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E-raamat: Observational Measurement of Behavior

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  • Formaat: 280 pages
  • Ilmumisaeg: 26-Apr-2018
  • Kirjastus: Brookes Publishing Co
  • Keel: eng
  • ISBN-13: 9781681252476
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Observational Measurement of BehaviorSuurem pilt
  • Formaat: 280 pages
  • Ilmumisaeg: 26-Apr-2018
  • Kirjastus: Brookes Publishing Co
  • Keel: eng
  • ISBN-13: 9781681252476
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This comprehensive textbook introduces graduate students to observational measurement of behavior; it is also a
valuable core resource for any clinician whose work includes observational research. The book discusses in depth the
theoretical considerations underlying observational research and provides specific recommendations for effective
techniques and practices.


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.

STUDENTS WILL:
  • Fully understand key methods of observational research and measurement
  • Get comprehensive information on both foundational and advanced topics
  • Learn from real-world examples based on the authors' extensive experience
  • Apply specific recommendations for effective techniques and best practices

SELECTED TOPICS COVERED: validity and reliability * representativeness * measurement theory * behavior sampling and coding * observer training * metrics of observational variables * modifying and designing coding manuals * sequential analysis * generalizability theory

ONLINE COMPANION MATERIALS: To enhance their courses, instructors will get a full package of online materials, including two licenses for observational software, video clips students can use to practice coding behaviors, a suggested schedule for a semester-long course, exercises for students, and assignments with corresponding grading rubrics.

About the Online Companion Materials xi
About the Authors xiii
Preface xv
The Scope of This Book
xv
Topics and Corresponding
Chapters
xv
The Book's Iterative Teaching Style
xvi
Using the Online Companion Materials
xvii
Acknowledgments xix
Section I Foundational Topics
Chapter 1 Introduction to Systematic Observation and Measurement Contexts
3(20)
Systematic Observation Using Count Coding
3(6)
Alternatives to Systematic Observation
4(1)
Ways to Quantify Observations
4(2)
The Rationale for Systematic Observation Using Count Coding
6(3)
The Importance of Falsifiable Research Questions or Hypotheses
9(1)
Objects of Measurement: The Continuum of Context- Dependent Behaviors to Generalized Person Characteristics
10(5)
Context-Dependent Behaviors
10(1)
Generalized Person Characteristics
11(4)
Generalized Behavioral Tendencies
12(1)
Skills
13(2)
Judging the Relative Scientific Value of Different Measures
15(5)
Reliability
15(1)
Validity
16(1)
Ecological Validity
17(1)
Representativeness
18(2)
Conclusions and Recommendations
20(3)
Chapter 2 Validation of Observational Variables
23(22)
The Changing Concept of Validation
24(1)
Consequences of Not Attending to Validation
25(1)
Overview: Types of Validity by Objects of Measurement and Purposes
26(1)
Content Validation
27(1)
Varying Importance Ascribed to Content Validation
27(1)
Weaknesses of Content Validation
28(1)
Sensitivity to Change
28(3)
Influences on Sensitivity to Change
29(1)
Weakness of Sensitivity to Change as Way to Judge a Variable's Validity
30(1)
Criterion-Related Validation
31(1)
The Primary Appeal of Criterion-Related Validation
31(1)
Weaknesses of Criterion-Related Validation
32(1)
Construct Validation
32(6)
Convergent Validity
33(1)
Discriminative Validation Evidence
33(1)
Nomological Validation Evidence
34(1)
Weaknesses of Convergent Validity
34(1)
Methods That Combine Convergent and Divergent Validity
34(1)
Multitrait, Multimethod (MTMM) Validation
35(1)
Confirmatory Factor Analysis as a Method of Validation
35(3)
Putting It All Together With Literature Synthesis
38(1)
An Implicit Weakness of Science?
39(2)
Conclusions and Recommendations
41(4)
Chapter 3 Estimating Stable Measures of Generalized Person Characteristics Through Systematic Observation
45(16)
A Brief Overview of Measurement Theory
46(1)
Why Stable Estimates Maximize Convergent Construct Validity
46(2)
Two Ways to Stabilize Observational Measures
48(9)
Estimating Stable Skills Through Observation
49(1)
Definition of Measurement Context
49(1)
How Controlling Influential Contextual Variables Stabilizes Skill Estimates
50(1)
Why Skills Are Often Assessed in Clinics or Labs
50(2)
Estimating Stable Generalized Behavioral Tendencies Through Observation
52(10)
Representativeness, Revisited
52(1)
Definition of Contextual Measurement Error
53(1)
Contextual Measurement Error in Measures of Generalized Behavioral Tendencies
54(1)
How Averaging Scores Across Contexts Improves Measures of Generalized Behavioral Tendencies
55(2)
Naturalness of Observations and Representativeness, Revisited
57(1)
Computing Stability Coefficients
57(2)
Conclusions and Recommendations
59(2)
Chapter 4 Designing or Adapting Coding Manuals
61(18)
Definition of a Coding Manual
61(1)
Deciding Whether to Write a New Coding Manual
62(1)
Recommended Steps for Modifying or Designing Coding Manuals
62(13)
Define Start and Stop Coding Rules
62(2)
Conceptually Define the Object of Measurement
64(1)
Define the Highest Level of Codable Behavior
64(1)
Determine the Level of Distinction Coders Have to Make
65(2)
Organize the Coded Categories into Mutually Exclusive Sets
67(1)
Decide How to Use Physically Based and/or Socially Based Definitions
68(1)
Define the Lowest-Level Categories
69(2)
Determine Sources of Conceptual and Operational Definitions
71(3)
Define Segmenting Rules
74(1)
The Potential Value of Flowcharts
75(1)
Recommended Length of Coding Manuals
75(1)
Conclusions and Recommendations
76(3)
Chapter 5 Coding
79(20)
The Elements of an Observational Measurement System
79(1)
Behavior Sampling
80(9)
The Superordinate Distinctions: Continuous Versus Intermittent
80(1)
The Subordinate Distinctions: Continuous Versus Intermittent
81(2)
Timed-Event Sampling
81(1)
Event Sampling
82(1)
Interval Sampling
82(1)
Types of Interval Sampling
83(5)
Whole-Interval Sampling
84(1)
Momentary-Interval Sampling
84(1)
Partial-Interval Sampling
85(3)
Summary of Interval Sampling
88(1)
Which Dimension of Behavior Should Be Estimated
88(1)
Summary of Behavior Sampling
88(1)
Participant Sampling
89(1)
Focal Sampling
90(1)
Multiple-Pass Sampling
90(1)
Conspicuous Sampling
90(1)
Reactivity
90(2)
When to Code Relative to When the Behavior Occurs
92(2)
Live Coding
92(1)
Coding From Recorded Sessions
93(1)
Recording Coding Decisions
94(1)
Paper and Pencil
94(1)
Observational Software
95(1)
Conclusions and Recommendations
95(4)
Chapter 6 Common Metrics of Observational Variables
99(18)
Definition of Metric
100(1)
Quantifiable Dimensions of Behavior
100(1)
Proportion Metrics
101(6)
How Proportion Metrics Change the Meaning of Observational Variables
102(1)
Scrutinizing Proportions
103(1)
An Implicit Assumption of Proportion Metrics
104(1)
Testing Whether the Data Fit the Assumption of Proportion Metrics
105(1)
Consequences of Using a Proportion When the Data Do Not Fit the Assumption
106(1)
Alternative Methods to Control Influential Contextual Variables
107(1)
Statistical Control
107(1)
Procedural Control
108(1)
Aggregate Measures of Generalized Person Characteristics
108(3)
Weighted Counts
110(1)
Unit-Weighted Aggregates
110(1)
Group Analysis of Observational Variables
111(3)
Transforming the Metric
112(1)
Bootstrapping
112(2)
Analyzing Count Variables
114(1)
Conclusions and Recommendations
114(3)
Chapter 7 Training Observers and Preventing Observer Drift
117(20)
Point-by-Point Agreement and Disagreement
118(9)
Point-by-Point Agreement of Interval-Sampled Data
118(2)
Point-by-Point Agreement of Timed-Event Data
120(4)
Discrepancy Matrices
124(3)
Discrepancy Discussions
127(7)
Using Discrepancy Discussions to Train Observers
129(3)
Creating Criterion-Coding Standards
130(1)
Training Observers: Remaining Steps
131(1)
Preventing Observer Drift
132(8)
Choosing a Method of Selecting Sessions for Agreement Checks
132(1)
Preventing or Addressing Observer Drift: Remaining Steps
133(1)
Conclusions and Recommendations
134(3)
Chapter 8 Interobserver Reliability of Observational Variables
137(24)
General Principles of Interobserver Reliability Estimation
138(2)
Single-Case Design Concepts of Interobserver Reliability
140(9)
Session-Level Agreement Indices
142(7)
Summary-Level Agreement
142(1)
Point-by-Point Agreement
142(5)
Base Rate and All Indices of Point-by-Point Agreement
147(1)
Summary of Point-by-Point Agreement Indices
147(2)
Group-Design Concepts of Interobserver Reliability
149(4)
A Sample-Level Reliability Index: Intraclass Correlation
149(1)
Why Session-Level Reliability Is Insufficient for Group-Design Studies
150(2)
The Interpretation of IBM SPSS Software Output for ICC
152(1)
The Relation Between Interobserver Agreement and ICC
153(1)
The Special Case of Fidelity of Treatment Data
153(1)
Selection of Interobserver Reliability Index
154(1)
Consequences of Low or Unknown Interobserver Reliability
154(2)
Conclusions and Recommendations
156(5)
Section II Advanced Topics
Chapter 9 Introduction to Sequential Analysis
161(20)
About the Terminology Used in This
Chapter
162(1)
Sequential Versus Nonsequential Variable Metrics
162(1)
Requirements for Sequential Analysis
163(1)
Why Sequential Associations Are Insufficient for Causal Inferences
164(1)
Coded Units and Contingency Tables
164(1)
Four Types of Sequential Analysis
165(8)
Event Lag
166(1)
Event Lag With Pauses
167(3)
Concurrent Interval
170(1)
Interval Lag
171(2)
Observational Software for Sequential Analysis
173(1)
The Need to Control for Chance
173(1)
Indices of Sequential Association
174(4)
Transitional Probabilities
175(1)
Risk Difference
175(1)
Yule's Q
176(1)
Relative Advantages and Disadvantages Across Indices
177(1)
Conclusions and Recommendations
178(3)
Chapter 10 Research Questions Involving Sequential Associations
181(20)
Sequential Analysis in Within-Group and Between-Groups Designs
181(3)
Testing the Significance of Mean Sequential Associations
183(1)
Testing Between-Groups Differences in Mean Sequential Associations
183(1)
Testing Within-Group Differences in Mean Sequential Associations
183(1)
Testing Summary-Level Associations Between Participant Characteristics and Sequential Associations
184(1)
Sequential Analysis in Single-Case Designs
184(10)
The Meaning of Contingency in Behavior Analysis
185(1)
Why Significance Testing Is Controversial at the Individual Participant Level
186(1)
Types of Within-Participant Research Questions and Methods to Address Them
187(7)
Descriptive Questions to Inform or Supplement Single-Case Experiments
187(1)
Transitional Probability Comparisons and Contingency Space Analysis
188(2)
Contingency Indices as Dependent Variables in Single-Case Experimental Designs
190(2)
Contingency Indices as Procedural Fidelity Measures in Single-Case Experimental Designs
192(2)
Data Sufficiency for Sequential Analysis
194(2)
Consequences of Insufficient Data
195(1)
Defining Sufficient Data for Estimating Sequential Associations
195(1)
Proposed Solutions When Data Are Insufficient
196(1)
Conclusions and Recommendations
197(4)
Chapter 11 Generalizability Theory
201(16)
The Scope of This
Chapter
201(1)
Overview of G Theory and Definition of Terms
202(2)
A Sample Observer-by-Context G and D Study
204(5)
The Rationale for Preferring the Absolute G Coefficient
209(1)
Sample Applications of D Studies
209(1)
An Ongoing Controversy
210(2)
Conclusions and Recommendations
212(5)
Section III Putting It All Together
Chapter 12 Summary of Recommendations for Best Practices in Observational Measurement
217(12)
Identify Research Questions and Objects of Measurement
217(1)
Validate Observational Variables
218(2)
Design or Adapt Coding Manuals
220(1)
Select Each Component of the Coding Enterprise
220(2)
Select Observational Variable Metrics
222(1)
Train Observers
223(1)
Prevent, Detect, and Address Observer Drift
224(1)
Estimate, Report, and Interpret Interobserver Reliability
225(2)
Use Sequential Analysis to Address Research Questions Involving Sequential Associations or Contingencies
227(1)
Apply Generalizability Theory to Improve Reliability of Observational Measures of Generalized Person Characteristics
227(2)
Glossary 229(12)
Index 241
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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