Complete case logistic regression with a dichotomised continuous outcome: a simulation study

Background A complete case logistic regression will give a biased estimate of the exposure odds ratio only if there is a multiplicative interaction between the exposure and outcome with respect to the probability of missingness – whereas linear regression with a continuous outcome is biased in more scenarios, including when only the outcome causes missingness. It is not clear whether a complete case logistic regression will give a biased estimate of the odds ratio if missingness depends on a continuous outcome but this outcome is dichotomised for the analysis – a common situation in epidemiology. Methods We investigated this using a simulation study and data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK birth cohort. We also examined whether any bias could be reduced by including a proxy for the binary outcome as an auxiliary variable in multiple imputation. Results There was negligible bias in the exposure odds ratio when the probability of being a complete case was independently associated with the exposure and (continuous) outcome but important bias in the presence of an interaction, particularly at high levels of missing data. Inclusion of the proxy led to significant bias reductions when this had high sensitivity and specificity in relation to the study outcome. Conclusions The robustness of logistic regression to missing data is maintained even when the outcome is a binary version of a continuous outcome. Bias due to an interaction between the exposure and outcome in their effect on selection could be reduced by including proxies for the missing outcome as auxiliary variables in MI. If such proxies are available, we would recommend using MI over a complete case analysis because, in practice, it would be difficult to rule out an interaction.

Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

Objective We examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Background Most existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. Method Seventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. Results Outcome bias and contrast effect significantly influence human operators’ trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him/herself. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. Conclusion Human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. Application Understanding the trust adjustment process enables accurate prediction of the operators’ moment-to-moment trust in automation and informs the design of trust-aware adaptive automation.

CAN EDUCATION ENHANCE THE GENERAL TRUST OF INDIVIDUALS?—BASED ON CFPS2018 EMPIRICAL ANALYSIS

Education, as the most important form of human capital investment, has many influences on individual development. In addition to economic benefits, educational investment can improve the general trust of individuals and produce non-economic benefits. In the study of the causal effect of education on general trust, the estimation method of OLS has some problems such as endogeneity. In order to avoid the outcome bias caused by endogenous problems, this paper uses the implementation of compulsory education law as the instrumental variable, and uses the two-stage least squares estimation method to explore the impact of education on individuals' general trust.

Measuring individual differences in cognitive biases: The Cognitive Bias Inventory

This article described the behavioral measurement of six classic cognitive biases (framing, availability, anchoring, overconfidence, hindsight/outcome bias, confirmation bias). Each measure showed a satisfactory level of reliability with regard both to internal consistency (mean Cronbach’s alpha = .77) and temporal stability (mean test-retest correlation = .71). Multivariate analysis supported the hypothesis that each cognitive bias captures specific decision-making processes as the six biases: (a) were virtually uncorrelated (mean correlation = .08), thus indicating no general decision-making competence factor, (b) were moderately correlated with other relevant constructs (the A-DMC components, cognitive ability, decision-making styles, and personality factors), (c) were more related to performance on a narrow domain of decision-making (the ability to overcome an intuitive wrong answer as measured by the CRT) than to the general success in real-life decision-making as measured by the Decision Outcomes Inventory (DOI). We introduce this set of behavioral tasks as the Cognitive Bias Inventory (CBI), a psychometric tool allowing for the reliable assessment of individual differences in six common, independent cognitive shortcuts. The CBI appears as a useful tool for future research on decision-making competence and how it relates to decision errors.