Simultaneous analysis of individual and aggregate responses in psychometric data using multilevel modeling

The Influencing Factors of Haze Tolerance in China

Haze pollution has become the most serious environmental risk in China and generated a large amount of public concerns. Influencing almost all the citizens in the polluted area, it is necessary and important to take public perception as an essential element in haze abatement. From the perspective of social psychology, this paper explores haze tolerance in Beijing, Shanghai, and Guangzhou, also the key influential factors on haze tolerance from four dimensions: political trust, perceived risk, cost perception, and haze knowledge. Based on the sample of 517 respondents, the results show that compared with Shanghai and Guangzhou, Beijing residents had the lowest tolerance level of haze pollution but have the highest levels of trust in the government’s capacity to control haze and self-evaluation of their own haze knowledge. People in Shanghai had the lowest cost perception and the strongest willingness to acquire haze knowledge. Meanwhile, the empirical analysis revealed that political trust and cost perception could enhance the public’s haze tolerance while perceived risk and haze knowledge had negative impacts on tolerance. Also, our research could provide some suggestions to government officials when making policies for abating haze pollution from the perspective of social risk control. Policy makers are supposed to launch various policy instruments to control haze effectively and engage the citizens in the decision-making process to improve their political trust, and publicize the knowledge of haze pollution to help the public to acquire objective and scientific knowledge and diminish unnecessary worries.

Multilevel modeling versus cross-sectional analysis for assessing the longitudinal tracking of cardiovascular risk factors over time

Correlated data are obtained in longitudinal epidemiological studies, where repeated measurements are taken on individuals or groups over time. Such longitudinal data are ideally analyzed using multilevel modeling approaches, which appropriately account for the correlations in repeated responses in the same individual. Commonly used regression models are inappropriate as they assume that measurements are independent. In this tutorial, we use multilevel modeling to demonstrate its use for analysis of correlated data obtained from serial examinations on individuals. We focus on cardiovascular epidemiological research where investigators are often interested in quantifying the relations between clinical risk factors and outcome measures (X and Y, respectively), where X and Y are measured repeatedly over time, for example, using serial observations on participants attending multiple examinations in a longitudinal cohort study. For instance, it may be of interest to evaluate the relations between serial measures of left ventricular mass (outcome) and of its potential determinants (i.e., body mass index and blood pressure), both of which are measured over time. In this tutorial, we describe the application of multilevel modeling to cardiovascular risk factors and outcome data (using serial echocardiographic data as an example of an outcome). We suggest an analytical approach that can be implemented to evaluate relations between any potential outcome of interest and risk factors, including assessment of random effects and nonlinear relations. We illustrate these steps using echocardiographic data from the Framingham Heart Study with SAS PROC MIXED.

Colour helps to solve the binocular matching problem

The spatial differences between the two retinal images, called binocular disparities, can be used to recover the three-dimensional (3D) aspects of a scene. The computation of disparity depends upon the correct identification of corresponding features in the two images. Understanding what image features are used by the brain to solve this binocular matching problem is an important issue in research on stereoscopic vision. The role of colour in binocular vision is controversial and it has been argued that colour is ineffective in achieving binocular vision. In the current experiment subjects were required to indicate the amount of perceived depth. The stimulus consisted of an array of fronto-parallel bars uniformly distributed in a constant sized volume. We studied the perceived depth in those 3D stimuli by manipulating both colour (monochrome, trichrome) and luminance (congruent, incongruent). Our results demonstrate that the amount of perceived depth was influenced by colour, indicating that the visual system uses colour to achieve binocular matching. Physiological data have revealed cortical cells in macaque V2 that are tuned both to binocular disparity and to colour. We suggest that one of the functional roles of these cells may be to help solve the binocular matching problem.

Aggregate, disaggregate, and hybrid analyses of ecological risk perceptions

Laypeople's perceptions of health and safety risks have been widely studied, but only a few studies have addressed perceptions of ecological hazards. We assembled a list of 39 attributes of ecological hazards from the literatures on comparative risk assessment, ecological health, environmental conservation and management, environmental psychology, and risk perception. In Study 1, 125 laypeople evaluated 83 hazards on subsets of this attribute set. Factor analysis of attribute ratings (averaged over participants) revealed six oblique factors: ecological impacts, human impacts, human benefits, aesthetic impacts, scientific understanding, and controllability. These factors predicted mean judgments of overall riskiness, ecological riskiness, acceptability, and regulatory strictness. In Study 2, 30 laypeople each evaluated 34 hazards on 17 attributes and 3 dependent variables. Aggregate-level factor analysis of these data replicated the appropriate portion of the factor solution and yielded similar regression results. Parallel analyses at the individual-participant level yielded factors that explained less variance in judgments of overall riskiness, ecological riskiness, and acceptability. However, the decrease in explanatory power was much less than is often reported for disaggregate-level analyses of psychometric data. This discrepancy illustrates the importance of distinguishing between the level of analysis (aggregate versus disaggregate) and the focus of analysis (distinctions among hazards versus distinctions among participants). In a hybrid analysis, aggregate-level factor scores predicted individual participants' riskiness judgments reasonably well. Psychometric studies such as these provide a sound empirical basis for selecting attributes of ecological hazards for use in comparative risk assessment.

A new look at the psychometric paradigm of perception of hazards

The psychometric paradigm has been the most influential model in the field of risk analysis. The "cognitive maps" of hazards produced by the paradigm seem to explain how laypeople perceive the various risks they face. Because most of the studies used aggregated data, analyzed using principal component analysis, it is not known whether the model neglects individual differences in risk perception. There has been much criticism on the fact that few studies have examined individual differences in the cognitive representation of hazards. In order to detect and describe the internal structure of the three-way data, we conducted a three-way component analysis (3MPCA). Data for the present analysis were derived from a mail survey conducted in Switzerland. Participants were asked to judge 9 attributes for 26 hazards. Individual differences in the cognitive representation of hazards were correlated with external variables (e.g., general trust). The results suggest that methods permitting individual differences should be used more frequently and that utilizing different methods could provide greater insight into the cognitive representation of risks.