State-Space Modeling of Dynamic Psychological Processes via the Kalman Smoother Algorithm: Rationale, Finite Sample Properties, and Applications

Abstract

This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a Monte Carlo study is conducted. Results indicate that the estimates of factor loading matrix, transition matrix, and unique variances were asymptotically normal, accurate, precise, and robust, especially for moderate and long time series. The estimates of state residual variances were positively biased for shorter time series, but as the length of series increased, these estimates became accurate and precise. To illustrate the application of SSM the technique is applied to empirical multivariate time-series data on daily affect collected from 2 individuals in a dating couple.

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