Sammanfattning

The role of human factors in crash causation is a central theme in traffic psychology. Human factors are often roughly categorized into cognitive errors and a tendency to break rules. In data analysis, these psychological properties are treated as measurable, continuous quantities, quite alike weight, length and temperature. Their existence is inferred based on covariation among individual traffic behaviors, which for their part function as measurements of the level of these properties: for instance, driving under the influence of alcohol and speeding are thought to reflect the tendency to break traffic rules. The thesis examines joint variation among traffic behaviors and compares two competing explanations for the phenomenon: 1) The latent variable view of errors and violations, according to which covariation among traffic behaviors is explained by latent, unobservable psychological properties that cause variation in them and 2) The network view, according to which traffic behaviors interact directly with one another, which makes it unnecessary to posit unobservable psychological properties as explanations of behavior. Within traffic psychology, questions such as these are usually not explicitly raised; rather, latent variable models are used as the default tool in data analysis. This practice entails certain assumptions, such as that of the latent variable models measuring the same unobservable properties in the same way across groups of respondents. Moreover, more fundamental questions, such as the theoretical status of latent variables in terms of realist vs. constructionist commitments and the nature of the relationship between latent and observed variables are seldom considered. The present thesis addresses these issues. Studies I and II examine a central property of latent variable models of driver behavior: whether the same psychological properties can be measured in the same way across different subgroups of drivers that are defined based on age, sex and nationality. Both studies utilize rigorous latent variable measurement equivalence analyses. Study I concludes that if the latent variable view is adopted, patterns of covariation among self-reported traffic behaviors are sufficiently different across subgroups of Finnish respondents formed based on age and gender that the latent variables may well be specific to the group in question. Study II reaches a similar conclusion concerning social behavior (breaking rules in traffic) based on a comparison of young Finnish and Irish drivers. On the other hand, it shows that cognitive errors can more readily be interpreted as being related to similar – but not identical – latent variables across countries. Study III assumes a novel point of view, and examines interactions among individual traffic behaviors using psychological network models. This shifts the focus from abstract psychological properties to potentially causal relationships between traffic behaviors: drivers who are more likely to exceed speed limits are also more likely to end up driving close to another vehicle, for instance. In other words, edges in the network models are interpreted as causal hypotheses. Study III also presents Poisson regression models that predict crashes from self-reported traffic behaviors instead of latent variables. This enables various self-reported traffic behaviors to have differential associations with crashes, which is intuitively plausible as, for instance, the violations range from driving under the influence of alcohol to honking at others. The models are built and tested in independent sets of data, making it possible to avoid overfitting the predictive models to data at hand. This procedure, together with selecting variables based on regularized regression, is argued to have useful properties in predicting crashes in traffic psychology. As a whole, the thesis presents two new interpretations for the relationship between individual traffic behaviors and the psychological properties investigated within traffic psychology. First, the psychological properties may reduce to nametags for behaviors that co-occur in certain kinds of contexts and have no causal power of their own. Second, they may prove to be emergent properties arising from the interaction among the behaviors. These alternatives are discussed together with an intermediate view that combines the latent variable view and the network view. The thesis, then, positions itself as a part of recent psychometric discussion in which psychological properties are seen as being formed through the interaction of different behaviors, thoughts and emotions without necessarily treating psychological properties as unidimensional, measurable quantities.
Originalspråkengelska
Handledare
  • Summala, Heikki, Handledare
  • Vehkalahti, Kimmo, Handledare
  • Lappi, Otto, Handledare
UtgivningsortHelsinki
Förlag
Tryckta ISBN978-951-51-6790-3
Elektroniska ISBN978-951-51-6791-0
StatusPublicerad - 2020
MoE-publikationstypG5 Doktorsavhandling (artikel)

Bibliografisk information

M1 - 103 s. + liitteet

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  • 515 Psykologi

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