Abstract
Social capital is a central concept in social science research, and it is measured in diverse ways. Few measurement approaches take the network structure of complex institutional settings into account. In this study, using data from a large-scale school-based randomized field trial, we develop several factor analytic models to test the validity and reliability of a new survey battery capturing multiple dimensions of social capital in such settings. We demonstrate that it is important to account for institutional and network structure in social capital measures, and we show how social capital can be operationalized as the shared variance between different relational characteristics in complex settings with multiple subnetworks.
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Notes
Another way social capital scholars have moved beyond the single-item approach is by employing name or position generators (Van Der Gaag and Snijders 2004, 2005). These generators have many advantages; however, they focus attention on the individual and do not account for different subnetworks within complex settings. Position generators, which rely on occupational prestige, can be less useful in studies of schools or other institutional settings in which the occupations of many members are a relative constant (i.e. teachers), and members like parents may not know one another’s occupations.
Participants were administered written surveys in their native language. Parents completed pre-treatment surveys in person at the time of consent. The follow-up questionnaires were distributed by mail. Non-respondents after repeated reminders were surveyed over the phone. The response rate was 70 % for post-treatment parent surveys.
The Weighted Least Squares, Means and Variance Adjusted estimator in M-PLUS aids in this effort by placing the variables on a common underlying distribution with category thresholds as placeholders.
Whether or not teacher/classroom clustering is modeled when measuring social capital is also a theoretical question that must be answered based on the goals of an individual study. Because our aim is to capture whole-school social capital, variation between classrooms, which depends heavily on the quality and practices of the teachers themselves, is not of interest. However, if an investigator is interested in teacher effects, rather than school effects, the between-teacher variation would be essential. Therefore, researchers interested in teacher effects should not take steps to focus on within classroom variation, as we do in Models 2 and 4.
We have an insufficient number of questionnaire items to estimate a nine-factor model in which subnetworks and dimensions are separated. We highlight this as an avenue for future research. However, we reiterate that the analyses reported here show clearly that modeling the dimensions of social capital separately provides trivial improvements in fit.
There are 25 items in the SDQ. Items ask the teacher to assess several negative and positive child characteristics such as “shares readily” and “often looses temper.” The full battery is available in Goodman (1997).
The parent depression variable is created from an additive index of responses to the following: “over the last 2 weeks, how often have you experienced the following? Little interest or pleasure in doing things (Never, Several Days, Most Days, Every Day); feeling down depressed or hopeless (Never, Several Days, Most Days, Every Day).
Model 5 from Table 3 is excluded from this analysis, because we found that the fit of Model 6 (where items 8c and 9c were allowed to load onto intergenerational closure) was superior.
This is not the case for the individual survey items themselves, which are consistently weaker predictors. Latent traits generated from aggregating multiple measures tend to have stronger relationships with important outcomes as compared to single items due to reduced measurement error and the associated downward bias in the coefficients. The regression analysis provides evidence for that phenomenon.
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Acknowledgments
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Award # R305C050055 to the University of Wisconsin-Madison. The larger project was supported by the National Institute of Child Health and Human Development (Grant Number 1R01HD051762-01A2). Findings and conclusions are those of the authors and do not necessarily represent the views of the supporting agencies. We wish to thank Adam Gamoran, Ruth Lopez Turley, Lynn McDonald, and Carmen Valdez for access to the project data and comments. We also wish to thank Jackie Roessler, Vansa Shewakramani Hanson, the FAST project staff, members of the Interdisciplinary Training Program in the Education Sciences at The University of Wisconsin-Madison, and an anonymous reviewer for assistance and comments.
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Condon, M., Lavery, L. & Engle, P.J. Measuring Social Capital: Accounting for Nested Data and Subnetworks Within Schools. Soc Indic Res 126, 1189–1207 (2016). https://doi.org/10.1007/s11205-015-0945-2
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DOI: https://doi.org/10.1007/s11205-015-0945-2