Emanuela Ingusci, Mario Angelelli, Giovanna Alessia Sternativo, Alessia Anna Catalano, Elisa De Carlo, Claudio G. Cortese, Evangelia Demerouti, Enrico Ciavolino
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引用次数: 2
摘要
摘要本研究采用偏最小二乘结构方程模型(PLS-SEM)和验证性复合分析(CCA)，以意大利学生($$n=953$$ n = 953)为样本，重点分析了生命制作量表(LCS)的因子结构和模型规格。从PLS-CCA获得的验证结果中，我们发现了LCS子量表得分的反射性质，以及LCS得分的另一种测量模型，即二阶反射-反射模型。
A higher-order life crafting scale validation using PLS-CCA: the Italian version
Abstract In this study, we highlight Life Crafting Scale (LCS) factor structure and model specifications by using partial least squares structural equations modelling (PLS-SEM) and confirmatory composite analysis (CCA), with a sample of Italian students ( $$n=953$$ n=953 ). From the validation results obtained through PLS-CCA, we identify the emergence of both the reflective nature of the scores of the LCS subscale and an alternative measurement model of the LCS scores as a second-order reflective–reflective model.
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
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