Experimental And Quasi Experimental Designs
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📒Experimental And Quasi Experimental Designs For Research ✍ Donald Thomas Campbell
✏Experimental and Quasi experimental Designs for Research Book Summary :
📒Experimental And Quasi Experimental Designs For Generalized Causal Inference ✍ William R. Shadish
✏Experimental and Quasi experimental Designs for Generalized Causal Inference Book Summary : This long awaited successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation: Theoretical matters: Experimentation, causation, and validity Quasi-experimental design: Regression discontinuity designs, interrupted time series designs, quasi-experimental designs that use both pretests and control groups, and other designs Randomized experiments: Logic and design issues, and practical problems involving ethics, recruitment, assignment, treatment implementation, and attrition Generalized causal inference: A grounded theory of generalized causal inference, along with methods for implementing that theory in single and multiple studies
📒Experimental And Quasi Experimental Designs For Research ✍ Donald T. Campbell
✏Experimental and Quasi Experimental Designs for Research Book Summary : 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.
📒Quasi Experimental Research Designs ✍ Bruce A. Thyer
✏Quasi Experimental Research Designs Book Summary : This pocket guide describes the logic, design, and conduct of the range of such designs, encompassing pre-experiments, quasi-experiments making use of a control or comparison group, and time-series designs. While it can be utilized as a manual, this book is also valuable for practitioners seeking a greater conceptual understanding of quasi-experimental studies in social work literature. Human service professionals planning to undertake a program evaluation of their agency's services will find this book helpful in understanding the steps and actions needed to adopt a quasi-experimental strategy.
📒Experimental And Quasi Experimental Designs For Generalized Causal Inference ✍ William R. Shadish
✏Experimental and Quasi experimental Designs for Generalized Causal Inference Book Summary : This successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation: Theoretical matters: Experimentation, causation, and validity ; Quasi-experimental design: Regression discontinuity designs, interrupted time series designs, quasi-experimental designs that use both pretests and control groups, and other designs ; Randomized experiments: Logic and design issues, and practical problems involving ethics, recruitment, assignment, treatment implementation, and attrition ; Generalized causal inference: A grounded theory of generalized causal inference, along with methods for implementing that theory in single and multiple studies.
📒Using Propensity Scores In Quasi Experimental Designs ✍ William M. Holmes
✏Using Propensity Scores in Quasi Experimental Designs Book Summary : Using Propensity Scores in Quasi-Experimental Designs, by William M. Holmes, examines how propensity scores can be used to reduce bias with different kinds of quasi-experimental designs and to fix or improve broken experiments. Requiring minimal use of matrix and vector algebra, the book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of disciplines.
📒Methodology And Epistemology For Social Sciences ✍ Donald T. Campbell
✏Methodology and Epistemology for Social Sciences Book Summary : Selections from the work of an influential contributor to the methodology of the social sciences. He treats: measurement, experimental design, epistemology, and sociology of science each section introduced by the editor, Samuel Overman. Annotation copyright Book News, Inc. Portland, Or.
📒Graphical Models For Quasi Experimental Designs ✍ Yongnam Kim
✏Graphical Models for Quasi Experimental Designs Book Summary : Experimental and quasi-experimental designs play a central role in estimating cause-effect relationships in education, psychology, and many other fields of the social and behavioral sciences. This paper presents and discusses the causal graphs of experimental and quasi-experimental designs. For quasi-experimental designs the authors demonstrate that a causal treatment effect can be identified if they can isolate a subpopulation whose graph resembles the graph of an randomized controlled trial (RCT). For regression discontinuity (RD) designs, the corresponding subpopulation is given by the population in the very close neighborhood around the cutoff score; for the instrumental variable (IV) design, it is the subpopulation of compliers; for matching designs, it is the matched population and for propensity stratification (PS); for weighting designs it is the stratified or weighted population. The causal graphs also show that the identification of causal effects rests on stronger assumptions as the researchers' control over the study diminishes. More control usually implies the simpler data-generating mechanism and, thus, relative ease of identification. Figures are provided in the appendix. [SREE documents are structured abstracts of SREE conference symposium, panel, and paper or poster submissions.].
📒Research Methods For The Behavioral Sciences ✍ Frederick J. Gravetter
✏Research Methods for the Behavioral Sciences Book Summary : "Inviting and conversational, RESEARCH METHODS FOR THE BEHAVIORAL SCIENCES, Third Edition, leads students through the research process from start to finish. The text opens with tips and strategies for generating research ideas, moves to selecting measures and participants, and then offers an examination of research strategy and design. This step-by-step approach emphasizes the decisions researchers must make at each stage of the process. The authors avoid a "cookbook" approach to the facts by linking terminology with applied concepts; their "lecture in a book" style makes the text accessible to students by emphasizing discussion and explanation of topics. Each chapter ends with a set of student exercises and activities."--Publisher's website.
📒Evaluation Theory Models And Applications ✍ Daniel L. Stufflebeam
✏Evaluation Theory Models and Applications Book Summary : The golden standard evaluation reference text Now in its second edition, Evaluation Theory, Models, and Applications is the vital text on evaluation models, perfect for classroom use as a textbook, and as a professional evaluation reference. The book begins with an overview of the evaluation field and program evaluation standards, and proceeds to cover the most widely used evaluation approaches. With new evaluation designs and the inclusion of the latest literature from the field, this Second Edition is an essential update for professionals and students who want to stay current. Understanding and choosing evaluation approaches is critical to many professions, and Evaluation Theory, Models, and Applications, Second Edition is the benchmark evaluation guide. Authors Daniel L. Stufflebeam and Chris L. S. Coryn, widely considered experts in the evaluation field, introduce and describe 23 program evaluation approaches, including, new to this edition, transformative evaluation, participatory evaluation, consumer feedback, and meta-analysis. Evaluation Theory, Models, and Applications, Second Edition facilitates the process of planning, conducting, and assessing program evaluations. The highlighted evaluation approaches include: Experimental and quasi-experimental design evaluations Daniel L. Stufflebeam's CIPP Model Michael Scriven's Consumer-Oriented Evaluation Michael Patton's Utilization-Focused Evaluation Robert Stake's Responsive/Stakeholder-Centered Evaluation Case Study Evaluation Key readings listed at the end of each chapter direct readers to the most important references for each topic. Learning objectives, review questions, student exercises, and instructor support materials complete the collection of tools. Choosing from evaluation approaches can be an overwhelming process, but Evaluation Theory, Models, and Applications, Second Edition updates the core evaluation concepts with the latest research, making this complex field accessible in just one book.