An integer programming approach to item pool design

Presented is an integer-programming approach to item pool design that can be used to calculate an optimal blueprint for an item pool to support an existing testing program. The results are optimal in the sense that they minimize the efforts involved in actually producing the items as revealed by current item writing patterns. Also presented is an adaptation of the models for use as a set of monitoring tools in item pool management. The approach is demonstrated empirically for an item pool designed for the Law School Admission Test

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits

A model for optimal constrained adaptive testing

A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum information at the current ability estimate fixing the items previously administered. Then the item with maximum information is selected from the test. All test assembly is optimal due to the use of a linear programming model that is automatically updated to allow for the attributes of the items already administered as well as the new value of the ability estimator. A simulation study using a pool of 753 items from the Law School Admission Test (LSAT) shows that for adaptive tests of realistic lengths the ability estimator did not suffer any loss of efficiency from the presence of 433 constraints on the item selection process

A model for optimal constrained adaptive testing

A model for constrained computerized adaptive testing is proposed in which the information in the test at the trait level (0) estimate is maximized subject to a number of possible constraints on the content of the test. At each item-selection step, a full test is assembled to have maximum information at the current 0 estimate, fixing the items already administered. Then the item with maximum in-formation is selected. All test assembly is optimal because a linear programming (LP) model is used that automatically updates to allow for the attributes of the items already administered and the new value of the 0 estimator. The LP model also guarantees that each adaptive test always meets the entire set of constraints. A simulation study using a bank of 753 items from the Law School Admission Test showed that the 0 estimator for adaptive tests of realistic lengths did not suffer any loss of efficiency from the presence of 433 constraints on the item selection process

LSAT Performance with Regional, Gender, and Racial/Ethnic Breakdowns: 2007-2008 Through 2013-2014 Testing Years

This report by Dalessandro and colleagues summarizes LSAT performance scores from 2007-2014. The document goes into great detail on the structure of the exam, and contains many data points regarding race, gender, and location. Many findings were discovered, including that test taking volume decreased by 38.5 percent from 2014 to 2009. Despite these changes, the overall distribution of LSAT scores remained fairly constant. The largest amount of test takers took the exam at the September/October and December administrations. On average, test takers who took the LSAT in either June or September/October reported slightly higher scores than those taking the exam in December or February