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Mark L Davison - One of the best experts on this subject based on the ideXlab platform.
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Meta-analytic criterion Profile Analysis.
Psychological methods, 2020Co-Authors: Brenton M. Wiernik, Mark L Davison, Michael P. Wilmot, Deniz S. OnesAbstract:Intraindividual patterns or configurations are intuitive explanations for phenomena, and popular in both lay and research contexts. Criterion Profile Analysis (CPA; Davison & Davenport, 2002) is a well-established, regression-based pattern matching procedure that identifies a pattern of predictors that optimally relate to a criterion of interest and quantifies the strength of that association. Existing CPA methods require individual-level data, limiting opportunities for reAnalysis of published work, including research synthesis via meta-Analysis and associated corrections for psychometric artifacts. In this article, we develop methods for meta-analytic criterion Profile Analysis (MACPA), including new methods for estimating cross-validity and fungibility of criterion patterns. We also review key methodological considerations for applying MACPA, including homogeneity of studies in meta-analyses, corrections for statistical artifacts, and second-order sampling error. Finally, we present example applications of MACPA to published meta-analyses from organizational, educational, personality, and clinical psychological literatures. R code implementing these methods is provided in the configural package, available at https://cran.r-project.org/package=configural and at https://doi.org/10.17605/osf.io/aqmpc. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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confirmatory factor Analysis and Profile Analysis via multidimensional scaling
Multivariate Behavioral Research, 2007Co-Authors: Mark L Davison, Claudine L. FrisbyAbstract:This paper describes the Confirmatory Factor Analysis (CFA) parameterization of the Profile Analysis via Multidimensional Scaling (PAMS) model to demonstrate validation of Profile pattern hypotheses derived from multidimensional scaling (MDS). Profile Analysis via Multidimensional Scaling (PAMS) is an exploratory method for identifying major Profiles in a multi-subtest test battery. Major Profile patterns are represented as dimensions extracted from a MDS Analysis. PAMS represents an individual observed score as a linear combination of dimensions where the dimensions are the most typical Profile patterns present in a population. While the PAMS approach was initially developed for exploratory purposes, its results can later be confirmed in a different sample by CFA. Since CFA is often used to verify results from an exploratory factor Analysis, the present paper makes the connection between a factor model and the PAMS model, and then illustrates CFA with a simulated example (that was generated by the PAMS m...
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Estimating Cognitive Profiles Using Profile Analysis via Multidimensional Scaling (PAMS)
Multivariate behavioral research, 2004Co-Authors: Se-kang Kim, Craig L. Frisby, Mark L DavisonAbstract:Two of the most popular methods of Profile Analysis, cluster Analysis and modal Profile Analysis, have limitations. First, neither technique is adequate when the sample size is large. Second, neither method will necessarily provide Profile information in terms of both level and pattern. A new method of Profile Analysis, called Profile Analysis via Multidimensional Scaling (PAMS; Davison, 1996), is introduced to meet the challenge. PAMS extends the use of simple multidimensional scaling methods to identify latent Profiles in a multi-test battery. Application of PAMS to Profile Analysis is described. The PAMS model is then used to identify latent Profiles from a subgroup (N = 357) within the sample of the Woodcock-Johnson Psychoeducational Battery-Revised (WJ-R; McGrew, Werder, & Woodcock, 1991; Woodcock & Johnson, 1989), followed by a discussion of procedures for interpreting participants' observed score Profiles from the latent PAMS Profiles. Finally, advantages and limitations of the PAMS technique are d...
Jenő Gubicza - One of the best experts on this subject based on the ideXlab platform.
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Practical Applications of X-Ray Line Profile Analysis
Materials Science and Engineering, 2017Co-Authors: Jenő GubiczaAbstract:In the previous chapters, the theory and the main methods of diffraction peak Profile Analysis were presented. Additionally, the specialties in the measurement and the evaluation of line Profiles in the cases of thin films and single crystals were discussed. In this chapter, some practical considerations are given in order to facilitate the evaluation of peak Profiles and the interpretation of the results obtained by this method. For instance, the procedures for instrumental correction are overviewed. Additionally, how the prevailing dislocation slip systems and twin boundary types in hexagonal polycrystals can be determined from line Profiles is shown. Besides the dislocation density, the vacancy concentration can also be obtained by the combination of electrical resistivity, calorimetric, and line Profile measurements. The crystallite size and the twin boundary frequency determined by X-ray peak Profile Analysis are compared with the values obtained by the direct method of transmission electron microscopy. Furthermore, the limits of line Profile Analysis in the determination of crystallite size and defect densities are given. Finally, short overviews on the results obtained by peak Profile Analysis for metals, ceramics, and polymers are presented.
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X-Ray Line Profile Analysis in Materials Science
2014Co-Authors: Jenő GubiczaAbstract:Crystalline materials are never ideal, perfectly ordered single crystals because they contain lattice defects such as grain boundaries, planar faults, dislocations, disclination, vacancies, and intersticial atoms. The deviation from the perfect single crystalline state is referred to as microstructure of materials. The microstructure is important in determining the physical, chemical, and mechanical properties of materials. X-ray line Profile Analysis (XLPA) is an effective and non-destructive method for the characterization of the microstructure in crystalline materials. This book presents both the theoretical background and practical implementation of x-ray line Profile Analysis.
Sunjun - One of the best experts on this subject based on the ideXlab platform.
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Skew detection using wavelet decomposition and projection Profile Analysis
Pattern Recognition Letters, 2007Co-Authors: Lishutao, Shenqinghua, SunjunAbstract:In this paper, a novel document skew detection algorithm based on wavelet decompositions and projection Profile Analysis is proposed. First, the skewed document images are decomposed by the wavelet...
Claudine L. Frisby - One of the best experts on this subject based on the ideXlab platform.
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confirmatory factor Analysis and Profile Analysis via multidimensional scaling
Multivariate Behavioral Research, 2007Co-Authors: Mark L Davison, Claudine L. FrisbyAbstract:This paper describes the Confirmatory Factor Analysis (CFA) parameterization of the Profile Analysis via Multidimensional Scaling (PAMS) model to demonstrate validation of Profile pattern hypotheses derived from multidimensional scaling (MDS). Profile Analysis via Multidimensional Scaling (PAMS) is an exploratory method for identifying major Profiles in a multi-subtest test battery. Major Profile patterns are represented as dimensions extracted from a MDS Analysis. PAMS represents an individual observed score as a linear combination of dimensions where the dimensions are the most typical Profile patterns present in a population. While the PAMS approach was initially developed for exploratory purposes, its results can later be confirmed in a different sample by CFA. Since CFA is often used to verify results from an exploratory factor Analysis, the present paper makes the connection between a factor model and the PAMS model, and then illustrates CFA with a simulated example (that was generated by the PAMS m...
D. K. Srivastava - One of the best experts on this subject based on the ideXlab platform.
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On the suitability of peak Profile Analysis models for estimating dislocation density
Materials Science and Engineering: A, 2017Co-Authors: Malvika Karri, J.b. Singh, K.v. Manikrishna, Bhupendra K. Kumawat, Naveen Kumar, D. K. SrivastavaAbstract:Abstract Work-hardening in crystalline materials, i.e., increase in their flow stress during plastic deformation, follow a square root dependence on density of dislocations present. Dislocation density, defined as the length of dislocations per unit volume (m/m3), is thus an important parameter for simulating flow properties of a material. A number of techniques based on line Profile Analysis of x-ray diffraction peaks have evolved over the years for estimating dislocation density in addition to direct measurements on the basis of transmission electron microscopy. However, all these techniques suffer from certain limitations and the effectiveness of a specific technique is difficult to establish as different researchers have used different techniques to estimate dislocation density on individual samples. In the present work, suitability of x-ray line Profile Analysis techniques, based on moment Analysis of tail portions of individual diffraction peaks (termed as variance methods), for estimating dislocation densities has been verified on the basis of their estimation in commercially pure aluminium as well as pure copper samples deformed for varying degrees of deformation. The accuracy of estimated values of dislocation density has been confirmed: (i) by comparing them with those estimated by transmission electron microscopy Analysis; and, (ii) on the basis of variations in their values that are expected to follow the observed work hardening behaviour. This study has established that line Profile Analysis of x-ray diffraction peaks obtained using a laboratory x-ray diffractometer can make a reliable estimate of dislocation density in metallic samples provided they contained a significant amount of deformation.
