Gary H. McClelland

Why do people pay taxes

Abstract Why do people pay taxes when they have an opportunity, even an incentive, to evade? The experimental results in this paper suggest that tax compliance occurs because some individuals overweight the low probability of audit, although such overweighting is not universal. The results also indicate that compliance does not occur simply because individuals believe that evasion is wrong, since subject behavior is unchanged by the use of either neutral or loaded terms. Finally, there is evidence that individuals pay taxes because they value the public goods that their taxes finance. In short, individuals exhibit much diversity in their behavior.

Estimating and testing mediation and moderation in within-subject designs.

Analyses designed to detect mediation and moderation of treatment effects are increasingly prevalent in research in psychology. The mediation question concerns the processes that produce a treatment effect. The moderation question concerns factors that affect the magnitude of that effect. Although analytic procedures have been reasonably well worked out in the case in which the treatment varies between participants, no systematic procedures for examining mediation and moderation have been developed in the case in which the treatment varies within participants. The authors present an analytic approach to these issues using ordinary least squares estimation.

Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression

It is common for researchers discovering a significant interaction of a measured variable X with a manipulated variable Z to examine simple effects of Z at different levels of X. These “spotlight” tests are often misunderstood even in the simplest cases, and it appears that consumer researchers are unsure how to extend them to more complex designs. The authors explain the general principles of spotlight tests, show that they rely on familiar regression techniques, and provide a tutorial demonstrating how to apply these tests across an array of experimental designs. Rather than following the common practice of reporting spotlight tests at one standard deviation above and below the mean of X, it is recommended that when X has focal values, researchers should report spotlight tests at those focal values. When X does not have focal values, it is recommended that researchers report ranges of significance using a version of Johnson and Neymans test the authors term a “floodlight.”

Misleading Heuristics and Moderated Multiple Regression Models

Moderated multiple regression models allow the simple relationship between the dependent variable and an independent variable to depend on the level of another independent variable. The moderated relationship, often referred to as the interaction, is modeled by including a product term as an additional independent variable. Moderated relationships are central to marketing (e.g., Does the effect of promotion on sales depend on the market segment?). Multiple regression models not including a product term are widely used and well understood. The authors argue that researchers have derived from this simpler type of multiple regression several data analysis heuristics that, when inappropriately generalized to moderated multiple regression, can result in faulty interpretations of model coefficients and incorrect statistical analyses. Using theoretical arguments and constructed data sets, the authors describe these heuristics, discuss how they may easily be misapplied, and suggest some good practices for estimating, testing, and interpreting regression models that include moderated relationships.

Negative Consequences of Dichotomizing Continuous Predictor Variables

Marketing researchers frequently split (dichotomize) continuous predictor variables into two groups, as with a median split, before performing data analysis. The practice is prevalent, but its effects are not well understood. In this article, the authors present historical results on the effects of dichotomization of normal predictor variables rederived in a regression context that may be more relevant to marketing researchers. The authors then present new results on the effect of dichotomizing continuous predictor variables with various nonnormal distributions and examine the effects of dichotomization on model specification and fit in multiple regression. The authors conclude that dichotomization has only negative consequences and should be avoided.

Designing, Testing, and Interpreting Interactions and Moderator Effects in Family Research

This article is a primer on issues in designing, testing, and interpreting interaction or moderator effects in research on family psychology. The first section focuses on procedures for testing and interpreting simple effects and interactions, as well as common errors in testing moderators (e.g., testing differences among subgroup correlations, omitting components of products, and using median splits). The second section, devoted to difficulties in detecting interactions, covers such topics as statistical power, measurement error, distribution of variables, and mathematical constraints of ordinal interactions. The third section, devoted to design issues, focuses on recommendations such as including reliable measures, enhancing statistical power, and oversampling extreme scores. The topics covered should aid understanding of existing moderator research as well as improve future research on interaction effects.

Preference Reversals and the Measurement of Environmental Values

Numerous studies have demonstrated that theoretically equivalent measures of preference, such as choices and prices, can lead to systematically different preference orderings, known as preference reversals. Two major causes of preference reversals are the compatibility effect and the prominence effect. The present studies demonstrate that the combined effects of prominence and compatibility lead to predictable preference reversals in settings where improvements in air quality are compared with improvements in consumer commodities by two methods-willingness to pay for each improvement and choice (For which of the two improvements would you pay more? Which improvement is more valuable to you?). Willingness to pay leads to relatively greater preference for improved commodities; choice leads to relatively greater preference for improved air quality. These results extend the domain of preference reversals and pose a challenge to traditional theories of preference. At the applied level, these findings indicate the need to develop new methods for valuing environmental resources.

Insurance for Low-Probability Hazards: A Bimodal Response to Unlikely Events

Two insurance experiments using real-money consequences and multiple rounds to provide experience are described. In the first experiment, subjects bid for insurance to prevent a fixed loss of

What’s Next? Judging Sequences of Binary Events

4 at probabilities ranging from .01 to .9. Mean bids were near expected value except at the lowest probability of.01, for which a very bimodal distribution was observed (some subjects bid zero and others bid much more than expected value). A second experiment explored this bimodality at a probability of .01 with loss increased to

Can Hypothetical Questions Reveal True Values? A Laboratory Comparison of Dichotomous Choice and Open-Ended Contingent Values with Auction Values

40. A similar bimodal distribution was obtained that persisted over 50 rounds of experience. These laboratory results are consistent with field evidence for low-probability hazards, for which people appear either to dismiss the risks or to worry too much about them.