The Experts below are selected from a list of 141 Experts worldwide ranked by ideXlab platform
John T Farrar - One of the best experts on this subject based on the ideXlab platform.
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Development of and psychometric testing for the Brief Pain Inventory-Facial in patients with facial pain syndromes.
Journal of neurosurgery, 2010Co-Authors: John T Farrar, John Y. K. Lee, H. Isaac Chen, Christopher Urban, Anahita Hojat, Ephraim W. Church, Sharon X. XieAbstract:Object Outcomes in clinical trials on trigeminal pain therapies require instruments with demonstrated reliability and validity. The authors evaluated the Brief Pain Inventory (BPI) in its existing form plus an additional 7 facial-specific items in patients referred to a single neurosurgeon for a diagnosis of facial pain. The complete 18-item instrument is referred to as the BPI-Facial. Methods This study was a cross-sectional analysis of patients who completed the BPI-Facial. The diagnosis of classic versus atypical trigeminal neuralgia (TN) was made before analyzing the questionnaire results. A hypothesis-driven Factor analysis was used to determine the principal components of the questionnaire. Item reliability and questionnaire validity were tested for these specific constructs. Results Data from 156 patients were analyzed, including 114 patients (73%) with classic and 42 (27%) with atypical TN. Using orthomax Rotation Factor analysis, 3 Factors with an eigenvalue > 1.0 were identified—pain intensity, ...
Jane Galbraith - One of the best experts on this subject based on the ideXlab platform.
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analysis of multivariate social science data second edition
2008Co-Authors: David J. Bartholomew, Fiona Steele, Irini Moustaki, Jane GalbraithAbstract:Preface Setting the Scene Structure of the book Our limited use of mathematics Variables The geometry of multivariate analysis Use of examples Data inspection, transformations, and missing data Cluster Analysis Classification in social sciences Some methods of cluster analysis Graphical presentation of results Derivation of the distance matrix Example on English dialects Comparisons Clustering variables Further examples and suggestions for further work Multidimensional Scaling Introduction Examples Classical, ordinal, and metrical multidimensional scaling Comments on computational procedures Assessing fit and choosing the number of dimensions A worked example: dimensions of color vision Further examples and suggestions for further work Correspondence Analysis Aims of correspondence analysis Carrying out a correspondence analysis: a simple numerical example Carrying out a correspondence analysis: the general method The biplot Interpretation of dimensions Choosing the number of dimensions Example: confidence in purchasing from European Community countries Correspondence analysis of multiway tables Further examples and suggestions for further work Principal Components Analysis Introduction Some potential applications Illustration of PCA for two variables An outline of PCA Examples Component scores The link between PCA and multidimensional scaling and between PCA and correspondence analysis Using principal component scores to replace the original variables Further examples and suggestions for further work NEW! Regression Analysis Basic ideas Simple linear regression A probability model for simple linear regression Inference for the simple linear regression model Checking the assumptions Multiple regression Examples of multiple regression Estimation and inference about the parameters Interpretation of the regression coefficients Selection of regressor variables Transformations and interactions Logistic regression Path analysis Further examples and suggestions for further work Factor Analysis Introduction to latent variable models The linear single-Factor model The general linear Factor model Interpretation Adequacy of the model and choice of the number of Factors Rotation Factor scores A worked example: the test anxiety inventory How Rotation helps interpretation A comparison of Factor analysis and principal components analysis Further examples and suggestions for further work Software Factor Analysis for Binary Data Latent trait models Why is the Factor analysis model for metrical variables invalid for binary responses? Factor model for binary data using the item response theory approach Goodness-of-fit Factor scores Rotation Underlying variable approach Example: sexual attitudes Further examples and suggestions for further work Software Factor Analysis for Ordered Categorical Variables The practical background Two approaches to modeling ordered categorical data Item response function approach Examples The underlying variable approach Unordered and partially ordered observed variables Further examples and suggestions for further work Software Latent Class Analysis for Binary Data Introduction The latent class model for binary data Example: attitude to science and technology data How can we distinguish the latent class model from the latent trait model? Latent class analysis, cluster analysis, and latent profile analysis Further examples and suggestions for further work Software NEW! Confirmatory Factor Analysis and Structural Equation Models Introduction Path diagram Measurement models Adequacy of the model Introduction to structural equation models with latent variables The linear structural equation model A worked example Extensions Further examples Software NEW! Multilevel Modeling Introduction Some potential applications Comparing groups using multilevel modeling Random intercept model Random slope model Contextual effects Multilevel multivariate regression Multilevel Factor analysis Further examples and suggestions for further work Further topics Estimation procedures and software References Index Further reading sections appear at the end of each chapter.
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analysis of multivariate social science data
2008Co-Authors: David J. Bartholomew, Fiona Steele, Irini Moustaki, Jane GalbraithAbstract:Preface Setting the Scene Structure of the book Our limited use of mathematics Variables The geometry of multivariate analysis Use of examples Data inspection, transformations, and missing data Cluster Analysis Classification in social sciences Some methods of cluster analysis Graphical presentation of results Derivation of the distance matrix Example on English dialects Comparisons Clustering variables Further examples and suggestions for further work Multidimensional Scaling Introduction Examples Classical, ordinal, and metrical multidimensional scaling Comments on computational procedures Assessing fit and choosing the number of dimensions A worked example: dimensions of color vision Further examples and suggestions for further work Correspondence Analysis Aims of correspondence analysis Carrying out a correspondence analysis: a simple numerical example Carrying out a correspondence analysis: the general method The biplot Interpretation of dimensions Choosing the number of dimensions Example: confidence in purchasing from European Community countries Correspondence analysis of multiway tables Further examples and suggestions for further work Principal Components Analysis Introduction Some potential applications Illustration of PCA for two variables An outline of PCA Examples Component scores The link between PCA and multidimensional scaling and between PCA and correspondence analysis Using principal component scores to replace the original variables Further examples and suggestions for further work NEW! Regression Analysis Basic ideas Simple linear regression A probability model for simple linear regression Inference for the simple linear regression model Checking the assumptions Multiple regression Examples of multiple regression Estimation and inference about the parameters Interpretation of the regression coefficients Selection of regressor variables Transformations and interactions Logistic regression Path analysis Further examples and suggestions for further work Factor Analysis Introduction to latent variable models The linear single-Factor model The general linear Factor model Interpretation Adequacy of the model and choice of the number of Factors Rotation Factor scores A worked example: the test anxiety inventory How Rotation helps interpretation A comparison of Factor analysis and principal components analysis Further examples and suggestions for further work Software Factor Analysis for Binary Data Latent trait models Why is the Factor analysis model for metrical variables invalid for binary responses? Factor model for binary data using the item response theory approach Goodness-of-fit Factor scores Rotation Underlying variable approach Example: sexual attitudes Further examples and suggestions for further work Software Factor Analysis for Ordered Categorical Variables The practical background Two approaches to modeling ordered categorical data Item response function approach Examples The underlying variable approach Unordered and partially ordered observed variables Further examples and suggestions for further work Software Latent Class Analysis for Binary Data Introduction The latent class model for binary data Example: attitude to science and technology data How can we distinguish the latent class model from the latent trait model? Latent class analysis, cluster analysis, and latent profile analysis Further examples and suggestions for further work Software NEW! Confirmatory Factor Analysis and Structural Equation Models Introduction Path diagram Measurement models Adequacy of the model Introduction to structural equation models with latent variables The linear structural equation model A worked example Extensions Further examples Software NEW! Multilevel Modeling Introduction Some potential applications Comparing groups using multilevel modeling Random intercept model Random slope model Contextual effects Multilevel multivariate regression Multilevel Factor analysis Further examples and suggestions for further work Further topics Estimation procedures and software References Index Further reading sections appear at the end of each chapter.
John Y. K. Lee - One of the best experts on this subject based on the ideXlab platform.
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Development of and psychometric testing for the Brief Pain Inventory-Facial in patients with facial pain syndromes.
Journal of neurosurgery, 2010Co-Authors: John T Farrar, John Y. K. Lee, H. Isaac Chen, Christopher Urban, Anahita Hojat, Ephraim W. Church, Sharon X. XieAbstract:Object Outcomes in clinical trials on trigeminal pain therapies require instruments with demonstrated reliability and validity. The authors evaluated the Brief Pain Inventory (BPI) in its existing form plus an additional 7 facial-specific items in patients referred to a single neurosurgeon for a diagnosis of facial pain. The complete 18-item instrument is referred to as the BPI-Facial. Methods This study was a cross-sectional analysis of patients who completed the BPI-Facial. The diagnosis of classic versus atypical trigeminal neuralgia (TN) was made before analyzing the questionnaire results. A hypothesis-driven Factor analysis was used to determine the principal components of the questionnaire. Item reliability and questionnaire validity were tested for these specific constructs. Results Data from 156 patients were analyzed, including 114 patients (73%) with classic and 42 (27%) with atypical TN. Using orthomax Rotation Factor analysis, 3 Factors with an eigenvalue > 1.0 were identified—pain intensity, ...
F Chin - One of the best experts on this subject based on the ideXlab platform.
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peak to average power reduction using partial transmit sequences a suboptimal approach based on dual layered phase sequencing
IEEE Transactions on Broadcasting, 2003Co-Authors: A S Madhukumar, F ChinAbstract:A high peak-to-average power ratio (PAPR) is a major shortcoming in multicarrier systems, as it causes nonlinearity in the transmitter, degrading the performance of the system significantly. Partial transmit sequences (PTS) is one of the best methods in reducing PAPR, in which the information-bearing subcarriers are divided into M disjoint subblocks, each controlled by a phase Rotation Factor which brings PAPR down. Though PAPR reduction by PTS is more effective with more subblocks, there is a corresponding exponential increase in complexity. In this paper, a novel implementation of PTS is presented, in which a dual-layered approach is employed to reduce the complexity.
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A novel two-layered suboptimal approach to partial transmit sequences
The 57th IEEE Semiannual Vehicular Technology Conference 2003. VTC 2003-Spring., 2003Co-Authors: Wong Sai Ho, A S Madhukumar, F ChinAbstract:A high peak-to-average power ratio (PAPR) is a major shortcoming in multicarrier systems, as it causes non-linearity in the transmitter, degrading the performance of the system significantly. Partial transmit sequences (PTS) is one of the best methods in reducing PAPR, in which the information-bearing subcarriers are divided into M disjoint subblocks, each controlled by a phase Rotation Factor which brings PAPR down. Though PAPR reduction by PTS is more effective with more subblocks, there is a corresponding exponential increase in complexity. In this paper, a novel implementation of PTS is presented which a dual-layered approached is employed to reduce complexity.
Emre Senol-durak - One of the best experts on this subject based on the ideXlab platform.
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The Development and Psychometric Properties of the Turkish Version of the Stress Appraisal Measure
European Journal of Psychological Assessment, 2013Co-Authors: Mithat Durak, Emre Senol-durakAbstract:This study examines the psychometric properties of the Stress Appraisal Measure (SAM) across three separate and independent samples. A calibration study was conducted with a sample of university students (n = 461), resulting in a 5-Factor model based on a parallel analysis and a principal axis Factor analysis with direct oblimin Rotation. Factor invariance across males and females, convergent validity, and discriminant validity were tested using a second sample of university students (n = 751) and adults (n = 548). The 5-Factor model obtained in the calibration study was replicated employing a confirmatory Factor analysis of the data from the validation study. Factor invariance across males and females was confirmed. In addition to satisFactory internal consistencies, the correlation of the SAM scales with a conceptually related measure (state anxiety) and a conceptually unrelated measure (social desirability) support the convergent and discriminant validity of the SAM.
