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 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).

 This volume presents detailed discussions of regression models that are appropriate for a variety of discrete dependent variables. Clear language guides the reader briefly through each step of the analysis, using SPSS and result presentation to enhance understanding of the important link function.

 The complexity of social problems necessitates that social work researchers understand and apply multivariate statistical methods in their investigations. In this pocket guide, the authors introduce readers to three of the more frequently used multivariate methods in social work research with an emphasis on basic statistics. The primary aim is to prepare entry-level doctoral students and early career social work researchers in the use of multivariate methods by providing an easy-to-understand presentation, building on the basic statistics that inform them. The pocket guide begins with a review of basic statistics, hypothesis testing with inferential statistics, and bivariate analytic methods. Subsequent sections describe bivariate and multiple linear regression analyses, one-way and two-way analysis of variance (ANOVA) and covariance (ANCOVA), and path analysis. In each chapter, the authors introduce the various basic statistical procedures by providing definitions, formulas, descriptions of the underlying logic and assumptions of each procedure, and examples of how they have been used in social work research literature, particularly with diverse populations. They also explain estimation procedures and how to interpret results. The multivariate chapters conclude with brief step-by-step instructions for conducting multiple regression analysis and one-way ANOVA in Statistical Package for the Social Sciences (SPSS), and path analysis in Amos, using data from the National Educational Longitudinal Study of 1988 (NELS: 88). As an additional supplement, the book offers a companion website that provides more detailed instructions, as well as data sets and worked examples.

 When used in tandem, systematic reviews and meta-analysis-- two distinct but highly compatible approaches to research synthesis-- form a powerful, scientific approach to analyzing previous studies. But to see their full potential, a social work researcher must be versed in the foundational processes underlying them. This pocket guide to Systematic Reviews and Meta-Analysis illuminates precisely that practical groundwork.In clear, step-by-step terms, the authors explain how to format topics, locate and screen studies, extract and assess data, pool effect sizes, determine bias, and interpret the results, showing readers how to combine reviewing and meta-analysis correctly and effectively. Each chapter contains vivid social work examples and concludes with a concise summary and notes on further reading, while the book's glossary and handy checklists and sample search and data extraction forms maximize the boo'ks usefulness.Highlighting the concepts necessary to understand, critique, and conduct research synthesis, this brief and highly readable introduction is a terrific resource for students and researchers alike.

 Clinical Data-Mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making and practitioner reflection. Depending upon the type of data mined, CDM can be qualitative or quantitative; it is generally retrospective, but may be meaningfully combined with original data collection.Any research method that relies on the contents of case records or information systems data inevitably has limitations, but with proper safeguards these can be minimized. Among CDM's strengths however, are that it is unobtrusive, inexpensive, presents little risk to research subjects, and is ethically compatible with practitioner value commitments. When conducted by practitioners, CDM yields conceptual as well as data-driven insight into their own practice- and program-generated questions.This pocket guide, from a seasoned practice-based researcher, covers all the basics of conducting practitioner-initiated CDM studies or CDM doctoral dissertations, drawing extensively on published CDM studies and completed CDM dissertations from multiple social work settings in the United States, Australia, Israel, Hong Kong and the United Kingdom. In addition, it describes consulting principles for researchers interested in forging collaborative university-agency CDM partnerships, making it a practical tool for novice practitioner-researchers and veteran academic-researchers alike.As such, this book is an exceptional guide both for professionals conducting practice-based research as well as for social work faculty seeking an evidence-informed approach to practice-research integration.

 A Social Justice Approach to Survey Design and Analysis is written for students, teachers, researchers and anyone who is interested in conducting research. It draws heavily on current discussions regarding social justice, equity, health disparities and social determinants of health to provide a framework for researchers to use both to engage in social justice research as well as to evolve as social justice practitioners. This research book includes a framework of the continuum of social justice research, a presentation on how to provide an active voice for the community in the design and exaction of research, examples of social justice data sources along with how researchers have used that data to measure social inequities, and an overview of how to analyze data, using the social justice research framework. The book also includes several in depth case scenarios that highlight how social justice research has been used to document, monitor and evaluate inequities encountered by underserved populations

 Survival analysis is a class of statistical methods for studying the occurrence and timing of events. With clearly written summaries and plentiful examples, this pocket guide will put this important statistical tool in the hands of many more social work researchers than have been able to use it before.

 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.

 This pocket guide provides an in-depth introduction to 29 of the most widely used data sets in social work and the social sciences. Readers will find in-depth information about each data set, how to locate and use the data, what types of questions the data may answer, and the key variables in the data.

 Grounded Theory (GT) is one of the oldest and most often used forms of qualitative research. Unlike other methods, GT is used to develop theory. Grounded Theory has great potential for social work because in order to conduct theory-based practice, social workers need middle-range theories that are neither highly abstract nor difficult to apply in real life. Social work and Grounded Theory focus on the interaction of individual and society. GT studies can provide theories about how individuals navigate their surroundings that can be tested in social work practice and, ultimately, be used to guide social work practitioners. In this volume, readers will find discussions of the common roots of social work and Grounded Theory, the basic characteristics of grounded theory research, and issues of quality in grounded theory research. In addition, practical guidelines and suggestions are provided for conducting grounded theory research, from writing the proposal to advanced data analysis. Exemplars from social work literature are used to illustrate grounded theory research in different social work fields. With practice exercises, guidelines for formulating problems and gathering and analyzing data, tips for working with software, consideration of ethical and Institutional Review Board issues, and discussion of new developments such as mixed-method and synthesis, this pocket guide offers social work researchers a strong, practical introduction to GT research.

 Finally, a practical guide to mixed methods research has been written with health and human services professionals in mind. Watkins and Gioia review the fundamentals of mixed methods research designs and the general suppositions of mixed methods procedures, look critically at mixed method studies and models that have already been employed in social work, and reflect on the contributions of this work to the field. But what is most important is that they lead the reader through considerations for the application of the mixed methods research in social work settings. The chapters of this book are structured so that readers can (figuratively) walk through the mixed methods research process using nine steps. Chapters one, five, and six provide supplemental material meant to serve as grounding for chapters two, three, and four, which outline nine steps in the mixed methods research process, and specific to social work research. This is a short and practical guide not just for learning about mixed methods research, but also doing it.

 Social work researchers often conduct research with groups that are diverse in terms of gender, sexual orientation, race or ethnic background, or age. Consequently, social work researchers must take great care to establish research-design equivalence at all phases of the research process (e.g., problem formulation, research design, sampling, measurement selection, data collection, and data analysis); otherwise, the results might reflect methodological flaws rather than true group differences and therefore lead to erroneous conclusions. This book introduces the methodological precautions that must be taken into consideration when conducting research with diverse groups. Multigroup Confirmatory Analysis (MG-CFA) using structural equation modeling (SEM) was utilized to demonstrate how to assess seven types of measurement and structural equivalence that Milfont and Fischer (2010) have deemed important for studies with diverse samples. A hypothetical example was provided to illustrate how to design a study with good research-design equivalence. A case example was provided to demonstrate how to conduct an MG-CFA for each type of measurement and structural equivalence discussed. The Mplus syntax used to conduct the MG-CFA was provided. The results from the MG-CFA analyses were written up as they would be for publication.

 When social workers draw on experience, theory, or data in order to develop new strategies or enhance existing ones, they are conducting intervention research. This relatively new field involves program design, implementation, and evaluation and requires a theory-based, systematic approach. Intervention Research presents such a framework. The five-step strategy described in this brief but thorough book ushers the reader from an idea's germination through the process of writing a treatment manual, assessing program efficacy and effectiveness, and disseminating findings. Rich with examples drawn from child welfare, school-based prevention, medicine, and juvenile justice, Intervention Research relates each step of the process to current social work practice. It also explains how to adapt interventions for new contexts, and provides extensive examples of intervention research in fields such as child welfare, school-based prevention, medicine, and juvenile justice, and offers insights about changes and challenges in the field. This innovative pocket guide will serve as a solid reference for those already in the field, as well as help the next generation of social workers develop skills to contribute to the evolving field of intervention research.

 In social sciences, education, and public health research, researchers often conduct small pilot studies (or may have planned for a larger sample but lost too many cases due to attrition or missingness), leaving them with a smaller sample than they expected and thus less power for their statistical analyses. Similarly, researchers may find that their data are not normally distributed -- especially in clinical samples -- or that the data may not meet other assumptions required for parametric analyses. In these situations, nonparametric analytic strategies can be especially useful, though they are likely unfamiliar. A clearly written reference book, Data Analysis with Small Samples and Non-Normal Data offers step-by-step instructions for each analytic technique in these situations. Researchers can easily find what they need, matching their situation to the case-based scenarios that illustrate the many uses of nonparametric strategies. Unlike most statistics books, this text is written in straightforward language (thereby making it accessible for nonstatisticians) while providing useful information for those already familiar with nonparametric tests. Screenshots of the software and output allow readers to follow along with each step of an analysis. Assumptions for each of the tests, typical situations in which to use each test, and descriptions of how to explain the findings in both statistical and everyday language are all included for each nonparametric strategy. Additionally, a useful companion website provides SPSS syntax for each test, along with the data set used for the scenarios in the book. Researchers can use the data set, following the steps in the book, to practice each technique before using it with their own data. Ultimately, the many helpful features of this book make it an ideal long-term reference for researchers to keep in their personal libraries.