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. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. 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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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发展科学中环境统计研究的框架。


心理学家倾向于依赖对一段时间内环境的口头描述，使用“不可预测”、“可变”和“不稳定”等术语。这些术语通常可以有不同的解释。这种模糊性模糊了结构和测量之间的匹配，从而造成了研究之间的混乱和不一致。为了更好地描述环境特征，该领域需要一个共享框架，以清晰的术语组织随时间变化的环境描述：作为统计定义。在这里，我们首先借鉴生物学、人类学、生态学和经济学等其他学科发展的理论，提出这样一个框架。然后，我们通过量化纽约市 (NYC) 15 年犯罪率公开纵向数据集中的“不可预测性”来应用我们的框架。该案例研究表明，不同地区的不同“不可预测性统计数据”之间的相关性只是中等。这意味着纽约市内的区域在不可预测性方面的排名不同，具体取决于使用的定义以及计算统计数据的空间尺度。此外，我们还探讨了不可预测性统计数据与来自公开的纽约市调查数据的失业、贫困和教育程度衡量标准之间的关联。在我们的案例研究中，这些指标与犯罪率的平均水平相关，但与犯罪率的不可预测性几乎没有关系。我们的案例研究说明了使用正式框架来理清环境不同属性的优点。为了方便使用我们的框架，我们提供了一个友好的分步指南，用于识别重复测量数据集中的环境结构。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。