השוואת שיטות
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| Quadratic Assignment Procedure× | Network Autocorrelation Model× | |
|---|---|---|
| תחום | Sociology | Sociology |
| משפחה≠ | Process / pipeline | Regression model |
| שנת המקור≠ | 1976 (QAP); 1988 (network application) | 1980 (spatial/network models); 2002 (weight matrix) |
| הוגה השיטה≠ | Lawrence Hubert & James Schultz; David Krackhardt | Patrick Doreian; Roger Leenders (weight-matrix synthesis) |
| סוג≠ | Permutation-based test of association between two matrices | Regression with an autoregressive term on a network weight matrix |
| מקור מכונן≠ | Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. DOI ↗ | Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI ↗ |
| כינויים | QAP correlation, QAP permutation test, matrix permutation test, Hubert-Schultz QAP | network effects model, social influence model, network disturbances model, autoregressive network model |
| קשורות | 4 | 4 |
| תקציר≠ | 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. | 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. |
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