Simonet Daniel V, Miller Katherine E, Askew Kevin L, Sumner Kenneth E, Mortillaro Marcello, Schlegel Katja
Department of Psychology, Montclair State University, Montclair, NJ 07043, USA.
Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA.
J Intell. 2021 Mar 5;9(1):14. doi: 10.3390/jintelligence9010014.
Drawing upon multidimensional theories of intelligence, the current paper evaluates if the Geneva Emotional Competence Test (GECo) fits within a higher-order intelligence space and if emotional intelligence (EI) branches predict distinct criteria related to adjustment and motivation. Using a combination of classical and S-1 bifactor models, we find that (a) a first-order oblique and bifactor model provide excellent and comparably fitting representation of an EI structure with self-regulatory skills operating independent of general ability, (b) residualized EI abilities uniquely predict criteria over general cognitive ability as referenced by fluid intelligence, and (c) emotion recognition and regulation incrementally predict grade point average (GPA) and affective engagement in opposing directions, after controlling for fluid general ability and the Big Five personality traits. Results are qualified by psychometric analyses suggesting only emotion regulation has enough determinacy and reliable variance beyond a general ability factor to be treated as a manifest score in analyses and interpretation. Findings call for renewed, albeit tempered, research on EI as a multidimensional intelligence and highlight the need for refined assessment of emotional perception, understanding, and management to allow focused analyses of different EI abilities.
基于多元智能理论，本文评估了日内瓦情绪能力测试（GECo）是否符合高阶智能空间，以及情商（EI）分支是否能预测与适应和动机相关的不同标准。通过结合经典模型和S-1双因素模型，我们发现：（a）一阶斜交双因素模型能很好地呈现EI结构，其中自我调节技能独立于一般能力发挥作用，且与EI结构具有相当的拟合度；（b）在以流体智力为参照的一般认知能力之上，剩余的EI能力能独特地预测相关标准；（c）在控制了流体一般能力和大五人格特质后，情绪识别和调节对平均绩点（GPA）和情感投入的预测呈相反方向的增量效应。心理测量分析表明，只有情绪调节在一般能力因素之外具有足够的确定性和可靠方差，才能在分析和解释中作为显性分数对待，这限制了研究结果。研究结果呼吁对作为多元智能的EI进行新的、尽管有所缓和的研究，并强调需要对情绪感知、理解和管理进行精细评估，以便对不同的EI能力进行有针对性的分析。
