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
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
  • Social sciences
  • Steekproeven
  • Sozialwissenschaften
  • Stichprobennahme
  • Sampling (Statistics)
  • Statistical hypothesis testing
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.
Dimensions
24 cm
Extent
xxi, 367 pages
Isbn
9780761908852
Isbn Type
(hardcover : acid-free paper)
Lccn
98043490
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
  • (OCoLC)40193548
  • (OCoLC)ocm40193548
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.
Dimensions
24 cm
Extent
xxi, 367 pages
Isbn
9780761908852
Isbn Type
(hardcover : acid-free paper)
Lccn
98043490
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
  • (OCoLC)40193548
  • (OCoLC)ocm40193548

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