Observational Measurement of Behavior

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ISBN: 9780826137975 | 0826137970
Cover: Paperback
Copyright: 2/1/2010

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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.

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

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