Covariation and Relative Consistency

Absolute stability and absolute consistency imply predictability. However, absolute consistency cannot satisfy our need for causal knowledge—a need that ordinary people and scientists share (Kelley, 1973). Knowing that my neighbor is absolutely stable and consistent does not tell me why this is the case. I could conceive many causes. Perhaps my neighbor has a strong helping norm (Schwartz, 1977), a strong sense of social responsibility (Berkowitz & Daniels, 1964), a strong need for approval (Crowne & Marlowe, 1964), or an excessive need for consistency (Lecky, 1945). He might also water my plants because he wants to stay away from his overbearing wife. Invariant behavior cannot teach me which explanation is correct or how much each factor contributes to my neighbor's behavior.

Insights into causality require variation. Without variation, the laws that generate data cannot be identified. Variation on one dimension is insufficient for causal analyses (Kenny, 1979). If my neighbor helped more in some situations than in others, I could not possibly explain his inconsistency unless I had identified at least one other dimension on which the situations also differ beside the amount of help I received. Using value theory (Tolman, 1932), I might speculate that gains and losses cause variation across situations. Helping takes more effort and provides less rewards in some situations than in others. I might conduct a cost-benefit analysis and compute a net outcome for each situation. I could then explore whether this net outcome covaries with helping, which is often the case (Piliavin, Dovidio, Gaertner, & Clark, 1981).

Such a covariation indicates relative consistency. This means that my neighbor's behavior although not absolutely consistent, did not differ in an arbitrary fashion between situations. Rather, the variation was systematic and lawful. It was relatively consistent because more help was provided when the net value of helping was high than when the net value of helping was low.

Relative consistency is a general concept. Its specific meaning depends on which facets of the data box are combined in search of lawfulness (Ozer, 1986). Relative consistency can occur across time (= relative stability), situations, types of behavior, modes of behavior, methods, and across other dimensions. Relative consistency across methods, often called convergence, is crucial in the context of this handbook. Convergence among methods is an essential criterion for their quality (Brunswik, 1934; Campbell & Fiske, 1959). Ideally, different methods for measuring the same property of objects will be perfectly consistent. In this case, the methods measure the same property—whatever it is. Given their equivalence, the method used holds little significance. More important, each measure could be trusted, especially if methods were heterogeneous (Houts, Cook, & Shadish, 1986). If my neighbor said that he had watered my plants and if I could feel that the soil of my plants was wet, I would feel confident that both methods are trustworthy.

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