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E-grāmata: Observational Measurement of Behavior

  • Formāts: 240 pages
  • Izdošanas datums: 16-Feb-2010
  • Izdevniecība: Springer Publishing Co Inc
  • Valoda: eng
  • ISBN-13: 9780826137982
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  • Formāts: 240 pages
  • Izdošanas datums: 16-Feb-2010
  • Izdevniecība: Springer Publishing Co Inc
  • Valoda: eng
  • ISBN-13: 9780826137982
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Yoder and Symons bring decades of work to bear and it shows....[ The book is] presented with broad scholarship and conceptual depth.

Roger Bakeman, PhD Professor Emeritus Georgia State University

This outstanding volume transcends the typical treatment of behavior observation methods in introductory research texts. Yoder and Symons articulate a set of measurement principles that serve as the foundation for behavior observation as a scientific tool.

William E. MacLean Jr., PhD Executive Director Wyoming Institute for Disabilities University of Wyoming

This comprehensive textbook introduces graduate students to the competent conduct of observational research methods and measurement. The unique approach of this book is that the chapters delineate not only the techniques and mechanics of observational methods, but also the theoretical and conceptual underpinnings of these methods.

The observational methods presented can be used for both single-subject and group-design perspectives, showing students how and when to use both methodologies. In addition, the authors provide many practical exercises within chapters as well as electronic media files of a sample observation session to code with multiple behavior sampling methods.

Key topics:





Improving measurement of generalized characteristics through direct observation and the generalizability theory Developing coding schemes and designing or adapting coding manuals Determining sampling methods and metrics for observational variables Training observers and assessing their agreement Performing sequential analysis on observational data Assessing the validity of observational variables
List of Figures
xiii
List of Tables
xv
Foreword xvii
Preface xix
Acknowledgements xxiii
Introduction and Measurement Contexts
1(16)
Overview
1(1)
Systematic Observation
2(1)
Count Coding Systems
3(1)
Importance of Falsifiable Research Questions or Hypotheses
4(1)
Behavior as ``Behavior'' Versus Behavior as a Sign or Indicant of a Construct
4(1)
Two Interpretations of Operationalism
5(2)
Distinction Between Context-Dependent Behavior and Generalized Tendencies to Behave
7(1)
Rationale for Identifying How We Are Conceptualizing Our Object of Measurement
8(4)
Influential Variables of a Measurement Context
9(1)
Structuredness
9(1)
Ecological Validity
10(1)
Representativeness
10(1)
Tension Between Structuredness and Ecological Validity
11(1)
Recommendations for Measuring Generalized Characteristics From Observations
12(1)
Potential Disadvantages of Systematic Observational Count Measurement
13(1)
Recommendations
14(1)
References
15(2)
Improving Measurement of Generalized Characteristics Through Direct Observation and Generalizability Theory
17(18)
Overview
17(1)
Two Concepts of Measurement
17(2)
Generalizability Theory as a Measurement Theory for Vaganotic Measures
19(1)
Example: Generalizability (G) Study With Multiple Sessions as a Single Facet
20(3)
Consequences of a Low G Coefficient
23(1)
Decision Studies
24(1)
McWilliam and Ware as an Example of a Two-Faceted Decision Study
25(1)
Practice Using a G Calculator on Data From a Two-Faceted G and D Study
26(4)
Accuracy of D Study Projections
30(1)
Implications of the Lessons of G and D Studies for Single-Subject Research
31(1)
A Dilemma
32(1)
Recommendations
33(1)
References
33(2)
Designing or Adapting Coding Manuals
35(18)
Overview
35(1)
Selecting, Adapting, or Creating a Coding Manual
36(12)
Definition of a Coding Manual
36(1)
Relation of the Coding Manual to the Research Questions and Predictions
36(1)
Recommended Steps for Modifying or Designing Coding Manuals
37(1)
Conceptually Defining the Context-Dependent Behavior or the Generalized Characteristic
37(1)
Deciding the Level of Detail at Which the Behaviors Should Be Distinguished
38(1)
Physically Based Definitions, Socially Based Definitions, or Both?
39(1)
Defining the Lowest Level Categories
40(2)
Sources of Conceptual and Operational Definitions
42(4)
Defining Segmenting Rules
46(1)
Defining When to Start and Stop Coding
47(1)
The Potential Value of Flowcharts
48(1)
Do Coding Manuals Need to Be Sufficiently Short to Be Included in Methods Sections?
49(1)
Recommendations
49(2)
References
51(2)
Sampling Methods
53(20)
Overview
53(1)
The Elements of a Measurement System
53(1)
Behavior Sampling
54(5)
Continuous Behavior Sampling
54(1)
Intermittent Behavior Sampling
55(1)
Interval Sampling
56(2)
How Does Interval Sampling Estimate Number and Duration?
58(1)
Participant Sampling
59(1)
Focal Sampling
59(1)
Multiple Pass Sampling
60(1)
Conspicuous Sampling
60(1)
Reactivity
60(2)
Live Coding Versus Recording the Observation for Later Coding
62(2)
Recording Coding Decisions
64(2)
Practice Recording Session
66(3)
Recommendations
69(1)
References
70(3)
Common Metrics of Observational Variables
73(20)
Overview
73(1)
Definition of Metric
74(1)
Quantifiable Dimensions of Behavior
74(1)
Proportion Metrics
75(1)
Proportion Metrics Change the Meaning of Observational Variables
75(10)
Scrutinizing Proportions
77(1)
An Implicit Assumption of Proportion Metrics
78(1)
Testing Whether the Data Fit the Assumption of Proportion Metrics
79(1)
Consequences of Using a Proportion When the Data Do Not Fit the Assumption
80(5)
Alternative Methods to Control Nuisance Variables
85(1)
Statistical Control
85(1)
Procedural Control
85(1)
Transforming Metrics of Observational Variables in Group Statistical Analyses
86(2)
Scales of Measurement for Observational Variables
88(2)
Observational Variables in Parametric Analyses
90(1)
Recommendations
90(1)
References
91(2)
Introduction to Sequential Analysis
93(26)
Overview
93(1)
Definitions of Terms Used in This
Chapter
94(1)
Sequential Versus Nonsequential Variables
94(1)
Sequential Associations Are Not Sufficient Evidence for Causal Inferences
95(1)
Coded Units and Exhaustiveness
96(2)
Three Major Types of Sequential Analysis
98(3)
Event-Lag Sequential Analysis
98(1)
Time-Lag Sequential Analysis
99(1)
Time-Window Sequential Analysis
100(1)
The Need to ``Control for Chance''
101(1)
How Sequential Data Are Represented Prior to Contingency Table Organization
102(1)
Contingency Tables
103(8)
Proper 2 x 2 Contingency Table Construction of Two Streams of Data for Concurrent Analysis
105(1)
Proper 2 x 2 Contingency Table Construction From One Stream of Data for Event-Lag Sequential Analysis
105(3)
Simulation Study to Compare Results From Two Ways to Construct Contingency Tables
108(1)
Contingency Tables for Time-Window Lag Sequential Analysis
109(2)
Transitional Probability
111(5)
Transitional Probabilities in Backward Sequential Analysis
113(2)
Summary of Transitional Probabilities
115(1)
Recommendations
116(1)
References
116(3)
Analyzing Research Questions Involving Sequential Associations
119(22)
Overview
119(1)
Computer Software to Aid Sequential Analysis
120(1)
Practice Exercise Using MOOSES Software to Conduct Time-Window Analysis
120(5)
Yule's Q
125(1)
What Is ``Enough Data'' and How Do We Attain It?
126(5)
Proposed Solutions for Insufficient Data
129(2)
Sequential Association Indices as Dependent Variables in Group Designs
131(2)
Testing the Significance of a Mean Sequential Association
131(1)
Testing the Between-Group Difference in Mean Sequential Associations
132(1)
Testing the Within-Subject Difference in Sequential Associations
132(1)
Testing the Significance of the Summary-Level Association Between a Participant Characteristic and a Sequential Association Between Behaviors
133(1)
Statistical Significance Testing of Sequential Associations in Single Cases
133(3)
A Caveat Regarding the Use of Yule's Q
136(1)
Recommendations
137(1)
References
138(3)
Observer Training, Observer Drift Checks, and Discrepancy Discussions
141(18)
Overview
141(1)
Three Purposes of Point-by-Point Agreement on Coding Decisions
141(1)
Two Definitions of Agreement
142(3)
Agreement Matrices
145(3)
Discrepancy Discussions
148(1)
Criterion Coding Standards
149(2)
Observer Training
151(2)
Method of Selecting and Conducting Agreement Checks
153(2)
Retraining When Observer Drift Is Identified
155(1)
Recommendations
156(1)
References
156(3)
Interobserver Agreement and Reliability of Observational Variables
159(24)
Overview
159(1)
Additional Purposes of Point-by-Point Agreement
159(1)
Added Principles When Agreement Checks Are Used to Estimate Interobserver ``Reliability'' of Observational Variable Scores
160(4)
Exhaustive Coding Spaces Revisited
164(3)
The Effect of Chance on Agreement
167(1)
Common Indices of Point-by-Point Agreement
168(6)
Occurrence Percentage Agreement
168(1)
Nonoccurrence Percentage Agreement
168(1)
Total Percentage Agreement
169(1)
Kappa
169(2)
Base Rate and Chance Agreement Revisited
171(1)
Summary of Point-by-Point Agreement Indices
172(2)
Intraclass Correlation Coefficient as an Index of Interobserver Reliability from the Vaganotic Concept of Measurement
174(4)
Options for Running ICC With SPSS
175(1)
Between-Participant Variance on the Variable of Interest Affects ICC
175(2)
Using ICC as a Measure of Interobserver Reliability for Predictors and Dependent Variables in Group Designs
177(1)
The Interpretation of SPSS Output for ICC
177(1)
The Conceptual Relation Between Interobserver Agreement and ICC
178(1)
Consequences of Low or Unknown Interobserver Reliability
178(2)
Recommendations
180(1)
References
181(2)
Validation of Observational Variables
183(22)
Overview
183(1)
The Changing Concept of Validation
184(1)
Understanding Which Types of Validation Evidence Are Most Relevant for Different Research Designs, Objects of Measurement, and Research Purposes
185(1)
Content Validation
186(2)
Definition of Content Validation
186(1)
Different Traditions Vary on the Levels of Importance Placed on Content Validation
187(1)
Weaknesses of Content Validation
188(1)
Sensitivity to Change
188(2)
Definition of Sensitivity to Change
188(1)
Influences on Sensitivity to Change
189(1)
Weakness of Sensitivity to Change
190(1)
Treatment Utility
190(3)
Definition of Treatment Utility
190(2)
Weaknesses of Treatment Utility
192(1)
Criterion-Related Validation
193(1)
Definition of Criterion-Related Validation
193(1)
Primary Appeal of Criterion-Related Validation
193(1)
Weaknesses of Criterion-Related Validation
194(1)
Construct Validation
194(6)
Definition of Construct Validation
194(1)
Discriminative Validation
195(1)
Nomological Validation
196(1)
Multitrait, Multimethod Validation
197(3)
An Implicit ``Weakness'' of Science?
200(2)
Recommendations
202(1)
References
202(3)
Glossary 205(16)
Index 221
Paul Yoder, PhD is a Professor in Vanderbilt University's Department of Special Education.

Frank Symons, PhD is Associate Professor in the Department of Educational Psychology at the University of Minnesota. He is the current director of the Observational Methods Lab at University of Minnesota.