Diana C. Mutz
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691144511
- eISBN:
- 9781400840489
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691144511.003.0001
- Subject:
- Sociology, Social Research and Statistics
This introductory chapter traces the development of population-based experiments and highlights some of their advantages over traditional experiments and surveys. There is a tendency to think about ...
More
This introductory chapter traces the development of population-based experiments and highlights some of their advantages over traditional experiments and surveys. There is a tendency to think about population-based survey experiments as simply a hybrid methodology that melds certain characteristics of surveys and experiments. But to say this tells nothing about which advantages and disadvantages of each methodology are inherited. The chapter argues that population-based survey experiments are instead more akin to an agricultural hybrid that produces something that was not present in either of the two original plants. To the extent that population-based survey experiments can be implemented with effective treatments and with the same degree of control over random assignment as in the lab, it is the only kind of research design capable of straightforwardly estimating population average treatment effects without complex statistical machinations.Less
This introductory chapter traces the development of population-based experiments and highlights some of their advantages over traditional experiments and surveys. There is a tendency to think about population-based survey experiments as simply a hybrid methodology that melds certain characteristics of surveys and experiments. But to say this tells nothing about which advantages and disadvantages of each methodology are inherited. The chapter argues that population-based survey experiments are instead more akin to an agricultural hybrid that produces something that was not present in either of the two original plants. To the extent that population-based survey experiments can be implemented with effective treatments and with the same degree of control over random assignment as in the lab, it is the only kind of research design capable of straightforwardly estimating population average treatment effects without complex statistical machinations.
Diana C. Mutz
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691144511
- eISBN:
- 9781400840489
- Item type:
- book
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691144511.001.0001
- Subject:
- Sociology, Social Research and Statistics
Population-based survey experiments have become an invaluable tool for social scientists struggling to generalize laboratory-based results, and for survey researchers besieged by uncertainties about ...
More
Population-based survey experiments have become an invaluable tool for social scientists struggling to generalize laboratory-based results, and for survey researchers besieged by uncertainties about causality. Thanks to technological advances in recent years, experiments can now be administered to random samples of the population to which a theory applies. Yet until now, there was no self-contained resource for social scientists seeking a concise and accessible overview of this methodology, its strengths and weaknesses, and the unique challenges it poses for implementation and analysis. Drawing on examples from across the social sciences, this book covers everything you need to know to plan, implement, and analyze the results of population-based survey experiments. But it is more than just a “how to” manual. This book challenges conventional wisdom about internal and external validity, showing why strong causal claims need not come at the expense of external validity, and how it is now possible to execute experiments remotely using large-scale population samples. Designed for social scientists across the disciplines, the book provides the first complete introduction to this methodology and features a wealth of examples and practical advice.Less
Population-based survey experiments have become an invaluable tool for social scientists struggling to generalize laboratory-based results, and for survey researchers besieged by uncertainties about causality. Thanks to technological advances in recent years, experiments can now be administered to random samples of the population to which a theory applies. Yet until now, there was no self-contained resource for social scientists seeking a concise and accessible overview of this methodology, its strengths and weaknesses, and the unique challenges it poses for implementation and analysis. Drawing on examples from across the social sciences, this book covers everything you need to know to plan, implement, and analyze the results of population-based survey experiments. But it is more than just a “how to” manual. This book challenges conventional wisdom about internal and external validity, showing why strong causal claims need not come at the expense of external validity, and how it is now possible to execute experiments remotely using large-scale population samples. Designed for social scientists across the disciplines, the book provides the first complete introduction to this methodology and features a wealth of examples and practical advice.
Diana C. Mutz
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691144511
- eISBN:
- 9781400840489
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691144511.003.0009
- Subject:
- Sociology, Social Research and Statistics
This chapter discusses how population-based survey experiments can be especially valuable to more particularistic research because the target of applicability is known and specified. With ...
More
This chapter discusses how population-based survey experiments can be especially valuable to more particularistic research because the target of applicability is known and specified. With particularistic research, population-based survey experiments are important not so much to explore boundaries of generalizability as to establish that the empirical cause and effect relationship works as predicted on the targeted population. By increasing the variety of experimental subjects, the settings in which research is done, and the kinds of treatments and measures that are utilized, population-based survey experiments may produce greater awareness of the boundaries of various social science theories. Whether this kind of activity is welcome or not, it remains an important contribution to social science knowledge.Less
This chapter discusses how population-based survey experiments can be especially valuable to more particularistic research because the target of applicability is known and specified. With particularistic research, population-based survey experiments are important not so much to explore boundaries of generalizability as to establish that the empirical cause and effect relationship works as predicted on the targeted population. By increasing the variety of experimental subjects, the settings in which research is done, and the kinds of treatments and measures that are utilized, population-based survey experiments may produce greater awareness of the boundaries of various social science theories. Whether this kind of activity is welcome or not, it remains an important contribution to social science knowledge.
Jorge Delva, Paula Allen-Meares, and Sandra L. Momper
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195382501
- eISBN:
- 9780199777419
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195382501.003.0003
- Subject:
- Social Work, Research and Evaluation
This chapter illustrates the conduct of population-based studies by describing the implementation of a large school-based survey of substance use in several Central American countries. The project's ...
More
This chapter illustrates the conduct of population-based studies by describing the implementation of a large school-based survey of substance use in several Central American countries. The project's methodology followed an etic approach although considerable work was conducted to validate and harmonize the instrument across countries and sites. The project's implementation involved a number of activities that required collaborators to navigate different cultural and geopolitical situations, some of them of a very sensitive nature, some of which we describe in this chapter. Through these examples, we hope readers will acquire a better understanding of the inner workings of these complicated multi-national projects. We also highlight the importance of building partnerships between country researchers and international organizations, including a discussion of protection issues for human subjects, and we provide an example of power analyses and an analytic strategy of accounting for clustering when conducting statistical analyses with these large studies.Less
This chapter illustrates the conduct of population-based studies by describing the implementation of a large school-based survey of substance use in several Central American countries. The project's methodology followed an etic approach although considerable work was conducted to validate and harmonize the instrument across countries and sites. The project's implementation involved a number of activities that required collaborators to navigate different cultural and geopolitical situations, some of them of a very sensitive nature, some of which we describe in this chapter. Through these examples, we hope readers will acquire a better understanding of the inner workings of these complicated multi-national projects. We also highlight the importance of building partnerships between country researchers and international organizations, including a discussion of protection issues for human subjects, and we provide an example of power analyses and an analytic strategy of accounting for clustering when conducting statistical analyses with these large studies.
