Measuring social value orientation by model-based scoring

Q1 Mathematics Behaviormetrika Pub Date : 2023-11-13 DOI:10.1007/s41237-023-00211-4

Keiko Mizuno, Hiroshi Shimizu

引用次数: 0

Abstract

Abstract This study proposes a method of measuring social value orientation using model-based scoring and a task suitable for such scoring. We evaluated this method by means of parameter recovery simulation (Study 1), and we examined its retest reliability (Study 2) and its predictive validity (Study 3). The results indicate that the proposed method has low bias and sufficient predictive validity. While the improvement in predictive validity of altruism was negligible and comparable to previous scoring methods in terms of confidence intervals, the measurement of equality using the proposed model and task combination produced a moderate correlation that was not observed with other methods. Although SVO is a concept used primarily in psychology, the model assumed in this study is mathematically equivalent to a well-known economics model. We, therefore, suggest that this method may lead to cross-disciplinary research.

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基于模型的评分法测量社会价值取向

摘要本研究提出了一种基于模型的社会价值取向评分方法，并提出了一种适合于该评分的任务。我们通过参数恢复模拟(Study 1)对该方法进行了评价，并对其重测信度(Study 2)和预测效度(Study 3)进行了检验。结果表明，该方法具有低偏倚和足够的预测效度。虽然利他主义的预测效度的提高可以忽略不计，并且在置信区间方面可以与以前的评分方法相媲美，但使用所提出的模型和任务组合测量的平等性产生了与其他方法未观察到的适度相关性。虽然SVO是一个主要用于心理学的概念，但本研究中假设的模型在数学上等同于一个众所周知的经济学模型。因此，我们建议这种方法可能会导致跨学科的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Behaviormetrika Mathematics-Analysis

CiteScore

5.10

自引率

0.00%

发文量

期刊介绍： Behaviormetrika is issued twice a year to provide an international forum for new theoretical and empirical quantitative approaches in data science. When Behaviormetrika was launched in 1974, the journal advocated data science, as an interdisciplinary field that included the use of statistical methods to extract meaningful knowledge from data in its various forms: structured or unstructured. Behaviormetrika is the oldest journal addressing the topic of data science. The first editor-in-chief of Behaviormetrika, Dr. Chikio Hayashi, described data science in this way:“Data science is not only a synthetic concept to unify statistics, data analysis, and their related methods; it also comprises its results. Data science is intended to analyze and understand actual phenomena with ‘data.’ In other words, the aim of data science is to reveal the features or the hidden structure of complicated natural, human, and social phenomena using data from a different perspective from the established or traditional theory and method.” Behaviormetrika is a fully refereed international journal, which publishes original research papers, notes, and review articles. Subject areas suitable for publication include but are not limited to the following methodologies and fields. Methodologies Data scienceMathematical statisticsSurvey methodologiesArtificial intelligence Information theoryMachine learning Knowledge discovery in databases (KDD)Graphical modelsComputer scienceAlgorithms FieldsMedicinePsychologyEducationEconomicsMarketingSocial scienceSociologyPolitical sciencePolicy scienceCognitive scienceBrain science