*Patrick Dattalo*

- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195378351
- eISBN:
- 9780199864645
- Item type:
- book

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195378351.001.0001
- Subject:
- Social Work, Research and Evaluation

Random sampling (RS) and random assignment (RA) are considered by many researchers to be the definitive methodological procedures for maximizing external and internal validity. However, there is a ...
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Random sampling (RS) and random assignment (RA) are considered by many researchers to be the definitive methodological procedures for maximizing external and internal validity. However, there is a daunting list of legal, ethical, and practical barriers to implementing RS and RA. While there are no easy ways to overcome these barriers, social workers should seek and utilize strategies that minimize sampling and assignment bias. This book is a single source of a diverse set of tools that will maximize a study's validity when RS and RA are neither possible nor practical. Readers are guided in selecting and implementing an appropriate strategy, including exemplar sampling, sequential sampling, randomization tests, multiple imputation, mean-score logistic regression, partial randomization, constructed comparison groups, propensity scores, and instrumental variables methods. Each approach is presented in such a way as to highlight its underlying assumptions, implementation strategies, and strengths and weaknesses.Less

Random sampling (RS) and random assignment (RA) are considered by many researchers to be the definitive methodological procedures for maximizing external and internal validity. However, there is a daunting list of legal, ethical, and practical barriers to implementing RS and RA. While there are no easy ways to overcome these barriers, social workers should seek and utilize strategies that minimize sampling and assignment bias. This book is a single source of a diverse set of tools that will maximize a study's validity when RS and RA are neither possible nor practical. Readers are guided in selecting and implementing an appropriate strategy, including exemplar sampling, sequential sampling, randomization tests, multiple imputation, mean-score logistic regression, partial randomization, constructed comparison groups, propensity scores, and instrumental variables methods. Each approach is presented in such a way as to highlight its underlying assumptions, implementation strategies, and strengths and weaknesses.

*Paul Clarke and Rebecca Hardy*

- Published in print:
- 2007
- Published Online:
- September 2009
- ISBN:
- 9780198528487
- eISBN:
- 9780191723940
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198528487.003.0007
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology

This chapter begins by describing helpful typologies of missing data based on pattern and non-response mechanisms. It then summarizes a collection of commonly used but imperfect methods for dealing ...
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This chapter begins by describing helpful typologies of missing data based on pattern and non-response mechanisms. It then summarizes a collection of commonly used but imperfect methods for dealing with missing data at the analysis stage. Three more rigorous methods, maximum likelihood, multiple imputation, and weighting, are also considered.Less

This chapter begins by describing helpful typologies of missing data based on pattern and non-response mechanisms. It then summarizes a collection of commonly used but imperfect methods for dealing with missing data at the analysis stage. Three more rigorous methods, maximum likelihood, multiple imputation, and weighting, are also considered.

*Patrick Dattalo*

- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195378351
- eISBN:
- 9780199864645
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195378351.003.0003
- Subject:
- Social Work, Research and Evaluation

This chapter describes the following alternatives and complements to RS in terms of their assumptions, implementations, strengths, and weaknesses: (1) randomization tests; (2) multiple imputation; ...
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This chapter describes the following alternatives and complements to RS in terms of their assumptions, implementations, strengths, and weaknesses: (1) randomization tests; (2) multiple imputation; and (3) mean-score logistic regression. Randomization tests are statistical alternatives to RS. Multiple imputation is a statistical supplement RS. Mean-score logistic regression is a statistical alternative or supplement to RS.Less

This chapter describes the following alternatives and complements to RS in terms of their assumptions, implementations, strengths, and weaknesses: (1) randomization tests; (2) multiple imputation; and (3) mean-score logistic regression. Randomization tests are statistical alternatives to RS. Multiple imputation is a statistical supplement RS. Mean-score logistic regression is a statistical alternative or supplement to RS.

*Shinichi Nakagawa*

- Published in print:
- 2015
- Published Online:
- April 2015
- ISBN:
- 9780199672547
- eISBN:
- 9780191796487
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199672547.003.0005
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology

Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. The common methods used to deal with missing data are to delete cases containing missing data, ...
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Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. The common methods used to deal with missing data are to delete cases containing missing data, and to use the mean to fill in missing values. However, these ‘traditional’ methods will result in biased estimation of parameters and uncertainty, and reduction in statistical power. Now, better missing data procedures such as multiple imputation and data augmentation are readily available and implementable. This chapter introduces the basics of missing data theory—most importantly, the three missing data mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR); the chapter also explains relevant concepts of importance such as EM algorithms and MCMC procedures. This chapter enables the application of proper missing data procedures, in particular multiple imputation, using R packages.Less

Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. The common methods used to deal with missing data are to delete cases containing missing data, and to use the mean to fill in missing values. However, these ‘traditional’ methods will result in biased estimation of parameters and uncertainty, and reduction in statistical power. Now, better missing data procedures such as multiple imputation and data augmentation are readily available and implementable. This chapter introduces the basics of missing data theory—most importantly, the three missing data mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR); the chapter also explains relevant concepts of importance such as EM algorithms and MCMC procedures. This chapter enables the application of proper missing data procedures, in particular multiple imputation, using R packages.