Measurement properties of the Inventory of Cognitive Bias in Medicine (ICBM)

The Measurement of Individual Differences in Cognitive Biases: A Review and Improvement

Individual differences have been neglected in decision-making research on heuristics and cognitive biases. Addressing that issue requires having reliable measures. The author first reviewed the research on the measurement of individual differences in cognitive biases. While reliable measures of a dozen biases are currently available, our review revealed that some measures require improvement and measures of other key biases are still lacking (e.g., confirmation bias). We then conducted empirical work showing that adjustments produced a significant improvement of some measures and that confirmation bias can be reliably measured. Overall, our review and findings highlight that the measurement of individual differences in cognitive biases is still in its infancy. In particular, we suggest that contextualized (in addition to generic) measures need to be improved or developed.

Assessment of cognitive biases and biostatistics knowledge of medical residents: a multicenter, cross-sectional questionnaire study

PURPOSE

The aim of this study is to determine the perceived familiarity of medical residents with statistical concepts, assess their ability to integrate these concepts in clinical scenarios, and investigate their susceptibility to the gambler's fallacy and the conjunction fallacy.

METHODS

A multi-institutional, cross-sectional survey of Greek medical residents was performed. Participants were asked to indicate their familiarity with basic statistical concepts and answer clinically oriented questions designed to assess their biostatistics knowledge and cognitive biases. Univariate, bivariate, and multivariate statistical models were used for the evaluation of data.

RESULTS

Out of 153 respondents (76.5% response rate), only two participants (1.3%) were able to answer all seven biostatistics knowledge questions correctly while 29 residents (19%) gave incorrect answers to all questions. The proportion of correct answers to each biostatistics knowledge question ranged from 15 to 51.6%. Residents with greater self-reported familiarity were more likely to perform better on the respective knowledge question (all p<0.01). Multivariate analysis of the effect of individual resident characteristics on questionnaire performance showed that previous education outside Greece, primarily during medical school, was associated with lower biostatistics knowledge scores (p<0.001). A little more than half of the respondents (54.2%) answered the gambler's fallacy quiz correctly. Residents with higher performance on the biostatistics knowledge questions were less prone to the gambler's fallacy (odds ratio 1.38, 95% confidence intervals 1.12-1.70, p=0.003). Only 48 residents (31.4%) did not violate the conjunction rule.

CONCLUSIONS

A large number of medical residents are unable to correctly interpret crucial statistical concepts that are commonly found in the medical literature. They are also especially prone to the gambler's fallacy bias, which may undermine clinical judgment and medical decision making. Formalized systematic teaching of biostatistics during residency will be required to de-bias residents and ensure that they are proficient in understanding and communicating statistical information.

Factor analysis methods and validity evidence: a review of instrument development across the medical education continuum

PURPOSE

Instrument development consistent with best practices is necessary for effective assessment and evaluation of learners and programs across the medical education continuum. The author explored the extent to which current factor analytic methods and other techniques for establishing validity are consistent with best practices.

METHOD

The author conducted electronic and hand searches of the English-language medical education literature published January 2006 through December 2010. To describe and assess current practices, she systematically abstracted reliability and validity evidence as well as factor analysis methods, data analysis, and reported evidence from instrument development articles reporting the application of exploratory factor analysis and principal component analysis.

RESULTS

Sixty-two articles met eligibility criteria. They described 64 instruments and 95 factor analyses. Most studies provided at least one source of evidence based on test content. Almost all reported internal consistency, providing evidence based on internal structure. Evidence based on response process and relationships with other variables was reported less often, and evidence based on consequences of testing was not identified. Factor analysis findings suggest common method selection errors and critical omissions in reporting.

CONCLUSIONS

Given the limited reliability and validity evidence provided for the reviewed instruments, educators should carefully consider the available supporting evidence before adopting and applying published instruments. Researchers should design for, test, and report additional evidence to strengthen the argument for reliability and validity of these measures for research and practice.

Evaluating health information technology in community-based settings: lessons learned

Implementing health information technology (IT) at the community level is a national priority to help improve healthcare quality, safety, and efficiency. However, community-based organizations implementing health IT may not have expertise in evaluation. This study describes lessons learned from experience as a multi-institutional academic collaborative established to provide independent evaluation of community-based health IT initiatives. The authors' experience derived from adapting the principles of community-based participatory research to the field of health IT. To assist other researchers, the lessons learned under four themes are presented: (A) the structure of the partnership between academic investigators and the community; (B) communication issues; (C) the relationship between implementation timing and evaluation studies; and (D) study methodology. These lessons represent practical recommendations for researchers interested in pursuing similar collaborations.