Munoz-Rubke Felipe, Kafadar Karen, James Karin H
Cognitive Science Program, Indiana University Bloomington, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
Program in Neuroscience, Indiana University, Bloomington, USA.
Psychol Res. 2018 Jul;82(4):787-805. doi: 10.1007/s00426-017-0864-8. Epub 2017 Apr 25.
The concrete-abstract categorization scheme has guided several research programs. A popular way to classify words into one of these categories is to calculate a word's mean value in a Concreteness or Imageability rating scale. However, this procedure has several limitations. For instance, results can be highly distorted by outliers, ascribe differences among words when none may exist, and neglect rating trends in participants. We suggest using an alternative procedure to analyze rating scale data called median polish analysis (MPA). MPA is tolerant to outliers and accounts for information in multiple dimensions, including trends among participants. MPA performance can be readily evaluated using an effect size measure called analog R and be integrated with bootstrap 95% confidence intervals, which can prevent assigning inexistent differences among words. To compare these analysis procedures, we asked 80 participants to rate a set of nouns and verbs using four different rating scales: Action, Concreteness, Imageability, and Multisensory. We analyzed the data using both two-way and three-way MPA models. We also calculated 95% CIs for the two-way models. Categorizing words with the Action scale revealed a continuum of word meaning for both nouns and verbs. The remaining scales produced dichotomous or stratified results for nouns, and continuous results for verbs. While the sample mean analysis generated continua irrespective of the rating scale, MPA differentiated among dichotomies and continua. We conclude that MPA allowed us to better classify words by discarding outliers, focusing on main trends, and considering the differences in rating criteria among participants.
具体-抽象分类方案指导了多个研究项目。将单词分类到这些类别之一的一种常用方法是在具体性或可想象性评级量表中计算单词的均值。然而，这个过程有几个局限性。例如，结果可能会受到异常值的严重扭曲，在可能不存在差异的情况下归因于单词之间的差异，并忽略参与者的评级趋势。我们建议使用一种称为中位数平滑分析（MPA）的替代程序来分析评级量表数据。MPA对异常值具有耐受性，并考虑了多个维度的信息，包括参与者之间的趋势。MPA的性能可以使用一种称为模拟R的效应量度量轻松评估，并与自举95%置信区间相结合，这可以防止在单词之间赋予不存在的差异。为了比较这些分析程序，我们让80名参与者使用四种不同的评级量表对一组名词和动词进行评级：动作、具体性、可想象性和多感官。我们使用双向和三向MPA模型分析了数据。我们还计算了双向模型的95%置信区间。用动作量表对单词进行分类揭示了名词和动词的词义连续体。其余量表对名词产生了二分或分层结果，对动词产生了连续结果。虽然样本均值分析无论评级量表如何都产生连续体，但MPA区分了二分法和连续体。我们得出结论，MPA使我们能够通过丢弃异常值、关注主要趋势并考虑参与者之间评级标准的差异来更好地对单词进行分类。
