Choice homophily, which is the tendency to choose to associate with people who are more similar to oneself, is particularly interesting because it can indicate a directional relationship with attraction, and cognitive biases towards certain individuals or groups, unlike forms of homophily that occur between people by chance, or homophily identified in existing relationships. Here we are specifically interested in the directional relationship from identifying similarities with another person to attraction towards further interaction with that person.
Typically, experimental manipulation of similarity when investigating whom people are interested in interacting with has involved questionnaires about attitudes, for example asking participants whether they agree that having money is an important life goal. By asking participants to rate statements about an interaction partner, and varying the proportion of traits that they share with that partner, it has been shown that relative number of shared attitudes have a consistent positive relationship with interpersonal attraction [ 3 ] of a kind likely to cause people to pursue friendships with one another outside experimental settings.
In-group membership has generally been studied independently from choice homophily.
However, the relationship between this effect and choice homophily as a consequence of shared traits remains unclear, especially as they focus on different trait types. In the case of arbitrary group assignment, transforming the dichotomous group distinction i. Real-world categories that act as markers for groups of individuals, such as age, are rarely dichotomous, so in these cases we might expect minimal group effects to become so diluted that they become irrelevant [ 24 ].
While dilution of in-group effects with increasing complexity of trait category could occur, evidence shows that in fact real social categories do define the kinds of social networks that we interact in [ 33 ]. Studies in relational demographics a research field that looks more specifically at similarities between people who form friendships in the workplace have typically demonstrated that people are likely to share demographic characteristics with people that they interact with, including age for example [ 34 , 35 ].
However, as this study looked at existing friendships, it could not demonstrate a causal relationship in which different shared traits preceded the establishment of a relationship with a stranger. As there is a tendency to lose contact with friends with whom we have less in common [ 20 , 22 ], and to conform with the norms of our social groups [ 36 , 37 ], it is still unclear which traits have a causal influence on willingness to pursue a friendship with a given individual, especially when time or distance place a maintenance stress on those relationships [ 38 ].
A stochastic agent-based modeling framework has also been used recently to infer which features might determine friendship formation using online social network data [ 39 ], but this did not directly manipulate shared traits to determine which could be most influential when interacting with a stranger. Different forms of similarity have been measured when studying choice homophily and minimal group membership. Experiments on choice homophily have relied on statements about attitude e.
Given that minimal group membership research demonstrates that changes in social closeness occur due to arbitrary categorization, the kinds of similarity are thought to be relatively unimportant. In contrast, research on relational demography has used basic demographic information such as age, gender, race [ 35 ] , and homophily has typically looked at the effect of shared values. Only a few studies have combined different types of trait in similarity-attraction paradigms, and these have typically been typically interested in just one trait notably race [ 41 , 42 ].
Here we combine different trait types in order to determine whether there is a consistent relationship between some particular traits and positive evaluation of a potential social partner. Traits were selected that indicate status homophily based on formal, informal or ascribed status and value homophily based on traits such as shared hobbies, interests or tastes [ 33 , 43 ].
Status homophily can be further separated into two main types: 1 major socio-demographic factors that we are born with and remain unchanged throughout life e. While studies in relational demography have typically focused on socio-demographic features and homophily studies more generally focus on values, there is as yet no experimental evidence assessing whether and how these different forms of similarity interact to influence attraction towards strangers during the initiation of friendships.
Another important distinction to make here is between choice homophily and induced homophily [ 44 ]. While choice homophily is a consequence of actively choosing to interact with a person with whom we share characteristics, induced homophily is that which occurs as a consequence of the people we meet and have the opportunity to interact with.
In particular, this distinction is important when connecting the field of relational demographics with homophily research. As relational demography looks at people who work together, it primarily identifies induced homophily and has tended to show more similarities in status traits age, gender, class , while homophily research has focused on value traits.
In this study, we use an online design, in which participants have no prior knowledge about a partner, to ensure that we are solely measuring choice homophily, meaning that our results relate to whom people are initially most attracted to rather than friendships that occur through forced convenience.
There is currently some contention over the measurement of attraction [ 45 ], so here we will evaluate several potential measures that are relevant to an online experimental paradigm. While we primarily focus on rating scales e. As a third measure of affiliation, we use the Inclusion of Other in Self Scale [ 47 ].
Since choice homophily has been shown to be a robust effect, we first assess which of these measures demonstrate the expected effect, then test which traits contribute most towards the values of those measures. In Experiment 1, we test five measures in order to evaluate which demonstrate the well-documented positive linear relationship with increased proportion of shared traits. We then use these same measures in Experiment 2 to determine which traits are most important in attraction to similar others. In line with an extensive existing literature, we expected participants to feel more positive towards partners that they believed they shared more traits with.
Beyond this primary hypothesis, we tested which traits are the best predictors of positive evaluation of a partner. If we find no consistent relationship between trait types and positive evaluation of a partner, then similarity-attraction may act purely as a minimal group membership effect. All participants gave written informed consent and the experiment was approved by the University of Oxford Central University Research Ethics Committee Ref.
Participants were recruited via Maximiles, a commercial online survey management system, and completed the study online using their own computers. A within-subject design was used to test whether sharing behavioral traits with others could influence attraction. Participants were introduced to a series of partners whom they were told were human but who in fact were computers with whom they shared varying numbers of traits, and were asked a number of questions to determine how positively they felt about each partner.
The independent variables were partner type i.
Dependent variables were a conformity task outlined in the Procedure below , and a set of questions designed to assess positivity felt towards the partner, which might lead to attraction in real settings of friendship initiation. Participants read an information sheet, provided consent, and were asked to read instructions which included a practice of the conformity estimation task.
They then completed a survey with limited answer options provided using drop-box menus. After completing the questionnaire, participants were provided with an answer profile that they were told corresponded to a partner.
The order of different partner types sharing different numbers of traits was counterbalanced between participants. Participants were told to read through this profile, and informed that they would be asked to recall some of these traits later in the experiment. The traits that would be shared with each partner were randomly determined. For traits that were not shared the computer partner was randomly assigned one of the other possible answers from the drop-box menu. Any traits that were shared with one partner would not be shared with another partner, which meant that all participants experienced sharing each of the possible traits.
In order to measure conformity we used a task previously developed by Castelli et al. This was presented to participants as the main task of the experiment and they did not engage in any other form of interaction with partners. Following this estimation task, participants were given a set of further questions, and asked to rate them on a standard Likert scale 1—7. The relevant questions were:. How willing would you be to work with the same partner again on a different task?
How much do you think you would like your partner? If you discovered your partner had cheated during the estimation task, how likely would you be to report it to the experimenter? Participants were finally asked to recall four of the traits of their partner. This task has been widely used and provides a reliable index of how engaged an individual feels with someone else.https://akbyoclimat.tk
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The same tasks were repeated for each of the partners. Following this procedure participants were asked to complete a very brief personality-type questionnaire [ 48 ], and to give their current home town. Finally participants were asked to read a full debrief of the experiment, and were given the opportunity to withdraw their data from the study without any penalty. In total, participants were tested from a UK population N female: , Mdn age group: 36—45 , and all details as entered into the survey are given in S1 Table.
All data are provided as DataExp1 at doi The experiment used a within-subject design, with three different partner types as the independent variable. Partners either shared nine out of fourteen traits Partner A , five out of fourteen traits Partner B , or no traits Partner C with the participant. The procedure was as given above, and all questions asked are given in S1 Table , along with proportions of participants choosing each option. Questions assessed both demographic and value based traits: age, gender, ethnicity, natal location, religion, current location, occupation, income, highest level of education, music taste, political views, hobbies, sense of humor and ethical beliefs.
Questionnaire and estimation task data were first assessed to determine which dependent variables were most relevant to sharing traits with another person. Estimates in the conformity task were subtracted from the answers given by the computer partner, with absolute values of these used as an indicator of estimate proximity. These values were log-transformed to normality for use in statistical tests. IOS picture ratings were transformed to numerical values from 1 to 7, with 1 representing most separate. These values were negatively skewed, and were binned into ratings of 1, 2—3, 4—5 and 6—7 for statistical analysis, but non-parametric tests on raw data gave comparable results to those reported here.
We first tested which of the five measures of attraction would best demonstrate a linear relationship with shared traits, as should occur in an indicator of choice homophily. We used a repeated measures MANOVA to test the effect of partner type A, B or C on the estimation task results, ratings of desire to interact again, likeability and reporting of cheating of a partner, and the binned IOS scale ratings. These results are summarised in Fig 1 , and demonstrate that only the measures of likeability and IOS scale showed the expected relationship, with increased attraction towards people with whom more traits were shared.
This means that only these measures appear to index the choice homophily relationship that we are expecting, so these measures will be used to determine which traits contribute most towards positive ratings of strangers. Following this, we tested which traits had the largest influence on positive evaluation of a partner. Partner likeability, and IOS scale ratings were each used to measure the importance of sharing different types of trait with a partner because they seem to best represent choice homophily.
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Each trait was coded for whether it was shared with a partner or not using a 1 or 0 respectively. These coded variables were then entered into a multilevel linear model, using individual IDs as Level 2 predictive factors with random intercepts effectively normalising for individual variability in baseline response and removing the problem of non-independence of data points , and traits as Level 1 predictive factors with fixed intercepts and fixed slopes.
All instances in which participants recalled less than one out of four features of their partner correctly were excluded from this analysis on the basis that in these trials participants had not remembered a sufficient amount of information from the profile given 43 data points out of a total of The statistics program R version 3. All 14 factors were entered into a model for each dependent variable, with each factor acting as an additional predictor with no interaction terms, so the model for likeability would be given by each letter represents a coefficient :.
The most important predictor of likeability and IOS scale ratings was taste in music, with political views second most important. Religion also acted as a significant predictor of both ratings, while choice of ethical statements, ethnicity, and natal area were only predictors of one of the measures.
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Given that the results are directly modelling ratings using coding for whether a trait was shared or not they can be interpreted directly in terms of the contribution each trait made towards likeability i. So coefficients of up to 0.
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The individual coefficients for the five variables that differ significantly from zero ethnicity, religion, musical taste, political views and ethical statement range from 0. Although this is a small effect, it is of a magnitude that we might expect given the contrived setting in which the experiment was conducted; critically, the valence of each of the different traits is of more importance than the effect size per se in this paradigm.
An alternative way to express the effect here is against baseline ratings i. Note that shared hobbies acted as a negative predictor of IOS ratings, suggesting that having hobbies in common with a stranger made people view them as less closely linked to themselves; however, this result was not replicated in likeability ratings and should be interpreted with caution.
Other than this negative effect of hobbies, the overall pattern of results suggests that value traits were important predictors of positivity expressed towards a stranger. In Experiment 1, we tested which measures of attraction would demonstrate the predicted linear relationship with shared traits, then tested which traits were most relevant predictors of these measures.
Experiment 2 was designed to focus specifically on the traits that were shown to be the most important predictors of homophily in Experiment 1. By doing this, we both ensure that the modelling results are consistent, and check that, in a simpler situation with fewer shared categories and fewer partners, participants are influenced by similar factors. We here report only results directly relating to this question, using shared traits to predict ratings of likeability and ratings of IOS scale, as these measures were shown to best reflect the expected linear relationship with shared traits.