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| MRQAP Network Regression× | 사회 연결망 분석× | |
|---|---|---|
| 분야≠ | Sociology | 네트워크 분석 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 1988 (MRQAP); 2007 (double-semipartialing test) | 1934 (sociometry); 1994 (modern formalization) |
| 창시자≠ | David Krackhardt; David Dekker, David Krackhardt & Tom Snijders | Moreno, J.L.; formalized by Wasserman & Faust |
| 유형≠ | Permutation-based multiple regression for dyadic (matrix) outcomes | Structural/relational analysis framework |
| 원전≠ | Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 별칭 | MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression | SNA, network analysis, sociometric analysis, relational analysis |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
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