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For a multilevel model with two levels and only a random intercept, the quality of different estimators of the random intercept is examined. Analytical results are given for the marginal model interpretation where negative estimates of the variance components are allowed for. Except for four or five level‐2 units, the Empirical Bayes Estimator (EBE) has a lower average Bayes risk than the Ordinary Least Squares Estimator (OLSE). The EBEs based on restricted maximum likelihood (REML) estimators of the variance components have a lower Bayes risk than the EBEs based on maximum likelihood (ML) estimators. For the hierarchical model interpretation, where estimates of the variance components are restricted being positive, Monte Carlo simulations were done. In this case the EBE has a lower average Bayes risk than the OLSE, also for four or five level‐2 units. For large numbers of level‐1 (30) or level‐2 units (100), the performances of REML‐based and ML‐based EBEs are comparable. For small numbers of level‐1 (10) and level‐2 units (25), the REML‐based EBEs have a lower Bayes risk than ML‐based EBEs only for high intraclass correlations (0.5).

The role of information processing in understanding people's responses to risk information has recently received substantial attention. One limitation of this research concerns the unavailability of a validated questionnaire of information processing. This article presents two studies in which we describe the development and validation of the Information‐Processing Questionnaire to meet that need. Study 1 describes the development and initial validation of the questionnaire. Participants were randomized to either a systematic processing or a heuristic processing condition after which they completed a manipulation check and the initial 15‐item questionnaire and again two weeks later. The questionnaire was subjected to factor reliability and validity analyses on both measurement times for purposes of cross‐validation of the results. A two‐factor solution was observed representing a systematic processing and a heuristic processing subscale. The resulting scale showed good reliability and validity, with the systematic condition scoring significantly higher on the systematic subscale and the heuristic processing condition significantly higher on the heuristic subscale. Study 2 sought to further validate the questionnaire in a field study. Results of the second study corresponded with those of Study 1 and provided further evidence of the validity of the Information‐Processing Questionnaire. The availability of this information‐processing scale will be a valuable asset for future research and may provide researchers with new research opportunities.

This study aims to test the pathways of change from individual exposure to smoke-free legislation on smoking cessation, as hypothesized in the International Tobacco Control (ITC) Conceptual Model. A nationally representative sample of Dutch smokers aged 15 years and older was surveyed during 4 consecutive annual surveys. Of the 1,820 baseline smokers, 1,012 participated in the fourth survey. Structural Equation Modeling was employed to test a model of the effects of individual exposure to smoke-free legislation through policy-specific variables (support for smoke-free legislation and awareness of the harm of [secondhand] smoking) and psychosocial mediators (attitudes, subjective norm, self-efficacy, and intention to quit) on quit attempts and quit success. The effect of individual exposure to smoke-free legislation on smoking cessation was mediated by 1 pathway via support for smoke-free legislation, attitudes about quitting, and intention to quit smoking. Exposure to smoke-free legislation also influenced awareness of the harm of (secondhand) smoking, which in turn influenced the subjective norm about quitting. However, only attitudes about quitting were significantly associated with intention to quit smoking, whereas subjective norm and self-efficacy for quitting were not. Intention to quit predicted quit attempts and quit success, and self-efficacy for quitting predicted quit success. Our findings support the ITC Conceptual Model, which hypothesized that policies influence smoking cessation through policy-specific variables and psychosocial mediators. Smoke-free legislation may increase smoking cessation, provided that it succeeds in influencing support for the legislation.