Introduction to measurement -- The one-parameter model -- Joint maximum likelihood parameter estimation -- Marginal maximum likelihood parameter estimation -- The two-parameter model -- The three-parameter model -- Rasch models for ordered polytomous data -- Non-Rasch models for ordered polytomous data -- Models for nominal polytomous data -- Models for multidimensional data -- Linking and equating -- Differential item functioning -- Multilevel IRT models.

In the decade of the 1970s, item response theory became the dominant topic for study by measurement specialists. But, the genesis of item response theory (IRT) can be traced back to the mid-thirties and early forties. In fact, the term "Item Characteristic Curve," which is one of the main IRT concepts, can be attributed to Ledyard Tucker in 1946. Despite these early research efforts, interest in item response theory lay dormant until the late 1960s and took a backseat to the emerging development of strong true score theory. While true score theory developed rapidly and drew the attention of leading psychometricians, the problems and weaknesses inherent in its formulation began to raise concerns. Such problems as the lack of invariance of item parameters across examinee groups, and the inadequacy of classical test procedures to detect item bias or to provide a sound basis for measurement in "tailored testing," gave rise to a resurgence of interest in item response theory. Impetus for the development of item response theory as we now know it was provided by Frederic M. Lord through his pioneering works (Lord, 1952; 1953a, 1953b). The progress in the fifties was painstakingly slow due to the mathematical complexity of the topic and the nonexistence of computer programs.

Item Response Theory clearly describes the most recently developed IRT models and furnishes detailed explanations of algorithms that can be used to estimate the item or ability parameters under various IRT models. Extensively revised and expanded, this edition offers three new chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo methods. It includes discussions on issues related to statistical theory, numerical methods, and the mechanics of computer programs for parameter estimation, which help to build a clear understanding of the computational demands and challenges of IRT estimation procedures.

By using familiar concepts from classical measurement methods and basic statistics, this book introduces the basics of item response theory (IRT) and explains the application of IRT methods to problems in test construction, identification of potentially biased test items, test equating and computerized-adaptive testing. The book also includes a thorough discussion of alternative procedures for estimating IRT parameters and concludes with an exploration of new directions in IRT research and development.

First thorough treatment of multidimensional item response theory Description of methods is supported by numerous practical examples Describes procedures for multidimensional computerized adaptive testing

This volume introdudes social science students and researchers to the theory and practice of the highly powerful methods of nonpatametric item response theory (IRT).

Every year roughly 100,000 fatal and injury crashes occur in the United States involving large trucks and buses. The Federal Motor Carrier Safety Administration (FMCSA) in the U.S. Department of Transportation works to reduce crashes, injuries, and fatalities involving large trucks and buses. FMCSA uses information that is collected on the frequency of approximately 900 different violations of safety regulations discovered during (mainly) roadside inspections to assess motor carriers' compliance with Federal Motor Carrier Safety Regulations, as well as to evaluate their compliance in comparison with their peers. Through use of this information, FMCSA's Safety Measurement System (SMS) identifies carriers to receive its available interventions in order to reduce the risk of crashes across all carriers. Improving Motor Carrier Safety Measurement examines the effectiveness of the use of the percentile ranks produced by SMS for identifying high-risk carriers, and if not, what alternatives might be preferred. In addition, this report evaluates the accuracy and sufficiency of the data used by SMS, to assess whether other approaches to identifying unsafe carriers would identify high-risk carriers more effectively, and to reflect on how members of the public use the SMS and what effect making the SMS information public has had on reducing crashes.