validity threats

Validity Threats and Psychometric Forensics

Nathan Thompson, PhDApril 10, 2024

Validity threats are issues with a test or assessment that hinder the interpretations and use of scores, such as cheating, inappropriate use of scores, unfair preparation, or non-standardized delivery.  It is important to establish a test security plan to define the threats relevant for you and address them.

Validity, in its modern conceptualization, refers to evidence that supports our intended interpretations of test scores (see Chapter 1 of APA/AERA/NCME Standards for full treatment).   The word “interpretation” is key because test scores can be interpreted in different ways, including ways that are not intended by the test designers.  For example, a test given at the end of Nursing school to prepare for a national licensure exam might be used by the school as a sort of Final Exam.  However, the test was not designed for this purpose and might not even be aligned with the school’s curriculum.  Another example is that certification tests are usually designed to demonstrate minimal competence, not differentiate amongst experts, so interpreting a high score as expertise might not be warranted.

Validity threats: Always be on the lookout!

Test sponsors, therefore, must be vigilant against any validity threats.  Some of these, like the two aforementioned examples, might be outside the scope of the organization.  While it is certainly worthwhile to address such issues, our primary focus is on aspects of the exam itself.

Which validity threats rise to the surface in psychometric forensics?

Here, we will discuss several threats to validity that typically present themselves in psychometric forensics, with a focus on security aspects.  However, I’m not just listing security threats here, as psychometric forensics is excellent at flagging other types of validity threats too.

Threat Description Approach Example Indices
Collusion (copying) Examinees are copying answers from one another, usually with a defined Source. Error similarity (only looks at incorrect) 2 examinees get the same 10 items wrong, and select the same distractor on each B-B Ran, B-B Obs, K, K1, K2, S2
Response similarity 2 examinees give the same response on 98/100 items S2, g2, ω, Zjk
Group level help/issues Similar to collusion but at a group level; could be examinees working together, or receiving answers from a teacher/proctor.  Note that many examinees using the same brain dump would have a similar signature but across locations. Group level statistics Location has one of the highest mean scores but lowest mean times Descriptive statistics such as mean score, mean time, and pass rate
Response or error similarity On a certain group of items, the entire classroom gives the same answers Roll-up analysis, such as mean collusion flags per group; also erasure analysis (paper only)
Pre-Knowledge Examinee comes in to take the test already knowing the items and answers, often purchased from a brain dump website. Time-Score analysis Examinee has high score and very short time RTE or total time vs. scores
Response or error similarity Examinee has all the same responses as a known brain dump site All indices
Pretest item comparison Examinee gets 100% on existing items but 50% on new items Pre vs Scored results
Person fit Examinee gets the 10 hardest items correct but performs below average on the rest of the items Guttman indices, lz
Harvesting Examinee is not actually taking the test, but is sitting it to memorize items so they can be sold afterwards, often at a brain dump website.  Similar signature to Sleepers but more likely to occur on voluntary tests, or where high scores benefit examinees. Time-Score analysis Low score, high time, few attempts. RTE or total time vs. scores
Mean vs Median item time Examinee “camps” on 10 items to memorize them; mean item time much higher than the median Mean-Median index
Option flagging Examinee answers “C” to all items in the second half Option proportions
Low motivation: Sleeper Examinees are disengaged, producing data that is flagged as unusual and invalid; fortunately, not usually a security concern but could be a policy concern. Similar signature to Harvester but more likely to occur on mandatory tests, or where high scores do not benefit examinees. Time-Score analysis Low score, high time, few attempts. RTE or total time vs. scores
Item timeout rate If you have item time limits, examinee hits them Proportion items that hit limit
Person fit Examinee attempt a few items, passes through the rest Guttman indices, lz
Low motivation: Clicker Examinees are disengaged, producing data that is flagged as unusual and invalid; fortunately, not usually a security concern but could be a policy concern. Similar idea to Sleeper but data is quite different. Time-Score analysis Examinee quickly clicks “A” to all items, finishing with a low time and low score RTE, Total time vs. scores
Option flagging See above Option proportions

Psychometric Forensics to Find Evidence of Cheating

An emerging sector in the field of psychometrics is the area devoted to analyzing test data to find cheaters and other illicit or invalid testing behavior.  There is a distinction between primary and secondary collusion, and there are specific collusion detection indices and methods to investigate aberrant testing behavior, such as

While research on this topic is more than 50 years old, the modern era did not begin until Wollack published his paper on the Omega index in 1997. Since then, the sophistication and effectiveness of methodology in the field has multiplied, and many more publications focus on it than in the pre-Omega era. This is evidenced by not one but three recent books on the subject:

  1. Wollack, J., & Fremer, J. (2013).  Handbook of Test Security.
  2. Kingston, N., & Clark, A. (2014).  Test Fraud: Statistical Detection and Methodology.
  3. Cizek, G., & Wollack, J. (2016). Handbook of Quantitative Methods for Detecting Cheating on Tests.

 

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Nathan Thompson, PhD

Nathan Thompson earned his PhD in Psychometrics from the University of Minnesota, with a focus on computerized adaptive testing. His undergraduate degree was from Luther College with a triple major of Mathematics, Psychology, and Latin. He is primarily interested in the use of AI and software automation to augment and replace the work done by psychometricians, which has provided extensive experience in software design and programming. Dr. Thompson has published over 100 journal articles and conference presentations, but his favorite remains https://scholarworks.umass.edu/pare/vol16/iss1/1/ .
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