Older participants reported smaller social networks, largely because of reporting fewer peripheral others; yet older age was associated with better well-being

Age differences in reported social networks and well-being. Bruine de Bruin, Wändi, Parker, Andrew M., Strough, JoNell. Psychology and Aging, Nov 07, 2019. https://psycnet.apa.org/buy/2019-64493-001

Abstract: Social networks can consist of close friends, family members, and neighbors as well as peripheral others. Studies of social networks and associations with well-being have mostly focused on age-restricted samples of older adults or specific geographic areas, thus limiting their generalizability. We analyzed 2 online surveys conducted with RAND’s American Life Panel, a national adult life span sample recruited through multiple probability-based approaches. In Survey 1, 496 participants assessed the sizes of their social networks, including the number of close friends, family members, neighbors, and peripheral others. Of those, 287 rated their social satisfaction and well-being on Survey 2. Older participants reported smaller social networks, largely because of reporting fewer peripheral others. Yet older age was associated with better well-being. Although the reported number of close friends was unrelated to age, it was the main driver of well-being across the life span—even after accounting for the number of family members, neighbors, and peripheral others. However, well-being was more strongly related to social satisfaction than to the reported number of close friends—suggesting that it is the perception of relationship quality rather than the perception of relationship quantity that is relevant to reporting better well-being. We discuss implications for social network interventions that aim to promote well-being.

Check also Older age was correlated to better scores on each of the four financial decision‐making measures, and has more experience‐based knowledge, & less negative emotions about financial decisions (both of which are particularly helpful for better financial decision-making):

Age differences in financial decision making: The benefits of more experience and less negative emotions. Wiebke Eberhardt, Wändi Bruine de Bruin, JoNell Strough. Journal of Behavioral Decision Making, https://www.bipartisanalliance.com/2018/08/older-age-was-correlated-to-better.html

And Age differences in moral judgment: Older adults are more deontological than younger adults. Simon McNair, Yasmina Okan, Constantinos Hadjichristidis, Wändi Bruine de Bruin. Journal of Behavioral Decision Making, https://www.bipartisanalliance.com/2018/06/age-differences-in-moral-judgment-older.html

DISCUSSION

In a national adult life-span sample, we found support for four predictions from the
conceptual framework provided by the Convoy Model (Antonucci et al., 2013) and Socio
emotional Selectivity Theory (Carstensen, 2006), pertaining to age differences in social
networks, as well as associations with social satisfaction and well-being across the life span.
First, we found that older adults had smaller social networks than younger adults, but that the
number of close friends was unrelated to adult age. Younger adults had especially large
social networks consisting of mostly peripheral others, perhaps because online social
networking sites have facilitated the maintenance of increasingly large and mostly impersonal
social networks (Chang et al., 2015; Ellison, Steinfeld, & Lampe, 2007; Manago, Taylor, &
Greenfield, 2012; Valenzuela, Park, & Kee, 2009; Yu et al., 2018). Yet, our findings from
this national adult life span sample are consistent with previous observations in the offline
social networks of San Francisco Bay area residents (English & Carstensen, 2014; Fung et
al., 2001) and US residents before the widespread use of the internet (Morgan, 1988), as well
as more recent observations in the online social networks of Facebook users (Chang et al.,
2015; Yu et al., 2018). Additionally, older age in our national adult life-span sample was
associated with reporting social networks that included fewer family members and more
neighbors. A review of studies with older adults suggested that friends and neighbors may be
more important than family members kp"qnfgt"cfwnvuÓ"uqekcn"pgvyqtmu for promoting well
being (Pinquart & Sörensen, 2000). In older West Berlin residents, close friends and
neighbors were found to take over social and instrumental support functions to replace
unavailable family members (Lang & Carstensen, 1994).
Second, qnfgt"cfwnvuÓ"smaller networks did not appear to undermine their social
satisfaction or well-being. Although the two measures were highly correlated, reports of
social satisfaction were unrelated to age while reports of well-being increased with age.
Age differences in social networks 16

Other studies that have also suggested that life satisfaction and well-being tend to be
preserved or improve with older age (Carstensen et al., 2000, 2011; Charles et al., 2001;
Kessler & Staudinger, 2009).
Third, the reported number of close friends was associated with reported social
satisfaction and reported well-being across the adult life span. The relationship between the
number of close friends and well-being held even after accounting for the number of family
members, neighbors, and peripheral others Î which were not additionally associated with
well-being. The relationship of the reported number of close friends with greater social
satisfaction and well-being did not vary with age, suggesting the importance of close
friendships across the life span. This finding is consistent with observed patterns among
Facebook users, who reported greater well-being if they perceived more Òactual friendsÓ in
their online social networks (Chang et al., 2015). However, in the off-line social networks of
San Francisco Bay area residents (Fung et al., 2001), there was some evidence that reporting
more close friendships was related to lower happiness among younger adults, in line with the
idea that close relationships can also be emotionally taxing (Birditt et al., in press; Hartup &
Stevens, 1999). Indeed, younger adults report more problems and negative interactions in
their close social relationships as compared to older adults (Akiyama, Antonucci, Takahashi,
& Langfahl, 2003; Birditt et al., in press; Schlosnagle & Strough, 2017), which may partially
explain why we found that younger adults reported lower well-being despite having similar
numbers of close friends as older adults.
Our fourth main finding is that the reported number of close friends no longer
predicted well-being after taking into account the significant relationship between social
satisfaction and well-being. Thus, the quality of close friendships seems more important than
their quantity, for promoting well-being. Our analyses of a national adult life-span sample
confirmed patterns that had been observed in studies with age-restricted samples of older dults (Cornwell & Waite, 2009; Pinquart & Sörensen, 2000), and with a geographically
restricted adult life span sample recruited from the San Francisco Bay area (Fung et al.,
2001).
Our combined findings suggest support for a conceptual framework consisting of the
Convoy Model (Antonucci et al., 2013) and Socio-emotional Selectivity Theory, which
predicts smaller social networks of emotionally close relationships in older age, with benefits
to well-being. The Convoy Model posits that these age differences in social network size and
composition reflect age differences in personal and situational factors (Antonucci et al.,
2013). However, all findings held despite taking into account potential age differences in
self-reported health, income, and demographics. Possibly, age differences in other
unmeasured factors may have played a role. Socio-emotional Selectivity Theory suggests
that older adults may make intentional choices about their social networks, so as to optimize
emotional experiences (English & Carstensen, 2014). Although our secondary analyses can
not provide direct insight into the deliberate nature of age-related changes in centering social
networks more on emotionally gratifying close relationships, findings from the Berlin Aging
Study have shown that the main reason for discontinuing relationships in older adulthood
may be a lack of interest rather than lack of opportunity (Lang, 2000). Moreover, a survey of
a national adult life span sample revealed that younger, not older, people reported wishing
they had more friends (Lansford, Sherman, & Antonucci, 1998). Yet, our findings also
suggest that, as compared to younger adults, older adults count more neighbors among their
social contacts, which was unrelated to their social satisfaction and well-being. Thus, not all
of older adults' social contacts may be deliberately selected (or avoided) to promote better
well-being.
 One limitation of our research is its cross-sectional correlational nature, which
precludes conclusions about causality or developmental changes with age. Additionally, did not have access to participants' actual social networks. It is possible that younger adults
exaggerated their reported social networks, or that older adults underestimated theirs.
However, our findings suggest that these perceptions of social networks are relevant to later
reports of social satisfaction and well-being as provided on a separate survey. Another
potential limitation is that, despite relatively good response rates, our national life span
sample may have had limited representativeness due to selection effects. Although our
demographic control variables were in line with those in the literature on age differences in
social networks (e.g., Chang et al., 2015; Lang & Carstensen, 1994; Morgan, 1988), it is
possible that unmeasured variables such as personality characteristics may have contributed
to our findings.
Furthermore, the surveys we analyzed did not ask participants to distinguish between
social contacts who were maintained online or face to face. There may have been age
differences in the number of contacts maintained online or face-to-face with younger adults
maintaining especially large online social networks with many peripheral others (Chang et
al., 2015; Ellison, Steinfeld, & Lampe, 2007; Manago et al., 2012; Valenzuela, Park, & Kee,
2009; Yu et al., 2018). However, distinguishing between online and face-to-face contacts
may not actually be possible, because online communications are typically used to
supplement face-to-face and telephone communications with existing social contacts (Bargh
& McKenna, 2004; Wellman, Haase, Witte, & Hampton, 2001). Moreover, the importance
of friendships for well-being has been reported in studies of off-line social networks and
online social networks (e.g., Fung et al., 2001; Chang et al., 2015). While the nature of
friendships and time spent face to face may change over the life span, their social meaning
and importance to well-being does not (Hartup & Stevens, 1999).
Our findings suggest that interventions that aim to improve well-being may benefit
from helping recipients to foster close social relationships. Such interventions may require different approaches among older adults, as compared to younger adults. Indeed, developing
effective interventions requires a deeper understanding of those issues that audience members
need and want to have addressed (Bruine de Bruin & Bostrom, 2013). For example, older
adults may be most interested in interventions that help them to maintain their existing close
friendships. As noted by Fung et al. (2001), older people may actively resist encouragements
to increase their social networks through senior centers or visitation programs, because
meeting new people may no longer be as important to them (see also Carstensen & Erickson,
1986; Korte & Gupta, 1991). Rather, older adults may be better able to reduce feelings of
loneliness when being provided with internet and computer training (Choi, Kong, & Jung,
2012), perhaps because it helps them to stay in touch with those social contacts they care
most about (McAndrew & Jeong, 2012; Thayer & Ray, 2006).
Younger adults, on the other hand, may be most interested in growing their social
networks, but may benefit from learning how to do so while avoiding problems with their
friendships and draining their emotional resources (Birditt et al., in press; Hartup & Stevens,
1999; Schlosnagle & Strough, 2017). Pro-social interventions may be able to help younger
adults to grow their social networks in a positive manner: Pre-adolescents who were asked to
engage in three acts of kindness (vs. to visit three places) increased their popularity among
peers as well as their well-being (Layous, Nelson, Oberle, Schonert-Reichl, Lyubomirsky,
2012).
Moreover, a review of interventions that targeted lonely adults of all ages suggested
that providing cognitive behavioral therapy that aimed to improve maladaptive social
cognitions (or heightened negative attention to social threats, which exacerbate feelings of
sadness and loneliness) may be more effective than social activity interventions (Masi, Chen,
Hawkley, & Cacioppo, 2011). A review of interventions that promote the self-expression of
gratitude has suggested a beneficial effect on feelings of social connectedness and well-being (Armenta, Fritz, & Lyubomirsky, 2016). Indeed, our findings suggest that, across the life
span, satisfaction with social relationships may be more important than the quantity of close
friends, for promoting well-being.

Why Boredom Is Interesting

Why Boredom Is Interesting. Erin C. Westgate. Current Directions in Psychological Science, November 8, 2019. https://doi.org/10.1177/0963721419884309

Abstract: Is boredom bad? It is certainly common: Most everybody gets bored. There is a sense that boredom sometimes causes bad things to happen (e.g., substance use, self-harm) and sometimes causes good things to happen (e.g., daydreaming, creativity), but it is hard to understand what boredom does without first understanding what it is. According to the meaning-and-attentional-components (MAC) model of boredom and cognitive engagement, the emotion of boredom signals deficits in attention and meaning. Much like pain, it may not be pleasant, but boredom critically alerts us that we are unable or unwilling to successfully engage attention in meaningful activities. Whether that is good or bad rests ultimately on how we respond.

Keywords: boredom, meaning, attention, motivation, emotion

When a Russian man stole an army tank and drove it into a local supermarket (Kiryukhinia & Coleman, 2018), you would have been forgiven for thinking he had good reason. Nope, reported journalists: He was just bored.
Tales of bored troublemakers abound. From the odd— bored shopworkers cremating a mouse (“‘Bored’ Workers ‘Cremated Mouse,’” 2019)—to the disturbing—an Irishman caught aiming his pellet gun at drivers (Ferguson & McLean, 2019)—these news stories appear regularly, and the explanation “I was bored” resonates and perplexes. What is it about boredom that drives people to steal military equipment, watch movies on the job, and lay mice to rest? Is boredom really that nefarious?
It is certainly common: Most everybody gets bored (e.g., Chin, Markey, Bhargava, Kassam, & Loewenstein, 2017). Boredom is especially common at work, where it is linked to productivity loss and burnout (Fisher, 1993). It is also common in schools: Students get bored, and bored students do not do very well (Pekrun, Goetz, Daniels, Stupnisky, & Perry, 2010). Indeed, there is growing suspicion that boredom lies behind many socially destructive behaviors, including self-harm, compulsive gambling, and substance use (Mercer & Eastwood, 2010; Weybright, Caldwell, Ram, Smith, & Wegner, 2015). Yet, at the same time, there are calls from public intellectuals for people to experience more boredom in the belief that it leads to greater well-being (Paul, 2019). Who is right? To understand when boredom is good (and when it is bad), we first need to understand what boredom is.
Attention and Meaning: Boredom’s Key Ingredients
If you are reading this, you have almost certainly had the lamentable experience of reading a boring article. We all know the feeling: Dread and irritation build, your mind wanders, you check the clock and remaining page count, or even surrender and sneak a glimpse at your phone. In short, you are bored. But why? There could be something amiss with the environment—too much constraint or too little stimulation or arousal (Berlyne, 1960). According to attentional theories, such environmental features foster understimulation that makes it difficult to focus (Csikszentmihalyi, 2000; Eastwood, Frischen, Fenske, & Smilek, 2012). There is excellent evidence that difficulty paying attention translates into feelings of boredom and that understimulation can cause inattention. But such theories do not account for times when inattention is the result of overstimulation—too much going on rather than too little—and overlook a greater problem: Sometimes attention is not the issue.
Many functional approaches to boredom set attention aside to consider its underlying purpose; their proponents argue that boredom is a signal meant to alert people to underlying problems, most often concerning goals, meaning, or opportunity costs (e.g., van Tilburg & Igou, 2012). If inattention results in boredom, such individuals argue, it is because inattention is an indirect signal that what you are doing lacks value or meaning. But that does not explain instances when people are bored during otherwise meaningful activities.
Which is it then? Is boredom caused by inattention resulting from understimulation? Or is boredom caused by a lack of meaning? Both are (partially) right.1 The meaning-and-attentional-components (MAC) model of boredom and cognitive engagement unifies past work that has examined attention, meaning, and their environmental correlates in isolation and brings these ideas together to explain what boredom is and why we experience it.

From 2014...The Price of Envy—An Experimental Investigation of Spiteful Behavior

From 2014...The Price of Envy—An Experimental Investigation of Spiteful Behavior. Inga Wobker. Managerial and Decision Economics, April 21 2014. https://doi.org/10.1002/mde.2672

Abstract: When receiving less resources than a competitor, envy may be evoked that may result in spiteful behavior. This paper applies evolutionary theory to understand envy and its outcomes. A theoretical framework is developed that is based on the cause–effect relationships of unequal outcomes, envy, defection of cooperation, and welfare loss. To test this framework, an experiment with 136 participants is run. The results confirm that receiving less than another can indeed lead to experiences of envy and defection of future cooperation, producing a welfare loss of one‐sixth.

4.1. Discussion

The overall objective of this research was to study envyand its influence on spiteful behavior in an experimentalsetting with economic relevance. Evolutionary theoryprovided a supportive framework for studying the issue.

An unequal distribution of resources led to feelingsof envy in those who were worse off, which is in linewith prior literature (Hill and Buss, 2008b; Leach,2008). One-third of the losers chose to act spitefullyand reduce the other players’ balances. In a similar experiment by Celse (2009), participants with different levels of endowment could reduce the other players’ payoff at own cost. Of the participants with a lowerendowment than their opponents (equal to the losers inthis experiment), 31.9% reduced the other player’s balance at a personal cost. This may indicate that the rateof approximately one-third of the agents who are willing to behave spitefully is stably distributed in the population. However, one has to bear in mind that different expectations about the outcomes of the resource division probably will moderate the satisfaction with the outcome. For example, if an actor only expects a fraction of the outcome of what the others obtain, this outcome of the resource division is, however, still profitable for her or him, when actually receiving less than others, in this case, this does not trigger envy.

Although spiteful behavior as a reaction to resourcedeficiencies may be the best individual strategy (Hilland Buss, 2008b), it is certainly not the best strategy for a group or organization as a whole, as it produces welfare losses (Garay and Móri, 2011). Recognizing the impact of envy for agents may lead to new understandings of inefficient organizations and welfare losses and may help to develop approaches that better manage the destructive influence of emotions (more precisely,social emotions—those triggered by social comparisons), on behavior (Manner and Gowdy, 2008).

Agents who acted spitefully stated several motivations for their actions. Their attitude—that if they could not have the money, then no one should have it—is a common feature of envy (Feather and Nairn, 2005) and supports the evolutionary perspective that when itis not possible for a person to obtain the resource, nocompetitor should have it either, in order to preserve relative fitness (Hill and Buss, 2008a, 2008b). Some agents expressed the opinion that the distribution wasunjust as insofar that the opposing agents won and that they themselves did not. This attitude corresponds tothe traditional scholarly view that subjective assump-tions of undeserved advantage trigger envy (Featherand Sherman, 2002; Feather and Nairn, 2005; Smithand Kim, 2007), which, in turn, triggers ill will. If an agent is observed to have something that he or sheshould not have—even though this state of not deserving may be very subjective—it is understandable that the envious agent would feel hostile toward that enviedagent (Smithet al., 1994). Motivation that arises fromreasons of distributive justice is a very subjective response. Both agents had exactly the same chance of winning, so no objective criteria of fairness have been corrupted. Retaliation of the winner could be interpretedas a punishment for a defection of equity (Xiao andHouser, 2005; Axelrod and Hamilton, 2006). When an agent reduces the other agent’s prize and explains the decision on the simple basis that it is an option of the game, the stated motivation can be interpreted as apossible expression of disguised envy. A logical hypothesis for this behavior is that the agents neededto justify the reduction to themselves and to the experimenter and wanted to blame their desire to reduce on features of the game rather than on their envy.

5. CONCLUSION
The overall objective of this research was to studythe influence of envy on spiteful behavior and to understand the negative effects of envy on inter-firm and intra-firm relations. The evolutionary theory provided a supportive framework for studying the issue. In the study designed, agents played a lottery that provided them with unequal distribution and could subsequently create financial harm for each other at their own cost. One-third of thelosers in the lottery acted spitefully and reducedthe winners’balances by half. The observed welfare losses accumulated to one-seventh of thetotal income value.Addressing the broader aspects of envy, in orderto fully understand the nature of envy and its implications for relationships, more research is needed (Smith and Kim, 2007). Systematically integrating envy and other other-regarding preferences into economic modeling can provide a refreshing viewpoint in the investigation of human behavior (Horstet al., 2006; Kirman and Teschl, 2010). It will be interesting to see whether, and in what ways, this study will help to motivate researcher sto focus research on this fascinating emotion in this field—where, despite the relevance of inter-firm and intra-firm relations, the concept of envy is still neglected. It may be hoped for that adopting an evolutionary perspective on these questions will lead to more effective management strategies fo rdealing with envy

Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? It seems positive for lonely guys.

Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support. David A. Cole et al. Cyberpsychology, Behavior, and Social Networking, Vol. 22, No. 11, , Nov 7 2019. https://doi.org/10.1089/cyber.2019.0035

Abstract: In a two-wave, 4-month longitudinal study of 308 adults, two hypotheses were tested regarding the relation of Twitter-based measures of online social media use and in-person social support with depressive thoughts and symptoms. For four of five measures, Twitter use by in-person social support interactions predicted residualized change in depression-related outcomes over time; these results supported a corollary of the social compensation hypothesis that social media use is associated with greater benefits for people with lower in-person social support. In particular, having a larger Twitter social network (i.e., following and being followed by more people) and being more active in that network (i.e., sending and receiving more tweets) are especially helpful to people who have lower levels of in-person social support. For the fifth measure (the sentiment of Tweets), no interaction emerged; however, a beneficial main effect offset the adverse main effect of low in-person social support.


Discussion

This study examined the longitudinal effects of TU as a
means for offsetting the adverse effects of low social support
on depressive thoughts and symptoms. Two key results
emerged: first, support emerged for our corollary to the social
compensation (or poor-get-richer) hypothesis but not for the
rich-get-richer hypothesis. Four aspects of TU were related
to reductions in depressive thoughts and symptoms, but only
for people with low initial levels of in-person social support.
Second, conveying positive sentiment through Twitter predicted
a reduction in depressive thoughts and feelings, irrespective
of people’s level of in-person social support. Below,
we elaborate on these findings and their implications.
Our first set of findings was consistent with our corollary
to the social compensation hypothesis. People with low social
support showed improvements in depressive thoughts
and feelings over time if they reported four markers of TU:
following more people on Twitter, having more people follow
them on Twitter, posting more Tweets, and having more
of their posts retweeted by others. These markers were unrelated
to depressive thoughts and feelings of people who
already had high levels of in-person social support. These
results support Baker and Algorta’s observation that the effect
of social media on depression-related outcomes is
complicated by social, psychological, and behavioral moderators.
8 The current research provides longitudinal evidence
that in-person social support may be one such moderator.
These results also suggest that social media might be a way
to combat the adverse effects of low social support on mental
health. This possibility is commensurate with the conventional
wisdom that having one or two good friends in one
social niche can offset social adversity in other social niches.
48,49 Perhaps social media platforms represent a modern
version of such niches. We urge caution along these lines as
previous research has also shown that the relation of Twitter
with depression-related outcomes varies as a function of how
(and when) Twitter is used6,7,50–54 (also refer literature reviews
by Guntuku et al.55 and Hur and Gupta56).
Three reasons for this finding are possible.9,57 One is that
meeting people with similar interests or characteristics may
be easier online than in person, especially when such people
are not available within one’s in-person social networks.57–61
For people who lack these affiliations, connecting with others
online may have an especially strong impact. A second
explanation is that the online channels of communication are
simpler, such that people who find it challenging to develop
supportive in-person social networks may be more effective
in the more restricted online world of social media. One’s
ability to interpret nonverbal cues, one’s physical characteristics,
one’s proper use of vocal tone, and one’s timing of
social responses may be less important online than in person.
Nesi et al. referred to this as cue absence in their transformation
theory.62–65 In a related vein, online interactions tend
to be asynchronous. Delays between online communications
might allow people the time to compose more effective responses.
62 Understanding the mechanisms that underlie these
results represents an important avenue for future research.
A third explanation for these results is that the value added
by having online followers may not be as beneficial to people
who already have strong in-person social support, at least
insofar as reducing depressive thoughts and symptoms are
concerned. Some evidence even suggests that having a very
large number of online friends may actually be associated
with negative outcomes.66,67 The current interaction plots
in Figure 1 somewhat reflect this possibility, in that some of
our TU variables appeared to have adverse effects for people
who had strong in-person social supports. We caution
against overinterpreting this result, however, as the slopes for
participants with low social support were not statistically
significant.
Our second set of findings was the significant main effect of
Twitter sentiment, which offset the adverse effect of low inperson
social support. Two aspects of this result deserve
emphasis. First, this finding cannot be explained as consequences
of depression, as it derives from longitudinal analyses
in which prior levels of depression were statistically controlled.
Second, these results were not moderated by level of
social support. The effects of positive sentiment applied to
people at all levels of social support. Indeed, positive Twitter
sentiment offset much of the depressive effects of low inperson
social support. People with problematic social networks
but highly positive Twitter sentiment had similar levels
of depressive symptoms as did people with strong social
networks but more negative Twitter sentiment, reminiscent of
Granovetter’s early work on the strength of weak ties.68
Shapiro and Margolin’s extensive literature review describes
at least four reasons why effective use of online social
media platforms could offset the adverse effects of problematic
face-to-face relationships, especially with respect to
cognitive and emotional outcomes.57 First, people can engage
in selective self-presentation more easily online than in person.
By crafting carefully their online communications and
constructing their online persona, some people can accrue
more positive feedback online than they can in person, which
may in turn result in improvement on psychological outcomes.
64,65 Second, connecting with similar people or with
people who share similar interests may be easier for some
people online than in person, especially when such affiliations
are not available within in-person social networks.58–61 Third,
through the Internet, communicating with others from more
diverse intellectual, political, and social backgrounds can
expand one’s self-identity while enhancing feelings of belongingness
and affiliation.69 Trepte et al. hypothesized that
large, diverse groups may feel more connected with each
other online and are thus more likely to support each other.70
Fourth, self-disclosure may be easier online than in person,
potentially facilitating online social relationships or enabling
people to practice for in-person relationships.71,72
Taken together, these results begin to suggest interesting
supplemental strategies in the prevention of depression in
people who are at risk because of low social support. The
current findings, derived from one of very few longitudinal
studies in this area, increase our understanding about prospective
(not just correlational) relations and could have
implications for the use of social media in prevention research.
12 A powerful next step will be true experimental
research designs in which positive use of social media is
actively manipulated, so that its causal effect on mental
health outcomes can be assessed. If successful, online social
skills training could become a valuable component of comprehensive
depression prevention efforts.
Several shortcomings of this study suggest important avenues
for future research. The first focuses on our sentiment
analysis. Although examining the actual sentiment conveyed
by Twitter communications is a powerful step, in-depth
content analysis of people’s Tweets could reveal more about
more specific aspects of people’s communications that might
be responsible for the relation of sentiment with depressionrelated
outcomes. Furthermore, in short textual passages
(such as Tweets), it is extremely difficult to reliably measure
issues such as sarcasm and irony. Also, some kinds of negatively
toned messages (e.g., expressing distress) could serve
as triggers for positive responses (e.g., emotional support).
Second, depressive thoughts and symptoms are extremely
important mental health outcomes, emblematic of one of the
most common and debilitating classes of mental illnesses;
however, many other important clinical outcomes should
be explored, including Internet addiction, social anxiety,
and obsessive-compulsive disorder.73–75 Third, our study
focused only on Twitter. Other social media platforms exist
generate very different kinds of risks and benefits, which
should be explored. Fourth, we used an observational/
correlation research design, which leaves various ‘‘third
variables’’ uncontrolled. Random assignment to high versus
low Twitter conditions could control for self-selection factors
such as extraversion or level of depression. Fifth, although
use of MTurk for participant recruitment has certain
strengths, weaknesses have also been documented. These
include crosstalk among participants, misrepresentation of
personal characteristics to qualify for studies, and provision
of unreliable results.76–78 Although these issues do seem to
be characteristic of some MTurk participants, research shows
that these problems actually occur at similar rates in samples
obtained from more conventional methods.79 Future studies
should examine the generalizability of the current results
across a wider variety of populations.

About the Implicit Association Tests (IATs)... Predicting Behavior With Implicit Measures: Disillusioning Findings

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions. Franziska Meissner, Laura Anne Grigutsch, Nicolas Koranyi, Florian Müller and Klaus Rothermund. Front. Psychol., November 8 2019. https://doi.org/10.3389/fpsyg.2019.02483

Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.

Why does he act like this? Why does she not do what she intended to do? In our everyday life, we often try to find explanations for the behavior of others, and of ourselves, respectively. Explaining and predicting behavior is also of key interest across all fields of scientific psychology, especially when it comes to deviations between individuals’ actual behavior and the attitudes, goals, or values held by these very individuals. Why do people discriminate although they report to hold egalitarian values? Why do they not quit smoking although they know that smoking is bad? Why is there a gap between people’s self-reported attitudes and actual behavior?

Dual-process or dual-system models attribute seemingly inconsistent behavior to the triumph of an impulsive system over a reflective system of behavior control (e.g., Strack and Deutsch, 2004; Hofmann et al., 2009; Kahneman, 2011). The notion that the prediction of behavior could be improved considerably if one succeeds in measuring the processes of the impulsive system (Hofmann et al., 2007; Friese et al., 2008; Hofmann and Friese, 2008) fueled research applying so-called implicit measures of attitudes. The most popular of these measures, the Implicit Association Test (IAT, Greenwald et al., 1998) evoked enthusiastic hopes regarding its predictive value. Unfortunately, however, the IAT and its derivatives have not met these expectations.

In this article, we review findings illustrating reasons for the IAT’s unsatisfying predictive value, as well as promising ways forward. We will outline that in order to improve the predictive power of implicit measures, differentiation is key. We will argue that future research should put more emphasis on the underlying processes and concepts behind these measures. We begin with sketching the discrepancy between individuals’ behaviors and their self-expressed attitudes. We then summarize the (mostly unsatisfying) attempts to close this attitude-behavior gap with the help of implicit measures. In the main part of this article, we identify features of implicit measures that are responsible for their weak predictive validity. We review findings illustrating each of these problematic aspects along with specific, sophisticated solutions providing promising directions for future research.

Closing Thoughts

In this article, we presented an overview of possible reasons for the weak relationship between implicit measures like the IAT and behavioral criteria. We outlined that the unsatisfying predictive value of the IAT is due to (1) extraneous influences like recoding, (2) the measurement of liking instead of wanting, (3) the measurement of associations instead of complex beliefs, and/or (4) a conceptual mismatch of predictor and criterion. We presented precise solutions for each of these problems. More precisely, we suggested to switch to procedural variations that minimize extraneous influences (i.e., the SB-IAT, Teige-Mocigemba et al., 2008; and the IAT-RF; Rothermund et al., 2009), and to apply sophisticated analysis tools (i.e., the ReAL model, Meissner and Rothermund, 2013) that separate relevant processes from those extraneous influences. Second, we presented an overview of different implicit measures that go beyond the measurement of evaluative associations, and instead quantify actual implicit wanting (e.g., the W-IAT, Koranyi et al., 2017). Third, we pointed to implicit measures of beliefs (e.g., the PEP, Müller and Rothermund, 2019) that allow a more nuanced view on individual attitudes and values than measures that tap into associations. Finally, we emphasized the importance of measuring behavior proper and outlined that implicit measures incorporating contextual information might be more adequate in assessing the structure of implicit attitudes or beliefs and their implications for behavior (Casper et al., 2011; Kornadt et al., 2016). Each of the recent developments presented in the current paper has the potential to increase the predictive power of implicit measures. Future research will also have to clarify whether a combination of these approaches may lead to further improvement. Inspired by the fruitful research on dual-process or dual-systems models, we further suggest to invest in theoretical considerations: Which forms or aspects of behavior should be related to which processes involved in which implicit measures? Differentiation is key, with regard to both the predictor and the criterion.

We strongly argue not to take the validity of implicit measures like the IAT for granted. Instead, we should take into account the complexity of these measures, especially when it comes to the predictive value for real-life behavior. As outlined in the current review, the past 20 years of research have provided us with a number of good reasons for why the IAT and its derivatives did not succeed in closing the attitude-behavior gap, and enriched our toolbox with promising, sophisticated improvements. Future research will benefit from harnessing the power of such a more differentiated view on implicit measures.