Journal of Personality 00:00, Month 2015

Ordinary Social Interaction and the Main Effect Between Perceived Support and Affect

C 2015 Wiley Periodicals, Inc. V

DOI: 10.1111/jopy.12190

Brian Lakey, Randy J. Vander Molen, Elizabeth Fles, and Justin Andrews Grand Valley State University

Abstract Relational regulation theory hypothesizes that (a) the main effect between perceived support and mental health primarily reflects ordinary social interaction rather than conversations about stress and how to cope with it, and (b) the extent to which a provider regulates a recipient’s mental health primarily reflects the recipient’s personal taste (i.e., is relational), rather than the provider’s objective supportiveness. In three round-robin studies, participants rated each other on supportiveness and the quality of ordinary social interaction, as well as their own affect when interacting with each other. Samples included marines about to deploy to Afghanistan (N 5 100; 150 dyads), students sharing apartments (N 5 64; 96 dyads), and strangers (N 5 48; 72 dyads). Perceived support and ordinary social interaction were primarily relational, and most of perceived support’s main effect on positive affect was redundant with ordinary social interaction. The main effect between perceived support and affect emerged among strangers after brief text conversations, and these links were partially verified by independent observers. Findings for negative affect were less consistent with theory. Ordinary social interaction appears to be able to explain much of the main effect between perceived support and positive affect.

People who believe their family and friends will help during times of need (i.e., perceived support) have better mental health than those who doubt their social networks. For example, people with low perceived support have higher rates of major depressive disorder (Lakey & Cronin, 2008), more severe posttraumatic stress disorder (Brewin, Andrews, & Valentine, 2000), and more psychotic symptoms (Gayer-Anderson & Morgan, 2013), as well as more psychological distress (Barrera, 1986; Cohen & Wills, 1985), including low positive and high negative affect (Finch, Okun, Pool, & Ruehlman, 1999). Stress and coping theory (Lazarus & Folkman, 1984) guides most social support research and describes the role of specific supportive actions that buffer the effects of stress (Barrera, 1986; Cohen & Wills, 1985). However, many important links between perceived support and mental health occur regardless of the presence of stress (i.e., main effects) and do not involve stress buffering. Relational regulation theory (RRT) offers an explanation for main effects and emphasizes the role of ordinary social interaction (Lakey & Orehek, 2011). The present studies tested RRT hypotheses. Stress buffering and main effects are two important ways in which social support is linked to mental health (Cohen & Wills, 1985). Stress buffering occurs when people with high social support are protected from the bad effects of stress. Such effects

are indicated by Stress 3 Support interactions in analyses such as multiple regression. Main effects occur when people with high support have better mental health regardless of the presence of stress, including its complete absence. Main effects are most clearly revealed in simple correlations between support and mental health in the absence of Stress 3 Support interactions. Main effects can also occur in tandem with stress buffering effects (see Cohen & Wills, 1985, for a detailed discussion of main and stress buffering effects). Stress and coping theory is well suited to explain stress buffering effects for social support. According to the theory, people with high perceived support receive enacted support (e.g., advice or reassurance) that protects them from stress (i.e., stress buffering) when enacted support matches the demands of the stressor (Cohen & Hoberman, 1983; Cutrona & Russell, 1990). Yet, stress and coping theory is also commonly used to explain main effects between perceived support and mental health. This is problematic for several reasons (see Lakey & David Kenny provided assistance with SOREMO. Will Woods, Alex Banker, and Travis Whitaker served as raters for Study 3. Marcus Dickson provided advice regarding instant messaging software for Study 3. Correspondence concerning this article should be addressed to Brian Lakey, Department of Psychology, 2224 Au Sable Hall, Allendale, Michigan 49401-9403. Email: [email protected]

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Orehek, 2011, for a review). Conceptually, stress and coping theory seems ill suited to explain main effects because the theory predicts stress buffering effects specifically, and main effects do not, by themselves, reflect stress buffering. In addition, there are several empirical problems with stress and coping explanations for main effects. First, the correlation between perceived and enacted support is not sufficiently strong to suggest that perceived support is based mostly on enacted support (Barrera, 1986; Haber, Cohen, Lucas, & Baltes, 2007). Second, the receipt of enacted support does not consistently have main effects with better mental health (Finch et al., 1999), and it is often linked to worse mental health (Bolger, Zuckerman, & Kessler, 2000). Thus, it is important to develop theoretical explanations for main effects between perceived support and mental health, as many of the observed links between the two constructs are main effects. For example, nearly all of the links between perceived support and mental health cited in the first paragraph are main effects. According to RRT, main effects between perceived support and mental health reflect processes whereby people regulate their affect, thought, and action through ordinary, moment-bymoment social interaction involving conversation and shared activity. Over time, these interactions lead to enduring effects on mental health, including mental disorders. Ordinary conversation involves topics such as the weather, children, sports, TV, music, and movies, among many others. Ordinary shared activity includes work, games, exercise, cooking, housework, and watching TV, among others. Further, who and what regulates a recipient well is largely a matter of the recipient’s personal tastes (i.e., regulation is relational). For example, conversation about jazz might regulate Recipient A well, but it might dysregulate Recipient B. Conversation about the British royal family might show the opposite pattern. Although ordinary, such interactions cause most of the main effect between perceived support and mental health. There is substantial indirect evidence for the role of ordinary social interaction in main effects between perceived support and mental health (Lakey & Orehek, 2011). For example, generic relationship quality, but not enacted support, could explain perceived support’s link to low distress (Kaul & Lakey, 2003; Mak, Bond, Simpson, & Rholes, 2010). Discussing positive events was linked to greater well-being, but talking about stress and coping was not (Hicks & Diamond, 2008). An offer of companionship was preferred to an offer of enacted support (Clark, MacGeorge, & Robinson, 2008). Companionship was more closely linked to well-being than was enacted support (Rook, 1987). Interactions involving fun and relaxation were linked to adjustment, but enacted support was not (Hays & Oxley, 1986). Providers’ responses to recipients’ good events were more strongly linked to perceived support than were responses to negative events (Gable, Gosnell, Maisel, & Strachman, 2012). Ordinary conversation was linked to favorable affect as people went about their daily lives (Mehl, Vazire, Holleran, & Clark, 2010), as well as to relationship satisfaction (Schrodt, Soliz, &

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Braithwaite, 2008). Although these studies offer indirect support for RRT, they do not offer precise tests of the theory. None of the measures used in the studies were designed to test RRT mechanisms, and thus the relevance of the findings to RRT is unclear. For example, Kaul and Lakey (2003) studied generic relationship quality, but this construct lacks specificity about the types of interactions that generate perceived support and mental health. In addition, few of these studies tested whether ordinary social interaction could account for main effects between perceived support and mental health, and few isolated the relational aspect of perceived support. A key part of RRT is that the regulation of affect is primarily relational as defined by the social relations model (SRM; Kenny, 1994; Kenny, Kashy, & Cook, 2006). For example, relational perceived support occurs when a recipient sees a provider as unusually supportive: more supportive than how the recipient typically sees other providers, and more supportive than how the provider is typically seen. For example, Abbey sees Melissa as more supportive than how Abbey typically sees other people, and as more supportive than how Melissa is seen by others. Relationship effects are defined quantitatively: Rij 5 Xij – Ri – Pj 1 M, where Xij is Recipient i’s rating of Provider j’s supportiveness, Ri is Recipient i’s mean ratings of providers’ supportiveness, Pj is Provider j’s mean supportiveness score across recipients, and M is the grand mean. Relationship effects are mathematically identical to Person 3 Situation interactions as defined by Endler and Hunt (1969), as well as if-then situation– behavior profiles (Mischel & Shoda, 1995). In addition to relationship effects, the SRM defines two other effects of interest. Recipient effects (Ri) reflect trait-like perceived support in that some recipients characteristically perceive providers as more supportive than do other recipients, even when recipients rate the same providers. Provider effects (Pj) indicate the extent to which recipients agree that some providers are more supportive than others. Perceived support is primarily relational. Relationship effects accounted for 62% of the variance in perceived support, recipient effects accounted for 27%, and provider effects accounted for only 7% in a recent meta-analysis (Lakey, 2010). Thus, who is supportive is mostly a matter of taste, but a substantial portion reflects recipients’ trait-like predispositions to see others as supportive. Relatively little reflects the consensus among observers that some providers are more supportive than others (i.e., provider effects). This pattern has been observed in studies in which U.S. sorority members rated other sisters (Lakey, McCabe, Fisicaro, & Drew, 1996, Study 2), U.S. Ph.D. students rated program faculty members (Lakey et al., 1996, Study 1), Dutch and Italian nuclear family members rated each other (Branje, van Aken, & van Lieshout, 2002; Lanz, Tagliabue, & Rosnati, 2004), U.S. medical residents rated clinical mentors (Giblin & Lakey, 2010), and British elite youth athletes rated coaches (Rees, Freeman, Bell, & Bunney, 2012). To summarize, RRT predicts that (a) the main effects between perceived support and mental health reflect ordinary conversation and shared activities primarily, and (b) the

Ordinary Social Interaction

regulation of affect is primarily relational. Previous research suggests that ordinary social interaction plays a role in perceived support, but this research was not designed to test RRT and may not have captured RRT mechanisms. In addition, although perceived support is primarily relational, it is unknown whether ordinary social interaction is primarily relational. Further, it is unknown whether ordinary interaction can account for the relational main effects between perceived support and mental health. Although not predicted by RRT, the present research also investigated the role of consensually supportive providers.1 Stress and coping social support theory implies that there are objectively supportive people and actions (Hobfoll, 2009). Thus, there should be good agreement among observers that some providers are more supportive than others. This assumption is reflected in the design of most social support interventions in which select providers are assigned to recipients (Hogan, Linden, & Najarian, 2002). Presumably, providers are chosen because of their apparent objective supportiveness. Yet, as just described, there is comparatively little interobserver agreement that some providers are more supportive than others. Nonetheless, small provider effects might have disproportionately large effects on mental health. Although previous research has investigated consensually supportive actions (Collins & Feeney, 2000), to our knowledge no research has tested the extent to which consensually supportive providers elicit better affect in recipients compared to less supportive providers.

Overview of the Current Studies The primary goal of the current studies was to test RRT’s hypothesis that ordinary social interaction can explain relational main effects between perceived support and positive affect, as well as low negative affect. Our primary focus was on ordinary conversation. In three round-robin studies, each member of four-person groups rated each other on supportiveness, the quality of their ordinary conversation, and affect experienced when with each participant. The round-robin design permitted the estimation of the strength of relationship, recipient, and provider effects, as well as the estimation of correlations among constructs for each effect. The studies included marines about to deploy to Afghanistan (Study 1), students sharing apartments (Study 2), and strangers participating in brief text conversations (Study 3). If ordinary conversation can explain relational main effects between perceived support and affect, then (1) ordinary conversation should be as strongly relational as is perceived support, (2) relational conversation and support should be highly correlated, (3) relational conversation and support should have the same patterns of correlations with affect, and (4) the main effects among relational affect, perceived support, and perceived ordinary conversation should be mostly redundant. Thus, when perceived ordinary conversation is controlled, perceived support’s links to affect should be mostly eliminated. When perceived support is controlled, ordinary conversation’s links to

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Figure 1 According to RRT, perceptions of affect, perceived support, and ordinary social interaction emerge simultaneously from ordinary social interaction.

affect should be mostly eliminated. We expect redundancy because RRT predicts that perceptions of ordinary conversation, support, and affect emerge simultaneously from actual ordinary social interaction (Figure 1). When people report perceptions of well-established relationships at the same point in time, there is no reason to believe that perceptions of ordinary conversation precede perceptions of support. Analyses that presumed that one construct preceded the others could be misleading. Thus, we predict that perceived conversation and support will be mostly redundant in their main effects with affect. Study 2 also investigated shared activity, permitting tests of the following RRT hypotheses: (5) Shared activity should be as strongly relational as is perceived support, (6) relational shared activity should be strongly correlated with perceived conversation and support, (7) relational shared activity and support should have the same patterns of correlations with affect, and (8) links among relational shared activity, perceived support, and affect should be mostly redundant. Study 2 also assessed social conflict to examine the extent to which ordinary social interaction might merely reflect low conflict. Social conflict has been hypothesized as an explanation for links between perceived support and mental health (Coyne & DeLongis, 1986). According to this view, low perceived support reflects high social conflict, but conflict has the primary effect on mental health. Similarly, interpersonal conflict might also cause any link between poor mental health and low-quality ordinary social interaction. Even though RRT predicts that ordinary conversation is an important cause of perceived support and affect, the designs of Studies 1 and 2 cannot establish the temporal order of the constructs. Study 3 was designed to test the hypothesis that (9) a relational main effect between perceived support and affect will

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emerge when strangers have ordinary brief conversations. By studying strangers in brief interactions, we can determine that their entire acquaintance is composed of ordinary social interaction. Study 3 also tested whether (10) relational links among perceptions of conversation quality, support, and affect will be mostly redundant. Finally, each study also examined the extent to which more consensually supportive providers elicited more favorable affect in recipients than did less supportive providers (i.e., provider effects).

STUDY 1 Method Participants. One hundred U.S. Marine Corps reservists (100% male; 85% European ancestry; median age 5 23) participated prior to their deployment to Afghanistan. Participants were members of one of three infantry platoons. Within platoons, marines were organized into four-member fire teams who shared a particular task. For example, four marines haul and operate a mortar. Thus, fire team members are well acquainted, as they had typically trained together for years. Procedure. Marines rated each other at desks arranged in circles within a large room. Participants were far enough apart that they could not observe each other’s responses to the questionnaires. Each participant wore a name tag displaying his subject number. Each platoon participated in a separate session on a single day at a Marine Reserves center. The measures were perceived support and ordinary conversation, as well as positive and negative affect. Measures. Perceived support was assessed with seven support items from the Quality of Relationships Inventory (Pierce, Sarason, & Sarason, 1991). An example item is, “Can you depend on this person to help you if you really need it?” Internal consistencies2 were .88 (relationship), .90 (provider), and 1.00 (recipient). As indicated by the formulas, a reliability of 1 indicates that there was no Recipient 3 Item variance. Positive and negative affect were assessed with the Positive and Negative Affectivity Schedule (Watson, Clark, & Tellegen, 1988). This 20-item scale yields separate scores for positive affect (PA) and negative affect (NA). Example items are “proud” and “enthusiastic” (positive affect) and “nervous” and “jittery” (negative affect). Internal consistency reliability was .87 (relationship), .99 (provider), and 1.00 (recipient) for positive affect and .93 (relationship), .91 (provider), and 1.00 (recipient) for negative affect. Participants rated affect experienced when interacting with each participant. Ordinary conversation was rated with the Ordinary Conversation Scale, an eight-item questionnaire developed by Travis Sain and Brian Lakey to assess RRT mechanisms. No items refer to conversations about stress and how to cope with it. Items involve subjective evaluations of the quality of

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conversations because the measure was intended to explain responses to perceived support items that are subjective in nature. Originally, 20 items were written, and the scale was shortened by selecting items with the highest loadings on the first principal component across three samples (including Study 1, but not Studies 2 and 3). Beyond the studies presented here, the measure’s construct validity is supported by three studies (Woods, Lakey & Sain, 2015) that found strong correlations between ordinary conversation and perceived support, moderate correlations between ordinary conversation and affect, and low correlations between ordinary conversation and enacted support. Internal consistency reliability in the present sample was .86 (relationship), 1.00 (provider), and .98 (recipient). The items are “I enjoy talking with him because we have interesting conversations that last a long time; It is difficult to find something he and I both want to talk about; It is hard to have a conversation with him because he repeatedly says things that have no relevance to what I am talking about; When we have a conversation, we can go back and forth for as long as we want; My conversations with him usually end quickly; I hardly ever change the subject when talking to him because he always has something interesting to talk about; It is hard to talk with him because he never has anything new to say; I normally forget our conversations soon after they are done.” Statistical Analyses. Statistical analyses proceeded in two stages. First, we estimated the relative strength of relationship, provider, and recipient effects for all constructs. Second, we estimated correlations among constructs for each of these effects. We estimated recipient, provider, and relationship effects with the computer program SOREMO (Kenny, 1998). Odd and even items were aggregated to form two indicators of each construct to distinguish relationship effects from error (Kenny, 1994). We calculated scores for each construct, for each effect, using the definitional formulas in the social relations model with SOREMO, following previous practice (Cook & Kenny, 2004; Kwan, John, Kenny, Bond, & Robins, 2004; Kwan, John, Robins, & Kuang, 2008). Effect scores were then analyzed using conventional correlational and multiple regression analyses in STATA (SOREMO does not calculate multiple regression analyses). We used percentile bootstrapping with 1,000 resamples to calculate statistical significance. We resampled groups rather than individual scores, as groups represent independent observations. Correlations estimated in this way were nearly identical to estimates from SOREMO (Kenny, 1998).

Results and Discussion RRT predicts that the relative strength of relationship, provider, and recipient effects should be similar for perceived support and ordinary conversation. As predicted, there were strong relationship effects for both perceived support and ordinary conversation. The extent to which a recipient saw a provider as supportive, or as eliciting ordinary conversation, was mostly a

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Table 1 Study 1: Proportion of Variance Explained and Standard Errors

Perceived support Relational Provider Recipient Ordinary conversation Relational Provider Recipient Positive affect Relational Provider Recipient Negative affect Relational Provider Recipient

Proportion of Variance

Standard Error

.54* .21* .25*

.08 .07 .10

.67* .10* .23*

.09 .06 .12

.37* .57* .06

.05 .10 .04

.79* .14* .08

.35 .06 .08

Note. The table reports relative variance partitioning from SOREMO, which does not include effects due to items. For simplicity, Provider 3 Item, Recipient 3 Item, and Relationship 3 Item interactions are not reported, but they are available upon request. Median unstable construct variance was .00 (rounded) for recipient, .02 for provider, and .11 for relationship components. *p < .05.

matter of the recipient’s personal taste (Table 1). Significant provider effects indicated that there was some agreement among recipients that some providers were more supportive and elicited better conversation than other providers. Significant recipient effects indicated that some recipients characteristically rated providers as more supportive and as eliciting better conversation than did other recipients, even though recipients rated the same providers. There were also significant relational and provider effects on positive and negative affect. The extent to which a provider elicited affect in a recipient was strongly idiosyncratic to the recipient (i.e., was relational). In addition, some providers elicited better affect in recipients than did other providers, on average, across recipients (i.e., provider effects). There were no significant recipient effects on affect. If ordinary conversation can explain the main effect between perceived support and affect, then (a) relational support and ordinary conversation should be highly correlated and (b) the two constructs should have similar links to affect. As predicted, relational support and ordinary conversation were highly correlated (Table 2). In addition, relational conversation had similar correlations to relational affect, as did relational support. When a provider had unusually good conversations with a recipient, the provider also elicited unusually high positive affect and low negative affect in the recipient. Similarly, when a recipient saw a provider as unusually supportive, the provider also elicited unusually high positive affect and unusually low negative affect in the recipient. According to RRT, perceptions of ordinary interactions, support, and affect emerge simultaneously from actual interactions. If so, then controlling for perceived conversation quality should

mostly eliminate the main effect between perceived support and affect and vice versa. Perceived support accounted for 29% of the variance in positive affect (Table 2), but when ordinary conversation was controlled in multiple regression, this dropped to 6% (b 5 .33; p < .05), a reduction of 79%. Ordinary conversation also accounted for 29% of the variance in positive affect (Table 2), but when perceived support was controlled, this dropped to 5% (b 5 .31; p < .05), a reduction of 83%. A more precise test of RRT is that perceived support’s and perceived conversation’s links to affect are mostly redundant. As predicted, the overlap between perceived support and ordinary conversation in predicting positive affect (R2 5 .23) was approximately four times larger than each variable’s independent link. For low negative affect, perceived support accounted for 19% of the variance, and this dropped to 7% (b 5 –.38; p < .05) when ordinary conversation was controlled, a reduction of 63%. Ordinary conversation accounted for 12% of the variance in low negative affect, and this dropped to 0% (b 5 –.09; ns) when perceived support was controlled. The redundancy between perceived support and ordinary conversation in predicting low negative affect (R2 5 .13) was about twice the size as perceived support’s unique link. An additional goal was to examine whether consensually supportive providers elicited more favorable affect in recipients than did less supportive providers (i.e., provider effects). Although there is relatively little agreement that some providers are more supportive than others, provider effects for perceived support might have especially large impacts on affect. In the present study, consensually supportive providers elicited high positive affect in recipients, but not low negative affect (Table 2). Similarly, providers who were consensually good conversationalists elicited high positive affect but not low negative affect in recipients. Multiple regression analyses indicated that both provider supportiveness (b 5 .23; p < .05) and ordinary conversation (b 5 .35; p < .05) were uniquely linked to recipient positive affect. To our knowledge, this is the first Table 2 Study 1: Correlations Among Constructs

Perceived support Relational Provider Recipient Ordinary conversation Relational Provider Recipient Positive affect Relational Provider Recipient

Ordinary Conversation

Positive Affect

Negative Affect

.70* .42* .80*

.54* .38* ##

–.44* .02 ##

— — —

.54* .45* ##

–.35* –.14 ##

— — —

— — —

–.17 .06 ##

Note. N 5 300 for relationship effects; N 5 100 each for recipient and provider effects. ## indicates that there was no significant variance for a variable in SOREMO, and thus correlations involving that variable were not estimated. *p < .05.

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estimate of the extent to which consensually supportive providers can elicit favorable affect in recipients. Finally, we report correlations for recipient trait effects for completeness. Recipients who characteristically saw providers as supportive also characteristically reported good conversations. There were no significant recipient effects for affect, so correlations were not estimated for those. In summary, the results of Study 1 were mostly consistent with RRT: (1) Perceived support and ordinary conversation were strongly relational, (2) they were strongly correlated, (3) they had the same pattern of correlations with positive and negative affect, and (4) their links to affect were mostly redundant.

STUDY 2 Study 2 attempted to replicate Study 1 with a new sample of well-acquainted participants: college roommates who had been sharing an apartment for several months. Additionally, Study 2 tested RRT hypotheses regarding shared activity and examined whether effects for ordinary social interaction might merely reflect low conflict. RRT predicts that shared activity should (1) be mostly relational, (2) be strongly correlated with relational support and ordinary conversation, (3) have the same pattern of correlations with affect as do relational support and ordinary conversation, and (4) be redundant with relational support’s and ordinary conversation’s links to affect. Recent research found that shared activity acted as predicted by RRT, but the authors did not separate relationship from provider effects (Woods et al., 2015). Study 2 corrected this limitation by using a round-robin design. Finally, Study 2 examined whether ordinary social interaction’s links to affect might only reflect low social conflict. Coyne and DeLongis (1986) hypothesized that social conflict caused the link between low perceived support and poor mental health, and the same might apply to ordinary social interaction.

Method Participants. Sixteen groups of four college students who had shared an apartment for at least 3 months participated (93% female; 91% European ancestry; median age 5 20.) Each participant received $10. Procedure. Participants were recruited with flyers posted on campus and distributed in classrooms. In a laboratory session, each participant sat at a separate table in a large room and rated each other on supportiveness, ordinary conversation, shared activity, conflict, and affect. Each participant wore a name tag displaying her subject number. Participants were far enough apart that they could not observe each other’s responses to the questionnaires. Measures. Participants completed the same measures of affect and ordinary conversation as in Study 1. Internal consistency

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reliability was .93 (relationship), .98 (provider), and .63 (recipient) for ordinary conversation; .85 (relationship), .98 (provider), and .90 (recipient) for positive affect; and .87 (relationship), 1.00 (provider), and 1.00 (recipient) for negative affect. Perceived social support was assessed with 12 items from the Social Provisions Scale (Cutrona & Russell, 1987), which has established reliability and validity. An example item is “Does your relationship with your roommate provide you with a sense of emotional security and well-being?” Internal consistency reliability was .89 (relationship), .99 (provider), and .95 (recipient). Shared activity was assessed with the eight-item Shared Activity Scale (Woods et al., 2015). Shared activity has been linked to perceived support, ordinary conversation, high positive affect, and low negative affect (Woods et al., 2015), although previous work has not isolated relationship from provider effects. An example item is “We always find something to do that we can both get enjoyment from.” Internal consistency reliability was .80 (relationship), .98 (provider), and .73 (recipient). Conflict was measured with nine items from the Quality of Relationships Inventory (QRI; Pierce et al., 1991). This scale has shown good construct validity and reliability and is widely used in research. An example item is “How much do you argue with this person?” Internal consistency reliability was .83 (relationship), 1.00 (provider), and .00 (recipient). The zero indicates that there was no recipient variance.

Results and Discussion First, we attempted to replicate Study 1’s findings. Perceived support and ordinary conversation were strongly relational (Table 3) and were strongly correlated (Table 4). Whether a recipient saw a provider as supportive or as eliciting quality conversation was strongly a matter of the recipient’s personal taste. When a recipient saw a provider as eliciting unusually good conversation, the recipient also saw the provider as unusually supportive (Table 4). Relational support and ordinary conversation were linked to positive affect, whereas only relational support was linked to low negative affect. A provider who elicited unusually positive affect in a recipient was seen as unusually supportive and as eliciting unusually good conversation. A provider who was seen as unusually supportive elicited low negative affect. Shared activity was also strongly relational (Table 3). Whether a recipient saw a provider as sharing high-quality activity was strongly idiosyncratic to the recipient. Relational shared activity was linked to relational support and ordinary conversation (Table 4). A provider who shared unusually high-quality activity with a recipient was seen as unusually supportive and as eliciting high-quality conversation. A provider who elicited unusually high-quality shared activity in a recipient elicited unusually high positive affect, but not low negative affect, in the recipient.

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Table 3 Study 2: Proportion of Variance Explained and Standard Errors

Perceived support Relational Provider Recipient Ordinary conversation Relational Provider Recipient Shared activity Relational Provider Recipient Conflict Relational Provider Recipient Positive affect Relational Provider Recipient Negative affect Relational Provider Recipient

Proportion of Variance

Standard Error

.52* .32* .16

.14 .14 .12

.67* .30* .04

.12 .16 .09

.52* .28* .20

.12 .14 .12

.84* .14 .03

.25 .15 .18

.41* .23* .36*

.08 .10 .10

.86* .07 .07

.32 .07 .05

Note. The table reports relative variance partitioning from SOREMO, which does not include effects due to items. For simplicity, Provider 3 Item, Recipient 3 Item, and Relationship 3 Item interactions are not reported, but they are available upon request. Median unstable construct variance was .02 for recipient, .01 for provider, and .11 for relationship components. *p < .05.

RRT predicts that most of relational support’s links to affect should be redundant with ordinary social interaction. When ordinary conversation and shared activity were controlled, perceived support’s link to positive affect dropped from explaining 35% of the variance (Table 4) to explaining 3% (b 5 .22; p < .05), a reduction of 91%. Consistent with RRT and Study 1, multiple regression analyses indicated that most of (37%) the three constructs’ links to positive affect overlapped, but that ordinary conversation (b 5 .22; p < .05) and shared activity (b 5 .32; p < .05) had their own unique links. We did not conduct multiple regression analyses for negative affect, as only perceived support was linked to low negative affect. This was inconsistent with Study 1, as well as with RRT. There was substantial inter-rater agreement among recipients that some providers were more supportive than others (i.e., provider effects; Table 3), and the size of the effect was larger than in previous studies (Lakey, 2010). Consensually supportive providers elicited more positive affect in recipients than did less supportive providers, but not less negative affect (Table 4). There were also significant provider effects for ordinary conversation and shared activity. Some providers were consensually better conversationalists than other providers, and some providers consistently engaged recipients in more shared activity. Providers who were consensually good conversationalists or engaged recipients in more activity also elicited high positive affect, but not low negative affect. Multiple regression analyses indicated that both ordinary conversation (b 5 .39; p < .05) and shared activity (b 5 .42; p < .05) had significant unique links to positive affect, but perceived support did not (b 5 .08). An additional goal of Study 2 was to examine the extent to which relational conflict could account for ordinary social

Table 4 Study 2: Correlations Among Constructs

Perceived support Relational Provider Ordinary conversation Relational Provider Shared activity Relational Provider Conflict Relational Provider Positive affect Relational Provider Negative affect Relational Provider

Ordinary Conversation

Shared Activity

Conflict

Positive Affect

Negative Affect

.63* .86*

.69* .82*

–.20 ##

.59* .76*

–.32* ##

— —

.68* .77*

–.10 ##

.58* .78*

–.08 ##

— —

–.10 ##

.63* .78*

–.13 ##

— —

–.16

.22*

— —

–.00 ##

— —

— —

Note. N 5 192 for relationship effects; N 5 64 for provider effects. Only one variable had significant recipient effects, so correlations for recipient effects were not estimated. ## indicates that there was no significant variance for a variable in SOREMO, and thus correlations involving that variable were not estimated. *p < .05.

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interaction’s links to affect. Conflict was almost exclusively relational (Table 3), and the provider who elicited unusually high conflict in a recipient also elicited unusually high negative affect. In addition, relational conflict was not significantly linked to ordinary conversation and shared activity, and thus it could not explain ordinary social interaction’s links to affect. Strong relationship effects on conflict are consistent with other research on aggression among children (Coie et al., 1999) and family negativity (Cook, Kenny, & Goldstein, 1991). In summary, ordinary social interaction was strongly relational and was strongly linked to relational support, but not to conflict. Further, ordinary social interaction was linked to positive affect and overlapped with most of perceived support’s link to positive affect. These findings replicated results for ordinary conversation in Study 1. However, unlike Study 1, and contrary to RRT, relational ordinary social interaction was not linked to low negative affect.

STUDY 3 RRT predicts that ordinary conversation is an important cause of perceived support and affect, but Studies 1 and 2 could not determine their temporal order. Perceived support might develop first and cause perceptions of conversation. To address this limitation, Study 3 participants were strangers whose acquaintance was based entirely on participation in the study, which consisted primarily of a brief text conversation. If ordinary conversation causes perceived support and affect, Study 3 should observe (a) strong relational effects on perceived support and perceived ordinary conversation, as well as (b) significant relational links among perceived support, perceived ordinary conversation, and favorable affect. Text conversations have the additional advantage of providing a transcript of each conversation. This allows independent observers to confirm participants’ perceptions of high-quality, ordinary conversation. Study 3 also investigated the role of the perceived similarity of providers to recipients. One of the best markers that a recipient will see a provider as supportive is the extent to which the recipient sees the provider as similar to herself in attitudes and values (Lakey & Orehek, 2011). According to RRT, perceived similarity and support are linked because similarity marks the extent to which dyad members like the same activities and conversation topics. If so, relational perceived similarity should be linked to ordinary conversation, as well as to perceived support and affect.

Method Participants. Twelve groups of four students (71% female; 75% of European ancestry; median age 5 18) enrolled in introductory psychology participated and received partial course credit. Procedure. Students enrolled in introductory psychology signed up for the study using online software. Students

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assembled in a hallway and were ushered into a small room containing computer cubicles. After participants introduced themselves to each other, each participant rated her acquaintance with the other participants. For this purpose, each participant wore a name tag displaying her subject number. Next, each participant completed demographic forms and wrote a list of 4–5 preferred conversation topics. Then each participant was seated in a cubicle with a personal computer and had a 10-minute text conversation with another participant via Yahoo Instant Messenger. The order of conversation partners was randomized. To guide the interaction, each participant was shown her partner’s list of preferred topics before each conversation. After each conversation, participants rated the supportiveness and similarity of her conversation partner, the quality of the conversation, and affect elicited during the conversation. This sequence was repeated until each participant had a conversation with every other participant. An electronic transcript of each conversation was saved by the experimenter. If any participant reported that she had had a conversation with any other participant before the experiment, the entire group’s data were excluded from the current analyses. That is, to be included in the analyses, both dyad members had to report that they had never had a conversation. The data from eight of the original 20 groups were discarded for this reason. The results from the discarded groups were essentially identical to the results for the retained groups. Measures. Participants rated ordinary conversation and affect with regard to the text conversation using the same measures as in Studies 1 and 2. Internal consistency reliability was .80 (relationship), 1.00 (provider), and .93 (recipient) for ordinary conversation; .71 (relationship), 1.00 (provider), and .91 (recipient) for positive affect; and .64 (relationship) and .00 (recipient) for negative affect. Reliability could not be calculated for provider effects for negative affect, as provider and Provider 3 Item variances were zero. Perceived provider supportiveness was assessed with seven items from the Social Provisions Scale (Cutrona & Russell, 1987), as modified by Neely et al. (2006) for use with brief conversations among strangers. An example item is “I had a sense of emotional security and well-being with this person.” Internal consistency reliability was .72 (relationship), 1.00 (provider), and .71 (recipient). Each recipient rated the perceived similarity of each provider in attitudes and values with the six-item measure from Lakey et al. (2002). Internal consistency reliability was .88 (relationship), .82 (provider), and .85 (recipient). An example item is “This person is similar to me in values.” Finally, five observers rated each conversation for quality using a modified version of the Ordinary Conversation Scale. Observers were blind to participants’ status on all other measures. Inter-rater reliability was .68, and internal consistency of the items was .98. For rater data, we used SOREMO to calculate relational scores for each dyad, and these scores were used in the main statistical analyses. Raters did not attempt to distinguish

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Table 5 Study 3: Proportion of Variance Explained and Standard Errors

Perceived support Relational Provider Recipient Ordinary conversation Relational Provider Recipient Perceived similarity Relational Provider Recipient Positive affect Relational Provider Recipient Negative affect Relational Provider Recipient

Proportion of Variance

Standard Error

.62* .10 .29*

.13 .10 .10

.70* .14* .16

.13 .07 .17

.56* .08 .37*

.10 .09 .13

.36* .06* .58*

.08 .03 .22

.79* .00 .21

.21 .00 .18

Note. The table reports relative variance partitioning from SOREMO, which does not include effects due to items. For simplicity, Provider 3 Item, Recipient 3 Item, and Relationship 3 Item interactions are not reported, but they are available upon request. Median unstable construct variance was .10 for recipient, .00 (rounded) for provider, and .24 for relationship components. *p < .05.

between each member of a dyad, so we did not estimate recipient and provider scores.

Results and Discussion As predicted, on the basis of mere 10-minute text conversations among strangers, large relationship effects emerged for perceived support (Table 5). Further, there was a main effect between relational support and positive affect, as well as low negative affect (Table 6). When a recipient saw a provider as unusually supportive, the provider elicited unusually high positive and low negative affect in the recipient. As predicted, recipients’ perceptions of the quality of ordinary conversation were mostly relational and were linked to relational support and positive affect (Table 5). When a provider elicited unusually highquality conversation in a recipient, the provider was seen as unusually supportive and elicited unusually positive affect (Table 6). Multiple regression analyses indicated that perceived relational support’s link to positive affect was mostly shared with perceived ordinary conversation. When ordinary conversation was controlled, perceived support’s link to positive affect was reduced from accounting for 22% of the variance (Table 6) to 3% (b 5 .20; p < .05), a reduction of 86%. Together, the two constructs accounted for 40% of relational positive affect. Nearly half (19%) of this effect was shared between perceived support and ordinary conversation. A similarly large portion

reflected ordinary conversation’s unique link (b 5 .50; DR2 =.18; p < .05). In contrast to RRT predictions, perceived relational ordinary conversation was not linked to low negative affect. Perceived relational similarity was a strong marker for perceived support and ordinary conversation (Table 6). When a recipient saw a provider as unusually similar to the recipient, the recipient saw the provider as unusually supportive and as eliciting unusually high-quality conversation. These findings are consistent with RRT’s explanation that the link between perceived support and similarity reflects the extent to which dyad members like the same conversation topics. In addition, a provider seen as unusually similar elicited unusually high positive affect and low negative affect in a recipient. Woods et al. (2015) also found that similarity was a good indicator of ordinary conversation, as well as positive and low negative affect. Finally, when observers rated a conversation as high in quality, the recipient perceived the provider to be supportive, and as similar to herself in attitudes and values. There were marginally significant links between observer ratings of conversation quality and recipients’ ratings of conversation quality, as well as recipients’ high positive and low negative affect (Table 6).

GENERAL DISCUSSION The studies presented here are among the first to test RRT’s predictions that the main effect between perceived support and affect reflects ordinary conversation primarily. According to RRT, people regulate their affect, action, and thought on a moment-by-moment basis through ordinary conversation. Over time, these interactions lead to enduring differences in mental health, including mental disorders. Moreover, who and what regulates a recipient well is primarily a matter of the recipient’s personal tastes (i.e., is relational). RRT does not rely upon stress and coping processes. Although hypotheses about stress buffering effects can be derived from RRT, the theory was developed to apply to main effects. The current findings were highly consistent with RRT for positive affect. In each study, ordinary conversation was strongly relational and had strong main effects with relational support and positive affect. Furthermore, when ordinary conversation was controlled, perceived support’s main effects with positive affect were greatly reduced. In fact, most of perceived support’s and ordinary conversation’s main effects with positive affect were redundant with each other’s. In Study 3, ordinary conversation preceded the development of relational support, and support’s main effect on affect emerged from mere 10minute text conversations among strangers. We assume that the situational constraints of Study 3 limited participants to only ordinary conversations. Inspection of the transcriptions indicated that no participants talked about stress and how to cope with it. Study 2 also assessed shared activity. Shared activity was strongly relational and was linked to relational support and ordinary conversation. When shared activity and conversation

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Table 6 Study 3: Correlations Among Constructs

Perceived support Relational Provider Recipient Ordinary conversation Relational Provider Recipient Perceived similarity Relational Provider Recipient Positive affect Relational Provider Recipient Negative affect Relational Provider Recipient Observer-rated ordinary conversation Relational Provider Recipient

Ordinary Conversation

Perceived Similarity

Positive Affect

Negative Affect

Observer- Rated Conversation Quality

.54* ## ##

.66* ## ##

.47* ## ##

–.28* ## ##

.23*

— — —

.58* ## .67*

.61* .69* .52*

–.01 ## ##

.18†

— — —

.70* ## .23

–.37* ## ##

.26*

— — —

–.21 ## ##

.20†

— — —

–.25†

— — —

Note. N 5 144 for relationship effects; N 5 48 each for recipient and provider effects. ## 5 indicates that there was no significant variance for a variable in SOREMO, and thus correlations involving that variable were not estimated. 5 Not applicable because observers rated ordinary conversation for the dyad, which did not permit isolating recipient and provider effects. † *p < .05. p < .10.

quality were controlled, perceived support’s main effect with positive affect was greatly reduced. Most of shared activity’s links to positive affect were redundant with perceived support’s and ordinary conversation’s links. The current studies’ findings regarding positive affect are replicated in three additional studies (Woods et al., 2015). In two studies, participants rated their parents and closest peers. This design combines relational and provider effects into a single social influence, but the vast majority of the social influence reflects relationship effects (Lakey & Orehek, 2011). Socially influenced conversation quality and shared activity had the same pattern of links with positive affect as did perceived support. When ordinary conversation was controlled, the main effect between perceived support and positive affect was mostly eliminated. In a third study, strangers played video games together and then rated each other on support, affect, and conversation. The main effect between perceived support and positive affect emerged in this study, even though the task allowed for very little opportunity for conversation. Thus, across the current studies and Woods et al., there is strong evidence that ordinary social interaction can explain most of the main effect between perceived support and positive affect. For negative affect, the current findings were less consistent with RRT. Relational support was linked to low negative affect in all studies, but recipient-rated ordinary conversation was

linked to low negative affect only in Study 1. These findings might mean that RRT does not apply well to negative affect. Another possibility is that the link between ordinary conversation and low negative affect emerges primarily in well-established relationships. This hypothesis is suggested by two studies of participants’ relationships with their parents and closest peers described in the previous paragraph (Woods et al., 2015). These studies found strong links between ordinary conversation and low negative affect, and when ordinary conversation was controlled, most of the main effect between perceived support and low negative affect was eliminated. Thus, counting the current studies and Woods et al. (2015), three of four studies of wellestablished relationships found main effects between conversation quality and low negative affect. In addition, both studies of strangers in the current studies and Woods et al. (2015) found no significant links between conversation quality and low negative affect. These findings suggest that ordinary conversation’s link to low negative affect might emerge only in well-established relationships. In some relationships, conversation might become increasingly irritating over time. Such irritation could reflect repetition whereby some people say the same things over and over. Irritation could also emerge later in relationships because people withhold potentially irritating opinions and information in earlier stages of relationships. Current research findings cannot yet resolve the issue, and additional studies are needed.

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In the current studies, as well as in Woods et al. (2015), there remained significant residual main effects between perceived support and positive as well as low negative affect, when ordinary social interaction was controlled. This indicates that there is something about the belief that others would help that goes beyond ordinary social interaction. How does this work? One possibility is that stress buffering through enacted support creates confidence in one’s friends and family that endures beyond the time of stress and directly influences affect in the absence of stress (i.e., a main effect). This residual effect might also reflect capitalization support in response to positive events, which has been shown to be linked to perceived support (Gable et al., 2012). Another possibility derived from RRT is that residual perceived support reflects a belief in the loyalty of friends and family to be consistently available to help regulate a recipient through ordinary social interaction. For example, we can count on a loyal friend to attend our dinners and parties, as well as accompany us to movies, sporting events, or concerts. Consistent with previous research, there was comparatively modest agreement among recipients that some providers were more supportive than others (i.e., provider effects). When participants knew each other well (Studies 1 and 2), the weighted average provider effect was .25. This value is larger than the meta-analytic estimate of .07 (Lakey, 2010) and is consistent with inter-observer agreement for personality (Branje, van Aken, van Lieshout, & Mathijssen, 2003; Kenny, Albright, Malloy, & Kashy, 1994; Park, Kraus, & Ryan, 1997). Although provider effects seem modest compared to relationship effects, provider effects could have disproportionately large influences on mental health. Yet, in the current studies, the main effect between support and positive affect for provider effects was about the same size as the main effect for relationship effects. In addition, there were no links between perceived support and low negative affect for provider effects. Thus, the relatively modest size of provider effects for perceived support was not offset by unusually strong links to affect. Consistent with RRT and previous findings, there were strong relationship effects on perceived support. When recipients knew providers well (Studies 1 and 2), the weighted average effect size was .53, comparable to the meta-analytic estimate of .62 (Lakey, 2010). Our study involving strangers provided a very similar estimate. Ordinary conversation and shared activity had similarly sized relationship effects. Thus, a provider’s supportiveness and the extent to which he or she elicits highquality, ordinary social interaction is strongly a matter of the recipient’s personal taste. There were also strong relationship effects on positive and negative affect. Positive and negative affect are not only traitlike, but they also vary strongly depending upon with whom one interacts. Negative affect was strongly relational, replicating Ingraham and Wright’s (1987) research. The provider who elicited negative affect in one recipient would not typically elicit negative affect in other recipients. For positive affect, the size of provider and relationship effects was similar. Some providers consistently elicited high positive affect in all recipients studied,

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but recipients also differed substantially in how they reacted to the same providers. Perceived support also partly reflected a trait-like tendency to see all providers as supportive, a well-established finding (Lakey, 2010; Lakey & Cassady, 1990). When recipients knew providers well (Studies 1 and 2), recipient effects accounted for 20% of perceived support, which compares with the metaanalytic estimate of 27% (Lakey, 2010). Similar effects were observed for our study of strangers. Ordinary conversation was significantly trait-like in only one of three studies, and shared activity had no significant trait variance in Study 2. Woods et al. (2015) also found no significant trait effects for ordinary conversation or shared activity. Thus, lacking a strong trait-like component seems to be one way in which ordinary social interaction differs from perceived support.

LIMITATIONS AND IMPLICATIONS The current studies are not without limitations. (a) Participants were young people in each study and were college students in two of the three. Findings might differ for older participants. (b) In two studies, we did not verify that links between perceived support and ordinary conversation actually reflected social interaction. Recipients might have inferred support and conversations from previously made personality impressions (Klein, Loftus, Trafton, & Fuhrman, 1992). (c) RRT predicts the specific aspects of conversations that lead to favorable affect and perceived support, but these aspects were not assessed. (d) The temporal ordering of ordinary conversation and support judgments was determined only in Study 3. In Studies 1 and 2, we cannot rule out the possibility that ordinary conversation was inferred from previously made support judgments. (e) In Studies 1 and 2, we could not rule out that ordinary conversation was inferred from the receipt of enacted support. Yet Study 3 ruled out the role of enacted support by studying strangers, and Woods et al. (2015) found that enacted support could not explain effects for ordinary conversation. RRT and the current findings have implications for designing social support interventions. Most social support interventions have assigned support providers to at-risk recipients (Hogan et al., 2002). The providers are typically assumed to be consensually supportive, in that most recipients will find the assigned providers to be supportive. In addition, the goal of most providers is to offer enacted support that will buffer the effects of stress. Unfortunately, when subjected to randomized controlled trials, such interventions have not been as effective as hoped (Hogan et al., 2002). RRT offers an alternative approach. RRT predicts that it will be more effective to match specific providers with specific recipients such that unusually supportive relationships emerge. In addition, if the goal of the intervention is to boost mental health through main effects, support providers should create opportunities for ordinary conversation and shared activities.

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CONCLUSION

References

In conclusion, the present studies were consistent with RRT’s predictions that the main effect between perceived support and positive affect primarily reflects ordinary social interaction. As proposed by Thoits (1985) 30 years ago, “aspects of regularized social interaction and not emotional support dimensions per se, are responsible for maintaining well-being. What we recognize as dimensions of emotional support and main effects of support are simply byproducts of these more abstract socialpsychological processes” (pp. 57–58). To this we add that the interaction partners who maintain well-being are primarily a matter of the recipient’s personal tastes (i.e., is relational). Finally, we emphasize that we do not advocate discarding perceived support and replacing it with ordinary social interaction. The main effect between perceived support and mental health is strong and well replicated. It is a defining empirical finding in the field. Rather, we seek to better clarify the types of psychological processes that give rise to this effect.

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Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Justin Andrews was supported by the Ronald E. McNair Postbaccalaureate Achievement Program (U.S. Department of Education). Notes 1. In our team’s previous work, we used the term objectively supportive for consensually supportive. 2. Internal consistency formulas were ar 5 r2r/[r2r 1 (r2rxi/ni)] for recipient effects, ap 5 r2p/[r2p 1 (r2pxi/ni)] for provider effects and arel 5 r2rel/[r2rel 1 (r2relxi/ni)] for relationship effects, for which r indicates recipients, p indicates providers, rel indicates relationships, i indicates items, and ni indicates the number of items. There are two items in these analyses: the mean of even- and odd-numbered items. Interactions involving items are the same as unstable construct variance in the SRM (Bonito & Kenny, 2010). We did not use the reliability formulas for recipient (actor) and provider (partner) effects in Bonito and Kenny (2010), as their formulas included generalizability across both items and relationships, and we were only interested in generalizability across items—the conventional focus of internal consistency reliability. The internal consistency reliability formula for relationship effects is the same as Bonito and Kenny (2010).

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