Discriminant – Offers a choice of variable selection methods, statistics at each step and in a final summary output is displayed at each step and/or in final form. TwoStep Cluster Analysis – Group observations into clusters based on nearness criterion, with either categorical or continuous level data specify the number of clusters or let the number be chosen automatically.Statistics are displayed at each stage to help you select the best solution. Distance or similarity measures are generated by the Proximities procedure. Analyze raw variables or choose from a variety of standardizing transformations. Hierarchical Cluster Analysis – Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case in a separate cluster and combines clusters until only one is left.Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only. K-means Cluster Analysis – Used to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases but which requires you to specify the number of clusters. Also, scores can be saved as variables for further analysis Three methods of computing factor scores.Five methods of rotation, including direct oblimin and promax for nonorthogonal rotations. In IBM SPSS Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering:
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |