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.

Correcting an analysis of variance for clustering

A great deal of educational and social data arises from cluster sampling designs where clusters involve schools, classrooms, or communities. A mistake that is sometimes encountered in the analysis of such data is to ignore the effect of clustering and analyse the data as if it were based on a simple random sample. This typically leads to an overstatement of the precision of results and too liberal conclusions about precision and statistical significance of mean differences. This paper gives simple corrections to the test statistics that would be computed in an analysis of variance if clustering were (incorrectly) ignored. The corrections are multiplicative factors depending on the total sample size, the cluster size, and the intraclass correlation structure. For example, the corrected F statistic has Fisher's F distribution with reduced degrees of freedom. The corrected statistic reduces to the F statistic computed by ignoring clustering when the intraclass correlations are zero. It reduces to the F statistic computed using cluster means when the intraclass correlations are unity, and it is in between otherwise. A similar adjustment to the usual statistic for testing a linear contrast among group means is described.

Problems with change in R2 as applied to theory of reasoned action research

The paradigm of choice for theory of reasoned action research seems to depend largely on the notion of change in variance accounted for (DeltaR2) as new independent variables are added to a multiple regression equation. If adding a particular independent variable of interest increases the variance in the dependent variable that can be accounted for by the list of independent variables, then the research is deemed to be 'successful', and the researcher is considered to have made a convincing argument about the importance of the new variable. In contrast to this trend, I present arguments that suggest serious problems with the paradigm, and conclude that studies on attitude-behaviour relations would advance the field of psychology to a far greater extent if researchers abandoned it.

Separating response-execution bias from decision bias: arguments for an additional parameter in Ratcliff's diffusion model

Diffusion model data analysis permits the disentangling of different processes underlying the effects of experimental manipulations. Estimates can be provided for the speed of information accumulation, for the amount of information used to draw conclusions, and for a decision bias. One parameter describes the duration of non-decisional processes including the duration of motor-response execution. In the default diffusion model, it is implicitly assumed that both responses are executed with the same speed. In some applications of the diffusion model, this assumption will be violated. This will lead to biased parameter estimates. Consequently, we suggest accounting explicitly for differences in the speed of response execution for both responses. Results from a simulation study illustrate that parameter estimates from the default model are biased if the speed of response execution differs between responses. A second simulation study shows that large trial numbers (N>1,000) are needed to detect whether differences in response-execution times are based on different execution times.

Covariances between regression coefficient estimates in a single mediator model

This study presents formulae for the covariances between parameter estimates in a single mediator model. These covariances are necessary to build confidence intervals (CI) for effect size measures in mediation studies. We first analytically derived the covariances between the parameter estimates in a single mediator model. Using the derived covariances, we computed the multivariate-delta standard errors, and built the 95% CIs for the effect size measures. A simulation study evaluated the accuracy of the standard errors as well as the Type I error, power, and coverage of the CIs using various parameter values and sample sizes. Finally, we presented a numerical example and a SAS MACRO that calculates the CIs for the effect size measures.

Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis

The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the intervention and control groups that captures the treatment effect is rarely reported. This article first reviews 2 classes of formulas for effect sizes associated with classical repeated-measures designs that use the standard deviation of either change scores or raw scores for the denominator. It then broadens the scope to subsume GMA and demonstrates that the independent groups, within-subjects, pretest-posttest control-group, and GMA designs all estimate the same effect size when the standard deviation of raw scores is uniformly used. Finally, the article shows that the correct effect size for treatment efficacy in GMA--the difference between the estimated means of the 2 groups at end of study (determined from the coefficient for the slope difference and length of study) divided by the baseline standard deviation--is not reported in clinical trials.

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.

[Scientific productivity and Hirsch's h index of Spanish social psychology: convergence between productivity indexes and comparison with other areas]

This article describes Hirsch's h index as a measure of researchers' scientific productivity and quality, examining its convergent validity with other productivity and quality measures (total citations, maximum citation, sexennials, years since earning Ph.D., and years as a full professor). It shows the distribution of the index among Spanish Social Psychology professors, and a comparison is made among the ten professors with highest h index of the six knowledge areas into which academic Psychology is divided in Spain. The results show that the index largely correlates with the citations but only very modestly with the number of sexennial investigations. The distributions by areas show that Psychobiology and Basic Psychology, as a whole, have a higher h index than the other areas, with Social Psychology in fourth place. Lastly, we discuss the implications of these results and we offer some recommendations for using the index in academic and research decision making.

Crossed categorization beyond the two-group model

Four studies examined processing of in-group and out-group information with stimuli that are more complex than those used in previous crossed categorization studies. A diverse set of predictions is generated by previous theoretical work to account for how participants will integrate information stemming from multiple group memberships. Heuristic, threshold-based processing of information was supported over algebraic processing. Participants appeared to divide stimuli into "in-grouplike" and "out-grouplike" metacategories. However, the threshold at which this distinction was made, and whether it was based on in-group favoritism or out-group derogation, was influenced by the nature of the situation and the task participants performed. Advantages of crossed categorization research that moves beyond the traditional two-group model are discussed.

Power and action in critical theory across disciplines: implications for critical community psychology

Although critical scholarship and community psychology share similar aspirations, the links between them remain unexplored and under-theorized. In this article we explore the implications of critical scholarship in various specialties for the field of community psychology. To understand the contributions of critical scholarship to a theory of power and action for social change, we conducted a systematic analysis of a ten-year period of publications in seven journals associated with the critical scholarship tradition. We created precise criteria for the concepts of power and action and applied them to the publications. Results indicate an interesting paradox at play. Whereas community psychology is more action oriented than critical scholarship, its actions fall short of challenging institutionalized power structures and the status quo; and whereas critical scholarship is more challenging of the status quo than community psychology in theory, it has failed to produce viable actions that challenge the status quo. We discuss the implications of this state of affairs for the development of a more critical community psychology.

Quantifying social asymmetric structures

Many social phenomena involve a set of dyadic relations among agents whose actions may be dependent. Although individualistic approaches have frequently been applied to analyze social processes, these are not generally concerned with dyadic relations, nor do they deal with dependency. This article describes a mathematical procedure for analyzing dyadic interactions in a social system. The proposed method consists mainly of decomposing asymmetric data into their symmetric and skew-symmetric parts. A quantification of skew symmetry for a social system can be obtained by dividing the norm of the skew-symmetric matrix by the norm of the asymmetric matrix. This calculation makes available to researchers a quantity related to the amount of dyadic reciprocity. With regard to agents, the procedure enables researchers to identify those whose behavior is asymmetric with respect to all agents. It is also possible to derive symmetric measurements among agents and to use multivariate statistical techniques.

When group members admit to being conformist: the role of relative intragroup status in conformity self-reports

Five studies examined the hypothesis that people will strategically portray the self as being more group influenced the more junior they feel within the group. Among social psychologists (Study 1), ratings of self-conformity by group members were greater when the status of the participant was low than when it was high. These effects were replicated in Studies 2, 3, and 4 in which relative intragroup status was manipulated. In Study 3, the authors found junior group members described themselves as more conformist than senior members when they were addressing an ingroup audience, but when they were addressing an outgroup audience the effect disappeared. Furthermore, junior members (but not senior members) rated themselves as more conformist when they were led to believe their responses were public than when responses were private (Study 5). The discussion focuses on the strategic processes underlying low-status group members' self-reports of group influence and the functional role of conformity in groups.

Actor-observer differences in realism in confidence and frequency judgments

Taking a social psychological approach to metacognitive judgments, this study analyzed the difference in realism (validity) in confidence and frequency judgments (i.e., estimates of overall accuracy) between one's own and another person's answers to general knowledge questions. Experiment 1 showed that when judging their own answers, compared with another's answers, the participants exhibited higher overconfidence, better ability to discriminate correct from incorrect answers, lower accuracy, and lower confidence. However, the overconfidence effect could be attributable to the lowest level of confidence. Furthermore, when heeding additional information about another's answers the participants showed higher confidence and better discrimination ability. The overconfidence effect of Experiment 1 was not found in Experiment 2. However, the results of Experiment 2 were consistent with Experiment 1 in terms of discrimination ability, confidence, and accuracy. Finally, in both experiments the participants gave lower frequency judgments of their own overall accuracy compared with their frequency judgments of another person's overall accuracy.

Perceptions of trait typicality in gender stereotypes: examining the role of attribution and categorization processes

Gender stereotypes are understood as the ascription of different personality traits to men and women. Data from American and Italian samples showed that consistent with the attribution hypothesis, the estimated prevalence of a trait in a target group predicted perceptions of trait typicality well. In contrast, there was no support for the categorization hypothesis, according to which perceived differences in trait prevalence between groups should independently predict trait typicality. Nevertheless, participants overestimated gender differences in personality as predicted by the principle of intercategory accentuation. The implications of these findings for the rationality and accuracy of gender stereotyping are discussed.

[Psychological and sociologic requirements to prepare for anesthesia and surgery of patients with intracranial invasive processes]

Response to anxiety has been analyzed in 195 patients operated because of intracranial expansive process and influence of standard and individualized preoperative preparations over it.

Childhood abuse, household dysfunction, and the risk of attempted suicide throughout the life span: findings from the Adverse Childhood Experiences Study

CONTEXT

Suicide is a leading cause of death in the United States, but identifying persons at risk is difficult. Thus, the US surgeon general has made suicide prevention a national priority. An expanding body of research suggests that childhood trauma and adverse experiences can lead to a variety of negative health outcomes, including attempted suicide among adolescents and adults.

OBJECTIVE

To examine the relationship between the risk of suicide attempts and adverse childhood experiences and the number of such experiences (adverse childhood experiences [ACE] score).

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study of 17 337 adult health maintenance organization members (54% female; mean [SD] age, 57 [15.3] years) who attended a primary care clinic in San Diego, Calif, within a 3-year period (1995-1997) and completed a survey about childhood abuse and household dysfunction, suicide attempts (including age at first attempt), and multiple other health-related issues.

MAIN OUTCOME MEASURE

Self-reported suicide attempts, compared by number of adverse childhood experiences, including emotional, physical, and sexual abuse; household substance abuse, mental illness, and incarceration; and parental domestic violence, separation, or divorce.

RESULTS

The lifetime prevalence of having at least 1 suicide attempt was 3.8%. Adverse childhood experiences in any category increased the risk of attempted suicide 2- to 5-fold. The ACE score had a strong, graded relationship to attempted suicide during childhood/adolescence and adulthood (P<.001). Compared with persons with no such experiences (prevalence of attempted suicide, 1.1%), the adjusted odds ratio of ever attempting suicide among persons with 7 or more experiences (35.2%) was 31.1 (95% confidence interval, 20.6-47.1). Adjustment for illicit drug use, depressed affect, and self-reported alcoholism reduced the strength of the relationship between the ACE score and suicide attempts, suggesting partial mediation of the adverse childhood experience-suicide attempt relationship by these factors. The population-attributable risk fractions for 1 or more experiences were 67%, 64%, and 80% for lifetime, adult, and childhood/adolescent suicide attempts, respectively.

CONCLUSIONS

A powerful graded relationship exists between adverse childhood experiences and risk of attempted suicide throughout the life span. Alcoholism, depressed affect, and illicit drug use, which are strongly associated with such experiences, appear to partially mediate this relationship. Because estimates of the attributable risk fraction caused by these experiences were large, prevention of these experiences and the treatment of persons affected by them may lead to progress in suicide prevention.

The Electronically Activated Recorder (EAR): a device for sampling naturalistic daily activities and conversations

A recording device called the Electronically Activated Recorder (EAR) is described. The EAR taperecords for 30 sec once every 12 min for 2-4 days. It is lightweight and portable, and it can be worn comfortably by participants in their natural environment. The acoustic data samples provide a nonobtrusive record of the language used and settings entered by the participant. Preliminary psychometric findings suggest that the EAR data accurately reflect individuals' natural social, linguistic, and psychological lives. The data presented in this article were collected with a first-generation EAR system based on analog tape recording technology, but a second generation digital EAR is now available.

The decade 1989-1998 in Spanish psychology: an analysis of research in social psychology

In this study, a detailed exploration is carried out of the production of research and theory in social psychology in the Spanish context. The main research areas are: Work and organizational psychology, social health psychology, community and social services psychology, environmental research, judicial and political psychology, psychosocial theory and meta-theory, social psychology of language, research on emotion, group processes and social identity. The growing importance of social psychology within the framework of Spanish psychology is emphasized, and the relation with specific social problems from the national context, and the paradoxically scarce originality of the theoretical perspectives and the leading research, strongly influenced by Anglo Saxon social psychology, is commented upon.

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.

Analysis of clustered data in community psychology: with an example from a worksite smoking cessation project

Although it is common in community psychology research to have data at both the community, or cluster, and individual level, the analysis of such clustered data often presents difficulties for many researchers. Since the individuals within the cluster cannot be assumed to be independent, the use of many traditional statistical techniques that assumes independence of observations is problematic. Further, there is often interest in assessing the degree of dependence in the data resulting from the clustering of individuals within communities. In this paper, a random-effects regression model is described for analysis of clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent, but do assume data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting these effects for the dependency resulting from the clustering of the data. Models are described for both continuous and dichotomous outcome variables, and available statistical software for these models is discussed. An analysis of a data set where individuals are clustered within firms is used to illustrate features of random-effects regression analysis, relative to both individual-level analysis which ignores the clustering of the data, and cluster-level analysis which aggregates the individual data.