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Author (up) 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 2022 Publication Behavior Research Methods Abbreviated Journal Behav. Res. Methods  
  Volume Early Access Issue Pages  
  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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  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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