Detecting measurement bias in respondent reports of personal networks |
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Affiliation: | 1. Department of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea;2. Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea |
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Abstract: | Inaccuracy of sociometric reports poses a serious challenge to social network analysis. Nevertheless, researchers continue to draw potentially misleading conclusions from flawed data. We consider two particular types of systematic error in measurement of network size: individuals over/underreporting others (expansiveness bias), and individuals being over/underreported by others (attractiveness bias). We examine evidence of individual variation in these biases in one apparently typical sociometric dataset. We specifically suggest that variation in expansiveness bias may commonly distort findings concerning characteristics of individual networks (e.g. size, range, density), and properties of whole networks (e.g. inequality, transitivity, clustering, and blockmodels). We suggest methodological improvements and urge further research. |
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