1. Главная
  2. Ресурсы
  3. Библиотечный поиск
An EmpiricalEvaluation ofAlternative Methodsof Estimation forConfirmatory FactorAnalysis With OrdinalData. David B. FloraPatrick J. Curran2004 год

An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.
статья из журнала

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items).A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS.Importantly, this approach assumes that a continuous, normal latent process determines each observed variable.The extent to which violations of this assumption undermine CFA estimation is not well-known.In this article, the authors empirically study this issue using a computer simulation study.The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality.Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples.Finally, robust WLS performed well across all conditions.Variables characterized by an ordinal level of measurement are common in many empirical investigations within the social and behavioral sciences.A typical situation involves the development or refinement of a psychometric test or survey in which a set of ordinally scaled items (e.g., 0 = strongly disagree, 1 = neither agree nor disagree, 2 = strongly agree) is used to assess one or more psychological constructs.Although the individual items are designed to measure a theoretically continuous construct, the observed responses are discrete realizations of a small number of categories.Statistical methods that assume continuous distributions are often applied to observed measures that are ordinally scaled.In circumstances such as these, there is the potential for a critical mismatch between the assumptions underlying the statistical model and the empirical characteristics of the data to be analyzed.This mismatch in turn undermines confidence in the validity of the conclusions that are drawn from empirical data with respect to a theoretical model of interest (e.g., Shadish, Cook, & Campbell, 2002).This problem often arises in confirmatory factor analysis (CFA), a statistical modeling method commonly used in many social science disciplines.CFA is a member of the more general family of structural equation models (SEMs) and provides a powerful method for testing a variety of hypotheses about a set of measured variables.By far the most common method of estimation within CFA is maximum likelihood (ML), a technique which assumes that the observed variables are continuous and normally distributed (e.g., Bollen, 1989, pp.131-134).These assumptions are not met when the observed data are discrete (as occurs when using ordinal scales), thus significant problems can result when fitting CFA models
Год издания: 2004
Авторы: David B. Flora, Patrick J. Curran
Издательство: American Psychological Association
Источник: Psychological Methods
Ключевые слова: Sensory Analysis and Statistical Methods, Advanced Statistical Methods and Models, Advanced Statistical Modeling Techniques
Другие ссылки: Psychological Methods (HTML)
Europe PMC (PubMed Central) (PDF)
Europe PMC (PubMed Central) (HTML)
PubMed Central (HTML)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (PDF)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (HTML)
PubMed (HTML)