Gonzaga et al. (2007) show that partners who are similar in their self-ratings of personality traits experience similar emotions in many situations, facilitating understanding each other’s spontaneous and intuitive responses to circumstances and events in their common environment (Shiota et al., 2006).

According to the Vulnerability Stress Adaptation Model (Karney and Bradbury, 1995) personality traits are distant causes of behavior. In the context of stress research, personality traits are understood as vulnerabilities (in experiencing events as more or less stressful) and as specific skills (needed in coping with and adapting to stressful situations). When the partners agree in perceiving events or circumstances as stressful and in how to cope with stress, it is easier to avoid and to overcome tensions that could impair RS.

At the level of reflected reasoning (for a comparison of intuitive and deliberate judgment, see Gigerenzer, 2007; Kruglanski and Gigerenzer, 2011), partners with a high degree of similarity will support each other’s world view and beliefs (Morry and Graines, 2005). They find themselves in situations of misunderstanding and conflict of values, attitudes and intentions less often, possibly due to congruent use of heuristics in judgment and decision making. Consequently, they will be better able to develop a mutually satisfying relationship.

Similarity implies equity, i.e., the subjective value of resources given and received is well balanced (Austin and Walster, 1974). Thus, even being similar in negative traits can have some merits as, for instance, equality in physical attractiveness is a moment of stability for the partnership (White, 1980).

Perceived similarity is both a cause and an effect of liking (Morry and Graines, 2005; Montoya, 2008; Morry et al., 2011). When RS is high (for some other reason than similarity), perceiving the partner as being similar is cognitively consistent (Simon et al., 2015), therefore rewarding and thus contributing to a positive link between similarity and RS.

The generally assumed positive effect of similarity on RS has been questioned by Shiota and Levenson (2007), who present data from middle aged couples with positive effects of dissimilarity on RS. The authors explain their unexpected findings that dissimilar partners provide a stimulating variety of experiences to each other and develop a kind of specialization in coping with their daily life tasks both of which foster RS. We have to consider a specific form of dissimilarity, that is, complementarity: the partners’ opposite characteristics provide satisfaction by compensating each other’s weaknesses (e.g., Markey and Markey, 2007). For example, take the case of a couple where one partner is dominant while the other is submissive (Markey et al., 2010). Presumably, this form of complementarity prevents a couple from constant conflict imminent in a relationship with two highly dominant partners. Dominance-submissiveness, though, is one of the few dimensions where dissimilarity is related to positive outcomes in a social interaction (e.g., Tiedens and Fragale, 2003). Actually, the literature provides many more instances of negative effects of dissimilarity than positive effects of complementarity. Thus, it is reasonable to assume that a score of global dissimilarity as given with distance in our studies generally will be correlated with negative experiences in social relationships (see Appendix for an illustration of the trivariate relationship).

Originally developed as proxies for Cattell’s 16PF (Brandstätter, 1988; Cattell and Schuerger, 2003), the scales were later adapted as short substitutes for the five global scales of the German version of the NEO-PI-R (Ostendorf and Angleitner, 2005; Brandstätter, 2012). In our studies, however, the focus is not on the five global scales, but on the item-based personality profiles, specifically on their global positivity and on the global (dis)similarity between the two partners’ personality profiles. In the person-centered approach, the larger number of scales – 32 items instead of 5 scales – has the advantage of a higher reliability of the (dis)similarity and positivity measures and of a higher completeness of the trait facets included. Although, completing the PASK5 questionnaire only takes about 7 min, internal consistency, stability, and construct validity of the PASK5 are very similar to those of the NEO-PI-R (cf. Brandstätter, 2012).

An important characteristic of the self-ratings of personality traits is their positivity or social desirability. One can hardly speak of positivity in rating personality traits without referring to response sets, such as acquiescence or social desirability. Response sets are commonly treated as a nuisance in personality assessment. Not so by Ferrando et al. (2009) who have conceptualized a participant’s socially desirable responses as valuable information for predicting and explaining behavior in social situations. Adopting this approach, we assume that positivity of personality self-ratings is a summary of sufficiently realistic self-attributions of valuable personality traits.

There are, of course, various ways of assessing the positivity of a personality profile¹. For example, experts could judge the positivity of the items. Weighting the participants’ responses to the items with the experts’ social desirability scores for the items and aggregating the weighted responses would result in a reasonable global positivity measure. Alternatively, one could simply add the Big Five values (with reversed scoring for Neuroticism) to produce a composite of positive personality characteristics. In contrast, our measure of positivity follows a different strategy, taking into account the fact that people tend to give more or less positive self-ratings to their personality traits. Individual differences in positivity of self-ratings are an important personal characteristic. The more extreme the ratings, the more positive the traits of the rater. Consequently, our positivity measure is based on the distribution of answers on 9-point bipolar rating scales for value-laden traits. Such a distribution is usually highly skewed, positively when the left pole is positive (e.g., well-balanced versus irritable) or negatively, when the right pole is positive (e.g., gets worried easily versus emotionally stable). Thus, the individual frequency of extreme scores (1, 2, 8, 9) provides a reasonable measure of positivity, not seriously disrupted by rare cases of extreme negative self-attributions. In our case, scores higher than two and lower than eight are neglected when calculating the positivity measure².

Due to the design of the PASK5 and its use in characterizing oneself, extremity means positivity and is, as a consequence of the scale design, highly correlated in the pilot study (r = 0.95, n = 1183) with the intra-individual scatter (standard deviation) of a person’s responses to the 32 profile items, which would justify using positivity and scatter as equivalent measures.

Our positivity measure is peculiar in that persons with the same positivity score can differ widely in its composition. One might excel with items indicating Agreeableness, another with items in the domain of Conscientiousness, a third with items rooted in Extroversion, etc. Therefore, participants themselves determine what kind of experience or behavior they perceive as positive or negative by giving specific items extreme (i.e., positive), others only moderate to low ratings. Such an idiosyncratic definition of positivity may be part of its expected success in predicting RS. Because people generally tend to self-attribute positive traits, the PASK5 and similar questionnaires tell us what kinds of behavior and subjective experience are judged on average as being positive and also how positive a participant perceives herself/himself to be.

In the perception of people, counting the daily frequencies of events, such as friendly acts in a partnership, can be more relevant and informative for judging the quality of a relationship than averaging scores on a Likert scale. In this context, Kruglanski and Gigerenzer (2011, p. 101) state: “To estimate a criterion, do not estimate weights, but simply count the number of positive cues.” A measure of positivity that is based on this heuristic is admittedly unconventional, but possibly a better predictor of RS than alternative measures, at least when PASK5 or a similar questionnaire is used to self-rate personality traits.

Moreover, we can defend our positivity measure by pointing to its reliability and its validity when it is combined with distance in predicting RS. In our pilot study on personality aspects of academic performance (with n = 1183 students applying to a college of health sciences), the split-half reliability coefficients of distance and positivity (Form A and Form B of the PASK5) were r = 0.64 and r = 0.68, corresponding to quite reliable coefficients α = 0.78 and α = 0.81, respectively.

In the same sample, the correlations of positivity with the global scales of the Five Factor Model (FFM) are: Conscientiousness r = 0.48, Neuroticism r = -0.20, Agreeableness r = 0.24, Openness r = 0.19, and Extroversion r = 0.35. Altogether, in a multiple regression analysis, 57% of the variance of positivity are explained by the Big Five. The positivity variable covers the valence aspects of the PASK5-based global personality dimensions quite well. The Big Five have quite often proved to be valid predictors of RS, as reported by Dyrenforth et al. (2010) and Furler et al. (2013), for example. Hence, we can expect that positivity, encompassing the socially desirable aspects of the FFM dimensions, combined with distance, will also be a valid predictor in our studies of RS.

Among the variety of (dis)similarity indices (Kenny and Acitelli, 1994; Kenny et al., 2006; Furr, 2008, chapter 12), we consider McCrae’s (2008) index of profile agreement (I_pa), designed for z-transformed variables, to be the most useful for studying a combination of extremity and (dis)similarity effects. It is based on the mean (ΣM²) and difference (Σd²) of the partners’ z-transformed ratings and implies moderating the negative effects of distance by giving a bonus to the congruence of extreme scores. This index of profile agreement is described by the following equation:

k = number of items, M = mean of the two partners’ z-scores of item responses, and d = signed difference between the two z-scores.

Although, inspired by McCrae (2008), we chose a modified approach, (a) separating the distance and extremity components of the profile similarity index and using these components as the only predictor variables in our model, (b) basing the extremity score not on the two partners’ average squared z-scores, but on the individual frequency of extreme scores, thus allowing extremity to be interpreted as positivity for the individual profile, and (c) defining distance (D) as the average (across the 32 items of the PASK5) of nearly normally distributed absolute differences (not of squared differences, D²) between two personality profiles. All these adaptations had been proved to work properly in the pilot study.

The global distance measure D is a composite of the partners’ differences in elevation (i.e., intra-individual average of scores), shape (i.e., idiosyncratic pattern of an individual’s scores), and scatter (i.e., intra-individual variance of scores) of their personality profiles. In order to keep the complexity of the models at a moderate level, and because their effects on RS were assumed to be similar, these components of D have not been analyzed separately (see Decuyper et al., 2012 for separate analyses of the components).

Wood and Furr (2016) point to a special problem that arises with any kind of similarity measure (cf. also Furler et al., 2013). These measures would automatically be linked to RS, simply because they would be confounded with the positivity of the ratings. Controlling measures of similarity for positivity of the personality profiles has become a methodological must in recent years, as for example Wood and Furr (2016) have shown for a variety of personality inventories and similarity measures. They speak of a Normative Desirability Confound (NDC), which all kinds of global similarity indices are said to be fraught with.

It is therefore commonly recommended (e.g., Kenny et al., 2006; Dyrenforth et al., 2010; Wood and Furr, 2016) that (dis)similarity effects should be controlled for stereotypes, i.e., for people’s average responses as females and males, or as leaders and followers, by subtracting the average (normative) profile from the individual profiles before calculating the ‘distinctive’ similarity score.

Our solution to the problem is different: Instead of controlling stereotype effects by subtracting the average (normative) profile from the individual profile, we control distance for positivity by including both distance and positivity as predictors of RS in our models.

In the pilot study with students applying for admission to a competitive higher education program, we found that (a) the distance between the individual personality profiles and the collective profile of the accepted students, and (b) positivity of the individual profiles, were valid negative and positive predictors, respectively, of the first semester grade point average. This had been expected; what was unexpected, however – a kind of serendipity – was that distance and positivity served as reciprocal suppressors of irrelevant components of the predictors (for the concept of reciprocal suppressor variables see Maassen and Bakker, 2001). Following this methodological hint from our research on person-environment fit, we stated the hypothesis, which applies to all five of our studies, that including positivity in addition to distance or distance in addition to positivity as predictors of RS will enhance the negative β-coefficients of distance and the positive β-coefficients of positivity.

Following the advice of Arbuckle and Wothke (1999), or Kenny et al. (2006), we aimed to reduce the complexity of models and secure a higher level of generalizability by setting some parameters equal, for instance, across the social roles of the partners (men vs. women, or leaders vs. followers), if this was both theoretically and statistically reasonable.

We always tested the fit of measurement models (representing the relationship between the latent constructs and their indicators) before testing the full structural models. For the sake of brevity, we will not report the generally good fit indices of the various measurement models, instead reporting only the fit of the overall models.

In the following reports on our studies, we first present two cross-sectional analyses, one with a convenience sample of students at the University of Zurich (n = 60 couples; Study 1) and another with a sample recruited by students of the Johannes Kepler University of Linz among their relatives and acquaintances (n = 66 couples). Cross-sectional Study 2 is a close replication of Study 1, whereas Study 3 resumes the longitudinal data of Study 2. Study 4 deals with time sampling data collected from a community sample using a time sampling diary (n = 34 couples). Finally, Study 5 is an attempt to show that the central constructs of distance and positivity are applicable to quite different dyads, namely to leaders and followers in a large Swiss organization (n = 91 leader–follower dyads). Sample sizes³ were determined by the availability of participants who were able to motivate their (dyadic) partners to concurrently answer the questionnaires. If our theoretically founded expectations are correct, the results of the five studies will support the hypothesis that distance combined with positivity are highly valid predictors of RS.

Sixty heterosexual couples (average age 23.8 years, SD = 5.23), mostly students of the University of Zurich, completed the PASK5 and then indicated on 5-point scales (1 = never, 2 = rarely, 3 = sometimes, 4 = often, and 5 = almost always) (a) the frequency of negative emotions (depression, anger, disappointment, contempt, nervousness), and (b) the frequency of positive emotions (joy, sympathy, strength, liking, pride) they had experienced in their partnership during the past 2 weeks (emotion scale with frequencies of negative emotions reversed). In addition, they gave an overall evaluation of their relationship by answering the question “How happy is your relationship at this point in time?” from 1 = very unhappy to 6 = very happy (Hahlweg, 1996).

Based on actor–partner interdependence model (APIM) and structural equation modeling (SEM), we tested the effects of distance and positivity on RS. Each of the latent predictor variables has two indicators, one derived from Form A of the PASK5 (16 items) and the other from Form B (16 items with reversed coding). Latent constructs with less than three indicators often cause identification problems in measurement models which, according to Arbuckle and Wothke (1999) and Little et al. (2002), can be avoided by setting the error variances of pairs of indicators equal, thus securing their identification. We adopted this strategy.

Table 1 presents descriptives and correlations of the central variables. Model B (Figure 1) differs from Model A (no figure) by setting the coefficients of distance as couples’ effects, the two actor effects, and the two partner effects of positivity to be equal across gender. In addition, for each of the five pairs of indicators, the error variances were set equal. Because the fit of Model B is not significantly poorer than that of Model A (Δχ²= 0.965; df = 4; p = 0.915 of the SPSS model comparison test), we chose the more parsimonious Model B as most appropriate from both a theoretical and statistical perspective.

As expected, the paths from distance to female (β = -0.71) and male RS (β = -0.77) are negative and the actor effects of female and male positivity, (β = 0.62 and β = 0.66, respectively), are positive. The partner effects of female and male positivity are positive, too, but not significant (β = 0.35 and β = 0.31). On the whole, Model B displays a good fit to the data (χ² = 35.95, p = 0.289, CFI = 0.975, RMSEA = 0.046).

The pattern of regression and correlation coefficients of the independent variables reveals suppressor effects. The regression coefficients of female positivity and male positivity with female and male relationship satisfaction, respectively, are positive, whereas the regression coefficients of distance in relation to female and male relationship satisfaction, respectively, are negative. Suppressor effects are revealed by the fact that distance is positively related to positivity, but negatively to relationship satisfaction. Excluding either the distance or the two positivity variables from the model dramatically reduced the explained variance. In terms of effect sizes (of regression coefficients connecting predictors with the criteria of RS) the results are situated in the range of medium to large. The results of Study 1 support quite impressively the theoretical predictions: a negative relationship for distance and a positive relationship for positivity.

Whereas Study 2 is an independent replication of Study 1 with data collected cross-sectionally at one measurement time, longitudinal Study 3 is an extension of Study 2. More concretely, Study 3 is based on the same sample and material as Study 2 (see Methods section of Study 2 for detailed information), but on partially different variables (i.e., partners’ ratings instead of self-ratings). Instead of rating one’s own personality, participants here rated the personality of their partner. By changing the source of the ratings, we aimed at testing whether the theoretically postulated effects generalize to different operationalizations of distance and positivity. Study 3 includes three measurement points data with missing data at Times 2 and 3 imputed (n = 66 couples cross-sectionally measured at Time 1 with no missing data versus n = 66 with n = 22 imputed missing data). In the longitudinal Study 3, we analyzed the intercept and slope of the RS data collected at three points in time (6 months interval) with missing data at Times 2 and 3 imputed.

In Study 3, we are confronted with a situation that is called wave non-response (Schafer and Graham, 2002, p. 150) meaning that part of the sample did not participate at all measurement occasions. Basing the analyses on the widely used procedure of listwise deletion would drastically reduce sample size. Graham (2009, p. 554) delineates various criteria for using methods imputing missing values. First, the method should yield unbiased parameter estimates over a wide range of parameters. Second, there should be a method for assessing the degree of uncertainty about parameter estimates. Third, the method should have good statistical power. The so-called full-information maximum likelihood (FIML) methods do meet these criteria and are available in SEM software like AMOS (Arbuckle and Wothke, 1999) that we used.

Table 3 presents descriptives and correlations of the central variables. The participants of Study 3 provided data on relationship satisfaction three times. Thus, we can analyze the influence of distance and positivity on the change in their relationship satisfaction. Because the processes that led to initial relationship satisfaction were assumed to be similar to those that lead to further change, we expected that change would be predicted by the same variables as the initial relationship satisfaction (for different similarity effects on change in long-term marriages, see Shiota and Levenson, 2007).

Following Kenny et al. (2006, p. 363 f.), we tested structural equation models including the intercepts and slopes of the trajectories of RS as latent endogenous variables and distance together with positivity as latent exogenous variables. According to Kenny et al. (2006, p. 110), the exogenous variables female positivity (cFposip), male positivity (cMposip), and distance (cpappap) are centered at the grand mean of the variables, that is, the mean of partner ratings. In order to cover a broader variety of predictors, the distance and positivity scores are not based on self-ratings, as in Study 1 and Study 2, but on how the partner was rated. A SEM analysis based on personality self-ratings (not presented here in detail) led to a very similar structure of the coefficients.

The indicators of intercept and slope are the equally weighted averages of the three scales of relationship satisfaction (emotion scale, happy time scale, and Relationship Assessment Scale; see above), each measured three times. Their internal consistency coefficients are in the fully acceptable range of 0.73 < α < 0.91.

We tested Model B (Figure 3) for which we set the paths from distance to both intercepts and both slopes and the paths from female and male positivity to the female and male intercepts equal across gender, against Model A (no figure) without all these constraints. The correlations of the corresponding error and residual scores and the correlations of observed independent variables are treated as free parameters in both models. The fit of Model B is not significantly poorer than that of Model A [Δχ²(6) = 9.372; p = 0.154], thus justifying our preference for Model B (Model B: CFI = 0.970; RMSEA = 0.078).

The distance variable (cpappap) significantly negatively predicted female and male intercepts (β = -0.50 and β = -0.65, respectively). The paths between the distance variable and female and male slopes were negative but not significant (β = -0.22 and β = -0.12, respectively). In contrast, female (cfposip) and male (cmposip) positivity on female/male intercepts (actor effects) were significantly positive (β = 0.27 and β = 0.35, respectively). Female (cfposip) and male positivity (cmposip) on female/male slopes (actor effects) were negative and not significant (β = -0.14 and β = -0.07, respectively). The partner effects of female and male positivity on intercepts are positive and significant (β = 0.33 and β = 0.24). The partner effects of female and male positivity on slopes are negative and not significant (β = -0.13 and β = -0.23). The positivity variables have positive coefficients for intercept and negative coefficients for slopes. Our assumption that initial RS and RS change would be influenced by positivity and distance in a similar way is not supported by the data.

Participants with a high degree of positivity tend to see a decrease in RS in the course of 12 months. This could mean they have higher, but unrealistic perceptions and expectations at the beginning which lead to a drop in RS. Regression toward the mean may also partly explain the opposite signs of the coefficients in predicting intercept versus predicting slope. Finally, initial RS has a longer history, which may have been partly influenced by different traits to the short-term changes later in the partnership (see Shiota and Levenson, 2007, for more reasons why intercept and slope can be influenced by different traits).

A community sample of 34 married couples varying in age between 20 and 59 years participated in Study 4, with 35.9 and 39.8 years as the average age of wives and husbands, respectively. The education level varied between 20% elementary school, 30% apprenticeship, 20% high-school diploma, and 30% college or university degree. On 28 consecutive days, four times a day, at randomly selected times (programmed on a special wristwatch), participants recorded their own and, if present, their partner’s mood (-1, 0, and +1) and their partner’s presumable affection for the participant (9-point scale), in addition to other characteristics of the situation which were only relevant to a prior study (Wagner and Brandstätter, 1994).

Measures of RS were derived from the following three time sampling variables, referring to a target person’s state of mind as described by self-ratings or by partner ratings: (a) the wife’s mood as judged by the husband (component of the wife’s RS) and the husband’s mood as judged by the wife (component of the husband’s RS), (b) the wife’s affection for the husband as perceived by the husband (component of the wife’s RS) and the husband’s affection for the wife as perceived by the wife (component of the husband’s RS), and (c) the wife’s mood in the presence of the husband (component of the wife’s RS) and the husband’s mood in the presence of the wife (component of the husband’s RS).

Aggregating the records separately over the first, second, and third part of equally frequent consecutive observations led to a time series of measures of RS with coefficients α = 0.54, α = 0.65, and α = 0.44 for Time 1, Time 2, and Time 3. Again, we applied the model of Figure 3 with intercepts and slopes of RS as dependent variables. Distance and positivity as independent variables are based on self-ratings. No partner ratings were collected in this study.

Table 4 presents descriptives and correlations of the central variables. We compared Model B (Figure 4; residual variances and β-coefficients predicting RS set equal across gender) with Model A (no figure; without any restraints) and opted for Model B as most adequate in terms of theoretical considerations and empirical fit, which is not significantly different from the fit of Model A (Δχ²= 8.457; df = 8; p = 0.390). On the whole, Model B displays a good fit to the data (CFI = 0.977, RMSEA = 0.055).

As expected, both paths from distance (cpadi) to female slope and male slope are negative and significant (β = -0.46 and β = -0.47). The β-coefficients of distance predicting the female and male intercepts are in the expected direction, but not statistically significant (β = -0.14 and β = -0.09). The same applies to actor effects of female (cFpos) and male (cMpos) positivity that are in the expected direction but not significant predicting the female and male slopes (β = 0.25 and β = 0.28) and female and male intercepts (β = 0.11 and β = 0.07), respectively. With respect to partner effects, female positivity is significantly related to male slope (β = 0.32) but (not significantly) related to male intercept (β = 0.17). Further, male positivity is significantly related to female slope (β = 0.33) but (not significantly) related to female intercept (β = 0.29). The distance effects of Study 4, as those of Study 3, are – with some variation in the size of the coefficients – negative on all four dependent variables. Contrary to Study 3, where the effects of positivity are positive on intercept and negative (close to zero) on slope, in the present study all positivity effects are (some significantly) positive. Moreover, intercept and slope are predicted by the same variables (distance throughout with negative signs and positivity throughout with positive signs), but not with the same weights.

The participants of Study 5 were 91 leaders (29 women) and 91 followers (53 women). On average, the leaders were 45 (SD = 8.6), the followers 42 (SD = 12.5) years old. The participants were recruited by (a) sending a link from our study website to the human resources representative of a leading Swiss insurance company, and (b) the human resource manager sending e-mails (with a link to our website) to 420 German-speaking managers who regularly interacted with their subordinates. Having answered their own questionnaires, the supervisors invited a strictly randomly selected follower by e-mail (with a shared code for dyad identification and a link to our study website) to participate in the study. As an acknowledgment, the leaders and followers received a report of the general results and a book after the end of the study. Among other questionnaires, which are not relevant in the present context, the participants answered Form A of the PASK5 as a self-rating questionnaire, an adaptation of Hassebrauck’s (1991) relationship satisfaction scale for the leader–follower context with seven items, α = 0.80 (leader) and α = 0.85 (follower), and the Job Diagnostic Survey of Hackman and Oldham (1975) in German as a measure of job satisfaction, with an acceptable internal consistency of the 5-items, α = 0.63 (leader) and α = 0.73 (follower), in the present sample.

Table 5 presents descriptives and correlations of the central variables. With Model B (Figure 5), the two actor effects, the two partner effects, the two distance effects, and the residual variances were set equal across the social roles (leader and follower). In addition, the error variances of both indicators of leader satisfaction and follower satisfaction, respectively, were set equal. In contrast, Model A (no figure) does not have these constraints. Because the fit of Model B was not significantly poorer than the fit of Model A (Δχ² = 4.638; df = 6; p = 0.591), we preferred Model B which reveals significant actor effects for leaders’ and followers’ positivity as well as significant negative effects for distance.

Only Form A of the PASK5 was answered in this study, preventing the use of two parallel indicators of the latent constructs. Therefore, distance and positivity are represented in Figure 5 as observed variables.

A person’s positivity score is positively correlated with her/his own relationship satisfaction (β_leader = 0.42 and β_follower = 0.44), but not with the partner’s relationship satisfaction (both βs = 01; Figure 5). When the actor and partner effects are in addition set equal, the fit deteriorates drastically (Δχ² = 23-405; df = 4; p = 0.000) proving that the actor-partner difference is highly significant. In Study 5, the actor effects are much stronger than the partner effects. This could mean that the leader–follower dyads are socially less closely interconnected than the couples in Studies 1–4.

As expected, the β-coefficients of distance predicting the leader’s and the follower’s satisfaction are significantly negative (β_leader = -0.29 and β_follower = -0.30). The pattern of correlations and regression coefficients can be interpreted as a small (not significant) suppressor effect which is illustrated by comparing the regression coefficients of distance (in predicting, for instance, follower satisfaction) before (β = -0.12) and after (β = -0.18) including positivity scales (leader’s positivity, Lpos, and follower’s positivity, Fpos) in the regression model. More important, as to be seen in Figure 5, the paths to leader satisfaction and follower satisfaction are significant; because of the negative weight of distance and the positive weight of Lpos and Fpos, respectively, there is a suppressor effect statistically asserted.

In each of the five studies, distance is a significant negative predictor of the partners’ RS, particularly when the models include positivity of their self-rated personality profiles, defined by the 32 items of the PASK5. In contrast to the contradictory results reported in the literature, the importance of global distance (average across 32 items of absolute differences between the two partners’ personality self-ratings) for RS has been unequivocally corroborated.

The global variable positivity is a valid predictor of RS, too, in particular when it is combined with distance. Positivity absorbs the valence aspects of the 32 items of the PASK5, indirectly also those of the Big Five. Furr (2008) reports high correlations between the average individual profile and the social desirability profile of expert ratings. Our positivity scale is highly correlated with the profile similarity between the individual profile and the average profile. Focusing on the effects of global distance and global positivity, we have not delved into narrower domains of similarity as, for instance, Zentner (2005) did for the domains of the Big Five.

Positivity (P) means positive self-rating, that is, a form of generalized attitude of individuals concerning their interaction with the social surrounding. It is often but not in general conceptually connected with positive relationship as central for relationship satisfaction (RS). Consequently, it is positive correlated with RS but distinctively different from it. It is also connected with distance of self-rating (DS) of two partners, meaning that a higher positivity reduces the distance to all (important) partners.

We already could show that the effects of positivity and distance are not restricted to the specific form of measuring these constructs. Substituting self-ratings by partner-ratings in Study 3 leads to very similar results: positive effects on RS of positivity and negative effects of distance. A further argument for the generalizability of the positivity construct is given by the fact that defining positivity in terms of the two most extreme scores (1, 9) only did not affect the results. In any case, the reciprocal suppressor effects of the two predictor variables were found again.

It will be remembered that a reciprocal suppressor effect is revealed when adding a second predictor (e.g., positivity) increases the coefficient of the first (e.g., distance) and vice versa. This implies that the zero order correlation between predictor X and criterion Z is lower than the partial correlation between X and Z, when controlling for the second predictor Y (Maassen and Bakker, 2001). Having found suppressor effects for distance and positivity in the pilot study, we had expected that combining distance with positivity in each of the five studies would also lead to suppressor effects, which should result in a higher predictability of RS than that reported in recent publications (Barelds, 2005; Luo et al., 2008; Dyrenforth et al., 2010; Furler et al., 2013). Our expectations are clearly supported by the data. The seemingly high validity of the distance-positivity model of RS might call for special care in interpreting the construct validity of the latent variables, possibly under the premise: “It is too good to be true.” However, discounting the scientific value of a model because the effect sizes are astoundingly (unbelievably) high would seem to be the wrong conclusion.

It is worth noting that controlling for the main effects of personality traits is usually associated with smaller effects of similarity (Dyrenforth et al., 2010). However, the opposite is true for distance and positivity in our studies. Obviously, contrary to past empirical evidence, the constructs distance and positivity have some unique features which increase their predictive validity and which may be relevant beyond the field of relationship satisfaction.

For a better understanding of the suppressor effects, we must realize – something we did not appreciate at the beginning of our analyses – that a positive correlation between distance and positivity follows from the very construction of the distance and positivity scales: the more extreme (i.e., positive) a partner’s ratings are, the more room there is for high distance scores if the positivity scores of the two partners are only moderately correlated, as is the case in our studies. Note that with different operationalizations of distance (e.g., communicator’s posture, Mehrabian, 1968; similarity in person perception, Alves et al., 2016; psychological distance of an event in terms of time or space, Van Boven et al., 2010) there is a negative correlation between distance and variables of positivity (such as positive attitudes, liking). In our studies, the positive correlation between distance and positivity (due to the specific operationalization) implies a suppressor effect, when RS is negatively correlated with distance and positively with positivity.

In the first and, most pronounced, in the fifth study, the actor effects are stronger than the partner effects (see Watson et al., 2004; Donnellan et al., 2007; Luo et al., 2008; Dyrenforth et al., 2010; Furler et al., 2013 for similar results). One may assume that the members of the leader–follower dyads are less mutually dependent than the members of the couples in the other studies. Actor effects may also be stronger since cause and effect remain within the person, while partner effects are socially transactional (cf. Zentner, 2005; Kenny et al., 2006, p. 149/150). Nevertheless, actor effects are not generally stronger than partner effects, as is shown by Studies 3 and 4. Thus, future research needs to clarify the conditions that make actor or partner effects relatively more important.

We found that both aspects of RS (intercept and slope) can be predicted by distance and positivity, albeit partly with different weights and, in the case of positivity, even with different signs. In our studies, the intercept is the estimated RS status at the beginning of observation (time zero). One must take into consideration that the relationships already have a more or less extended history at time zero. It seems plausible that the intercept and slope should call for somewhat different explanations. Future studies may collect retrospective information on the history of the relationship in order to better understand its development from the first date till time zero of the study.

Going beyond partnership research, the concepts of distance and positivity can be applied whenever the profile of an individual is matched with the profile of a group or of an environment’s demands, as we have seen in the pilot study. The person-environment fit, represented by a small distance between two personality profiles, can be an important condition for well-being and achievement. The distance-positivity model has the potential to serve as a remarkable improvement in predicting similarity effects of various kinds.

In designing questionnaires with personality descriptive adjectives or behavioral statements, one generally tries to avoid items with explicit valence connotations. We guess, however, that the combination of distance and positivity works best when the items have a moderate valence connotation connected with bipolar items like those of the PASK5.

The distance-positivity model of RS maintains its validity when self-ratings are substituted by partner ratings (Study 3). Although comparing the predictive validity of various measures of distance and positivity and of various perspectives (self- or partner perspective) was not our primary goal, there is some empirical evidence suggesting that the benefits of suppressor effects in predicting RS are linked to the type of ratings, in that partner ratings somewhat outperformed the predictive validity of self-ratings (with RS as criterion).

Although many questions still remain open, we expect that distance combined with positivity will prove useful beyond the field of dyadic relationship research (Karney and Bradbury, 1995). Analyzing profiles that are based on the 32 items of the PASK5 and not, as is more common, on the scales of the Five Factor Model, forgoes exploring the connection between personality types and RS (see, for example, Altmann et al., 2013). However, being aware of the limits imposed by the small sample sizes, which may render complex models particularly problematic, we chose distance and positivity of personality profiles as the minimum number of theoretically meaningful explaining variables. Choosing and specifying models not exclusively a priori, but to some extent post hoc on the basis of fit indices, as we did, bears some risk of capitalizing on chance. However, the consistent replication of the basic results in five studies is a strong argument for trusting our findings.