Entries by Nathan Thompson, PhD

What Is The Standard Error of Measurement?

The standard error of measurement (SEM) is one of the core concepts in psychometrics.  One of the primary assumptions of any assessment is that it is accurately and consistently measuring whatever it is we want to measure.  We, therefore, need to demonstrate that it is doing so.  There are a number of ways of quantifying […]

Psychometrist: What do they do?

A psychometrist is an important profession within the world of assessment and psychology.  Their primary role is to deliver and interpret assessments, typically the sorts of assessments that are delivered in a one-on-one clinical situation.  For example, they might give IQ tests to kids to identify those who qualify as Gifted, then explain the results […]

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What is Classical Item Difficulty (P Value)?

One of the core concepts in psychometrics is item difficulty.  This refers to the probability that examinees will get the item correct for educational/cognitive assessments or respond in the keyed direction with psychological/survey assessments (more on that later).  Difficulty is important for evaluating the characteristics of an item and whether it should continue to be part of […]

Item Banks: 6 Ways To Improve

The foundation of a decent assessment program is the ability to develop and manage strong item banks. Item banks are a central repository of test questions, each stored with important metadata such as Author or Difficulty. They are designed to treat items are reusable objects, which makes it easier to publish new exam forms. Of […]

Responses in Common (RIC) Index

This collusion detection (test cheating) index simply calculates the number of responses in common between a given pair of examinees.  For example, both answered ‘B’ to a certain item regardless of whether it was correct or incorrect.  There is no probabilistic evaluation that can be used to flag examinees.  However, it could be of good […]

Exact Errors in Common (EEIC) collusion detection

Exact Errors in Common (EEIC) is an extremely basic collusion detection index simply calculates the number of responses in common between a given pair of examinees. For example, suppose two examinees got 80/100 correct on a test. Of the 20 each got wrong, they had 10 in common. Of those, they gave the same wrong […]

Errors in Common (EIC) Exam Cheating Index

This exam cheating index (collusion detection) simply calculates the number of errors in common between a given pair of examinees.  For example, two examinees got 80/100 correct, meaning 20 errors, and they answered all of the same questions wrongly, the EIC would be 20. If they both scored 80/100 but had only 10 wrong questions […]

Harpp, Hogan, & Jennings (1996): Response Similarity Index

Harpp, Hogan, and Jennings (1996) revised their Response Similarity Index somewhat from Harpp and Hogan (1993). This produced a new equation for a statistic to detect collusion and other forms of exam cheating: . Explanation of Response Similarity Index EEIC denote the number of exact errors in common or identically wrong, D is the number […]

Harpp & Hogan (1993): Response Similarity Index

Harpp and Hogan (1993) suggested a response similarity index defined as Response Similarity Index Explanation EEIC denote the number of exact errors in common or identically wrong, EIC is the number of errors in common. This is calculated for all pairs of examinees that the researcher wishes to compare.  One advantage of this approach is that […]