Quantifying changes in the environment across time

What is it about?

Psychologists tend to rely on verbal descriptions of the environment over time, using terms like “unpredictable,” “variable,” and “unstable.” These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying “unpredictability” in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different “unpredictability statistics” across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets.

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Photo by Icons8 Team on Unsplash

Photo by Icons8 Team on Unsplash

Why is it important?

Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. Using formal definitions of constructs, rather than verbal definitions, facilitates the integration of findings across studies and even fields. Our case study features environment-level data, such as crime rates. However, our framework is also readily applicable to more narrow, individual-level data, such as repeated measures of the social environment. Quantifying how an individual's narrow environment changes in relation to their broader, surrounding environment helps us to better characterize lived experiences of individuals.