Psychometrics and Educational Measurement

The Section “Psychometrics and Educational Measurement” seeks to publish original methodological and applied research that target measurement in psychology and education. In this section, articles are published that focus on

• Methodological developments in psychological and educational measurement that propose new methods;
• Discussions of theory, practice, and foundations of psychological and educational measurement
• Applications of innovative methodology;
• Empirical and conceptual comparisons of different psychometric methods;
• Critical reviews on current practice and perspectives of methodology in psychometrics;
• Tutorial papers about the applicability of a particular psychometric method;
• Software papers (e.g., for R packages), software tutorials, and software reviews.

Typical classes of statistical methods and approaches can include, but are not limited to:

• Item response models
• Latent class models
• Structural equation models
• Mixed effects models
• Multivariate data analysis
• Diagnostic classification models
• Psychometric network models
• Linking and equating
• Validity and validation
• Reliability and generalizability
• Adaptive and multistage testing
• Analysis of process data (e.g., log data and reaction times)
• Methodology for large-scale assessment studies
• Psychometric approaches in educational data mining and learning analytics
• Machine learning and artificial intelligence in educational measurement
• Computational issues, algorithms, and software development in psychometrics