The Computerized Construction of a Matched Sample

The use of matched sampling in experimental and nonexperimental research has been frustrated by the problems of attrition and incomplete matching. The amount of attrition and incomplete matching. The amount of attrition of sample size and incomplete matching are shown to be a function of various aspects of the research, including the level of measurement of the matching variables, the sampling situation, the definition of a match, and the means used to construct matches. Several basic and heretofore neglected considerations in the conceptualization of a "match" are discussed. These considerations and a rarallel set of desiderata became apparent during the computerized construction of two matched samples of black and white college graduates. Three approaches to the computerization of matching are described, the last of which-"guaranteed variable caliper matching"- meets the desiderata. We describe our clerical and field procedures from the computerized matching to the final matching based on completed interviews. The degree to which both computer matching and these procedures controlled attition and incomplete matching is assessed. The quality of matches is found to be fairly high and attrition has been virtually elliminated, thus demonstrating the possibilities of our approach

Attrition · Calipers · Conceptualization · Econometrics · Matching (statistics) · Sample (material) · Sample size determination · Sampling (signal processing) · Set (abstract data type) · Statistics · Artificial Intelligence · Computer Science · Higher Education Research Studies · Innovative Teaching Methodologies in Social Sciences · Mathematics · Medicine · Survey Methodology and Nonresponse

• Canadian Encouragement of Higher Educational Participation

  Sheldon Goldenberg•Comparative Sociology•1976

  Incluir item

• Statistical Matching Of Different Source

  Biancamaria Zavanella•Income and Wealth Distribution,…•1990

  Incluir item

• Matched Sampling for Causal Effects

  Open Access•Donald B Rubin•Matched Sampling for Causal Effects•2006

  Incluir item

• Assignment to Treatment Group on the Basis of a Covariate

  Open Access•Donald B Rubin•Journal of Educational Statistics•1977

  Incluir item

• Matching Methods for Causal Inference

  Elizabeth A Stuart•Statistical Science•2010

  Incluir item

• Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

  Paul R Rosenbaum, Donald B Rubin•The American Statistician•1985

  Incluir item

• Income advantages of communist party members before and During the transformation process

  Open Access•Willem‐Jan Verhoeven, Willem-Jan Verhoeven et al.•European Societies•2008

  Incluir item

• National Differences in Individual Modernity

  Alex Inkeles•National Character•2017

  Incluir item

• Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies

  Open Access•Herbert L Smith•Sociological Methodology•1997

  Incluir item

• Statistical Matching Using File Concatenation With Adjusted Weights and Multiple Imputations

  Donald B Rubin•Journal of Business and Economic…•1986

  Incluir item

• Agricultural trade liberalization and domestic fertilizer use

  Open Access•Fei Nie, Jian Li et al.•Ecological Economics•2022

  Incluir item

• Matching Estimators of Causal Effects

  Open Access•Stephen L Morgan, David J Harding•Sociological Methods & Research•2006

  Incluir item

• Socioeconomic background and racial earnings inequality

  Open Access•Jeremy Pais•Social Science Research•2011

  Incluir item

• Statistical procedures and their mathematical bases.

  Charles C Peters, Walter R Van Voorhis•1940

  Incluir item

• The Conduct of Inquiry

  Ma Natividad Jiménez Salas, Abraham Kaplan•Revista española de la opinión…•1967

  Incluir item