Causal Analysis with Panel Data / Edition 1

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Overview

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.

Incl. modeling change w/panel data/models of reciprocal causation/measurement error models/LISREL matrices, notation

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Product Details

  • ISBN-13: 9780803938960
  • Publisher: SAGE Publications
  • Publication date: 1/28/1995
  • Series: Quantitative Applications in the Social Sciences Series , #105
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 104
  • Sales rank: 1,057,657
  • Product dimensions: 5.50 (w) x 8.40 (h) x 0.40 (d)

Meet the Author

Reseach in Professor Finkel’s laboratory focuses on the long-term survival and evolution of bacteria, including understanding natural systems and applications to electricity production. Among the topics of current study in the laboratory are (1) the mechanisms of survival in many organisms, including population structure at the genetic & genomic level and studies of the GASP (growth advantage in stationary phase) phenotype; (2) the generation of bacterial diversity in different environments, including the roles of error-prone DNA polymerases in generating diversity; (3) the role of DNA-protein interactions in controlling gene expression, organizing the chromosome, and protecting DNA during the stationary phase of the bacterial lifecycle, focusing on the role of the Dps protein, (4) the use of DNA as a nutrient and parallels to natural competence and transformation; and (5) the long-term survival ability of bacteria in electricity-generating microbial fuel cells (MFCs). Model organisms in the laboratory include: Escherichia coli, Shewanella oneidensis, Pseudomonas aeruginosa, and Streptococcus mutans.

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Table of Contents

Introduction
Modeling Change with Panel Data
Models with Reciprocal Causation
Measurement Error Models
Models of Spurious Association
Concluding Note on Causal Inference in Panel Analysis

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