Statistical Applications in Educational Measurement

Research output: Contribution to journalArticlepeer-review


Educational measurement assigns numbers to individuals based on observed data to represent individuals educational properties such as abilities, aptitudes, achievements, progress, and performance. The current review introduces a selection of statistical applications to educational measurement, ranging from classical statistical theory (e.g., Pearson correlation and the Mantel-Haenszel test) to more sophisticated models (e.g., latent variable, survival, and mixture modeling) and statistical and machine learning (e.g., high-dimensional modeling, deep and reinforcement learning). Three main subjects are discussed: evaluations for test validity, computer-based assessments, and psychometrics informing learning. Specific topics include item bias detection, high-dimensional latent variable modeling, computerized adaptive testing, response time and log data analysis, cognitive diagnostic models, and individualized learning.

Original languageEnglish (US)
Pages (from-to)439-461
Number of pages23
JournalAnnual Review of Statistics and Its Application
StatePublished - Mar 7 2021


  • adaptive learning
  • cognitive diagnostic models
  • computerized adaptive testing
  • educational measurement
  • reliability
  • response time
  • validity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Statistical Applications in Educational Measurement'. Together they form a unique fingerprint.

Cite this