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~ Steven Eric Finkel
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Introduction
Panel data - information gathered from the same individuals or units at several different points in time - are commonly used in the social sciences to test theories of individual and social change. This book highlights the developments in this technique in a range of disciplines and analytic traditions. Providing an overview of models appropriate for the analysis of panel data, the book focuses specifically on the area where panels offer major advantages over cross-sectional research designs: the analysis of causal interrelationships among variables. Finkel demonstrates how panel data offer multiple ways of strengthening the causal inference process. He also explores how to estimate models that contain a variety of lag specifications, reciprocal effects and imperfectly measured variables.
- 出版社: SAGE Publications Inc (1995年1月17日)
- 丛书名: Quantitative Applications in the Social Sciences
- 平装: 104页
- 语种： 英语
- Contents
- 1. Introduction
- 2. Modeling Change With Panel Data
- 2.1 Change-Score Models and the Role of Lagged Endogenous Variables
- 2.2 Estimation of the Static-Score Model
- 2.3 Alternative Lag Specifications
- 2.4 Problems in the Estimation of Panel Models
- 3. Models of Reciprocal Causation
- 3.1 Cross-Lagged Effects Models
- 3.2 Synchronous Effects Models
- 3.3 Cross-Lagged and Synchronous Effects Models
- 4. Measurement Error Models
- 4.1 Basic Concepts
- 4.2 Single-Indicator Models
- 4.3 Multiple-Indicator Models
- 5. Spurious Association and Autocorrelated Disturbances
- 5.1 Common Factor Models
5.2 Unmeasured Variable Models - 6. Concluding Note on Causal Inference in Panel Analysis
Notes
References
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