Experimental and Quasi-experimental Designs for Generalized Causal Inference PDF Download
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Author: William R. Shadish
Publisher: Cengage Learning
ISBN:
Category : Education
Languages : en
Pages : 664
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Book Description
Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions.
Author: William R. Shadish
Publisher: Cengage Learning
ISBN:
Category : Education
Languages : en
Pages : 664
Get Book Here
Book Description
Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions.
Author: Judea Pearl
Publisher: Createspace Independent Publishing Platform
ISBN: 9781507894293
Category : Causation
Languages : en
Pages : 0
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Book Description
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
Author: Donald T. Campbell
Publisher: Ravenio Books
ISBN:
Category : Psychology
Languages : en
Pages : 172
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Book Description
We shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control.
Author: Dek Terrell
Publisher: Emerald Group Publishing
ISBN: 1789739594
Category : Business & Economics
Languages : en
Pages : 427
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Book Description
Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
Author: Gary King
Publisher: Princeton University Press
ISBN: 0691034710
Category : Social Science
Languages : en
Pages : 259
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Book Description
Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?
Author: Patrick Suppes
Publisher: Springer Science & Business Media
ISBN: 9401723001
Category : Science
Languages : en
Pages : 506
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Book Description
The thirty-one papers collected in this volume represent most of the arti cles that I have published in the philosophy of science and related founda tional areas of science since 1970. The present volume is a natural succes sor to Studies in the Methodology and Foundations of Science, a collection of my articles published in 1969 by Reidel (now a part of Kluwer). The articles are arranged under five main headings. Part I contains six articles on general methodology. The topics range from formal methods to the plurality of science. Part II contains six articles on causality and explanation. The emphasis is almost entirely on probabilistic approaches. Part III contains six articles on probability and measurement. The impor tance of representation theorems for both probability and measurement is stressed. Part IV contains five articles on the foundations of physics. The first three articles are concerned with action at a distance and space and time, the last two with quantum mechanics. Part V contains eight articles on the foundations of psychology. This is the longest part and the articles reflect my continuing strong interest in the nature of learning and perception. Within each part the articles are arranged chronologically. I turn now to a more detailed overview of the content. The first article of Part I concerns the role of formal methods in the philosophy of science. Here I discuss what is the new role for formal methods now that the imperialism of logical positivism has disappeared.
Author: Guido W. Imbens
Publisher: Cambridge University Press
ISBN: 0521885884
Category : Business & Economics
Languages : en
Pages : 647
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Book Description
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Author: Ellen Perecman
Publisher: SAGE
ISBN: 141291681X
Category : Reference
Languages : en
Pages : 281
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Book Description
This text contains a collection of essays and bibliographies providing both novice and experienced scholars with invaluable and accessible insights, as well as references to a select list of critical texts pertaining to a wide array of social science methods and practices useful when doing fieldwork.
Author: Peter Spirtes
Publisher: Springer Science & Business Media
ISBN: 1461227488
Category : Mathematics
Languages : en
Pages : 551
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Book Description
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
Author: Stephen L. Morgan
Publisher: Cambridge University Press
ISBN: 1139465902
Category : Social Science
Languages : en
Pages : 291
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Book Description
Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual's labor market earnings? Did the use of the butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? If so, was the number of miscast votes sufficiently large to have altered the election outcome? At their core, these types of questions are simple cause-and-effect questions. Simple cause-and-effect questions are the motivation for much empirical work in the social sciences. This book presents a model and set of methods for causal effect estimation that social scientists can use to address causal questions such as these. The essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics.
