4 Results

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In the following chapter, results regarding the different hypotheses are reviewed. Section 4.1 presents the results of a number of factor analyses, which were used to create composite scales whenever possible. In Section 4.2, the outcomes of the hypotheses regarding main effects of fluid intelligence, crystallized intelligence, and openness to experience on the MU process are described. Section 4.3 addresses the hypotheses regarding the dyadic effects of these factors on the MU process. Finally, Section 4.4 compares gifted to non-gifted individuals in terms of their social and general adjustment.

4.1  Data Reduction

4.1.1  Ego-Centered Social Relationship Quality

4.1.1.1 Samples 1 and 2

In Samples 1 and 2, participants used an ego-centered social relationships instrument (Neyer, 1997) to rate each contact person on the following dimensions: importance, felt closeness, frequency of conflict, quality of communication, emotional support, and felt understanding. Correlations between these variables ranged between -.22 (between conflict and felt understanding) and .71 (between closeness and importance).33 Principal component factor analysis (with Varimax rotation) was performed to reduce the number of variables, which resulted in two factors with Eigenvalues greater than 1 that explained 55% and 18% of the total variance. Inspection of the unrotated factor loadings yielded clearly interpretable factors (see Table 8), with high primary (≥│.74│) and low secondary loadings (≤│.34│). Accordingly, a Relationship Quality Scale was formed by averaging the items loading on the first factor, which had very good reliability (α = .87). Because the current study was only interested in MU as an indicator of relationship quality, the conflict item that dominated the second factor was not used in further analyses.

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Table 8 Loadings of Social Relationships Ratings on Relationship Quality Factors in Samples 1-3

Identified Factors

Sample 1.I-2

 

Sample 1.II a

Quality

Conflict

Quality

Quality

Conflict

Importance b

.74

.34

Closeness

.84

.19

.80

.78

.29

Conflict

-.17

.90

-.41

-.29

.92

Communication

.76

-.26

.84

.84

.04

Support

.85

.12

.79

.77

.30

Understanding

.85

-.20

.85

.84

-.10

Acceptance b

.79

.79

-.18

% explained variance

55%

18%

58%

56%

18%

Note. Factor loadings greater than .4 printed in bold.
a Results are only based on the analysis of Mensa members’ responses
b The importance item was not assessed in Samples 1.II and 3, whereas the acceptance item was not assessed in Samples 1.I and 2

4.1.1.2 Sample 1.II

In Sample 1.II, the ego-centered relationships questionnaire was supplemented with an item measuring the amount of acceptance felt in the relationship but did not longer include an item measuring the importance of the relationship (all other items remained the same as in Sample 1-2). As can be seen in Table 8, a factor analysis of all items resulted in a clear two factor structure, explaining 56% and 18% of all variance. Because of the incomplete overlap of items between Samples 1-2 and 1.II, coefficients of congruence could not be calculated to quantify factor resemblance. However, inspection of the factor loadings in Table 8 suggests clearly comparable factors. Like in Sample 1-2, a Relationship Quality scale was formed by averaging all items that loaded highly on the first factor (α = .86).

4.1.1.3 Sample 3

Sample 3 completed the same relationship questionnaire as Sample 1.II (i.e., with acceptance but without importance ratings). Factor analysis of these items showed that, unlike in Sample 1-2 and 1.II, all evaluative items, including the ratings for conflict frequency, loaded on a single factor. However, because the (absolute) factor loading for conflict (.41) was considerably lower than for the other items (range .78-.85) and to improve comparability with the composite measures collected in Samples 1 and 2, it was decided to treat conflict as a separate dimension of relationship evaluation and to average the remaining items in a composite scale with excellent reliability (α = .87).

4.1.2 Post-Interaction Ratings in Sample 4

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In Sample 4, after the end of their conversation, participants completed the interaction evaluation questionnaire (see Appendix 7.4). The items of this post-interaction questionnaire were factor-analyzed to reduce the number of variables. Results of this analysis are presented in the following section.

4.1.2.1 Assessment of Conversation Quality

The post-interaction questionnaire items were factor-analyzed separately for each interaction role (i.e., interviewer/interviewee). Table 9 shows the factor solutions for the participants’ evaluations of the interviewer, separately for self-ratings and partner-ratings. As can be seen, analysis of interviewers’ self-ratings resulted in a three-factor solution that explained 55% of the total variance. In contrast, analysis of the interviewees’ ratings of the interviewers’ behavior resulted in two factors that explained 53% of the variance.

The factor loadings of the self-ratings of the active interviewer role are displayed in the left three columns of Table 9. As can be seen, the first factor was dominated by items that tap into a successful understanding of the interview partner. Participants with high scores on this factor stated they could comprehend their partners well and had the impression that their partners were successful in explaining the meaning of their life domains. Accordingly, this factor was labeled Interviewer Understanding (IU). The second factor was dominated by items emphasizing a smooth, pleasant and synchronized conversation. This factor was labeled Interviewer Flow (IF). Finally, the third factor was almost exclusively dominated by the (reverse coded) dissatisfaction item. This factor was labeled Interviewer Satisfaction (IS).

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As stated above, the factor analysis of the ratings for the interaction half in which the participants played the role of interviewee only resulted in a two-factor solution. An inspection of the factor loadings shows that the first factor was dominated by items expressing satisfaction with the interviewer’s behavior (e.g., saying the right things, providing ample opportunity to explain the importance of life domains) and a subjective feeling of being understood. Accordingly, this factor was called Partner Understood (PU). The second factor was characterized by items that emphasized a smooth, relaxed, and satisfactory conversation. This factor was labeled Partner Flow (PF).

Despite the similar labeling of the first two factors of the post-interaction ratings of conversation quality, there were differences in accent. Indeed, coefficients of congruence between the first two factors were .85 for the first and .75 for the second factor, suggesting broadly similar, but by no means identical factors. Inspection of the factor loadings showed that this was mainly caused by Item 2 (interviewer made constructive remarks) and 8 (interviewer followed up on the interviewee’s thoughts). Whereas these items loaded on the Flow factor of the interviewer ratings, they contributed to the Understood factor of the partner-ratings. Presumably, this is due to the fact that interviewers can discriminate between (covert) subjective understanding and (overt) communicative behavior (e.g., it is possible to understand another person without succeeding to communicate this feeling to him/her), whereas the interviewee cannot. For both interaction halves, participants’ factor scores on the interviewer and interviewee “Understanding” factors were used as variables in subsequent analyses.

4.1.2.2 Effect of Manipulation on Intelligence Ratings

As stated previously (Section 3.2.3), an experimental manipulation was carried out before the participants started to interact. Specifically, in half of the interactions (n = 39), participants were given feedback about the relative difference in measured intelligence between them. In half of these cases (n = 19), they were told that the test had indicated a large intelligence difference between them, whereas in the other half (n = 20), they were told that there were only minimal differences. It was expected that this manipulation would affect perceived intelligence differences, so that participants in the difference-feedback condition would perceive a lot of differences, whereas participants in the similarity-feedback condition would not.

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Table 9 Loadings of Ratings of Interviewer Behavior on Conversation Quality Factors in Sample 4

Self-rated interviewer behavior

Partner-rated interviewer behavior

Item

Content

IU

IF

IS

PU

PF

1

I [the interviewer] could comprehend well why the discussed life domains are so important to my interview partner [me]

.80

.00

.11

.66

.31

2

I [the interviewer] often said things that did not contribute much to the conversation (R)

.00

.65

-.31

.69

.05

3

This conversation half went smoothly

.01

.73

.24

.20

.82

4

The interviewee [I] succeeded in explaining the interviewer [me] what personal meaning the discussed life domains have for him/her [me]

.79

.13

.26

.61

.30

5

I [the interviewer] showed great interest in the things my interviewer partner [I] said

.59

.46

-.08

.57

.41

6

I did not enjoy the conversation (R)

.43

.32

.37

.19

.56

7

My conversation partner [I] had little opportunity to explain why certain life domains are important to him/her [me] (R)

.60

.02

.01

.75

-.01

8

It was often difficult for me [the other person] to follow the thoughts of the interviewed person [my thoughts] with my [his/her] questions (R)

.21

.72

.01

.67

.22

9

I felt relaxed during this conversation half

.27

.62

.30

.27

.82

10

I [the interviewer] showed my conversation [me] partner that I [he/she] understood, what he/she [I] said

.49

.31

-.14

.72

.32

11

I was very dissatisfied with the conversation (R)

.04

.02

.85

.06

.62

Note. Factor loadings greater than .40 printed in bold.
IU = Interviewer Understanding, IF = Interviewer Flow, IS = Interviewer Satisfaction, PU = Partner Understanding, PF = Partner Flow, (R) = reverse coded

The effectiveness of the experimental manipulation was tested with a t-test of the difference between independent sample means. Specifically, participants in dyads who were told to be very similar in intelligence were compared to participants in dyads who were told to be different. As the dependent variable, the absolute difference between participants’ ratings of themselves and their partners was used (i.e., perceived difference). Because the dependent variable varied across individual participants (in contrast to the experimental manipulation, which varied across dyads), the model was tested with participants as between-subjects unit (n = 77). In contrast to expectations, however, results did not show a significant difference between the two feedback groups, t(76) = 1.08, p = .14 (one-sided). Accordingly, the manipulation was deemed a failure and its effects were not further analyzed.34

4.1.3 Agreement Across Data Sources

To assess data quality, the level of agreement across data sources was calculated. First, it was tested whether persons who thought they were very understanding as interviewers actually had partners who felt understood during that interaction half. In addition, self- and partner-ratings were correlated with the aggregated MU observations by the student assistant judges. Table 10 shows the results of these analyses. As can be seen, all correlations were positive and at least marginally significant. Specifically, if interviewers reported being very understanding, their interaction partners felt better understood during the corresponding interaction half (rs = .31 and .26 for the first and second interaction halves, respectively). In addition, both partners’ impression of the degree of MU was corroborated by the observational ratings, with correlations ranging between .22 and .36 (all ps ≤ .10).

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Table 10 Correlations Between Self-Reported and Observed Indices of MU

IU(2)

PU(2)

PU(1)

MU(1)

MU(2)

IU(1)

.12

.27*

.31**

.22†

.23†

IU(2)

.26*

.66**

.31**

.28*

PU(2)

.14

.22†

.36**

PU(1)

.34**

.30**

MU(1)

.74**

Note. IU = Self-rated understanding as interviewer, PU = felt understanding as partner, MU = mutual understanding rated by outside observers; index of interaction half in brackets
** p < .01, * p < .05, † p < .10

To obtain at a dyadic measure of MU, a factor analysis of both participants’ self-rated Interviewer and Interviewee Understanding, and the amount of observed MU for each interaction half (i.e., six variables) was conducted. Inspection of the scree plot suggested a one-factor solution, with the first factor explaining 45% of the variance, and factor loadings ranging between .50 and .76. Accordingly, the average of these six variables was taken as a composite index of MU, which had an acceptable reliability (α = .74), especially considering the heterogeneity of information sources. In addition, similar composite indices were created for each interaction half (because these consisted of only three items each, reliabilities were deflated to .54 and .57 for the first and second half, respectively).

4.1.4 Interdependence of Data

An important issue in using dyadic data is that the individual members of the dyad can be interdependent. That is, for whatever reason, members may resemble each other in terms of certain characteristics. Failing to account for this interdependency may lead to biased conclusions (Kenny & la Voie, 1985). Because the dyads were formed unsystematically (with the exception of pretest intelligence) and did not have contact previous to the interaction, interdependence in Study 4 can only be due to the interaction itself (e.g., when both participants act in a relaxed way because of a nice conversation). Accordingly, no dyadic associations between partners on any of the pretest measures were expected.

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In line with the expectation of non-interdependence, (intraclass) correlational analyses showed that correlations between participants’ crystallized and fluid intelligence scores ranged between -.11 and .03 and were not statistically significant, ps > .30. Similarly, openness to experience did not correlate across interaction partners, r = -.05, p = .69. Finally, both participants’ self-ratings and partner-ratings were uncorrelated between dyadic partners, r = -.05 and .02 (ps > .60), respectively. Accordingly, it was not necessary to account for dyadic interdependency regarding the current predictor variables.

Because the data were virtually non-interdependent, applying the formula by Burr and Nesselroade (1990) results in estimates of the reliability of the difference scores between two participants that approximate the reliability of the corresponding scales. The only exception is the difference between participants’ self-ratings of intelligence and their ratings of the intelligence of their interaction partners, which were significantly correlated (r = .40, p < .01). When the reliability of the single-item measure is estimated at .70, then the reliability of the (intra-individual) difference score does not exceed .50. Of course, the low reliability of the intra-individual difference score also affects the results of Studies 1-3.

4.2 Main Effects of Intelligence and Dispositional Valuations

The current study tested a number of hypotheses regarding the main effects of intelligence and dispositional valuations on the level of MU in social relationships. Specifically, it was hypothesized that fluid intelligence, crystallized intelligence, and openness to experience are positively related to MU.

4.2.1  Samples 1-2

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To address the main effect hypotheses regarding fluid intelligence and openness to experience on the amount of MU in relationships (Main Effect Hypothesis 1 and 3, respectively), the ego-centered network data were analyzed using HLM. On the relationship-specific Level 1, the composite relationship quality index served as the dependent variable. The intercept (i.e., average level of relationship quality within a person’s social network) served as outcome variables in a participant-specific Level 2 regression. As predictors of the intercept35, self-rated intelligence and openness to experience were included. All continuous Level 2 variables were standardized to facilitate comparison of the HLM coefficients. Level 2 random effects were included for both intercept and the IQ x relationship quality slope parameter. In addition, gender, age, and network size36 were entered as control variables on Level 2.

Table 11 shows the regression coefficients of the HLM analysis of main effects in Samples 1-2 and 3. As can be seen, the Table is divided into two horizontal halves covering the different samples. The first line of each half specifies the Level 2 intercept (the average relationship quality), followed by the slope of the association between rated intelligence and relationship quality. In lines 2-5, Level 2 moderators of the Level 1 intercept are displayed. For example, in Sample 1-2, an elevation of 1 SD in Openness increases the Level 1 intercept by .04 points.

As can be seen in Table 11, the parameter linking partner intelligence with relationship quality was significant and positive on Level 1, indicating that relationships with more intelligent network partners were rated as higher in quality. Thus, Main Effect Hypothesis 1 was confirmed regarding the relationship-specific Level 1. In contrast, self-rated intelligence on Level 2 had a significantly negative influence on the average level of relationship quality. That is, highly intelligent individuals reported less satisfying and understanding relationships, which is inconsistent with Main Effect Hypothesis 1. Finally, participants’ openness to experience was positively related to average relationship quality on Level 2, but this effect was not significant. Accordingly, Main Effect Hypothesis 3 was not supported.

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Table 11 HLM Regression Coefficients of the Effect of Rated Intelligence, Openness, and Vocabulary on Relationship Quality in Samples 1-2 and 3

Level 1 (relationship partner)

Relationship quality

intercept

Rated partner IQ

slope

Sample 1-2

  

Level 2

-Openness

 

-Self-rated IQ

-0.21**

-Network size

-0.07**

-Age

-0.01

-Female gender

0.03

Sample 3

 

0.41**

Level 2

-Openness

0.01

-Vocabulary

 

-Network size

-0.04*

-Age

-0.06*

-Female gender

0.28**

Note. IQ = intelligence rating, corrected for age and gender. All continuous variables were standardized before entering in the analysis. In Samples 1-2 (Level 1, N = 7,863; Level 2, N = 410), random effects were estimated at .36, .20, and .86 for the Level 1 intercept, the IQ x relationship quality slope, and the residual variance, respectively (ps < .01). In Sample 3 (Level 1, N = 5,153; Level 2, N = 511), the corresponding estimates were .50, .22, and .79 (ps < .01).
** p < .01, * p < .05

In the current case, there were extreme differences between the Mensa (Sample 1.I) and alumni (Sample 2) in the mean level and range of the single item intelligence rating. Specifically, the average (single-item) self-rated intelligence was 19.7 for the Mensa members against 16.2 for the university alumni, a highly significant difference, F(1, 431) = 555.74, p = .01. In addition, Mensa members had a SD of .62 on the single item rating, whereas university alumni had a SD of 2.15. An F-test showed that this difference is significant, F(221, 219) = 3.49, p = .01. Because of these large differences in distribution, the single item intelligence rating is confounded with sample membership (i.e., a participant with a self-rating of 20 points almost certainly belongs to the Mensa sample).

Because of the potentially unmeasured selection bias accompanying Mensa membership (besides having a high intelligence), the single item intelligence rating scale is not ideal to test for the effects of intelligence on social relationships. Sample differences regarding the four-item intelligence self-concept scale were comparably smaller, though significant, F(1, 435) = 82.47, p < .01, whereas the SD of this scale did not differ between the Mensa members (SD = .68) and the university alumni (SD = .73), F(222, 223) = 1.09, p = .26. When the single-item intelligence ratings were replaced with the intelligence self-concept scale, no significant effect on the relationship quality intercept was found (p = .27), even though the trend was again negative (b = -0.03). Accordingly, the negative effect of intelligence self-ratings found for the single item measure was not replicated for the intelligence self-concept scale.

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In sum, a negative/null association between participants’ self-rated intelligence and their social network’s average level of MU was found on Level 2. In contrast, a positive association between participants’ rating of their network partners’ intelligence and the quality of their relationship was found on Level 1. Thus, results differed between the relationship-specific (dyadic) Level 1 and the participant-specific Level 2. In interpreting this discrepancy, it should be noted that these two levels are mathematically independent of each other and represent different research questions. For example, whereas the Level 2 association between rated intelligence and MU is dependent of the relationship quality intercept, the association on Level 1 is independent of this parameter.

4.2.2 Sample 1.II

Like in Sample 1.I, 39 Mensa members filled out the NEO-FFI personality questionnaire, provided a list of their network partners and rated each partner’s intelligence and the quality of the corresponding relationship. In addition, a total of 172 network partners rated their own openness to experience and intelligence as well as the quality of the relationship with the Mensa member. Because the intelligence self-ratings of the Mensa members did not show any variation, they could not be used to assess main effects. In addition, it turned out that also the network partners’ ratings of the Mensa members’ intelligence were extremely right skewed (kurtosis -2.35, se .18, p < .01). As 42% of the network partners used the highest or second-highest intelligence rating (i.e., 19 or 20), many were apparently aware of the gifted status of the Mensa members, most likely because of their publicly known membership in an organization for the gifted. Because the intelligence ratings of the network partners were highly confounded with the availability of this information, they were not further used in the subsequent analyses.

Because two rating sources (Mensa members and network partners) for both dependent and independent variables were available, main effects were tested with four separate HLM analyses. The dependent variable in these regressions was the amount of relationship quality, predicted by ratings of partner intelligence while controlling for the total size of the social network and participants’ gender.37 Table 12 shows the outcomes of these analyses. Note that this Table is divided into four rows, depending on the source and the target of the intelligence and relationship quality ratings (in the Table, the source of a rating is placed left of colon, whereas the target is placed on the right; in the case of self-ratings, source and targets are identical). As can be seen, the effects of intelligence ratings on relationship quality were only statistically significant when the Mensa member provided both intelligence and relationship quality ratings. Because the positive association between intelligence and relationship quality ratings found in Samples 1.I and 2 was only replicated in one out of four possible analyses, Main Effect Hypothesis 1 received only weak support.

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Table 12 HLM Regression Coefficients of the Effect of Ratings of Network Partner Intelligence on Relationship Quality in Sample 1.II

Level 1 (relationship partner)

Relationship quality

intercept

Rated partner IQ

slope

MM: Quality/MM: IQ

0.24

0.24**

Level 2

-Network size

0.00

-Female gender

0.04

MM: Quality/NP: IQ

0.40**

-0.05

Level 2

-Network size

-0.03

-Female gender

-0.06

NP: Quality/MM: IQ

0.01

-0.04

Level 2

-Network size

-0.01

-Female gender

0.02

NP: Quality/NP: IQ

0.02

-0.07

Level 2

-Network size

0.00

-Female gender

-0.02

Note. MM = Mensa member, NP = network partner, IQ = intelligence rating, corrected for age and gender. All continuous variables were standardized before entering in the analysis. Level 1, N = 172; Level 2, N = 39. Mean random effects (across all four analyses) were estimated at .26, .10, and .89 for the Level 1 intercept, the IQ x relationship quality slope, and the residual variance, respectively (ps ≤ .36). The table is divided into four horizontal parts, depending on the source (left of colon) and target , whereas the target (right of colon) of the intelligence ratings rating
** p < .01, * p < .05

4.2.3 Sample 3

Like in Samples 1-2, a HLM analysis with vocabulary and openness as Level 2 main effect variables and rated network partner intelligence as Level 1 main effect variables was carried out. Instead of intelligence self-ratings (not assessed in this Sample), the vocabulary test score was included as a measure of crystallized intelligence. Like in the previous analyses, gender, age, and total network size were included as Level 2 covariates.

Table 11 (lower part) shows the results of the HLM analysis. As can be seen, neither the effect of vocabulary nor the effect of openness was significant on Level 2. As found in Samples 1-2, however, the main effect of rated partner intelligence on relationship quality was significant and positive. This is consistent with Main Effect Hypothesis 1, which predicts that intelligence and relationship quality are positively associated.

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In sum, results for Sample 3 resulted in positive results for Main Effect Hypothesis 1, but no support for Main Effect Hypotheses 2 and 3. Consistent with Main Effect Hypothesis 1 and replicating results from Samples 1-2, participants’ ratings of their network partners’ intelligence (on Level 1) were positively associated with relationship quality. In contrast, vocabulary level as assessed with a psychometric test (on Level 2) was not associated with relationship quality, which disconfirms Main Effect Hypothesis 2. Finally, self-ratings of openness to experience (on Level 2) were not significantly associated with relationship quality, which disconfirms Main Effect Hypothesis 3.

4.2.4 Sample 4

According to the current Main Effect Hypotheses, MU should be positively related to intelligence, vocabulary, and openness to experience. In Sample 4, self-ratings, ratings by the interaction partner, and intelligence test results were available as information sources for fluid intelligence. In addition, vocabulary was measured with the help of a psychometric test (MWT), and openness with an established self-rating instrument (NEO-FFI). The level of observed and self-reported MU after the interaction served as dependent variable.

The intercorrelation matrix of the different predictor measures (see Table 13) was inspected to see whether it would be possible to create aggregated variables. As can be seen, self-ratings of intelligence were significantly correlated with psychometric numerical intelligence and openness to experience. Vocabulary was significantly correlated with openness to experience and marginally significantly with numerical intelligence. These correlations are consistent with previous research (e.g., Ashton et al., 2000; Paulhus et al., 1998), but they are not high enough to create composite measures.38 Unexpectedly, partner-ratings of intelligence and figural intelligence were unrelated to the other predictor variables.39

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Table 13 Correlations Among Predictor Variables in Sample 4 (Intelligence and Openness to Experience)

IQ SR

IQ PR

IQ NUM

IQ VOC

IQ FIG

O

IQ SR

1.00

.03

.23**

-.03

.04

.20*

IQ PR

1.00

-.01

 

-.03

-.11

IQ NUM

1.00

.13†

.10

.04

IQ VOC

1.00

.01

.17*

IQ FIG

1.00

-.05

O

1.00

Note. IQ SR = Self-rated IQ, IQ PR = Rating by interaction partner IQ, IQ NUM = numerical IQ, IQ FIG = figural IQ, IQ VOC = Vocabulary, O = Openness. Ns ranged from 139 (correlations with intelligence ratings) to 200 (correlations among pretest measures).
** p < .01, * p < .05, † p < .10

Main effects in Sample 4 were tested by a series of correlational analyses. As can be seen in Table 14, main effects were limited to partner-ratings of intelligence, and psychometrically assessed vocabulary. Interestingly, main effects differed between participants (i.e., first vs. second interviewer) but generalized across interaction halves (i.e., first vs. second half). That is, effects that were found for the first interviewer were not replicated for the second interviewer (and vice versa), but the effects (and lack of effects) of both persons’ personality were not constrained to one interaction half.

Across both interaction roles and interaction halves, two main effects of intelligence were significant. To begin with, the first interviewer’s level of vocabulary was significantly positively related to MU during the conversation. The positive effect of crystallized intelligence is consistent with Main Effect Hypothesis 2. However, no effect was found for the second interviewer’s vocabulary. Second, the rating of the first interviewer’s intelligence by his or her interaction partner was positively related to MU. However, no effect was found for the first interviewer’s ratings of the intelligence of the second interviewer. Besides the effects of the first interviewer’s vocabulary level and intelligence ratings by his/her partner, no other personality variables exerted a significant main effect on MU. That is, no significant main effects were found for self-ratings of intelligence, psychometric (figural, numerical) intelligence, or self-reported openness to experience.

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Table 14 Correlations Between Intelligence/Dispositional Valuations and MU in Sample 4

 

Target of personality assessment

Interviewer(1)

Interviewer(2)

Source

MU

MU(1)

MU(2)

MU

MU(1)

MU(2)

 

 

-.12

-.19

-.01

.08

.15

-.01

 

.35**

.35**

.28**

-.07

-.09

-.04

Intelligence tests

-Numerical IQ

-.04

.00

-.08

.08

.07

.07

-Figural IQ

-.03

-.03

-.02

-.03

-.03

-.01

-Vocabulary

.30**

.27**

.27**

-.03

-.06

.01

Openness to experience

.13

.14

.09

.12

.15

.06

Note. MU = Composite measure of self-reported and observed mutual understanding (interaction half in brackets)
** p < .01, * p < .05

In sum, Main Effect Hypothesis 1 received weak support, with the ratings of the first interviewers’ intelligence by their partners being positively related to MU, but not the ratings of the second interviewers’ intelligence. Because the positive association between intelligence and MU was not replicated with other intelligence indices, this finding does not seem very robust, however. Second, Main Effect Hypothesis 2 received mixed support, with the level of vocabulary being positively associated with MU for the first but not for the second interviewer. Third, Main Effect Hypothesis 3 received no support since openness to experience was not related to the level of MU.

4.3 Dyadic Effects of Intelligence and Dispositional Valuations

In the following section, the current study’s dyadic effect hypotheses are addressed. As a reminder, the prediction was made that between-person differences in intelligence and dispositional valuations are related to impairments in the MU process. Specifically, this involved the following variables:

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Dyadic Effect Hypothesis 1: Between-person differences in fluid intelligence are negatively related to MU.

Dyadic Effect Hypothesis 2: Between-person differences in crystallized intelligence are negatively related to MU.

Dyadic Effect Hypothesis 3: Between-person differences in openness to experience are negatively related to MU.

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Dyadic Effect Hypothesis 4: Between-person differences in interests and values are negatively related to MU.

4.3.1  Sample 1.I

Because the extremely high intelligence level of the Mensa members makes it impossible to discriminate between main and dyadic effects in this sample (see Section 3.4.3), no tests of dyadic effects were conducted (for tests of main effects, see Section 4.2).

4.3.2 Sample 1.II

Because of the previously discussed problems with the self- and partner-ratings of intelligence of the Mensa members (see Section 3.4.3), these indices could not be used to test dyadic effects. In contrast, both dyadic partners completed the Openness to Experience scale of the NEO-FFI, so this information was available to calculate absolute difference scores that were used in three HLM analyses. First, an analysis was carried out to assess the effect of between-person differences in openness on a composite index of relationship quality (average of both dyadic partners’ ratings). However, because the degree of convergence across both raters was only modest (r = .24, p = .01), two separate HLM analyses were calculated to assess the impact of between-persons difference in openness on each participant’s idiosyncratic relationship quality ratings. As was done for the test of main effects in this sample (see Section 4.2.2), Mensa members’ gender and network size were included as control variables.

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Inconsistent with Emergent Effect Hypothesis 4, results did not show any effects of between-person differences in openness on any of the three relationship quality indices. Specifically, the absolute difference score of the two dyadic partners’ openness values were neither associated with the average of both persons’ relationship quality ratings (b = -.07, p = .40), nor to partners’ idiosyncratic quality ratings (bs = -.02 and -.11 for Mensa members’ and relationship partners’ quality ratings, respectively, ps > .20).

4.3.3 Sample 2

In Sample 2, dyadic effects were studied in the following way. For every relationship, the difference between the university alumni’s self-reported intelligence and their ratings of their network partners’ intelligence was calculated. This difference score had an average of 2.7 (SD = 3.2), indicating that the alumni saw themselves as more intelligent than the people with whom they frequently interacted. To calculate the impact of between-person intelligence differences, the absolute value of this difference score was inserted in an HLM analysis as a Level 1 predictor of relationship quality.40 As before, age, gender, and network size were entered as control variables.

Table 15 shows the results of the HLM analysis for the effects of absolute differences scores on the quality of social relationships. As can be seen, there was a significantly negative effect of absolute intelligence differences on relationship quality. Thus, when participants perceived larger between-person differences in intelligence, they also perceived the quality of the social relationship as lower, which is consistent with Dyadic Effect Hypothesis 1.

↓102

In only 4.3% of all relationships, participants rated their network partners as more than one scale point higher in intelligence than themselves (vs. 57.8% lower in intelligence). As a result, if there is a detrimental effect of intelligence differences when the network partner is rated as more intelligent, this is likely obscured by the sheer number of relationships where the opposite is the case. Because of the bias towards higher self-ratings, the difference score can also be seen as an index of the degree to which contact persons have a lower intelligence than the alumni.

To test the notion that communication is also hampered when a contact person has a higher intelligence level, two additional dummy variables were created. First, a dummy was created for all dyads in which participants rated a contact person as more than one scale point lower in intelligence than themselves (this concerned 5,006 dyads). The same was done for dyads in which participants rated a contact person as more than one scale point higher in intelligence (this concerned 376 dyads). Results showed that the dummy that specified relationships in which the network partner was rated as less intelligent was associated with lower levels of relationship quality (b = -.49, p = .00). In contrast, there was a non-significantly positive effect of the dummy that specified relationships in which the network partner was rated as moreintelligent (b = .15, p = .25).

Although the lack of significance for the dummy specifying relationships with more intelligent network partners may be the result of unequal cell sizes, the positive sign clearly disconfirms Dyadic Effect Hypothesis 1, which states that differences in intelligence should be related to lower levels of MU. Instead, the pattern of findings is more consistent with a positive main effect of participants’ ratings of their network partners’ intelligence.

↓103

In sum, the Dyadic Hypothesis that intelligence differences between persons are related to lower levels of MU received modest support. True, when participants perceived network partner as lower in intelligence than themselves, the relationship was rated as lower in quality. Yet the crucial prerequisite for a demonstration of a dyadic effect (lower quality ratings for relationships with network partners that are perceived as higher in intelligence) was not met in the current study. Accordingly, the significant „dyadic” effect of intelligent difference is more likely due to a main effect of rated intelligence on social relationship quality.

Table 15 HLM Regression Coefficients of the Effect of Between-Person Differences in Rated Intelligence and Relationship Quality in Sample 2

Level 1 (relationship partner)

Relationship quality

intercept

Between-person IQ
difference slope

  

-0.29**

Level 2

-Network size

-0.08**

-Age

0.01

-Female gender

0.10*

Note. IQ = intelligence rating, corrected for age and gender. All continuous variables were standardized before entering in the analysis. Level 1, N = 4,537; Level 2, N = 205. Mean random effects (across all four analyses) were estimated at .30, .19, and .90 for the Level 1 intercept, the IQ x relationship quality slope, and the residual variance, respectively (ps < .01).
** p < .01, * p < .05

4.3.4 Sample 4

In Sample 4, univariate between-person differences in intelligence and dispositional valuations were operationalized as the absolute difference between interaction partners’ scores. Second, the Euclidean distance between interaction partners’ personality profiles was calculated as an index of multivariate between-person differences.41 Note that, unlike in the previous samples, relative intelligence differences in intelligence ratings were much more balanced in terms of which person was perceived as more intelligent. In fact, the mean relative difference between self-ratings and partner-ratings was -0.27 (SD = 2.42), indicating that participants on average rated themselves as .27 scale points less intelligent than they rated their network partners. Indeed, no less than 67% of all participants regarded their interaction partner as equally or more intelligent as themselves.

↓104

To investigate the effect of perceived intelligence differences more fully, three different indices were created. First, an index of self-concept similarity was calculated by taking the absolute difference of both participants’ self-ratings of intelligence. Second, an index of target agreement, operationalized as the absolute difference between participants’ self-rating and the rating provided by their partners, was calculated. Third, an index of perceived similarity was calculated by taking the difference between individuals’ ratings of their own intelligence and their partners’ intelligence.

Table 16 contains correlations between MU (composite of participants’ ratings and behavioral observations) and between-person differences in intelligence/dispositional valuations. As can be seen, only one dyadic effect was marginally significant: The level of disagreement regarding the first interviewer was negatively correlated with the amount of MU. Thus, conversations between participants with very divergent opinions about the intelligence of the first interviewer were less understanding than conversations in which there was a large degree of agreement on this issue. The level of agreement regarding the second interviewer was not associated with MU.

Apart from the effect of intersubjective agreement regarding the first interviewer’s intelligence level, no other dyadic effects were significant. Between-person differences in psychometric intelligence tests and self-reported openness were unrelated to the level of MU. Also, profile similarity regarding the psychometric intelligence measures (i.e., numerical and figural intelligence, and vocabulary), the Big Five factors (excluding openness), the AIST interests scales, and the Rokeach value rankings was not significantly related to MU. Accordingly, the emergent effect hypotheses did not receive much support.

↓105

Table 16 Correlations Between MU and Between-Person Differences in Intelligence and Dispositional Valuations in Sample 4

Impact of absolute difference

MU

MU(1)

MU(2)

 

 

-Self-concept similarity a

  

-.06

-Target agreement (Interviewer 1) a

-.22†

-.17

-.23†

-Target agreement (Interviewer 2) a

-.06

.04

-.15

-Perceived similarity (Interviewer 1) a

-.05

-.05

-.04

-Perceived similarity (Interviewer 2) a

.07

.08

.04

Intelligence tests

-Numerical IQ

 

-.06

.05

-Figural IQ

-.09

-.01

-.16

-Vocabulary

-.01

-.03

.02

Openness

.04

-.08

.14

Profile similarity

- Intelligence + vocabulary

-.04

-.06

-.01

- FFM (excl. Openness)

.10

.09

.09

- Values

-.12

-.07

-.14

- Interests

-.10

-.11

-.07

a More positive values are indicative of less similarity
Note. MU = Composite measure of self-reported and observed mutual understanding (interaction half in brackets), FFM = Factors of the Five-Factor Model of personality description. N = 68-72.
** p = .01, * p = .05, † p = .10

4.4 Group Differences

In the previous Sections 4.2 and 4.3, main and dyadic effects of intelligence and dispositional valuation were discussed. In the following section, it is tested what effect the combined impact of these factors has in a sample that is extreme with regard to one important cognitive personality trait: intelligence. Specifically, it was tested to what degree intellectually gifted individuals, highly achieving university alumni, and averagely achieving alumni in Samples 1-2 differ from each other in terms of a number of (indirect) indicators of adjustment and MU: Neuroticism, general self-esteem, self-esteem of relationships with people of the same and opposite gender, self-esteem of relationships with parents, loneliness, and social network size. Because MU is hypothesized to be an important first step in the establishment of intimate relationships, these comparisons indirectly address the following hypothesis:

Extreme group hypothesis: Intellectually gifted individuals experience a lower level of MU in their social relationships

4.4.1  Differences in Self-report Scales

↓106

Multivariate analyses of variance (MANOVA) were carried out with sample membership (Mensa member, highly achieving alumni, and averagely achieving alumni) as between-subjects factor and the above described adjustment and MU indicators as dependent variables. Except for neuroticism, significant group differences were found for all variables (see Table 17 and Figure 6), multivariate F(6, 431) = 27.82, p < .01. Planned contrasts showed that this was due to differences between Mensa members and alumni (ps ≤ .01 for all variables except neuroticism). In contrast, differences between highly and averagely achieving university alumni were not significant except for self-esteem of opposite-sex relationships, with somewhat higher values for the high achievers (p = .02).

Table 17 Differences Between Mensa Members and University Alumni in Self-Reported Social Adjustment

 

Sample

Mean

SD

F a

d b

Neuroticism

Mensa members

2.65

0.84

2.01

0.15

High achievers

2.50

0.64

Average achievers

2.57

0.61

General self-esteem

Mensa members

3.72

1.03

5.88**

-0.30

High achievers

4.03

0.76

Average achievers

3.96

0.73

Opposite-sex self-esteem

Mensa members

3.50

0.95

 

-0.43

High achievers

4.00

0.74

Average achievers

3.74

0.83

Same-sex self-esteem

Mensa members

3.23

0.86

46.19**

-0.90

High achievers

3.95

0.70

Average achievers

3.93

0.72

Parents self-esteem

Mensa members

3.26

1.00

31.31**

-0.72

High achievers

4.00

0.85

Average achievers

3.88

0.92

Loneliness

Mensa members

2.30

0.70

49.37**

0.92

High achievers

1.73

0.44

Average achievers

1.79

0.42

Size of social network

Mensa members

16.41

9.06

29.86**

-0.71

High achievers

22.91

8.39

Average achievers

22.29

8.51

Note. Because of the lack of significant differences between high achievers and average achievers, these groups were pooled together in the comparisons with the Mensa members.
a Based on the difference between Mensa members and university alumni
b Calculated with Cohen’s formula d = (M 1 - M 2) / SD p , where M 1 is the mean for the Mensa group, M 2 is the mean for the alumni, and SD p is the pooled SD across the entire sample
** p < .01, * p < .05

An inspection of the effect sizes of the difference between the Mensa members and university alumni42 showed that the difference between both groups in terms of general and opposite-sex self-esteem were small to modest following Cohen’s guidelines. However, the differences regarding self-esteem of the relationships with parents and same-sex peers, subjective feelings of loneliness, and total network size ranged between .70 and .90 and can be described as large (i.e., around .80).

↓107

Figure 6. Mean Level Differences in Self-Rated Social Adjustment Between Intellectually Gifted Mensa Members, Highly Achieving University Alumni, and Averagely Achieving University Alumni.

Group comparisons between Mensa members and university alumni are somewhat biased because these groups differed in a number of respects. For example, Mensa members were older and less educated, which may have affected the comparisons. To correct for this potential bias, GLM analyses with participant age as a covariate and gender, sample, questionnaire format, and their interaction terms as a between-subjects factors were carried out.

Findings showed that the multivariate effect of participant sample remained significant, F(6, 401) = 4.55, p < .01. Inspection of univariate follow-up tests showed that Mensa members no longer differed from the university alumni in terms of general self-esteem and total network size. However, the effects for the self-esteem of same-sex, opposite-sex and parental relationships, and loneliness remained significant (ps ≤ .03). Accordingly, the lower social self-esteem and higher loneliness of the Mensa members was not a by-product of differences in gender, age, education or questionnaire format. This result is consistent with the Extreme Group Hypothesis.

↓108

Finally, an especially stringent test was created to compare Mensa members and highly achieving university alumni in terms of their social adjustment. This test included the average absolute difference between participants’ and their network partners’ rated intelligence as a control variable (in addition to the covariates listed above). The inclusion of this control variable provides an indication whether the differences in adjustment between Mensa members and university graduates are due to larger between-person differences in intelligence (as the Simonton model would predict) or to other, unknown factors (e.g., selection bias). Results of this stringent analysis replicated the lower level of social adjustment for Mensa members, multivariate F(6, 318) = 3.25, p < .01. In contrast, the multivariate effect of intelligence differences became only marginally significant after controlling for Mensa membership, F(6, 318), = 1.90, p = .08. This clearly speaks against the notion that the lower social adjustment of the Mensa members is due to between-person differences in intelligence.

4.4.2 Differences in Social Network Composition

As described above, group comparisons between Mensa members and university alumni uncovered significant differences in terms of their social self-esteem and feelings of loneliness. These differences were found in spite of the absence of sample differences in more generalized emotional tendencies such as Neuroticism and general self-esteem. Because MU theoretically is an important determinant of the quality of social relationships, the lower relationship quality observed for gifted individuals indirectly supports the hypothesis that they experience a lower level of MU in their social relationships.

To investigate whether this hypothesis is also confirmed by the ego-centered network data, a HLM analysis was carried out. As covariates , network size43, age, gender, and education were inserted as covariates. To test whether there are group differences in the overall level of MU, a dummy variable indicating Mensa membership was inserted as a predictor of the relationship quality intercept and as a moderator of the effect of different relationship categories. This procedure allowed a fine grained analysis of the impact of Mensa membership on specific types of relationships. For example, a negative association between Mensa membership and the parent-dummy x relationship quality slope would indicate that Mensa members have less satisfying relationships with their parents. Because the importance of certain relationships may vary across different age groups (Carstensen, 1992), participant age was included as an additional predictor of the relationship category slopes.

↓109

Results of the analysis are shown in Table 18. As can be seen, Mensa members did not differ from the university alumni in terms of the average relationship intercept. Thus, the level of MU in relationships with „typical”44 network partners did not differ between both groups. However, there were consistent moderator effects of Mensa membership on the impact of relationship category. First, across the entire sample, relationships with parents were rated as above-average in quality, but this effect was less strong for the Mensa members (Δ = -.44, p < .01). Second, an even stronger relative effect was found for other family members, with university alumni perceiving a larger increase in quality than the Mensa members (Δ = -.38, p < .01). Third, across the entire sample, relationships with romantic partners were rated as higher in quality (+ 1.86, p = .01), but less so for Mensa members (Δ = -.27, p = .03).

Table 18 HLM Regression Coefficients of the Effect of Ratings of Network Partner Intelligence and Mensa Membership on Relationship Quality Across Different Relational Categories

Level 1 (relationship-specific)

Intercept

IQ

Parent

Family

Partner

Friend

 

-0.60**

 

1.21**

0.81**

1.86**

 
 

 

-0.09

-0.01

 

-0.38**

-0.27*

-0.12

 

-0.02

 

0.00

-0.15*

0.06

-0.06

0.02

 

0.10**

 

-0.04

Note. IQ = intelligence rating, corrected for age and gender. All continuous variables were standardized before entering in the analysis. Level 1, N = 7,808; Level 2, N = 405. Mean random effects (across all four analyses) were estimated at .35, .20, and .74 for the Level 1 intercept, the IQ x relationship quality slope, and the residual variance, respectively (ps < .01).
** p < .01, * p < .05

4.4.3 Compensation Mechanisms

↓110

In light of the above-described social adjustment problems of Mensa members, it is interesting that (controlling for age and education) the current study did not find any differences between Mensa members and university alumni in terms of general self-esteem. This raises the possibility of a potential compensatory function of the Mensa membership. That is, it may be that gifted individuals who experience social adjustment problems somehow “compensate” this threat to their self-esteem by means of their higher self-concept of intelligence, of which the Mensa membership is the symbolic emblem.45

If the above interpretation is correct, then the Mensa members should have a lower social-concept, a higher intellectual self-concept, and an equally high general self-concept when compared to the university alumni. To test this notion, a GLM analysis with participant age and education as a covariate and Mensa-membership, gender, sample, questionnaire format, and their interaction terms as between-subjects factors were carried out (i.e., equivalent to the procedure described in Section 4.4.1). As dependent variables, the self-concept scale of intelligence and the mean of the self-concept with relationships with parents, same-sex, and opposite-sex peers were used. Results indicated that Mensa members (M = 3.33, SD = .66) had a significantly lower social self-concept than the university alumni (M = 3.95, SD = .53), F(1) = 19.33, p < .01. By comparison, Mensa members (M = 3.89, SD = .68) had a higher intellectual self-concept than the alumni (M = 3.28, SD = .74), F(1) = 35.10, p < .01.46 Because the group comparison reported above showed no differences in general self-esteem, the above results are consistent with the compensation hypothesis.

In sum, comparisons between Mensa members and university alumni resulted in a number of clearly delineated social differences. After controlling for age, gender, and education, Mensa members had lower self-esteem of relationships with peers of the same and opposite sex, a lower self-esteem of the relationship with parents, and a higher self-perceived loneliness. In terms of relationships with specific partners, these self-views were corroborated for relationships with family members and partners. Note that the differences in social adjustment were found despite a lack of effects for more generalized constructs such as Neuroticism or general self-esteem. In addition, Mensa members did not differ from university alumni in terms of their relationships with friends, colleagues, neighbors, and co-workers, so the differences found for family members and partners were not due to generalized response tendencies in completing the social network questionnaire. Because of these consistent social adjustment differences to the disadvantage of the Mensa members, the Extreme Group Hypothesis received strong support.


Footnotes and Endnotes

33  Because the current study was not interested in possible idiosyncratic differences in evaluations of relationship quality, these analyses ignored the nested structure of the data, treating Level 1 relationships as the information unit.

34  The student assistant in charge of the experiment rated the intelligence of both interaction partners in 114 out of 144 cases. As the assistant also assigned the participants to similar or dissimilar dyads on the basis of their intelligence score, it is no surprise that this impacted her ratings. In line with expectations, she perceived an average difference of .82 points in similar dyads, against 1.38 points in dissimilar dyads, which is statistically significant, F = 10.37, p = .01.

35  No covariates were included as predictors of the intelligence rating x relationship quality slope because the analysis focuses on main effects, not on moderator effects.

36  Network size was included as a control variable to avoid diluting effects: If participants are not very selective in listing contact persons, they are more likely to include less intimate relationships, which might lead to decreased mean levels of relationship quality.

37  Different from the previous analyses, age was not included because the relatively high number of missing values for this variable would have reduced the already limited sample size.

38  Applying the Spearman-Brown formula shows that aggregating scales that are significantly correlated (e.g., self-ratings of intelligence and numerical intelligence) would result in inadequate reliability levels (i.e., alpha levels lower than .37).

39  The lack of associations with partner intelligence could be due to the limited amount of information on which participants could base their judgments. The lack of association with figural intelligence may be due to range restriction and the fact that this measure was based on a speeded test, whereas the other intelligence measures were not.

40  Because the difference score varies across different relationships, it was deemed a Level 1 property. Note, however, that intelligence differences calculated in this way are not pure Level 1 measures because they are dependent on Level 2 information (i.e., the self-rated IQ of the university alumni).

41  Using the formula where dij is the Euclidean distance between persons i and j, m represents the number of personality dimensions, and xia and xja are the scores on personality variable a for person i and j, respectively.

42  High and average achievers were pooled together because they did not significantly differ from each other in their level of social adjustment.

43  Because only 19% of Mensa members vs. 74% of alumni used the Internet version of the questionnaire, the cell sizes for this comparison were relatively unbalanced. Because of this, the inclusion of both Mensa membership and questionnaire format would lead to colinearity among predictors (r = .54, p = .01) and bias the linear HLM analysis. Note, however, that the most important difference between both questionnaire formats seems to be the smaller number of contact persons in the Internet condition. Because the total size of the ego-centered network was included as a covariate in the HLM analysis, concerns about this possible source of bias can be somewhat assuaged.

44  In this case, „typical” refers to a relationship with a person who does not belong to any of the dummy-coded categories (i.e., who is not a family member, friend or partner).

45  Alternatively, it may be that the lower social self-concept of the Mensa members is a result of an intra-individual contrast effect resulting from their high self-concept of intelligence (Marsh, 1986). Note, however, that the highly achieving university alumni can also be expected to have a high self-concept of intelligence, yet their social self-concepts are significantly more positive when compared to the norm data reported by Schwanzer (2002; z-scores = .18 and .50 for the self-concept of peers of the same and opposite sex, respectively, ps < .01).

46  Note that results did not change when the Mensa members were compared to the subgroup of highly achieving alumni (M = 3.34, SD = .78), F(1) = 23.04, p < .01



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