So sánh phương pháp

	MRQAP Network Regression	Quadratic Assignment Procedure
Lĩnh vực	Sociology	Sociology
Họ	Regression model	Process / pipeline
Năm ra đời	1988 (MRQAP); 2007 (double-semipartialing test)	1976 (QAP); 1988 (network application)
Người khởi xướng	David Krackhardt; David Dekker, David Krackhardt & Tom Snijders	Lawrence Hubert & James Schultz; David Krackhardt
Loại	Permutation-based multiple regression for dyadic (matrix) outcomes	Permutation-based test of association between two matrices
Công trình gốc	Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381.	Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381.
Tên gọi khác	MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression	QAP correlation, QAP permutation test, matrix permutation test, Hubert-Schultz QAP
Liên quan	4	4
Tóm tắt	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.	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.