Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields.
(eBook)

Book Cover
Published:
Alexandria : American Counseling Association, 2011.
Format:
eBook
Edition:
1st ed.
ISBN:
9781119019299
Physical Desc:
1 online resource (276 pages)
Status:
ProQuest Ebook Central (Western)
Description

"Dr. Dimitrov has constructed a masterpiece-a classic resource that should adorn the shelf of every counseling researcher and graduate student serious about the construction and validation of high quality research instruments. -Bradley T. Erford, PhD Loyola University Maryland Past President, American Counseling Association   "This book offers a comprehensive treatment of the statistical models and methods needed to properly examine the psychometric properties of assessment scale data. It is certain to become a definitive reference for both novice and experienced researchers alike." -George A. Marcoulides, PhD University of California, Riverside   This instructive book presents statistical methods and procedures for the validation of assessment scale data used in counseling, psychology, education, and related fields. In Part I, measurement scales, reliability, and the unified construct-based model of validity are discussed, along with key steps in instrument development. Part II describes factor analyses in construct validation, including exploratory factor analysis, confirmatory factor analysis, and models of multitrait-multimethod data analysis. Traditional and Rasch-based analyses of binary and rating scales are examined in Part III. Dr. Dimitrov offers students, researchers, and clinicians step-by-step guidance on contemporary methodological principles, statistical methods, and psychometric procedures that are useful in the development or validation of assessment scale data. Numerous examples, tables, and figures provided throughout the text illustrate the underlying principles of measurement in a clear and concise manner for practical application.

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Language:
English

Notes

Description
"Dr. Dimitrov has constructed a masterpiece-a classic resource that should adorn the shelf of every counseling researcher and graduate student serious about the construction and validation of high quality research instruments. -Bradley T. Erford, PhD Loyola University Maryland Past President, American Counseling Association   "This book offers a comprehensive treatment of the statistical models and methods needed to properly examine the psychometric properties of assessment scale data. It is certain to become a definitive reference for both novice and experienced researchers alike." -George A. Marcoulides, PhD University of California, Riverside   This instructive book presents statistical methods and procedures for the validation of assessment scale data used in counseling, psychology, education, and related fields. In Part I, measurement scales, reliability, and the unified construct-based model of validity are discussed, along with key steps in instrument development. Part II describes factor analyses in construct validation, including exploratory factor analysis, confirmatory factor analysis, and models of multitrait-multimethod data analysis. Traditional and Rasch-based analyses of binary and rating scales are examined in Part III. Dr. Dimitrov offers students, researchers, and clinicians step-by-step guidance on contemporary methodological principles, statistical methods, and psychometric procedures that are useful in the development or validation of assessment scale data. Numerous examples, tables, and figures provided throughout the text illustrate the underlying principles of measurement in a clear and concise manner for practical application.
Local note
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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Citations
APA Citation (style guide)

Dimitrov, D. M. (2011). Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields. Alexandria, American Counseling Association.

Chicago / Turabian - Author Date Citation (style guide)

Dimitrov, Dimiter M. 2011. Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields. Alexandria, American Counseling Association.

Chicago / Turabian - Humanities Citation (style guide)

Dimitrov, Dimiter M, Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields. Alexandria, American Counseling Association, 2011.

MLA Citation (style guide)

Dimitrov, Dimiter M. Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields. Alexandria, American Counseling Association, 2011.

Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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5050 |a Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields -- Table of Contents -- Preface -- Acknowledgments -- About the Author -- Part I: Scales, Reliability, and Validity -- Chapter 1: Variables and Measurement Scales -- 1.1 Variables in Social and Behavioral Research -- 1.1.1 Observable Versus Latent Variables -- 1.1.2 Continuous Versus Discrete Variables -- 1.2 What Is Measurement? -- 1.3 Levels of Measurement -- 1.3.1 Nominal Scale -- 1.3.2 Ordinal Scale -- 1.3.3 Interval Scale -- 1.3.4 Ratio Scale -- 1.4 Typical Scales for Assessment in Counseling -- 1.4.1 Binary Scales -- 1.4.2 Rating Scales -- 1.5 Scaling -- 1.5.1 What Is Scaling? -- 1.5.2 Dimensionality -- 1.5.3 Likert Scaling -- 1.5.4 Guttman Scaling -- 1.5.5 Thurstone Scaling -- 1.5.6 Transformations of Scale Scores -- Summary -- Chapter 2: Reliability -- 2.1 What Is Reliability? -- 2.2 Classical Concept of Reliability -- 2.2.1 True-Score Model -- 2.2.2 Congeneric Measures -- 2.2.3 Calibration of Measures -- 2.2.4 Definition of Reliability -- 2.2.5 Standard Error of Measurement (SEM) -- 2.3 Types of Reliability -- 2.3.1 Internal Consistency Reliability -- 2.3.2 Test-Retest Reliability -- 2.3.3 Alternate Forms Reliability -- 2.3.4 Criterion-Referenced Reliability -- 2.3.5 Interrater Reliability -- 2.4 Stratified Alpha -- 2.4.1 Stratified Alpha for Two Scales -- 2.4.2 Stratified Alpha for Three Scales -- 2.5 Maximal Reliability of Congeneric Measures -- Summary -- Chapter 3: Validity -- 3.1 What Is Validity? -- 3.2 Unified Construct-Based Model of Validity -- 3.2.1 Content Aspect of Validity -- 3.2.2 Substantive Aspect of Validity -- 3.2.3 Structural Aspect of Validity -- 3.2.4 Generalizability Aspect of Validity -- 3.2.5 External Aspect of Validity -- 3.2.6 Consequential Aspect of Validity -- 3.2.7 Additional Notes on Validity -- Summary.
5058 |a Chapter 4: Steps in Instrument Development -- 4.1 Definition of Purpose -- 4.1.1 Intended Decision -- 4.1.2 Constraints -- 4.1.3 Frame of Reference -- 4.2 Instrument Specifications -- 4.2.1 Construct Definition -- 4.2.2 Construct Models -- 4.2.3 Content Specification -- 4.2.4 Scoring and Scaling Models -- 4.3 Item Development -- 4.3.1 Selection of Item Format -- 4.3.2 Expert Review -- 4.3.3 Pilot Studies -- Summary -- Part II: Factor Analysis in Construct Validation -- Chapter 5: Exploratory Factor Analysis -- 5.1 Correlated Variables and Underlying Factors -- 5.2 Basic EFA Models -- 5.2.1 Full Component Model -- 5.2.2 Common Factor Model -- 5.3 The Principal Factor Method of Extracting Factors -- 5.3.1 Principal Factor Extraction -- 5.3.2 Principal Component Analysis -- 5.3.3 Principal Factor Analysis -- 5.3.4 Component Analysis Versus Factor Analysis -- 5.4 Rotation of Factors -- 5.4.1 Simple Structure -- 5.4.2 Orthogonal Rotation -- 5.4.3 Oblique Rotations -- 5.4.4 Interpreting Factors -- 5.5 Some Basic Properties -- 5.5.1 Eigenvalues and Related Properties -- 5.5.2 Sum of Squared Loadings -- 5.6 Determining the Number of Factors -- 5.6.1 "Eigenvalues of One or Higher" Criterion -- 5.6.2 Scree Test -- 5.6.3 Parallel Analysis -- 5.6.4 Velicer's MAP Test -- 5.6.5 Computer Programs for PA and Velicer's MAP Test -- 5.6.6 Modified PA of Ordered Categorical Data -- 5.7 Higher-Order Factors -- 5.8 Sample Size for EFA -- 5.9 Data Adequacy for EFA -- 5.9.1 Bartlett's Test of Sphericity -- 5.9.2 Measures of Sampling Adequacy -- 5.10 EFA With Categorical Data -- 5.11 EFA in Collecting Evidence of Construct Validity -- 5.11.1 EFA for Convergent and Discriminant Evidence of Validity -- 5.11.2 Some New Trends in Using EFA for Validation Purposes -- Summary -- Chapter 6: Confirmatory Factor Analysis -- 6.1 Similarities and Differences of EFA and CFA.
5058 |a 6.2 CFA Model Specification -- 6.3 Dependent and Independent Variables in CFA -- 6.4 CFA Model Parameters -- 6.5 CFA Model Identification -- 6.6 Evaluation of CFA Model Adequacy -- 6.6.1 The Assumption of Multivariate Normality -- 6.6.2 Goodness-of-Fit Indices -- 6.6.3 Analysis of Residuals -- 6.6.4 Modification Indices -- 6.6.5 Interpretability of Model Parameters -- 6.6.6 Cross-Validation -- 6.7 Factorial Invariance Across Groups -- 6.7.1 What Is Factorial Invariance? -- 6.7.2 Configural Invariance -- 6.7.3 Measurement Invariance -- 6.7.4 Structural Invariance -- 6.8 Testing for Factorial Invariance -- 6.8.1 Approaches to Testing for Factorial Invariance -- 6.8.2 Chi-Square Difference Test for Nested Models -- 6.8.3 CFI Difference -- 6.8.4 Partial Invariance -- 6.9 Comparing Groups on Constructs -- 6.9.1 Why Compare Groups on Constructs? -- 6.9.2 Group-Code (MIMIC) Modeling -- 6.10 Higher-Order CFA -- 6.10.1 What Is Higher-Order CFA? -- 6.10.2 Identification and Acceptability of Higher-Order Models -- 6.10.3 Schmid-Leiman Transformation -- 6.10.4 Testing for Measurement Invariance of Second-Order Models -- 6.11 Points of Caution in Testing for Factorial Invariance -- 6.11.1 The Role of Referent Indicators -- 6.11.2 The Role of Item Parceling -- 6.11.3 Measurement Invariance, Prediction Invariance, and Selection Invariance -- 6.12 Sample Size for CFA -- Summary -- Chapter 7: CFA-Based Models of Multitrait-Multimethod Data -- 7.1 Conventional MTMM Analysis -- 7.2 The Standard CFA Model -- 7.3 The CU Model -- 7.4 The CU-CFA Model -- 7.5 The Correlated Trait-Correlated Method Minus One [CTC(M - 1)] Model -- 7.6 The Random Intercept Factor Model -- 7.7 The Hierarchical CFA (HCFA) Model -- 7.8 The Multilevel CFA (ML-CFA) Method -- 7.9 Conventional MTMM Analysis Using Latent Variable Modeling -- 7.10 Brief Guidelines for Selecting Models of MTMM Data.
5058 |a Summary -- Part III: Psychometric Scale Analysis -- Chapter 8: Conventional Scale Analysis -- 8.1 Analysis of Binary Scales -- 8.1.1 Analysis of Binary Items -- 8.1.2 Distribution of Total Test Scores -- 8.2 Analysis of Rating Scales -- 8.2.1 Item Selection -- 8.2.2 Joining Adjacent Response Categories -- 8.2.3 Probability Tests for Likert-Type Scales -- 8.3 Estimation of Reliability for Congeneric Measures -- Summary -- Chapter 9: Rasch-Based Scale Analysis -- 9.1 Rasch Model for Binary Data -- 9.1.1 Rasch Model and Logit Scale -- 9.1.2 Item Characteristic Curve -- 9.1.3 Test Characteristic Curve -- 9.1.4 Item Information Function -- 9.1.5 Test Information Function -- 9.1.6 Fit Analysis -- 9.1.7 Person-Item Distribution Map for Binary Scales -- 9.2 Rating Scale Model (RSM) -- 9.2.1 Basic RSM Concepts -- 9.2.2 Threshold (or Step) Disordering -- 9.2.3 Category Disordering -- 9.2.4 Optimizing Rating Scale Category Effectiveness -- 9.2.5 Person-Item Distribution Map for Rating Scales -- 9.2.6 Testing for Multidimensionality -- Summary -- References -- Index -- Technical Support -- End User License Agreement.
520 |a "Dr. Dimitrov has constructed a masterpiece-a classic resource that should adorn the shelf of every counseling researcher and graduate student serious about the construction and validation of high quality research instruments. -Bradley T. Erford, PhD Loyola University Maryland Past President, American Counseling Association   "This book offers a comprehensive treatment of the statistical models and methods needed to properly examine the psychometric properties of assessment scale data. It is certain to become a definitive reference for both novice and experienced researchers alike." -George A. Marcoulides, PhD University of California, Riverside   This instructive book presents statistical methods and procedures for the validation of assessment scale data used in counseling, psychology, education, and related fields. In Part I, measurement scales, reliability, and the unified construct-based model of validity are discussed, along with key steps in instrument development. Part II describes factor analyses in construct validation, including exploratory factor analysis, confirmatory factor analysis, and models of multitrait-multimethod data analysis. Traditional and Rasch-based analyses of binary and rating scales are examined in Part III. Dr. Dimitrov offers students, researchers, and clinicians step-by-step guidance on contemporary methodological principles, statistical methods, and psychometric procedures that are useful in the development or validation of assessment scale data. Numerous examples, tables, and figures provided throughout the text illustrate the underlying principles of measurement in a clear and concise manner for practical application.
588 |a Description based on publisher supplied metadata and other sources.
590 |a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650 0|a Counseling - Evaluation.
655 4|a Electronic books.
77608|i Print version:|a Dimitrov, Dimiter M.|t Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields|d Alexandria : American Counseling Association,c2011|z 9781556202957
7972 |a ProQuest (Firm)
85640|u https://ebookcentral.proquest.com/lib/wscc-ebooks/detail.action?docID=1832731|z Click to View