Written in non-technical language, this popular and practical volume has been completely updated to bring readers the latest advice on major issues involved in longitudinal research. It covers: research design strategies; methods of data collection; and how longitudinal and cross-sectional research compares in terms of consistency and accuracy of results.
Written in non-technical language, this popular and practical volume has been completely updated to bring readers the latest advice on major issues involved in longitudinal research. It covers: research design strategies; methods of data collection; and how longitudinal and cross-sectional research compares in terms of consistency and accuracy of results.
Scott Menard is a Professor of Criminal Justice at Sam Houston State University and a research associate in the Institute of Behavioral Science at the University of Colorado, Boulder. He received his A.B. at Cornell University and his Ph.D. at the University of Colorado, Boulder, both in Sociology. His interests include quantitative methods and statistics, life course criminology, substance abuse, and criminal victimization. His publications include Longitudinal Research (second edition Sage 2002), Applied Logistic Regression Analysis (second edition Sage 2002), Good Kids from Bad Neighborhoods (Cambridge University Press 2006, with Delbert S. Elliott, Bruce Rankin, Amanda Elliott, William Julius Wilson, and David Huizinga), Youth Gangs (Charles C. Thomas 2006, with Robert J. Franzese and Herbert C. Covey), and the Handbook of Longitudinal Research (Elsevier 2008), as well as other books and journal articles in the areas of criminology, delinquency, population studies, and statistics.
Inhaltsangabe
1 Introduction Definition 2 The Purposes of Longitudinal Research Age, Period, and Cohort Effects Causal Relationships Serendipity and Intentionality in Longitudinal Data 3 Designs for Longitudinal Data Collection Not-Quite-Longitudinal Designs Total Population Designs Repeated Cross-Sectional Designs Revolving Panel Designs Longitudinal Panel Designs Other Variations 4 Issues in Longitudinal Research Genesis Versus Prediction Changes in Measurement Over Time Panel Attrition Treatment of Missing Data in Longitudinal Research Repeated Measurement and Panel Conditioning Respondent Recall The Costs of Longitudinal Research 5 Longitudinal Analysis Longitudinal versus Cross-Sectional Statistical Methods Types of Longitudinal Causal Models Measuring Change Deterministic versus Probabilistic Models Pooling Cross-Sectional and Time Series Data Time Series Analysis Methods for Short Time Serieswith Many Cases Methods for Long Time Seres with Many Cases Longitudinal Versus Cross-Sectional Data and Analysis Notes References
1 Introduction Definition 2 The Purposes of Longitudinal Research Age, Period, and Cohort Effects Causal Relationships Serendipity and Intentionality in Longitudinal Data 3 Designs for Longitudinal Data Collection Not-Quite-Longitudinal Designs Total Population Designs Repeated Cross-Sectional Designs Revolving Panel Designs Longitudinal Panel Designs Other Variations 4 Issues in Longitudinal Research Genesis Versus Prediction Changes in Measurement Over Time Panel Attrition Treatment of Missing Data in Longitudinal Research Repeated Measurement and Panel Conditioning Respondent Recall The Costs of Longitudinal Research 5 Longitudinal Analysis Longitudinal versus Cross-Sectional Statistical Methods Types of Longitudinal Causal Models Measuring Change Deterministic versus Probabilistic Models Pooling Cross-Sectional and Time Series Data Time Series Analysis Methods for Short Time Serieswith Many Cases Methods for Long Time Seres with Many Cases Longitudinal Versus Cross-Sectional Data and Analysis Notes References
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