structure invariance of the temperament and character inventory (tci)

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Structure invariance of the Temperament and CharacterInventory (TCI)

Kamel Gana*, Raphael Trouillet

Department of Psychology, Groupe d’Analyse Psychometrique des Conduites,

3 Palce Godefroy de Bouillon, University of Nancy 2, 54015 Nancy, France

Received 26 November 2001; received in revised form 23 September 2002; accepted 6 November 2002

Abstract

Based on Cloninger’s psychobiological model of personality, the Temperament and Character Inventory(TCI) was designed to assess seven dimensions reflecting two major components of personality, tempera-ment and character. Confirmatory factor analysis (CFA) was used in order to evaluate the internal struc-ture of the TCI. Using the general approach to representing personality constructs suggested by Bagozziand Heatherton (1994), 25 measurement models were tested. None of them provided a compelling fit to oursample data (N=689). Moreover, the reliability of some of the subscales of the TCI was very weak. Wediscuss the importance of CFA on construct validity.# 2003 Elsevier Ltd. All rights reserved.

Keywords: Temperament and Character Inventory; TCI; Factorial structure; Confirmatory factor analysis

All scientific theories need measurement of the constructs underlying them. Personality theoriesare, of course, similarly concerned. Personality is usually defined as a sum of stable and habitualpatterns of behavior that are characteristic of an individual. Combination of this pattern ofbehavior leads to individual differences in personality (Maddi, 2000). Psychology and psychiatryalways aimed not only at organizing these differences into theoretically relevant taxonomicstructures, but also at measuring them in the most reliable and valid manner. For example, theTemperament and Character Inventory (TCI), a 226-item, true-false, self-report questionnaire,developed by Cloninger to assess seven dimensions of personality, is an operationalization of hisbiosocial theory (Cloninger, 1986, 1998; Cloninger & Svrakic, 1997; Cloninger, Przybeck,Svrakic, & Wetzel, 1994; Cloninger, Svrakic, & Przybeck, 1993). This seven-factor model assumes

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Personality and Individual Differences 35 (2003) 1483–1495

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E-mail address: [email protected] (K. Gana).

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that personality structure is composed of four temperaments and three characters (see Table 1).The temperament dimensions, which are thought to have some genetic basis, refer to tendenciesto react to emotional stimuli (i.e. anger, fear, disgust. . .). The temperament dimensions are thefollowing: (1) Novelty seeking (NS) refers to the tendency to initiate exploratory activity and tohave curiosity about new stimuli. This dimension is assessed by the sum of four subscales mea-

Table 1TCI scales and subscales means (M), standard deviations (S.D.) and a coefficients

Scale/subscale
No. of items M (S.D.) a
Temperament

Novelty Seeking (NS)
Novelty seeking 40 19.0 (6.1) 0.79 NS1 Exploratory excitability vs. rigidity 11 6.1 (2.6) 0.70
NS2
Impulsiveness vs. reflection 10 4.0 (2.4) 0.69 NS3 Extravagance vs. reserve 9 5.0 (2.0) 0.67 NS4 Disorderliness vs. regimentation 10 4.0 (1.9) 0.42
Harm Avoidance (HA)
Harm avoidence 35 17.7 (1.9) 0.86 HA1 Anticipatory worry vs. optimism 11 5.1 (2.6) 0.71
HA2
Fear of uncertainty vs. confidence 7 4.1 (1.7) 0.56 HA3 Shyness vs. gregariousness 8 4.4 (2.2) 0.73 HA4 Fatigability vs. vigor 9 4.1 (2.2) 0.71
Reward Dependence (RD)
Reward dependence 24 15.9 (3.9) 0.71 RD1 Sentimentality vs. insensitiveness 10 7.3 (1.8) 0.53 RD3 Attachment vs. detachment 8 4.9 (2.2) 0.73
RD4
Dependence vs. independence 6 3.6 (1.4) 0.41
Persistence (PS)
Persistence 8 4.6 (1.9) 0.61
Character

Self-Directedness (SD)
Self-directedness 44 31.0 (6.8) 0.84
SD1
Responsibility vs. blaming 8 6.0 (1.9) 0.72 SD2 Purposeful vs. lack of goal direction 8 5.2 (1.8) 0.58 SD3 Resourcefulness vs. apathy 5 3.4 (1.4) 0.58 SD4 self-acceptance vs. self-striving 11 7.7 (2.3) 0.71
SD5
congruent second nature 12 8.5 (2.4) 0.70
Cooperativeness (C)
Cooperativeness 42 32.6 (5.3) 0.81
C1
Social acceptance vs. intolerance 8 6.8 (1.5) 0.66 C2 Empathy vs. social disinterest 7 5.2 (1.5) 0.47 C3 Helpfulness vs. unhelpfulness 8 6.3 (1.3) 0.50
C4
Compassion vs. revengefulness 10 7.5 (2.5) 0.80 C5 Pure-hearted vs. self-serving 9 6.9 (1.4) 0.36
Self-transcendence (ST)
Self-transcendence 33 13.4 (5.9) 0.83
ST1
Self-forgetful vs. self-conscious 11 5.5 (3.1) 0.60 ST2 Transpersonal identification 9 3.3 (2.3) 0.62 ST3 Spiritual acceptance vs. materialism 13 4.5 (2.0) 0.73
1484 K. Gana, R. Trouillet / Personality and Individual Differences 35 (2003) 1483–1495

suring more specific related traits: Exploratory excitability (11 items), Impulsiveness (10 items),Extravagance (9 items) and Disorderliness (10 items). (2) Harm avoidance (HA) reflects a ten-dency to inhibit behavior when faced with new situations. This dimension is assessed by the meanof four subscales: Anticipatory worry (11 items), Fear of uncertainty (7 items), Shyness (8 items)and Fatigability (9 items). (3) Reward dependence (RD) reflects a tendency to maintain behaviorspersonally rewarding. This dimension encompasses three subscales: Sentimentality (10 items),Attachment (8 items) and Dependence (6 items). (4) Persistence (PS) corresponds to a tendency tomaintain behaviors despite frustration and fatigue. Its an 8-item one-single dimension. Thecharacter dimensions, which are more environmentally influenced than temperament is, reflect‘‘individual differences in self-object relationships’’ (Cloninger, Bayon, & Svrakic, 1998). Thecharacter dimensions of the TCI are the following: (1) Self-directedness (SD) refers to self-accep-tance, responsibility and to the tendency to adapt behavior according with ones own goals, that isto be autonomous. This dimension is assessed as the sum of five subscales measuring more spe-cific related traits: Responsibility (8 items), Purposeful (8 items), Resourcefulness (5 items), self-acceptance (11 items), congruent second nature (12 items). (2) Cooperativeness (C) refers to theability to identify with and to accept other people, that is to be an integral part of the group. Thisdimension includes Social acceptance (8 items), Empathy (7 items), Helpfulness (8 items), Com-passion (10 items), and Pure-hearted (9 items). (3) Self-transcendence (ST) reveals a tendency tohave a belief system and feel part of nature and the universe. This dimension encompasses threesubscales: Self-forgetfulness (11 items), Transpersonal identification (9 items) and Spiritualacceptance (13 items).The TCI currently enjoys a broad success as can be testified by its translation in several foreignlanguages including Czech (Kozeny & Hoschl, 1999), Dutch (De la Rie, Duijsens, & Cloninger,1998; Duijsens, Spinhoven, Goekoop, Spermon, & Evrelings-Bontekoes, 2000), French (Pelissolo& Lepine. 1997; Hansenne, Le Bon, Gauthier, & Ansseau, 2001), Japanese (Tomita, Aoyama,Kitamura, Sekiguchi, Murai, & Matsuda, 2000), Spanish (Gutierrez et al., 2001), Swedish(Brandstrom et al., 1998). This success is imputable at least to three reasons: (1) Cloninger’spsychobiological model seems to be consistent with the basic construct of personality accepted bymost personality researchers. This model supports theoretical founding and empirical findings ofpreviously well-known models including those of Eysenck (1981), Gray (1970, 1987), and Zucker-man (1991; Zuckerman & Cloninger, 1996). (2) The Cloninger dimensional model can be usedclinically to generate DSM categorical diagnoses. For example, an individual who is very low onNS, HA, RD temperament dimensions, and on SD and C character dimensions has a high prob-ability of having a schizoid personality. An individual who is very low in all three character dimen-sions (SD, C, ST) has a high probability to be considered as melancholic (Cloninger et al., 1998). (3)The TCI proved to have good psychometric properties. However, the validity of the Temperamentdimensions from the genetic point of view is still polemical (Herbst, Zonderman, McCrae, & Costa,2000), and the factorial structure of the TCI remains problematic. Indeed, the seven dimensions donot appear each time a factor analysis is used, and, as far as we know, confirmatory factor analyses(CFA) have never been performed to verify the structure of the TCI. If the large number of items inthis questionnaire could dissuade one from testing a total disaggregation model, one could testpartial disaggregation/aggregation models (Bagozzi & Heatherton, 1994).And because the TCI is explicitly developed for clinical use, its factorial validity has to be proved.The aim of the present study was to gain insight into this issue by verifying the universality of the

K. Gana, R. Trouillet / Personality and Individual Differences 35 (2003) 1483–1495 1485

psychobiological seven-factor model in a French sample. Normative data were obtained from arepresentative sample of the French population by Pelissolo and Lepine (2000), and from repre-sentative French-speaking subjects from Belgium by Hansenne et al. (2001). Factorial structure ofthe TCI was explored in these studies as in those of Brandstrom et al. (1998) and Cloninger et al.(1993) by separate principal component analyses (PCA), one for the temperament dimensionsand one for the character dimensions. And as stated by Mulaik (1987) ‘‘the conclusionsof exploratory factor analyses are never complete without a subsequent confirmatory factoranalysis’’ (p. 267).To test the internal structure of the TCI, we chose to use the framework for representing per-sonality constructs proposed by Bagozzi and Heatherton (1994; see also Bagozzi & Edwards,1998). This framework depicts measurement models at various levels of abstraction.The total aggregation model is the most abstract representation of a construct. Here subscalesare treated as indicators of a single factor (dimension). Fig. 1 shows a total aggregation modelapplied to the TCI. It should be noted here that we treat Temperament and Character separatelyas they have been factor analyzed by Cloninger et al. (1993).The partial aggregation model is less abstract than the total aggregation model if one considersthat the total scale is represented with multiple facets, each operationalized with subscales asindicators. Fig. 2 depicts first-order partial aggregation models applied to the two components ofthe TCI. The main advantage of the total and the partial aggregation models is that they can beeasily used as predictor or predicted variables in a structural equation model. It should be notedhere that the TCI has been exclusively used at the total aggregation level.The total disaggregation model is the most atomistic representation of a scale if one takes intoaccount that each item is used as an indicator of component, as a facet or as a global factor. Fig. 3

Fig. 1. Total aggregation models of TCI components.


presents a first-order total disaggregation model for the TCI. The total disaggregation modelprovides the most ‘‘fine-grained analyses’’ of a construct because psychometric properties areevaluated for each individual item. However, according to Bagozzi and Heatherton (1994), ‘‘asthe number of items per factor increases and sample sizes increase, it is likely that many totaldisaggregation models fail to fit the data satisfactorily’’ (p. 43).1 However, in their recentsimulation studies, Marsh, Hau, Balla, and Grayson (1998), found that it is better to have moreindicators per factor even when sample size is small or moderate. They also found that dis-aggregated solutions performed better than parceled ones. These results support their ‘‘more isbetter’’ assumption. Using more indicators per factor leads to fewer nonconverged and impro-per solutions and more stable parameter estimates. It should be noted here that these simula-tions were based on three-factor models in which the maximum number of items per factor was12.

Fig. 2. Partial aggregation models of TCI components.

1 The last level of abstraction depicted by Bagozzi and Heatherton (1994) and Bagozzi and Edwards (1998) is the

partial disaggregation model ‘‘which is structuraly similar to the the total disaggregation model but differs in the wayitems are handled and indicators are formed’’ (1998, p. 10). This model is omitted here because it’s not applicable to theTCI.


1. Method

1.1. Data

Weobtained responses to the French version of the TCI (Pelissolo&Lepine, 1997) from 689 persons(492 female) aged 18–76 years (mean=35.1, S.D.=0.14.7). All of themwere contacted by our studentswho handed out about 1000 questionnaires. It was a convenient sample from the central part ofFrance. No exclusion criteria were used. The participants answered either ‘‘true’’ or ‘‘false’’ to each ofthe 226 items of the TCI. They returned the completed questionnaires under cover(envelope given withthe questionnaire) to the students. They were also informed of the anonymous nature of the study.

1.2. Statistical analyses

All the measurement models shown in Figs. 1–3, depicting a-priori hypotheses about relation-ships between indicators (observed variables) and underlying (latent) factors, were assessed withCFA. CFA is a subset of structural equation modeling (SEM) that is widely used in psychometricresearch, particularly in validation studies (Byrne, 1994). It is a way of determining whether a

Fig. 3. Total disaggregation models of TCI components.


sample data set is consistent with a predefined factor structure. Several fit indexes can be used toassess the plausibility of the theory-driven a-priori models, including the w2 statistic, the adjustedgoodness of fit index (AGFI), the comparative fit index (CFI), and the root mean square error ofapproximation (RMSEA; Hu & Bentler, 1999). And because our data were not multivariatenormal, robust statistics associated with the maximum likelihood method of estimation, providedby EQS (Bentler, 1995), were requested. The robust chi-square statistic (S–B w2), developed bySatorra and Bentler (1994), incorporates a scaling correction for the w2 statistic when distribu-tional assumptions are transgressed. And a robust version of CFI (R-CFI) can be computed byusing the S–B w2 instead of the usual w2 statistic.

2. Results

2.1. Descriptive statistics and reliability of TCI Scales and subscales

The mean scores for each of the seven personality scales and their subscales are displayed inTable 1. We obtained estimates of internal consistency for each scale and subscale of the TCI bythe use of Kuder–Richardson formula (K–R20). As indicated in the last column of Table 1, theinternal consistency coefficients for the TCI Scales are higher than 0.70, except for Persistence(0.61), probably due to the smallness of the number of items (8 items). Fourteen of the 24 TCIsubscales had coefficients lower than 0.70. The highest coefficient, 0.80, was found for C4 and thelowest one, 0.36, was found for C5.

2.2. CFA model analyses

2.2.1. Total aggregation models (Fig. 1)A test of the measurement model with temperament as latent factors and Novelty Seeking,Harm avoidance, Reward Dependence and Persistence as indicators (Fig. 1a) generated animproper solution (one negative residual variance). By forcing these parameters to be nonnegative,EQS provides a good fit to the model. According to Marsh et al. (1998), it seems reasonable toevaluate the fitting of improper solutions. Of course, interpretation of findings from impropersolutions should be done cautiously. Presented with findings of S–B w2(2, N=689)=29.10,R-CFI=0.80 and RMSEA=0.17 (90% confidence interval (CI)=0.13–0.22), it seems obvious thatthe total aggregation model of Temperament does not represent a good fit to the present data.Because only three indicators were used (Self-directedness, Cooperativeness and Self-transcen-dence), the total aggregation model of Character (Fig. 1b) is just identified and thus fits the dataperfectly. Then, and as suggested by Bagozzi and Edwards (1998), to achieve overidentification,the three factor loadings were constrained to be equal. The results were indicative of an ill-fittingmodel: S–B w2(2, N=689)=66.75, R-CFI=0.17 and RMSEA=0.22. It is clear then that thehypothesis of equal factor loadings must be rejected.

2.2.2. Partial aggregation models (Fig. 2)The findings showed poor overall fits for both partial aggregation model of Temperament(Fig. 2a) (S–B w2(49, N=689)=379.4, R-CFI=0.78, RMSEA=0.10 (90% CI=0.09–0.11) and


partial aggregation model of Character (Fig. 2b) (S–B w2(49, N=689)=305.6, R-CFI=0.86,RMSEA=0.08 (90% CI=0.07–0.09). All parameter estimates were, however, significantly posi-tive in both models. In the Temperament Model, two of the four factors were independent ofeach other. The correlation between the Persistence factor and the Reward dependence factor waszero, and the correlation between Reward dependence and Harm avoidance was 0.08. The othercorrelations between factors were low (Persistence–Harm avoidance=�0.10, P<0.05; Persis-tence–Novelty seeking=�0.17, P<0.05) to moderate (Novelty seeking–Harm avoidance=�0.45,

Table 2Goodness-of-fit indexes for models representing each dimension of the Temperament and Character Inventory (TCI)

TCI dimensions
No. ofitems
S–B w2
d.f. R-CFI RMSEA 90% CI ofRMSEA
Factorloadingsranged

No. ofloadings>0.40

Novelty seeking (NS)
40 One-factor model 3340.20 740 0.38 0.072 0.069–0.074 0.10–0.50 6 Four-factor first-order mode 2250.50 734 0.64 0.055 0.053–0.058 0.12–0.73 20
Higher order model
2270.20 737a 0.64 0.055 0.053–0.058 0.11–0.73 20
Harm avoidance (HA)
35
One-factor model
2644.90 560 0.61 0.075 0.072–0.078 0.17–0.64 19 Four-factor first-order mode 2201.90 554 0.69 0.067 0.064–0.070 0.20–0.70 23 Higher order model 2224.20 556 0.68 0.067 0.064–0.070 0.19–0.70 23
Reward dependence (RD)
24 One-factor model 844.90 252 0.65 0.061 0.057–0.065 0.09–0.75 5
Three-factor first-order model
600.80 249 0.80 0.048 0.043–0.052 0.11–0.76 10 Higher order model 600.80 249 0.80 0.048 0.043–0.052 0.11–0.76 10
Persistence
08
One-factor model
66.90 20 0.91 0.059 0.044–0.075 0.15–0.64 5
Self-directedness (SD)
44
One-factor model
2865.10 902 0.54 0.060 0.058–0.062 0.09–0.54 15 Five-factor first-order model 2167.80 892 0.70 0.049 0.046–0.051 0.19–0.64 25 Higher order model 2178.20 897 0.70 0.049 0.046–0.051 0.19–0.65 25
Cooperativeness (C)
42 One-factor model 2181.90 819 0.53 0.058 0.056–0.061 0.10–0.65 8 Five-factor first-order model 1503.10 809 0.76 0.043 0.040–0.046 0.15–0.74 19
Higher order model
1554.10 814 0.75 0.044 0.042–0.047 0.13–0.74 19
Self-transcendence (ST)
33
One-factor model
1870.80 495 0.63 0.065 0.062–0.068 0.14–0.60 13 Three-factor first-order model 1772.40b 492 0.67 0.062 0.058–0.065 0.18–0.63 19 Higher order model 1772.40b 492 0.67 0.062 0.058–0.065 0.18–0.63 19
a The model was estimated with an equality constraint placed on two second-order disturbance terms needed to

achieve the identification of the model.b ML estimation with Amos 4 was used because EQS failed to perform the model.


P<0.05 ; Novelty seeking–Reward dependence=0.45, P<0.05). Thus, the four components seemdistinct. In the Character model, there was no relation between Cooperativeness and Self-trans-cendence (r=0.08) and the correlation between Self-directedness and Self-transcendence was low(r=�0.14, P<0.05) also the correlation between Self-directedness and Cooperativeness wasmoderate (r=0.34, P<0.05). These findings suggest that three facets of Character demonstratesatisfactory discriminant validity.

2.2.3. Total disaggregation models (Fig. 3)Because the EQS program does not handle more than 99 observed variables, CFAs for the twototal disaggregation models were conducted with the SEPATH module of STATISTICA (Stat-Soft, 1996) using the maximum likelihood estimation method. The results showed poor overallfits both for the total disaggregation model of Temperament (Fig. 3a) (w2(5558,N=689)=14593.1, CFI=0.41, RMSEA=0.066 (90% CI=0.065–0.067) and the total dis-aggregation model of Character (Fig. 3b) (w2(6899, N=689)=14999, CFI=0.47,RMSEA=0.051 (90% CI=0.050–0.052). Here the parameter estimates for more ‘‘fine-grainedanalyses’’ have a particular relevance. In the Temperament model, all factor loadings (except fourof them) were statistically significant, but very modest, varying from 0.04 to 0.37. Correlationsamong the four factors were quite similar to those in the partial aggregationmodel (Fig. 2a). Rewarddependence and Harm avoidance were independent from each other (r=0.01), as were Rewarddependence and Persistence (r=0.04). The correlation betweenNovelty seeking andHarm avoidancefactors was�0.38 (P<0.05), the correlation between Novelty seeking and Reward dependence was0.29 (P<0.05) and the correlation between Persistence factor and Novelty seeking factor was�0.33(P<0.05). These findings suggest that the four components are distinct. In the Character model, threeof the 119 factor loadings were not significant. Significant factor loadings ranged from 0.04 to 0.28,which are particularly small. The correlations among factors were low, pointing to the conclusion oftheir discriminant validity: the correlation between Cooperativeness and Self-transcendence was 0.10(P<0.05), the correlation between Cooperativeness and Self-directedness was 0.24 (P<0.05), andbetween Self-directedness and Self-transcendence was�0.22 (P<0.05).

2.2.4. Total disaggregation models of each dimension of the TCIHere to get more ‘‘fine-grained analyses’’, we chose to evaluate a set of three models separatelyfor each of the seven dimensions of the TCI, except for Persistence which is designed as an eight-item single factor. These evaluations consist of a simple one-factor model, a multiple-factor first-order model consistent with the design of each dimension of the TCI (Table 1), and a second-order factor model in which relations among first-order factors were hypothesized to reflect asingle higher order factor representing the dimension of the TCI. Thus, 19 models were evaluatedhere, three for each of the six dimensions, and one single-factor model for Persistence. Table 2summarizes the results for each model. The findings showed poor overall fit for all the modelsevaluated. It can be seen that, for any dimension, the one-factor solution yielded the worst fit. Itcan also be seen that, save for Persistence, the CFI value never reached the 0.90 critical levelsuggesting a well-fitting model (see the questioning of this conventional cutoff criterion by Hu &Bentler, 1999). A key finding here concerns the fit of individual parameters, where it was foundthat only 102 of the 226 item loadings reached 0.40 on their posited underlying dimension, whichis the widely accepted minimum in CFA (Heck, 1998).


3. Discussion

We undertook confirmatory factor analyses in order to examine the ‘‘cross-cultural’’ factorialinvariance of Cloninger et al’s (1994) TCI. Despite the fact that this model is assumed to be basedon genetics and neuropsychobiological foundations of personality, its factorial structure needs tobe well established. And despite the fact that the usefulness and the flexibility of CFA for theconstruct validation processes are now well demonstrated, TCI, as far as we know, has never beensubmitted to such analyses. Tomita et al. (2000) performed a CFA of a short Japanese version ofthe TCI containing only 122 items.2

In this study, we analyzed the full version of the TCI (226 items), and we tested severalmeasurement models representing tendencies influencing responses to this scale. These modelsseparately evaluated the two components, and each dimension of the TCI at different levels ofabstraction. The most abstract representation of a measure leads to a total aggregation model. Atthis level, two models in which dimensions (scales) were used as indicators of a component (i.e.temperament and character) were tested. The most concrete representation of a measure leads toa total disaggregation model in which items are treated individually. Under this level, two modelsin which dimensions of each component (i.e. temperament and character) had individual items asindicators were tested. In both models, dimensions were allowed to freely correlate: a four-factorfirst-order model underlying 107 items for temperament, and a three-factor first-order modelunderlying 119 items for character. Plus, as in Tomita et al. (2000), each dimension of the TCIwas analyzed separately. For each dimension, except for Persistence, three hypothetical repre-sentations were tested: a one-factor first-order model, a multidimensional model (based on facetsspecified in Cloninger’s model), and a single higher order model, in which intercorrelationsbetween facets in the multidimensional model were replaced by a secondary factor. A partialaggregation model represents a measure at an intermediate level of abstraction. Under this level,we fit two models. The first one is a temperament four-factor first-order model in which theirrespective subscales were treated as indicators, and a second one was a character three-factorfirst-order model in which their respective subscales were treated as indicators. It should be notedhere that the TCI has exclusively been used at the total aggregation level (see e.g. the recent paperof Vedeniapin, Anokhim, Sirevaag, Rohrbaugh, & Cloninger, 2001).Let us now turn to the results obtained in our study. The most general findings are: (1) thereliability of some of the subscales was, as in Brandstrom et al. (1998), very poor. In fact, 14 ofthe 24 subscales showed a values unacceptably low (ranging from 0.36 to 0.69). Brandstrom andCloninger (Brandstrom et al., 1998) did not consider this lack of internal consistency as a seriousproblem. They argued that all important conclusions drawn in both clinical and epidemiologicalstudies were based on scores from dimensions (higher-level scales) which are a summation ofsubscales. One can then wonder about the usefulness and the relevance to specify subscales in theTCI. And this questioning leads to another one: are scales (dimensions) of the TCI unidimen-sional, multidimensional (represented by related subscales) or hierarchical? Our second mostrelevant general finding gives an answer to this question. (2) None of the 25 models we tested in

2 Several studies used CFA to identify the structure of Cloninger’s Tridimensional Personality Questionnaire (TPQ,

a 100-item self-report questionnaire), which is the « ancestor » of the TCI. For instance: Bagby, Parker, and Joffe(1992), and Parker, Bagby, and Joffe (1996).


this study provided an adequate fit to our sample data. Thus, there is no evidence from ourfindings that could support using either a single score for each scale (dimension) or a score foreach subscale. Indeed, none of the representations of the TCI we hypothesized fit the data. Itseems clear that neither partial and total aggregation models nor total disaggregation modelsconstituted adequate representations of the TCI. Tomita et al. (2000) concluded, based on theirCFA of a 122-item short version of the TCI completed by a Japanese sample, that the dis-aggregation models (they named ‘‘item-to-scale’’ models) using the whole items as indicators ofscales did not represent a good fit to their data. They argued that the factorial replicability of theTCI could be difficult to achieve because the construction of this measure ‘‘was based not onresults of factor analysis, as with NEO-PI, but on theoretical considerations’’ (p. 723). We thinkthat there is no incompatibility between a theory-based approach and factorial approach in testconstruction. Factor analysis, both exploratory and confirmatory, helps one to understand thetheoretical constructs (Mulaik, 1987). While exploratory factor analysis serves essentially forgenerating hypotheses, CFA fundamentally serves for testing them subsequently. And the use-fulness of CFA in construct validation is now well known and well-documented. In fact, stan-dardized factor loadings generated by CFA could be considered as ‘‘validity coefficients’’ (Bollen,1989). In other words, a factor loading serves as an index of the validity of the item as indicatorof the underlying construct it is intended to measure. The widely accepted minimum value of thisvalidity coefficient is 0.40 (Heck, 1998). Using this cutoff criterion, only 102 of the 226 items ofthe TCI could be considered as valid indicators of the constructs they are purported to measure.This result authorizes us to agree with De la Rie et al. (1998) that ‘‘the operationalization ofseveral subscales needs to be refined to improve the discrimination from other scales ’’ (p. 371). Itis important here to emphasize that validity is always temporary because the construct validationis often a perpetual process. The TCI is also concerned. Thus, the best way to improve a measureis to choose and keep valid indicators. CFAs of responses to the full 226-item questionnaire fromvarious samples and cultures are needed to achieve the factorial structure invariance of the scale.And because the use of dichotomous data in CFA is always a source of problems, Schumackerand Beyerlein (2000) recommended the use of the multinomial full information based on ModernItem Response Theory. This seems a fruitful direction for further research concerning the TCI.

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structure invariance of the temperament and character inventory (tci)

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