Causal Models in Experimental Designs
Autor H. M. Blalocken Limba Engleză Paperback – 15 iul 2007
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Specificații
ISBN-13: 9780202309729
ISBN-10: 020230972X
Pagini: 298
Ilustrații: 1
Dimensiuni: 152 x 229 x 19 mm
Greutate: 0.45 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom
ISBN-10: 020230972X
Pagini: 298
Ilustrații: 1
Dimensiuni: 152 x 229 x 19 mm
Greutate: 0.45 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom
Cuprins
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 *
Descriere
A companion volume to the "Causal Models in the Social Sciences", this work includes articles, the majority of which concern panel designs involving repeated measurements while a smaller cluster involves discussions of how experimental designs may be improved by more explicit attention to causal models.