Which statement best describes the relationship between attitude similarity and attraction?

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journal article

Sociometry

Vol. 27, No. 2 [Jun., 1964]

, pp. 115-127 [13 pages]

Published By: American Sociological Association

//doi.org/10.2307/2785710

//www.jstor.org/stable/2785710

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Abstract

Two conditions that promote attraction between members of a dyad are perceived similarity of alter to self and interpersonal congruency, a state existing when perceived self and self as ego imagines he is perceived by alter are congruent. These conditions were studied by having subjects describe themselves and their best friend of the same sex on ten ranking scales for social needs. Analysis of these data, conducted so as to control for possible artifacts, provided significant support for perceived similarity and interpersonal congruency as correlates of interpersonal attraction.

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Abstract

In their early stages, social networking sites [SNSs] focused primarily on sharing personal information via user profiles, enabling users to discover similarities in demographics, interests, and attitudes. A great deal of empirical evidence indicates that when users discover these types of similarities, even when this discovery is mediated, they become more attracted to each other [Montoya et al., 2008]—the so-called similarity-attraction effect [Byrne, 1971]. Previous similarity-attraction effect manipulations include personality traits [Banikiotes & Neimeyer, 1981; Bleda, 1974], attitudes [Yeong Tan & Singh, 1995], ethnic backgrounds [Hu et al., 2008], facial features [Bailenson et al., 2008], and voice features [Nass & Brave, 2005], among others.

Similarity-attraction has been a topic of investigation for more than half a century, but the recent growth of mediated communication inspires new questions regarding similarity and interpersonal attraction. In this paper, we explore a previously unstudied type of interpersonal similarity: similarity in the timing of activities. That is, beyond knowing that someone else holds the same attitudes as oneself, shares a common background, or performs the same activities, is similarity-attraction amplified when two people know that they are doing the same activity at the same time? With the dramatic growth of real-time applications—such as Facebook and Twitter—one can be presented with contemporaneity information without actively interacting with the other person. Does this computer-mediated simultaneity influence people's feelings of connectedness and belonging? Does similarity-attraction extend to similarity in the timing of activities?

Background on The Similarity-Attraction Effect

The empirical evidence for similarity-attraction is so compelling that Byrne and Rhamey [1965] early on labeled the positive relationship between respondents' similarity and the attraction between respondents the Law of Attraction. After numerous replications in multiple domains using different similarity manipulations, Berger [1975] proclaimed that similarity-attraction is “one of the most robust relationships in all of the behavioral sciences.”

The explanations for the origin of similarity-attraction are, however, multifold and often disputed. Initially, no explanations for the effect were offered, and similarity-attraction was regarded to be self-evident—as we see even today by the lack of explanations for similarity-attraction in many social psychology textbooks [Heine et al., 2009]. When explanations are proposed, one of the most popular views is based on people's innate desire to be consistent with societal norms and values. This explanation assumes that the discovery of interpersonal similarity leads to the validation of one's own characteristics and views by providing consensus support [Byrne & Clore, 1970]. The validation then leads to a higher perceived appropriateness of one's current beliefs, attitudes, behaviors, and traits.

Byrne [1971] and Clore and Byrne [1974] extended this explanation in their formulation of the reinforcement-affect model. This model is based on the assumptions that: [a] people experience stimuli as rewarding or punishing and seek out those that are rewarding, [b] positive feelings—affect—are associated with rewarding stimuli, and [c] other people are liked or disliked according to their association with rewarding or punishing stimuli. That is, we learn to associate positive feelings with people that are linked to rewards. Instances of interpersonal similarity function as rewarding stimuli, which leads people to associate positive feelings with similar others, which in turn leads people to be more attracted to similar others.

Another popular explanation for similarity-attraction is the emergence of positive feelings stemming from smooth and rewarding interactions, which are more likely to arise when communicating with people who are similar to oneself [Berscheid & Walster, 1978] than when communicating with dissimilar others. While this explanation is similar to the explanation of Clore and Byrne [1974] in its emphasis on rewards experienced by positive stimuli, Berscheid & Walster [1978] specifically focus on the smooth interactions that are likely to occur with similar others as opposed to the positive feelings stemming from discovering instances of interpersonal similarity. Through these smooth interactions with others, similarity-attraction partly satisfies a person's “Need to Belong” [Baumeister, 1995].

Other explanations put forward for similarity-attraction include enhanced reciprocal liking towards similar others [Condon & Crano, 1988] or a desire to satisfy implicit egoism [Jones et al., 2004]. One final explanation for the similarity-attraction effect inverts these arguments: A person's default state is to like everyone, and dissimilarity leads to repulsion [Rosenbaum, 1986]. Rosenbaum supports this view by showing several experiments in which the standard experimental paradigm of the SAE is extended by adding a “no-interaction” control group. His work shows that attraction ratings in the no-interaction and in the similar other groups are comparable, while the ratings in the nonsimilar other group are significantly lower.

Despite the overwhelming empirical evidence supporting the existence of similarity-attraction as well as the numerous plausible mechanisms identified to explain it, several questions have been raised regarding the importance and integrity of the effect. Some authors have discounted the effect as merely resulting from demand characteristics operating in experiments [Sunnafrank, 1991] or other methodological flaws [Bochner, 1991]. Morry [2007] questions the claimed causality of similarity-attraction, and Sunnafrank and Miller [1981] demonstrate that similarity-attraction is diminished greatly when allowing for initial interactions between participants in laboratory studies. Overall, these results lead critics to conclude that similarity-attraction only exists in a laboratory setting using ad-hoc dyads and is of no practical importance in a real-world setting. Meta-analysis of numerous SAE studies both within and outside of the laboratory indeed shows that while similarity manipulations have strong effects on attraction in laboratory settings, these effects are limited in real long-term relationships [Montoya et al., 2008]. In the next section, we will explain why these criticisms do not discredit similarity-attraction as an important psychological effect in real-time computer-mediated interactions.

Types of Similarity in New Media

The most common laboratory paradigm in similarity-attraction research is the phantom-other technique [Smith, 1957; Byrne, 1961]. In laboratory experiments using the phantom-other technique, the target is often unknown to participants. Participants are then presented with details about the nonpresent target, such as age, personality, judgments, or social status. This phantom-other technique leads to the strongest similarity-attraction effects.

Coincidentally, these laboratory characteristics frequently hold for a wide variety of computer-mediated communications that take place in new media and SNSs: A large number of “friends” in people's social networks are relatively unfamiliar, not physically present, and their profiles present details not commonly discovered in face-to-face interactions. Thus, while similarity-attraction has been hard to replicate outside of the laboratory, the situation is now reversed: Real life, through SNSs and other mediated communication, has replicated the laboratory conditions in which similarity-attraction was first observed. It is thus plausible that the laboratory studies that support the similarity-attraction effect will possess high external validity in these new media contexts.

There are many types of similarity encountered by people when using new media like SNSs. Initially, SNSs enabled people to explore and experience “who-similarity": similarity in demographic features such as ethnic background or religious affiliation. As personal profiles and information streams on SNSs grew, people were also able to discover “what-similarity": similarity in attitudes, activities, and hobbies. The use of social GPS tracking, as is done by applications like Foursquare, has even increased the salience of “where-similarity": similarity in location.

Recent advancements—specifically, real-time social technologies—have created the ability to discover a new type of similarity that has not been previously examined either in the laboratory or in the field: when-similarity. While it was already possible to connect with remote others in real-time since the emergence of chat rooms and instant messaging [see e.g. Baker, 2008], experiencing co-occurrence of [remote] activities in time without an explicit conversation or shared activity is relatively novel. In this paper, we create a situation in which co-occurrence of remote activities is experienced without introducing additional confounds that would naturally arise during an explicit conversation.

Real-time services like Facebook and Twitter enable users to experience similarity with others in the timing of activities. With millions of people broadcasting their current activity status in real-time repeatedly during the day, it is highly likely users will experience some form of when-similarity: Users discover that at the point in time that they are carrying out a specific activity, someone in their extended social network is carrying out that same activity, without an active conversation or conscious joint activity of the two parties involved. Due to the growing prominence of real-time services both on the web as well as on other devices such as mobile phones, it is worthwhile to explore whether this new type of similarity also enhances attraction and thus supports the similarity-attraction effect.

Overview of the Studies

In this paper, we describe two studies in which when-similarity [Study 1] and both when- and who-similarity [Study 2] are manipulated. The effects on the participant's perception of a target other are measured. Based on the strong evidence that supports similarity-attraction in settings similar to those experienced by users of real-time services, we hypothesize that similarity-attraction will also hold for this new type of similarity. That is, we expect that users who discover when-similarity with a target through computer-mediated communication channels will feel closer to the target. This expectation is already supported by a number of sociological investigations into the effects of the timing of events such as religious festivities: When a group of individuals perform an activity or ritual at the same time, simultaneity plays a key role in the formation of feelings of connectedness within the social group. [Durkheim, 1912 / 2008; Horton, 1967; Lee & Liebenau, 2000; Zerubavel, 1982].

In both of the presented studies, we use a novel method to manipulate when-similarity while controlling for other types of similarity. We are the first to isolate the effects of the timing of activities from confounds such as the type of activity being performed. The mediated nature of the interactions in the experiment enables us to manipulate the timing of activities while keeping the pattern of activity types constant and while avoiding confounds caused by physical proximity. Our manipulation of when-similarity differs from traditional experimental manipulations in that the two experimental groups—those high and low in when-similarity—are created dynamically based on the behavior of participants. We explain this method in detail.

Study 1: The Effects of When-Similarity

Method

This study tests the effects of when-similarity—while controlling for other types of similarity—on the participants' evaluations of others. Interpersonal attraction, after a weeklong intervention manipulating when-similarity, was measured using both social connectedness and intimacy scales. When-similarity was manipulated between participants.

Participants

Twenty-four United States college students participated in this balanced, between-participants experiment. Thirteen [54.2%] participants were female, and gender was balanced as much as possible across the conditions. The average age of respondents was 20.1 [SD = 1.61] years. Participants received partial course credit for their participation.

Procedure

First, participants were asked to complete a brief personal profile, which asked for their gender, age, area[s] of academic study, and favorite pastimes. Participants were then ostensibly partnered with another participant of the same gender. Participants were provided with the name of their partner for the duration of the experiment, and no other information about the partner was disclosed. In reality, participants' partners were not actual participants, and partners' names were selected to provide a perceived gender match with the participant.

Next, participants received six text messages per day on their mobile phones over the course of five days. Each message contained the question: “What are you doing right now?” Participants were instructed to reply with a number from 1 to 5, which represented different behavioral categories: 1: Eating, 2: Studying, 3: Physical Activity, 4: Relaxing, and 5: Working. These categories were pretested to resonate with the participant population and were found exclusive as well as exhaustive: a 2-day pretest showed that for each moment in the day that they were queried, students [N = 11] were able to pick exclusively one of the five provided categories as their current activity.

A few minutes after responding to the text message, participants received a follow-up message stating the activity their partner was ostensibly performing at that same point in time, and whether their own activities matched their partner's. Participants were not provided with any information about their partner other than these activity messages. After 5 days of text messages, summing to 5 × 6 = 30 messages, participants answered an online questionnaire to evaluate their partner and their perceived relationship with their partner.

We chose text messaging as our medium, as opposed to an SNS, since respondents would have direct access to their devices to be able to report on their activities at the moment the messages were received. Furthermore, the text messages allowed us to fully control the conversation with the ostensible other with less risk of directed online searches to get in contact via other means. Thus, text messaging was chosen mainly for methodological ease. We do not feel our results are restricted to text messaging but rather are representative of a much broader class of mediated real-time interactions.

When-Similarity Manipulation

To manipulate when-similarity while controlling for other types of similarity, half of the participants were assigned to the Similar Timing condition and half were assigned to the Dissimilar Timing condition. In both conditions, to keep constant what the partner was doing, the ostensible partner responded in such a way that after all six text messages had been sent for a specific day, the partner had studied twice and performed each of the other behavioral categories only once. This constraint on the activity pattern of the ostensible partner was imposed to prevent the actual activity from influencing the perceptions of the partner. By providing the same “activity profile” in both of our conditions, we minimize the effect of what-similarity.

The two conditions differed only in when the participants were told their partners were performing these activities. In the Dissimilar Timing condition, the response, when possible, consisted of a different behavioral category than the one performed by the participant at that point in time. For example, if the participant indicated she was “eating” at the time she received a message, the response message would be any [random] behavioral category other than eating—unless all other behavioral categories had already been exhausted that day, leaving “eating” to be the only valid remaining response. In the Similar Timing condition, the response was, when possible, in the same behavioral category. Since no background information about the participants' ostensible partners was presented, this experimental setup also controlled for possible confounds of who- or where-similarity [beyond the matching gender].

Validity of the When-Similarity Manipulation

Something to keep in mind is that ostensible partner responses generated by our algorithm depend on the activities that are performed by our participants. Thus, to evaluate the validity of our when-similarity manipulation, we need to determine whether there would always be a difference between the number of simultaneous activity occurrences for the Similar and Dissimilar Timing groups regardless of whether our participants' overall activity pattern were different. We addressed this by conducting simulations of our response algorithm for different possible participant activity patterns. For N = 20 participants per simulation [M = 1000], we took six draws from a five-category multinomial—corresponding to the six activity messages sent daily. By changing the initial probabilities of the five activity categories, we were able to test the consistency of our algorithm's responses to different participant activity patterns [i.e., what are the generated responses from the ostensible partner if the participant performs the same activity at all times?].

Figure 1 shows the simulation results for the actual observed activity probabilities in our study [Row 1], a flat activity pattern in which the simulated participants performed each of the activities equally often [Row 2], and a severely skewed distribution of activities in which simulated participants spent far more time studying than performing any of the other activities [row 3]. The distribution of responses by the ostensible partner [Column 2] is the same for all simulations due to the restricted daily activity pattern implemented to control for what-similarity [i.e., study twice a day and perform all other categories only once]. Column 3 shows the distribution of the percentage of simultaneous activities in the Similar Timing and the Dissimilar Timing conditions—that is, the times in the simulation when the activities of the participant and the ostensible partner would match. In each of the simulated scenarios, including the severely skewed scenario [Row 3, Column 3], the number of matches in activity responses produced by the algorithm differed significantly between the Dissimilar Timing and Similar Timing conditions. Thus, our when-similarity manipulation creates distinct numbers of activity matches between the Similar Timing and the Dissimilar Timing groups while controlling for what-similarity even in situations where the activities of the participant are skewed1.

Figure 1

Evaluation by means of simulation of the when-similarity algorithm for different true population distributions of activities. The column on the right shows the distribution of the number of matches over all simulated experiments. Rows show different participant activity patterns: Row 1 shows the actual activity pattern of our participants, Row 2 shows an evenly distributed activity pattern, and Row 3 shows a highly skewed pattern. Even in the last case, the response generated by our matching algorithm led to a significant difference in the number of matches between the two conditions.

In this study, the activity of the ostensible partner matched that of the participant 50.6% of the time in the Similar Timing condition and 3.0% of the time in the Dissimilar Timing condition. Thus, the actual number of instances of experienced when-similarity in the two conditions is in accordance with those produced in the simulations.

Measures

Participants evaluated their partner after the one-week manipulation using three rating scales. The first scale was a 6-item, 7-point Social Connectedness Scale [Cronbach's α = 0.94] [Van Bel et al., 2009]. The endpoints of the items were labeled “[1] Totally disagree” to “[7] Totally agree.” This social connectedness scale consisted of items addressing the feelings of closeness and shared thoughts between the participant and their partner [e.g. “I often know what my partner feels” and “I feel that my partner often knows what I think”].

The second scale was the Inclusion of the Other in the Self [IOS] Scale. The IOS measures perceived intimacy [Aron et al., 2006] using a single, 7-point pictorial item. Each of the pictures shows two circles labeled “You” and “Your Partner.” In each picture, the circles overlap more and more—from nontouching to almost fully overlapping. Participants are asked which of the pictures most closely represents the relationship with their ostensible partner.

Finally, participants were asked to state how much they agreed with the statement “I would like to meet my partner” on a 7-point scale. The end-points of this scale—which was specifically designed for the purposes of this experiment—were labeled “[1] Completely disagree” to “[7] Completely agree.”

Results

A MANOVA with when-similarity as an independent factor and the three attitudinal measures as dependent factors showed a strong multivariate main effect of when-similarity-attraction on the overall partner evaluations, F[3,20] = 10.58, p < 0.001, η2 = 0.61. Table 1 presents the mean scores of each dependent variable and the outcomes of separate t-tests for the effect of when-similarity. For each of the three dependent measures, participants in the Similar Timing condition scored significantly higher than participants in the Dissimilar Timing condition.

Table 1

Comparisons of attitudes towards the ostensible partner in the Dissimilar Timing and Similar Timing conditions. N = 24