Time Series and Stochastic Processes

Peter Halpin, Lu Ou, Michelle LaMar

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter addresses some statistical modeling approaches for time series data and discusses their potential for psychometric applications. We adopt a broad conceptualization of time series, including under this rubric any type of data that involves serial statistical dependence. Such dependence may be represented in continuous time, discrete time, or in a purely sequential manner. This chapter begins by discussing the relationships among these three representations and offers some general advice on when each might prove useful. We then provide an overview of three modeling frameworks that exemplify the different representations of statistical dependence: Markov decision processes, state-space modeling, and temporal point processes. For each modeling framework, we discuss its specification, its psychometric interpretation, and provide a brief numeric example including R code.

Original languageEnglish (US)
Title of host publicationMethodology of Educational Measurement and Assessment
PublisherSpringer Nature
Number of pages22
StatePublished - 2021

Publication series

NameMethodology of Educational Measurement and Assessment
ISSN (Print)2367-170X
ISSN (Electronic)2367-1718


  • Markov decision process
  • Process data
  • State-space modeling
  • Temporal point process

ASJC Scopus subject areas

  • Education
  • Development


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