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      Having Friends and Feeling Lonely : A Daily Process Examination of Transient Loneliness, Socialization, and Drinking Behavior

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      Personality and Social Psychology Bulletin
      SAGE Publications

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          Abstract

          Loneliness is a well-known indicator of relationship deficits, with potentially severe consequences on health and well-being (Perlman & Peplau, 1981). Research has used cross-sectional methods to examine behavioral consequences of loneliness (e.g., Cacioppo et al., 2002). However, within-person associations between daily fluctuations in loneliness and subsequent behavioral outcomes have yet to be explored. Using a sample of community-dwelling adults, the authors examined associations between daily loneliness on daily time with others, and subsequent context-specific alcohol consumption (i.e., social and solitary consumption), and individual differences in these patterns of behavior. Daytime loneliness significantly and uniquely predicted patterns of social behavior and context-specific consumption; time with others mediated loneliness-social consumption associations, but not loneliness-solitary consumption relationships. These findings contribute to existing literature by demonstrating the unique properties of solitary versus social consumption as behavioral responses to loneliness, thus addressing inconsistent findings regarding the effects of loneliness on alcohol consumption.

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          Most cited references29

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          Diary methods: capturing life as it is lived.

          In diary studies, people provide frequent reports on the events and experiences of their daily lives. These reports capture the particulars of experience in a way that is not possible using traditional designs. We review the types of research questions that diary methods are best equipped to answer, the main designs that can be used, current technology for obtaining diary reports, and appropriate data analysis strategies. Major recent developments include the use of electronic forms of data collection and multilevel models in data analysis. We identify several areas of research opportunities: 1. in technology, combining electronic diary reports with collateral measures such as ambulatory heart rate; 2. in measurement, switching from measures based on between-person differences to those based on within-person changes; and 3. in research questions, using diaries to (a) explain why people differ in variability rather than mean level, (b) study change processes during major events and transitions, and (c) study interpersonal processes using dyadic and group diary methods.
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            Centering Decisions in Hierarchical Linear Models: Implications for Research in Organizations

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              Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations.

              The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects. Simulations show that the estimates are unbiased under most conditions. Confidence intervals based on a normal approximation or a simulated sampling distribution perform well when the random effects are normally distributed but less so when they are nonnormally distributed. These methods are further developed to address hypotheses of moderated mediation in the multilevel context. An example demonstrates the feasibility and usefulness of the proposed methods.
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                Author and article information

                Journal
                Personality and Social Psychology Bulletin
                Pers Soc Psychol Bull
                SAGE Publications
                0146-1672
                1552-7433
                March 10 2015
                March 10 2015
                : 41
                : 5
                : 615-628
                Article
                10.1177/0146167215569722
                25758705
                14aaf6b9-8b4c-41d0-9eb8-8cbe2f946910
                © 2015
                History

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