Survey-Based Measurement of Public Management and Policy Networks

被引:37
作者
Henry, Adam Douglas [1 ]
Lubell, Mark
McCoy, Michael [2 ]
机构
[1] Univ Arizona, Sch Govt & Publ Policy, Tucson, AZ 85721 USA
[2] Univ Calif Davis, Urban Land Use & Transportat Planning Ctr, Inst Transportat Studies, Davis, CA 95616 USA
关键词
NAME GENERATOR; RISK;
D O I
10.1002/pam.21623
中图分类号
F [经济];
学科分类号
02 ;
摘要
Networks have become a central concept in the policy and public management literature; however, theoretical development is hindered by a lack of attention to the empirical properties of network measurement methods. This paper compares three survey-based methods for measuring organizational networks: the roster, the free-recall name generator, and a hybrid name generator that combines these two classic approaches. Results indicate that the roster and free-recall name generator methods both suffer from important limitations. The roster method tends to measure many linkages among a limited set of network actors, whereas the name generator tends to measure fewer linkages among a larger set of network actors. Using survey data on policy networks within California regional planning processes (N = 752), we find that the hybrid method strikes an effective balance between these techniques. The hybrid approach performs well in terms of identifying a large number of network actors and interconnections between them. Although no survey-based measurement technique is perfect, this study suggests that the hybrid name generator is an excellent alternative for the measurement of complex networks with large or shifting boundaries that encompass a diverse set of actors.
引用
收藏
页码:432 / 452
页数:21
相关论文
共 42 条
[1]   Big questions in public network management research [J].
Agranoff, R ;
McGuire, M .
JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY, 2001, 11 (03) :295-326
[2]  
[Anonymous], 1991, Journal ofTheoretical Politics
[3]  
[Anonymous], 2020, MAKING POLICY HAPPEN, DOI DOI 10.4324/9781003060697-5
[4]   Self-Organizing Policy Networks: Risk, Partner Selection, and Cooperation in Estuaries [J].
Berardo, Ramiro ;
Scholz, John T. .
AMERICAN JOURNAL OF POLITICAL SCIENCE, 2010, 54 (03) :632-649
[5]   INFORMANT ACCURACY IN SOCIAL-NETWORK DATA .5. AN EXPERIMENTAL ATTEMPT TO PREDICT ACTUAL COMMUNICATION FROM RECALL DATA [J].
BERNARD, HR ;
KILLWORTH, PD ;
SAILER, L .
SOCIAL SCIENCE RESEARCH, 1982, 11 (01) :30-66
[6]  
Bilmes J., 2002, SOC NETWORKS
[7]   Organizing Babylon - On the different conceptions of policy networks [J].
Borzel, TA .
PUBLIC ADMINISTRATION, 1998, 76 (02) :253-273
[8]   Network inference, error, and informant (in)accuracy: a Bayesian approach [J].
Butts, CT .
SOCIAL NETWORKS, 2003, 25 (02) :103-140
[9]  
Carrington P.J., 2005, Models and Methods in Social Network Analysis, V28
[10]   The stability of centrality measures when networks are sampled [J].
Costenbader, E ;
Valente, TW .
SOCIAL NETWORKS, 2003, 25 (04) :283-307