Georgia State University, P.O. Box 3991, Atlanta, GA, 30302-3991, USA.
University of Cologne, Cologne, Germany.
Psychometrika. 2019 Sep;84(3):772-780. doi: 10.1007/s11336-019-09677-2. Epub 2019 Jul 10.
Parceling-using composites of observed variables as indicators for a common factor-strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference.
分箱——使用观测变量的组合作为共同因子的指标——可以增强载荷，但会减少指标的数量。当每个因子有许多观测变量，且载荷和因子相关性较强时，因子不确定性会降低。已证明分箱不能降低因子不确定性。在特殊情况下，对于每个分箱中包含的所有项目，载荷与残差方差的比值都相同，分箱不会影响因子不确定性。否则，分箱会使因子不确定性恶化。虽然因子不确定性不会影响与因子模型相关的参数估计、标准误差或拟合指数，但它确实会产生不确定性，从而危及有效的推断。
