来自杂志 Journal of abnormal child psychology 的文献。
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魏魏魏
(2022-09-07 11:47):
#paper doi:10.1007/s10802-010-9396-z. Journal of Abnormal Child Psychology, (2010), Mother and Adolescent Reports of Associations Between Child Behavior Problems and Mother-Child Relationship Qualities: Separating Shared Variance from Individual Variance. 基于共同命运模型(Common Fate Model, CFM)的研究很少，所以看到了2010年的文献，只为更好地学习这种方法。共同命运模型很适合研究夫妻、母子和父子关系这种双方成员共享生活环境的人，即双方受到共同的环境变量影响，在一些变量上双方具有相似性。关系中的双方都需要在相关变量上报告自己的情况，这样就形成了配对数据(dyadic data)，而且，双方的数据会存在依存性(interdependence)，这也就打破了传统的相关分析需要变量各自独立的假设前提，此时共同命运模型可以解决这个问题。再有，传统研究只考察了单个被试在自变量和结果变量上的情况，这可能会出现因数据有共同来源而导致的共同方法变异(Common method variance)，这会使最终结果的变异被夸大或缩小，也会影响我们对实际情况的准确认识。此时，这个模型也很有优势，因为它引进了另一个关系被试的情况，使得数据的来源多元化。基于共同命运模型的分析除了考察单个被试内变量的相关情况，也考察了被试间在同样的变量上的相关情况，还考察了关系水平上自变量与结果变量的相关情况。在这个过程中，关系双方共享因素带来的变异被分解了出来，帮助人们更好地了解了自变量与因变量的真实关系。当前研究考察了青少年行为问题与母子关系品质的关系，在两个变量上，母子双方有共同的认识，彼此间也会存在差异。基于共同命运模型，该研究同时考察了母子在相同变量上的情况。在具体分析中，除了考察子女报告的变量间的相关情况，也考察了母亲报告的变量间的相关情况，还从母子关系水平上分析了变量间的相关，并同时在模型中分别分析了母子在变量间的相关情况，并比较了多个相关系数之间在大小上的差异情况。最终发现了不同于基于传统研究的发现结果。
Abstract:
This study contrasts results from different correlational methods for examining links between mother and child (N = 72 dyads) reports of early adolescent (M = 11.5 years) behavior problems and …
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This study contrasts results from different correlational methods for examining links between mother and child (N = 72 dyads) reports of early adolescent (M = 11.5 years) behavior problems and relationship negativity and support. Simple (Pearson) correlations revealed a consistent pattern of statistically significant associations, regardless of whether scores came from the same reporter or from different reporters. When correlations between behavior problems and relationship quality differed, within-reporter correlations were always greater in magnitude than between-reporter correlations. Dyadic (common fate) analyses designed for interdependent data decomposed within-reporter correlations into variance shared across reporters (dyadic correlations) and variance unique to specific reporters (individual correlations). Dyadic correlations were responsible for most associations between adolescent behavior problems and relationship negativity; after partitioning variance shared across reporters, no individual correlations emerged as statistically significant. In contrast, adolescent behavior problems were linked to relationship support via both shared variance and variance unique to maternal perceptions. Dyadic analyses provide a parsimonious alternative to multiple contrasts in instances when identical measures have been collected from multiple reporters. Findings from these analyses indicate that same-reporter variance bias should not be assumed in the absence of dyadic statistical analyses.
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