Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Network Autocorrelation Model× | MRQAP Network Regression× | |
|---|---|---|
| Область | Sociology | Sociology |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980 (spatial/network models); 2002 (weight matrix) | 1988 (MRQAP); 2007 (double-semipartialing test) |
| Автор метода≠ | Patrick Doreian; Roger Leenders (weight-matrix synthesis) | David Krackhardt; David Dekker, David Krackhardt & Tom Snijders |
| Тип≠ | Regression with an autoregressive term on a network weight matrix | Permutation-based multiple regression for dyadic (matrix) outcomes |
| Основополагающий источник≠ | Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI ↗ | Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. DOI ↗ |
| Другие названия | network effects model, social influence model, network disturbances model, autoregressive network model | MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression |
| Связанные | 4 | 4 |
| Сводка≠ | The network autocorrelation model adapts spatial-econometric regression to social networks to estimate peer influence: it explains an actor's outcome — an attitude, behavior, or performance — as a function of their own covariates plus a weighted average of their network partners' outcomes. The autocorrelation parameter ρ captures the strength of social influence, and the network weight matrix W encodes who influences whom and how strongly. | 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. |
| ScholarGateНабор данных ↗ |
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