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Eysenck Personality Questionnaire
Eysenck Personality Questionnaire
Eysenck Personality Questionnaire
Eysenck Personality Questionnaire
Eysenck Personality Questionnaire
Structural and Functional
Equivalence of the Eysenck Personality Questionnaire Within and Between
Countries
Dianne A. van Hemert
Fons J. R. van de Vijver
Ype H. Poortinga
Tilburg University, The
Netherlands
James Georgas
University of Athens,
Greece
Abstract
The question was examined as to whether scores at the individual level and scores at the country level on the four scales of the Eysenck Personality Questionnaire (EPQ) have the same psychological meaning. Using data of 24 countries, it was found that the EPQ has different factorial structures at both levels. Both the Lie scale and the Psychoticism scale were shown to jeopardize cross-level equivalence. For further exploration of the meaning of the EPQ scales within countries and between countries country-level correlations were calculated with a variety of country characteristics such as Gross National Product, political indices, religiosity, Hofstede's measures, and subjective well-being. Significant findings for 38 countries included correlations of the EPQ scales with Hofstede’s Masculinity, Diener’s Subjective Well-Being, religiosity, the number of deaths in a country due to political violence, and bribery. The most striking finding was a substantial negative correlation of the Lie scale with Gross National Product and other wealth-related indices.
The Eysenck Personality Questionnaire was published in 1975 (Eysenck & Eysenck, 1975) and consists of four scales. The Psychoticism scale (EPQ-P) was designed to measure tough-mindedness, the Extraversion scale (EPQ-E) was meant to measure extraversion versus introversion, and the Neuroticism scale (EPQ-N) was constructed to measure emotionality or emotional instability. The interpretation of the Lie scale (EPQ-L) is less straightforward. From the beginning, Eysenck and Eysenck (1976) have acknowledged that the EPQ-L, besides (or as part of) measuring a tendency to fake good, reflects a stable personality characteristic, namely social conformity. This was confirmed by McCrae and Costa (1983), who found the EPQ-L to be substantially related to neuroticism and extraversion. They concluded that social desirability scales more likely measure a personality characteristic than a response set (see also Ones, Viswesvaran & Reiss, 1996, for a similar conclusion). Šipka (1988) questioned the use of the EPQ-L as a dimension of personality in cross-cultural comparison until there is more clarity as to its nature.
Over the years, many cross-cultural studies have been done with the EPQ. In studying the construct equivalence of the EPQ scales across countries two general strategies can be followed. First, one can research the internal structure of a concept (structural equivalence). Barrett, Petrides, Eysenck, and Eysenck (1998) established structural equivalence of the EPQ over 34 countries, which means that the factorial structure is (nearly) identical in each of these countries. This is an important finding. At the same time, the question remains unanswered whether differences in scores between countries have the same psychological meaning as differences in scores within countries. Do individual differences in, say, extraversion have the same meaning as country differences or do the latter differences have to be accounted for by other factors, such as cross-culturally different response sets? The second way to assess cross-cultural construct equivalence is to examine (parts of) the nomological networks of the instrument in the different cultures (Van de Vijver & Leung, 1997). This strategy can be said to focus on functional equivalence, as it refers to the functional context of the concept.
In the present study two issues are addressed. The first concerns the structural equivalence of the Eysenck Personality Questionnaire at within- and between-country levels. The second issue attends to the functional equivalence through examining at both levels relationships with variables that can be seen as part of the nomological network of the EPQ scales. Secondary analyses of previously published studies are used to address these issues.
To evaluate whether the EPQ scales provide useful dimensions to describe individual differences across countries, the structure found in various countries can be compared to a reference population using target rotation (Van de Vijver & Leung, 1997). Quite a few studies have done this, using an English sample as reference (e.g., Barrett & Eysenck, 1984; Eysenck, Barrett, Spielberger, Evans, & Eysenck, 1986; Eysenck & Haraldsson, 1983). In these studies the EPQ was administered in some country and the factorial structure was compared to the UK norm groups, using a procedure described by Kaiser, Hunka, and Bianchini (1971) for factor comparison (e.g., Eysenck & Eysenck, 1983). It was found that this comparison method could yield misleadingly high coefficients of factorial similarity (Bijnen, Van der Net, & Poortinga, 1986; Bijnen & Poortinga, 1988). Moreover, simulation studies have shown that target rotation procedures such as those cited by Eysenck and Eysenck (1983) do not have a high power to detect biased items (Van de Vijver & Poortinga, 1994). More definite evidence was derived by Barrett et al. (1998) using an improved comparison procedure, which also showed factorial similarity of the EPQ across 34 countries.
There is a second aspect to structural equivalence in cross-cultural comparisons, namely whether it holds for both individuals and populations. A necessary condition for equality of psychological meaning of individual (within-country) and country (between-country) scores is the equality of the factor structure at these two levels.1 Dissimilarity of within-country and between-country factor structures means that different underlying factors account for scores at the two levels. An illustrative example of different relations at different levels was described by Myers and Diener (1996). In most nations there is hardly a relationship between income and happiness. Yet, people in rich countries are generally happier than people in less prosperous countries. A way to establish equivalence of constructs at different levels was proposed by Muthén (1991, 1994). He describes a multilevel factor analysis or covariance structure analysis, based on a comparison of the pooled within-sample structure with the between samples structure. The pooled within-sample covariance matrix is an average of the covariance matrices of the separate samples, each weighted according to sample size. The between-samples matrix is computed on the basis of the aggregated sample means of the various variables. Muthén’s procedure, developed for confirmatory factor models, can be easily extended to exploratory factor analytic models (Van de Vijver & Poortinga, 2001). The strategy amounts to first factor analyzing the pooled within-country correlation matrix and the between-country correlation matrix and then evaluating the agreement of the loadings after rotation of the two solutions. The factorial agreement can be evaluated by a congruence coefficient. A second method to assess factor similarity involves a bootstrap procedure that makes it possible to examine the statistical significance of congruence coefficients (Chan, Ho, Leung, Chan, & Yung, 1999). Both procedures are discussed in more detail in the Method section.
An evaluation of the nomological network of the EPQ requires correlations between context variables and the EPQ scales at individual and country level. Many studies have investigated EPQ correlates within countries and some studies have done the same across countries. However, no studies have attempted to compare correlates of the EPQ scales at both levels. Because of the amount of within-country studies, the present literature review is limited to studies concerning correlates across countries. For a summary of findings at the individual level the reader is referred to Table 4.
Lynn has done extensive research on the relationship between the EPQ scales, Hofstede’s measures, and demographic variables at country level (e.g., Lynn, 1971, 1981; Lynn & Hampson, 1975, 1977). Lynn (1981) postulated that cross-cultural differences in neuroticism are related to differences in stress. He argues that stress is linked to political, social, and economic instability, as well as to war and climate. Also, Lynn indicates that higher levels of extraversion were found in more affluent nations. Lynn (1971) calculated rank correlations for 11 countries between anxiety, as measured by Cattell’s 16 Personality Factor Test, and various country variables. Anxiety can be regarded as related to neuroticism. Significant positive correlations were found with alcoholism and average temperature in the hottest month of the year, and a significant negative correlation with rates of psychosis. Lynn (1981) reported a highly significant negative correlation (r = -.62) between EPQ-P and per capita income across 12 nations.
In a recent study, Lynn and Martin (1995) reported national means of the E, N, and P scales for 37 countries. These means were correlated with demographic variables such as national rates of suicide, homicide and alcoholism, with economic data such as per capita income, and with work attitude data such as work ethic, competitiveness and an anxiety index. The latter was derived from Hofstede (1976), who measured anxiety with a single item, namely “How often do you feel nervous or tense at work?” Significant negative correlations were found between psychoticism and work ethic, and between extraversion and rate of suicide, while positive correlations were reported between neuroticism and Hofstede’s anxiety index, and between extraversion and rate of homicide. No significant correlations were found between the EPQ scales and per capita income.
Lester (1988) found in a sample of 18 industrialized nations anxiety scores to be significantly correlated with suicide rate (r = .45), and extraversion scores with homicide rates (r = .60). His study of neuroticism and extraversion in 32 nations (Lester, 2000) rendered similar results as Lynn's (1971; Lynn & Hampson, 1975) studies.
Arrindell et al. (1997) correlated subjective well-being with various countrylevel variables, among which EPQ-P, EPQ-E and EPQ-N (obtained from Lynn & Martin, 1995), across 20 countries. They found subjective well-being to be negatively correlated with neuroticism and psychoticism. Positive correlations were found between neuroticism and Hofstede’s (1980) dimensions of Power Distance, Masculinity and Uncertainty Avoidance, as well as for psychoticism and Power Distance.
Ones et al. (1996) meta-analyzed data on social desirability and correlated the data with (among other variables) the Big Five personality dimensions. Social desirability was found to be related to emotional stability (r = .37), conscientiousness (r = .20), and years of education (r = -.18). In a survey Ross and Mirowsky (1984) found that respondents in the USA and Mexico with lower socio-economic positions, higher age, or Mexican descent scored higher in acquiescence and social desirability. They concluded that less powerful social groups give more socially desirable responses. They state that the same tendency occurs among social groups that stress the importance of keeping up a proper image, because socially desirable responses may be seen as strategies for presenting a good face.
Warnecke et al. (1997) reported higher social desirability scores in the USA among both African American and Mexican American respondents compared to non-Hispanic Whites, after controlling for gender, age, education, and income. Other studies also have shown higher social desirability scores among Blacks than among Whites in the USA (e.g. Crandall, Crandall, & Katkovsky, 1965; Edwards & Riordan, 1994; Fisher, 1967; Johnson & Van de Vijver, 2000; Klassen, Hornstra, & Anderson, 1975).
Williams, Satterwhite, and Saiz (1998) measured favorability (which appears to be closely related to social desirability) in ten countries with the Adjective Check List (Gough & Heilbrun, 1965). This 300-item list of person-descriptive terms was presented to samples of students, who were asked to indicate the favorability of each adjective on a 5-point scale. Reanalyzing these data, Poortinga and Van de Vijver (2000; see also Johnson & Van de Vijver, 2000) found a significant correlation (r = -.84) between the average favorability of all items and a socioeconomic index.
Bond and Smith (1996) performed a meta-analysis of studies using Asch’s line judgment task, which can be considered an alternative measure for social conformity. Across 17 countries they found significantly higher levels of conformity for countries with higher collectivism scores, indicated by three surveys assessing a country’s individualism or collectivism, among which Hofstede's (1980) index.
A number of studies have attempted to find a relationship between the EPQ scales and religiosity or attitudes towards religion. Unfortunately, correlations are generally calculated only within countries. Although the present section focuses on correlations across countries, some of these within-country studies are mentioned in order to formulate expectations concerning the relationship between religiosity and personality across countries. In most studies a negative relation between psychoticism and religiosity is found (Francis, 1992; Heaven, 1990; Kay, 1981; Lewis & Maltby, 1995; Svensen, White, & Caird, 1992). Francis, Lewis, Brown, Philipchalk, and Lester (1995) compared students from the UK, USA, Canada, and Australia and concluded that psychoticism is fundamental to individual differences in religiosity. Extraversion has been reported to be negatively correlated to religiosity (Francis, Pearson, Carter, & Kay; 1981), but the relationship is not consistent (e.g., Caird, 1987). For EPQ-N sometimes positive and sometimes negative correlations are reported. Finally, Lie scale scores have been found to be positively correlated with religiosity (Francis, 1985).
In summary, although several studies have addressed the structural equivalence of the EPQ between two or more countries, they only considered equivalence within countries. However, one cannot be certain that differences in EPQ scores at the individual level have the same meaning at the country level. Thus, our first research question addresses the structural equivalence of the EPQ within and between countries through factor analyses. Another way to study equivalence is through examination of the nomological network. Quite a few studies have reported correlations between the EPQ and context variables, both within and between countries, but, again, no study has compared correlations at both levels. Therefore, the second question addresses the functional equivalence of the EPQ at individual and country level.
Method
Studies included in the secondary analyses were found by searching PsycLit (now called PsycInfo), using the keywords EPQ and Eysenck. All volumes of the journal Personality and Individual Differences were also searched. Further studies were found through "snowballing" on the basis of references in literature already identified. Studies were included if the averages on all four scales of the EPQ were available.2 Only data on the 1975 version of the EPQ were used; EPQ-R, EPI and JEPQ data were not included because of lack of comparability with the EPQ and an insufficient number of relevant studies. As our aim was to compare data on normal adult populations, studies with children, clinical samples or specific groups that presumably show extreme scores on (some of) the scales, such as alcoholics and gamblers, were not included in the study. Each separate group of respondents in the same report, for which data were reported (e.g., men and women), was considered as a separate sample. This procedure resulted in 153 studies, which provided data for 333 separate samples. The data set contained studies in 38 countries with a total of 68,374 respondents. Source, year of publication, composition of the sample (general or students), sex, means and standard deviations of the four EPQ scales, intercorrelations between the scales, and reliabilities of the scales were recorded, in so far as they were available. This data set is called the total data set.
A second data set was used for the multilevel analyses, hereafter called the multilevel data set. This set consisted of all studies from the first data set that provided data on intercorrelations of the EPQ scales. Fifty-one (33.3%) studies with a total of 25,922 respondents were found to meet these requirements, including 96 separate samples, from 24 countries.
Many studies had to be dropped because insufficient information was provided by the author(s). For example, it was not always clear which version of the EPQ was used. EPQ–R (revised) and the EPQ-RSS (short version) were regularly referred to as EPQ. Additionally, not all required information could be found in each study. In some cases the number of women and men in the sample was not specified. Furthermore, some researchers did not fully describe the cultural composition of their samples.
Both data sets contained more male samples: 45.6% of the samples in the total data set and 49.0% in the multilevel data set were male. In the total data set 17.7% and in the multilevel data set 7.3% of the studies did not report data on gender composition.
The age of the respondents ranged from 15 to 70.1 years for the total data set and from 15 to 60 for the multilevel data set. The distribution of age was positively skewed in both data sets. Mean ages were 27.46 (SD = 9.30) and 27.01 (SD = 8.89), respectively. The value of the mode (21.00 in both data sets) reflects the overrepresentation of students in EPQ research: 28.8% and 28.1% respectively of the samples consisted solely of students. Data on the subjects’ age were missing for 39.6% and 14.6% of the studies respectively.
The publication dates of studies ranged from 1977 to 1997. There were no earlier reports as the EPQ was only published in 1975. The mode was 1984 and the median 1985 for both data sets.
A practical problem in comparing means on the EPQ scales across countries lies in the fact that the scales as used do not contain equal numbers of items. As a scale score is a sum of item scores, scale means had to be adjusted by multiplication with correction factors (see Barrett & Eysenck, 1984).3 This correction procedure assumes that the deleted items had average endorsement values. Reliabilities were corrected to what they would be if scales would all have the same length as the English versions, using the Spearman-Brown formula (Lord & Novick, 1968). The number of items in the four scales ranged from 16 to 25 in EPQ-P, from 16 to 23 in EPQ-E, from 18 to 23 in EPQ-N and from 19 to 24 in EPQ-L. Sample sizes, corrected scale means and corrected scale reliabilities for all countries are shown in Table 1. The means across the countries varied considerably, although means on the EPQ-P were generally the lowest and means on the EPQ-E the highest. Reliabilities of the EPQ-P tended to be considerably lower than reliabilities of the other scales.
Multilevel Factor Analysis
First, multilevel analyses were performed according to the adaptation of Van de Vijver and Poortinga (2001) of the Muthén strategy (1991, 1994). The procedure was as follows:
It should be noted that the main interest here is to compare factor structures at individual and country level. The above-described analyses do not focus on the dimensionality of the EPQ at either level. This implies that neither the size nor the sign of factor loadings were of interest. Structural equivalence would be challenged by a dissimilarity of structure, whatever the dimensionality found in the factor analysis.
Lower bounds of congruence coefficients (i.e., threshold values below which factors are taken to be dissimilar) were estimated using a bootstrap procedure proposed by Chan et al. (1999). This bootstrap procedure allows an estimate of the standard error of variable congruence coefficients; it evaluates the similarity of a variable, comparing the loadings of that variable across factors (analogous to a factor congruence coefficient which evaluates the agreement of the loadings of a factor across variables). A raw data matrix was generated which yielded correlations equal to the pooled-within correlations. From this data matrix 1000 samples were drawn. Each sample consisted of 270 observations (being the original average sample size), drawn with replacement from the original data matrix. The distribution of the congruence coefficients for a variable was used to determine the lower bound (alpha = .05).
As no data at the individual level were available, functional equivalence at the two levels was established by comparing previously reported individual level correlations between the EPQ scales and other variables with correlations between the EPQ scales and a number of country variables. The latter were drawn from several sources.
Ecosocial factors. Georgas and Berry (1995) combined several ecological and social indicators to six categories or factors. The Ecological factor included highest and lowest average temperature and highest monthly level of precipitation. The Economical factor included GNP per capita, daily calorie intake as a percentage of the recommended amount, consumption of commercial energy per capita, percentage of population working in agriculture, percentage of population working in industry, percentage of population working in services, and electricity consumption per capita. The third factor concerned the Educational system, and included the teacher-pupil ratio in the first level, the gross enrollment in the first, the second and the third level, and the percentage of adult illiterates. The fourth factor regarded the means of Mass communication, reflected in the number of public and private telephones per 100 inhabitants, newspapers (circulation per 1,000 inhabitants), televisions (number of receivers per 1,000 inhabitants) and radios (number of receivers per 1,000 inhabitants). The fifth factor, Population, included infant mortality rates, life expectancy at birth, crude death rate, crude birth rate and rate of population increase. Where indicators were not available for a country in our data set, these were added by using the Demographic Yearbook 1987 (United Nations, 1990a), the UNESCO Statistical Yearbook 1990 (United Nations, 1990c), and the Historical Climatology Series (National Climatic Data Center, 1991). An overall score on Affluence (Georgas, Van de Vijver & Berry, 2001) was calculated by factor analyzing all variables used for calculation of the five ecosocial factors. The resulting factor scores on the single factor were used as a measure of Affluence. Data on the five ecosocial factors and Affluence were available for all 38 countries.
Additional economics variables. The Gini index expresses the degree of income inequality in a society. Indices for 29 countries (ranging from 1987 to 1995) were collected from the World Development Report (World Bank, 1999). The Human Development Index (United Nations, 1990b) was available for 36 countries. This index measures development in three areas (life expectancy, adult literacy rate, and Gross Domestic Product per Capita) in relation to other countries.
Sociopolitical factors. Five variables were selected to denote the social and political atmosphere in countries. Humana (1986) collected data from several United Nations instruments and constructed an index for rights and freedoms in 40 categories, the Human Rights Index (34 countries). Indices for Political Rights and for Civil Liberties in the year 1984 to 1985 were available for 35 countries (Gastil, 1985). Stability of Democracy (Inglehart, 1997) refers to the number of years of continuous democracy (24 countries). Vanhanen’s (1997) Index of Democratization over the year 1980 is a weighted combination of two indicators of dimensions of democracy, namely Competition (smaller parties’ share of the votes) and Participation (percentage of total population who voted in the election).
Death rates. Death rates per 100,000 inhabitants by homicide, suicide, and liver cirrhosis (as an indicator of alcoholism) were collected (following Lynn & Martin, 1995) using the Demographic Yearbook 1987 (United Nations, 1990a). Data were available for 31 countries on homicide and suicide rates, and for 30 countries on liver cirrhosis resulting in death. Further, as a measure of political instability the data by Taylor and Jodice (1983) on death due to political violence between 1948 and 1977 were used. This measure, available for all countries, was corrected for population size.
Bribe and corruption. Subjective data on bribery for 1999, obtained through polls in emerging market countries, were provided by Transparency International (Pope, 1999). This Bribe Payers Index was available for 14 countries. The Corruption Perceptions Index from the same source indicates business people's opinion on corruption in a large amount of countries in the period 1988-1992 (30 countries).
Religiosity. An analysis was done on a part of the 1990-1991 World Values Survey (Inglehart, 1993, 1997). This study included 47,871 respondents from 39 countries. It provided data on a large range of topics related to religion, like the meaning of life, religious services, the role of churches and praying. Six items on the experience and practice of religion were selected from a larger number of indices to form a scale of religiosity. Examples of items are: “Do you find that you get comfort and strength from religion?” “How important is God in your life?” and “How often do you pray to God outside of religious services?” The six items yielded a one-factor solution in factor analysis at aggregated (country) level, with an eigenvalue of 5.39 (89.8% of variance explained). Cronbach’s alpha was .83. These data on religion were available for 22 countries.
Anxiety. Mean scores on the question ‘How often do you feel nervous or tense at work?’ (Hofstede, 1976) as published by Lynn (1981) were used for 23 countries.
Hofstede’s measures. Data for 23 countries were available on Hofstede’s (1980) Individualism, Power Distance, Uncertainty Avoidance and Masculinity.
Subjective Well-being. A measure for subjective well-being was derived from Diener, Diener, and Diener (1995). This value combines scores from several surveys and was available for 30 countries.
Schwartz’s Values. Schwartz (1994) provided data on values in 22 of the countries in the present study. Factor analysis by Georgas et al. (2001) yielded two bipolar orthogonal factors, labelled Autonomy and Hierarchy. Scores on these two dimensions were used as indicators.
Pace of Life. Levine and Norenzayan (1999) studied the pace of life in 31 countries. An index was established from three variables: Average walking speed downtown, the speed with which postal clerks completed a simple request, and the accuracy of public clocks. The Pace of Life index was reported to correlate positively with GNP (r = .74; p < .01). Nineteen of the countries overlapped with countries in the present study.
Structural equivalence at the individual and the country level was established first by performing multilevel analyses following the five steps described before. First all four scales were factor analyzed. Across all countries (N = 24) a two-dimensional structure was found. In the second step the pooled within-country correlation matrix and the between-country correlation matrix were calculated. Table 2 shows both the within-countries and the between-country correlation matrices. The third step involved checking the factorial agreement of each country by comparing the factor solution from the overall pooled-within correlation matrix with the factor solution of the correlation matrix of the country means. It turned out that four countries did not meet the requirements of an agreement coefficient > .90. These countries were China, India, Japan, and Uganda. Sample characteristics in these countries did not distinctly differ from the other countries. Although the data for China, Japan, and Uganda were based on only one study, this was also the case for 13 of the other countries. Target rotation and calculation of the congruence coefficients took place in the fourth step. Coefficients for the total set were .96 and .78; after deleting these countries from this analysis, Tucker’s coefficient was .99 for the first factor, but only .88 for the second factor (Table 3). Thus, the first factor can be considered equivalent at individual level and country level, whereas the second factor did not meet the requirements.
In order to examine the influence each of the four scales exerts on the equivalence at individual and country level, four further analyses were performed, each time omitting one scale (fifth step). When the EPQ-P was omitted Israel showed a Tucker’s coefficient below .90 on the second factor (indicating poor equivalence between the Israel matrix and the pooled within-country correlation matrix) and was removed. Congruence coefficients (after target rotation) reached values of 1.00 and .99 for the two-factor solution. The deletion of the EPQ-E resulted in coefficients of .64 and .77 respectively, and the deletion of EPQ-N led to coefficient values of .90 and .89 respectively. In both these cases all countries showed sufficient agreement (i.e., Tucker's coefficient > .90) with the factors based on the corresponding within-country matrix. Finally, when leaving out the EPQ-L, China, Japan, Uganda, and Yugoslavia showed a less than adequate fit and were removed. After extraction of, again, two factors, agreement coefficients (after target rotation) of 1.00 and 1.00 were found (before deletion of the four countries Tucker’s coefficients were .99 and .97).
These findings suggest that EPQ-E and EPQ-N define a single bipolar factor that shows good structural equivalence within and across countries. EPQ-P and EPQ-L, however, are more problematic. Elimination of either of the two is sufficient to establish the functional equivalence of the three remaining scales.
In order to evaluate the equivalence of the four variables within and across countries, congruence coefficients for the four variables were examined using the Chan et al. (1999) procedure. Congruence coefficients for the EPQ scales were .54 (EPQ-P), 1.00 (EPQ-E), .96 (EPQ-N), and .93 (EPQ-L). Bootstrapping (with an alpha level of .05) resulted in critical values of .91, .90, .96, and .95, for the four scales, respectively. An observed coefficient smaller than the critical value points to incongruence (Chan et al., 1999). Applied to the present data, it can be concluded that both the EPQ-E and EPQ-N are congruent within and across countries, while the EPQ-P and EPQ-L are incongruent. This result is in agreement with the findings from the multilevel exploratory factor analyses.
To study functional equivalence, within-country correlations were collected from previously published studies and compared with between-country correlations calculated from the total data set. Table 4 shows the nature of correlations found between the EPQ scales and context variables within and between countries. Because of relatively small sample sizes, correlations at country level hardly ever reach significance. Therefore, the patterning of the correlations is more informative than their level of significance. For the Lie scale it was established that correlations with affluence or socioeconomic status are negative at both levels. Subjective well-being appeared to be related to the Lie scale differently within and between countries. However, the reasons for these differences between countries are still unclear. Therefore, further correlations at country level were examined.
Correlations between the EPQ scales and the country characteristics are presented in Table 5. It should be noted that findings in the preceding section cast doubt on the exact meaning of some of the EPQ scales at country level. Also, sample sizes are relatively small because of limited overlap between available data, reaching a maximum of 38. A striking finding is the large number of significant correlations with EPQ-L. High positive correlations were found between the EPQ-L and the Ecology (r = .69; p < .01) and Population (r = .51; p < .01) factors. Extremely hot and humid (tropical) countries, and countries with high birth and death rates and low life expectancy score higher on social desirability. Strong negative correlations were found with the ecosocial factors Economy (r = -.64; p < .01), Education (r = -.63; p < .01), Communication (r = -.70; p < .01), and with Affluence (r = -.69; p < .01). Strong negative correlations were also found for GNP and the Human Development Index. Apparently, richer countries score lower on the EPQ-L. An identical pattern could be seen for all sociopolitical variables: Lower social desirability scores were related to a higher level of democracy and more observance of citizens' rights. As for death rates, a significant negative correlation was found between the EPQ-L and the number of deaths by suicide (r = -.45; p < .05). The occurrence of bribery and corruption went together with high social desirability. A negative relation was found between the EPQ-L and Individualism (r = -.68; p < .01). This is not surprising as individualism is known to correlate with GNP, a finding confirmed in the present study (r = .66; p < .01). Power distance was positively correlated with EPQ-L. Diener’s Subjective Well-Being, also known to be related to GNP, correlates negatively with the EPQ-L. Finally, the Pace of Life index correlated positively with the EPQ-L, and negatively with GNP (r = -.55; p < .05). This result is in line with Levine and Norenzayan (1999), who reported a negative correlation between Pace of Life and GNP.
The EPQ-P was significantly correlated with bribery. The often reported negative correlation between EPQ-P and religiosity in individual level studies was not replicated at country level (e.g., Francis, 1992; Heaven, 1990; Kay, 1981; Lewis & Maltby, 1995; Svensen et al., 1992). Also, more masculine countries displayed higher scores on psychoticism. Subjective well-being was negatively correlated with EPQ-P. EPQ-E was correlated significantly with death resulting from political violence, the Bribe Payers Index and religiosity. EPQ-N correlated with masculinity. The two factors representing Schwartz’ values did not show significant correlations with any of the EPQ scales.
Thus, most significant correlations were found with the EPQ-L. In general, these correlations involve GNP related variables. To explore what other factors exert influence, partial correlations for all country variables with the EPQ scales were calculated, controlling for Affluence. All significant correlations with EPQ-L disappeared, except for the positive correlation with the Ecological factor (r = .39; p < .05), the negative correlations with the Population factor (r = -.40; p < .05) and suicide rate (r = -.39; p < .05). Incidentally significant partial correlations with the other EPQ scales were also observed. However, we did not find any consistent interpretations for these.
Finally, the reliabilities of EPQ-P, EPQ-E, and EPQ-N were correlated with the country scores on EPQ-L and with GNP. A factor analysis of EPQ-P, EPQ-E, and EPQ-N reliabilities at country level yielded a one-factor solution with an eigenvalue 1.94, explaining 65.0% of the variance. Factor scores on this factor showed a correlation of -.47 (p < .01) with the mean country score on EPQ-L, indicating that reliabilities of the PEN scales are lower when scores on the Lie scale are higher. Further, correlations between this reliability factor and GNP (r = .45; p < .05), and both Affluence (r = .68) and the Education Factor (r = .61) were significant (p < .01). This finding implies that social desirability is indeed related to reliability of the other EPQ scales, and that on average reliabilities are lower in less affluent countries.
Two questions were examined. The first concerned the multilevel structure of the Eysenck Personality Questionnaire. Intercorrelations between the EPQ scales were compared within and between countries. First, in some countries (China, India, Japan, and Uganda) the correlations of the four EPQ scales differed from those found elsewhere. Whether this is due to translations, sample characteristics, differences in administration, or differences in construct is not clear. Second, whereas the constructs of extraversion and neuroticism appear to have the same psychological meaning within and across countries, this does not hold for psychoticism and social desirability. Hence, the psychological meaning of differences in score levels is not equivalent at individual and country level, at least not for psychoticism and social desirability.
It may be argued that carrying out a factor analysis with only four input variables (the four EPQ scale scores) is debatable, as the number of scales is small and the underlying scales are assumed not to be strongly related. However, the aim of the factor analysis was not the examination of the dimensionality of the EPQ within and/or between countries, but the establishment of the (dis)similarity between factor structures at both levels. For this purpose a factor analysis with four variables can be regarded as acceptable. Also, the results from the bootstrap procedure can be considered a confirmation of the findings.
The second research question involved the relationship of the EPQ scales and context variables at individual and country level. It should be noted that the nature of the results does not permit any statements about the direction of causality. The clearest results were correlations with the Lie scale. At both levels more affluence was accompanied by less social desirability. At the individual level subjective well-being seemed to be positively related to social desirability or conformity, while for countries the opposite was true. Correlations across countries were further explored.
The main finding was the strong negative correlation of social desirability with Gross National Product and related economic variables. The joint underlying factor here is affluence. The negative correlations between the Lie scale and the factors Education and Communication, Hofstede’s Individualism, and Subjective Well-Being also are in agreement with this finding. All these variables are significantly correlated with GNP and directly or indirectly reflect GNP or other wealth-related indices. This strong social desirability effect can be interpreted in two ways, in line with two existing models. First, it can be considered as due to method bias. This form of bias refers to artefacts (for example instrument or administration characteristics) influencing the results of an entire instrument (e.g., Van de Vijver & Leung, 1997). According to this explanation differences in social desirability should be considered as a kind of response bias. This explanation is supported by the finding that a higher level of social desirability is related to lower reliabilities of the other three EPQ scales. However, this finding is not in line with findings by Grimm and Church (1999) who reported that cross-cultural differences in means on personality measures were ‘only modestly’ confounded by response biases.
Second, the results can be interpreted as reflecting differences in social-psychological functioning as a consequence of ecocultural conditions, such as GNP. In this case, there is a psychological relationship between affluence and social desirability. A sociocultural explanation for differences in social-psychological functioning can be considered as well. Ross and Mirowsky (1984) state that less powerful groups are more prone to socially desirable responding, less powerful groups are often less affluent groups. People from these groups are forced to behave according to social norms because they depend on the approval of other people. This would suggest a relation at country level between social desirability and power-related variables. At an individual level social desirability could easily be a measure of ‘social naïveté’ or conformity (see Eysenck & Eysenck, 1975; Furnham, 1986; Ones et al., 1996), social desirability being a personality characteristic. However, the present findings could not support a relation between social desirability and power-related variables, like Power Distance or Hierarchy, after controlling for GNP. More research is needed to further explore which of the two interpretations of country differences in social desirability, method bias or conformity, is more likely to be true.
Due to the nature of the data, we could not establish to what extent bias in individual items has contributed to inequivalence. Although it has to be assumed that item bias is present, it probably plays a small part in the correlational relationships analysed in the present study. The basis for this assertion is the high agreement across countries.
It should be noted that Lynn and Martin's (1995) findings were not replicated, as no significant correlations were found between extraversion and any of the death rate indices. However, suicide rate was related to the Lie scale. Also, Lynn’s hypotheses about the association between political, social, and economic instability, and climate (Lynn, 1981), and neuroticism across countries were not supported. The same applies to his claims about the relationship between extraversion and income. Most likely, sample differences explain the failure to find similar results, as the countries and the studies from which scores were derived only partially overlap. Moreover, Lynn's results were based on various questionnaires, such as the EPI, MPI, and EPQ. Except for the 1995 study, the sample size in Lynn's studies was considerably lower than in the present study. Furthermore, Lynn did not evaluate relations at the individual level.
In
summary, two EPQ scales (Extraversion and Neuroticism) were convincingly
equivalent at an individual and country level. Very few significant
correlations with country variables could be found for these scales.
For the two other scales (Psychoticism and Lie) aggregating individual
scores to country level seems to affect the psychological meaning of
the scores. For the Lie scale significant correlations with many country
level variables were found; all of these seem to reflect differences
in affluence. Thus, it was established quite convincingly that social
desirability scores are higher in countries that are poorer, less educated,
less egalitarian and more collectivistic.
Footnotes
1In the present article the term individual level refers to within-country scores or matrices and the term country level refers to between-country scores or matrices (see for similar terminology, e.g., Hofstede, 1991; Leung, 1989; Leung & Bond, 1989; Van de Vijver & Leung, in press; Van de Vijver & Poortinga, 2001).]
2In some cases, however, the data on the L scale or the P scale were missing.
3The
8 most heavily corrected countries were Bangladesh, Brazil, Iceland,
Nigeria, Norway, Spain, Sri Lanka, and Russia. In later analyses these
countries did not show a deviant pattern of agreement with the overall
structure.
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Table 1
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aDashes indicate no studies were suitable for the multilevel analyses
bPsychoticism
cExtraversion
dNeuroticism
eLie
fMeans across
all studies, weighted by their sample size
Table 2
Intercorrelations between EPQ scales for the Multilevel Data Seta
| Scale | P | E | N | L |
| Psychoticism (P) | -- | -.25 | .37 | -.02 |
| Extraversion (E) | -.00 | -- | -.35 | -.39 |
| Neuroticism (N) | .15 | -.17 | -- | -.35 |
| Lie (L) | -.27 | -.06 | -.21 | -- |
aCorrelations above the diagonal represent the between-country correlations, correlations below the diagonal represent the pooled within-country correlations
Factor loadings (Varimax Rotated), Eigenvalues of Between and Within Correlation Matrices, and Agreement Indices
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Note. Two factors were drawn in each analysis to enable target rotation later
aPsychoticism
bExtraversion
cNeuroticism
dLie
eSquare root
of the mean squared difference per scale
Table 4
Sign and Strength of Correlations Found between EPQ scales and Context Variables Within-Countries and Between-Country
| P | E | N | L | |||||
| Wa | Bb | W | B | W | B | W | B | |
| Affluence | n.s. | n.s. | + | n.s. | - | n.s. | - | - |
| Suicidec | + | n.s. | n.s. | n.s. | + | n.s. | n.s. | - |
| Alcoholism | ? | n.s. | + | n.s. | n.s. | n.s. | n.s. | n.s. |
| Religiosity | - | n.s. | n.s. | + | -+ | n.s. | + | n.s. |
| Subjective
well-being |
? | - | + | n.s. | - | n.s. | + | - |
aWithin-countries correlations, as found in literature (see sources)
bBetween-country correlations, as found in the present study
cNumber of suicide attempts and suicide ideation
Note. Nature of correlations is denoted by ‘’n.s.’’ (no significant correlation), ‘’-‘’ (a negative correlation), ‘’+’’ (a positive correlation), or a combination of these (''-+''). A lack of findings is denoted by ''?''.
Sources: Pritchard & Kay, 1993; Ross
& Mirowsky, 1984; Lolas, Gomez, & Suarez, 1991; Schuckit, Klein,
Twitchell, & Smith, 1994; Francis, 1992; Francis et al., 1981; Caird,
1987; Francis, 1985; DeNeve & Cooper, 1998
Table 5
Correlations (Pearson) between Mean Scale Scores and Context Variables for the Total Data set
| Country variable | Pa | Eb | Nc | Ld | |
| Ecosocial factors | |||||
| Economy | -.23 | .10 | -.04 | -.64** | |
| Ecology | .07 | .05 | -.11 | .69** | |
| Communication | -.15 | .10 | -.00 | -.70** | |
| Education | -.09 | -.09 | .10 | -.63** | |
| Population | .04 | .18 | -.06 | .51** | |
| Affluence | -.13 | -.03 | .04 | -.69** | |
| Additional economics variables | |||||
| Gross National Product | -.19 | .11 | -.06 | -.67** | |
| Gini index | -.11 | .26 | -.05 | .29 | |
| Human Development Index | -.06 | -.14 | .03 | -.57** | |
| Sociopolitical factors | |||||
| Human Rights Index | -.08 | .21 | -.03 | -.49** | |
| Political Rightse | -.10 | .12 | .13 | -.46** | |
| Civil Libertiesf | -.19 | .24 | .03 | -.53** | |
| Stability of Democracy | -.20 | .20 | -.11 | -.63** | |
| Index of Democratization | -.06 | .28 | -.08 | -.48** | |
| Death rates | |||||
| Homicide | -.10 | .02 | .07 | .16 | |
| Suicide | .12 | -.13 | -.07 | -.45* | |
| Liver Cirrhosis | -.09 | -.25 | -.04 | -.19 | |
| Deaths from Political Violenceg | -.17 | .35* | -.29 | .19 | |
| Bribe and corruption | |||||
| Bribe Payers Indexh | .58* | -.73** | .27 | .73** | |
| Corruption Perceptions Indexi | .03 | .05 | .14 | .71** | |
| Religiosity | -.34 | .66** | -.11 | .04 | |
| Anxietyj | .22 | .04 | .34 | .06 | |
| Hofstede’s measures | |||||
| Individualism | -.11 | .18 | .05 | -.68** | |
| Masculinity | .47* | -.04 | .63** | -.17 | |
| Power Distance | .26 | -.39 | .25 | .58** | |
| Uncertainty Avoidance | .14 | -.05 | .23 | .30 | |
| Subjective well-being | -.41* | .29 | -.23 | -.57** | |
| Schwartz' values | |||||
| Autonomy factor | .07 | -.11 | .14 | -.11 | |
| Hierarchy factor | .24 | .04 | -.08 | .28 | |
| Pace of lifek | .23 | .13 | -.19 | -.63** | |
aPsychoticism
bExtraversion
cNeuroticism
dLie
efhijkOriginal sign changed
gCorrected for population size
*p < .05. **p < .01.
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