McGaghie, William C. PhD; Cohen, Elaine R.; Wayne, Diane B. MD
There is no such thing as a valid test!” assert Clauser and colleagues.1 These scholars teach that validity is not a property of tests or examinations. Instead, validity is about the accuracy of decisions made from test scores for a focused reason. This rationale comes from advances in test score interpretation and use based chiefly on the work of Michael Kane.2–4 Kane presents a framework for test score interpretation that uses an argument-based approach to validity. According to this framework, an argument about the validity of a test score must be structured, coherent, and evidence based. The argument should progress from a test's origins to its administration, scoring, and interpretation. The argument-based approach involves a cascaded chain of reasoning and evidence that leads to claims about test score validity for a specific purpose, in a particular context, with a singular population.
After test design and development, the chain begins with scoring, evidence that the test was administered properly and that scores were derived and recorded accurately. The second component, generalization, involves evidence about score reliability including item or case sampling, test length, and score precision. The third component, extrapolation, requires “evidence that the observations represented by the test score are relevant to the target proficiency or construct measured by the test.”1 Finally, “the decision/interpretation component of the argument requires evidence in support of any theoretical framework required for score interpretation or evidence in support of decision rules.”1 An argument about the validity of a test score interpretation depends on logically consistent evidence for each of the four components and the integrity of the overall chain of reasoning.
The three-step United States Medical Licensing Examination (USMLE) is a key feature of medical personnel evaluation in North America. The purpose of the USMLE, expressed in the 2010 Bulletin of Information, is to provide “individual medical licensing authorities (‘state medical boards’) … a common evaluation system for applicants for medical licensure.”5 However, since the 1993 inception of the exam, O'Donnell and colleagues6 acknowledge that USMLE “board scores are often used for nonlicensure-related purposes [including] evaluation of examinees' levels of academic achievement, the evaluation of educational programs, and the selection of examinees into residency programs.” There are interpretive risks involved in using scores from a test like the USMLE for purposes beyond its pass/fail licensure intent. O'Donnell and colleagues6 caution, “If the USMLE is to be used for nonlicensure-related decisions, it is important to be able to interpret correctly the scores away from the pass/fail point.”
The Standards for Educational and Psychological Testing,7 published by the American Educational Research Association, the American Psychological Association, and the National Council for Measurement in Education, are the “gold standard” regarding the use of test scores for key personnel decisions. These standards have been endorsed by the American Board of Medical Specialties and the National Board of Medical Examiners (NBME). The standards assert that “appropriate use and sound interpretation of test scores … are the responsibility of the test user.” Specifically, Standard 1.3 states,
If validity for some common or likely interpretation [e.g., postgraduate residency selection] has not been investigated, or if the interpretation is inconsistent with available evidence, that fact should be made clear and potential users should be cautioned about making unsupported interpretations.7
Despite the assertion that USMLE scores are to be used only for licensure decisions, the Federation of State Medical Boards of the United States and the NBME allow USMLE Part 1 and 2 scores to be used for another nonvalidated purpose—residency application via the Electronic Residency Application Service (ERAS). The 2010 Bulletin of Information5 tells prospective residents, “If you use ERAS, you may request electronic transmittal of your USMLE transcript to residency programs that participate in ERAS.”
Residency program directors routinely use USMLE scores in the applicant selection process, despite its licensure intent. To illustrate, Green and colleagues8 recently reported the results of a national program directors survey on selection criteria for postgraduate residencies. Across all medical specialties, program directors ranked USMLE Step 1 and Step 2 scores second and fifth, respectively, in importance for resident selection. These findings are confirmed by a 2008 survey of residency program directors conducted by the National Resident Matching Program. In this large sample of almost 2,000 program directors, USMLE Step 1 score was the factor most commonly used when selecting candidates to interview.9 This assumes a validity argument can be made that links USMLE Step 1 and 2 scores with variables that matter in residency education.10 Such correlations have been demonstrated in studies involving supervisors' ratings of resident performance as outcome measures, although coefficients are modest and USMLE scores are overinterpreted.11–13 Research also shows that subjective clinical ratings of trainee performance frequently yield unreliable data that are subject to many sources of bias.14 A recent systematic review covering the medical education literature from 1955 to 2004 demonstrates that research to verify the presumptive correlation of USMLE scores (or their predecessors) and objective measures of medical trainees' clinical skills has not yet been reported.15
Given this history, is there strong validity evidence about using USMLE Step 1 and 2 scores for postgraduate residency selection beyond their licensure intent? Is the validity argument for residency selection structured, coherent, and evidence based?
The objectives of this report are to (1) study the chain of reasoning and evidence that underlies the use of USMLE Step 1 and 2 scores for postgraduate medical resident selection, and (2) evaluate the validity argument about the utility of USMLE scores for resident selection.
This is a research synthesis using the “critical review” approach advocated by Norman and Eva.16,17 These scholars argue that research reviews should be deliberately selective and critical, not exhaustive. This study extracts and summarizes (1) USMLE Step 1 and 2 scores and (2) reliable clinical performance data drawn from nine research reports published by Northwestern University investigators from 2005 to 2010. These were the only studies found in a search conducted during spring 2010 that assess the correlation between USMLE Step 1 and 2 scores and objective, reliable clinical performance evaluations. Our search strategy covered three literature databases (MEDLINE, Web of Knowledge, PsychINFO) and employed search terms and concepts (e.g., medical education, residency training, clinical skills, USMLE) and their Boolean combinations. We searched from 1990 to April 2010. We also reviewed reference lists of all selected manuscripts to identify additional reports. The intent was to perform a detailed and thorough search of peer-reviewed publications that have been judged for academic quality to assess the correlation between USMLE scores and clinical performance of advanced medical students and postgraduate trainees.
The research synthesis of the nine reports involves data from 393 medical students and residents across the five-year time span. The majority of participants were enrolled in Northwestern undergraduate and postgraduate training programs. However, nephrology fellows from three metropolitan Chicago programs also participated. The performance data concern clinical skill acquisition by third-year medical students, internal medicine residents, emergency medicine residents, and nephrology fellows. The skills include cardiac auscultation, central venous catheter (CVC) insertion, advanced cardiac life support (ACLS), communication with patients, thoracentesis, and temporary hemodialysis catheter (THDC) insertion. This study is a variation on the theme of secondary data analysis, synthesis, and presentation promoted by research methods scholars.18
We extracted and tabulated correlations between USMLE Step 1 and 2 scores and reliable clinical performance scores from the nine research reports. Spearman rho correlations were calculated in each study to evaluate the association of USMLE Step 1 and 2 scores with reliable measures of student, resident, or subspecialty fellow acquisition of key clinical skills. Correlations are reported from the actual data and also corrected for attenuation (unreliability). Reliability coefficients (KR-21, alpha, and kappa) are data quality estimates ranging from 0.00 to 1.00. Reliability values above 0.80 are considered acceptable for research and evaluation. Measures of clinical skills include an audiovisual evaluation of cardiac auscultation19 and observational checklist evaluations of CVC insertion, ACLS, communication with patients, thoracentesis, and THDC insertion.
Measures of clinical skills were diverse. Cardiac auscultation skills were assessed by the trainee's ability to perform a physical exam and formulate a clinical diagnosis based on findings. ACLS skills were evaluated by participants' team leadership and communication in addition to medical knowledge and patient care regarding basic and advanced patient resuscitation. Communication skills were measured by 14 physician attributes rated by patients. Three skills were predominantly technical (CVC insertion, THDC insertion, thoracentesis). However, these procedural assessments also included components such as history taking, medical decision making, and patient communication.
A summary of the correlations of USMLE Step 1 and 2 scores with reliable measures of clinical skill acquisition among medical student, resident, and fellow participants from the nine studies is presented in Table 1.20–28 For USMLE Step 1, the correlations range from −0.05 to 0.29 (median = 0.02); none are statistically significant. For USMLE Step 2, the correlations range from −0.16 to 0.24 (median = 0.18); one is statistically significant, yet accounts for a meager proportion of the variation among the scores (0.232 = 5%). When correlations are corrected for attenuation, they range from −0.06 to 0.33 (median = 0.03) for USMLE Step 1. For Step 2, the corrected correlations range from −0.03 to 0.27 (median = 0.22).
USMLEs are carefully crafted measures of acquired medical knowledge that are administered and scored under standardized conditions. These characteristics fulfill the scoring link in the validity argument chain. There is evidence5 that USMLE Step 1 and Step 2 scores are highly reliable to satisfy the generalization link in the validity argument chain. However, USMLE Step 1 and Step 2 scores fall short on grounds of extrapolation because they lack association with measures of clinical skills that matter among advanced medical students, residents, and subspecialty fellows. The validity argument also breaks down in terms of decision/interpretation because the absence of an empirical link between USMLE scores and measured clinical skill acquisition shows that the examination scores do not have clinical correlates. By contrast, there is much evidence from medical education that multiple-choice test scores are correlated strongly with other multiple-choice test scores.29,30 In this context, high correlations among scores are due to common measurement methods rather than a link to a consistent trait like clinical competence.29
Use of USMLE Step 1 and 2 scores for postgraduate resident selection is a decision rule that is not evidence based unless the target outcome is another multiple-choice test. In this case, the validity argument makes sense only if the purpose of resident selection is to choose trainees who achieve high USMLE Step 1 and Step 2 scores and high scores on multiple-choice specialty board examinations. Measures used for resident selection that do not capture “real world” skills needed for clinical practice contribute little to the chain of validation reasoning whose end point is measured clinical competence.31
This research synthesis demonstrates that USMLE Step 1 and 2 scores are not correlated with reliable measures of students', residents', and fellows' clinical skill acquisition—cardiac auscultation, CVC insertion, ACLS, communication with patients, thoracentesis, and THDC insertion. These are competencies and skills that matter on clinical and professional grounds. Studying these correlations at multiple levels—students, junior and senior postgraduate trainees, and subspecialty fellows—shows that USMLE scores do not correlate with clinical skills near the time of the examinations or during subsequent clinical training.
The argument that USMLE Step 1 and 2 scores are valid predictors of clinical performance that matters is not sustained by the evidence presented here. Links in the validity argument involving extrapolation and decision/interpretation are not supported by these data, and the integrity of the chain of reasoning is broken. This idea is not new. Scholars have pointed out for at least 20 years that USMLE Step 1 and 2 scores, and their predecessors, are not designed for use in postgraduate resident selection and are not linked with clinical performance.32,33
The results of this data synthesis are consistent, but we acknowledge that the number of studies reviewed is small and primarily from trainees at one institution. Also, we reviewed a wide range of skills among medical trainees, yet it is impossible to assess all physician skills for correlations with USMLE Step 1 and 2 scores.
What are alternative approaches to sort and select physicians for competitive postgraduate residency positions? Are there measures that are better linked with clinical competence acquisition? Several studies have been reported that hold promise for improved measurement policy and practice about selecting medical learners. The measures include the University of Michigan's Postgraduate Orientation Assessment,34 an OSCE for incoming residents; the Israeli MOR (a Hebrew acronym for “selection for medicine”), a simulation-based assessment center for evaluating the personal and interpersonal qualities of medical school candidates35; and the multiple mini-interview developed at McMaster University to evaluate medical candidates at undergraduate and postgraduate levels.36 Each of these measurement procedures relies on practical evaluations of candidates' technical, professional, and interpersonal skills rather than measures of acquired knowledge. Strengths of these studies include assessment of skills needed for actual patient care and use of assessment measures that yield reliable data. This approach measures competence rather than intelligence37 and is designed to select doctors who will provide high-quality patient care rather than achieve high multiple-choice test scores. Further study is needed to link assessment strategies such these with enhanced residency selection procedures and subsequent trainee performance. It is also necessary to develop and adopt these alternative approaches on a larger scale for availability to program directors nationally.
The USMLE Step 1 and 2 examinations and their scores are designed to contribute to medical licensure decisions. Use of these scores for other purposes, especially postgraduate residency selection, is not grounded in a validity argument that is structured, coherent, and evidence based. Continued use of USMLE Step 1 and 2 scores for postgraduate medical residency selection decisions is discouraged.
The authors are indebted to Steven M. Downing, S. Barry Issenberg, and Emil R. Petrusa for critical comments about earlier drafts of the manuscript.
Dr. McGaghie's contribution was supported in part by the Jacob R. Suker, MD, professorship in medical education at Northwestern University and by grant UL1 RR 025741 from the National Center for Research Resources, National Institutes of Health. The National Institutes of Health had no role in the preparation, review, or approval of the manuscript.
1Clauser BE, Margolis MJ, Swanson DB. Issues of validity and reliability for assessments in medical education. In: Holmboe ES, Hawkins RE, eds. Practical Guide to the Evaluation of Clinical Competence. Philadelphia, Pa: Mosby Elsevier; 2008.
2Kane MT. Validation. In: Brennan RL, ed. Educational Measurement. 4th ed. Westport, Conn: American Council on Education and Praeger Publishers; 2006.
3Kane MT. An argument-based approach to validity. Psychol Bull. 1992;112:527–535.
4Kane MT. Content-related validity evidence in test development. In: Downing SM, Haladyna TM, eds. Handbook of Test Development. Mahwah, NJ: Lawrence Erlbaum Associates; 2006:131–153.
5United States Medical Licensing Examination 2010. Bulletin of Information. Philadelphia, Pa: Federation of State Medical Boards of the United States and the National Board of Medical Examiners; 2009.
7American Educational Research Association; American Psychological Association; National Council on Measurement in Education. Standards for Educational and Psychological Testing. Washington, DC: American Educational Research Association; 1999.
10Downing SM. Validity: On the meaningful interpretation of assessment data. Med Educ. 2003;37:830–837.
14Williams RG, Klamen DA, McGaghie WC. Cognitive, social, and environmental sources of bias in clinical competence ratings. Teach Learn Med. 2003;15:270–292.
15Hamdy H, Prasad K, Anderson MB, et al. BEME systematic review: Predictive values of measurements obtained in medical schools and future performance in medical practice. Med Teach. 2006;28:103–116.
16Norman G, Eva KW. Quantitative Research Methods in Medical Education. Edinburgh, UK: Association for the Study of Medical Education; 2008.
17Eva KW. On the limits of systematicity. Med Educ. 2008;42:852–853.
18Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, Mass: Houghton Mifflin; 2002.
19The MIAMI Group. UMedic User Manual. Miami, Fla: Gordon Center for Research in Medical Education, University of Miami Miller School of Medicine; 2007.
20Butter J, McGaghie WC, Cohen ER, Kaye M, Wayne DB. Simulation-based mastery learning improves cardiac auscultation skills in medical students. J Gen Intern Med. 2010;25:780–785.
21Barsuk JH, McGaghie WC, Cohen ER, Balachandran JS, Wayne DB. Use of simulation-based mastery learning to improve the quality of central venous catheter placement in a medical intensive care unit. J Hosp Med. 2009;4:397–403.
22Barsuk JH, McGaghie WC, Cohen ER, O'Leary KS, Wayne DB. Simulation-based mastery learning reduces complications during central venous catheter insertion in a medical intensive care unit. Crit Care Med. 2009;37:2697–2701.
23Wayne DB, Butter J, Siddall VJ, et al. Simulation-based training of internal medicine residents in advanced cardiac life support protocols: A randomized trial. Teach Learn Med. 2005;17:210–216.
24Wayne DB, Butter J, Siddall VJ, et al. Mastery learning of advanced cardiac life support skills by internal medicine residents using simulation technology and deliberate practice. J Gen Intern Med. 2006;21:251–256.
25Wayne DB, Didwania A, Cohen ER, Schroedl C, McGaghie WC. Improving the quality of cardiac arrest medical team responses at an academic teaching hospital. Am J Respir Crit Care Med. 2010;181:A1453.
26Makoul G, Krupat E, Chang C-H. Measuring patient views of physician communication skills: Development and testing of the Communication Assessment Tool. Patient Educ Couns. 2007;67:333–342.
27Wayne DB, Barsuk JH, O'Leary KS, Fudala MJ, McGaghie WC. Mastery learning of thoracentesis skills by internal medicine residents using simulation technology and deliberate practice. J Hosp Med. 2008;3:48–54.
28Barsuk JH, Ahya SN, Cohen ER, McGaghie WC, Wayne DB. Mastery learning of temporary hemodialysis catheter insertion skills by nephrology fellows using simulation technology and deliberate practice. Am J Kidney Dis. 2009;54:70–76.
29Forsythe GB, McGaghie WC, Friedman CP. Construct validity of medical clinical competence measures: A multitrait-multimethod matrix study using confirmatory factor analysis. Am Educ Res J. 1986;23:315–336.
30Perez JA, Greer S. Correlation of United States Medical Licensing Examination and internal medicine in-training examination performance. Adv Health Sci Educ Theory Pract. 2009;14:753–758.
35Ziv A, Rubin O, Moshinsky A, et al. MOR: A simulation-based assessment centre for evaluating the personal and interpersonal qualities of medical school candidates. Med Educ. 2008;42:991–998.
36Eva KW, Reiter HI, Trinh K, et al. Predictive validity of the multiple mini-interview for selecting medical trainees. Med Educ. 2009;43:767–775.
37McClelland DC. Testing for competence rather than for “intelligence.” Am Psychol. 1973;28:1–14.