Suitability of the HAM-Nat test and TMS module "basic medical-scientific understanding" for medical school selection

Tests with natural-scientific content are predictive of the success in the first semesters of medical studies. Some universities in the German speaking countries use the 'Test for medical studies' (TMS) for student selection. One of its test modules, namely "medical and scientific comprehension", measures the ability for deductive reasoning. In contrast, the Hamburg Assessment Test for Medicine, Natural Sciences (HAM-Nat) evaluates knowledge in natural sciences. In this study the predictive power of the HAM-Nat test will be compared to that of the NatDenk test, which is similar to the TMS module "medical and scientific comprehension" in content and structure.

162 medical school beginners volunteered to complete either the HAM-Nat (N=77) or the NatDenk test (N=85) in 2007. Until spring 2011, 84.2% of these successfully completed the first part of the medical state examination in Hamburg. Via different logistic regression models we tested the predictive power of high school grade point average (GPA or "Abiturnote") and the test results (HAM-Nat and NatDenk) with regard to the study success criterion "first part of the medical state examination passed successfully up to the end of the 7(th) semester" (Success7Sem). The Odds Ratios (OR) for study success are reported.

For both test groups a significant correlation existed between test results and study success (HAM-Nat: OR=2.07; NatDenk: OR=2.58). If both admission criteria are estimated in one model, the main effects (GPA: OR=2.45; test: OR=2.32) and their interaction effect (OR=1.80) are significant in the HAM-Nat test group, whereas in the NatDenk test group only the test result (OR=2.21) significantly contributes to the variance explained.

On their own both HAM-Nat and NatDenk have predictive power for study success, but only the HAM-Nat explains additional variance if combined with GPA. The selection according to HAM-Nat and GPA has under the current circumstances of medical school selection (many good applicants and only a limited number of available spaces) the highest predictive power of all models.