Exploring the "like" in the psychological interaction of users on fan community: A netnography analysis.

Little is known about the psychology behind fans joining fan community pages in a blog context; the factors driving them to like, share, and comment on posts on fan community pages; or the manner in which fans experience and interact with such pages. These topics were not given sufficient explanation in past research. This study aimed to explore the special situations and unique online experiences that fans community experience in a blog context. A netnography analysis was conducted through online interviews and field observations. Three phases of contextual experiences were determined, including observing and collecting data online, active participation, and emergent design. The contribution of this study is its establishment of the fan community experience model, which is a substantive theory, and its suggestion of nine propositions that can provide insights into fan community page interaction and experience models.

MCMC estimation for the p(2) network regression model with crossed random effects.

The p(2) model is a statistical model for the analysis of binary relational data with covariates, as occur in social network studies. It can be characterized as a multinomial regression model with crossed random effects that reflect actor heterogeneity and dependence between the ties from and to the same actor in the network. Three Markov chain Monte Carlo (MCMC) estimation methods for the p(2) model are presented to improve iterative generalized least squares (IGLS) estimation developed earlier, two of which use random walk proposals. The third method, an independence chain sampler, and one of the random walk algorithms use normal approximations of the binary network data to generate proposals in the MCMC algorithms. A large-scale simulation study compares MCMC estimates with IGLS estimates for networks with 20 and 40 actors. It was found that the IGLS estimates have a smaller variance but are severely biased, while the MCMC estimates have a larger variance with a small bias. For networks with 20 actors, mean squared errors are generally comparable or smaller for the IGLS estimates. For networks with 40 actors, mean squared errors are the smallest for the MCMC estimates. Coverage rates of confidence intervals are good for the MCMC estimates but not for the IGLS estimates.

Testing reciprocity in social interactions: a comparison between the directional consistency and skew-symmetry statistics.

In the present article, we focus on two indices that quantify directionality and skew-symmetrical patterns in social interactions as measures of social reciprocity: the directional consistency (DC) and skew-symmetry indices. Although both indices enable researchers to describe social groups, most studies require statistical inferential tests. The main aims of the present study are first, to propose an overall statistical technique for testing null hypotheses regarding social reciprocity in behavioral studies, using the DC and skew-symmetry statistics (Phi) at group level; and second, to compare both statistics in order to allow researchers to choose the optimal measure depending on the conditions. In order to allow researchers to make statistical decisions, statistical significance for both statistics has been estimated by means of a Monte Carlo simulation. Furthermore, this study will enable researchers to choose the optimal observational conditions for carrying out their research, since the power of the statistical tests has been estimated.

The impact of income inequality on individual and societal health: absolute income, relative income and statistical artefacts.

The relative income hypothesis, that relative income has a direct effect on individual health, has become an important part of the literature on health inequalities. This paper presents a four-quadrant diagram, which shows the effect of income, relative income and aggregation bias on individual and societal health. The model predicts that increased income inequality reduces average health regardless of whether relative income affects individual health. If relative income does have a direct effect then societal health will decrease further.