Canessa Enrique, Chaigneau Sergio E, Moreno Sebastián, Lagos Rodrigo
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. 2023 Feb;55(2):554-569. doi: 10.3758/s13428-022-01811-w. Epub 2022 Mar 22.
In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data's statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.
在概念属性规范研究（CPNs）中，参与者列出描述一组概念的属性。从CPNs中，可以计算出许多不同的参数，比如语义丰富度。一个普遍被忽视的问题是，这些值只是真实未知总体参数的点估计。在本研究中，我们展示了一个R包，它能让我们将这些值视为总体参数估计。与此相关的是，CPNs中的一个常见做法是，为每个概念列出属性的参与者数量相等（即标准化样本量）。正如我们通过示例所说明的，这个过程对数据的统计分析有负面影响。在此，我们认为更好的方法是标准化覆盖率（即抽样属性占描述一个概念的属性总数的比例），以便在各个概念间实现相似的覆盖率。当标准化覆盖率而非样本量时，CPN中的一组概念更有可能都表现出相似的代表性。此外，通过计算覆盖率，研究者可以判断CPN是否达到了足够高的覆盖率，从而其结果可能适用于其他研究。我们在当前研究中提供的R包允许计算覆盖率，并估计达到目标覆盖率所需的参与者数量。我们通过在真实和模拟的CPN数据上使用该R包展示了这种抽样程序。
