Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (485 pages)

Edition: 1st ed.

ISBN: 9783031509513

Note: Intro -- Contents -- Abbreviations -- Symbols -- 1 General Introduction -- 1.1 Psychological Assessment and Normative Data -- 1.2 The Traditional Normative Method -- 1.3 Issues with the Traditional Normative Method -- 1.3.1 The Boundary Problem -- 1.3.2 The Splitting Problem -- 1.4 The Regression-Based Normative Approach -- 1.5 Outline of the Book -- 1.6 Setting the Scene -- References -- 2 The R Programming Language -- 2.1 What Is R? -- 2.2 Installing R and R Packages -- 2.3 Basic R Operators and Functions -- 2.4 Generic Functions -- 2.5 Data Frames -- 2.6 Missing Data -- 2.7 Reading-in and Exploring Datasets -- 2.8 Plotting Functions -- References -- 3 Normative Data Accounting for a Binary Independent Variable -- 3.1 Regression Models with One Binary Independent Variable -- 3.1.1 Estimation of the Regression Parameters -- 3.1.2 Inference for the Regression Parameters -- 3.2 Establishing Regression-Based Normative Data Accounting for One Binary Independent Variable -- 3.2.1 Stage 1: Building the Regression Model -- 3.2.2 Stage 2: Deriving Normative Data -- 3.2.2.1 Manual Normative Conversions -- 3.2.2.2 Normative Tables -- 3.2.2.3 Automatic Scoring Program -- 3.2.3 Type I and II Errors -- 3.3 Case Studies -- 3.3.1 Case Study 1: The GCSE Dataset -- 3.3.1.1 Exploratory Analyses -- 3.3.1.2 Conducting the Regression-Based Normative Analyses -- 3.3.2 Case Study 2: The TMAS Dataset -- 3.3.2.1 Exploratory Analyses -- 3.3.2.2 Conducting the Regression-Based Normative Analyses -- 3.3.2.3 An Alternative Dummy-Coding Scheme -- References -- 4 Assumptions of the Normal Error Regression Model -- 4.1 Homoscedasticity -- 4.1.1 Testing Homoscedasticity -- 4.1.1.1 Graphical Checks -- 4.1.1.2 Formal Test -- 4.1.2 Problems with Heteroscedasticity in a Regression-Based Normative Data Context -- 4.1.2.1 Impact on Stage 1: Building the Regression Model. , 4.1.2.2 Impact on Stage 2: Deriving the Normative Data -- 4.1.3 Accounting for Heteroscedasticity: Summary -- 4.1.3.1 Stage 1 -- 4.1.3.2 Stage 2 -- 4.1.3.3 Special Case: An Intercept-Only Model -- 4.2 Normality of the Errors -- 4.2.1 Testing Normality -- 4.2.1.1 Graphical Checks -- 4.2.1.2 Formal Test -- 4.2.1.3 Difficulties in Assessing Normality -- 4.2.2 Problems with Non-normality in a Regression-Based Normative Data Context -- 4.2.2.1 Impact on Stage 1: Building the Regression Model -- 4.2.2.2 Impact on Stage 2: Deriving the Normative Data -- 4.2.3 Accounting for Non-normality: Summary -- 4.2.3.1 Stage 1 -- 4.2.3.2 Stage 2 -- 4.3 Dealing with Violations of the Normality and/or Homoscedasticity Assumptions: A Simulation Study -- 4.4 Independence of the Errors -- 4.5 Absence of Outliers -- 4.6 Model Assumptions Are Remedial Actions: Summary -- 4.7 Case Studies -- 4.7.1 Impact of the Homoscedasticity and Normality Assumptions on the Behavior of the NormDataPackage -- 4.7.2 Case Study 1: The GCSE Dataset -- 4.7.2.1 Testing Model Assumptions -- 4.7.2.2 Impact of Tested Model Assumptions on Stage 2: Deriving Normative Data -- 4.7.3 Case Study 2: The TMAS Dataset -- 4.7.3.1 Testing Model Assumptions -- 4.7.3.2 Impact of Tested Model Assumptions on Stage 2: Deriving Normative Data -- References -- 5 Normative Data Accounting for a Non-binary Qualitative Independent Variable -- 5.1 Regression Models with One Non-binary Qualitative Independent Variable -- 5.1.1 Estimation of the Model Parameters -- 5.1.2 Inference for the Model Parameters -- 5.1.2.1 T* Test-Statistics -- 5.1.2.2 The General Linear Test Approach -- 5.2 Testing Model Assumptions When X Is a Non-binary Qualitative Variable -- 5.2.1 Homoscedasticity -- 5.2.1.1 Graphical Checks -- 5.2.1.2 Testing Homoscedasticity. , 5.3 Establishing Regression-Based Normative Data Accounting for One Non-binary Qualitative Independent Variable -- 5.3.1 Stage 1: Building the Regression Model -- 5.3.2 Stage 2: Deriving Normative Data -- 5.4 Case Studies -- 5.4.1 Impact of the Model Assumptions on the Behavior of the NormData Package -- 5.4.2 Case Study 1: The ``Openness'' Scale of the Personality Dataset -- 5.4.2.1 Exploratory Analyses -- 5.4.2.2 Conducting the Regression-Based Normative Analyses -- 5.4.3 Case Study 2: The ``Fruits'' Score of the Fluency Dataset -- 5.4.3.1 Exploratory Analyses -- 5.4.3.2 Conducting the Regression-Based Normative Analyses -- References -- 6 Normative Data Accounting for a Quantitative IndependentVariable -- 6.1 Regression Models with One Quantitative Independent Variable -- 6.1.1 Higher-Order Polynomial Models -- 6.1.2 Well-Formulated Higher-Order Polynomial Models -- 6.1.3 Exploring the Statistical Relationship of the MeanStructure -- 6.1.4 Formal Approaches to Identify the Optimal Order of a Polynomial Model -- 6.1.4.1 The GLT Approach -- 6.1.4.2 T* Test-Statistics -- 6.1.4.3 GLT Approach Versus T* Test-Statistics -- 6.2 Testing Model Assumptions When X Is a Quantitative Variable -- 6.2.1 Homoscedasticity -- 6.2.1.1 Graphical Checks -- 6.2.1.2 Formal Test -- 6.3 Establishing Regression-Based Normative Data Accounting for One Quantitative Independent Variable -- 6.3.1 Stage 1: Building the Regression Model -- 6.3.1.1 The Mean Structure -- 6.3.1.2 The Residual Structure -- 6.3.2 Stage 2: Deriving Normative Data -- 6.4 Case Studies -- 6.4.1 Impact of the Model Assumptions on the Behavior of the NormData Package -- 6.4.2 Case Study 1: The Substitution Dataset -- 6.4.2.1 Exploratory Analyses -- 6.4.2.2 Conducting the Regression-Based Normative Analyses -- 6.4.3 Case Study 2: The VLT Dataset -- 6.4.3.1 Exploratory Analyses. , 6.4.3.2 Conducting the Regression-Based Normative Analyses -- Stage 1: Building the Regression Model -- Stage 2: Deriving Normative Data -- References -- 7 Normative Data Accounting for Multiple Qualitative and/or Quantitative Independent Variables -- 7.1 Regression Models with Multiple Independent Variables -- 7.1.1 Additive Models -- 7.1.1.1 Inference for the Regression Parameters of Additive Models -- 7.1.1.2 Impact of Centering on the Normative Data -- 7.1.2 Interaction Models -- 7.1.2.1 Inference for Interaction Models -- 7.1.2.2 Impact of Centering on the Normative Data -- 7.1.3 The Principle of Marginality -- 7.1.4 Assumed Statistical Relationship for the Mean Structure -- 7.2 Testing Model Assumptions -- 7.2.1 Homoscedasticity -- 7.2.1.1 Models That Include Only Qualitative Independent Variables -- 7.2.1.2 Models That Include At Least One Quantitative Independent Variable -- 7.2.2 Impact of Model Misspecification on the Assumptions -- 7.2.3 Multicollinearity -- 7.2.3.1 Detecting Multicollinearity -- 7.2.3.2 Considerations on Multicollinearity in a Normative Data Setting -- 7.3 Establishing Regression-Based Normative Data Accounting for Multiple Independent Variables -- 7.3.1 Stage 1: Building the Regression Model -- 7.3.1.1 The Mean Structure -- 7.3.1.2 The Residual Structure -- 7.3.2 Stage 2: Deriving Normative Data -- 7.4 Case Studies -- 7.4.1 Impact of the Model Assumptions on the Behavior of the NormData Package -- 7.4.2 Case Study 1: The Substitution Dataset -- 7.4.2.1 Exploratory Analyses -- 7.4.2.2 Conducting the Regression-Based Normative Analyses -- Model-Building of the Mean Structure: Evaluating the Interaction Terms -- Model-Building of the Mean Structure: Higher-Order Polynomial Terms -- Model-Building of the Mean Structure: Main Effects -- Building the Variance Prediction Function -- Model Assumptions -- Generalized VIF. , Assumed Statistical Relationship for the Mean Structure -- The Stage.2.NormTable() Function -- The Stage.2.AutoScore() Function -- 7.4.3 Case Study 2: the VLT Dataset -- 7.4.3.1 Exploratory Analyses -- 7.4.3.2 Conducting the Regression-Based Normative Analyses -- Model-Building of the Mean Structure: Evaluating Interaction Terms -- Model-Building of the Mean Structure: Higher-Order Polynomial Terms -- Model-Building: Main Effects -- The Final Stage 1 Mean Prediction Function -- Model Assumptions -- Assumed Statistical Relationship of the Mean Structure -- Building the Variance Prediction Function -- The Stage.2.NormTable() Function -- The Stage.2.AutoScore() Function -- References -- 8 Quantifying Uncertainty in Regression-Based Norms -- 8.1 The Bootstrap -- 8.2 Simulation Studies -- 8.2.1 Simulation Study 1 -- 8.2.2 Simulation Study 2 -- 8.3 A Note on Sample Size Computations for Regression-Based Normative Studies -- 8.4 Case Studies -- 8.4.1 Case Study 1: The GCSE Dataset -- 8.4.2 Case Study 2: The Substitution Dataset -- References -- A Statistical Tables -- A.1 Standardized Test Scores and Residuals (i.e., υ"0362υ0 and δ"0362δ0) with Their Corresponding Estimated Percentile Ranks (π"0362π0) Based on the Standard Normal Distribution -- A.2 Absolute Values of the T* Test-Statistics and Their Corresponding p-Values for Different Degrees of Freedom (Two-Sided Tests) -- B R Code -- B.1 Code to Reproduce the Plots on Page 390 -- B.2 Code to Reproduce the Plots on Page 415 -- B.3 Code to Reproduce the Plots on Page 420 -- B.4 Code to Reproduce the Sample Size Estimation Example -- Index.

Additional Edition: Print version: Van der Elst, Wim Regression-Based Normative Data for Psychological Assessment Cham : Springer,c2024 ISBN 9783031509506

Language: English

UID:

Format: XI, 482 p. 164 illus., 75 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031509513

Content: Over the last 20 years, so-called regression-based normative methods have become increasingly popular. In this approach, regression models for the mean and the residual variance structure are used to derive the normative data. The regression-based normative approach has some important advantages over the traditional normative approach, e.g., it allows for deriving more fine-grained norms and typically requires a substantially smaller sample size to derive accurate norms.This book focuses on regression-based methods to derive normative data. The target audience are psychologists and other researchers in the behavioral sciences who are interested in deriving normative data for psychological tests (e.g., cognitive tests, questionnaires, rating scales, etc.). The book provides the essential theoretical background that is needed to understand the methodology, with a strong emphasis on the practical/real-life application of the methodology. To this end, the book is also accompanied by an open-source software package (the R library NormData) that is used to exemplify how normative data can be derived in several case studies. Provides a solid introduction in regression-based normative methods without being overly technical; Comes with a comprehensive open-source software package to help efficiently derive regression-based normative data; Focuses strongly on the practical application of the methodology using various real-life case studies. .

Note: General introduction.-The R programming language -- Normative data accounting for a binary independent variable -- Assumption of the normal error regression model -- Normative data accounting for a non-binary qualitative independent variable -- Normative data accounting for a quantitative independent variable -- Normative data accounting for multiple qualitative and/or quantitative independent variables -- Quantifying uncertainty in regression-based norms.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031509506

Additional Edition: Printed edition: ISBN 9783031509520

Additional Edition: Printed edition: ISBN 9783031509537

Language: English

DOI: 10.1007/978-3-031-50951-3

URL: https://doi.org/10.1007/978-3-031-50951-3

UID:

Format: 1 online resource (485 pages)

Edition: 1st ed. 2024.

ISBN: 9783031509513

Content: Over the last 20 years, so-called regression-based normative methods have become increasingly popular. In this approach, regression models for the mean and the residual variance structure are used to derive the normative data. The regression-based normative approach has some important advantages over the traditional normative approach, e.g., it allows for deriving more fine-grained norms and typically requires a substantially smaller sample size to derive accurate norms.This book focuses on regression-based methods to derive normative data. The target audience are psychologists and other researchers in the behavioral sciences who are interested in deriving normative data for psychological tests (e.g., cognitive tests, questionnaires, rating scales, etc.). The book provides the essential theoretical background that is needed to understand the methodology, with a strong emphasis on the practical/real-life application of the methodology. To this end, the book is also accompanied by an open-source software package (the R library NormData) that is used to exemplify how normative data can be derived in several case studies. Provides a solid introduction in regression-based normative methods without being overly technical; Comes with a comprehensive open-source software package to help efficiently derive regression-based normative data; Focuses strongly on the practical application of the methodology using various real-life case studies. .

Note: General introduction.-The R programming language -- Normative data accounting for a binary independent variable -- Assumption of the normal error regression model -- Normative data accounting for a non-binary qualitative independent variable -- Normative data accounting for a quantitative independent variable -- Normative data accounting for multiple qualitative and/or quantitative independent variables -- Quantifying uncertainty in regression-based norms.

Additional Edition: Print version: Van der Elst, Wim Regression-Based Normative Data for Psychological Assessment Cham : Springer,c2024 ISBN 9783031509506

Language: English

DOI: 10.1007/978-3-031-50951-3