Mattsson Markus, Hailikari Telle, Parpala Anna
Centre for University Teaching and Learning (HYPE), University of Helsinki, Helsinki, Finland.
Front Psychol. 2020 Apr 30;11:742. doi: 10.3389/fpsyg.2020.00742. eCollection 2020.
Quantitative research into the nature of academic emotions has thus far been dominated by factor analyses of questionnaire data. Recently, psychometric network analysis has arisen as an alternative method of conceptualizing the composition of psychological phenomena such as emotions: while factor models view emotions as underlying causes of affects, cognitions and behavior, in network models psychological phenomena are viewed as arising from the interactions of their component parts. We argue that the network perspective is of interest to studies of academic emotions due to its compatibility with the theoretical assumptions of the control value theory of academic emotions. In this contribution we assess the structure of a Finnish questionnaire of academic emotions using both network analysis and exploratory factor analysis on cross-sectional data obtained during a single course. The global correlational structure of the network, investigated using the spinglass community detection analysis, differed from the results of the factor analysis mainly in that positive emotions were grouped in one community but loaded on different factors. Local associations between pairs of variables in the network model may arise due to different reasons, such as variable A causing variation in variable B or vice versa, or due to a latent variable affecting both. We view the relationship between feelings of self-efficacy and the other emotions as causal hypotheses, and argue that strengthening the students' self-efficacy may have a beneficial effect on the rest of the emotions they experienced on the course. Other local associations in the network model are argued to arise due to unmodeled latent variables. Future psychometric studies may benefit from combining network models and factor models in researching the structure of academic emotions.
迄今为止，对学术情绪本质的定量研究主要由问卷数据的因素分析主导。最近，心理测量网络分析作为一种概念化情绪等心理现象构成的替代方法应运而生：虽然因素模型将情绪视为情感、认知和行为的潜在原因，但在网络模型中，心理现象被视为由其组成部分的相互作用产生。我们认为，网络视角对于学术情绪研究具有重要意义，因为它与学术情绪控制价值理论的理论假设相一致。在本论文中，我们使用网络分析和探索性因素分析，对在一门课程中获得的横断面数据进行分析，以评估一份芬兰学术情绪问卷的结构。使用自旋玻璃社区检测分析研究的网络全局相关结构，与因素分析结果的不同之处主要在于，积极情绪被归为一个社区，但加载在不同因素上。网络模型中变量对之间的局部关联可能由于不同原因而出现，例如变量A导致变量B的变化，反之亦然，或者由于一个潜在变量同时影响两者。我们将自我效能感与其他情绪之间的关系视为因果假设，并认为增强学生的自我效能感可能对他们在课程中体验到的其他情绪产生有益影响。网络模型中的其他局部关联被认为是由于未建模的潜在变量而产生的。未来的心理测量研究可能会受益于在研究学术情绪结构时将网络模型和因素模型结合起来。
