قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| MRQAP Network Regression× | Dyadic Analysis× | |
|---|---|---|
| المجال | Sociology | Sociology |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 1988 (MRQAP); 2007 (double-semipartialing test) | 1981 |
| صاحب الطريقة≠ | David Krackhardt; David Dekker, David Krackhardt & Tom Snijders | Holland & Leinhardt (p1); Kenny (Social Relations Model) |
| النوع≠ | Permutation-based multiple regression for dyadic (matrix) outcomes | Analysis of the dyad as the unit, decomposing relational effects |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة | MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression | dyad analysis, dyadic data analysis, social relations model, dyad census |
| ذات صلة | 4 | 4 |
| الملخص≠ | Multiple regression quadratic assignment procedure (MRQAP) extends QAP to the regression setting: it predicts a dependent relational matrix from several independent relational matrices on the same actors — for example, modeling who collaborates with whom as a function of who is co-located, who shares a department, and who has prior friendship. Coefficients are estimated by ordinary least squares on the vectorized matrices, but significance is assessed by permutation, because dyadic dependence invalidates the standard regression standard errors. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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