Causal Analysis with Panel DataPanel data, which consist of 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 provides an overview of models that are appropriate for the analysis of panel data, focusing specifically on the area where panels offer major advantages over cross-sectional research designs: the analysis of causal interrelationships among variables. Without "painting" panel data as a cure all for the problems of causal inference in nonexperimental research, the author shows how panel data offer multiple ways of strengthening the causal inference process. In addition, he shows how to estimate models that contain a variety of lag specifications, reciprocal effects, and imperfectly measured variables. Appropriate for readers who are familiar with multiple regression analysis and causal modeling, this book will offer readers the highlights of developments in this technique from diverse disciplines to analytic traditions. |
Contents
Models of Reciprocal Causation | 22 |
Measurement Error Models | 45 |
Spurious Association and Autocorrelated | 70 |
Concluding Note on Causal Inference | 87 |
| 93 | |
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assumed assumptions autocorrelated disturbances B₁ candidate ratings causal inference causal system common factor model correlated errors cross-lagged and synchronous cross-lagged effects cross-lagged model cross-sectional degrees of freedom dependent variable direct causal effects disturbance terms effects between variables endogenous variables error term error variances exogenous free parameters group memberships GROUPS₂ identification and candidate included influence instrumental variable latent variable legal protest potential LISREL matrix maximum likelihood estimates measurement error measurement models methods negative observed indicators observed variables omitted variables overidentified panel analysis panel data panel models panel studies panel waves parameter estimates party identification previous chapter procedures regression reliability single-indicator specification spurious association stability standardized coefficients static-score model statistically significant structural disturbances structural effects structural equation substantive synchronous effects model theoretical three-wave model true-score two-wave model U₁ uncorrelated unmeasured variables variances and covariances waves of data waves of observation X₁ Y₁ Y₂ zero