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• W164649316 abstract "Toward a Model of Comparison-Induced Density Effects Jessica M. Choplin (jchoplin@depaul.edu) DePaul University Department of Psychology 2219 North Kenmore Avenue Chicago, IL 60614-3504 end of these distributions will be smaller than the portion of the range at the sparse end. To cover the entire range of values the remaining verbal labels or category ratings would have to be assigned asymmetrically. In this paper, I propose yet another possible explanation for density effects. Namely, that some density effects might be comparison-induced (Choplin & Hummel, 2002). Verbal comparisons will tend to bias values apart in dense regions making the slope of the evaluation function steep and bias values together in sparse regions making the slope of the evaluation function shallow. These biases would make evaluation functions concave for positively skewed distributions and convex for negatively skewed distributions. The assignment of verbal labels or category ratings to these biased values might explain why values drawn from positively skewed distributions are often judged larger than are values drawn from negatively skewed distributions. I start by reviewing the basic tenets of Comparison- Induced Distortion Theory (Choplin & Hummel, 2002) and describing how comparisons could produce density effects. I present simulation data to demonstrate signature characteristics of comparison-induced density effects and how they differ from density effects produced by other mechanisms. I then describe an experiment in which I found preliminary support for the view that some density effects might be comparison-induced. Abstract A model of the effects of distribution density on evaluations of attribute values is proposed in which biases created by language-expressible magnitude comparisons (e.g., “I waited longer for the bus today than I did yesterday”) serve as the mediating mechanism. The biases created by comparisons as well as the mechanisms by which comparison-induced biases could produce density effects are described. Simulation data demonstrate signature characteristics of comparison-induced density effects. An experiment found preliminary evidence in support of the view that some density effects might be comparison-induced. Density Effects Evaluations of attribute values such as grades (Wedell, Parducci, & Roman, 1989), taste (Riskey, Parducci, & Beauchamp, 1979), visual velocity (Sokolov, Pavlova, & Ehrenstein, 2000), prices (Niedrich, Sharma, & Wedell, 2001), income (Hagerty, 2000) and so forth often depend upon the density—or frequency—of the distribution from which judged values are drawn (Krumhansl, 1978; Parducci, In particular, evaluation functions are typically concave (downward) for positively skewed distributions and convex (concave upward) for negatively skewed distributions. Values drawn from positively skewed distributions are also often judged larger than are values drawn from negatively skewed distributions. Several explanations for these effects have been proposed. Parducci’s (1965) Range-Frequency Theory assumes that people are aware of and use percentile rank information to evaluate attribute values. Range-Frequency Theory explains the finding that evaluation functions are often concave for positively skewed distributions, because the density at the lower end of positively skewed distributions gives low values larger percentile rank scores than they would have had otherwise. The slope of the function becomes shallow at the sparse upper end of the distribution where percentile rank scores increase at a slower rate. The reverse pattern of changes in percentile rank scores in negatively skewed distributions explains the finding that evaluation functions are often convex for negatively skewed distributions. Haubensak (1992) suggested an alternative explanation for density effects on evaluations of sequentially presented values. He argued that since people do not know the distribution density and range in advance, they tend to assume that early values are typical or average and assign them intermediate verbal labels or category ratings. After these initial labels or category ratings have been assigned, people are obliged to use them consistently. Since early values are most likely to come from the dense portion of skewed distributions, the portion of the range at the dense Comparison-Induced Distortion Theory The basic idea behind Comparison-Induced Distortion Theory (Choplin & Hummel, 2002) is that language- expressible magnitude comparisons suggest quantitative values. To investigate the meanings of English age comparisons Rusiecki (1985) gave his participants sentences such as “Mary is older than Jane” and “Martin’s wife is older than Ken’s wife” and asked them to report the ages they imagined. Rusiecki found considerable agreement in the values imagined by his participants. In response to the comparison “Mary is older than Jane” participants imagined Mary to be 20.2 years on average and Jane to be 17.9 years on average. In response to the comparison “Martin’s wife is older than Ken’s wife” participants imagined Martin’s wife to be 37.2 years on average and Ken’s wife to be 33.0 years on average. Of particular interest to the current discussion, the age differences imagined by Rusiecki’s (1985) participants were remarkably similar. Regardless of the particular ages they imagined, participants imagined a difference between the ages of approximately 2 to 5 years (slightly larger for larger values)—not 1 month or 30 years. Inspired by these results, Rusiecki argued that comparisons suggest quantitative differences between compared values. I will henceforth call" @default.
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• W164649316 title "Toward a Model of Comparison-Induced Density Effects" @default.
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