A Robust Method for Detecting Item Misfit in Large-Scale Assessments

Viable methods for the identification of item misfit or Differential Item Functioning (DIF) are central to scale construction and sound measurement. Many approaches rely on the derivation of a limiting distribution under the assumption that a certain model fits the data perfectly. Typical DIF assumptions such as the monotonicity and population independence of item functions are present even in classical test theory but are more explicitly stated when using item response theory or other latent variable models for the assessment of item fit. The work presented here provides a robust approach for DIF detection that does not assume perfect model data fit, but rather uses Tukey's concept of contaminated distributions. The approach uses robust outlier detection to flag items for which adequate model data fit cannot be established.

Electronic cigarette use and its association with asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap syndrome among never cigarette smokers

INTRODUCTION

Although smoking is a strong risk factor for lung diseases including asthma, COPD, and asthma-COPD overlap syndrome (ACOS), studies are needed to examine the association between e-cigarettes and asthma, COPD, and ACOS. This study evaluated the association between e-cigarette use and self-reported diagnosis of asthma, COPD, and ACOS using a large nationally representative sample of adults aged ≥18 years in the United States.

METHODS

Cross-sectional data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2016 to 2018 were used to examine self-reported information on current e-cigarette use, demographic variables, and asthma and COPD status among never cigarette smokers (n=8736). Asthma and COPD were measured by self-reported diagnosis, and respondents who reported having both diagnoses were then classified as having ACOS. Of the 469077 never cigarette smokers, 4368 non-e-cigarette users were 1:1 propensity score-matched to e-cigarette users on age, sex, race/ethnicity and education level. We used multinomial logistic regression to examine association between current e-cigarette use and self-report asthma, COPD, and ACOS while controlling for marital status and employment in addition to matching variables.

RESULTS

Compared with never e-cigarette users, e-cigarette users had increased odds of self-reported ACOS (OR=2.27; 95% CI: 2.23–2.31), asthma (OR=1.26; 95% CI: 1.25–1.27) and COPD (OR=1.44; 95% CI: 1.42–1.46).

CONCLUSIONS

Our findings suggest that e-cigarette use is associated with an increased odds of self-reported asthma, COPD, and ACOS among never combustible cigarette smokers. BRFSS provides cross-sectional survey data, therefore a causal relationship between e-cigarette use and the three lung diseases cannot be evaluated. Future longitudinal studies are needed to validate these findings.

Electronic cigarette use and its association with asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap syndrome among never cigarette smokers

INTRODUCTION

Although smoking is a strong risk factor for lung diseases including asthma, COPD, and asthma-COPD overlap syndrome (ACOS), studies are needed to examine the association between e-cigarettes and asthma, COPD, and ACOS. This study evaluated the association between e-cigarette use and self-reported diagnosis of asthma, COPD, and ACOS using a large nationally representative sample of adults aged ≥18 years in the United States.

METHODS

Cross-sectional data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2016 to 2018 was used to examine self-reported information on current e-cigarette use, demographic variables, and asthma and COPD status among never cigarette smokers (n=8736). Asthma and COPD were measured by self-reported diagnosis, and respondents who reported having both diagnoses were then classified as having ACOS. Of the 46079 never cigarette smokers, 4368 non-e-cigarette smokers were 1:1 propensity score-matched to e-cigarette smokers on age, sex, race/ethnicity and education level. We used multinomial logistic regression to examine association between current e-cigarette use and self-report asthma, COPD, and ACOS while controlling for marital status and employment in addition to matching variables.

RESULTS

Compared with never e-cigarette smokers, e-cigarette smokers had increased odds of self-reported ACOS (OR=2.27; 95% CI: 2.23–2.31), asthma (OR=1.26; 95% CI: 1.25–1.27) and COPD (OR=1.44; 95% CI: 1.42–1.46).

CONCLUSIONS

Data from this large nationally representative sample suggest that e-cigarette use is associated with increased odds of self-reported asthma, COPD, and ACOS among never combustible cigarette smokers. The odds of ACOS were twice as high among e-cigarette users compared with never smokers of conventional cigarettes. The findings from this study suggest the need to further investigate the long-term and short-term health effects of e-cigarette use, since the age of those at risk in our study was 18–24 years.

Modeling Item Revisit Behavior: The Hierarchical Speed-Accuracy-Revisits Model

This article presents a new approach to the analysis of how students answer tests and how they allocate resources in terms of time on task and revisiting previously answered questions. Previous research has shown that in high-stakes assessments, most test takers do not end the testing session early, but rather spend all of the time they were assigned to take the test. Rather than being an indication of speededness, this was found to be caused by test takers' tendency to revisit previous items even if they already provided answers to all questions. In accordance with this information, the proposed approach models revisit patterns simultaneously with responses and response times to gain a better understanding of the relationship between speed, ability, and revisit tendency. The empirical data analysis revealed that examinees' tendency to revisit items was strongly related to their speed and subgroups of examinees displayed different test-taking behaviors.