Social Comparison as the Thief of Joy: Emotional Consequences of Viewing Strangers’ Instagram Posts

This experiment investigates the emotional consequences of viewing strangers’ positive posts on Instagram. From a social comparison perspective, strangers’ positive posts on social media are expected to negatively affect viewers’ emotions. From an emotional contagion perspective, strangers’ positive posts should positively affect viewers’ emotions. The current lab experiment examines both the social comparison and the emotional contagion perspective while taking individual differences into account. Participants viewed positive, neutral, or no posts of confederates. In support of the social comparison perspective, individuals who tend to compare themselves to others reported lower positive affect if they had viewed positive posts than if they had viewed neutral or no posts. In support of the emotional contagion perspective, individuals who tend not to engage in social comparison reported higher positive affect after viewing positive posts than after viewing neutral or no posts. These findings indicate that individual differences in processing tendencies lead people to respond to social information on social media in opposite ways.     

Free choice and contextually permitted actions

We present a solution to the paradox of free choice permission by introducing strong and weak permission in a deontic logic of action. It is shown how counterintuitive consequences of strong permission can be avoided by limiting the contexts in which an action can be performed. This is done by introducing the only operator, which allows us to say that only is performed (and nothing else), and by introducing contextual interpretation of action terms.This author gratefully acknowledges the hospitality of Linköping University during revision of this paper.This research of J.-J.Ch.Meyer and R.J.Wieringa is partially supported by ESPRIT BRWG project No.8319 ModelAge.     

Exploratory Factor Analysis With Small Sample Sizes

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to which a small N solution can sustain the presence of small distortions such as interfactor correlations, model error, secondary loadings, unequal loadings, and unequal p/f. Factor recovery was assessed in terms of pattern congruence coefficients, factor score correlations, Heywood cases, and the gap size between eigenvalues. A subsampling study was also conducted on a psychological dataset of individuals who filled in a Big Five Inventory via the Internet. Results showed that when data are well conditioned (i.e., high λ, low f, high p), EFA can yield reliable results for N well below 50, even in the presence of small distortions. Such conditions may be uncommon but should certainly not be ruled out in behavioral research data.