Center for Cognition Research (CINCO), School of Psychology, Universidad Adolfo Ibáñez, Av. Presidente Errázuriz 3328, Las Condes, Santiago, Chile.
Faculty of Engineering and Science, Universidad Adolfo Ibáñez, Av. P. Hurtado 750, Lote H, Viña del Mar, Chile.
Behav Res Methods. 2021 Feb;53(1):354-370. doi: 10.3758/s13428-020-01439-8.
Conceptual properties norming studies (CPNs) ask participants to produce properties that describe concepts. From that data, different metrics may be computed (e.g., semantic richness, similarity measures), which are then used in studying concepts and as a source of carefully controlled stimuli for experimentation. Notwithstanding those metrics' demonstrated usefulness, researchers have customarily overlooked that they are only point estimates of the true unknown population values, and therefore, only rough approximations. Thus, though research based on CPN data may produce reliable results, those results are likely to be general and coarse-grained. In contrast, we suggest viewing CPNs as parameter estimation procedures, where researchers obtain only estimates of the unknown population parameters. Thus, more specific and fine-grained analyses must consider those parameters' variability. To this end, we introduce a probabilistic model from the field of ecology. Its related statistical expressions can be applied to compute estimates of CPNs' parameters and their corresponding variances. Furthermore, those expressions can be used to guide the sampling process. The traditional practice in CPN studies is to use the same number of participants across concepts, intuitively believing that practice will render the computed metrics comparable across concepts and CPNs. In contrast, the current work shows why an equal number of participants per concept is generally not desirable. Using CPN data, we show how to use the equations and discuss how they may allow more reasonable analyses and comparisons of parameter values among different concepts in a CPN, and across different CPNs.
概念属性标准化研究（CPN）要求参与者生成描述概念的属性。根据这些数据，可以计算出不同的指标（例如，语义丰富度、相似性度量），然后用于研究概念，并作为精心控制的实验刺激源。尽管这些指标已经证明了它们的有用性，但研究人员通常忽略了它们只是真实未知总体值的点估计值，因此只是粗略的近似值。因此，尽管基于 CPN 数据的研究可能会产生可靠的结果，但这些结果可能是普遍的和粗粒度的。相比之下，我们建议将 CPN 视为参数估计程序，研究人员只能获得未知总体参数的估计值。因此，更具体和细粒度的分析必须考虑这些参数的可变性。为此，我们从生态学领域引入了一个概率模型。它的相关统计表达式可用于计算 CPN 参数及其相应方差的估计值。此外，这些表达式可用于指导采样过程。CPN 研究中的传统做法是在各个概念中使用相同数量的参与者，直观地认为练习将使计算出的指标在概念和 CPN 之间具有可比性。相比之下，当前的工作展示了为什么每个概念的参与者数量相等通常不是理想的。我们使用 CPN 数据展示了如何使用这些方程，并讨论了它们如何允许更合理地分析和比较 CPN 中不同概念之间以及不同 CPN 之间的参数值。
