Wise and Kong (2005) defined an index to flag examinees not putting forth minimal effort, based on their response time. It is called the response time effort (RTE) index. Let K be the number of items in the test. The RTE for each examinee j is where TCji is 1 if the response time on […]
The Holland K index and variants are probability-based indices for psychometric forensics, like the Bellezza & Bellezza indices, but make use of conditional information in their calculations. All three estimate the probability of observing wij or more identical incorrect responses (that is, EEIC, exact errors in common) between a pair of examinees in a directional […]
Guttman errors are a concept derived from the Guttman Scaling approach to evaluating assessments. There are a number of ways that they can be used. Meijer (1994) suggests an evaluation of Guttman errors as a way to flag aberrant response data, such as cheating or low motivation. He quantified this with two different indices, G […]
Test security is an increasingly important topic. There are several causes, including globalization, technological enhancements, and the move to a gig-based economy driven by credentials. Any organization that sponsors assessments that have any stakes tied to them must be concerned with security, as the greater the stakes, the greater the incentive to cheat. And threats […]
Psychometric forensics is a surprisingly deep and complex field. Many of the indices are incredibly sophisticated, but a good high-level and simple analysis to start with is overall time vs. scores, which I call Time-Score Analysis. This approach uses simple flagging on two easily interpretable metrics (total test time in minutes and number correct raw score) […]
A psychometrician is a data scientist who studies how to develop and analyze exams so that they are reliable, valid, and fair. Using psychometrics, Psychometricians implement aspects of engineering, data science, and machine learning to ensure that tests provide accurate information about people, so we can be confident about decisions based on test scores. They […]
Artificial intelligence (AI) and machine learning (ML) have become buzzwords over the past few years. As I already wrote about, they are actually old news in the field of psychometrics. Factor analysis is a classical example of ML, and item response theory (IRT) also qualifies as ML. Computerized adaptive testing (CAT) is actually an application […]
If you have worked in the field of assessment and psychometrics, you have undoubtedly encountered the word “standard.” While a relatively simple word, it has the potential to be confusing because it is used in three (and more!) completely different but very important ways. Here’s a brief discussion. Standard = Cutscore As noted by the […]
The generalized partial credit model (GPCM, Muraki 1992) is an item response theory (IRT) model designed to work with items that are partial credit. That is, instead of just right/wrong as possible, scoring an examinee can receive partial points for completing some aspects of the item correctly. For example, a typical multiple-choice item is scored […]
If you are dealing with data science, which psychometrics most definitely is, you’ve probably come across R. It is an environment that allows you to implement packages for many different types of analysis, which are built by a massive community of data scientists around the world. R has become one of the two main languages […]
