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"Factor analysis"
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Exploratory factor analysis
\"Understanding factor analysis is key to understanding much published research in the social and behavioral sciences. This volume provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other issues in EFA such determining the appropriate sample size and handling missing data, and it offers brief introductions to EFA with structural equation modeling and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website\"-- Provided by publisher.
Book
Development and Validation of the Camouflaging Autistic Traits Questionnaire (CAT-Q)
There currently exist no self-report measures of social camouflaging behaviours (strategies used to compensate for or mask autistic characteristics during social interactions). The Camouflaging Autistic Traits Questionnaire (CAT-Q) was developed from autistic adults’ experiences of camouflaging, and was administered online to 354 autistic and 478 non-autistic adults. Exploratory factor analysis suggested three factors, comprising of 25 items in total. Good model fit was demonstrated through confirmatory factor analysis, with measurement invariance analyses demonstrating equivalent factor structures across gender and diagnostic group. Internal consistency (α = 0.94) and preliminary test–retest reliability (r = 0.77) were acceptable. Convergent validity was demonstrated through comparison with measures of autistic traits, wellbeing, anxiety, and depression. The present study provides robust psychometric support for the CAT-Q.
Journal Article
Program FACTOR at 10: Origins, development and future directions
We aim to provide a conceptual view of the origins, development and future directions of FACTOR, a popular free program for fitting the factor analysis (FA) model.
The study is organized into three parts. In the first part we discuss FACTOR in its initial period (2006-2012) as a traditional FA program with many new and cutting-edge features. The second part discusses the present period (2013-2016) in which FACTOR has developed into a comprehensive program embedded in the framework of structural equation modelling and item response theory. The third part discusses expected future developments.
at present FACTOR has attained a degree of technical development comparable to commercial software, and offers options not available elsewhere.
We discuss several shortcomings as well as points that require changes or improvements. We also discuss the functioning of FACTOR within its community of users.
Journal Article
Prehistoric adaptation in the American Southwest
This resource is about post-Pleistocene adaptive change among the aboriginal cultures of the mountains and deserts of Arizona and New Mexico.
Book
Factor VIII–Mimetic Function of Humanized Bispecific Antibody in Hemophilia A
Emicizumab is a humanized bispecific antibody that mimics the cofactor function of factor VIII. In a dose-escalation study in Japanese persons with hemophilia A, including those with factor VIII inhibitors, emicizumab markedly reduced the number of bleeding episodes.
Hemophilia A is a serious bleeding disorder caused by a deficiency of clotting factor VIII. Approximately 50% of patients have severe hemophilia A,
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defined as less than 1% residual factor VIII activity (<1 IU per deciliter).
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Such patients have severe bleeding from early childhood, and without appropriate treatment, recurrent bleeding into joints can lead to irreversible hemoarthropathy.
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,
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Standard treatment for hemophilia A includes regular prophylaxis and episodic treatment with recombinant or plasma-derived factor VIII. The goals of prophylaxis with factor VIII are to increase factor VIII activity to at least a moderate level (1 to 5 IU per deciliter) . . .
Journal Article
Meta-analysis of structural evidence for the Hierarchical Taxonomy of Psychopathology (HiTOP) model
The Hierarchical Taxonomy of Psychopathology (HiTOP) is a classification system that seeks to organize psychopathology using quantitative evidence - yet the current model was established by narrative review. This meta-analysis provides a quantitative synthesis of literature on transdiagnostic dimensions of psychopathology to evaluate the validity of the HiTOP framework.
Published studies estimating factor-analytic models from
(
diagnoses were screened. A total of 120,596 participants from 35 studies assessing 23
diagnoses were included in the meta-analytic models. Data were pooled into a meta-analytic correlation matrix using a random effects model. Exploratory factor analyses were conducted using the pooled correlation matrix. A hierarchical structure was estimated by extracting one to five factors representing levels of the HiTOP framework, then calculating congruence coefficients between factors at sequential levels.
Five transdiagnostic dimensions fit the
diagnoses well (comparative fit index = 0.92, root mean square error of approximation = 0.07, and standardized root-mean-square residual = 0.03). Most diagnoses had factor loadings >|0.30| on the expected factors, and congruence coefficients between factors indicated a hierarchical structure consistent with the HiTOP framework.
A model closely resembling the HiTOP framework fit the data well and placement of
diagnoses within transdiagnostic dimensions were largely confirmed, supporting it as valid structure for conceptualizing and organizing psychopathology. Results also suggest transdiagnostic research should (1) use traits, narrow symptoms, and dimensional measures of psychopathology instead of
diagnoses, (2) assess a broader array of constructs, and (3) increase focus on understudied pathologies.
Journal Article
Best Practices for Your Exploratory Factor Analysis: A Factor Tutorial
ABSTRACT Context: exploratory factor analysis (EFA) is one of the statistical methods most widely used in administration; however, its current practice coexists with rules of thumb and heuristics given half a century ago. Objective: the purpose of this article is to present the best practices and recent recommendations for a typical EFA in administration through a practical solution accessible to researchers. Methods: in this sense, in addition to discussing current practices versus recommended practices, a tutorial with real data on Factor is illustrated. The Factor software is still little known in the administration area, but is freeware, easy-to-use (point and click), and powerful. The step-by-step tutorial illustrated in the article, in addition to the discussions raised and an additional example, is also available in the format of tutorial videos. Conclusion: through the proposed didactic methodology (article-tutorial + video-tutorial), we encourage researchers/methodologists who have mastered a particular technique to do the same. Specifically about EFA, we hope that the presentation of the Factor software, as a first solution, can transcend the current outdated rules of thumb and heuristics, by making best practices accessible to administration researchers. RESUMO Contexto: a análise fatorial exploratória (AFE) é um dos métodos estatísticos mais utilizados em administração. No entanto, sua prática corrente coexiste com regras de bolso e heurísticas proferidas há meio século. Objetivo: o propósito deste artigo é apresentar as melhores práticas e recomendações recentes para uma AFE típica em administração através de uma solução prática acessível aos pesquisadores. Métodos: nesse sentido, além de serem discutidas as práticas correntes versus as práticas recomendadas, ilustra-se um tutorial com dados reais no Factor, um software ainda pouco conhecido na área de administração, porém freeware, fácil de usar (point and click) e poderoso. O passo a passo ilustrado no artigo, além das discussões levantadas e de um exemplo adicional, também é disponibilizado no formato de vídeos tutoriais. Conclusão: através da metodologia didática proposta (artigo-tutorial + vídeo-tutorial), incentivamos os pesquisadores/metodologistas que dominam alguma técnica particular a fazerem o mesmo. Especificamente sobre a AFE, esperamos que a apresentação do software Factor, como uma primeira solução, possa transcender as regras de bolso e heurísticas correntes ultrapassadas, ao tornar acessíveis as melhores práticas para os pesquisadores da administração.
Journal Article
How Do Employees Perceive Corporate Responsibility? Development and Validation of a Multidimensional Corporate Stakeholder Responsibility Scale
Recent research on the microfoundations of corporate social responsibility (CSR) has highlighted the need for improved measures to evaluate how stakeholders perceive and subsequently react to CSR initiatives. Drawing on stakeholder theory and data from five samples of employees (N = 3,772), the authors develop and validate a new measure of corporate stakeholder responsibility (CStR), which refers to an organization's context-specific actions and policies designed to enhance the welfare of various stakeholder groups by accounting for the triple bottom line of economic, social, and environmental performance; it is conceptualized as a superordinate, multidimensional construct. Results from exploratory factor analyses, first- and second-order confirmatory factor analyses, and structural equation modeling provide strong evidence of the convergent, discriminant, incremental, and criterion-related validities of the proposed CStR scale. Two-wave longitudinal studies further extend prior theory by demonstrating that the higher-order CStR construct relates positively and directly to organizational pride and perceived organizational support, as well as positively and indirectly to organizational identification, job satisfaction, and affective commitment, beyond the contribution of overall organizational justice, ethical climate, and prior measures of perceived CSR.
Paper
OpenMx 2.0: Extended Structural Equation and Statistical Modeling
The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.
Journal Article