Nested multiple imputation in large-scale assessments

Issues in large - scale assessments Issues in Large - scale Assessments

In large-scale assessments such as state-wide testing programs, national sample-based surveys and international comparative studies, there are many steps involved in the measurement and reporting of student achievement. Typically, the steps involved include the following:  Items are developed and test instruments are assembled;  Student samples are selected (for sample-based surveys);  Tests...

Title: Multiple Imputation for Large-Scale Educational Assessments: A Fully Bayesian Approach using Stan Authors and Affiliations:

Computer-Based Large-Scale Assessments in Germany

In nearly all countries, technological changes in the field of digital media have raised great interest in procedures for testing students' performance by means of computer-based assessment. For extensive international tests (large-scale assessments) such as the Programme for International Student Assessment (PISA) or Trends in International Mathematics and Science Study (TIMSS), pilot studies ...

Large-scale Imputation for Complex Surveys

Much of the recent research into imputation methodology has focused on developing optimal procedures for a single variable or set of variables, where the patterns of missingness and underlying distributions follow standard distributions. In contrast, it is frequently necessary to impute for many variables from a single survey, with an even larger set of potential covariates and complex covarian...

Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys

In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian, joint modeling approach to multiple imputation fo...