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An Ego-Network Approach to Understanding Educator and School Ties to Research: From Basic Statistics to Profiles of Capacity

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Networks, Knowledge Brokers, and the Public Policymaking Process

Abstract

Prior literature suggests a social network perspective is instructive for understanding and building research-use capacity. Although educators’ professional networks have been found to contribute the implementation of reform, professional learning, and instructional improvement, evidence about their role in supporting schools’ use of research remains thin. This chapter presents the application of network methods—specifically, ego-network methods—to understanding the flow of research-based ideas and practices into schools, drawing on the work of the Center for Research Use in Education. An ego-network approach proved useful in unpacking educators’ networks for connecting with research, including helping us to better understand the ecology of research mediation in education, to operationalize network concepts, and to develop indicators of educator and school-level capacity.

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Notes

  1. 1.

    It is not possible to determine whether non-response indicates failure to complete the item or the absence of any ties to educational research resources.

  2. 2.

    Individuals are excluded for privacy purposes and organization names that reveal sample participants are given pseudonyms.

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Correspondence to Elizabeth N. Farley-Ripple .

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Appendices

Appendix A

figure afigure afigure a

Appendix B: Model Comparison Fit Statistics for MLPA

No. of profiles

AIC

BIC

SABIC

LMR LRT (p)

BLRT (p)

Entropy

Level-1

2

−532.78

−488.49

−520.24

 <0.001

 <0.001

0.84

3

−1107.52

−1045.52

−1089.97

0.0938

 <0.001

0.91

4

−1556.68

−1476.97

−1534.12

0.0030

 <0.001

0.93

5

−1888.10

−1790.68

−1860.52

 <0.001

 <0.001

0.94

Level-2 with 4 level-1 profiles

2

−3061.73

−2949.07

−3018.95

  

0.91

3

−3067.13

−2934.0

−3016.56

  

0.92

  1. Note AIC = Akaike information criteria; BIC = Bayesian information criterion; SABIC = sample size-adjusted Bayesian information criterion; LMR LRT = Lo-Mendel-Rubin likelihood ratio test; BLRT = bootstrapped log likelihood ratio test.

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Farley-Ripple, E.N., Yun, JY. (2021). An Ego-Network Approach to Understanding Educator and School Ties to Research: From Basic Statistics to Profiles of Capacity. In: Weber, M.S., Yanovitzky, I. (eds) Networks, Knowledge Brokers, and the Public Policymaking Process. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78755-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-78755-4_7

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  • Publisher Name: Palgrave Macmillan, Cham

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