Patrick Dattalo – författare

A researcher's decision about the sample to draw in a study may have an enormous impact on the results, and it rests on numerous statistical and practical considerations that can be difficult to juggle. Computer programs help, but no single software package exists that allows researchers to determine sample size across all statistical procedures. This pocket guide shows social work students, educators, and researchers how to prevent some of the mistakes that would result from a wrong sample size decision by describing and critiquing four main approaches to determining sample size. In concise, example-rich chapters, Dattalo covers sample-size determination using power analysis, confidence intervals, computer-intensive strategies, and ethical or cost considerations, as well as techniques for advanced and emerging statistical strategies such as structural equation modeling, multilevel analysis, repeated measures MANOVA and repeated measures ANOVA. He also offers strategies for mitigating pressures to increase sample size when doing so may not be feasible. Whether as an introduction to the process for students or as a refresher for experienced researchers, this practical guide is a perfect overview of a crucial but often overlooked step in empirical social work research.

A researcher's decision about the sample to draw in a study may have an enormous impact on the results, and it rests on numerous statistical and practical considerations that can be difficult to juggle. Computer programs help, but no single software package exists that allows researchers to determine sample size across all statistical procedures. This pocket guide shows social work students, educators, and researchers how to prevent some of the mistakes that would result from a wrong sample size decision by describing and critiquing four main approaches to determining sample size. In concise, example-rich chapters, Dattalo covers sample-size determination using power analysis, confidence intervals, computer-intensive strategies, and ethical or cost considerations, as well as techniques for advanced and emerging statistical strategies such as structural equation modeling, multilevel analysis, repeated measures MANOVA and repeated measures ANOVA. He also offers strategies for mitigating pressures to increase sample size when doing so may not be feasible. Whether as an introduction to the process for students or as a refresher for experienced researchers, this practical guide is a perfect overview of a crucial but often overlooked step in empirical social work research.

Random sampling and random assignment 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 random sampling and random assignment. While there are no easy ways to overcome these barriers, social workers should seek and utilize strategies that minimize sampling and assignment bias. These methodological and statistical strategies form the book's core. In step-by-step chapters liberally illustrated with examples using a variety of software packages, Dattalo guides readers in selecting and implementing an appropriate strategy. Readers will gain confidence in using such techniques as exemplar sampling, sequential sampling, randomization tests, multiple imputation, mean-score logistic regression, partial randomization, constructed comparison groups, instrumental variables methods, and propensity scores. Each approach will be cataloged in such a way as to highlight its underlying assumptions, implementation strategies, and strengths and weaknesses. Screen shots, annotated resources, and a companion website make this a valuable tool for students, teachers, and researchers seeking a single source that provides a diverse set of tools that will maximize a study's validity when random sampling and random assignment are neither possible nor practical.

A researcher's decision about the sample to draw in a study may have an enormous impact on the results, and it rests on numerous statistical and practical considerations that can be difficult to juggle. Computer programs help, but no single software package exists that allows researchers to determine sample size across all statistical procedures. This pocket guide shows social work students, educators, and researchers how to prevent some of the mistakes that would result from a wrong sample size decision by describing and critiquing four main approaches to determining sample size. In concise, example-rich chapters, Dattalo covers sample-size determination using power analysis, confidence intervals, computer-intensive strategies, and ethical or cost considerations, as well as techniques for advanced and emerging statistical strategies such as structural equation modeling, multilevel analysis, repeated measures MANOVA and repeated measures ANOVA. He also offers strategies for mitigating pressures to increase sample size when doing so may not be feasible. Whether as an introduction to the process for students or as a refresher for experienced researchers, this practical guide is a perfect overview of a crucial but often overlooked step in empirical social work research.

Random sampling and random assignment 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 random sampling and random assignment. While there are no easy ways to overcome these barriers, social workers should seek and utilize strategies that minimize sampling and assignment bias. These methodological and statistical strategies form the book''s core. In step-by-step chapters liberally illustrated with examples using a variety of software packages, Dattalo guides readers in selecting and implementing an appropriate strategy. Readers will gain confidence in using such techniques as exemplar sampling, sequential sampling, randomization tests, multiple imputation, mean-score logistic regression, partial randomization, constructed comparison groups, instrumental variables methods, and propensity scores. Each approach will be cataloged in such a way as to highlight its underlying assumptions, implementation strategies, and strengths and weaknesses. Screen shots, annotated resources, and a companion website make this a valuable tool for students, teachers, and researchers seeking a single source that provides a diverse set of tools that will maximize a study''s validity when random sampling and random assignment are neither possible nor practical.

Multivariate procedures allow social workers and other human services researchers to analyze complex, multidimensional social problems and interventions in ways that minimize oversimplification. This pocket guide provides a concise, practical, and economical introduction to four procedures for the analysis of multiple dependent variables: multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). Each procedure will be presented in a way that allows readers to compare and contrast them in terms of (1) appropriate research context; (2) required statistical assumptions, including levels of measurement of variables to be modeled; (3) analytical steps; (4) sample size; and (5) strengths and weaknesses. This invaluable guide facilitates course extensibility in scope and depth by allowing instructors to supplement course content with rigorous statistical procedures. Detailed annotated examples using Stata, SPSS (PASW), SAS, and Amos, together with additional resources, discussion of key terms, and a companion website, make this an unintimidating guide for producers and consumers of social work research knowledge.