Evaluation of social network measurement instruments |
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| Institution: | 1. Viterbi Department of Electrical Engineering, Technion, Haifa, Israel;2. Yahoo Research, Haifa, Israel;1. GRMEP/MSU General Surgery Residency, 221 Michigan Street 200A, Grand Rapids, MI 49503, USA;2. Department of General Surgery, Michigan State University, Grand Rapids, MI, USA;3. GRMEP Research Department, Grand Rapids, MI, USA;4. Division of Trauma Surgery, Spectrum Health, Grand Rapids, MI, USA;1. Hannover Medical School (MHH), Institute for History, Ethics, and Philosophy of Medicine, Germany;2. University of Duisburg-Essen, Ethics Committee of the Medical Faculty, Germany;3. University of Gießen, Research Ethics Committee, Germany;4. Ludwig Maximilians Universität München (LMU), Ethics Committee, Germany;5. University of Kiel, Ethics Committee of the Medical Faculty, Germany;6. Humboldt-Universität zu Berlin, Faculty of Law, Germany;7. University Hospital of Wuerzburg, Interdisciplinary Bank of Biomaterials and Data Wuerzburg (ibdw), Germany;8. University of Kiel, Chair of Public and Medical Law, Germany;9. Technische Universität München (TUM), University Hospital Klinikum Rechts der Isar, Germany;10. University of Mannheim, Professorship for Civil Law, Law of Civil Procedure, Private International Law and Comparative Law, Germany;11. Hannover Medical School (MHH), Research Ethics Committee, Germany |
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| Abstract: | This paper evaluates the reliability and validity of network measurement instruments for measuring social support. The authors present and discuss the results from eight experiments which were designed to analyze the quality of four measurement scales: (1) binary, (2) categorical, (3) categorical with labels, and (4) line production, as well as two measurement techniques for listing alters (free recall and recognition). Reliability and validity were estimated by the true score multitrait–multimethod (MTMM) approach. Meta-analysis of factors affecting the reliability and the validity of network measurement was done by multiple classification analysis (MCA). The results show that the binary scale and the first presentation of measurement instruments are the least reliable. Surprisingly, the two data collection techniques (free recall and recognition) yield equally reliable data. |
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