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      Time series analysis for psychological research: examining and forecasting change

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          Abstract

          Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

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          Effects of daily stress on negative mood.

          This article examines the influence of daily stressors on mental health in a community sample. Ss were 166 married couples who completed diaries each day for 6 weeks. In pooled within-person analyses, daily stressors explained up to 20% of the variance in mood. Interpersonal conflicts were by far the most distressing events. Furthermore, when stressors occurred on a series of days, emotional habituation occurred by the second day for almost all events except interpersonal conflicts. Contrary to certain theoretical accounts, multiple stressors on the same day did not exacerbate one another's effects: rather an emotional plateau occurred. Finally on days following a stressful event, mood was better than it would have been if the stressor had not happened. These results reveal the complex emotional effects of daily stressors, and in particular they suggest that future investigations should focus primarily on interpersonal conflicts.
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            Using internet searches for influenza surveillance.

            The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.
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              The Role of Time in Theory and Theory Building

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                09 June 2015
                2015
                : 6
                : 727
                Affiliations
                [1] 1Department of Psychological Sciences, Purdue University West Lafayette, IN, USA
                [2] 2Department of Psychology, University of Central Florida Orlando, FL, USA
                [3] 3Department of Statistics, Purdue University West Lafayette, IN, USA
                Author notes

                Edited by: Holmes Finch, Ball State University, USA

                Reviewed by: Anne C. Black, Yale University, USA; Tim J. Croudace, University of Dundee, UK

                *Correspondence: Andrew T. Jebb, Department of Psychological Sciences, Purdue University, 703 Third Street, West Lafayette, IN 47907, USA ajebb@ 123456purdue.edu

                This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2015.00727
                4460302
                26106341
                32a781c9-df46-47b9-9765-771048cacf34
                Copyright © 2015 Jebb, Tay, Wang and Huang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 March 2015
                : 15 May 2015
                Page count
                Figures: 10, Tables: 4, Equations: 12, References: 76, Pages: 24, Words: 19698
                Categories
                Psychology
                Methods

                Clinical Psychology & Psychiatry
                time series analysis,longitudinal data analysis,forecasting,regression analysis,arima

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