BackgroundThis research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way.MethodsUsing data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage.ResultsModels including place-based measures of land use emerged as the best models among the set of tested models. These were superior to models that included measures of age and socioeconomic status. The best models for commercial and street robbery include three measures of ordinary businesses, liquor stores, and spatial lag.ConclusionsModels based on information theory offer a useful alternative to significance testing when a strong theoretical framework guides the selection of model sets. Theoretically relevant ‘ordinary businesses’ have a greater influence on robbery than socioeconomic variables and most measures of discretionary businesses.

中文翻译：

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犯罪学研究中假设检验的信息论方法

背景研究表明，在选择最佳拟合模型时，Akaike信息标准（AIC）可以替代无效假设重要性检验。它提供了一个示例来说明如何以这种方式使用AIC。方法使用来自威斯康星州密尔沃基的数据，我们测试了街头抢劫和商业抢劫中基于地点的预测变量模型。我们建立模型以平衡解释力和简约性。测度包括不同类型企业的存在，特定年龄段和社会劣势。结果模型（包括基于地点的土地利用测度）在测试的模型集中成为了最佳模型。这些模型优于包括年龄和社会经济地位衡量指标的模型。商业和街头抢劫的最佳模型包括对普通企业，酒类商店和空间滞后的三种度量。结论当强大的理论框架指导模型集的选择时，基于信息论的模型为有意义性检验提供了有用的替代方法。从理论上讲，“普通企业”对抢劫的影响要大于社会经济变量和大多数可衡量企业的衡量标准。