IVs and DVs

IVs and DVs
Sample Answer for IVs and DVs Included After Question
General QDAFI Grading Rubric
Q: What question did the researchers ask? 2/2 for the full question 1.5/2 for mostly correct (missing a detail, etc.) 1/2 for along the right lines but missing a major point 0/2 for not at all correct D: What did the researchers do? 2/2 for including most or all IVs and DVs 1.5/2 for including some IVs and DVs 1/2 failing to include most IVs and DVs 0/2 for not including any A: What is the author’s rationale? 2/2 including the rationale for the research q and what they expect (hypothesis) 1.5/2 for including rationale or hypothesis 1/2 not including either but there is something to give points for (along the right lines) 0/2 for not including anything relevant F: What did they find? 2/2 including all relevant findings (at least main one) and not including numbers or exact stats 1.5/2 for including some relevant findings but not the main one 1/2 not including either but there is something to give points for (along the right lines) 0/2 for not including anything relevant I: What is the author’s interpretation? 2/2 including the summary of what they found and their interpretation. Alternatively, including their explanation, some large issue with it and the student’s interpretation 1.5/2 for including some of the above 1/2 not including either but there is something to give points for (along the right lines) 0/2 for not including anything relevant Sample Explanations – for grading In your ‘Q’ section: Try to aim to answer the specific research question the authors are asking. Are they looking at just any predictors or are there specific predictors they’re aiming to study? In your ‘D’ section: Clearly state in your sentences the author’s IVs, DVs, controls (if any), and methods of measurement. You don’t need to list the specific tests in your ‘D’, just the constructs of which they measure. The reader would only need to know these if they were planning on replicating the study. In your ‘A’ section: You’ve got part of it there (the hypothesis), but you left out the “why”….. Why do they think they’ll find this? What is the motivating drive that led them to do ‘D’ to find ‘Q’? In your ‘A’ section, remember to include both the author’s rationale and their hypothesis. You have a good hypothesis here, but you’re lacking the complete rationale (why they think they’ll find this). In your ‘A’ section: You’ve got part of it there (the rationale), but you left out the hypothesis. Think of this section as an “if, then” statement. IF (the rationale and motivating drive behind the research question are true), THEN (this is what they think will happen). You don’t have to write it in this manner, but it will help with the thought process. In your ‘A’ section, remember to include both the author’s rationale and their hypothesis (what they think their outcome will be from this study). You have a good rationale here, but you’re lacking the hypothesis. In your ‘A’ section remember to include both the authors’ rationale and their hypothesis: Think of this section as an ” if, then” statement. IF (the rationale and motivating drive behind the research question are true. – “Why do they think they’ll find this? What is the motivating drive that led them to do ‘D’ to find ‘Q’?”), THEN (this is what they think will happen). You don’t have to write it in this manner, but it will help with the thought process. In your ‘I’ section: full credit here would include the summary of what they found and their interpretation. Alternatively, include their explanation, some large issue with it and the student’s interpretation In your ‘I’ section: Be sure to include significant real-world factors these finds could or could not have, along with any issues there could be with this study (e.g., reliability, generalizability, etc). 697667 research-article2017 PSSXXX10.1177/0956797617697667 Corrigendum Corrigendum: Birds of a Feather Do Flock Together: Behavior-Based PersonalityAssessment Method Reveals Personality Similarity Among Couples and Friends Psychological Science 2017, Vol. 28(3) 403­ © The Author(s) 2017 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797617697667 https://doi.org/10.1177/0956797617697667 www.psychologicalscience.org/PS Original article: Youyou, W., Schwartz, H. A., Stillwell, D., & Kosinski, M. (2017). Birds of a feather do flock together: Behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychological Science. Psychological Science, 28, 276–284. doi:10.1177/0956797616678187 As a result of an oversight, the order in which the authors of this article were listed was incorrect. The correct order of authorship is as follows: Wu Youyou, David Stillwell, H. Andrew Schwartz, and Michal Kosinski 678187 research-article2016 PSSXXX10.1177/0956797616678187Youyou et al.Personality Similarity Research Article Birds of a Feather Do Flock Together: Behavior-Based Personality-Assessment Method Reveals Personality Similarity Among Couples and Friends Psychological Science 2017, Vol. 28(3) 276­–284 © The Author(s) 2017 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797616678187 www.psychologicalscience.org/PS Wu Youyou1,2, David Stillwell3, H. Andrew Schwartz4, and Michal Kosinski5 1 Department of Psychology, University of Cambridge; 2Kellogg School of Management, Northwestern University; 3Judge Business School, University of Cambridge; 4Department of Computer Science, Stony Brook University; and 5Graduate School of Business, Stanford University Abstract Friends and spouses tend to be similar in a broad range of characteristics, such as age, educational level, race, religion, attitudes, and general intelligence. Surprisingly, little evidence has been found for similarity in personality—one of the most fundamental psychological constructs. We argue that the lack of evidence for personality similarity stems from the tendency of individuals to make personality judgments relative to a salient comparison group, rather than in absolute terms (i.e., the reference-group effect), when responding to the self-report and peer-report questionnaires commonly used in personality research. We employed two behavior-based personality measures to circumvent the referencegroup effect. The results based on large samples provide evidence for personality similarity between romantic partners (n = 1,101; rs = .20–.47) and between friends (n = 46,483; rs = .12–.31). We discuss the practical and methodological implications of the findings. Keywords similarity, personality assessment, reference-group effect, social network, close relationships Received 5/4/15; Revision accepted 10/16/16 It is well established that in close relationships, individuals tend to be similar in a wide range of characteristics (McPherson, Smith-Lovin, & Cook, 2001), including age, education, race, religion, attitudes, and general intelligence (Rushton & Bons, 2005). Surprisingly, little evidence has been found for personality—a fundamental psychological construct that underpins much of the variation in human behaviors. Most past research has shown no or only weak similarity in personality between partners and between friends (Altmann, Sierau, & Roth, 2013; Anderson, Keltner, & John, 2003; Beer, Watson, & McDade-Montez, 2013; Botwin, Buss, & Shackelford, 1997; Buss, 1984a; Funder, Kolar, & Blackman, 1995; Rushton & Bons, 2005; Watson, Beer, & McDade-Montez, 2014; Watson, Hubbard, & Wiese, 2000a; Watson et al., 2004), with occasional findings indicating moderate similarity in the Big Five factors of Openness to Experience and Conscientiousness between romantic partners (Donnellan, Conger, & Bryant, 2004; McCrae et al., 2008; Watson, Hubbard, & Wiese, 2000b). This has led researchers to maintain the conclusion drawn by an early theorist that “mating is essentially random for personality differences” (Eysenck, 1990, p. 252). We argue that the lack of consistent evidence for personality similarity among couples or friends stems from the reliance on self-report and peer report1 of personality in a majority of previous studies. These assessment methods are unsuitable for studying the similarity effect, because they are affected by a tendency of the respondents to Corresponding Author: Wu Youyou, Northwestern Institute on Complex Systems, Chambers Hall, 600 Foster St., Evanston, IL 60208-4057 E-mail: yw341@cam.ac.uk Personality Similarity judge themselves relative to a salient comparison group, rather than in absolute terms (the reference-group effect; Heine, Buchtel, & Norenzayan, 2008; Heine, Lehman, Peng, & Greenholtz, 2002). For instance, an introverted engineer might perceive himself as relatively extraverted if he is surrounded by a group of even more introverted engineer friends. The same bias affects peer report as well; the introverted friends of the engineer might also see him as extraverted by comparing him with themselves. In fact, some widely used personality questionnaires specifically instruct people to describe themselves “in relation to other people you know” (e.g., the International Personality Item Pool, IPIP, measuring the five-­ factor model of personality; Goldberg et al., 2006). Several studies have found that self-reports of personality do not always correspond with behavioral measures (Heine et al., 2008; Ramírez-Esparza, Mehl, ÁlvarezBermúdez, & Pennebaker, 2009). The authors of these studies have suggested that the reference-group effect is a possible explanation. Subsequent experimental studies confirmed that the reference-group effect indeed pertains to questionnaire-based personality judgments (Credé, Bashshur, & Niehorster, 2010; Wood, Brown, Maltby, & Watkinson, 2012). We therefore argue that self- and peer report are inappropriate methods for studying personality similarity, because they amplify the differences in actual personality and obscure the similarity among partners and friends, who likely unconsciously treat one another as reference groups. Indeed, rare evidence of personality similarity emerged from a few studies relying on personality measures that are less susceptible to the reference-group effect. For example, Botwin et al. (1997) and Buss (1984a) measured personality using independent interviewers’ ratings and found similarity among spouses. Admittedly, this type of measure is still subject to the reference-group effect because the interviewer has his or her own reference group, but it affects both dyad members equally and therefore does not obscure the similarity between them. Buss (1984b) also found similarity between romantic partners by measuring personality using self- and peer-reported frequencies of certain personality-related behaviors (the act-frequency approach; Buss & Craik, 1983). Introversion, for example, was assessed by asking participants to judge whether in the last 3 months they “watched the soap opera on TV” or “went for a long walk alone” (Buss, 1984b, p. 368). This approach focuses on concrete behaviors and thus leaves less room for subjective comparisons. In light of these mixed findings, we aimed to address the reference-group effect and reexamine the existence of personality similarity between romantic couples and 277 between friends. We employed two behavior-based personality measures to circumvent the reference-group effect. The first approach measured personality using a common type of digital footprint: Facebook Likes. Facebook users generate Likes by clicking a Like button on Facebook Pages related to products, famous people, books, etc.2 This feature allows users to express their preferences for a variety of content. It has been shown that Likes can be used to accurately assess people’s personality (Kosinski, Stillwell, & Graepel, 2013; Youyou, Kosinski, & Stillwell, 2015). For example, people who score high on Extraversion tend to Like “partying,” “dancing,” and celebrities such as “Snooki” (a reality-TV personality).3 The second approach measured personality using digital records of language use: Facebook status updates. Facebook users write status updates to share their thoughts, feelings, and life events with friends. Previous research has consistently found links between personality and language use (Hirsh & Peterson, 2009; Mehl, Gosling, & Pennebaker, 2006; Tausczik & Pennebaker, 2010). Extraverts, for example, tend to use more words describing positive emotions (e.g., “great,” “happy,” or “amazing”; H. A. Schwartz et al., 2013) than introverts do. Several studies have demonstrated accurate personality assessment based on people’s language use in social media (Farnadi et al., 2014; Sumner, Byers, Boochever, & Park, 2012), including Facebook status updates (Park et al., 2014; H. A. Schwartz et al., 2013). For both Likes-based and language-based approaches, we measured personality in the following way. First, we obtained a sample of participants with both self-reports of personality and Facebook data. Next, we built a series of predictive models to link self-reports of personality with Likes or language use, respectively. This process allowed us to establish which digital signals were indicative of specific personality traits. The resulting models were then applied to a separate sample of romantic partners and friends to generate personality scores for these participants. The personality scores were correlated between dyad members to measure similarity. Notably, although both Likes-based and languagebased models are developed based on participants’ selfreported personality scores, they do not inherit the reference-group effect. The reference-group bias that contaminates personality similarity is a result of individuals using different standards, norms, or reference groups to evaluate themselves. In our analysis, the same personality-prediction models were applied to the entire sample, and therefore the evaluation standards were uniform across all participants. Youyou et al. 278 Method Likes-based personality assessment Participants The Likes-based personality-assessment model was built using Sample 1 following the procedure described in detail in Youyou et al. (2015). We first transformed participants’ Like data into a matrix, in which each row represented a participant, and each column represented a Like. The (i, j) entry was set to 1 if participant i liked object j, and 0 otherwise. A substantial number of Likes were associated with only a few participants in this sample, and some participants had only a small number of Likes. Since the assessment models leveraged the association between liking certain things and having a particular personality type, it was necessary to have enough combinations of personality profiles and Likes as training examples. The matrix was therefore trimmed so that participants with fewer than 20 Likes and Likes associated with fewer than 20 participants were removed. The resulting matrix consisted of 295,320 participants (rows) and 148,128 unique Likes (columns). For each of the five personality traits, a linear regression model was fitted to predict the self-reported personality scores from the participant-Like matrix (each column was treated as a variable); a combination of L1 (least absolute shrinkage and selection operator, or LASSO; Tibshirani, 1996) and L2 (ridge; Hoerl & Kennard, 1970) penalties were used for the models.4 A 10-fold cross-validation was applied in each model to avoid overfitting. The resulting five models, one for each personality trait, were applied to a separate sample of romantic couples (n = 990) and friends (n = 41,880) in Sample 3, to generate behavior-based personality scores for these participants. This sample contained only couples and friendship dyads in which both members had at least 20 Likes on their profile. The average number of Likes per participant was 159.4. We removed Likes shared between each pair of dyad members to ensure that they did not artificially inflate similarity. Such an overlap in Likes between dyad members was relatively low: friends, on average, shared 5.2 Likes, or 1.4% of their joint Likes; romantic partners shared 12.8 Likes, or 3.5% of their joint Likes. To evaluate the predictive accuracy of the Likes-based models, we correlated Likes-based and self-reported personality scores for a subset of participants in Sample 3 (note that the model was developed using Sample 1). After shared Likes were removed for all individuals, the correlations were as follows—Openness to Experience: r(22,692) = .39, Conscientiousness: r = .28, Extraversion: r = .31, Agreeableness: r = .25, and Neuroticism: r = .29. This study relied on three samples obtained from the myPersonality project (http://mypersonality.org). MyPersonality was a popular Facebook application that allowed users to take psychological tests and receive feedback on their scores. A portion of participants provided opt-in consent to allow us to record their test scores and contents of their Facebook profile. The average participant age was 24.1 years. Females constituted 61.1% of the sample, and males constituted 38.9%. Sample 1 was used to build the Likes-based personality-assessment models. It contained 295,320 participants who completed personality questionnaires and had at least 20 Likes on their Facebook profile. Sample 2 was used to develop the language-based personality-assessment models. It contained 59,547 participants who completed personality questionnaires and wrote at least 500 words across all of their status updates. Sample 3 was used to study the existence of personality similarity between romantic partners and between friends. It contained 247,773 individuals forming a total of 5,042 heterosexual romantic couples and 138,553 friendship dyads. Romantic couples were identified using the “relationship status” field of the Facebook profile, and friendship connections were identified using Facebook friend lists. To ensure that all dyads included in the analysis were independent from one another, we randomly chose one dyad for the individuals belonging to multiple friendship dyads. Self-report of personality Self-reports of personality were obtained using a 20- to 100-item IPIP questionnaire (Goldberg et al., 2006) measuring the widely accepted five-factor model of personality (Revised NEO Personality Inventory; Costa & McCrae, 1992). Reliability scores for the 100-item questionnaire, completed by 29.4% of the participants, were as follows—Openness to Experience: Cronbach’s α = .84, Conscientiousness: α = .92, Extraversion: α = .93, Agreeableness: α = .88, and Neuroticism: α = .93. Corresponding values for the 20-item version, completed by 56.4% of the participants, were as follows—Openness to Experience: Cronbach’s α = .48, Conscientiousness: α = .67, Extraversion: α = .73, Agreeableness: α = .58, and Neuroticism: α = .65. The remaining participants (14.2%) completed the IPIP questionnaire ranging from 30 to 90 items (in intervals of 10). Self-reports were available for all participants in Samples 1 and 2, and for 4,287 romantic couples and 103,329 friendship dyads in Sample 3. Language-based personality The language-based personality-assessment model was developed using Sample 2, with an open-vocabulary Personality Similarity approach similar to the one employed by Park et al. (2014). We first extracted words and phrases (i.e., sequence of words) from participants’ status updates and then transformed them into two types of predictors: (a) binary indicators of whether the participant used each word and (b) relative frequencies of each word or phrase (as compared with the total number of words that each participant wrote). Words and phrases used by less than 1% of the participants were excluded when we created the predictors. The two types of predictors were each represented as a matrix and underwent randomized principal-component analysis (RPCA; Martinsson, Rokhlin, & Tygert, 2011) independently. They were then combined into a single participant-language matrix. For each of the five personality traits, a linear regression model with an L2 (ridge) penalty was fitted to predict the self-reported personality scores from the participant-language matrix. A 10-fold cross-validation was applied in each model to avoid overfitting. Because words and phrases shared between two partners and between friends could artificially inflate personality similarity, it was necessary to control for the overlap in language between dyad members. However, we could not exclude all the words shared between dyad members (as we did with Likes), because most of the common words would be removed as a result, which would lower the predictive accuracy. Instead, we randomly split all the available words and phrases into two halves, and submitted each half to the procedures described in the previous paragraph. The two resulting matrices were regressed onto participants’ self-reported personality scores to build two independent sets of predictive models. Finally, the two sets of models were each applied to a different member of the dyad. This process ensured that even if two dyad members used the same words or phrases, the two different models applied to each of them separately would capture only distinct parts of the overlap. The two sets of models developed here were applied to 282 romantic couples and 5,674 friendship dyads in Sample 3. These dyads all consisted of members that both had status updates available and wrote at least 500 words across all of their status updates. Participants in this sample wrote 4,474 words on average. To determine the predictive accuracy of the languagebased models, we correlated language-based and selfreported personality scores for a subset of participants in Sample 3 (note that the model was developed using Sample 2). After an overlap in language was controlled for (by applying independent models to each dyad member), the correlations were as follows—Openness to Experience: r(2,718) = .37, Conscientiousness: r = .32, Extraversion: r = .34, Agreeableness: r = .30, and Neuroticism: r = .33. 279 Measuring similarity Similarity between dyad members was measured by correlating their scores on a given personality trait across all dyads. Correlations were calculated for self-report, Likes-based, and language-based measures, respectively, between partners and between friends. Additionally, we calculated correlations between one dyad member’s Likes-based score and this person’s partner’s or friend’s language-based score across all dyads. For romantic couples, personality scores were aligned by gender, and Pearson product-moment correlation coefficients were used. For friendship dyads, intraclass correlations were used because dyads cannot be aligned by gender, and the assignment of dyad members as person A or person B is arbitrary (see Watson et al., 2000b).5 Results The goal of this study was to examine the degree of personality similarity between romantic partners and between friends. The results based on three different personality measures are presented in Figure 1. The Likes-based scores between dyad members showed significant positive correlations across all five personality traits—romantic couples: mean r(988) = .24, 95% confidence interval (CI) = [.18, .30]; friends: mean r(83,758) = .14, 95% CI = [.13, .15].6 An even stronger effect was observed in the language-based results— romantic couples: mean r(280) = .38, CI = [.28, .48]; friends: mean r(11,346) = .24, 95% CI = [.22, .26]. Using both Likes-based and language-based measures, the correlations did not differ substantially between same-sex and opposite-sex friendships (all differences were .03 or less). In contrast, self-reports showed weak to negligible personality similarity for both romantic couples, mean r(4,285) = .10, 95% CI = [.07, .13], and friends, mean r(206,656) = .06, 95% CI = [.06, .07]. All these correlations were significant at p < .001. The strength of personality similarity became clear when compared with the similarity observed for other variables. Personality similarity was not as strong as similarity in age—romantic couples: r(2,458) = .81, 95% CI = [.80, .82], p < .001; friends: r(85,076) = .57, 95% CI = [.57, .58], p < .001. However, it was comparable to or stronger than similarity in IQ: r(550) = .21, 95% CI = [.13, .29], p < .001 (this sample included both romantic couples and friends because there were not enough romantic couples in which both partners had IQ scores, n = 44, to allow for a meaningful comparison). These similarity results were based on personality scores measured using nonoverlapping Likes and language features. This was because the overlap in Likes or Youyou et al. 280 O O r = .50 r = .50 .40 .40 .30 .30 .20 .20 N C .10 10 A N A E Similarity in Likes-Based Personality (Assessed via Online Behavior) C .10 E Similarity in Language-Based Personality (Assessed via Online Behavior) Romantic Couple Friendship Dyad O: Openness to Experience C: Conscientiousness E: Extraversion A: Agreeableness N: Neuroticism O r = .50 .40 O r = .50 .40 .30 .3 .30 .20 .20 N C .10 A E Similarity Between Likes-Based and Language-Based Personality (Assessed via Online Behavior) N C .10 .1 A E Similarity in Self-Reports of Personality (Assessed via Questionnaire) Fig. 1. Radar charts showing the similarity in the Big Five personality traits between romantic partners and between friends. Results are shown separately for analyses based on Facebook Likes, Facebook language use, the combination of these two measures, and self-report questionnaires. Personality Similarity language features between dyad members might have been driven by factors other than personality, such as a shared environment, shared culture, or interpersonal influence. However, it also might have been partially driven by actual personality similarity. Consequently, these results represent a lower-bound estimate of similarity—some effect was lost. To calculate the upper-bound estimate, we performed the same analyses without controlling for shared Likes or language features. As expected, the results showed a stronger level of similarity. The Likesbased correlations were as follows—romantic couples: mean r(1,082) = .33, 95% CI = [.28, .38]; friends: mean r(87,842) = .19, 95% CI = [.18, .20]; the language-based ones were as follows—romantic couples: mean r(280) = .41, 95% CI = [.31, .50]; friends: mean r(11,346) = .25, 95% CI = [.23, .27], all ps < .001. One potential problem with the preceding analyses was that the scores of both dyad members were based on the same type of data, namely, Likes or status updates. This was problematic, as Facebook’s News Feed and its recommendation system might cause an artificial covariation between friends’ Likes or status updates (i.e., common-method bias; Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). For example, Facebook recommends Pages to its users that are similar to the ones their friends liked. Also, users are constantly exposed to their friends’ status updates in the News Feed and are therefore prone to post about similar topics. While we already controlled for overlap in Likes and language features, to further reduce potential sources of bias, we correlated the Likesbased scores of one person with the language-based scores of that person’s partner or friend. The results were similar to the ones already reported: The average personality similarity across the five traits was as follows— romantic couples: mean r(1,055) = .31, 95% CI = [.26, .36], ps < .001; friends: mean r(32,552) = .19, 95% CI = [.18, .20], ps < .001. Additionally, a series of analyses was performed to rule out alternative explanations for the observed personality similarity. First, we calculated the correlations between random pairs of participants to gauge the baseline similarity between strangers. None of the correlations were significant (|rs| < .01) for random dyads. Second, dyad members’ scores were correlated controlling for the number of Likes that they had or the number of words written in the status updates. This was to ensure that similarity in predicted personality between partners or between friends was not due to having a similar number of digital signals. These partial correlations were very similar to the zero-order ones (within .02 of the original values). Third, we investigated the extent to which the observed personality similarity was a by-product of similarity in other traits (Buss, 1984a). To this end, we reran the 281 analyses while controlling for dyad members’ age and education. For education, we calculated correlations for a subsample of friendship dyads in which both members were college graduates. We used keywords such as “university” or “college” (excluding “community college”) in the school names shown on Facebook profiles to identify a sample of participants with higher education degrees. The level of similarity between friends did not change considerably after taking education into account: All correlations were within .04 of the original values. Unfortunately, information about education was not available for enough of the romantic couples to provide for a meaningful comparison (n = 17 for the Likes-based approach and n = 6 for the language-based approach). The analysis was therefore limited to friends only. Similarly, little change was observed when we controlled for age. For both romantic couples and friends, partial correlations that controlled for age were all within .03, compared with zero-order ones. The only exception was Conscientiousness, for which the correlations decreased on average by .10 for romantic couples and .08 for friends across the three methods. Nevertheless, the similarity in Conscientiousness remained significant at p < .001 (romantic partners: mean r = .30; friends: mean r = .16). Discussion Our findings provide evidence that romantic partners as well as friends are characterized by similar personalities. We measured personality traits relying on three different sources of data: traditional self-report questionnaires, digital records of behaviors and preferences, and language use. Relatively strong similarity was detected between romantic partners and between friends when we used Likes-based and language-based measures. By contrast, self-reports yielded only weak to negligible similarity. Across all three methods, stronger personality similarity was found for romantic couples than for friends. We also showed that dyadic similarity in most personality traits was unlikely to be driven simply by similarity in age or education. The only exception was dyad members’ similarity in Conscientiousness, which was partially explained by their similarity in age. Compared with the other four traits, Conscientiousness is most strongly positively associated with age, especially before the age of 30 (Donnellan & Lucas, 2008; Soto, John, Gosling, & Potter, 2011). Because 88% of the participants were between 18 and 30 years old, it is not surprising that partners’ and friends’ similarity in Conscientiousness was partially due to their similarity in age. In which of the five personality traits were romantic partners and friends most similar? After controlling for age, we found that Openness to Experience displayed Youyou et al. 282 the strongest similarity in self-reports, Likes-based results, and Likes-language correlations for both romantic couples and friendship dyads. Language-based results, however, showed the strongest effect in Extraversion. However, we cannot draw definitive conclusions on the basis of our present analysis, because (a) the patterns were not consistent across all the methods that we employed, and (b) the effect sizes could be influenced by several factors, such as the strength of the referencegroup effect, the accuracy of the assessment models, and common method bias. These factors might affect the five traits differently and to varying degrees. Together, these results challenge the widely accepted notion that individuals in close relationships are not similar in personality. We argue that the scarcity of the evidence for the similarity effect is likely due to the reference-group effect. Notably, our results are consistent with those obtained in rare previous studies that relied on personality-assessment methods resistant to the reference-group effect (Botwin et al., 1997; Buss, 1984a, 1984b). On the other hand, the fact that t