Causal Models in Experimental Designs

by H. M. Blalock

Overview

This is a companion volume to Causal Models in the Social Sciences, the majority of articles concern panel designs involving repeated measurements while a smaller cluster involve discussions of how experimental designs may be improved by more explicit attention to causal models. All of the papers are concerned with complications that may occur in actual research designs- as compared with idealized ones that often become the basis of textbook discussions of design issues.

Product Details

ISBN-13:	9781351529808
Publisher:	Taylor & Francis
Publication date:	07/12/2017
Sold by:	Barnes & Noble
Format:	eBook
Pages:	300
File size:	9 MB

About the Author

H. M. Blalock

Table of Contents

The Use of Causal Models in Experimental and Nonexperiment al Designs; 1: Logic of Causal Analysis: From Experimental to Nonexperimental Designs *; 2: Causal Models Involving Unmeasured Variables in Stimulus - Response Situations *; 3: Utilizing Causal Models to Discover Flaws in Experiments *; 4: Causal Models, Unobserved Variables, and Experimental Data *; 5: Inadvertent Manipulations of Dependent Variables in Research Designs; Part II: The Use of Causal Models in Panel Designs; 6: Separating Reliability and Stability in Test-Retest Correlation; 7: The Estimation of Measurement Error in Panel Data *; 8: Comment on “The Estimation of Measurement Error in Panel Data” *; 9: Problems in Estimating Measurement Error from Panel Data: An Example Involving the Measurement of Scientific Productivity *; 10: Estimating Measurement Error Using Multiple Indicators and Several Points in Time *; 11: The Stability and Reliability of Political Efficacy: Using Path Analysis to Test Alternative Models *; 12: Equilibrium and Identification in Linear Panel Models *; 13: Causal Inference in Cross-Lagged Panel Analysis* *; 14: The Reciprocal Effects of the Substantive Complexity of Work and Intellectual Flexibility: A Longitudinal Assessment *