Use of correlational data to examine the effects of risk perceptions on precautionary behavior

Abstract

The idea that perceptions of high personal risk lead people to adopt precautionary behavior (the “motivational hypothesis”) is mainly tested with correlational data. A review of studies from selective journals reveals a high proportion with methodological and conceptual problems that make them invalid as tests of this hypthesis. Three problems arc emphasized: (1) the misinterpretation of correlations from cross-sectional studies as testing the motivational hypothesis when they actually indicate the accuracy of risk perceptions; (2) the failure to control for prior behavior in prospective studies; and (3) the we of prospective studies in situations of little behavior change. Path models are used to help explain these problems. Recommendations for selecting research designs and for calculating the least problematic correlations are given, along with warnings about the many assumptions needed to interpret even these correlations. Summary Overall, 27 of the 61 cross-sectional analyses listed in Tables 2 and 3 were conducted using clearly inappropriate variables. The appropriateness of the remaining correlations rest on one or two rather questionable assumptions: (1) that people do not distort their risk perceptions to justify their behavior or intentions and (2) that negative screening results a problem like breast cancer do not affect perceptions of future risk. The correlations preferred for testing the motivational hypothesis are listed in Table 4. Summary When the amount of precautionary behavior in a population has become relatively stable and behavior at time t+1 is well-predicted from behavior at time t, no independent variable other than B_t will have much predictive value in a prospective design. To avoid this problem, research should be conducted at a time when a substantial change in behavior is occurring, such as soon after the risk is recognized. If this is not possible, interventions that lead people to change their behavior (e.g., by raising risk perceptions or by lowering barriers to action) are required. In effect, they remove the system from equilibrium and allow one to watch what happens as people seek a new equilibrium. The least satisfactory choice is to use a cross-sectional correlation, such as R _H∗B, that represents a summation over previous changes in behavior. The assumptions required to interpret such a correlation have been discussed earlier.