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Author Canessa, E.; Chaigneau, S.E.; Lagos, R.; Medina, F.A. doi  openurl
  Title How to carry out conceptual properties norming studies as parameter estimation studies: Lessons from ecology Type
  Year 2021 Publication Behavior Research Methods Abbreviated Journal Behav. Res. Methods  
  Volume 53 Issue Pages 354370  
  Keywords Conceptual properties norming studies; Property listing task; Parameter estimation; Sample size determination; Sample coverage  
  Abstract 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.  
  Address [Canessa, Enrique; Chaigneau, Sergio E.] Univ Adolfo Ibanez, Sch Psychol, Ctr Cognit Res CINCO, Av Presidente Errazuriz 3328, Santiago, Chile, Email: ecanessa@uai.cl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1554-351x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000551760700002 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1210  
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Author Canessa, E.; Chaigneau, S.E.; Moreno, S.; Lagos, R. doi  openurl
  Title CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies Type
  Year 2023 Publication Behavior Research Methods Abbreviated Journal Behav. Res. Methods  
  Volume 55 Issue Pages 554569  
  Keywords Conceptual properties norming studies; Property listing task; Parameter estimation; Sample size determination; Sample coverage  
  Abstract 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.
 
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1554-3528 ISBN Medium  
  Area Expedition Conference  
  Notes Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1538  
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