This book shows why and how the concepts, control and happenstance, are crucial to methodology and statistics, respectively. Control, as a means to reduce ambiguity, fulfills its function because of inductive rules. Statistical null hypothesis renders it possible to use test of statistical significance to rule out happenstance as explanation of research result. This book shows why and how the concepts, control and happenstance, are crucial to methodology and statistics, respectively. Control, as a means to reduce ambiguity, fulfills its function because of inductive rules. Statistical null hypothesis renders it possible to use tests of statistical significance to rule out happenstance as an explanation for research results. Basic concepts of descriptive statistics are introduced in the context of data collection, tabulation, derivation, and standardization. Issues related to psychometric measurement, correlation and regression are thereby explained as well. Random sampling distribution renders possible inferential statistics (viz., confidence interval, parameter estimation, hypothesis testing and goodness-of-fit). The book shows how standardizing random sampling distribution gives rise to parametric tests. In short, this book shows that research impartiality is possible despite the belief in (as well as instances of) “beauty is in the eye of the beholder.”

This innovative text offers a completely integrated approach to teaching research methods and statistics by presenting a research question accompanied by the appropriate methods and statistical procedures needed to address it. Research questions and designs become more complex as chapters progress, building on simpler questions to reinforce student learning. Using a conversational style and research examples from published works, this comprehensive book walks readers through the entire research process and includes ample pedagogical support for SPSS, Excel, and APA style.

""The authors have successfully integrated a technical and well rounded discussion of the evolution of mixed methods research balanced with practical application examples of using mixed methods for the pragmatic researcher.""

The Handbook of Mixed Methods in Social & Behavioral Research contains a gold mine of articles by leading scholars on what has come to be known as the third methodological movement in social research. Aimed at surveying the differing viewpoints and disciplinary approaches of mixed methods, this breakthrough book examines mixed methods from the research enterprise to paradigmatic issues to application. The book also discusses the strengths and weaknesses of mixed methods designs, and provides an array of specific examples in a variety of disciplines, from psychology to nursing. The book closes with a brief section on how to teach and perform collaborative research using a mixed methods research design. Written so that it can be used either as a pedagogical tool or as a reference for researchers, the book is rich in examples and includes a glossary, easy-to-follow diagrams, and tables to help readers become more familiar with the language and controversies in this evolving area.

The Updated Third Edition has been fully revised for the seventh edition of the Publication Manual of the American Psychological Association (2020), both in the APA style sections within content and the references. The language within the text has been updated to be as inclusive as possible regarding all aspects of identity. The APA sections on style, paper preparation, and ethics have been updated and the text itself has been formatted in the 7th edition style to better reflect the latest style guidance. Both comprehensive and clear, Research Methods for the Behavioral Sciences, Third Edition author Gregory J. Privitera employs a problem-focused approach to introduce research methods. A conversational writing tone speaks to learners directly, empowering students to view research methods as something they are capable of understanding and applying. Within each chapter, students draw conclusions by following the scientific process. To do enable this, Privitera fully integrates the research methods decision tree—from choosing a research design to choosing an appropriate statistic—to encourage students to select the most appropriate methodology for the research question they′re seeking to answer. Greg Privitera covers the full scope of methodologies from non-experimental to quasi-experimental to experimental in a straightforward, unbiased manner.

The authors of this unique text found that while most students can "crunch" the numbers quite easily and accurately with a calculator or computer, many have trouble seeing the "big picture" or seeing how research questions and design influence data analysis. As a result, the authors developed a semantically consistent framework that integrates traditional research approaches (experimental, quasi-experimental, comparative) into three basic kinds of research questions (difference, associational, and descriptive), which, in turn, lead to three kinds or groups of statistics with the same names. This text: *helps students become good consumers of research by demonstrating how to analyze and evaluate research articles; *offers a number of summarizing diagrams and tables that clarify confusing or difficult to learn topics; *points out the value of qualitative research and how it should lead quantitative researchers to be more flexible; *divides all quantitative research questions into five logically consistent categories that help students select appropriate statistics and understand their cause and effect; and *classifies design into three major types: between groups, within subjects, and mixed groups and shows that, although these three types use the same general type of statistics (e.g., ANOVA), the specific statistics in between-groups design are different from those in within-subjects and mixed groups.

Integrating Analyses in Mixed Methods Research goes beyond mixed methods research design and data collection, providing a pragmatic discussion of the challenges of effectively integrating data to facilitate a more comprehensive and rigorous level of analysis. Showcasing a range of strategies for integrating different sources and forms of data as well as different approaches in analysis, it helps you plan, conduct, and disseminate complex analyses with confidence. Key techniques include: Building an integrative framework Analysing sequential, complementary and comparative data Identifying patterns and contrasts in linked data Categorizing, counting, and blending mixed data Managing dissonance and divergence Transforming analysis into warranted assertions With clear steps that can be tailored to any project, this book is perfect for students and researchers undertaking their own mixed methods research.