No abstract

Interest in cross-cultural traffic signs is in part motivated by the increase of motorists driving in foreign countries. This study investigated comprehension levels of 100 international road signs and the effect of brief sign training with the associated referent (sign meaning) on subsequent comprehension. Using open-ended questions U.S. drivers were tested on their ability to correctly report the meaning and action associated with various international road signs. Later they were exposed to the textual referent in a brief 5 minute training exercise. Following the training exercise, comprehension was retested. For many signs initial comprehension levels were low and critical confusions (serious errors) were high. However, after a brief training session comprehension levels dramatically improved. The results indicate that U.S. drivers may have difficulty understanding traffic signage outside of the U.S. To some extent training of the sign meanings might counteract low comprehension and high critical confusions. More extensive training or redesign may be needed to ensure U.S. drivers understand particularly highly signs.

No abstract

Multimode data collection has emerged as a common approach for conducting household surveys in the United States. A number of different data collection schemes have been investigated, with an emphasis on collecting as many respondents by the Web prior to going to paper data collection to reduce costs. Despite this, little research has been conducted on the approaches to weighting data from multimode surveys. The typical approach assumes that all respondents should be treated the same regardless of mode even though it is well known that the response patterns by mode vary substantially. We examine an adaptive mode adjustment to address these differences and propose an imbalance measure to help determine the adjustment factor using ideas from responsive design. We then compare the effects of the alternative weighting method in two recent sequential mixed-mode surveys and show it appears to reduce bias while only slightly increasing variances of the estimates.