Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Quadratic Assignment Procedure× | Dyadic Analysis× | |
|---|---|---|
| Галузь | Sociology | Sociology |
| Родина≠ | Process / pipeline | Regression model |
| Рік появи≠ | 1976 (QAP); 1988 (network application) | 1981 |
| Автор методу≠ | Lawrence Hubert & James Schultz; David Krackhardt | Holland & Leinhardt (p1); Kenny (Social Relations Model) |
| Тип≠ | Permutation-based test of association between two matrices | Analysis of the dyad as the unit, decomposing relational effects |
| Основоположне джерело≠ | Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. DOI ↗ | Holland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association, 76(373), 33–50. DOI ↗ |
| Інші назви | QAP correlation, QAP permutation test, matrix permutation test, Hubert-Schultz QAP | dyad analysis, dyadic data analysis, social relations model, dyad census |
| Пов'язані | 4 | 4 |
| Підсумок≠ | The quadratic assignment procedure (QAP) is a permutation-based method for testing the association between two relational matrices measured on the same set of actors — for example, whether who advises whom is correlated with who is friends with whom. Because the dyads in a network are not independent, ordinary correlation and regression give invalid p-values; QAP fixes this by comparing the observed matrix correlation to a reference distribution generated by randomly relabeling the nodes of one matrix many times. | Dyadic analysis treats the dyad — the pair of actors and the relation between them — as the unit of analysis, separating the relational outcome into what each actor brings to all their relationships and what is unique to the specific pair. It spans the descriptive dyad census of network analysis and statistical frameworks such as Holland and Leinhardt's p1 model and Kenny's Social Relations Model, all of which respect the structural non-independence inherent in relational data. |
| ScholarGateНабір даних ↗ |
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