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The Resource Statistical strategies for small sample research, [edited by] Rick H. Hoyle

Statistical strategies for small sample research, [edited by] Rick H. Hoyle

Label
Statistical strategies for small sample research
Title
Statistical strategies for small sample research
Statement of responsibility
[edited by] Rick H. Hoyle
Contributor
Subject
Language
eng
Cataloging source
DLC
Dewey number
001.4/22
Illustrations
illustrations
Index
index present
LC call number
HA29
LC item number
.S7844 1999
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
Hoyle, Rick H
http://library.link/vocab/subjectName
  • Social sciences
  • Sampling (Statistics)
  • Statistical hypothesis testing
  • Sciences sociales
  • Steekproeven
  • Sozialwissenschaften
  • Stichprobennahme
Label
Statistical strategies for small sample research, [edited by] Rick H. Hoyle
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 8
  • Statistical Analyses Using Bootstrapping: Concepts and Implementation
  • Yiu-Fai Yung, Wai Chan
  • A Monte Carlo Experiment on Reaction Time
  • 82
  • What if the Population Distribution Is Not Known? The Bootstrap Method
  • 87
  • What Makes the Bootstrap Work?
  • 90
  • A Study of Factor Replicability Using the Bootstrap
  • 92
  • Preparing the Data Set
  • Implementing a Bootstrap Procedure: Some Suggested Guidelines
  • 99
  • 5.
  • Meta-Analysis of Single-Case Designs
  • Scott L. Hershberger, Dennis D. Wallace, Samuel B. Green, Janet G. Marquis
  • Treatment Effect Sizes for Single-Case Designs and Their Replicability
  • 109
  • Choice of Scores to Represent A and B Phases for Computing Effect Sizes
  • 109
  • Choice of Divisors for an Effect Size Index for AB Phases
  • 8
  • 116
  • Treatment and Replication Effects for Multiple AB Phases for a Study
  • 117
  • Combining Effect Size Measures and Evaluating Moderator Variables
  • 118
  • An Example Meta-Analysis
  • 123
  • Computation of Effect Sizes and Replicability Effects for a Single Study
  • 123
  • Computation of Effect Sizes and Replicability Effects for Six Studies
  • Describing the Data
  • 126
  • Combining Effect Sizes and Assessing Moderator Variables
  • 128
  • 6.
  • Exact Permutational Inference for Categorical and Nonparametric Data
  • Cyrus R. Mehta, Nitin R. Patel
  • Exact Permutation Tests for r [times] c Contingency Tables
  • 134
  • Unconditional Sampling Distributions
  • 135
  • 9
  • Conditional Sampling Distribution
  • 137
  • Exact p Values
  • 140
  • Application to a Variety of r [times] c Problems
  • 142
  • Exact Inference for Stratified Contingency Tables
  • 152
  • Stratified 2 [times] 2 Contingency Tables
  • 152
  • Running the EM Algorithm
  • Stratified 2 [times] c Contingency Tables
  • 157
  • Computational Issues
  • 161
  • Software and Related Resources for Exact Inference
  • 163
  • 7.
  • Tests of an Identity Correlation Structure
  • Rachel T. Fouladi, James H. Steiger
  • Why Test the Identity Correlation Structure Model?
  • 9
  • 168
  • Available Test Procedures
  • 170
  • Exact Test Procedure
  • 170
  • Asymptotic Test Procedures
  • 171
  • Testing Difference in Fit
  • 175
  • Choosing a Procedure
  • Running Data Augmentation and Generating Multiple Imputations
  • 175
  • Relevant Monte Carlo Literature
  • 177
  • Type I Error Control
  • 179
  • Power
  • 183
  • Robustness
  • 183
  • 8.
  • 10
  • Sample Size, Reliability, and Tests of Statistical Mediation
  • Rick H. Hoyle, David A. Kenny
  • Conceptualization of a Mediational Model
  • 197
  • Technical Issues Concerning Tests of Mediation
  • 198
  • Sample Size
  • 199
  • Collinearity Between Cause and Mediator
  • 201
  • Diagnostics
  • Unreliability of the Mediator
  • 201
  • Latent Variable Modeling of Mediation
  • 203
  • Monte Carlo Experiment
  • 204
  • Design
  • 204
  • Technical Details
  • 205
  • 1.
  • 10
  • Results
  • 205
  • 9.
  • Pooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor Analysis
  • John R. Nesselroade, Peter C. M. Molenaar
  • A Focus on Process
  • 224
  • Idiographic Emphases Within the Pursuit of Nomothetic Laws
  • 224
  • Multivariate Measurement and Analysis
  • Analyzing the Data
  • 225
  • Statement of the Problem
  • 226
  • What Is Needed?
  • 227
  • Earlier Work on the Problem
  • 227
  • Pooling Dynamic Structures Rather Than Individuals' Time Series
  • 227
  • Assessing the Poolability of Individual Covariance Structures: A Test of Ergodicity
  • 10
  • 228
  • Ergodicity
  • 228
  • Lagged Relationships
  • 229
  • The Statistical Test of "Poolability"
  • 230
  • Dynamic Factor Analysis of Pooled, Lagged Covariance Functions
  • 234
  • Testing the "Poolability" of the Participants' Covariance Functions
  • Combining the Results
  • 237
  • Fitting the Dynamic Factor Model to the Pooled Covariance Functions
  • 238
  • Estimates of Noise Series Parameters
  • 239
  • Interpretation of the Lagged Factor Loadings
  • 241
  • 10.
  • Confirmatory Factor Analysis: Strategies for Small Sample Sizes
  • Herbert W. Marsh, Kit-Tai Hau
  • 11
  • Proposed Strategies
  • 252
  • More Items Is Better
  • 252
  • Item Parcels
  • 253
  • Equal-Loading Strategy
  • 254
  • Convergence, Proper Solutions, and N
  • 254
  • A Simulation With Small N
  • Marsh, Hau, and Balla (1997) Study
  • 256
  • Convergence Behavior
  • 256
  • Effects of Number of Indicators and N in Confirmatory Factor Analysis
  • 257
  • A Comparison of Parcel and Item Solutions
  • 258
  • Extensions
  • 259
  • 11
  • Study 1
  • The Effects of Measured Variable Saturation, Number of Indicators, and N
  • 260
  • Study 2
  • The Effect of Imposing Equality Constraints to Improve the Behavior of Factor Solutions With Small N
  • 262
  • Conclusions, Implications, Limitations, and Directions for Future Research
  • 277
  • 11.
  • Small Samples in Structural Equation State Space Modeling
  • The Population
  • Johan H. L. Oud, Robert A. R. G. Jansen, Dominique M. A. Haughton
  • State Space Modeling by Means of Structural Equation Modeling
  • 288
  • Simulation Study
  • 291
  • Results of the Simulation Study
  • 300
  • Results for the Simulation on the Basis of True Model I: Observed State Variables (See Table 1)
  • 301
  • Results for the Simulation on the Basis of True Model II: Measurement Errors (See Table 2)
  • 12
  • 302
  • Results for the Simulation on the Basis of True Model III: Measurement Errors and Traits (Random Subject Effects)
  • 303
  • 12.
  • Structural Equation Modeling Analysis With Small Samples Using Partial Least Squares
  • Wynne W. Chin, Peter R. Newsted
  • Contrasting Partial Least Squares and Covariance-Based Structural Equation Modeling
  • 308
  • The Standard Partial Least Squares Algorithm
  • 315
  • Sampling Method
  • Multiblock Example
  • 316
  • Formal Specification of the Partial Least Squares Model
  • 321
  • Inner Model
  • 321
  • Outer Model
  • 322
  • Weight Relations
  • 324
  • On the Performance of Multiple Imputation for Multivariate Data With Small Sample Size
  • 15
  • Predictor Specification
  • 324
  • Sample Size Requirements Based on the Inside and Outside Approximations
  • 326
  • Model Evaluation
  • 328
  • Partial Least Squares Estimates: The Issue of Consistency at Large
  • 328
  • Monte Carlo Simulation
  • 331
  • Rates and Patterns of Missingness
  • 15
  • Imputation and Analysis
  • 16
  • Running the Simulation
  • 18
  • Criteria of Performance
  • 18
  • Bias
  • John W. Graham, Joseph L. Schafer
  • 18
  • Efficiency
  • 18
  • Coverage
  • 19
  • Rejection Rates
  • 19
  • Simulation Results
  • 20
  • Performance of Multiple Imputation
  • A Brief History of Missing-Data Procedures
  • 20
  • Performance of Complete Cases Analysis
  • 25
  • 2.
  • Maximizing Power in Randomized Designs When N Is Small
  • Anre Venter, Scott E. Maxwell
  • Within- Versus Between-Subjects Designs
  • 33
  • The Statistical Model and Assumptions
  • 34
  • 1
  • Relative Power and Precision of the Designs
  • 35
  • Numerical Example
  • 41
  • Qualifications
  • 44
  • Between-Subjects Designs
  • 45
  • The Statistical Model and Assumptions
  • 45
  • Overview of Multiple Imputation With NORM
  • Posttest-Only Versus Pretest-Posttest Design
  • 46
  • Unequal Allocation of Assessment Units Between The Pretest and Posttest
  • 52
  • The Intensive Design
  • 55
  • 3.
  • Effect Sizes and Significance Levels in Small-Sample Research
  • Sharon H. Kramer, Robert Rosenthal
  • Effect Sizes: An Introduction
  • 5
  • 60
  • Relationship Between Effect Sizes and Significance Tests
  • 60
  • Types of Effect Size Estimates
  • 62
  • Effect Sizes in Small-Sample Studies
  • 64
  • Counternull Value of an Effect Size
  • 66
  • Effect Sizes in Contrasts Within a Study: Three Types of rs
  • Using the NORM Program
  • 67
  • Effect Sizes Across Studies
  • 70
  • The Nature of Replication
  • 70
  • Meta-Analysis
  • 72
  • Meta-Analysis With a Small Number of Studies
  • 74
  • 4.
Control code
40193548
Dimensions
24 cm
Extent
xxi, 367 pages
Isbn
9780761908869
Isbn Type
(paperback)
Lccn
98043490
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
(OCoLC)40193548
Label
Statistical strategies for small sample research, [edited by] Rick H. Hoyle
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 8
  • Statistical Analyses Using Bootstrapping: Concepts and Implementation
  • Yiu-Fai Yung, Wai Chan
  • A Monte Carlo Experiment on Reaction Time
  • 82
  • What if the Population Distribution Is Not Known? The Bootstrap Method
  • 87
  • What Makes the Bootstrap Work?
  • 90
  • A Study of Factor Replicability Using the Bootstrap
  • 92
  • Preparing the Data Set
  • Implementing a Bootstrap Procedure: Some Suggested Guidelines
  • 99
  • 5.
  • Meta-Analysis of Single-Case Designs
  • Scott L. Hershberger, Dennis D. Wallace, Samuel B. Green, Janet G. Marquis
  • Treatment Effect Sizes for Single-Case Designs and Their Replicability
  • 109
  • Choice of Scores to Represent A and B Phases for Computing Effect Sizes
  • 109
  • Choice of Divisors for an Effect Size Index for AB Phases
  • 8
  • 116
  • Treatment and Replication Effects for Multiple AB Phases for a Study
  • 117
  • Combining Effect Size Measures and Evaluating Moderator Variables
  • 118
  • An Example Meta-Analysis
  • 123
  • Computation of Effect Sizes and Replicability Effects for a Single Study
  • 123
  • Computation of Effect Sizes and Replicability Effects for Six Studies
  • Describing the Data
  • 126
  • Combining Effect Sizes and Assessing Moderator Variables
  • 128
  • 6.
  • Exact Permutational Inference for Categorical and Nonparametric Data
  • Cyrus R. Mehta, Nitin R. Patel
  • Exact Permutation Tests for r [times] c Contingency Tables
  • 134
  • Unconditional Sampling Distributions
  • 135
  • 9
  • Conditional Sampling Distribution
  • 137
  • Exact p Values
  • 140
  • Application to a Variety of r [times] c Problems
  • 142
  • Exact Inference for Stratified Contingency Tables
  • 152
  • Stratified 2 [times] 2 Contingency Tables
  • 152
  • Running the EM Algorithm
  • Stratified 2 [times] c Contingency Tables
  • 157
  • Computational Issues
  • 161
  • Software and Related Resources for Exact Inference
  • 163
  • 7.
  • Tests of an Identity Correlation Structure
  • Rachel T. Fouladi, James H. Steiger
  • Why Test the Identity Correlation Structure Model?
  • 9
  • 168
  • Available Test Procedures
  • 170
  • Exact Test Procedure
  • 170
  • Asymptotic Test Procedures
  • 171
  • Testing Difference in Fit
  • 175
  • Choosing a Procedure
  • Running Data Augmentation and Generating Multiple Imputations
  • 175
  • Relevant Monte Carlo Literature
  • 177
  • Type I Error Control
  • 179
  • Power
  • 183
  • Robustness
  • 183
  • 8.
  • 10
  • Sample Size, Reliability, and Tests of Statistical Mediation
  • Rick H. Hoyle, David A. Kenny
  • Conceptualization of a Mediational Model
  • 197
  • Technical Issues Concerning Tests of Mediation
  • 198
  • Sample Size
  • 199
  • Collinearity Between Cause and Mediator
  • 201
  • Diagnostics
  • Unreliability of the Mediator
  • 201
  • Latent Variable Modeling of Mediation
  • 203
  • Monte Carlo Experiment
  • 204
  • Design
  • 204
  • Technical Details
  • 205
  • 1.
  • 10
  • Results
  • 205
  • 9.
  • Pooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor Analysis
  • John R. Nesselroade, Peter C. M. Molenaar
  • A Focus on Process
  • 224
  • Idiographic Emphases Within the Pursuit of Nomothetic Laws
  • 224
  • Multivariate Measurement and Analysis
  • Analyzing the Data
  • 225
  • Statement of the Problem
  • 226
  • What Is Needed?
  • 227
  • Earlier Work on the Problem
  • 227
  • Pooling Dynamic Structures Rather Than Individuals' Time Series
  • 227
  • Assessing the Poolability of Individual Covariance Structures: A Test of Ergodicity
  • 10
  • 228
  • Ergodicity
  • 228
  • Lagged Relationships
  • 229
  • The Statistical Test of "Poolability"
  • 230
  • Dynamic Factor Analysis of Pooled, Lagged Covariance Functions
  • 234
  • Testing the "Poolability" of the Participants' Covariance Functions
  • Combining the Results
  • 237
  • Fitting the Dynamic Factor Model to the Pooled Covariance Functions
  • 238
  • Estimates of Noise Series Parameters
  • 239
  • Interpretation of the Lagged Factor Loadings
  • 241
  • 10.
  • Confirmatory Factor Analysis: Strategies for Small Sample Sizes
  • Herbert W. Marsh, Kit-Tai Hau
  • 11
  • Proposed Strategies
  • 252
  • More Items Is Better
  • 252
  • Item Parcels
  • 253
  • Equal-Loading Strategy
  • 254
  • Convergence, Proper Solutions, and N
  • 254
  • A Simulation With Small N
  • Marsh, Hau, and Balla (1997) Study
  • 256
  • Convergence Behavior
  • 256
  • Effects of Number of Indicators and N in Confirmatory Factor Analysis
  • 257
  • A Comparison of Parcel and Item Solutions
  • 258
  • Extensions
  • 259
  • 11
  • Study 1
  • The Effects of Measured Variable Saturation, Number of Indicators, and N
  • 260
  • Study 2
  • The Effect of Imposing Equality Constraints to Improve the Behavior of Factor Solutions With Small N
  • 262
  • Conclusions, Implications, Limitations, and Directions for Future Research
  • 277
  • 11.
  • Small Samples in Structural Equation State Space Modeling
  • The Population
  • Johan H. L. Oud, Robert A. R. G. Jansen, Dominique M. A. Haughton
  • State Space Modeling by Means of Structural Equation Modeling
  • 288
  • Simulation Study
  • 291
  • Results of the Simulation Study
  • 300
  • Results for the Simulation on the Basis of True Model I: Observed State Variables (See Table 1)
  • 301
  • Results for the Simulation on the Basis of True Model II: Measurement Errors (See Table 2)
  • 12
  • 302
  • Results for the Simulation on the Basis of True Model III: Measurement Errors and Traits (Random Subject Effects)
  • 303
  • 12.
  • Structural Equation Modeling Analysis With Small Samples Using Partial Least Squares
  • Wynne W. Chin, Peter R. Newsted
  • Contrasting Partial Least Squares and Covariance-Based Structural Equation Modeling
  • 308
  • The Standard Partial Least Squares Algorithm
  • 315
  • Sampling Method
  • Multiblock Example
  • 316
  • Formal Specification of the Partial Least Squares Model
  • 321
  • Inner Model
  • 321
  • Outer Model
  • 322
  • Weight Relations
  • 324
  • On the Performance of Multiple Imputation for Multivariate Data With Small Sample Size
  • 15
  • Predictor Specification
  • 324
  • Sample Size Requirements Based on the Inside and Outside Approximations
  • 326
  • Model Evaluation
  • 328
  • Partial Least Squares Estimates: The Issue of Consistency at Large
  • 328
  • Monte Carlo Simulation
  • 331
  • Rates and Patterns of Missingness
  • 15
  • Imputation and Analysis
  • 16
  • Running the Simulation
  • 18
  • Criteria of Performance
  • 18
  • Bias
  • John W. Graham, Joseph L. Schafer
  • 18
  • Efficiency
  • 18
  • Coverage
  • 19
  • Rejection Rates
  • 19
  • Simulation Results
  • 20
  • Performance of Multiple Imputation
  • A Brief History of Missing-Data Procedures
  • 20
  • Performance of Complete Cases Analysis
  • 25
  • 2.
  • Maximizing Power in Randomized Designs When N Is Small
  • Anre Venter, Scott E. Maxwell
  • Within- Versus Between-Subjects Designs
  • 33
  • The Statistical Model and Assumptions
  • 34
  • 1
  • Relative Power and Precision of the Designs
  • 35
  • Numerical Example
  • 41
  • Qualifications
  • 44
  • Between-Subjects Designs
  • 45
  • The Statistical Model and Assumptions
  • 45
  • Overview of Multiple Imputation With NORM
  • Posttest-Only Versus Pretest-Posttest Design
  • 46
  • Unequal Allocation of Assessment Units Between The Pretest and Posttest
  • 52
  • The Intensive Design
  • 55
  • 3.
  • Effect Sizes and Significance Levels in Small-Sample Research
  • Sharon H. Kramer, Robert Rosenthal
  • Effect Sizes: An Introduction
  • 5
  • 60
  • Relationship Between Effect Sizes and Significance Tests
  • 60
  • Types of Effect Size Estimates
  • 62
  • Effect Sizes in Small-Sample Studies
  • 64
  • Counternull Value of an Effect Size
  • 66
  • Effect Sizes in Contrasts Within a Study: Three Types of rs
  • Using the NORM Program
  • 67
  • Effect Sizes Across Studies
  • 70
  • The Nature of Replication
  • 70
  • Meta-Analysis
  • 72
  • Meta-Analysis With a Small Number of Studies
  • 74
  • 4.
Control code
40193548
Dimensions
24 cm
Extent
xxi, 367 pages
Isbn
9780761908869
Isbn Type
(paperback)
Lccn
98043490
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
(OCoLC)40193548

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