The importance of personally relevant knowledge for pandemic risk prevention behavior: A multimethod analysis and two-country validation.

Pandemics threaten world stability; however, spread is mitigated with prevention behaviors. We introduce "personally relevant knowledge" to explain the knowledge-behavior gap (i.e., objective and subjective knowledge on information acquisition and behavioral change). Hypotheses are derived from prior knowledge literature, economic psychology, and relevance theory. Multimethod analysis (survey data, partial least squares structural equation path modeling [PLS-SEM], and an asymmetric information theoretic statistical analysis) is applied to H1N1 data from the USA and Australia. Personally relevant knowledge is an important addition to prior knowledge conceptualizations, and information theory uncovers asymmetric variable relationships concerning the knowledge-behavior gap, not captured by PLS-SEM.

aPRIDIT Unsupervised Classification with Asymmetric Valuation of Variable Discriminatory Worth.

Sometimes one needs to classify individuals into groups, but there is no available grouping information due to social desirability bias in reporting behavior like unethical or dishonest intentions or unlawful actions. Assessing hard-to-detect behaviors is useful; however it is methodologically difficult because people are unlikely to self-disclose bad actions. This paper presents an unsupervised classification methodology utilizing ordinal categorical predictor variables. It allows for classification, individual respondent ranking, and grouping without access to a dependent group indicator variable. The methodology also measures predictor variable worth (for determining target behavior group membership) at a predictor variable category-by-category level, so different variable response categories can contain different amounts of information about classification. It is asymmetric in that a "0" on a binary predictor does not have a similar impact toward signaling "membership in the target group" as a "1" has for signaling "membership in the non-target group." The methodology is illustrated by identifying Spanish consumers filing fraudulent insurance claims. A second illustration classifies Portuguese high school student's propensity to alcohol abuse. Results show the methodology is useful when it is difficult to get dependent variable information, and is useful for deciding which predictor variables and categorical response options are most important.