Prediction and judgment as indicators of sensitivity to covariation of continuous variables

Abstract

The present study used both judgments of strength of relationship and measures of the ability to predict one variable from another to assess subjects' sensitivity to the covariation of two continuous variables. In addition, one group of subjects judged strength of relationship after merely observing the presentation of 60 pairs of two-digit numbers, and a second group made strength judgments after being actively engaged in predicting one member of a pair when given the other. The prediction and judgment data provide different pictures of subjects' sensitivity to covariation. The subjects were quite poor at estimating strength of relationship but, by some measures, good at predicting one variable from another. Judgments were not strongly influenced by whether subjects had previously engaged in overt prediction. The implications of these results for the literature on covariation estimation are discussed.The ability to detect relationships between events in the environment and to use knowledge about these relationships to make predictions has come to be regarded as an important component of human intelligence. As Crocker (1981, p. 272) pointed out, "knowing whether events are related, and how strongly they are related, enables individuals to explain the past, control the present, and predict the future."The large literature that is concerned with people's ability to judge the degree to which imperfectly related events covary has often been characterized as indicating that people are poor at assessing covariation (e.g., Nisbett & Ross, 1980). Most research in the field has dealt with binary variables (e.g., presence or absence of symptom and presence or absence of disease), so that all cases fall into one of the cells of a 2 X 2 table. Empirical studies and theoretical analyses have been aimed at exploring reasons why performance is often poor and at delineating circumstances under which subjects tend to be more or less accurate according to some statistical measure of relationship (for reviews, see Alloy & Tabachnik, 1984;Crocker, 1981).The ability to estimate covariation between variables that can take on more than two values has also been characterized as being quite poor (e.g., Nisbett & Ross, 1980), despite the fact that few estimation studies have employed nonbinary variables and the fact that the older studies cited as providing evidence of conservatism (Beach & Scopp, 1966; Edick & Mills, 1967) by Beach and Scopp (1966) was a rating of degree of confidence that the relationship was positive or negative, whereas subjects in the Edick and Mills (1967) study made more than 1,000 covariationjudgments over 10 sessions.Jennings, Amabile, and Ross (1982) had subjects make covariationjudgments for several types of "theory-free" bivariate distributions in which the product-moment correlation (r) varied between 0 and 1. In one condition, after briefly studying 10 number pairs, subjects first judged whether the relationship between members of pairs was positive or negative, then indicated their subjective impression ...

Human contingency judgments: Rule based or associative?

“…With few exceptions (see Well, Boyce, Morris, Shinjo, & Chumbley, 1988), studies concerned with human contingency judgments have involved binary variables. In the simplest case, there are only two binary variables.…”

Covariation neglect among novice investors.

“…Jennings et al (1982) showed that individuals tend to underestimate low correlations between variables and that they only detect very high correlations (r Ͼ .8) with certainty. Well, Boyce, Morris, Shinjo, and Chumbley (1988) found that, although participants gave poor global judgments of strength of relationship, they were more accurate in predicting one variable from another. Using a prediction task, Malmi (1986) found that participants responded adequately to positive correlations in the data but showed a virtual lack of sensitivity to negative correlations.…”

Heuristics and biases in the "eyeballing" of data: The effects of context on intuitive correlation assessment.

“…The majority of research has required participants to estimate the strength of the relationship between two dichotomous variables. In contrast, the few studies that have examined numerical stimuli (i.e., perceived covariation between continuous or 591 ordinal variables) have reported relatively accurate perceptions of covariation (e.g., Alloy & Tabachnik, 1984;Baumgartner, 1995;Bettman, Creyer, John, & Scott, 1988;Lane, Anderson, & Kellam, 1985;Malmi, 1986;Wainer & Thissen, 1979;Well, Boyce, Morris, Shinjo, & Chumbley, 1988;Wright & Murphy, 1984). The experiments that follow were restricted to the case of numerical stimuli.…”