Personality and Individual Differences 50 (2011) 1168–1173

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Personality and Individual Differences

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Review

Childhood temperament: Dimensions or types?

Glenn D. Walters ⇑,1

Federal Correctional Institution Schuylkill, Pennsylvania, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 8 November 2010Received in revised form 8 February 2011Accepted 18 February 2011Available online 15 March 2011

Keywords:TemperamentTaxometric methodLatent structureFearfulnessPositive affectDifficultness

0191-8869/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.paid.2011.02.014

⇑ Address: Psychology Services, FCI-Schuylkill, P.Osylvania 17954-0700, United States. Tel.: +1 570 544

E-mail address: gwalters@bop.gov1 The assertions and opinions contained herein are t

and should not be construed as official or as reflecting tof Prisons or the United States Department of Justice.

In the Children of the National Longitudinal Survey of Youth (NLSY-C), mothers rated their 12–23 monthold toddlers on 11 temperament items. Three sets of items (three items per set) – one clustered aroundfearfulness, one clustered around positive affect, and one clustered around difficultness – were subjectedto taxometric analysis using mean above minus below a cut (MAMBAC), maximum covariance (MAX-COV), and latent-mode factor analysis (L-Mode). The results for all three sets of items showed consistentsupport for dimensional latent structure. When all nine items were simultaneously analyzed with finitemixture modeling the results were inconsistent with a categorical solution. The results of this study indi-cate that individual differences in childhood temperament – as measured by maternal ratings of children12–23 months of age – are quantitative (difference in degree) rather than qualitative (difference of kind).The implications of these results for understanding and assessing childhood temperament are discussed.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Typologies of temperament and personality go back to the an-cient Greeks and Romans. The modern era has seen a resurgenceof interest in temperament types, stimulated in part by the pio-neering work of Thomas and Chess (1977) who identified ninetemperament dimensions (activity, regularity, initial reaction,adaptability, intensity, mood, distractibility, persistence and atten-tion span, sensitivity) and three temperament types (easy, difficult,slow-to-warm-up). One limitation of Thomas and Chess’ three-group typology is that only two-thirds of children fit into one ofits three categories. Another limitation is that nine temperamentdimensions may be unwieldy for many applications. In contrastto descriptive taxonomies like the ‘‘Big Five’’ personality traits(McRae & Costa, 1990), biologically based theories of early temper-ament and personality identify three primary dimensions: ap-proach (behavioral activation, extraversion, positive affect),avoidance (behavioral inhibition, anxiety, negative affect), andaggression-dyscontrol (behavioral maintenance, impulsivity, non-specific arousal: Revelle, 1995). The purpose of this study was todetermine whether individual differences on temperament do-

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mains formed from these three dimensions are qualitative (differ-ences in kind) or quantitative (differences in degree) in nature.

The results of several studies (Aksan et al., 1999; Asendorpf &van Aken, 1999) imply that temperament types may, in fact, exist,but more definitive proof is required. Neither cluster analysis, theprincipal vehicle for identifying temperament types in the Ase-ndorpf and van Aken (1999) study, nor confirmatory factor analy-sis, the principal vehicle for identifying temperament types in theAksan et al. (1999) investigation, is particularly effective in differ-entiating between dimensional and categorical latent structure.Case-centered procedures like cluster analysis frequently over-identify the number of classes in a construct, assume discontinuityin a distribution, and are biased toward a categorical solution (Bau-er & Curran, 2003). Conversely, variable-centered procedures likeconfirmatory factor analysis are incapable of identifying a taxonicboundary between classes, assume continuity in a distribution,and are biased toward a dimensional solution (Ruscio, Haslam, &Ruscio, 2006). Meehl’s (1995, 2004) taxometric method, by con-trast, is designed specifically to discriminate between dimensionaland categorical latent structure and is not systematically biased to-ward either a dimensional or categorical solution.

Using three taxometric procedures – mean above minus below acut (MAMBAC: Meehl & Yonce, 1994), maximum covariance (Meehl& Yonce, 1996), and latent mode factor analysis (L-Mode: Waller &Meehl, 1998) – the latent structure of temperament was evaluatedin a large group of toddlers. The 25% base rate used to divide the sam-ple into putative taxon and complement groups was calculated fromthe proportion of children normally found in Thomas and Chess’(1977) difficult and slow-to-warm-up categories. These groups were

G.D. Walters / Personality and Individual Differences 50 (2011) 1168–1173 1169

then used to validate the taxometric method for use in the currentstudy. Few investigators adopt a purely categorical view of temper-ament but there is an implicit, and sometimes explicit, assumptionon the part of advocates of temperament typologies that there arefeatures of both dimensional and categorical structure in childhoodtemperament (see Asendorpf, Borkenau, Ostendorf, & van Aken,2001). If there is evidence of categorical latent structure in childhoodtemperament, however, it should be detected by the taxometricmethod. Given an absence of clear support for categorical latentstructure it was hypothesized that the latent structure of child tem-perament would be dimensional across several different tempera-ment domains.

2. Method

2.1. Participants

Participants were 2945 members of the National LongitudinalSurvey of Youth-Child (NLSY-C: Center for Human Resource Re-search, 2009) study who had been rated by their mothers on threefearful temperament items (n = 2887), three positive affect tem-perament items (n = 2888), three difficultness temperament items(n = 2878), or all nine items (n = 2854) sometime between 1982and 2002 when the child was between the ages of 12 and23 months. The gender breakdown for the full sample of 2945 par-ticipants was evenly split between boys (50.4%) and girls (49.6%).Ethnically, the full sample of 2945 participants was composed of55.6% white children, 25.5% black children, and 18.9% Hispanicchildren.

2.2. Measure

The How My Child Usually Acts temperament survey from theNLSY-C integrates elements of Rothbart’s (1981) Infant BehaviorQuestionnaire, Campos and Kagan’s compliance scale, and othertemperament items (Mott, Baker, Ball, Keck, & Lambert, 1995).The items on this inventory were completed by the mother whenthe child was 0–11, 12–23, and 24–83 months of age. Each itemon the How My Child Usually Acts survey is rated on a five-pointscale (5 = ‘‘almost always’’ and 1 = ‘‘almost never’’). The 11 itemsrated when the child was 12–23 months were considered for inclu-sion in this study given research showing that temperament doesnot become apparent and cannot be reliably measured until a childis at least 3–6 months old (Komsi et al., 2006). The internal consis-tency, stability, and construct validity of these ratings are adequate(Baydar, 1995). However, the mean intercorrelation between theeleven rated items fell below the .30 threshold recommended byMeehl (1995, 2004) for use in a taxometric analysis. As such, threesets of items were subjected to taxometric analysis: one set ofthree items from the NLSY-C Fearfulness (avoidance) scale, oneset of three items from the NLSY-C Positive Affect (approach) scale,and three unaffiliated items that suggested difficultness (aggres-sion), each with mean inter-indicator correlations >.30.

2.3. Procedure

Mothers rated their 12–23 month old children on 11 tempera-ment items but because the full set of 11 items achieved an aver-age inter-item correlation of only .14, circumscribed sets of itemssatisfying Meehl’s (1995, 2004) recommended threshold of .30were selected for analysis. The first three-item set came from theNLSY-C Fearfulness scale (afraid of strangers, afraid of animals, up-set when alone) and the second three-item set came from theNLSY-C Positive Affect scale (smiles/laughs with mom, smiles/laughs when alone, smiles/laughs in the bath). The third three-item

set did not come from a primary NLSY-C scale, but item contentsuggested difficultness and the item set was accordingly labeledthe Difficultness scale (mom has trouble calming, often fussy andirritable, often cries). The positive affect items were inverted (i.e.,multiplied by �1) so that higher scores on all nine items indicatedmore problematic temperament.

Ruscio’s (2009) taxometric software program for R languagewas used to perform mean above minus below a cut (MAMBAC:Meehl & Yonce, 1994), maximum covariance (MAXCOV: Meehl &Yonce, 1996), and latent mode factor analysis (L-Mode: Waller &Meehl, 1998) on the three temperament item sets. Summed inputMAMBAC was performed with 50 evenly spaced cuts, traditionalMAXCOV was performed with 25 overlapping windows, and L-Mode was calculated using base rate estimates from MAMBACand MAXCOV. The input indicators were summed for the MAMBACanalyses but not for the MAXCOV analyses in response to researchshowing that summed input MAMBAC is slightly more accuratethan traditional (all combinations of input and output indicators)MAMBAC and that traditional MAXCOV is significantly more accu-rate than summed input MAXCOV (Walters & Ruscio, 2009).

The MAMBAC, MAXCOV, and L-Mode curves were compared tocurves derived from a bootstrapping technique (20 simulated datasets for taxonic structure and 20 simulated data sets for dimen-sional structure) which takes into account the unique distribu-tional and correlational characteristics of the data (Ruscio,Ruscio, & Meron, 2007). The degree of fit between the data curveand two comparison curves is quantified in the comparison curvefit index (CCFI). The accuracy of the CCFI has been documentedin a series of Monte Carlo studies (Ruscio, 2007; Ruscio & Marcus,2007; Ruscio, Walters, Marcus, & Kaczetow, 2010; Ruscio et al.,2007; Walters, McGrath, & Knight, 2010; Walters & Ruscio, 2009,2010), particularly when an indeterminate category is used (Ruscioet al., 2010). CCFI scores between .55 and 1.00 were considered cat-egorical, CCFI scores between 0.00 and .45 were considered dimen-sional, and CCFI scores between .45 and .55 were consideredindeterminate.

Because separate taxometric analyses of the three temperamentdomains could potentially obscure a taxonic or categorical bound-ary that could have been identified had all nine items been ana-lyzed simultaneously, a finite mixture modeling (FMM) analysiswas computed with MPlus (Muthén & Muthén, 2007). Based onan Expectation Maximization (EM) algorithm, FMM seeks to repre-sent the heterogeneity in a construct with a finite number of latentclasses. The advantage that mixture modeling has over Meehl’staxometric method is that it does not require a certain minimumlevel of covariation between indicators (r P .30) to produce mean-ingful results.

3. Results

Preconditions for performing a taxometric analysis includedemonstrating that a sufficient degree of covariance exists be-tween the indicators (mean full sample inter-indicator r P .30),nuisance covariance is negligible (mean taxon and mean comple-ment inter-indicator r < .30), and indicator validity or the abilityof the indicators to differentiate between putative taxon and com-plement members is adequate (Cohen’s d P 1.25: Meehl, 1995).Putative taxon and complement groups were formed by convertingthe raw scores for each indicator into z-scores, summing the z-scores, taking the top quarter of summed z-scores – the proportionequal to Thomas & Chess’ (1977) difficult and slow-to-warm-upcategories – and assigning them to the putative taxon and assign-ing the remaining cases to the putative complement. The size ofthe putative taxon for the three indicator sets was 25.0% (fearful-ness), 23.3% (positive affect), and 22.8% (difficultness).

Table 1Descriptive statistics and validity estimates for the three fearfulness, three positiveaffect, and three difficultness temperament indicators.

Indicator Mean SD Skewb Kurtosisc Validityd

FearfulnessAfraid of strangers 2.38 1.45 0.65 �0.97 1.99Afraid of animals 1.87 1.33 1.37 0.52 1.96Upset when alone 2.48 1.37 0.57 �0.90 1.36Positive affectSmiles/laughs with mom 4.87a 0.49 4.94 28.93 1.27Smiles/laughs when alone 4.13a 0.94 0.94 0.35 1.63Smiles/laughs in the bath 4.54a 0.86 2.17 11.30 2.18DifficultnessMom has trouble calming 1.64 0.96 1.72 2.70 1.90Often fussy and irritable 2.33 0.86 0.51 �0.04 1.76Often cries 2.21 0.84 0.18 �0.29 1.43

Note: mean = total sample mean based on the results of a five-point (1–5) ratingscale; SD = standard deviation; N = 2887 for the fearfulness indicators, 2888 for thepositive affect indicators, and 2878 for the difficultness indicators.

a These are the non-inverted means for the positive affect items; inverted posi-tive affect item scores were used in the taxometric analyses.

b The standard error of measurement for skew was .05.c The standard error of measurement for kurtosis was .09.d Measured in effect size units (i.e., Cohen’s d). Under fearfulness d represents the

ability of each fearfulness indicator to separate the putative taxon (i.e., high fear-fulness) from the putative complement (i.e., low fearfulness) using a cutting scoreof 1.02 on the sum of the combined z-score conversions of the three indicators(corresponding to a taxon base rate = 25.0%). Under positive affect d represents theability of each positive affect indicator to separate the putative taxon (i.e., lowpositive affect) from the putative complement (i.e., high positive affect) using acutting score of 1.00 on the sum of the combined z-score conversions of the threeindicators (corresponding to a taxon base rate = 23.3%). Under difficultness d rep-resents the ability of each difficultness indicator to separate the putative taxon (i.e.,high difficultness) from the putative complement (low difficultness) using a cuttingscore of 1.05 on the sum of the combined z-score conversions of the three indicators(corresponding to a taxon base rate = 22.8%).

1170 G.D. Walters / Personality and Individual Differences 50 (2011) 1168–1173

Means, standard deviations, skew, kurtosis, and indicator valid-ity estimates for the three indicator sets are listed in Table 1. Thethree fearfulness indicators were sufficiently correlated (mean fullsample inter-indicator r = .30), free of nuisance covariance (meantaxon inter-indicator r = �.15, mean complement inter-indicatorr = �.04), and valid (mean d = 1.77) to perform a taxometric analy-sis. The three positive affect indicators also displayed sufficientindicator covariance (mean full sample inter-indicator r = .30), lackof nuisance covariance (mean taxon inter-indicator r = �.08, meancomplement inter-indicator r = �.01), and validity (mean d = 1.69)to permit taxometric analysis. Finally, the three difficultness indi-cators were sufficiently correlated (mean full sample inter-indica-tor r = .35), free of nuisance covariance (mean taxon inter-indicatorr = �.12, mean complement inter-indicator r = .09), and valid(mean d = 1.70) for taxometric analysis.

A taxometric analysis of the three fearfulness indicators re-vealed consistent support for dimensional latent structure (see Ta-ble 2). A mean CCFI of .276 is well below the threshold fordimensional latent structure (i.e., .450). When data for boys andgirls were analyzed separately the results were also consistentlydimensional. Table 2 further indicates that the positive affect anddifficultness item sets were also dimensional. The taxometriccurves for the MAMBAC (fearfulness), MAXCOV (positive affect),and L-Mode (difficultness) analyses, along with the categoricaland dimensional comparison curves, are reproduced in Figs. 1–3,respectively. To preserve space only three (one temperament do-main for each method) of the nine graphs are presented.

To test the possibility that the procedures used in the currentstudy to compute the taxometric analyses (i.e., separate analysesof the three temperament domains) concealed one or more catego-ries, finite mixture modeling was applied to the nine temperamentitems. Treating a decrease in BIC < 100 as trivial and a non-signifi-cant BLRT (p > .05) as a reasonable stopping point the mixture

modeling analysis was halted after the 13-class model was ana-lyzed (see Table 3). A review of the conditional response meansfor the 12 classes in the best fitting model revealed that differencesbetween classes were largely a matter of degree and thereforemore consistent with dimensional latent structure than with cate-gorical latent structure.

4. Discussion

Testing the latent structure of a construct is a two-step process.The goal of the first step is to ascertain the latent structure type(i.e., dimensional vs. categorical). The goal of the second step isto determine the latent structure model (i.e., the number of dimen-sions or the number of categories). Several recent Monte Carlostudies cogently demonstrate that the taxometric method is highlyeffective in answering Step 1 questions, regardless of the numberof dimensions or categories (Frazier, Ruscio, & Youngstrom, sub-mitted; McGrath & Walters, submitted; Ruscio, 2007; Ruscioet al., 2010; Walters et al., 2010). Other procedures, however, ap-pear to be better-equipped to answer Step 2 questions: factor anal-ysis in the case of dimensional latent structure (Ruscio et al., 2006)and mixture modeling in the case of categorical latent structure(McGrath & Walters, submitted). The current study addressed theStep 1 question of latent structure type with respect to childhoodtemperament and found consistent support for dimensional latentstructure. In fact, there was no evidence of a taxonic boundary inany of the taxometric or mixture modeling analyses. What thismeans is that individual differences in temperament, as measuredby maternal ratings of toddler fearfulness, positive affect, and dif-ficultness, are quantitative (difference in degree) rather than qual-itative (difference of kind).

One limitation of this study is that the three temperament do-mains with a sufficient number of correlating items (fearfulness,positive affect, difficultness) had to be analyzed separately becausethey did not correlate well enough to perform a taxometric analy-sis on the full item set. In an attempt to rule out the possibility thatthis procedure concealed a taxonic boundary, the nine items fromthe three domains were analyzed simultaneously with finite mix-ture modeling, a procedure, which while not as effective as taxo-metrics in making Step 1 decisions (Frazier et al., submitted;McGrath & Walters, submitted), does not require that indicatorscorrelate at least .30 with each other. The results of the mixturemodeling analyses revealed that a 12-class solution achieved anoptimal fit for the nine temperament items. The presence of 12classes for nine indicators and the quantitative nature of the differ-ences between ‘‘categories’’ are further evidence that the latentstructure of childhood temperament is dimensional. Even if twotaxonic boundaries (assuming that fearfulness, positive affect,and difficultness represent separate types) were obscured whenthe three temperament domains were analyzed separately, thethree classes created by these two taxonic boundaries do not ac-count for the majority of the 12 classes in the best fitting modelidentified by finite mixture modeling. The remaining nine classeswould have been identified by the individual taxometric analysesbecause they would have been located in one or more of the threedomains.

The current results have important implications for the etiologyand assessment of childhood temperament. Meehl (1977, 1992) ar-gued that categorical constructs have four possible causal path-ways: specific etiology, threshold effect, nonlinear interaction,and developmental bifurcation. Specific etiology requires the pres-ence of a small number of necessary and sufficient genetic and/orenvironmental conditions. A threshold effect, on the other hand,starts out as a continuum but then splits into two or more catego-ries after reaching a certain level of intensity, magnitude, or

Table 2Base rate and comparison curve fit index results for the fearfulness, positive affect, and difficultness indictor analyses broken down by gender.

Sample N MAMBAC MAXCOV L-Mode Average

BR CCFI BR CCFI Range M CCFI

FearfulnessTotal 2887 .80(.23) .271 .20(.03) .261 .265–.319 .295 .276Boys 1453 .16(.16) .331 .20(.04) .314 .362–.390 .376 .340Girls 1434 .37(.04) .338 .20(.03) .360 .297–.340 .324 .344Positive affectTotal 2888 .17(.17) .279 .10(.04) .426 .429–.453 .439 .381Boys 1452 .21(.15) .290 .09(.03) .496 .389–.421 .407 .398Girls 1436 .07(.11) .305 .12(.04) .254 .374–.430 .405 .321DifficultnessTotal 2878 .30(.12) .255 .26(.10) .299 .378–.387 .381 .312Boys 1452 .29(.25) .226 .36(.26) .258 .379–.384 .382 .289Girls 1426 .32(.11) .241 .25(.08) .368 .383–.409 .392 .334

Note: MAMBAC = mean above minus below a cut; MAXCOV = maximum covariance; L-Mode = latent mode factor analysis; average = average of the three procedures(MAMBAC, MAXCOV, and L-Mode); BR = taxon base rate with first number representing the mean taxon base rate across the individual curves and the second number (inparentheses) the standard deviation; CCFI = comparison curve fit index; range = range of CCFI values calculated with MAMBAC and MAXCOV base rate estimates; M = mean L-Mode CCFI value.

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Fig. 1. Average mean above minus below a cut (MAMBAC)-summed input data curve for the three fearfulness indicators (darker line) relative to simulated taxonic (left panel)and dimensional (right panel) data (lighter lines represent one standard deviation above and below the mean).

G.D. Walters / Personality and Individual Differences 50 (2011) 1168–1173 1171

involvement. Nonlinear interaction gives rise to a synergism that isgreater than the sum of the individual effects. Finally, developmen-tal bifurcation occurs when a dimensional causal process knifes offinto a category after a certain age or once a particular developmen-tal milestone is achieved. Asendorpf and colleagues (2001) appearto be proposing either a threshold effect or developmental bifurca-tion in explaining the etiology of their mixed categorical-dimen-sional model of temperament prototypes. Just the same, therewas no evidence in the current study of categorical latent struc-ture. Instead, the results were consistently dimensional, whichfrom a theoretical standpoint suggests that the etiology of temper-ament is additive. In the additive model a relatively large numberof causal factors, each wielding a relatively small effect, combine toform a construct. A person’s position on the dimension is thereforedetermined by the number of causal factors at play at any

particular point in time rather than a specific set or pattern of nec-essary and sufficient causal conditions.

The current results also have important assessment implica-tions. An instrument designed to assess a categorical constructshould accurately sort cases into their respective categories. Assuch, the items on measures of categorical constructs target thetaxonic boundary between the taxon and its complement. Dimen-sional constructs, by way of comparison, require an assessmentprocedure with a greater number and diversity of items thaninstruments designed to assess categorical constructs because in-stead of assigning cases to mutually exclusive categories, dimen-sional procedures must make fine distinctions along theircontinua. Accordingly, a dimensional assessment procedure re-quires items at various points along its continuum in order to bemaximally effective. The majority of self-report questionnaires

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Fig. 3. Latent mode factor analysis (L-Mode) data curve for the three difficultness indicators (dark solid line) relative to simulated taxonic (left panel) and dimensional (rightpanel) data (lighter lines represent one standard deviation above and below the mean) with the taxon base rate set at .28.

1172 G.D. Walters / Personality and Individual Differences 50 (2011) 1168–1173

used to assess temperament during middle-childhood and earlyadolescence more than likely meet these dimensional criteria. Thismay not be true, however, of parental ratings. The NLSY-C temper-ament items utilized a five-point Likert scale and some of thescales were as short as three items. To cover the full length ofthe temperament dimension it may be necessary to expand thesize of the temperament rating scale to 7 or 9 categories and in-crease the number of temperament rating items to 10 or 15.

If the dimensional results obtained in the current study can becross-validated in rating studies using data from both mothers andfathers and in self-report studies using data from older childrenand adolescents then the next step will be to determine the num-ber of temperament dimensions. As was mentioned earlier, thetaxometric method is incapable of accurately estimating the num-ber of dimensions in a dimensional construct or the number of cat-egories in a categorical construct and so answering Stage 2

Table 3Summary of finite mixture modeling results for the three fearfulness, three positiveaffect, and three difficultness indicators considered as a group (N = 2854).

k LL PAR p BIC DBIC

1 �35555.27 18 71253.762 �34154.71 28 <.001 68532.20 �2720.803 �32180.68 38 <.001 64663.71 �3868.494 �29841.79 48 <.001 60065.48 �4598.235 �28681.99 58 <.001 57825.47 �2240.016 �28254.32 68 <.001 57049.68 �775.797 �27982.86 78 <.001 56586.34 �463.348 �27701.65 88 <.001 56103.48 �482.869 �27506.03 98 <.001 55791.80 �311.68

10 �27312.49 108 <.001 55484.29 �307.5111 �27158.73 118 <.001 55256.33 �227.9612 �26901.81 128 <.001 54822.06 �434.2713 �26863.81 138 .100 54825.60 +3.54

Note: number of random starts set at 100 for k-1 through k-2, 200 for k-3 through k-4, 300 for k-5 through k-7, 500 for k-8 through k-10, and 1000 for k-11 through k-13; k = number of components or classes in the model; LL = log likelihood value;PAR = number of free parameters; p = significance level of the difference in loglikelihood of fit between the current model and previous (k � 1) model using theparametric bootstrapped likelihood ratio test (McLachlan & Peel, 2000);BIC = Bayesian information criterion (smaller values indicate better fit); DBIC = -change in BIC (� = current model has a better fit; + = previous model has a betterfit).

G.D. Walters / Personality and Individual Differences 50 (2011) 1168–1173 1173

questions in the case of a dimensional construct requires the use ofexploratory or confirmatory factor analysis. A number of factoranalytic studies on infant, child, and early adolescent temperamenthave been conducted over the years but the number of dimensionshas been found to vary as a consequence of the sample studied, therater or instrument used, and the extraction method employed.One thing is clear; very few studies have replicated Thomas andChess’ (1977) nine dimensions. Two of the more popular dimen-sional models of temperament are Rothbart’s three-factor solution(extraversion/surgency, negative affectivity, and effortful control:Rothbart, Ahadi, Hershey, & Fisher, 2001) and a five-factor model(social inhibition, negative emotionality, adaptability, activity le-vel, and task persistence) advanced by Presley and Martin (1994).Performing a Step 2 exploratory or confirmatory factor analysison childhood temperament data might be helpful in determiningwhich of these two models provides a better explanation for theStep 1 dimensional taxometric results obtained in the currentstudy.

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