Rex B. Kline – författare

Acclaimed for helping novice behavioral scientists hit the ground running as producers of meaningful research, this text now has been extensively revised with more than 50% new material, including current guidance on open science; transparency; replication; and quantitative, qualitative, and mixed methods reporting standards. The book provides the conceptual knowledge and practical skills needed to bridge the gap between introductory research design and analysis courses and execution of an independent study. In a candid, conversational style, Rex B. Kline guides the reader to choose appropriate research designs and analysis options; avoid common fallacies in interpreting the outcomes of statistical tests; make informed measurement choices; screen data for problems that could yield inaccurate results; and craft effective theses, journal articles, and presentations. Revised pedagogical features include engaging examples from published studies and student theses, as well as end-of-chapter exercises with answers.New to This Edition*Addresses critical "research crises" that have come to the fore in the last decade--and ways to remedy them.*New chapters on the replication crisis, reporting standards, the open-science movement, and statistics reform.*Extensively revised chapters on effect size estimation and psychometrics.*Updated discussions of how to write publishable journal articles and create effective presentations.

Acclaimed for helping novice behavioral scientists hit the ground running as producers of meaningful research, this text now has been extensively revised with more than 50% new material, including current guidance on open science; transparency; replication; and quantitative, qualitative, and mixed methods reporting standards. The book provides the conceptual knowledge and practical skills needed to bridge the gap between introductory research design and analysis courses and execution of an independent study. In a candid, conversational style, Rex B. Kline guides the reader to choose appropriate research designs and analysis options; avoid common fallacies in interpreting the outcomes of statistical tests; make informed measurement choices; screen data for problems that could yield inaccurate results; and craft effective theses, journal articles, and presentations. Revised pedagogical features include engaging examples from published studies and student theses, as well as end-of-chapter exercises with answers.New to This Edition*Addresses critical "research crises" that have come to the fore in the last decade--and ways to remedy them.*New chapters on the replication crisis, reporting standards, the open-science movement, and statistics reform.*Extensively revised chapters on effect size estimation and psychometrics.*Updated discussions of how to write publishable journal articles and create effective presentations.

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl’s structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and new self-tests on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor.New to This EditionChapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis. Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM. Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects. Pedagogical FeaturesNew self-tests of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers. End-of-chapter suggestions for further reading and exercises with answers. Troublesome examples from real data, with guidance for handling typical problems in analyses. Topic boxes on special issues and boxed rules to remember. Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the book’s detailed examples.

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl’s structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and new self-tests on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor.New to This EditionChapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis. Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM. Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects. Pedagogical FeaturesNew self-tests of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers. End-of-chapter suggestions for further reading and exercises with answers. Troublesome examples from real data, with guidance for handling typical problems in analyses. Topic boxes on special issues and boxed rules to remember. Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the book’s detailed examples.