Regime Switching State-Space Models Applied to Psychological Processes: Handling Missing Data and Making Inferences

• Published in: Psychometrika Vol. 77; no. 2; pp. 400 - 422
• Main Authors: Hamaker, E. L., Grasman, R. P. P. P.
• Format: Journal Article
• Language: English
• Published: New York Springer-Verlag 01.04.2012; Springer; Springer Nature B.V
• Subjects: Algorithms; Assessment; Behavioral Science and Psychology; Behavioral Sciences; Biological and medical sciences; Data; Data processing; Developmental Stages; Error of Measurement; Experimental Psychology; Females; Fundamental and applied biological sciences. Psychology; Hand; Humanities; Inferences; Latent Variables; Law; Mathematics; Maximum Likelihood Statistics; Menstrual cycle; Mental Disorders; Missing data; Pain; Performance evaluation; PMS; Post traumatic stress disorder; Posttraumatic Stress Disorder; Premenstrual syndrome; Psychiatry; Psychological Patterns; Psychology; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychometrics; Psychometrics. Statistics. Methodology; Resistance (Psychology); Simulation; Sleep; Sport Psychology; Statistical Inference; Statistical Theory and Methods; Statistics for Social Sciences; Statistics. Mathematics; Symptoms (Individual Disorders); Testing and Evaluation; Time series; state-space model; regime switching; Kalman filter; missing data; Missing data; Data handling; Inference; State space; Statistical estimation; Switching
• ISSN: 0033-3123; 1860-0980
• DOI: 10.1007/s11336-012-9254-8

Many psychological processes are characterized by recurrent shifts between distinct regimes or states. Examples that are considered in this paper are the switches between different states associated with premenstrual syndrome, hourly fluctuations in affect during a major depressive episode, and shifts between a “hot hand” and a “cold hand” in a top athlete. We model these processes with the regime switching state-space model proposed by Kim (J. Econom. 60:1–22, 1994 ), which results in both maximum likelihood estimates for the model parameters and estimates of the latent variables and the discrete states of the process. However, the current algorithm cannot handle missing data, which limits its applicability to psychological data. Moreover, the performance of standard errors for the purpose of making inferences about the parameter estimates is yet unknown. In this paper we modify Kim’s algorithm so it can handle missing data and we perform a simulation study to investigate its performance in (relatively) short time series in cases of different kinds of missing data and in case of complete data. Finally, we apply the regime switching state-space model to the three empirical data sets described above.