Moore Tyler M, Reise Steven P, Depaoli Sarah, Haviland Mark G
a Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania.
b Department of Psychology, University of California , Los Angeles.
Multivariate Behav Res. 2015;50(2):149-61. doi: 10.1080/00273171.2014.973990.
We describe and evaluate a factor rotation algorithm, iterated target rotation (ITR). Whereas target rotation (Browne, 2001) requires a user to specify a target matrix a priori based on theory or prior research, ITR begins with a standard analytic factor rotation (i.e., an empirically informed target) followed by an iterative search procedure to update the target matrix. In Study 1, Monte Carlo simulations were conducted to evaluate the performance of ITR relative to analytic rotations from the Crawford-Ferguson family with population factor structures varying in complexity. Simulation results: (a) suggested that ITR analyses will be particularly useful when evaluating data with complex structures (i.e., multiple cross-loadings) and (b) showed that the rotation method used to define an initial target matrix did not materially affect the accuracy of the various ITRs. In Study 2, we: (a) demonstrated the application of ITR as a way to determine empirically informed priors in a Bayesian confirmatory factor analysis (BCFA; Muthén & Asparouhov, 2012) of a rater-report alexithymia measure (Haviland, Warren, & Riggs, 2000) and (b) highlighted some of the challenges when specifying empirically based priors and assessing item and overall model fit.
我们描述并评估了一种因子旋转算法——迭代目标旋转(ITR)。目标旋转(Browne,2001)要求用户基于理论或先前研究预先指定一个目标矩阵,而ITR则从标准的解析因子旋转(即基于经验的目标)开始,随后进行迭代搜索过程以更新目标矩阵。在研究1中,我们进行了蒙特卡罗模拟,以评估ITR相对于克劳福德 - 弗格森家族的解析旋转在总体因子结构复杂度不同情况下的性能。模拟结果:(a)表明当评估具有复杂结构(即多重交叉载荷)的数据时,ITR分析将特别有用;(b)表明用于定义初始目标矩阵的旋转方法对各种ITR的准确性没有实质性影响。在研究2中,我们:(a)展示了ITR在对评分者报告的述情障碍量表(Haviland, Warren, & Riggs, 2000)进行贝叶斯验证性因子分析(BCFA;Muthén & Asparouhov, 2012)时作为确定基于经验的先验值的一种方法的应用;(b)强调了在指定基于经验的先验值以及评估项目和整体模型拟合时的一些挑战。