Publications and Research
Document Type
Article
Publication Date
6-9-2015
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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Data Science Commons, Longitudinal Data Analysis and Time Series Commons, Quantitative Psychology Commons, Statistical Models Commons
Comments
This article was originally published in Frontiers in Psychology, available at https://doi.org/10.3389/fpsyg.2015.00727
This work is distributed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).