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.

Self–Esteem among Chinese Cohorts: Its Temporal Trend and Its Relationships with Socioecological Factors, 1993–2016

“…Following previous work (Hamamura & Septarini, ; Trzesniewski & Donnellan, ), unstandardized coefficients were reported. Given that autocorrelation was a concern for time series analyses (Jebb et al, ; Varnumn & Grossmann, ), we conducted regression analyses accounting for autocorrelations using the R package [nlme] (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, ). When we compared the model with and without considering autocorrelations, the models accounting for autocorrelations were significantly improved statistically, p s < .004.…”

“…Neither study accounted for autocorrelation, which is an important issue in time series analyses (e.g. Jebb, Tay, Wang, & Huang, ; Varnumn & Grossmann, ).…”

Do changes in threat salience predict the moral content of sermons? The case of Friday Khutbas in Turkey

“…Time series data consist of multiple components, including trends, seasons, cycles, and residual values (Jebb, Tay, Wang, & Huang, ). Trends are the long‐term direction of changes in time series.…”

Buys-Ballot Technique for the Analysis of Time Series with a Cubic-Trend Component

“…In particular, the cubic trend model has found applications in Agronomy [19], Computational Statistics [11], Epidermology [20], Fishery [8], Meteorology [9] and Psychology [25,18].…”