The authors integrate research on impulsivity from the psychology area with standard economic theories of consumer demand to make novel predictions about the effects of market price changes on consumers’ food consumption behavior. The results from multiple studies confirm that consumers exhibit undesirable asymmetric patterns of demand sensitivity to price changes for healthy and unhealthy food. For healthy food, demand sensitivity is greater for a price increase than for a price decrease. For unhealthy food, the opposite holds true. The research further shows that the undesirable patterns are attenuated or magnified for key policy-relevant factors that have been shown to decrease or increase impulsive purchase behavior, respectively. As the rising obesity trend brings American consumers’ food consumption behavior under increased scrutiny, the focal findings hold significant implications for both public policy makers and food marketers.

Three experiments examine how prevention-focused and promotion-focused consumers evaluate the comparison brand and what information they anchor on in direct comparative ads framed positively or negatively. Negative (vs. positive) frames lead prevention-focused respondents to exhibit higher evaluations for the advertised brand and lower evaluations for the comparison brand. Under promotion focus, positive (vs. negative) frames lead to more favorable attitudes toward the advertised brand with no difference in attitudes for the comparison brand. Preference for consistency is posited as a possible process explanation. We also find an evaluation order effect: prevention-focused (promotion-focused) individuals evaluate the comparison (advertised) brand first. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc..

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise methods such as forward selection and backward elimination are provided. The user may specify Mallows's Cp, Akaike's information criterion, Akaike's corrected information criterion, Bayesian information criterion, or R 2 adjusted as the information criterion for the selection. When the user specifies the best subset option, the leaps-and-bounds algorithm (Furnival and Wilson, Technometrics 16: 499-511) is used to determine the best subsets of each predictor size. All the previously mentioned information criteria are reported for each of these subsets. We also provide options for doing variable selection only on certain predictors (as in [R] nestreg) and support for weighted linear regression. All options are demonstrated on real datasets with varying numbers of predictors.

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise methods such as forward selection and backward elimination are provided. The user may specify Mallows's Cp, Akaike's information criterion, Akaike's corrected information criterion, Bayesian information criterion, or R 2 adjusted as the information criterion for the selection. When the user specifies the best subset option, the leaps-and-bounds algorithm (Furnival and Wilson, Technometrics 16: 499-511) is used to determine the best subsets of each predictor size. All the previously mentioned information criteria are reported for each of these subsets. We also provide options for doing variable selection only on certain predictors (as in [R] nestreg) and support for weighted linear regression. All options are demonstrated on real datasets with varying numbers of predictors.

The relatively low level of concern about climate change among Americans has important implications for climate policy. While many studies have examined individual characteristics associated with climate change attitudes, fewer studies have considered the effects of environmental conditions on such attitudes. Here, we use two national samples of American adults to explore the impact of abnormal daily temperatures on levels of concern about climate change. We test the hypotheses that (1) abnormally warm temperatures, and (2) both abnormally warm and abnormally cool temperatures are associated with higher levels of concern. Using a generalized ordinal logit, we find that the quadratic form of deviation from mean temperature on the date of the survey is significantly associated with higher levels of concern, thus supporting the second hypothesis. We discuss several theoretical frameworks that may explain this result including availability bias, mental models, and implicit stimuli, and the implications for climate policy.