Descriptor
| Correlation | 3 |
| Equations (Mathematics) | 3 |
| Mathematical Models | 3 |
| Cluster Analysis | 2 |
| Comparative Analysis | 2 |
| Matrices | 2 |
| Algorithms | 1 |
| Classification | 1 |
| Cluster Grouping | 1 |
| Computer Simulation | 1 |
| Decision Making | 1 |
| More ▼ | |
Author
| Schweizer, Karl | 3 |
Publication Type
| Journal Articles | 3 |
| Reports - Evaluative | 3 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewedSchweizer, Karl – Applied Psychological Measurement, 1991
An equal-level approach is proposed for investigating multitrait-multimethod (MTMM) matrices with respect to other organizational units that contain additional information concerning a MTMM matrix's validity. The approach requires equality in "data level" before coefficients are submitted for evaluation. Disaggregation is central to…
Descriptors: Comparative Analysis, Correlation, Equations (Mathematics), Mathematical Models
Peer reviewedSchweizer, Karl – Multivariate Behavioral Research, 1992
Two versions of a decision rule for determining the most appropriate number of clusters on the basis of a correlation matrix are presented, applied, and compared with three other decision rules. The new rule is efficient for determining the number of clusters on the surface level for multilevel data. (SLD)
Descriptors: Cluster Analysis, Cluster Grouping, Comparative Analysis, Correlation
Peer reviewedSchweizer, Karl – Multivariate Behavioral Research, 1991
A mathematical formula is introduced for the effect of integrating data. A method is then derived to eliminate the effect from correlations of variables, including mean composites, thus allowing for a clustering algorithm that requires allocation of variables according to the magnitude of their correlations. Examples illustrate the procedure. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Computer Simulation


