Time series analysis for psychological research: examining and forecasting change

Area of Science:

• Psychological research
• Quantitative psychology
• Behavioral science

Background:

• Psychological research increasingly requires the integration of temporal dynamics.
• Longitudinal designs and analyses enable the formalization and testing of temporal theories.
• Psychological researchers often lack adequate training in analyzing complex longitudinal data.

Purpose of the Study:

• Introduce time series analysis (TSA) as a valuable analytic domain for psychological research.
• Discuss the characteristics of time series data relevant to psychological inquiry.
• Survey various TSA modeling techniques applicable to psychological research questions.

Main Methods:

• Discussion of time series data characteristics.
• Survey of time series modeling techniques (e.g., pattern description, seasonal effects, event impact assessment, forecasting).
• Illustrative example using online job search behavior data.
• Supplementary R software tutorial for TSA implementation.

Main Results:

• Time series analysis provides methods to describe patterns of change in psychological variables.
• TSA can model seasonal effects and assess the immediate and long-term impacts of events.
• Forecasting future psychological variable values is achievable using TSA techniques.

Conclusions:

• Time series analysis offers a powerful framework for psychological researchers to analyze temporal dynamics.
• The presented methods and example demonstrate the practical application of TSA in psychology.
• TSA enhances the ability to understand and predict psychological phenomena over time.