Factor structure of the pavlovian temperament survey in a Russian population: Comparison and preliminary findings

Person. indiuid.Dz$ Vol. 14, No. 4, pp. 557-563, 1993 Printed in Great Britain. All rights reserved

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Copyright 0 1993 Pergamon Press Ltd

FACTOR STRUCTURE OF THE PAVLOVIAN TEMPERAMENT SURVEY IN A RUSSIAN POPULATION: COMPARISON AND PRELIMINARY FINDINGS* MIKHAIL Institute

of Psychology,

Russian

Academy

V. BODUNOV

of Sciences,

Yaroslavskaya

13, 129366 Moscow,

Russia

(Received 17 April 1992) Summary--Our study aims were (a) to examine measurement properties of the Pavlovian Temperament Survey (PTS) on the Russian population and (b) to show that the same dimensions-Strength of Excitztion, Strength of Inhibition and Mobility of the central nervous system--exist in the Russian culture by comparison of factor patterns obtained on the Russian sample with factor pattern matrices revealed on the German population. The Russian version of the 252 item PTS was administered to a Moscow sample of 290 subjects. The results obtained showed a high level of reliability (internal consistency) of the PTS scales. Confirmatory factor modelling demonstrated satisfactory levels of convergent and discriminant validity of scale facets. Multi-sample analysis of the PTS factor structures in the Russian and German samples revealed their resemblance, which may indicate the cross-cultural similarity of underlying concepts.

INTRODUCTION

The Pavlovian Temperament Survey (PTS)t like its precursor-the Strelau Temperament Inventory(STI+is based on Pavlov’s theory of the central nervous system (CNS) properties. The inventory is of special interest because the descriptive typology was influenced directly by the theoretical, explanatory constructs proposed in Pavlov’s theory of the CNS properties. The three scales of the inventory were named according to Pavlov’s terminology Strength of Excitation (SE), Strength of Inhibition (SI) and Mobility (MO). In spite of the physiological terms used for labelling the properties to be measured by means of the inventory, it refers to overt behaviour. This means that the PTS, like the STI, is intended to measure temperamental traits, which are interpreted within the Pavlovian theory of properties of the CNS (Strelau, Angleitner & Ruth, 1990a). The items comprising the SE scale refer to readiness for activity and lack of emotional disturbances in highly stimulating situations or lack of evident changes in efficiency during intensive or long-lasting stimulation. The items of the SI scale concern the capacity for refraining from actions, delay of actions or ability to interrupt an action. The items included in the MO scale refer to the ability to react quickly and adequately to changing conditions (Strelau et al., 1990a). The ST1 was elaborated by Strelau in 1972 (Strelau, 1972). It was used in many countries and has proven to be a reliable and valid instrument for the measurement of generalized physiologically based temperamental traits with specific content (Strelau et al., 1990a; Carlier, 1985; Corulla, 1989; Daum, Hehl & Schugens, 1988; Daum & Schugens, 1986; San Martini & Mazzotti, 1990; Stelmack, Kruidenier & Anthony, 1985). In almost all of these investigations, positive correlations were revealed between the SE, SI, and MO scales of the ST1 with the highest correlation between the SE and MO scales. In factor analysis of items, two factors (or groups of factors) consistently appeared. One factor was largely composed of SI items and the other factor (or factors) composed of SE and MO items (Strelau et al., 1990a; Carlier, 1985; San Martini, Alessi & Borgogni, 1989; Stelmack et al., 1985). In several studies (Carlier, 1985; Stelmack et al., 1985), but especially in psychometric research by Strelau et al. (1990a), it was shown that the STI, in spite of its rather satisfactory construct *Paper presented at the International Workshop “Cross-Cultural Research on Temperament” of the European Association for Personality, Nieborow, Poland, September 1991. tFormer name was the Strelau Temperament Inventory-Revised (STI-R). To avoid confusions the senior authors of the STI-R decided to rename this inventory the Pavlovian Temperament Survey (PTS).

558

MIKHAIL V. BODUNOV

validity, was characterized by some psychometric disadvantages (extreme endorsements of many items, unsatisfactory item-scale correlations and others). These psychometric flaws as well as “the belief that the Pavlovian concepts of CNS properties are fruitful constructs to be applied in the research centered on biologically-based personality/temperament dimensions” (Strelau, Angleitner, Bantelmann & Ruth, 1990b, p. 210) stimulated Strelau and colleagues to revise the inventory. The revision was aimed at constructing the PTS on the basis of psychometric personality scale construction strategies proposed by Angleitner, John and Lijhr (1986). The PTS was standardized in Germany (Strelau et al., 1990b; Ruth, Angleitner & Strelau, 1991). One of the main advantages of the PTS, which is important for factor structure studies, is the two-level hierarchical composition of its scales. This allows the factor structure of the PTS to be assessed on the intermediate level of aggregated indexes of the main scales representing monodimensional construct measurement (Anderson & Gerbing, 1988). This study was undertaken with the following aims: (1) to examine measurement properties of the PTS on the Russian population; and (2) to show that the same dimensions (SE, SI, MO) exist in the Russian culture, by perusal of results of exploratory and confirmatory factor analyses applied to the Moscow sample data, and by comparison of factor patterns obtained on the Russian sample with factor pattern matrices produced by factor analysis of matrices of correlations between the PTS subscales, which were presented in the paper by Strelau et al. (1990b).

METHOD

The PTS was administered to a sample of 290 Ss (224 women, 66 men).* The mean age was 28.2 with a standard deviation of 6.4. The mean age of men was 30.6 (SD = 5.7), the mean age of women was 27.6 (SD = 6.4). The age of the sample ranged from 18 to 59. The level of education of Ss was measured by a 2-point scale: lo-year general secondary education was scored 1, and 5-year full courses of universities and professional colleges was scored 2. The complete 252 item version of the PTS was used. For 17 subscales (facets) and 3 scales, reliability coefficients (Cronbach alpha) and basic statistics were computed for men and women separately, and for the whole sample. Intercorrelations between subscales were factor analysed by means of the Principal components method with Varimax and Direct oblimin rotation of extracted vectors. The number of components to retain was evaluated by Horn’s (1965) parallel analysis method, which is based on the comparison of the eigenvalues actually obtained with the eigenvalues of a correlation matrix of random uncorrelated variables generated with the same sample size. Only those factors that had eigenvalues greater than the corresponding values of a random matrix were retained (Zwick & Velicer, 1986). Comparison of factor pattern matrices was made by means of the congruence coefficient and the Pearson correlation coefficient. This was achieved because pairs of examined factor pattern matrices were rotated to the same simple structure criterion (Barrett, 1986). For detailed inspection of factor structure and measurement peculiarities of the PTS, congeneric and confirmatory factor analyses were computed using the LISREL VI program (Joreskog & Sorbom, 1988; Hayduk, 1987). Taking into account the considerable dependency of the goodnessof-fit provided by LISREL on sample size (Bentler 8z Bonett, 1980) additional indexes showing better performance with large samples were used: i.e. the Tucker-Lewis index (TLI), incremental maximum likelihood fitting function (FFI), incremental Akaike information criterion (CAKI), and incremental scaled likelihood ratio (LHRI) (Marsh, Balla & McDonald, 1988). All indexes were Type 2 incremental: Type 2 indexes = It - nl/le - nl, where t is the value of an index for the target model, n is the value for the null model, and e is the expected value of the index if the target model is true (Marsh et al., 1988). To evaluate the cross-cultural reliability of the PTS factor structure, factor pattern similarity indexes mentioned above as well as the multi-sample LISREL analysis method (Joreskog & Sorbom, 1988) were used.

*The data was gathered

by Dr E. S. Romanova.

PTS factor structure: Russia

RESULTS

AND

559

DISCUSSION

As shown in Table 1, the reliability coefficients of the SE, SI, and MO scales were high and they were comparable to the values obtained in the study by Strelau et al. (1990b) for the pilot (252 items) and reduced (166 items, including 12 social desirability items) forms of the PTS. Our estimates reached values of 0.89, 0.79, and 0.91 for SE, SI, and MO, respectively, whereas for the reduced form these estimates were: 0.89, 0.85, and 0.89, respectively. The reliability values of the separate facets were not as high as those reported for the German reduced PTS. The mean values of the reliability of SE, SI, and MO facets were 0.64,0.53, and 0.74, respectively. For the reduced form, these values were 0.70, 0.71, and 0.77. This may be explained by the presence of some noisy and/or idiosyncratic items in each facet of the pilot form, that were eliminated during preparation of the reduced form. Their presence in the pilot form of the PTS, used in our study, primarily affected the specific and error portions of the facet variance and did not change its common factor portion. The means and standard deviations of the composite indexes and scales of the PTS are also shown in Table 1. Consistent with the study by Strelau eb al. (1990b), sex differences were found for the SE facets and scale. One-way ANOVA detected significant sex differences for the SE scale, as well as for SEl, SE3, SE4, SE6, and SE7 subscales. Men scored higher than women. This result may be considered an indirect indicator of external validity of the SE scale in the Russian form of the PTS. Education level differences were revealed for the SI scale and subscales SIl, S13, and S14. Higher level of education was associated with the higher values of the SI scale. As in the study by Strelau et al. (1990b), positive correlations were revealed between all PTS scales: the highest correlation (0.65) was between the SE and MO scales, whereas SE and SI (0.22), as well as SI and MO (0.18) showed low coefficients. As shown in Table 2, the correlations of the SE, SI, and MO scales with their corresponding subscales were generally higher then with non-corresponding subscales, and the correlations of the subscales with corresponding scales were higher then with non-corresponding scales. An exception was the SE2 facet which correlated with the MO scale (0.48) higher then with the SE scale (0.35). The interfacet correlations, presented in Table 3, demonstrate that the homofacet correlations show higher values then heterofacet ones, except the SE-MO block. On the whole the pattern of correlations between facets was very similar to that obtained in the study by Strelau et al. (1990b). Intercorrelations between facets were submitted to exploratory factor analysis (principal components method). Factor analysis was also applied to the correlation matrix presented in the paper by Strelau et al. (1990b). In all cases the Parallel analysis as a method of factor number identification suggested that three factors should be retained. Extracted factors were rotated to oblique simple structure by the Direct Oblimin method. The factor pattern matrix for the 17 PTS facets, obtained on the combined sample of Ss is given in Table 4. Three extracted factors (55.6% Table I. Means (M), medians (Me), standard deviations (SD) and Cronbach alpha coefficients (CA) for PTS scales and facets PTS scales/ facets SE SI MO SE1 SE2 SE3 SE4 SE5 SE6 SE7 SII s12 SI3 S14 s15 MO1 MO2 MO3 MO4 MO5

Females (n = 224)

Males (n = 66)

Full sample (n = 290)

M

Me

SD

CA

M

Me

SD

CA

M

Me

SD

CA

40.2 48.2 42.2 6.7 5.1 7.6 3.7 5.8 3.9 7.3 12.0 6.2 8.3 11.6 10.0 10.8 11.0 9.2 7.3 3.9

39.5 48.0 43.0 7.0 5.0 7.0 4.0 5.0 4.0 7.0 12.0 6.0 8.0 12.0 10.0 11.0 12.0 9.0 7.0 4.0

11.8 8.1 12.8 2.4 2.5 2.9 2.1 3.0 2.0 2.8 2.3 2.3 2.2 2.6 3.0 3.4 4.0 3.3 3.3 2.5

0.88 0.78 0.91 0.58 0.65 0.64 0.58 0.72 0.51 0.68 0.54 0.47 0.36 0.56 0.71 0.71 0.78 0.71 0.81 0.69

46.5 48.3 40.6 7.6 5.3 8.9 4.6 6.2 5.0 8.7 11.4 6.8 8.3 11.9 9.9 10.7 9.8 9.1 6.3 4.6

43.5 48.0 40.0 8.0 5.0 9.0 4.0 5.0 5.0 9.0 12.0 7.0 8.0 12.0 9.5 11.0 9.0 9.0 6.0 4.0

13.8 9.6 13.3 2.4 2.4 3.3 2.2 3.5 2.4 2.4 2.8 2.5 1.9 2.7 3.5 3.5 3.9 3.0 3.2 2.7

0.91 0.84 0.91 0.59 0.56 0.75 0.65 0.81 0.61 0.60 0.65 0.52 0.15 0.56 0.79 0.72 0.75 0.60 0.79 0.72

41.6 48.2 41.9 6.9 5.2 7.9 3.9 5.9 4.2 7.6 11.9 6.4 8.3 11.7 10.0 10.8 10.7 9.2 7.1 4.1

40.0 48.0 42.0 7.0 5.0 7.0 4.0 5.0 4.0 8.0 12.0 6.0 8.0 12.0 10.0

12.6 8.5 12.9 2.4 2.5 3.1 2.1 3.1 2.1 2.7 2.5 2.4 2.2 2.7 3.1 3.4 4.0 3.2 3.3 2.6

0.89 0.79 0.91 0.59 0.62 0.68 0.60 0.75 0.55 0.68 0.58 0.48 0.32 0.56 0.73 0.71 0.77 0.69 0.81 0.70

11.0 11.0 9.0 7.0 4.0

MIKHAIL V. B~DUNOV

560 Table 2. Correlations

(Pearson)

between the PTS scales and facets Scales

Scales/ facets

SE

SE SI MO SE1 SE2 SE3 SE4 SE5 SE6 SE7 SII SI2 SI3 SI4 SI5 MO1 MO2 MO3 MO4 MO5

SI

22” 65*’ 69” (56**) 52” (35”) 68” (51**) 79** (71**) 78** (65**) 69” (58’;) 70” (56**) -03 12 13 20** 26*’ 58** 49f’ 53** 37f’ 57**

18* 15* -2lf’ 02 38** 19.8 171 38** 64’. (41”) 59** (36**) 55** (33**) 74** (54**) 74**(49**) 21** 08 18’ 18* 07

lP < 0.01; **P < 0.001; decimal points coefficients are in parentheses.

Cronbach alaha

MO

are omitted.

89 79 91 59 62 68 60 75 55 68 58 48 32 56 13 71 17 69 81 70

29** 48” 36*’ 56” 57** 35” 51” -004 158 21** 04 21** 84” (73**) 82” (67”) 76*’ (62.‘) 77** (63**) 69*‘(56**) The part/whole

corrected

correlation

of explained variance) clearly correspond to the concepts covered by the PTS. The first factor (Fl) contains almost exclusively facets belonging to the MO scale. The second factor (F2) concerns mainly facets referring to the SI scale. The third factor (F3) references exclusively facets related to the SE scale. Factor pattern matrices obtained on male and female Ss separately were very similar to the factor pattern revealed on the combined sample (the Congruence/Pearson coefficients were of 0.94/0.94 and 0.98/0.98 for males and females, respectively). The same three factor structure was also obtained on groups of Ss with high and low education level (the total-group Congruence/Pearson coefficients were of 0.98/0.98 and 0.94/0.94 for high and low education level groups, respectively). This finding demonstrates the high level of replicability of the PTS factor structure across Ss and its independence of the sex and social status of Ss. For the evaluation of cross-cultural similarity of the PTS factor structure, the correlation matrix between 15 facets, presented in the paper by Strelau et al. (1990b), was factor analysed by the same methods. The factor pattern matrix for the German sample is presented in Table 5. The PTS factor structure, revealed on the German sample is almost identical to that obtained on the Russian sample. The matrices of correlations between factors were also very similar in the Russian and German samples. The revealed equivalence of the PTS factor structure in these two samples can be considered as evidence of cross-cultural identity of underlying concepts (Poortinga, 1989). Detailed analysis of the PTS factor structure was performed by means of the linear structure modelling with LISREL YI. It permitted us to test hierarchically nested models with different

Table 3. Correlations

(Pearson)

between the PTS facets

Facets

SE1

SE2

SE3

SE4

SE5

SE6

SE1

SII

s12

SI3

SI4

SI5

MO1

MO2

MO3

MO4

SE2 SE3 SE4 SE5 SE6 SE7 SII SI2 SI3 SI4 SI5 MO1 MO2 MO3 MO4 MO5

17 54 45 38 43 39 03 02 03 23 16 33 21 20 16 25

25 26 44 16 19 -21 -11 -06 -23 -10 35 41 37 27 48

46 33 42 23 -10 -02 01 09 06 34 34 31 15 27

59 56 59 15 20 24 29 33 50 41 48 34 45

44 55 -01 14 I2 I3 23 47 39 48 36 54

44 -01 09 07 20 17 38 26 32 13 30

06 27 23 28 39 47 34 40 38 43

14 22 39 34 02 -06 05 05 -07

28 30 29 I8 07 12 12 13

24 21 I8 16 26 14 06

45 07 -00 04 06 -03

23 12 I3 21 I2

63 54 60 49

50 53 43

;;

40

n = 290; r > 0.15: P < 0.01; r > 0.19: P < 0.001. Decimal

points are omitted.

PTS factor structure: Russia Table 4. Factor pattern matrix for the 17 PTS facets (n = 290, Principal Components, Direct Oblimin rotation)

561

Table 5. Factor pattern matrix for the 15 PTS facets (German samale. n = 506. Princioal Comoonents. Direct Oblimin rotation) Factors

Factors FI

F2

F2

F3

Facets

SE1 SE2 63 -43 SE3 SE4 SE5 51 SE6 SE7 SIl 65 SI2 58 SI3 54 SI4 71 SI5 68 MO1 74 MO2 77 MO3 72 MO4 77 MO5 69 MSA = 0.883; BTS = 1858.93, P < 0.000

81

SE1 49 SE2 SE3 59 SE5 SE6 SE7 Xl SI2 SI3 SI4 SI5 MO1 76 MO2 87 MO3 41 MO4 49 MSA = 0.815; BTS = 2087.86, P < 0.000

Facets

Fl

Correlations

F2 F3

73 56 40 72

Correlations

FI 12 34

F2 10

Decimal points are omitted; only loadings >0.4 are presented. MSA = Measure of Sampling Adequacy, BTS = Bartlett Test of Sphericity.

73 80 54 42 70 57 56 73 60

between factors

Fl 17 37

F2 F3

between factors

F3

F2 II

Decimal points are omitted; only loadings >0.4 are presented. MSA = Measurement of Sampling Adequacy, BTS = Bartlett Test of Sphericity.

degrees of restrictiveness. Six nested models were compared: (1) null model without general factors; (2) one general factor; (3) two uncorrelated factors (SE + MO, and SI); (4) three uncorrelated factors (SE, SI, and MO); (5) two correlated factors (SE + MO, and SI); and (6) three correlated factors (SE, SI, and MO). The data concerning the evaluation of the PTS factor models are given in Table 6. No model fits the data perfectly, but the model with three correlated factors, representing the PTS constructs, fits the data significantly better than the other ones. It should be mentioned that the model with two uncorrelated factors represents the data much better than the model with three uncorrelated factors. In order to improve the model the congeneric measurement modelling of the data with LISREL was performed on separate PTS scales. Two facets (SE2, and SE3) in the SE scale, as well as one facet (S13) in the SI scale with the least values of squared multiple correlation coefficients were excluded (see Table 7). The remaining facets fitted the data perfectly (P > 0.1). The reduced set of the PTS facets was analysed by means of linear structure modelling. As shown in Table 8, the model with three oblique factors appropriately accounted for the data. The LISREL adjusted goodness of fit index, as well as incremental fit indexes exceed the level of model acceptance (> 0.9). Convergent validity was assessed by determining whether each facet’s estimated loading on its underlying construct factor was significant (Anderson & Gerbing, 1988). All facet loadings on corresponding factors were greater then twice its standard error, showing significant convergent validity of the PTS facets. Discriminant validity was assessed for two estimated constructs by constraining correlation parameters (phi) between them to 1.0 and then performing Table 6. Evaluation of PTS factor models (17 facets, Moscow sample) Model specification Null model Single factor 2 Uncorrelated factors 3 Uncorrelated factors 2 Correlated factors 2 Correlated factors

PAR

Ksi

Chi-sq

df

TLI

FF12 CAKl2 LHRIZ

0 34 34

17 1 2

1901.70 671.66 543.18

153 119 119

0.59 0.69

0.69 0.76

0.68 0.75

0.35 0.45

34

3

598.38

119

0.65

0.73

0.72

0.41

35

2

523.62

118

0.70

0.77

0.76

0.47

37

3

410.94

116

0.78

0.83

0.83

0.58

PAR = number of free parameters to lx estimated in the model; Ksi = number of latent variables; TLI = Tucker-Lewis Index; FF12 = Type 2 incremental maximum likelihood fitting function; CAKIZ = Type 2 incremental Akaike information criterion; LHRl2 = Type 2 incremental scaled likelihood ratio.

562

MKHAILV. Table 7. Conaeneric

Scale

Step

SE

Excluded facet on the step

1 2 3

SI

I

MO

2 1

SE20.13) SE3 (0.30) S13<0.11)

EWDUNOV

factor models of the PTS scales

AGFI

df

Chisquare

0.83 0.83 0.97 0.96 0.98 0.97

14 9 5 5 2 5

96.16 60.41 7.55 11.22 3.02 7.48

Probability level of model acceptance 0.000 0.000 0. I83 0.047 0.220 0.187

In parentheses = squared multiple correlation coefficient of deleted facet estimated on the preceding step. On the first step none of the aspects were excluded. AGFI = LISREL Adjusted Goodness of Fit Index.

Table 8. Evaluation Model soecification Null model

Single factor 2 Uncorrelated factors 3 Uncorrelated’ factors 2 Correlated factors 3 Correlated factors

of PTS factor models (14 facets. Moscow samole) Ksi

Chi-so

df

TLI

FF12

0

14

1520.03

105

-

-

28 28

I 2

431.86 331.36

77 77

0.66 0.75

0.75 0.82

0.74 0.8 I

0.50 0.61

28

3

77

_

_

_

_

29

2

309.02

76

0.77

0.84

0.83

0.64

31

3

175.92

74

0.90

0.93

0.93

0.82

PAR

CAKIZ LHR12 -

-

PAR = number of free parameters to be estimated in the model; Ksi = number of latent variables; TLI = Tucker-Lewis Indexl; FF12 = Type 2 incremental maximum likelihood fitting function; CAK12 = Type 2 incremental Akaike information criterion; LHRIZ = Type 2 incremental scaled likelihood ratio. ‘LISREL failed to produce maximum likelihood estimates of parameters for this model because of poor initial estimates.

a chi-square difference test on the values obtained for the constrained and unconstrained models (Anderson & Gerbing, 1988). The chi-square tests for all pairs of factors showed that underlying constructs are not perfectly correlated. The level of cross-cultural invariability of the PTS structure was evaluated by means of multi-sample analysis with LISREL applied to correlation matrices between the PTS facets which were obtained on the Russian and German samples. This stacked model based on the hypothesis of equivalence of correlation matrices in question accounted for the data properly (the LISREL goodness of fit was of 0.95, incremental indexes of fit were ~0.9). In summary, the results obtained show that the Russian form of the PTS demonstrates an acceptable level of scale reliability as well as convergent and discriminant validity. The factor structure of the PTS is invariant across Ss. The identity of the factor structures in the Russian and German samples may indicate the cross-cultural similarity of underlying concepts. Acknowledgement-The this paper.

author

is grateful

to Professor

Robert

M. Stelmack

for his helpful comments

on earlier drafts

of

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