyses MAXEIG-HITMAX Taxonic plots Non-taxonic or Ambiguous Plots Mean P SD Inter-Indicator r Within-Complement r Within-Taxon r Mean Ind. Validity MAMBAC Taxonic plots Non-taxonic or cusped plots

30 15 .09 .05 .29 .14 .07 1.7 53 37

Note. Mean P = grand mean taxon base rate; SD = standard deviation of taxon base rate estimates; Mean Ind. Validity = estimated effect size of indicators in discriminating between latent classes. Inter-Indicator r = mean correlation between three indicators in full (mixed-class) sample; Within-Complement r = intracomplement between variable “nuisance” correlation; WithinTaxon r = intra-taxon between variable “nuisance” correlation. Inter-rater reliability = % agreement on MAXEIG and MAMBAC plots between investigators plot ratings and a blind independent rater.

good simple structure and overall good model fit among the complement class, all indicator variables (i.e., CASI items) were strongly associated (i.e., >.40) with their respective latent first-order factors and all first-order factors demonstrated significant loadings on the second-order global AS factor (Disease Concerns = .88, Unsteady Concerns = .88, Mental Illness Concerns = .26, and Social Concerns = .52). Indices of fit indicated good to excellent overall model fit, GFI = .965, Standardized RMR = .05, and RMSEA = .058 (90% CI = .055 to .062), NFI = .832, and TLI = .80. It is important to note that we did not refine the model based on preexisting factor analytic findings that identified poor fitting items (Silverman et al., 2003), observed poor fitting items, or modification indices. CFAs: taxon class (n = 385). Inconsistent with good simple structure and overall good model fit among the taxon class, 8 of the 13 variables demonstrated poor associations (<.40) with their respective latent first-order factors, indicating poor overall variable estimation. Specifically, all four items (i.e., items 3, 6, 9, 11) demonstrated poor associations with Factor I (disease concerns), two items (i.e., items 8, 10) demonstrated poor associations with Factor II (unsteady concerns), one item (i.e., item 12) demonstrated a poor association with Factor III (mental illness concerns), and one item (i.e. item 5) demonstrated a poor association with Factor IV (social concerns or fears of publicly observable anxiety reactions). Furthermore, all indices of fit indicated poor inadequate fit among the taxon class, GFI = .92, Standardized RMR = .08, and RMSEA = .082 (90% CI = .071 to .094),

NFI = .535, TLI = .486. Given that the hypothesized model demonstrated poor fit among the taxon class, in contrast to good fit among the complement class, tests of multigroup invariance between taxometrically indicated AS classes were inappropriate (Byrne, 2001). EFA: taxon class. Because the CFA of the fourfactor model of AS (Silverman et al., 2003) indicated acceptable fit with the CASI data for the taxometrically defined complement class but poor inadequate fit for the taxon class, an EFA was conducted to identify the possible latent factor structure of the AS taxon. One factor was extracted from the unrotated solution using the Kaiser rule (i.e., eigenvalue > 1.0; Kaiser, 1961). All other factors demonstrated eigenvalues < 1.0. As a rigorous consistency test for the purpose of factor extraction, parallel analysis was conducted. Parallel analysis also indicated a one-factor solution. Specifically, the one factor among the research data with an eigenvalue > 1.0 was greater than the 95th percentile random data eigenvalue (I 1.28 > II .86), indicating that the one factor extracted from the research data represented a substantive, nonspurious factor. Finally, the one-factor solution was interpretable within AS theory (Byrne, 2001; Reiss & McNally, 1985; see Discussion section for further explication of this matter). Table 2 Loadings and extraction communalities for the unidimensional factor solution of the anxiety sensitivity taxon among youth as indexed by the childhood anxiety sensitivity index CASI items

Loadings H2

Item 15: When I am afraid, I worry that I might be .60 crazy. Item 2: When I cannot keep my mind on school .52 work I worry that I might be going crazy. Item 17: I don’t like to let my feelings show. .36 Item 12: It scares me when I can’t keep my mind .36 on schoolwork. Item 1: I don’t want other people to know when I .36 feel afraid. Item 11: When my stomach hurts, I worry that I .34 might be really sick. Item 3: It scares me when I feel “shaky.” .32 Item 9: When I notice that my heart is beating fast, .16 I worry that there might be something wrong with me. Item 5: It is important to me to stay in control of my .10 feelings. Item 6: It scares me when I feel my heart beats .06 fast. Item 8: It scares me when I feel like I am going to .06 throw up. Item 4: It scares me when I feel like I am going to .06 faint. Item 10: It scares me when I have trouble getting −.08 my breath. Note. Salient loadings (>.30) are in boldface.

.36 .27 .13 .13 .13 .12 .11 .03

.01 .00 .00 .00 .01

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Principal axis factoring pattern matrices (loadings) and communalities of the one-factor solution are shown in Table 2. All three items of the unsteady concerns facet of AS (items 4, 8, 10), as well as two items from the disease concerns facet of AS (items 6, 9), and one social concerns item (item 5) were omitted empirically from the model because of weak, hyperplane or nonvocal loadings (i.e., items failing to have a salient loading > .30). Therefore, the unidimensional factor solution was composed of the three mental illness concerns items (2, 12,15), two disease or physical concerns items (3, 11), and two social concerns items (1, 17). However, the onefactor solution explained a relatively small percent of variance (9.9%), when a good fitting solution typically explains approximately 40% to 50% of variance (Gorsuch, 1983).

Discussion The MAXEIG test, the inchworm consistency test, the MAMBAC external consistency test, and comparisons to parameter-matched Monte Carlo simulated taxonic and dimensional data, converged on the conclusion that AS is taxonic in this large sample of youth. MAXEIG and MAMBAC plots were characteristically categorical, indicating latent discontinuity. The base rate estimate of the AS taxon class was .09 (the AS complement was .91). Results are consistent with previous taxometric work on AS using the 16-item ASI in young adults (Bernstein, Zvolensky, Feldner, Lewis, & LeenFeldner, 2005; Schmidt et al., 2005; Zvolensky et al., 2007) and the 16-item ASI and 36-item ASI-R among adults from different regions of the world (Bernstein, Leen-Feldner, et al., 2006; Bernstein, Zvolensky, Kotov, et al., 2006). Two additional taxometric findings are also noteworthy. First, unlike all other CASI-based manifest indicators of AS among youth, the social concerns manifest item-indicator did not validly discriminate between the taxonic latent classes of AS. In other words, similar levels of such social fears were observed continuously across the spectrum of each class and continuously across the taxonic latent classes . Thus, unlike all other facets of AS, social concerns among the AS taxon members may not differ categorically from social concerns among the complement class. In this regard, AS-related fears of publicly observable anxiety reactions demonstrates a continuous feature shared by discrete latent classes. Pickles and Angold (2003) described this type of phenomenon in arguing that simply because two groups are categorically different in some ways (e.g., in terms of disease concerns, mental illness concerns, etc.), it does not mean that all differences between such

groups must be categorical or discontinuous. Indeed, the present findings suggest that fears of publicly observable anxiety reactions do not differ categorically between AS latent classes. Thus, among youth, it appears that both normative and anxiety-vulnerability conferring forms of AS are similarly linked to youth’s social environment. This may be a function of the core role(s) of social context for adolescent development (Cook, Herman, Phillips, & Settersetn, 2002; Eder, 2002). Specifically, the important role and source of anxiety played by social context is of unique importance to the developmental challenges faced by youth (Kim, Conger, Lorenz, & Elder, 2001; LaGreca, Prinstein, & Fetter, 2001; Morison & Masten, 1991). What is at least somewhat encouraging or discouraging, depending on your perspective on the matter, is that social anxiety and anxiety about social evaluation may be an equal-opportunity problem for adolescents, regardless of their vulnerability for certain other forms of anxiety psychopathology, such as panic. These findings may also be further contextualized in terms of previous empirical AS-anxiety research that has found differential associations between AS factors and social anxiety (Taylor, Koch, & McNally, 1992). Specifically, Rodriguez and colleagues (2005) found that the social concerns or fears of publicly observable anxiety reactions facet of AS was uniquely associated with social anxiety, whereas the other facets of AS (e.g., physical concerns, mental illness concerns) were not related to social anxiety but instead to other anxiety problems such as panic. Future research could therefore examine whether the AS taxon confers vulnerability for certain anxiety disorders (e.g., panic, PTSD), but not social anxiety; the present latent structural findings may be interpreted to mean that both individuals in the complement and taxon classes ought to be at relatively equal risk for social anxiety outcomes insofar as AS-related fears of publicly observable anxiety reactions are related to social anxiety problems. Second, it also is important to note that the lower-order mental concerns factor demonstrated a relatively poor loading on the higher-order anxiety sensitivity global factor among the complement class but not the taxon class. This finding is theoretically consistent with previous taxometric findings indicating that this facet of AS may be the most powerful manifest index of the taxon and useful for discriminating between the latent classes (e.g., Zvolensky et al., 2007). Future study of this pattern of findings could benefit our understanding of the differential nature of the AS taxa in terms of mental concerns or fears of cognitive dyscontrol.

anxiety sensitivity and latent structure For instance, CFA research could examine whether mental concerns is not a facet of the normative form of AS and perhaps unique to the AS taxon dimensional structure; as another approach, future research could examine whether nomological relations between mental concerns among complement class members demonstrates a different pattern of relations with theoretically relevant anxiety vulnerability outcomes in comparison with the mental concerns among the taxon class. The CFAs indicated that the replicated four-factor hierarchical model of AS (Silverman et al., 2003) demonstrated good fit with CASI data for the complement class, but inadequate fit for the taxon class. These data suggest, consistent with prediction, the four-factor structure of AS (Silverman et al.) may in actuality reflect the factor structure of the normative form of AS, but not the high-risk form of AS. Furthermore, EFA indicated that the AS taxon demonstrates a unique factor solution. Specifically, the AS taxon factor structure is unique from that observed in previous factor analytic studies that did not discriminate between latent AS classes (Silverman et al.; Zinbarg et al., 1999), and from the factor structure of the complement class observed in the present investigation (i.e., CFA factor solution). Findings of the EFA suggested that the AS taxon demonstrates a unidimensional factor structure. Interestingly, the observed AS taxon unidimensional factor solution is consistent with the earliest theoretical conceptualizations of AS as a core, singular fear-of-fear, composed of unidimensionally inter-related fears of experiencing anxiety (Reiss & McNally, 1985). These fears include fears of mental illness, fears of disease or physical illness, and social concerns or fears of publicly observable anxiety reactions. Results of the present investigation indicate a number of theoretical and clinical implications. First, AS theory (Reiss & McNally, 1985) and nearly all subsequent study of AS (McNally, 2002) was premised on the theoretical assumption that AS is best conceptualized as an individual difference variable that exists in all persons in the same form, varying between individuals only by degree along a single latent dimension. However, the present factor analytic findings provide further support for the meaningfulness of the inference of AS taxonicity. More specifically, the present findings demonstrate that the latent structural nature of AS may be more accurately conceptualized as taxonic latent class structure composed of two types or forms of AS, each of which is characterized by its own unique, systematically meaningful latent continuity and dimensional structure. Additionally, the

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present factor analytic findings provide a unique form of empirical evidence supportive of the construct validity of the taxometrically based latent class inference of AS taxonicity. Specifically, the present CFA and EFA analyses of the taxometrically based latent AS classes provide further validity evidence, in the form of distinct latent dimensional structures, supporting the conjecture that the observed latent AS classes are indeed systematically different from one another. Such construct validity is of fundamental import, as simply observing that a construct is categorical via taxometric analyses may be of little consequence or interest (Widiger & Frances, 2002). Second, as contemporary theories of AS better address the latent structure of the construct across the life span, new hypotheses may follow about the relations between each discrete form of AS and anxiety outcomes among youth. That is, the refined definition of AS across the life span will help delineate additional parameters of the construct and focus research on it, as has been done for other psychopathology constructs in recent years (Davey, 1994; Lenzenweger & Korfine, 1992; Waller & Ross, 1997) and further elucidate developmental differences with respect to AS and its nomological network. Although previous taxometric study has aimed to understand AS as a single construct, with two underlying latent forms, the present findings may suggest that future research could fruitfully also evaluate continuous differences within each taxonic form of AS. For example, some sensitivity to, or intolerance of, anxiety-related cues may serve to alert the organism to threatening internal or external cues, whereas a lack thereof may hypothetically be associated with certain problems of disinhibition. Third, this and related investigations also help illuminate the potential limitations of the construct validity of our theoretical models of AS. Perhaps especially promising in addressing such limitations may be research that explores the latent structure of AS within a broader nomological network of affect sensitivity and tolerance. Indeed, extant theory and empirical study has focused on identifying and studying AS as a unique variable and affect sensitivity process but has not rigorously tested or developed theory regarding its likely relations with theoretically related cognitive-affective processes such as affect tolerance. Moreover, little theoretical or empirical study to date has explicitly evaluated the relations between affect sensitivity and tolerance more generally, although such study may have important clinical implications as these processes are at the heart of many third-wave behavior therapies as well as theoretical implications for

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integrating study of AS with affect regulatory theory and literature (see Zvolensky, Feldner, Leen-Feldner, & Yartz, 2005, for an expanded discussion of these issues). Fourth, the present findings and line of work may be used to refine the assessment of AS. For example, taxometric study of AS may enable the establishment of empirically based, nonarbitrary cutoffs to identify relatively young individuals at “high risk” for certain forms of anxiety psychopathology (Beauchaine, 2003). Thus, by more accurately identifying youth belonging to a high-risk AS latent class, researchers will be better positioned to develop and efficaciously deliver preventive interventions targeting the development of anxiety psychopathology (Feldner, Zvolensky, & Schmidt, 2004). This may be particularly important in the realm of AS, as such targeted preventive-intervention might be feasibly implemented prior to the expression of anxiety psychopathology. Furthermore, by better understanding the latent structure of AS, it may be possible to refine our understanding of the nature of, and mechanisms that are related to, change in this variable. AS appears to be malleable (Telch et al., 1993), yet little is known about the processes that underlie such change. Some researchers have suggested that taxa should change in an all-or-none, discontinuous mechanism rather than in a gradual, continuous process (Strube, 1989). The present findings, integrating taxometric and factor analytic models of AS, may further facilitate understanding in this area of AS change processes. Specifically, it may be the case that in addition to between-class categorical change, within-class continuity is related to differential, possibly linear, levels of risk for related outcomes. It therefore would be informative to study AS while considering its taxonic and withinclass dimensional properties during clinical intervention in an effort to better understand the nature of change-related processes. Limitations of the present investigation need to be recognized, each of which points to possible future directions for the study of AS vulnerability for anxiety psychopathology. One issue pertains to the fact that the conducted EFA analyses do not include the rigorous tests and fit indices afforded by CFA analyses. Such CFA techniques should thus be applied in additional samples to test the fit of the EFA-based factor model of the AS taxon found in the present investigation. We did not complete these analyses in the present study to prevent overmodeling of the data. Another area for future work is that, although large in overall size compared with prior taxometric and factor analytic studies of AS, the sample was not a normative probability-sample

of youth. Future study using epidemiological sampling techniques is important for replication, parameter estimation, and the derivation of a disseminable “CASI Taxon Scale” that is psychometrically sound and generalizable. It also would be fruitful to expand the population studied beyond non-treatment-seeking youth. It would therefore be important to conduct taxometric studies on clinical samples across the anxiety spectrum, as has recently been done with factor analytic work (Rodriguez, Bruce, Pagano, Spencer, & Keller, 2005). One alternative account of the observed differences in factor structure for the complement class compared with the taxon class may be related to differential levels of ASI score variability between classes. For example, lower ASI score variability among the taxon class could, in part, account for the observed factor structure differences between classes. However, this alternative hypothesis seems unlikely in the present investigation, as there were similarly high levels of variability for all AS indicators and total scores among both the complement and taxon classes. Although these data suggest that the factor analytic findings were not artifactually produced by differential levels of variability between taxometrically observed classes of AS, future investigations could further test this alternative account by oversampling taxon members (i.e., thereby equating sample size across AS classes). Another interpretative caveat is that the manifest indices of the cognitive construct were derived from a single self-report measure. Although this index is the most well-established measure of its kind and a standard approach for latent structural research on AS, future investigations could attempt replicate the present findings using a multimethod approach if alternative AS measures among youth became available. Finally, there is some inherent limitation of factor analysis, particularly EFA, and the fact that the existing measurement model of AS has been developed among mixed-class (population heterogeneous) samples. Specifically, it may theoretically be possible that the latent structural nature of the taxon is nonfactorial or nondimensional. For example, it may be that the identified factor solutions explain a small amount of variance because the latent structural nature of the taxon is solely categorical, whereas the latent structural nature of the complement class is continuous and multidimensional. In other words, among youth, it may be the case that it does not matter what “level” of AS a member of the taxon class experiences, but that categorical membership confers risk independent of within-class linear continuity. It may not be possible using factor analytic tools to rule out this nonfactorial alternative interpretation in that at

anxiety sensitivity and latent structure least one factor, by definition, is likely to be extracted by EFA from any set of data (even random data), regardless of its systematic meaningfulness. On the other hand, it may be possible that the unidimensional solution among the taxon explained little variance overall because the CASI was developed among mixed-class samples—that is, among samples that combine complement and taxon class members. Consequently, it may be the case that if the CASI was further developed to reflect the putative latent taxonic structure of AS (i.e., the measurement model was refined to match its latent structure), then subsequent factor analytic study of AS among taxon members may reveal a more construct-valid reflection of the dimensional nature of the anxiety high-risk form of AS. In sum, the present investigation is the first of its kind to attempt to build bridges between traditionally independent statistical approaches to the study of the latent structure of AS among youth. Findings suggest that this cognitive factor is taxonic and composed of two types or forms of AS; each of these forms may be characterized by a unique dimensional structure. These findings have important theoretical and clinical implications for better understanding the broader latent structural context of AS in terms of affect tolerance and anxiety vulnerability. Such results have direct implications for our understanding of existing AS theory, and hold promise for the refinement of ongoing and future efforts to reach theoretical and clinical advances intended to understand, measure, prevent, and treat anxiety psychopathology.

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