Multiple imputation (Rubin, 1987) is a well-known method for handling missing data. Applying the procedure to an incomplete data set results in several plausible complete versions of the incomplete data set which are then all analyzed with the same statistical analysis. In order to obtain one overall analysis that is used for interpretation, the analysis results of these several completed data sets are combined using specific combination procedures. For principal component analysis (PCA), Van Ginkel and Kroonenberg (2014) proposed generalized procrustes analysis (GPA; Gower, 1975; Ten Berge, 1977) to combine the results. To date, GPA seems to have been little used for combining PCA results in multiply imputed data sets, as shown from relatively few citations of Van Ginkel and Kroonenberg (2014) by applied research papers. One reason could be that there are only few software packages that have implemented GPA. Exceptions are the “shapes” package (Dryden & Mardia, 2016) in R (R Core Team, 2018) and the stand-alone program 3WayPack (Kroonenberg & De Roo, 2010). In addition, these software packages may not be well known by applied researchers, and it may not be obvious to them that they may also be used for combining the results of PCA in multiply imputed data. For these researchers, the authors developed a user-friendly SPSS subroutine which is specifically aimed at combining the results of PCA, as described by Van Ginkel and Kroonenberg (2014), and which can be applied completely within SPSS. To run the subroutine, one must first carry out a PCA on each of the imputed data sets in SPSS and save the results to a data file. Next, the subroutine may carry out the combining of the saved results using GPA. Within the subroutine, a number of required arguments and some optional arguments are specified. Among the most important optional arguments are the display of the Varimax rotated centroid solution in the output, and the display of loading plots of both the unrotated and Varimax rotated centroid solutions, along with their convex hulls (Van Ginkel & Kroonenberg, 2014).

中文翻译：

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SPSS语法，用于使用广义Procrustes分析组合多次插补数据集的主成分分析结果

多重插补（Rubin，1987）是一种处理缺失数据的众所周知的方法。将程序应用于不完整的数据集会导致不完整的数据集的多个合理的完整版本，然后使用相同的统计分析对它们进行全部分析。为了获得用于解释的整体分析，可以使用特定的组合过程将这几个完整数据集的分析结果组合在一起。对于主成分分析（PCA），Van Ginkel和Kroonenberg（2014）提出了广义过程分析（GPA; Gower，1975; Ten Berge，1977）来组合结果。迄今为止，GPA似乎很少用于将PCA结果合并到多个估算数据集中，如应用研究论文对Van Ginkel和Kroonenberg（2014）的引用相对较少。原因之一可能是只有很少的软件包实现了GPA。R中的“ shapes”包（Dryden＆Mardia，2016）（R Core Team，2018）和独立程序3WayPack（Kroonenberg＆De Roo，2010）是一个例外。此外，这些软件包可能不会被应用研究人员所熟知，并且对于它们来说也可能并不明显，它们也可用于将PCA的结果合并到多个估算数据中。对于这些研究人员，作者开发了一种用户友好的SPSS子例程，该子例程专门用于组合PCA的结果，如Van Ginkel和Kroonenberg（2014）所述，并且可以完全在SPSS中应用。要运行该子例程，必须首先对SPSS中的每个估算数据集执行PCA，然后将结果保存到数据文件中。下一个，该子例程可以使用GPA对保存的结果进行合并。在子例程中，指定了许多必需参数和一些可选参数。最重要的可选参数包括输出中Varimax旋转质心解的显示，未旋转和Varimax旋转质心解及其凸包的载荷图的显示（Van Ginkel＆Kroonenberg，2014）。