Overview

An essential textbook for anyone preparing to be a researcher, this comprehensive volume introduces graduate students to key principles of observational measurement of behavior. Based on a course the highly respected authors taught at Vanderbilt University and the University of Minnesota, this text delves deeply into a highly effective approach to observational measurement: systematic observation. Students will master both the theoretical principles of systematic observation and recommended research methods and techniques. They’ll learn from practical examples that illustrate complex concepts, clear explanations of recommended research methods, definitions of key terms, and exercises and assignments that help them practice putting principles into action. Online companion materials include two free licenses for proprietary observational software that students can use to complete the exercises and assignments in this book. Ideal for use in research methodology courses in diverse fields—including special education, communication sciences, psychology, and social work—this fundamental graduate text will prepare future researchers to skillfully collect, summarize, and communicate their observations of children’s behavior.

Full Product Details

Author:   Paul J. Yoder ,  Frank J. Symons ,  Blair Lloyd
Publisher:   Brookes Publishing Co
Imprint:   Brookes Publishing Co
Edition:   2nd Revised edition
ISBN:  

9781681252469

ISBN 10:   1681252465
Pages:   296
Publication Date:   30 June 2018
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

1: Introduction and Measurement Contexts Overview Definition of Systematic Observation Using Count Coding Rationale for Systematic Observation Using Count Coding Importance of Falsifiable Hypotheses The Continuum of State-Likeness to Trait-Likeness Context-Dependent Behavior Person Characteristics Generalized Behavioral Tendencies Skills The Relative Scientific Value of Different Objects of Measurement Ecological Validity and Representativeness Conclusions and Recommendations References 2: Validation of Observational Variables Overview The Changing Concept of Validation Understanding Which Types of Validation Evidence Are Most Relevant for Different Research Designs, Objects of Measurement, and Research Purposes Content Validation Definition of Content Validation Different Traditions Vary on the Levels of Importance Placed on Content Validation Weaknesses of Content Validation Sensitivity to Change Definition of Sensitivity to Change Influences on Sensitivity to Change Weaknesses of Sensitivity to Change Treatment Utility Definition of Treatment Utility Weaknesses of Treatment Utility Criterion-Related Validation Definition of Criterion-Related Validation Primary Appeal of Criterion-Related Validation Weaknesses of Criterion-Related Validation Construct Validation Definition of Construct Validation Discriminative Validation Nomological Validation Multitrait, Multimethod Validation An Implicit “Weakness” of Science? Recommendations References 3: Measuring Person Characteristics Overview Contextual Measurement Error Definition of Measurement Context A Brief Overview of Measurement Theory Definition of Contextual Measurement Error Representativeness Contextual Measurement Error in Measures Of Generalized Behavioral Tendencies Averaging Scores Across Contexts Improves Measures of Generalized Behavioral Tendencies Aggregates Tend to Improve Estimates of Known True Score. Aggregates Tend to Improve Construct Validity. Aggregates Tend to Improve Stability. Controlling Influential Contextual Variables Stabilizes Observed Scores for Highly Variable Person Characteristics Why Naturalistic Observations Are Not Necessarily More Representative Than Contrived Ones Why Skills Are Often Measured in Structured Measurement Contexts Why Skills Are Often Assessed in Clinics or Labs The Link Between Stability and Construct Validity Recommendations and Conclusions References 4: Designing or Adapting Coding Manuals Overview Definition of a Coding Manual Deciding Whether to Use an Existing Coding Manual or to Construct a New One Recommended Steps for Modifying or Designing Coding Manuals Defining When to Start and Stop Coding Conceptually Defining the Context-Dependent Behavior or the Generalized Characteristic Defining the Highest Level of Codable Behavior Determining the Level of Distinction Coders Have to Make Organizing the Coded Categories into Mutually-Exclusive Sets Physically Based Definitions, Socially Based Definitions, or Both? Defining the Lowest Level Categories Source of Conceptual and Operational Definitions A Qualitative Approach to Identifying Definitions Defining Segmenting Rules The Potential Value of Flowcharts Do Coding Manuals Need to be Sufficiently Short to be Included in Methods Sections? Recommendations and Conclusions References 5: Coding Overview The Elements of an Observational Measurement System Behavior Sampling The Superordinate Distinctions: Continuous versus Intermittent The Subordinate Distinctions: Timed-Event versus Event versus Interval Timed-Event Sampling Event Sampling Interval Sampling Types of Interval Sampling Whole Interval Sampling Momentary Interval Sampling Partial Interval Sampling The Importance of Knowing What Metric the Investigator Wants to Estimate Summary of Behavior Sampling Participant Sampling Focal Sampling Multiple Pass Sampling Conspicuous Sampling Reactivity Live Coding versus Recording the Observation For Later Coding Live Coding Coding from Recorded Sessions Recording Coding Decisions Recommendations and Conclusions References 6: Common Metrics of Observational Variables Overview Definition of Metric Quantifiable Dimensions of Behavior Proportion Metrics Proportion Metrics Change the Meaning of Observational Variables Scrutinizing Proportions An Implicit Assumption of Proportion Metrics Testing Whether the Data Fit the Assumption of Proportion Metrics Consequences of Using a Proportion When the Data Do Not Fit the Assumption Alternative Methods to Control Influential Contextual Variables Statistical Control Procedural Control Aggregate Measures of Generalized Person Characteristics Weighted Count Unit-Weighted Aggregates Group Analysis of Observational Variables Transforming the Metric Bootstrapping Recommendations and Conclusions References 7: Observer Training and Preventing Observer Drift Overview Point-by-point Agreement and Disagreement Point-by-point Agreement of Interval Sampled Data Point-by-point Agreement of Timed-Event Data Discrepancy Matrices Discrepancy Discussions Using Discrepancy Discussions to Train Observers Creating Criterion-coding Standards. Remaining Steps to Train Observers Preventing Observer Drift Method of Selecting Sessions for Agreement Checks Remaining Steps to Preventing or Addressing Observer Drift Recommendations References 8: Interobserver Agreement and Reliability of Observational Variables Overview Additional Purposes of Point-by-Point Agreement Added Principles When Agreement Checks Are Used to Estimate Interobserver “Reliability” of Observational Variable Scores Exhaustive Coding Spaces Revisited The Effect of Chance on Agreement Common Indices of Point-by-Point Agreement Occurrence Percentage Agreement Nonoccurrence Percentage Agreement Total Percentage Agreement Kappa Base Rate and Chance Agreement Revisited Intraclass Correlation Coefficient (ICC) as an Index of Interobserver Reliability in Group Designs Options for Running ICC With SPSS Between-Participant Variance on the Variable of Interest Affects ICC Using ICC as a Measure of Interobserver Reliability for Predictors and Dependent Variables in Group Designs The Interpretation of SPSS Output for ICC The Conceptual Relation Between Interobserver Agreement and ICC Consequences of Low or Unknown Interobserver Reliability Recommendations References 9: Introduction to Sequential Analysis Overview Definition of Terms Used in this Chapter Sequential versus Nonsequential Variables Sequential Associations are not Sufficient Evidence for Causal Inferences Coded Units and Exhaustiveness Contingency Tables Three Major Types of Sequential Analysis Event Lag Concurrent Interval Event Lag with Pauses (to replace time window method) Explanation for no longer focusing on time window Indices of Sequential Association: Controlling for Chance Existing Indices of Sequential Association: Advantages and Disadvantages Transitional/conditional probabilities Yule’s Q Risk Difference/Operant Contingency Value Other commonly-used indices and why we do not focus on them (e.g., z) Recommendations and Conclusions References 10: Identifying and Addressing Research Questions Involving Sequential Associations Overview Sequential Analysis in Group Designs Types of Research Questions and Methods to Address Them Indices of Sequential Association as Dependent Variables Testing the Significance of a Mean Sequential Association Testing the Between-Group Difference in Mean Sequential Associations Testing the Within-Subject Difference in Sequential Associations Sequential Analysis in Single-Case Designs Types of Research Questions Descriptive Questions Involving Behavior-Environment Associations to Inform Experimental Analyses Descriptive Questions to Inform Temporal Distribution of One or More Target Behaviors Indices of Sequential Association as Dependent Variables in Single-Case Designs Indices of Sequential Association to Inform Procedural Fidelity Methods to Address Sequential Analysis Research Questions in a Single Case Framework Why Significance Testing is Inappropriate at the Level of the Individual Participant More on the Term `Operant Contingency’ Summary of Analysis Methods Used in Behavior Analytic Literature Conditional Probabilities Lag Sequential Analysis Contingency Space Analysis What is “Enough Data” and How Do We Attain It? Proposed Solutions for Insufficient Data Summary and Recommendations References 11: Generalizability Theory Overview Scope of This Chapter Overview of G Theory and Definition of Terms An Example Observer by Context G and D Study Rationale for Preferring the Absolute G Coefficient Example Applications of D Studies An Ongoing Controversy Recommendations and Conclusions References 12: Best Practices in Observational Measurement Glossary Index

Reviews

Yoder, Lloyd and Symons have given us an observational methods opus that is rooted in the real world of human behavior. Their book is thorough, systematic, appropriately nuanced, concise and remarkably interesting. This book is a gift to present and future generations of behavioral scientists who's research is dependent on the observational measurement of human behavior. --Steven F. Warren, Ph.D.

"""Yoder, Lloyd and Symons have given us an observational methods opus that is rooted in the real world of human behavior. Their book is thorough, systematic, appropriately nuanced, concise and remarkably interesting. This book is a gift to present and future generations of behavioral scientists who's research is dependent on the observational measurement of human behavior."" --Steven F. Warren, Ph.D."

Author Information

Dr Paul J. Yoder, Ph.D. Professor, Department of Special Education, Peabody College, Vanderbilt University, Nashville, Tennessee 37203 Dr. Paul Yoder has been studying the transition from prelinguistic to linguistic communication in multiple populations with disabilities for over two decades. He is a co-designer of Milieu Communication Teaching and has contributed to several studies examining the efficacy of this treatment. He teaches methods and measurement at Vanderbilt University. Primary research activities of Frank J. Symons, Ph.D., are supported by the National Institute of Child Health and Human Development (NICHD), and they focus on improving the assessment and treatment of severe self-injurious behavior among individuals with developmental disabilities and pervasive developmental disorders. Dr. Symons was a research scientist at the Frank Porter Graham Child Development Center at the University of North Carolina at Chapel Hill and a postdoctoral fellow at the John F. Kennedy Center at the Peabody College of Vanderbilt University in Nashville, Tennessee. He is the co-author of Behavioral Observation: Technology and Applications in Developmental Disabilities (Paul H. Brookes Publishing Co., 2000).

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