Reliability and validity of the Italian version of the Temperament and Character Inventory-Revised in an outpatient sample

Comprehensive Psychiatry 48 (2007) 380 – 387 www.elsevier.com/locate/comppsych

Reliability and validity of the Italian version of the Temperament and Character Inventory-Revised in an outpatient sample Andrea Fossatia,4, C. Robert Cloningerb, Daniele Villaa, Serena Borronia, Federica Graziolia, Laura Giarollia, Marco Battagliaa, Cesare Maffeia a School of Psychology, Vita-Salute San Raffaele University, 20127 Milan, Italy Division of Biology and Biomedical Sciences, Washington University Medical School, Saint Louis, MO 63130, USA

b

Abstract Objective: The aim of this study was to evaluate the reliability and validity of the Temperament and Character Inventory-Revised (TCI-R) in an outpatient sample. Method: The TCI-R was administered to 404 consecutively admitted subjects. The TCI-R scale 1-month test-retest reliability and TCI-R/TCI convergent validity were assessed in 2 independent subsamples. Results: The TCI-R scales showed adequate Cronbach a values and acceptable 1-month test-retest reliability coefficients. Although many TCI-R facets showed factorial complexity, factor analysis results were consistent with the 7-factor structure of the TCI-R scales. The predictive validity of TCI-R profiles for personality disorder diagnoses was confirmed, with different combinations of temperament dimensions being associated with specific personality disorders. Conclusions: The TCI-R was a reliable and valid instrument for assessing temperament and character features, at least among Italian outpatients. The TCI-R psychometric properties support its clinical usefulness in the assessing of personality psychopathology. D 2007 Elsevier Inc. All rights reserved.

1. Introduction Cloninger has proposed temperament and character dimensions as constitutive elements of both normal and pathological personality [1,2]. Temperamental features are defined as individual differences that are presupposed to be independently inheritable, manifest in early life, and related to emotion regulation and perception-based habits and skills; at the opposite end, character dimensions are thought to represent individual differences in self-concepts, goals, and values, which develop during life through social experience [1,2]. Importantly, according to this model, individuals with the same temperament may behave in very different ways as a result of differences in character development [1,2]. Starting from these considerations and building on the previous experience of the Three-Dimensional Personality

4 Corresponding author. Servizio di Psicologia Clinica e Psicoterapia, Istituto Scientifico H San Raffaele, 20127, Milano, Italy. Tel.: +39 02 2643 3241; fax: +39 02 2643 3408. E-mail address: [email protected] (A. Fossati). 0010-440X/$ – see front matter D 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.comppsych.2007.02.003

Questionnaire—which was originally designed to measure 3 basic temperament dimensions [3,4]—Cloninger et al [5] developed the Temperament and Character Inventory (TCI), a 240-item true/false, self-report questionnaire, which was designed to assess 4 temperament dimensions (ie, Novelty Seeking [NS], Harm Avoidance [HA], Reward Dependence [RD], and Persistence [P]) and 3 character dimensions (ie, Self-directedness [SD], Cooperativeness [C], and Selftranscendence [ST]). Despite its clinical relevance and its good predictive validity features in deepening the comprehension of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Axis I [6] and Axis II [7,8] psychopathology and in gaining insight on Axis I/Axis II comorbidity [9], TCI showed controversial psychometric properties. On the one hand, TCI reliability studies consistently reported adequate reliability coefficient values for most of the scales. As a whole, reliability coefficient values (Cronbach a internal consistency and test-retest reliability) for TCI scales were adequate both in clinical [5,6] and nonclinical [1,10,11] samples, although RD and P scales showed somewhat low internal consistency reliability.

A. Fossati et al. / Comprehensive Psychiatry 48 (2007) 380 – 387

On the other hand, factor analysis studies yielded controversial results as to the latent structure of the TCI. Although some studies provided evidence for a 7-factor structure of the TCI facets [1,4,6,10], it should be observed that in these studies, temperament and character scales were factor-analyzed separately. When joint factor analyses of both temperament and character facets were attempted [11-14], some dimensions did not emerge as distinct factors, and temperament and character facets overlapped with one another, suggesting that the conceptual distinction between temperament and character may not be empirically supported [14,15]. Despite the widespread use and clinical relevance of the questionnaire, as a whole, these findings suggested the need for major revisions of the TCI. Accordingly, in 1999, Cloninger released the Temperament and Character Inventory-Revised (TCI-R). The TCI-R differs from the TCI in several aspects; the true-false item scale was replaced by Likert-type scale (1—definitely false; 2—mostly or probably false; 3—neither true nor false or about equally true or false; 4—mostly or probably true; 5—definitely true). The TCI-R contains the same number of items as the TCI (ie, 240); however, 189 items were not modified, and 51 items have been completely rewritten in the TCI-R (these included 5 validity items). In TCI, there was only 1 short scale measuring P scale and 3 scales measuring RD scale; in TCIR, both P scale and RD scale are now composed of 4 facets. Up to now, there has been just one study that tried to assess the reliability and validity issues of the TCI-R, at least in its French translation [16]; this study showed that the TCI-R is provided with adequate internal consistency and test-retest reliabilities and supported the 7-factor structure of the TCI-R facets, although no joint confirmatory factor analyses of temperament and character scales were performed, and a mixed sample (clinical and nonclinical subjects) was used. Starting from these considerations, this study aimed to assess TCI-R reliability and factor structure of the TCI-R in a clinical outpatient sample, trying also to investigate the convergent validity of TCI-R scores with TCI score in a random subsample. The predictive validity of TCI-R scales with respect to dimensionally assessed DSM-IV personality disorders (PDs) was also evaluated in this study. Differently from previous investigations, in this study, we aimed at testing the TCI-R factor structure using a confirmatory approach. Although maximum likelihood confirmatory factor analysis (MLCFA) should be considered in principle the method of choice for the analysis of latent dimensions underlying covariance matrices, its usefulness for personality data has been called into question [17,18]. The MLCFA is best suited for the analysis of simple structure models; in typical applications, variables are hypothesized to load on a single specified factor, and the loadings on the other factors are fixed at zero. Unfortunately, in personality research, the secondary loadings of the variables are usually not zero because of their factorial complexity or other methodolog-

381

ical aspects; in these cases, MLCFA with a simple structure model may not work appropriately, and other confirmatory factor analysis methods are required [17,18]. Thus, in this study, both multiple-group component analysis and Procrustes rotation, with Monte Carlo validation of fit indices, were used to assess the factor structure of the TCI-R.

2. Methods 2.1. Subjects The sample was composed of 504 psychiatric outpatients consecutively admitted to the Clinical Psychology and Psychotherapy Unit of the San Raffaele Hospital in Milan, Italy; this Unit is highly specialized in the diagnosis and psychotherapy treatment of personality psychopathology. The subjects voluntarily contacted the Unit asking for psychotherapy. All subjects volunteered to participate in the study after a detailed description was presented, and all were treated in accordance with the Ethical Principles of Psychologists and Code of Conduct [19]. To be included in the sample, participants should not meet any of the following exclusion criteria: (1) IQ less than 75 as assessed by the official Italian version of the Wechsler Adult Intelligence Scale-Revised [20]; (2) diagnosis of schizophrenia, schizoaffective disorder, schizophreniform disorder, delusional disorder, dementia, or organic mental disorder according to the diagnostic criteria listed in the DSM-IV; and (3) educational level lower than elementary school. The sample included 189 (37.5%) males and 315 (62.5%) females. The mean age was 34.02 years (standard deviation, 11.031). Among the 504 subjects, 289 (56.7%) received at least one DSM-IV Axis I diagnosis. Axis I disorders were assessed by the clinicians who followed the participants in psychiatric treatment. The most frequently diagnosed Axis I disorders were anxiety disorders (n = 84, 16.7%), substance abuse/dependence disorders (n = 105, 20.8%), eating disorders (n = 56, 11.1%), and mood disorders (n = 28, 5.6%); 18 subjects (3.6%) received other DSM-IV Axis I diagnosis (sleep disorders, sexual disorders, somatoform disorders, etc). According to the Structured Clinical Interview for DSMIV Axis II PDs (SCID-II, Version 2.0) [20], 332 subjects (65.9%) received at least one DSM-IV PD diagnosis. The most frequently diagnosed DSM-IV PDs were narcissistic PD (NPD; n = 79, 15.7%), passive-aggressive PD (PAPD; n = 77, 15.3%), avoidant PD (APD; n = 53, 10.5%), histrionic PD (HPD; n = 51, 10.1%), obsessive-compulsive PD (OCPD; n = 50, 9.9%), borderline PD (BPD; n = 24, 4.8%); 21 subjects (4.2%) received a PD not otherwise specified diagnosis. The higher prevalence of Axis II diagnoses compared with that of axis I disorders, which was observed in this sample, may be explained by the fact the Clinical Psychology and Psychotherapy Unit of the San Raffaele Hospital is highly specialized in the treatment of

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personality psychopathology. All subjects were administered the TCI-R. Convergent validity was assessed in a subsample of 65 subjects that also agreed to be administered the TCI, whereas test-retest reliability was evaluated in a second subsample of 30 subjects that were readministered the TCI-R roughly 4 weeks after the first administration. 2.2. Measures 2.2.1. Temperament and Character Inventory-Revised The TCI-R was translated from English into Italian by one of the authors (M. B.) and independently translated back to English by an English mother tongue professional translator. Main features of the TCI-R were described in detail earlier in this article. The TCI-R is a 240-item selfadministered questionnaire designed to measure 4 temperaments (NS, HA, RD, and P) and 3 character (SD, C, and ST) dimensions. The TCI-R items are listed in random order; roughly half of the items are scored in reverse order. 2.2.2. Structured Clinical Interview for DSM-IV Axis II PDs, Version 2.0 [21] The SCID-II is a 140-item semistructured interview that was designed to yield both categorical and dimensional assessment of the 10 PD diagnoses that are listed in the Axis II of the DSM-IV, as well as the 2 PDs (Depressive PD [DEPD] and PAPD) that were included in the DSM-IV for further studies. The SCID-II was translated by one of the authors (A. F.), and the adequacy of the Italian translation with respect to the original version was assessed through independent back versions by a professional English mother tongue translator. Subjects with Axis I disorders were administered the SCID-II during acute symptom remission. The SCID-II was administered by trained interviewers blind to the aims of the study and to TCI/TCI-R profiles. The SCID-II interviewers were psychiatrists or clinical psychologists with at least 6 months of specific training in the use of SCID-II interview. Interrater reliabilities for dimensionally assessed DSM-IV PDs were assessed in a subsample of 56 consecutively admitted subjects using a pairwise interview design; intraclass correlation coefficient values were acceptable for all PDs (median, 0.88; 25th percentile, 0.79; 75th percentile, 0.94). Both TCI-R and SCID-II were administered to the subjects as part of a standard diagnostic assessment procedure for the outpatients admitted to the Clinical Psychology and Psychotherapy Unit of the San Raffaele Hospital in Milan. 2.3. Statistical analyses 2.3.1. Item analyses and reliability Item-total correlation coefficients corrected for item-total overlap were computed to measure item validity; Cronbach a coefficient was used to assess the internal consistency reliability of the TCI-R facets and scales. Pearson r

coefficient was calculated to assess the temporal stability of TCI-R scores and the convergence between TCI and TCIR scores. 2.3.2. Multiple-group confirmatory component analyses In the sample, oblique multiple-group component analysis [22] was used to evaluate the factor structure of the TCI-R facets. The excessive number of items (n = 240) and the huge amount of noise that is usually present when itemlevel data are factor analyzed prevented from performing multiple-group component analyses on TCI-R items. Multiple-group component analyses define factors as the equally weighted sum of variables presumed to define each cluster [22,23]. The percentage of variance explained by the multiple-group component solution and the root-meansquare error (RMS) were used as fit indices. Next, the variance accounted for by the solution was compared with the variance explained by the first 7 principal components (PCs) [22]. To explore how much of the difference in explained variance is due to the tendency of PCs to capitalize upon chance, a cross-validation design was used; that is, the PC factor score weights from one subsample were used on the other sample. Shrinkage is defined as the difference in fit (ie, the percentage of explained variance) obtained using a group’s own weights vs the other group’s weights, averaged over the 2 samples [24]. This cross-validation was carried out using groups defined on the basis of random splitting. As a further measure of fit, the percentage of variance, explained by the multiple-group components defined by the hypothesized structure of the TCI-R items, was compared with the variance explained by a similar number of pseudofactors [24]. The pseudofactors were formed by randomly assigning variables to factors, maintaining the same number of variables on each pseudofactor as the corresponding theoretical TCI-R factor. 2.3.3. Procrustes oblique rotations Because the TCI-R scales represent dissociable, although interrelated domains of personality, the Promax procedure was used to rotate the extracted factors. Congruence coefficients (CC) were computed to evaluate if the rotated factors matched the binary target matrix of 1s and 0s representing the hypothesized factor loadings based on the 7-factor model of the TCI-R; a CC value of 0.90 is typically considered necessary to define a matching factor. To examine the extent to which differences between the target and the Promax matrix were due solely to the orientation of the axes, we performed oblique Procrustes rotation. After performing the targeted rotation, we computed factor, variable, and total CCs between the target matrix and the Procrustes-rotated replication matrix. Variables CC values indicate the extent to which variables load on their expected factors and do not load on other factors, and they reflect both variable convergent and discriminant validity issues.

A. Fossati et al. / Comprehensive Psychiatry 48 (2007) 380 – 387

The significance of CCs was tested by comparing the observed factor congruencies with the distribution of congruencies obtained after Procrustes rotation of the data to 1000 independent random targets that were obtained by randomly permuting the elements of the original target matrix. If the fit of the real data set was greater than 95% of the random target fits, we were able to conclude, with better than 95% confidence, that the fit of the real data is not simply due to capitalization on chance. 2.3.4. Linear and logistic regressions Both linear and logistic regression analyses were performed to evaluate the efficiency of TCI-R scales in predicting dimensionally assessed DSM-IV PDs and the presence of any DSM-IV PD diagnosis, respectively. Predictors to be included in the regression equation were selected using a stepwise algorithm; the F-to-enter and F-toremove P values were set to .05 and .10, respectively.

3. Results 3.1. Item analyses and reliability Descriptive statistics and internal consistency reliabilities (ie, Cronbach coefficient) of the TCI-R scales and facets are listed in Table 1. All TCI-R scales showed satisfactory Cronbach a values. On average, the internal consistency reliabilities were acceptable also for TCI-R facets (median a = .72, 25th percentile = .56, 75th percentile = .74), with the only possible exception being the Dependence facet. Itemtotal correlation values were on average in the acceptableto-good range. However, in several scales, a nonnegligible minority of items showing unsatisfactory (ie, b .20) itemtotal r values was observed; this result seems to suggest the need for further refinement of some TCI-R items. Although the small sample size limits the generalization of these findings, 1-month test-retest reliability coefficients were acceptable for all TCI-R scales, although NS, SD, and C total scores showed lower 1-month stability when compared with the other TCI-R scales. In particular, test-retest correlation (Spearman q) coefficients were 0.52, 0.74, 0.74, 0.78, 0.65, 0.60, and 0.80 for NS, HA, RD, P, SD, C, and ST, respectively. A moderate convergence between TCI-R and TCI scales was observed in this study; correlation coefficients were 0.77, 0.74, 0.46, 0.57, 0.69, 0.59, and 0.62 for NS, HA, RD, P, SD, C, and ST, respectively. Once these correlations were corrected for the attenuation due to measurement error, their corresponding values improved markedly for all scales (NS, r = 0.96; HA, r = 0.84; RD, r = 0.67; P, r = 0.78; SD, r = 0.80; C, r = 0.70; and ST, r = 0.75). 3.2. Multiple-group component analyses The 7 multiple-group components based on Cloninger’s model of TCI-R scales explained 61.4% of the total variance of TCI-R facets, whereas the first 7 PCs of the TCI-R facet

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Table 1 Descriptive statistics and internal consistency reliability (Cronbach a coefficients) of the TCI-R scales and facets on the whole sample TCI-R facets and scales

r¯ ij

n

a

% of items with r it b 0.20

% of items with r it b 0.10

r¯ it

Exploratory excitability (NS1) Impulsivity (NS2) Extravagance (NS3) Disorderliness (NS4) NS total score Anticipatory worry (HA1) Fear of uncertainty (HA2) Shyness (HA3) Fatigability (HA4) HA total Score Sentimentality (RD1) Openness to warm communication (RD2) Attachment (RD3) Dependence (RD4) RD total score Eagerness of effort (P1) Work hardened (P2) Ambitious (P3) Perfectionist (P4) P total score Responsibility (SD1) Purposeful (SD2) Resourcefulness (SD3) Self-acceptance (SD4) Enlightened second nature (SD5) SD total score Social acceptance (C1) Empathy (C2) Helpfulness (C3) Compassion (C4) Pure-hearted conscience (C5) C total score Self-forgetful (ST1) Transpersonal identification (ST2) Spiritual acceptance (ST3) ST total score

0.12

10

.56

30.0

20.0

0.30

0.23 0.29 0.13 0.11 0.20

9 9 7 35 11

.72 .78 .53 .81 .72

11.0 0.0 28.6 11.4 9.1

1.0 0.0 0.0 2.9 9.1

0.38 0.52 0.33 0.28 0.44

0.25

7

.70

0.0

0.0

0.42

0.35 0.32 0.20 0.14 0.21

7 8 33 8 10

.79 .79 .89 .55 .72

0.0 0.0 6.1 12.5 0.0

0.0 0.0 3.0 12.5 0.0

0.51 0.51 0.45 0.28 0.39

0.29 0.13 0.11 0.28

6 6 30 9

.71 .49 .79 .78

16.7 33.3 23.3 11.1

0.0 0.0 6.7 0.0

0.49 0.24 0.31 0.49

0.25 0.30 0.23 0.23 0.27 0.25 0.28

8 10 8 35 8 6 5

.73 .81 .70 .91 .74 .67 .66

0.0 0.0 0.0 2.9 0.0 16.6 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.45 0.50 0.38 0.48 0.43 0.43 0.44

0.25

10

.77

0.0

0.0

0.48

0.19

11

.73

0.0

0.0

0.40

0.14 0.25

40 8

.87 .73

12.5 0.0

5.0 0.0

0.36 0.44

0.18 0.12 0.21 0.12

5 8 7 8

.51 .52 .68 .55

20.0 37.5 28.6 50.0

0.0 12.5 14.3 12.5

0.27 0.27 0.46 0.20

0.13 0.22 0.27

36 10 8

.85 .74 .74

19.4 0.0 0.0

8.3 0.0 0.0

0.35 0.41 0.44

0.12

8

.54

37.5

12.5

0.38

0.17

26

.84

11.5

11.5

0.38

r¯ ij indicate average interitem correlation; r it, item-total correlation corrected for item-total overlap; r¯ it, average item-total correlation corrected for itemtotal overlap.

correlation matrix explained 64.5% of the total variance; the difference between the 2 component models was only 3.1%. The 10 shrinkage values that were computed in this study using the cross-validation method ranged from 3.0% to 4.4%, with an average value of 3.8%. In other words, the

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difference that was observed between the variance explained by the first 7 components and the variance explained by the 7 multiple-group components fell within the range of shrinkage values, that is, the estimate of the inflation of the percentage of variance explained by the first 7 PCs due to the tendency of PC models to capitalize upon chance. In turn, this implies that the relative lack of fit of the multiplegroup component model was only due to the PC tendency to capitalize upon chance. Interestingly, the pseudofactor model explained a much lower proportion of variance (44.8%), with an RMS residual value as large as 0.11. In contrast, the multiple-group components showed an RMS value of 0.05, which indicates good fit and which was not greater than the value observed for the first 7 PCs (RMS = 0.05). The multiple-group model of TCI-R scales was safely replicated across subsamples that were defined by sex, presence of any Axis I diagnosis, and presence of any Axis II diagnosis, as it was indicated by factor score correlations that ranged from 0.77 to 0.94 and by CC values that were greater than 0.90 for all factor comparisons. The structure coefficient matrix of the 7 multiple-group components is shown in Table 2. Although all the TCI-R facets showed substantial and significant ( P b .001) correlations with their respective TCI-R scales, a nonnegligible factor complexity was observed for several TCIR facets. Indeed, among the 174 correlations that were computed between each facet and the components on which it was not expected to load according to Cloninger’s theoretical model, 66 (37.9%) were positive and nonplanar (ie, z .10), and 24 (13.9%) were equal to or greater than .30. However, it should be stressed that only RD Dependence facet (RD4) showed poor discriminant validity because it correlated .48 with its component (ie, the TCIR RD scale) and .42 with the C component. As expected, the 7 components were significantly intercorrelated; the NS component was negatively correlated with the HA component (r = 0.20, P b .001), whereas the latter showed a moderate, negative association with the P (r = 0.35, P b .001) and S (r = 0.57, P b .001) components. The RD component was positively associated with C component (r = 0.46, P b .001), whereas the P component significantly correlated with the SD (r = 0.41, P b .001), C (r = 0.30, P b .001), and ST (r = .33, P b .001) character components. Finally, a significant association was observed between SD and C components (r = 0.41, P b .001). 3.3. Procrustes oblique rotations The first 8 eigenvalues of the TCI-R facet correlation matrix were 6.09, 3.37, 2.92, 2.52, 1.43, 1.34, 1.01, and 0.95, respectively. Thus, according to the Kaiser rule, that is, the eigenvalue-greater-than-one rule, only the first 7 components were retained for further analyses, a decision that was supported also by the scree test. After Promax rotation, only 2 PCs matched the corresponding theoretical target matrix; namely, PC4 closely matched TCI-R ST scale

Table 2 The TCI-R multiple group component analysis: structure coefficient matrix Multiple-group components 1 NS1 NS2 NS3 NS4 HA1 HA2 HA3 HA4 RD1 RD2 RD3 RD4 P1 P2 P3 P4 SD1 SD2 SD3 SD4 SD5 C1 C2 C3 C4 C5 ST1 ST2 ST3

2 0.50 0.71 0.78 0.68 0.02 0.05 0.14 0.06 0.05 0.32 0.30 0.14 0.11 0.10 0.18 0.29 0.04 0.08 0.05 0.24 0.27 0.14 0.04 0.11 0.21 0.22 0.22 0.05 0.01

3 0.17 0.00 0.03 0.10 0.73 0.71 0.68 0.78 0.11 0.26 0.09 0.20 0.31 0.42 0.35 0.31 0.29 0.41 0.52 0.26 0.41 0.16 0.21 0.20 0.00 0.05 0.09 0.19 0.00

4 0.14 0.07 0.23 0.12 0.07 0.07 0.13 0.05 0.61 0.72 0.71 0.48 0.11 0.08 0.05 0.5 0.2 0.06 0.01 0.12 0.02 0.30 0.42 0.40 0.30 0.38 0.13 0.21 0.04

5 0.10 0.24 0.20 0.22 0.37 0.22 0.20 0.45 0.17 0.09 0.11 0.01 0.78 0.87 0.79 0.84 0.12 0.44 0.53 0.05 0.39 0.23 0.19 0.35 0.18 0.24 0.26 0.33 0.09

6 0.14 0.27 0.12 0.26 0.54 0.24 0.36 0.40 0.21 0.12 0.06 0.01 0.30 0.41 0.32 0.39 0.70 0.72 0.82 0.64 0.68 0.34 0.23 0.33 0.27 0.29 0.13 0.02 0.10

7 0.13 0.27 0.12 0.26 0.12 0.02 0.08 0.19 0.32 0.29 0.18 0.42 0.32 0.34 0.17 0.21 0.25 0.24 0.30 0.33 0.28 0.80 0.70 0.71 0.77 0.76 0.08 0.28 0.09

0.11 0.03 0.02 0.12 0.03 0.12 -0.06 0.12 0.36 0.17 0.03 0.18 0.23 0.29 0.20 0.18 0.26 0.03 0.03 0.14 .02 0.07 0.29 0.03 0.07 0.19 0.83 0.86 0.81

NS1 indicates exploratory-excitability; NS2, impulsiveness; NS3, extravagance; NS4, disorderliness; HA1, anticipatory worry; HA2, fear of uncertainty; HA3, shyness; HA4, fatigability; RD1, sentimentality; RD2, openness to warm communication; RD3, attachment; RD4, dependence; P1, eagerness of effort; P2, work hardened; P3, ambitious; P4, perfectionist; SD1, responsibility; SD2, purposeful; SD3, resourcefulness; SD4, selfacceptance; SD5, enlightened second nature; C1, social acceptance; C2, empathy; C3, helpfulness; C4, compassion; C5, pure-hearted conscience; ST1, self-forgetful; ST2, transpersonal identification; ST3, spiritual acceptance.

(CC = 0.92), whereas PC6 closely resembled the expected structure of NS scale (CC = 0.91). After realigning the factor axes to the binary target matrix using Procrustes rotation, a moderate improvement in the measures of fit was observed (see Table 3). All factor CC coefficients exceeded the 95th percentile of the distribution of CC coefficient that was generated by performing 1000 Procrustes rotations, based on random target matrices that were generated by randomly permuting the elements of the original target matrix. The CC values suggestive of a close match between theoretical model and real data were observed for NS, P, and ST factors. The other CC values were large and significant, although they seemed to indicate that the theoretical model of the corresponding TCI-R scales was strongly related to, but not closely reproduced, by the remaining factors. Although all TCI-R facets showed substantial loadings on their theoretically expected factors, roughly 52% of the

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Table 3 Procrustes analysis results: factor loadings and CCs TCI-R facets NS1 NS2 NS3 NS4 HA1 HA2 HA3 HA4 RD1 RD2 RD3 RD4 P1 P2 P3 P4 SD1 SD2 SD3 SD4 SD5 C1 C2 C3 C4 C5 ST1 ST2 ST3 Factor/Overall CC a b

Factor loadings 1

2 0.78 0.91 0.92 0.72 0.14 0.19 0.06 0.05 0.05 0.13 0.08 0.01 0.13 0.07 0.01 0.22 0.16 0.11 0.07 0.20 0.40 0.03 0.10 0.01 0.16 0.10 0.26 0.08 0.11 0.91b

3 0.10 0.12 0.10 0.31 0.64 0.63 0.55 0.54 0.08 0.09 0.09 0.40 0.05 0.08 0.00 0.05 0.19 0.30 0.31 0.18 0.32 0.19 0.26 0.05 0.18 0.35 0.07 0.10 0.09 0.77b

4 0.06 0.11 0.17 0.04 0.23 0.26 0.17 0.16 0.45 0.89 0.85 0.42 0.05 0.07 0.15 0.04 0.10 0.07 0.17 0.30 0.09 0.19 0.32 0.39 0.01 0.08 0.11 0.02 0.09 0.81b

5 0.17 0.11 0.03 0.07 0.01 0.06 0.18 0.21 0.18 0.06 0.12 0.10 0.76 0.77 0.87 0.81 0.02 0.26 0.30 0.35 0.12 0.15 0.11 0.17 0.05 0.09 0.22 0.02 0.11 0.90b

6 0.19 0.18 0.12 0.37 0.45 0.24 0.37 0.24 0.48 0.04 0.13 0.11 0.05 0.16 0.01 0.12 0.58 0.42 0.60 0.60 0.47 0.12 0.01 0.16 0.23 0.34 0.17 0.03 0.03 0.73a

Variable CC

7 0.07 0.06 0.08 0.09 0.04 0.07 0.13 0.14 0.44 0.12 0.02 0.46 0.23 0.10 0.30 0.08 0.26 0.01 0.04 0.39 0.12 0.90 0.67 0.68 0.80 0.56 0.06 0.20 0.05 0.85b

0.08 0.07 0.01 0.10 0.03 0.04 0.06 0.14 0.17 0.03 0.02 0.35 0.04 0.07 0.01 0.02 0.21 0.00 0.04 0.06 0.05 0.05 0.20 0.12 0.07 0.22 0.82 0.82 0.92 0.92b

0.93a 0.96a 0.97a 0.82a 0.77a 0.84a 0.76a 0.80a 0.54 0.97a 0.97a 0.51 0.94a 0.95a 0.93a 0.95a 0.80a 0.71a 0.79a 0.67 0.66 0.94a 0.81a 0.82a 0.92a 0.71 0.90a 0.96a 0.97a 0.84b

Congruence higher than 95% of rotations from random data. Congruence higher than 99% of rotations from random data.

TCI-R facets showed CC variable values that were below 0.90, suggesting a high degree of factor complexity. This problem was particularly evident in the case of RD1 (Sentimentality), RD4 (Dependence), SD4 (Self-acceptance), and SD5 (Enlightened second nature) facets. In turn, the high degree of factor complexity of several TCI-R scales may explain the relative lack of close fit that was observed between some rotated factors and the theoretical model of facet assignment to scales. 3.4. Temperament and Character Inventory-Revised scale predictive validity Finally, we tested the validity of TCI-R scales in predicting any DSM-IV PD diagnosis and the individual dimensionally assessed DSM-IV PDs using stepwise logistic and linear regression analyses, respectively. Significant (ie, P b .05) predictors are shown in Table 4. Although the effect size estimates, that is, R 2 values, were small, nonetheless the results were consistent with Cloninger’s model of personality psychopathology [1]. In line with previous findings [1], TCI-R SD and C character scales seemed to represent general indicators of personality pathology because they predicted both the presence

of any PD diagnosis and the overall number of PD diagnoses. Consistently with Cloninger’s model of personality pathology [1], specific temperament configurations were useful in explaining both similarities and differences among DSM-IV PDs. For instance, both APD and dependent PD (DPD) were characterized by low NS and high HA scores; however, also the latter was predicted by high RD (as well as low SD). Interestingly, in line with what was observed in this study for all cluster B PDs, OCPD was characterized by low scores on the NS scale, but it was the only cluster C PD that was associated with high Persistence scores, rather than with high HA scores. Considering cluster A PDs, both schizotypal PD (SZPD) and schizoid PD subjects scored low on TCI-R RD scale, suggesting that aloofness may represent a dispositional trait that is common to both disorders. Consistently with its association with social anxiety and magical thinking, only SZPD showed elevations on the HA and ST scales. Novelty Seeking seemed to be a temperamental dimension common to all DSM-IV cluster B PDs; however, the low scores on SD character dimension may be useful in significantly discriminating BPD subject from subjects with

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A. Fossati et al. / Comprehensive Psychiatry 48 (2007) 380 – 387

Table 4 The TCI-R scales: regression analysis results TCI-R Scales NS HA RD P SD C ST R2

Standardized b coefficients Any PD

N. of PDs

APD

DPD

.21 .24

.15 .19 .21

OCPD

PAPD

DEPD

33

.22 .12

.17 .25 .09

PPD .09

SZPD .12 .16

SPD

.17

HPD

NPD

BPD

.22

.27

.22

.08a

.19 .15 .08a

.13

.19

.17 .22

.12a

.17a

.18

.17

.12 .02 (.98) .02 (.98)

ASPD

.13a

.12a

.17 .11a

.04a

.17 .05a

.03a

.12

.29

.09a

.17a

.09a

.03a

Regression coefficients in the case of any PD diagnosis were estimated using logistic regression analysis; exp(b) are listed between brackets. PPD indicates Paranoid PD; SPD, Schizoid PD; ASPD, Antisocial PD. The DSM-IV PDs are listed in SCID-II order. a P b .001.

other cluster B PD diagnoses. On the contrary, NPD diagnosis was significantly associated with low scores on C, as well as with high score on P scale. The high NS, low C profile characterized also HPD subjects, who also showed a significant elevation on the TCI-R RD scale. Interestingly, the TCI-R scale proved to be effective also in classifying the 2 PD diagnoses that were listed in the DSM-IV for further study, namely, DEPD and PAPD diagnoses. As shown in Table 4, the TCI-R profile of DEPD subjects was highly similar to the one observed for DPD subjects, whereas the temperament and character profile that were significantly associated with PAPD diagnosis closely resembled the TCI-R profile, that is, high NS scores and low C scores, of NPD and HPD subjects. In other words, the elevation on HA that differentiated PAPD subjects from NPD and HPD subjects, respectively, seems to suggest that PAPD may represent an anxious variant of narcissistic psychopathology. 4. Discussion The main purpose of this study was to assess various aspects of TCI-R reliability and validity in a large sample of Italian outpatients. In this study, internal consistency reliabilities were in the good-to-excellent range for all major TCI-R scales and showed a marked improvement with respect to those reported for the TCI, particularly when RD and P scales are considered [1,5,6,10,11,13]. With the exception of the Dependence facet (RD4), facet-level reliability analyses also suggested at least adequate Cronbach a values for all TCI-R facets. The reliability of TCI-R scale scores was also supported by test-retest correlations. Notwithstanding these positive findings, item analyses seemed to suggest possible further improvements of TCI-R scale internal consistency by revising a minority of items. Finally, the correlations between each TCI-R scale and the corresponding TCI scale seemed to imply a substantial overlap for NS, HA, P, SD, and ST scale scores; in other words, despite the small size of the convergent validity

subsample, this result suggests that TCI-R and TCI scales can be used interchangeably—on the condition that a common metric is used—to assess these 5 basic temperament and character dimensions. As a whole, both multiple-group component analysis and Procrustes rotation results were consistent with a 7-factor structure of the TCI-R scales. This result is particularly relevant because, differently from previous studies that were carried out using both TCI-R [16] and TCI [1,4,6,10] subscales, in this study, we jointly factor-analyzed temperament and character facets. Indeed, the 4 temperament latent dimensions and the 3 character latent variables clearly emerged as dissociable aspects of personality. However, it should be stressed that factor CCs indicated a close match between the empirically derived factor structures and the target matrix based on Cloninger’s model for only 3 TCI-R dimensions, namely NS, P, and ST, although all other CC values were large and significantly greater than those obtained by randomly permuting the elements of the target matrix. These findings, as well as the low CC values that were observed for several TCI-R facets, suggest that the factorial complexity of several TCI-R facets may be responsible for this partial reproduction of the theoretical model of facet attribution to TCI-R scales. Both logistic and linear regression analysis results were in agreement with previous findings on the predictive validity of TCI scales with respect to PD diagnoses [3,8,11]. In our opinion, the relatively small effect size (ie, R 2) values that were observed in this study largely reflect the use of different methods for assessing DSM-IV PDs and temperament/character dimensions. To our knowledge, this is the first report on the associations between DSM-IV PDs, temperament, and character dimensions that is based on the TCI-R. Consistent with previous findings [1,2], in this study, character dimensions—particularly SD and C scales—were general predictors of personality psychopathology, since they were significantly related to the presence of any PD diagnosis, as well as to the overall number of PD diagnoses.

A. Fossati et al. / Comprehensive Psychiatry 48 (2007) 380 – 387

On the contrary, the individual DSM-IV PD diagnoses were significantly associated with specific configurations of temperament and, to a lesser extent, character features. Interestingly, in line with previous findings based on the TCI [7,9], in this study, the TCI-R profile proved to be useful not only as a measure for identifying subjects with any PD diagnosis but also as an aid in the differential diagnoses between different specific DSM-IV PDs. As a whole, these findings suggest that TCI-R profiles may be clinically useful in deepening our understanding of PD psychopathology. These results should be considered in the light of several limitations. The sample was composed only of outpatients; this limits the ability to relate our findings to inpatient samples. Moreover, several sample peculiarities—for instance, the low base rate of mood disorders and the ratio of the Axis I/Axis II base rates—suggest extreme caution before extending the results of this study to other outpatient populations. These analyses should also be performed in an independent nonclinical sample to obtain normative data. The possible presence of some linguistic differences between the US and Italian versions of the TCI-R may have partly biased our results. Finally, great caution should be used in considering test-retest and convergent validity data because the small size of the sample limits the ability to generalize these findings. In summary, the TCI-R was a reliable and valid instrument for assessing temperament and character features, at least among Italian outpatients. These psychometric properties support the clinical usefulness of the TCI-R in the process of personality psychopathology assessment. References [1] Cloninger CR, Svrakic DM, Przybeck TR. A psychobiological model of temperament and character. Arch Gen Psychiatry 1993;50:975 - 90. [2] Cloninger CR, Svrakic DM. Integrative psychobiological approach to psychiatric assessment and treatment. Psychiatry 1997;60:120 - 41. [3] Cloninger CR. A systematic method for clinical description and classification of personality variants. A proposal. Arch Gen Psychiatry 1987;44:573 - 88. [4] Cloninger CR, Przybeck TR, Svrakic DM. The Three-Dimensional Personality Questionnaire: U.S. normative data. Psychol Rep 1991;69:1047 - 57. [5] Cloninger CR, Przybeck TR, Svrakic DM, Wetzel RD. The Temperament and Character Inventory (TCI): a guide to its development and use. St Louis (Miss)7 Center for Psychology of Personality, Washington University; 1994.

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[6] Sato T, Narita T, Hirano S, et al. Factor validity of the temperament and character inventory in patients with major depression. Compr Psychiatry 2001;42:337 - 41. [7] Fossati A, Donati D, Donini M, Novella L, Bagnato M, Maffei C. Temperament, character, and attachment patterns in borderline personality disorder. J Personal Disord 2001;15:390 - 402. [8] Svrakic DM, Whitehead C, Przybeck TR, Cloninger CR. Differential diagnosis of personality disorders by the seven-factor model of temperament and character. Arch Gen Psychiatry 1993;50:991 - 9. [9] Battaglia M, Przybeck TR, Bellodi L, Cloninger CR. Temperament dimensions explain the co-morbidity of psychiatric disorders. Compr Psychiatry 1996;37:292 - 8. [10] Brandstrom S, Schlette P, Przybeck TR, et al. Swedish normative data on personality using the Temperament and Character Inventory. Compr Psychiatry 1998;39:122 - 8. [11] De La Rie SM, Duijsens IJ, Cloninger CR. Temperament, character and personality disorders. J Personal Disord 1998;12:362 - 72. [12] Ball SA, Tennen H, Kranzler HR. Factor replicability and validity of the Temperament and Character Inventory in substance dependent patients. Psychol Assess 1999;11:514 - 24. [13] Gutierrez F, Torrens M, Boget T, et al. Psychometric properties of the Temperament and Character Inventory (TCI) questionnaire in a Spanish psychiatric population. Acta Psychiatr Scand 2001;103: 143 - 7. [14] Herbst JH, Zonderman AB, McCrae RR, Costa PT. Do the dimensions of the Temperament and Character Inventory map a simple genetic architecture? Evidence from molecular genetics and factor analysis. Am J Psychiatry 2000;157:1285 - 90. [15] Ando J, Suzuki A, Yamagata S, et al. Genetic and environmental structure of Cloninger’s temperament and character dimensions. J Personal Disord 2004;18:379 - 93. [16] Pelissolo A, Mallet L, Baleyte MG, Cloninger CR, Allilaire J. -F., Jouvent R. The Temperament and Character Inventory-Revised (TCIR): psychometric characteristics of the French version. Acta Psychiatr Scand 2005;112:126 - 33. [17] Church AT, Burke PJ. Exploratory and confirmatory tests of the Big Five and Tellegen’s three- and four-dimensional models. J Pers Soc Psychol 1994;66:93 - 114. [18] McCrae RR, Zonderman AB, Costa PTJR, Bond MH, Paunonen SV. Evaluating replicability of factors in the Revised NEO Personality Inventory: confirmatory factor analysis versus Procrustres rotation. J Pers Soc Psychol 1996;70:522 - 66. [19] American Psychological Association. Ethical principles of psychologists and code of conduct. Am Psychol 1992;47:1597 - 611. [20] Wechsler D. WAIS-R. Wechsler Adult Intelligence Scale Revised manual. Florence (Italy)7 Organizzazioni Speciali; 1997. [21] First MB, Spitzer RL, Gibbon M, Williams JBW, Benjamin L. Structured Clinical Interview for DSM-IV axis II personality disorders (SCID-II), version 2.0. New York7 Biometrics Research Department, New York State Psychiatric Institute; 1994. [22] Gorsuch RL. Factor analysis. Hillsdale (NJ)7 Erlbaum; 1983. [23] Nunnally JC, Bernstein IH. Psychometric theory. 3rd ed. New York (NY)7 McGraw-Hill; 1994. [24] Bernstein IH. Applied multivariate analysis. New York (NY)7 Spinger; 1988.