MRQAP Network Regression
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.
Lasīt pilno metodes aprakstu
Piesakieties ar bezmaksas kontu, lai lasītu šo sadaļu.
Metožu karte
Saistīto metožu apkaime — atlasiet mezglu, lai izpētītu.
Avoti
- Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. DOI: 10.1016/0378-8733(88)90004-4 ↗
- Dekker, D., Krackhardt, D., & Snijders, T. A. B. (2007). Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika, 72(4), 563–581. DOI: 10.1007/s11336-007-9016-1 ↗
Kā citēt šo lapu
ScholarGate. (2026, June 22). Multiple Regression Quadratic Assignment Procedure (MRQAP). ScholarGate. https://scholargate.app/lv/sociology/mrqap-network-regression
Kura metode?
Novietojiet šo metodi blakus tās tuvākajām radniecīgajām metodēm un lasiet tās līdzās — bibliotēka noliek grāmatas uz galda; izvēle ir jūsu.
- Dyadic AnalysisSociology↔ salīdzināt
- Network Autocorrelation ModelSociology↔ salīdzināt
- Quadratic Assignment ProcedureSociology↔ salīdzināt
- Sociālo tīklu analīzeTīklu analīze↔ salīdzināt
Uz to atsaucas
Līdzīgas metodes
Pamanījāt kļūdu šajā lapā? Ziņojiet vai ierosiniet labojumu →