Abstract
Reciprocal determinism refers to the situation where the underlying dynamic of an observed relationship is one of mutual influence. Each variable influences the other in a feedback loop. This notion is invoked in PISA to explain the relationship between students’ achievements and various aspects of their learning strategies, motivations, self-beliefs and preferences. But, in PISA, as in the literature as a whole, the reciprocal determinism of theory is seldom translated into an appropriate statistical model. Rather, in statistical analyses, the notion of mutual influence tends to be abandoned and the relationship is modeled simply as a one-way effect; in this case, the effect of a particular learning strategy on achievement. The most likely reason for this inconsistency is the widely-held belief that reciprocal determinism cannot be modeled with cross-sectional data. Longitudinal, repeated-measures data are considered necessary in order to estimate reciprocal determinism as cross-lagged effects. However, it is possible to model reciprocal effects with cross-sectional data by developing nonrecursive structural equation models in which these effects are represented as a feedback loop. This approach is not without its difficulties but, to the extent that these can be resolved, analyses in which the theoretical and statistical models are consistent become possible.
The discussion below is designed to illustrate this approach using as an example a nonrecursive structural equation model in which the mutual influence of self-efficacy and performance in mathematics is represented as a feedback loop. This model is estimated in each of 33 nations using PISA 2003 data.
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Williams, T., Williams, K. (2013). Modeling Reciprocal Determinism in PISA. In: Prenzel, M., Kobarg, M., Schöps, K., Rönnebeck, S. (eds) Research on PISA. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4458-5_4
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