Psychological Test and Assessment Modeling provides in a special issue a comprehensive overview of current approaches to analyze large scale assessment data and brings together different aspects of it. Guest Editors are Prof. Dr. Steffi Pohl (Freie Universität Berlin) and Dr. Christian Assmann (Otto Friedrich Universität Bamberg).
The special issue is interested in bringing together papers dealing with different methodological approaches of handling missing values in large scale studies. The papers stem from different disciplines, address different specific aspects of missing values, use different methodological perspectives, and apply their approaches to different large-scale assessment studies. By bringing them together in one issue, the editors and authors give an overview of the broad research field and enrich the view of the reader. The papers follow two different general approaches of dealing with missing values:
The first one uses model-based methods accounting for item nonresponse in competence test items modeled within Item Response Theory (IRT) models. These models have be proven to be very successful and the papers in the special issue further elaborate on them.
The other general approach is imputation, which is one of the state of the art methods for dealing with item nonresponse in variables not modeled within a latent variable framework. The papers in this special issue dealing with imputation extend that approach, making it applicable to the complex data structure of large-scale assessments.
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