Combining cognitive and statistical approaches to survey design

Sample surveys provide data for academic research, government policy-making, the media, and business. Statistical research aims to improve survey data by reducing extraneous sources of variability and thus increasing accuracy. Researchers have begun to use paradigms adapted from the cognitive sciences to study those sources of variability associated with the processes that the respondent undertakes in understanding questions, remembering, judging and estimating, and formulating answers. To generalize laboratory-based findings, researchers must begin to embed designed experiments that vary the questionnaire content into sample surveys of broad population. Issues associated with the design of and statistical inference from such embedded experiments are examined and illustrated with an example on the effects of context questions on responses in attitude surveys.     

The chemistry of solid-state electronics

Since the original theoretical insights of Bardeen and Shockley about 40 years ago, the progress of solid-state electronics has been paced by the ability to control chemical bonding structures, particularly at surfaces and interfaces. The functioning of solid-state devices depends on being able to produce interfacial structures with a minimum number of defective chemical bonds. A series of chemical discoveries and insights, on germanium (Ge) and silicon (Si) surfaces and gallium arsenide-aluminum arsenide (GaAs-AlAs) interfaces, has brought the electronics revolution to its present state of development. In most cases, the technological consequences of these accidental discoveries could not be accurately foreseen. With that caution, the technological prognosis for some current research is also reviewed.