With the permission of the university’s privacy commissioner („Datenschutzbeauftragter“), the examinations office of the Humboldt University Berlin was asked to provide the addresses of the university graduates whose final grade belonged to the top 14% of their peers within the same faculty. This criterion includes all participants who were at least one SD higher in achievement than their peers; although arbitrary, this cutoff secured an adequate samples size without being overinclusive.

Data collection was carried out in three waves between March 2003 and June 2003, when 322 former students of the Humboldt University Berlin and the Free University Berlin who had received their degrees during the past year were contacted. Because many alumni move to another city upon their graduation, it was expected that not all questionnaires would reach their target. In the first

For logistical reasons, the number of questionnaires mailed to invalid addresses could not be calculated for the other waves.

Between March 2003 and February 2004, the estimated 274 successfully delivered participation letters (85% of 322) sent to the high achieving graduates resulted in 174 filled-out P&P or Internet questionnaires. By applying the same exclusion criteria as in Sample 1, 20 respondents were excluded, resulting in a total number of 154 participants. This corresponds to an enrollment rate of 56%, which is quite high given the absence of financial reward and the fact that the questionnaire took about 60 minutes to fill out. Moreover, the gender composition of the sample was quite balanced, with 56% females. The mean age was 27.1 years (SD 2.3).

Parallel to contacting the top 16% achievers, university graduates who fell between the 42nd and 58th percentile in terms of academic achievement (average 16%) were contacted. Across all three waves, 317 former students were contacted. Against an estimated 269 effectively delivered questionnaires, a total of 89 people responded by contributing at least some data. Applying the criteria described above resulted in 18 excluded cases, bringing the total number of participants in this group to 70. The low response rate of 26% suggests that selection biases were strong in this sample. This was reflected in the more biased gender ratio (65% women), though the average age (27.8, SD 3.4) was very similar to that of the highly achieving group.

Some differences between the highly achieving and the average achieving alumni’s may be due to differences in the invitation letter they received. In the highly achieving pool, subjects were praised for their high achievement and told that their responses were particularly interesting to study the relation between giftedness and social relationships. In contrast, the average achieving subjects were told that the study was directed at gifted individuals but that academic achievement is not only determined by intellectual ability, so people with varying achievement levels were contacted. In hindsight, targeted individuals may have suspected that they had been assigned to the control group of less achieving subjects, which could have discouraged participation.

To create dyads with a maximum variance in psychometric intelligence levels, an experimental manipulation was performed. For this purpose, a composite intelligence score was calculated from the results of the available pretest information. As can be seen in Table 4, the vocabulary test was characterized by a marked range restriction (i.e., the SD was much smaller than the typical 15 points), which prohibited the usual procedure to z-standardize results. Instead, both the numerical and the vocabulary results were scored according to available norm data. For the numerical test, comparison data from a sample of 279 Gymnasium students were used, whereas the vocabulary test was normed according to data from 159 representative adults from the German ALLEE study (aging and life experience; Lang, Lüdtke, & Asendorpf, 2001).

As can be seen in Table 4, the norming procedure resulted in somewhat different mean IQ scores, with participants scoring higher on the numerical than on the vocabulary test. In light of the somewhat younger comparison group for the numerical test and the high proportion of participants from the natural sciences, this was not unexpected. The mean vocabulary IQ was about 10 points lower than the numerical IQ and the SD was markedly different from the 15 points that would be expected in a perfectly representative sample. Nevertheless, the composite mean value of 115.7 seemed a reasonable estimate of the average IQ of university students.

Unexpectedly, the numerical test was only weakly correlated with the vocabulary test (r = .13, p = .07). Because reliabilities for these tests were adequate, this does not seem to result from a psychometric artifact. Also, a method bias resulting from the Internet testing does not seem likely, since this would have created an artificial but systematic source of variance resulting in higher correlations. First, the truncated the size of the correlation could have been due to a restriction of range. Second the lack of association between the two tests may be due to an advanced level of cognitive specialization (Lubinski, Webb, Morelock, & Benbow, 2001) in Sample 4. For example, the average numerical IQ of mathematics students was 123.8 (SD = 12.3) vs. 117.6 (SD = 12.6) for language/arts students, which is a significant difference, F(2, 74) = 4.42, p = .04. In contrast, language students had a higher vocabulary IQ than mathematics students (M = 114.7 vs. 108.6, SD = 8.5 vs. 8.3, respectively), F(2, 74) = 9.52, p = .01.

After a sufficient number of participants (n ≥ 20) had completed the pretest questionnaire, same-sex dyads of minimum and maximum IQ differences were created alternately. When a participant was assigned to the minimum difference group, a partner was selected whose IQ differed less than two IQ points. In the maximum difference group, a partner with more than 10 points difference was selected. This resulted in 38 similar and 34 dissimilar dyads, with a mean IQ difference of .6 and 18.1 IQ points, respectively (p < .01). It should be noted, however, that the experimental manipulation assumes a general intelligence factor. Because of the lack of significant association between numerical intelligence and vocabulary, effects of the manipulation cannot be interpreted in this way.

^a See Wilhelm (2000)
^b See Amthauer et al. (2001)
^c Norm data provided by F. R. Lang (see also Lang et al. 2001)

Measure

Norm score (SD)

Alpha
reliability

Numerical test

121.3 (14.3)

279 Gymnasium students (average age 17.7 years) ^a

.77

Figural test (IST)

104.3 (8.8)

415 German young adults (age 26-30 years) ^b

.65

Vocabulary test (MWT)

110.0 (8.8)

159 German young adults (age 20-40 years) ^c

.71

Table 4 Normed Results and Psychometric Properties of Intelligence Tests Used in Sample 4

After both partners had completed the figural test, they were brought together into a comfortably furnished experimental room. In the middle of the room, a round table with two glasses and a water carafe was placed (see Appendix 7.1). The interaction evaluation questionnaires were also placed on this table. Each participant was randomly assigned to a seat that was fixed to a place to the left or right of the table. At the other side of the room, an unobtrusive camera was attached to the wall and filmed the interaction that followed.

After the participants were seated, a second manipulation was performed. In approximately half of the dyads (39), participants were given feedback regarding the relative similarity of their IQ results. In 19 dyads of the similarity condition, this feedback informed them that both participants had achieved highly comparable IQ scores. In 20 dissimilar dyads, the feedback stated that IQ results had been very different. No information regarding a single individual’s absolute intelligence level was given.

After the feedback manipulation, the student assistant asked the participants if they had any further questions and then left the room to a separate video control station, where she monitored and videotaped the interaction. Via a microphone, she remained in contact with the participants and explained that the interaction format required that the person in the right chair (seen from the camera) was first in talking about his or her important life domains. The person in the left chair was instructed to interview his or her partner about the reasons why he or she regarded this life domain as personally important.

It was explicitly stressed that the purpose of this interview was not for the interviewee to „justify” his or her life domain. Rather, the interviewer was to achieve an understanding of the subjective role of the domain in the life of the interaction partner.

It was also stressed that the interaction did not serve to draw conclusions about the participants’ level of ability. Rather, it was stressed that the study examined factors that play a role in human communication.

After the instruction, the participant in the left chair started the interaction by playing the role of the active interviewer whose task it was to explore the subjective meaning of the other person’s life domain. This interaction half was interrupted by the student assistant after 10 minutes to instruct the participants that they were to pick up the questionnaire on the table and fill out the section regarding the first interaction half (the student assistant kept monitoring the participants during this time). When the participants were ready, they proceeded to the second interaction half in which the roles of interviewer and interviewee were reversed. After this, the student assistant instructed the participants to fill out the second part of the questionnaire. When they were ready doing this, participants were thanked, debriefed, and paid.

Social network characteristics were sampled with a measure taken form Asendorpf and Wilpers (1998) and Neyer (1997) (see Appendix 7.2). In a first step, this measure requires participants to list all personally meaningful persons with whom they interact at least once per month. Contact persons were sampled from a wide range of family and non-family categories. Additionally, data on age, sex, duration of the relationship (between 1 = less than one year, and 4 = more than five years), and contact frequency (between 1 = once per month or less, and 5 = daily) were collected.

Table 6 summarizes some average features of the social networks of the different samples. As can be seen, Mensa participants reported an average of 16.4 network partners, whereas the university graduates reported an average of 22 to 23 partners. A similar difference was found for the category of friends, with Mensa members reporting less friends in their social network (4.8) compared to the alumni sample (between 9.1 and 10.3). Most participants in Samples 1 and 2 mentioned their mothers and fathers as members of their social network, whereas at least half also mentioned a romantic partner. On average, the social networks people reported were quite diverse in terms of demographic variables, with a reasonably balanced age distribution and gender ratio (close to 50% in all samples).

The social network data can be compared with data from Neyer (1999), who used an almost identical (P&P) instrument as used in the present study in a sample of N = 495 representative German adults (aged between 17.1 and 29.8 years, M = 24.3, SD = 3.7). In this study, participants reported an average number of 17.9 (SD = 8.5) network partners, with 51% females. On a 3-point Likert scale, they also reported the age of their network partner and the frequency of contact. The average age score was 2.2 (i.e., close to 2 = „about the same age”), and the average contact frequency category was 2.6 (i.e., between 2 = „multiple times a month” and 3 = „once a week”). Finally, participants listed 0.9 mothers, 0.9 fathers, 0.8 partners, and 5.7 friends. As can be seen in Table 6, the network composition reported by Neyer (1999) best matches the social network of Sample 1 (Mensa members).

In a second step, participants were asked to rate every contact person according to the following dimensions (in order of appearance): importance (what impact would the termination of the relationship have: 1 = I would feel better, 5 = I would be strongly burdened for a long time; assessed in Sample 2 and Sample 1.I), felt closeness of the relationship (1 = very distant, 5 = very close), frequency of conflict (1 = never, 5 = almost always), opportunity for meaningful communication (about themes that are important to you: 1 not at all, 5 = very good), availability of emotional support (1 = never, 5 = for almost every problem), felt understanding (1 = very much misunderstood, 5 = very much understood), and felt acceptance (assessed in Sample 3 and Sample 1.II) (to what degree do you feel accepted by this person: 1 = not at all accepted, 5 = completely accepted).

Note. k = average frequency of reporting a relationship category
^a Univariate difference between samples, df (between) = 3, df (within) = 985-992
* p = .05 ** p = .01

Sample 1:

MENSA members

Sample 3:

Internet users

F ^a

HA

AA

1.0 (0.3)

0.9 (0.4)

11.72**

k(father)

0.6 (0.5)

1.0 (0.3)

0.9 (0.4)

0.6 (0.5)

25.33**

k(friends)

4.4 (4.2)

10.6 (6.0)

9.0 (5.4)

3.7 (3.3)

126.77**

k(partner)

0.5 (0.5)

0.7 (0.5)

0.7 (0.5)

0.6 (0.5)

6.86**

k(total)

14.2 (9.5)

22.8 (8.5)

21.8 (8.8)

10.1 (6.1)

129.48**

age

39.7 (8.6)

35.2 (4.2)

36.8 (6.2)

36.0 (7.2)

21.70**

gender (% female)

0.5 (0.2)

0.5 (0.1)

0.5 (0.1)

0.6 (0.2)

2.75*

rated intelligence

15.9 (2.2)

14.6 (1.7)

14.5 (1.9)

13.3 (2.6)

93.19**

contact duration

3.3 (0.5)

3.2 (0.5)

3.3 (0.4)

3.3 (0.4)

4.14**

contact frequency

2.7 (0.7)

2.5 (0.5)

2.4 (0.5)

3.1 (0.7)

53.05**

Table 6 Social Network Composition Across Samples

To measure the participants’ self-assessments of the quality of their relationships with same-sex and opposite-sex peers, 8 items from the German translation of the Self Description Questionnaire III (SDQ-III; Marsh, 1992) were used (5-point Likert scale). These items were drawn from a study by Schwanzer (2002), who created short 4-item (half of them negatively framed) versions of the SDQ-III scales on the basis of item-total correlations (see Appendix 7.3). In the current study, both scales had good reliabilities (α = .81 for same-sex peers; .84 for opposite-sex peers).

Mixed with the SDQ-III items, 10 items from the German translation of the UCLA Loneliness Scale (Döring & Bortz, 1993) were included to measure subjective feelings of loneliness on a five-point Likert scale. Five of these items referred to social loneliness, whereas the remaining 5 concentrated more on emotional aspects of loneliness. Because these two facets correlated very highly (r = .71, p = .01), they were combined into a composite loneliness score (α = .83).

After each interaction, participants completed a short questionnaire (5-point Likert scale) assessing the level of felt understanding (4 items, e.g., „I [the interviewee] succeeded in explaining the interviewer [me] what personal meaning the discussed life domains have for me [him/her]”), empathic ability of the interviewer (4 items, e.g., „It was often difficult for me [the other person] to follow the thoughts of the interviewed person [my thoughts] with my [his/her] questions)”, interaction flow (4 items, e.g., „I did not enjoy the conversation”), and comfort (1 item, „I felt relaxed during the conversation”). Some of the items of this questionnaire were adapted from Hecht’s (1978) Communication Satisfaction Inventory, but others were especially constructed for the current dissertation (see Appendix 7.4).

Following the ratings of social relationship quality, participants were asked to rate their own intelligence (in Samples 1-2) and the intelligence of each contact person (Samples 1-4). For this, the unpublished „Intellectual Ability Questionnaire” developed by O. Wilhelm was used (2000; see Bailey & Lazar, 1976, for a similar measure). Participants were first instructed about the intelligence distribution in the population with the help of a graphical normal curve (see Appendix 7.5). In a next step, participants were asked to rate the intelligence of every contact person as well as their own intelligence on a 1 (0-5%) to 20 (95-100%) percentile scale.

Self-ratings of intelligence have been shown to be moderately accurate in predicting psychometric intelligence. In several reviews of the relevant literature, it has been stated that the validity of self-ratings approximates .30 (Furnham, 2001; Paulhus, Lysy, & Yik, 1998). Appendix 7.6 lists some empirical studies that calculated the correlation between psychometrically measured and self-rated intelligence. As can be seen, these studies report an average correlation of .29, which is consistent with previous reviews. This value is also somewhat similar to, though slightly lower than, agreement between self-ratings and informed acquaintances (Borkenau & Liebler, 1993: r = .29; Paulhus & Morgan, 1997: r = .37; but see Bailey & Mettetal, 1977b).

Especially with regard to self-ratings of intelligence, the level of predictive validity has been regarded as disappointing (Paulhus et al., 1998). Indeed, a correlation of .3 between measured and rated intelligence is considered small (Cohen, 1992). However, it should be noted that most studies using college students as participants suffer from restriction of range in intelligence, which leads to reductions of predictive correlations.

For example, Paulhus et al. (1998) used the Wonderlic intelligence test; instead of the typical SD of 7.1, they reported a SD of only 4.6.

Because of the evidence for the (modest) validity of intelligence ratings, the current study used them as proxies for general intelligence (Studies 1-3). However, it needs to be taken in mind that intelligence ratings are biased by a number of sources. First of all, such ratings are prone to self-serving biases (Gabriel, Critelli, & Ee, 1994; Dunning & Cohen, 1992). Second, intelligence ratings have been shown to be confounded by stereotypical influences associated with number of factors, such as gender (Furnham, 2001; Rammstedt & Rammsayer, 2000), age (Furnham, 2001), and physical attractiveness (Zebrowitz, Hall, Murphy, & Rhodes, 2002). To adjust for some of these stereotypical influences, the current dissertation used the residuals of a regression analysis predicting intelligence ratings with age and gender. The degree of physical attractiveness was not measured in the current study and could thus not be corrected for.

In the current study, there were significant differences in self ratings between Samples 1 and 2, F(3, 520) = 239.12, p < .01. As expected, Mensa members (Sample 1) rated themselves as very high in intelligence (M = 19.7, SD = 0.8), followed by the highly achieving (M = 16.3, SD = 2.1) and average achieving (M = 15.7, SD = 2.3) university alumni (Sample 2). The university students comprising Sample 4 had the lowest self ratings (M = 14.2, SD = 2.3). Average ratings by all samples of university students/alumni were higher than the scale midpoint, which is not unexpected given their high educational status. As can be seen in Table 6, network partners were also rated as above-average in intelligence, with the Mensa members reporting the most intelligent partners (M = 15.7, SD = 1.9), followed by the highly achieving and averagely achieving university alumni (M = 14.6, SD = 1.7 and M = 14.5, SD = 1.8, respectively) and the Internet users (Sample 3) coming last (M = 13.2, SD = 2.4). This difference is significant, F(3, 949) = 73.69, p < .01.

In Samples 1 and 2, the self-concept of intelligence was assessed alongside the SDQ-III scales (see Appendix 7.3) with four specifically devised items (half of them negatively formulated) that were calibrated towards a high intelligence level to avoid ceiling effects in the gifted sample (e.g., „compared to others, my level of intellectual abilities is unusually high”). This scale had a 1-5 Likert format and very good internal consistency (α = .87).

Numerical intelligence has been found to be a good estimate of fluid intelligence (Bickley et al., 1995), especially when tested in the context of new problems. Sample 4 took a test developed by O. Wilhelm (2000; see Appendix 7.7). This test requires participants to complete 17 series of 9 numbers. To find the solution, it is necessary to discover regularities in the first 7 numbers and then to apply this rule to the two empty slots. Item difficulties ranged from .94 (Item 4) to .53 (Item 12), with an average of .77 (average SD = .39). For the 144 participants who also completed the laboratory phase, alpha reliability was .77 (item total rs between .09 and .60), which is comparable to the .73 reported by Wilhelm (2000). The test loaded very highly on a general intelligence factor in a battery of 12 tests (Wilhelm, 2000).

As a test of figural intelligence, the Matrices subtest of the Intelligence Structure Test [Intelligenz-Struktur-Test] 2001-R (IST-2001; Amthauer et al. 2001) was used. This test is very much akin to the Raven’s Progressive Matrices test (see figure 4) that is considered as one of the best markers of fluid intelligence. The IST-2001 Matrices test consists of 18 series of 3 figures and 2 series of 8 figures that are built up according to some rule. Out of 4 alternatives, participants need to choose the figure that would complete the series. Following the manual, a time limit of 10 minutes was set for the test, which allowed participants to skip over certain items and proceed to the next one.

On average, participants answered 14.1 items, with difficulties for answered items ranging from .95 (item 1) to .16 (item 20), paralleling the values reported in the test manual. The reliability of this speeded test was calculated by correlating (using Spearman’s Rho) the number of correct odd items with the number of correct even items and correcting this index with the Spearman Brown formula. This resulted in an estimated reliability of .65, which comes close to the .70 reported in the manual for students of the Gymnasium. Applying the .70 reliability criterion that is acceptable according to Nunnaly (1978), this value is somewhat low, even for a short test of 20 items. Accordingly, results based on this test should be interpreted with some caution.

In Samples 3 and 4, vocabulary was measured with the Multiple-Choice Vocabulary Test [Mehrfachwahl Wortschatztest] (MWT; Lehrl, 1995). This test consists of 35 sets of five alternative letter combinations, only one of which is a correctly spelled word. In the manual, Lehrl summarizes the results from 26 studies that report a median correlation coefficient of .71 with several global intelligence tests. In both samples, a large number of items (12 in Sample 3, 13 in Sample 4) were answered correctly by almost all participants (difficulty ≥ .90; Items 1, 3, 4, 6-8, 10, 11, 14-16, 22, 26), with an average difficulty of around .70 (SD .11). The number of correctly answered items was used as the total score (α = .70 in both samples).

In Sample 1, a total number of 76 Mensa members (32%) provided the result of their latest IQ test results.

In most cases, this was the Mensa admission test. Note that the IQ tests were taken at different time points, which might have influenced results because secular gains in intelligence (Flynn, 2003) lead to an underestimation of average population performance when older test norms are applied.

Interests were measured with a short version of the German General Interests Structure Test [Allgemeiner Interessen-Struktur-Test] (AIST; Bergmann & Eder, 1992), with scales corresponding to Holland’s (1959) six basic interests (see Section 2.1.3). The unpublished short version (see Appendix 7.8) included 18 items and was developed by G. Nagy from the Berlin Max Planck Institute for Human Development on the basis of a factor analysis of the original 60 items (personal communication, 2 February, 2005). The items of the short form were selected on the basis of their discriminant factor loadings (i.e., high loadings on one factor, small loadings on all other factors), which makes them better suited for the calculation of profile similarity,

Items loading on more than one factor reduce the variability of the different scales, resulting in relatively flat profiles.

Values were assessed with a German version of the Rokeach Values Survey (RVS) adapted by Todt (1989). Participants were required to rank 17 end goals in terms of their subjective value. In the current study, the three most listed values were health, friendship, and love (rank M = 5.6, SD = 3.9 for health; M = 5.2, SD = 3.5 for friendship; M = 4.8, SD = 3.9 for love). The least important value in this sample was material wealth (rank M = 13.0, SD = 4.6). The median correlation between the different values was -.08

The negative correlation was expected because of the forced ranking procedure (e.g., if a value gets a rank of one, other values will automatically get lower ranks).

In Samples 1, 2 and 4, openness to experience was assessed with the German version of the NEO-Five Factor Inventory (NEO-FFF; Borkenau & Ostendorf, 1993), using a 5-point Likert scale. Sample 3 completed the German version of the Big Five Inventory (BFI; Rammstedt, 1997). To ensure BFI scales of equal length (the original instrument has 7-10 items per scale), the seven highest-loading items according to a study by Lang et al. (2001) were selected for use in the current study. With one exception, all items of the Openness scale were are formulated in the positive direction (items of the other Big Five scales are phrased in both the negative and positive direction). As can be seen in Table 7, the NEO Openness scale had quite low reliability in both the Mensa group and the alumni sample (α ≤ .70).

Inspection of the matrix of item-total correlations uncovered that this was mainly due to the negative item-total correlation of Item 8: „I think we should pay more attention to the opinion of our religious authorities in making ethical decisions”. Contrary to the indented purpose, the open individuals in Samples 1-2 generally agreed with this item, perhaps because of its emphasis on complicated ethical problems.

The current sample’s scores on the NEO-FFI scales were compared to norm data of 1,908 representative German adults (community sample), collected by Körner, Geyer, and Brähler (2002).

The Körner et al. (2002) data were deemed superior to data reported in the German NEO-FFI handbook (Borkenau & Ostendorf, 1993) that rely on a sample that is biased towards younger adults.

In all samples, extraversion, neuroticism, agreeableness, and conscientiousness (i.e., the remaining four Big Five factors) were assessed as control variables. As can be seen in Table 7, the reliability of these four scales was acceptable to good. Across all samples and factors, data were quite comparable with norm data, except for neuroticism in Sample 3, where the mean score trailed almost one SD below the population mean. On average, the studied samples were somewhat more extraverted, emotionally stable, and agreeable than the corresponding norm groups. The range in the four remaining traits was in no way restricted in range, with values tending instead towards somewhat higher diversity. Comparisons between Samples 1, 2, and 4 (i.e., the samples that completed the NEO-FFI) showed significant differences in extraversion, agreeableness, and conscientiousness, Fs (2, 710) > 17, ps < .01. Planned contrast showed that this was due to the lower extraversion and agreeableness of the Mensa sample compared the alumni and laboratory participants, Fs > 13, ps < .01, whereas the university alumni were more conscientious than the other two samples, Fs > 20, ps < .01.

Note. E = Extraversion, N = Neuroticism, O = Openness, C = Conscientiousness, A = Agreeableness
^a Norm data by Körner, Geyer, and Brähler (2002)
^b Norm data by Lang and Lüdtke (in preparation)

E

N

O

C

A

Alpha

.90

.63

.78

.83

Mean z ^a

0.04

-0.05

1.75

0.01

0.04

1.28

1.28

1.04

1.10

1.09

Alpha

.76

.70

.71

.87

Mean z ^a

-0.18

0.53

1.70

0.54

0.48

1.02

1.01

1.08

0.94

1.03

Alpha

.90

.85

.75

.82

.72

Mean z ^b

0.02

0.34

1.54

0.38

-0.09

1.06

1.09

1.16

0.96

0.99

.81

.87

.76

.84

.76

Mean z ^a

0.24

0.07

1.56

-0.11

0.34

SD

1.10

1.11

1.16

0.99

0.97

Aggregated mean z

0.03

0.22

1.64

0.21

0.19

Table 7 Psychometric Properties and Normed Scores of Big Five Scales Across Samples