3 resultados para multivariate analysis of covariance

em Helda - Digital Repository of University of Helsinki


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The objective of the study was to explore the dimensions of group identity in the guilds of World of Warcraft. Previous research shows that social interaction has an important role in playing games for many players. Social identities are an important aspect of self-concept and since group related cues are more salient than personal clues in computer-mediated communication, the social gaming experience was approached through group identity. In the study a new scale will be developed to measure the group identity in games. Secondary goal is to study how different guild attributes affect the group identity and third goal is to explore the connection between group identity and gaming experience and amount of play. Subjects were 1203 guild members and 106 players not in a guild. The data was gathered by an Internet survey which measured group identity with nine scales, gaming experience with three scales and guild attributes with four scales. Also various background data was gathered. The construct of group identity was analyzed with explorative factor analysis. The typical experiences of group identity was analyzed with cluster analysis and effects of guild attributes with multivariate analysis of covariance. As a result of the study a new scale was developed which measured group identity on six dimensions: self-stereotyping, public and private evaluation, importance, interconnection of self and others and awareness of content. Group identity was experienced strongest in elder middle-sized guilds that had formal rules and that emphasized social interaction. The players with strong group identity had more positive gaming experience and played World of Warcraft more per week than the players who were not in a guild or identified to guild weakly. This result encourages game developers to produce environments that enhance group identity as it seems to increase the enjoyment in games. As a whole this study proposes that group identity in guilds is constructed from the same elements as in traditional groups. If this is truly the case, guild membership may have similar positive effects on individual’s mental well-being as traditional positively evaluated group memberships have.

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The aim of the present study was to advance the methodology and use of time series analysis to quantify dynamic structures in psychophysiological processes and thereby to produce information on spontaneously coupled physiological responses and their behavioral and experiential correlates. Series of analyses using both simulated and empirical cardiac (IBI), electrodermal (EDA), and facial electromyographic (EMG) data indicated that, despite potential autocorrelated structures, smoothing increased the reliability of detecting response coupling from an interindividual distribution of intraindividual measures and that especially the measures of covariance produced accurate information on the extent of coupled responses. This methodology was applied to analyze spontaneously coupled IBI, EDA, and facial EMG responses and vagal activity in their relation to emotional experience and personality characteristics in a group of middle-aged men (n = 37) during the administration of the Rorschach testing protocol. The results revealed new characteristics in the relationship between phasic end-organ synchronization and vagal activity, on the one hand, and individual differences in emotional adjustment to novel situations on the other. Specifically, it appeared that the vagal system is intimately related to emotional and social responsivity. It was also found that the lack of spontaneously synchronized responses is related to decreased energetic arousal (e.g., depression, mood). These findings indicate that the present process analysis approach has many advantages for use in both experimental and applied research, and that it is a useful new paradigm in psychophysiological research. Keywords: Autonomic Nervous System; Emotion; Facial Electromyography; Individual Differences; Spontaneous Responses; Time Series Analysis; Vagal System

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Aneuploidy is among the most obvious differences between normal and cancer cells. However, mechanisms contributing to development and maintenance of aneuploid cell growth are diverse and incompletely understood. Functional genomics analyses have shown that aneuploidy in cancer cells is correlated with diffuse gene expression signatures and that aneuploidy can arise by a variety of mechanisms, including cytokinesis failures, DNA endoreplication and possibly through polyploid intermediate states. Here, we used a novel cell spot microarray technique to identify genes with a loss-of-function effect inducing polyploidy and/or allowing maintenance of polyploid cell growth of breast cancer cells. Integrative genomics profiling of candidate genes highlighted GINS2 as a potential oncogene frequently overexpressed in clinical breast cancers as well as in several other cancer types. Multivariate analysis indicated GINS2 to be an independent prognostic factor for breast cancer outcome (p = 0.001). Suppression of GINS2 expression effectively inhibited breast cancer cell growth and induced polyploidy. In addition, protein level detection of nuclear GINS2 accurately distinguished actively proliferating cancer cells suggesting potential use as an operational biomarker.