Markov chain models for inspecting response dynamics in psychological
  testing
Andrea Bosco
The importance of considering contextual probabilities in shaping response
patterns within psychological testing is underscored, despite the ubiquitous
nature of order effects discussed extensively in methodological literature.
Drawing from concepts such as path-dependency, first-order autocorrelation,
state-dependency, and hysteresis, the present study is an attempt to address
how earlier responses serve as an anchor for subsequent answers in tests,
surveys, and questionnaires. Introducing the notion of non-commuting
observables derived from quantum physics, I highlight their role in
characterizing psychological processes and the impact of measurement
instruments on participants' responses. We advocate for the utilization of
first-order Markov chain modeling to capture and forecast sequential
dependencies in survey and test responses. The employment of the first-order
Markov chain model lies in individuals' propensity to exhibit partial focus to
preceding responses, with recent items most likely exerting a substantial
influence on subsequent response selection. This study contributes to advancing
our understanding of the dynamics inherent in sequential data within
psychological research and provides a methodological framework for conducting
longitudinal analyses of response patterns of test and questionnaire.