Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Network Autocorrelation Model× | MRQAP Network Regression× | |
|---|---|---|
| Nyanja | Sociology | Sociology |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1980 (spatial/network models); 2002 (weight matrix) | 1988 (MRQAP); 2007 (double-semipartialing test) |
| Mwanzilishi≠ | Patrick Doreian; Roger Leenders (weight-matrix synthesis) | David Krackhardt; David Dekker, David Krackhardt & Tom Snijders |
| Aina≠ | Regression with an autoregressive term on a network weight matrix | Permutation-based multiple regression for dyadic (matrix) outcomes |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | network effects model, social influence model, network disturbances model, autoregressive network model | MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. |
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