title: Time series analysis for psychological research: examining and forecasting change.
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.
authors.affiliation: Department of Psychological Sciences, Purdue University West Lafayette, IN, USA.
authors.forename: Andrew T
authors.initials: AT
authors.lastname: Jebb
authors.affiliation: Department of Psychological Sciences, Purdue University West Lafayette, IN, USA.
authors.forename: Louis
authors.initials: L
authors.lastname: Tay
authors.affiliation: Department of Psychology, University of Central Florida Orlando, FL, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of Statistics, Purdue University West Lafayette, IN, USA.
authors.forename: Qiming
authors.initials: Q
authors.lastname: Huang
keywords: ARIMA; forecasting; longitudinal data analysis; regression analysis; time series analysis