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In this book, the authors provide a cogent review of statistical and interpretive procedures that, in combination, can be used to reduce the likelihood that tests contain items that favor members of one gender, age, racial, or ethnic group over equally able members of another group, for reasons that are unrelated to the objectives and purposes of measurement. Such test items are said to be biased against the equally able members of the group that is not favored. The methods described and illustrated in this book have the potential to reducing the incidence of tests that are, in their construction, biased against members of one or more groups. These methods have the potential of controlling an important source of invalidity when test results are interpreted.
A unique, practical manual for identifying and analyzing item bias in standardized tests. Osterlind discusses five strategies for detecting bias: analysis of variance, transformed item difficulties, chi square, item characteristic curve, and distractor response. He covers specific hypotheses under test for each technique, as well as the capabilities and limitations of each strategy.
This book provides a refined definition of standardized educational test fairness that can be utilized in multiple contexts to better understand the experiences and perspectives of diverse groups of test takers.
This book provides an up-to-date review of commonly undertaken methodological and statistical practices that are based partially in sound scientific rationale and partially in unfounded lore. Some examples of these “methodological urban legends” are characterized by manuscript critiques such as: (a) “your self-report measures suffer from common method bias”; (b) “your item-to-subject ratios are too low”; (c) “you can’t generalize these findings to the real world”; or (d) “your effect sizes are too low.” What do these critiques mean, and what is their historical basis? More Statistical and Methodological Myths and Urban Legends catalogs several of these quirky practices and outlines proper research techniques. Topics covered include sample size requirements, missing data bias in correlation matrices, negative wording in survey research, and much more.
Structural equation modeling (SEM) is becoming the central and one of the most popular analytical tools in the social sciences. Many classical and modern statistical techniques such as regression analysis, path analysis, confirmatory factor analysis, and models with both measurement and structural components have been shown to fall under the umbrella of SEM. Thus, the flexibility of SEM makes it applicable to many research designs, including experimental and non-experimental data, cross-sectional and longitudinal data, and multiple-group and multilevel data. In this eBook, you will find 19 cutting-edge papers from the Research Topic: Recent Advancements in Structural Equation Modeling (SEM). These 19 papers cover a wide variety of topics related to SEM, including: (a) analysis of different types of data (from cross-sectional data with floor effects to complex survey data and longitudinal data); (b) measurement-related issues (from the development of new scale to the evaluation of person fit and new ways to test measurement invariance); and (c) technical advancement and software development. We hope that the readers will gain new perspectives and be able to apply some of the new techniques and models discussed in these 19 papers.
The Handbook of Research Methods in Abnormal and Clinical Psychology presents a diverse range of areas critical to any researcher or student entering the field. It provides valuable information on the foundations of research methods, including validity in experimental design, ethics, and statistical methods. The contributors discuss design and instrumentation for methods that are particular to abnormal and clinical psychology, including behavioral assessment, psychophysiological assessment and observational methods. They also offer details on new advances in research methodology and analysis, such as meta-analysis, taxometric methods, item response theory, and approaches to determining clinical significance. In addition, this volume covers specialty topics within abnormal and clinical psychology from forensic psychology to behavior genetics to treatment outcome methods.
Approximately 1 in 54 children in the U.S. will be diagnosed with an autism spectrum disorder (ASD) and that number is expected to rise, according to the CDC. Applied Behavior Analysis (ABA) is one of the most common interventions for those with ASD. One of the biggest problems facing the field of ABA-based interventions is ineffectiveness of intervention approaches due to the rigid application of ABA-based interventions. A Progressive Approach to Applied Behavior Analysis provides practicing behavior analysts (e.g., BCBA, BCaBA, RBTs) and other clinicians with an in-depth introduction to a Progressive Approach to ABA and how it applies to common teaching methods within ABA-based interventions. This includes research and guidelines for implementing a Progressive Approach to ABA potentially increasing the likelihood of meaningful outcomes for the individuals with ASD. This will become the guide for practitioners on how to implement clinical judgement using in-the-moment assessment across various procedures.A comprehensive clinical guide to a Progressive Approach for Applied Behavior Analysis - Summarizes Autism Partnership Method and Progressive ABA - Explores how to use ABA for teaching and behavioral intervention - Discusses reinforcement conditioning, punishment, and token economies