Author
John J. McArdle
Other affiliations: University of Virginia, Max Planck Society, University of Hawaii at Manoa ...read more
Bio: John J. McArdle is an academic researcher from University of Southern California. The author has contributed to research in topics: Structural equation modeling & Cognition. The author has an hindex of 67, co-authored 200 publications receiving 16342 citations. Previous affiliations of John J. McArdle include University of Virginia & Max Planck Society.
Papers published on a yearly basis
Papers
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TL;DR: This review considers a common question in data analysis: What is the most useful way to analyze longitudinal repeated measures data and presents several classic SEMs based on the inclusion of invariant common factors and why these are so important.
Abstract: This review considers a common question in data analysis: What is the most useful way to analyze longitudinal repeated measures data? We discuss some contemporary forms of structural equation models (SEMs) based on the inclusion of latent variables. The specific goals of this review are to clarify basic SEM definitions, consider relations to classical models, focus on testable features of the new models, and provide recent references to more complete presentations. A broader goal is to illustrate why so many researchers are enthusiastic about the SEM approach to data analysis. We first outline some classic problems in longitudinal data analysis, consider definitions of differences and changes, and raise issues about measurement errors. We then present several classic SEMs based on the inclusion of invariant common factors and explain why these are so important. This leads to newer SEMs based on latent change scores, and we explain why these are useful.
1,509 citations
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TL;DR: Conceptual principles of multivariate methods of data analysis are presented in terms of substantive issues of importance for the science of the psychology of aging.
Abstract: We describe mathematical and statistical models for factor invariance. We demonstrate that factor invariance is a condition of measurement invariance. In any study of change (as over age) measurement invariance is necessary for valid inference and interpretation. Two important forms of factorial invariance are distinguished: "configural" and "metric". Tests for factorial invariance and the range of tests from strong to weak are illustrated with multiple group factor and structural equation modeling analyses (with programs such as LISREL, COSAN, and RAM). The tests are for models of the organization and age changes of intellectual abilities. The models are derived from current theory of fluid (Gf) and crystallized (Gc) abilities. The models are made manifest with measurements of the WAIS-R in the standardization sample. Although this is a methodological paper, the key issues and major principles and conclusions are presented in basic English, devoid of technical details and obscure notation. Conceptual principles of multivariate methods of data analysis are presented in terms of substantive issues of importance for the science of the psychology of aging.
1,465 citations
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TL;DR: A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM) that allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions.
Abstract: This report uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM). When merged with repeated-measures data, this technique permits the estimation of parameters representing both individual and group dynamics. The statistical basis of this model allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions. Aspects of these latent growth models are illustrated with a set of longitudinal WISC data from young children and by using the LISREL V computer program.
867 citations
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TL;DR: Prevalence rates from what is believed to be the first population-based study of cognitive impairment without dementia to include individuals from all regions of the country are reported, as well as rates of progression from cognitive impairmentWithout dementia to dementia and death.
Abstract: Results: In 2002, an estimated 5.4 million people (22.2%) in the United States age 71 years or older had cognitive impairment without dementia. Prominent subtypes included prodromal Alzheimer disease (8.2%) and cerebrovascular disease (5.7%). Among participants who completed follow-up assessments, 11.7% with cognitive impairment without dementia progressed to dementia annually, whereas those with subtypes of prodromal Alzheimer disease and stroke progressed at annual rates of 17% to 20%. The annual death rate was 8% among those with cognitive impairment without dementia and almost 15% among those with cognitive impairment due to medical conditions. Limitations: Only 56% of the nondeceased target sample completed the initial assessment. Population sampling weights were derived to adjust for at least some of the potential bias due to nonresponse and attrition. Conclusion: Cognitive impairment without dementia is more prevalent in the United States than dementia, and its subtypes vary in prevalence and outcomes.
795 citations
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01 Jan 1988TL;DR: The authors examined multivariate psychological change data using the 20th century developments of latent variable structural equation modeling, and used this dynamic equation, but here they also used this simple dynamic equation to examine multivariate psychology change data.
Abstract: The term “dynamic” is broadly defined as a pattern of change. Many scientists have searched for dynamics by calculating df/dt: the ratio of changes or differences d in a function f relative to changes in time t.This simple dynamic equation was used in the 16th and 17th century motion experiments of Galileo, in the 17th and 18th century gravitation experiments of Newton, and in the 19th century experiments of many physicists and chemists (see Morris, 1985). I also use this dynamic equation, but here I examine multivariate psychological change data using the 20th century developments of latent variable structural equation modeling.
643 citations
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TL;DR: In this paper, the authors examined the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model and found that the change was independent of both model complexity and sample size.
Abstract: Measurement invariance is usually tested using Multigroup Confirmatory Factor Analysis, which examines the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model. Although many studies have examined the properties of GFI as indicators of overall model fit for single-group data, there have been none to date that examine how GFIs change when between-group constraints are added to a measurement model. The lack of a consensus about what constitutes significant GFI differences places limits on measurement invariance testing. We examine 20 GFIs based on the minimum fit function. A simulation under the two-group situation was used to examine changes in the GFIs (ΔGFIs) when invariance constraints were added. Based on the results, we recommend using Δcomparative fit index, ΔGamma hat, and ΔMcDonald's Noncentrality Index to evaluate measurement invariance. These three ΔGFIs are independent of both model complexity and sample size, and are not correlated with the o...
10,597 citations
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TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Abstract: Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
10,568 citations
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
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TL;DR: In this article, the sensitivity of goodness of fit indexes to lack of measurement invariance at three commonly tested levels: factor loadings, intercepts, and residual variances was examined, and the most intriguing finding was that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items.
Abstract: Two Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or residual variances. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) appear to be equally sensitive to all 3 types of lack of invariance. The most intriguing finding is that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items: when the pattern of lack of invariance is uniform, the relation is nonmonotonic, whereas when the pattern of lack of invariance is mixed, the relation is monotonic. Unequal sample sizes affect changes across all 3 levels of invariance: Changes are bigger when sample sizes are equal rather than when they are unequal. Cutoff points for t...
6,202 citations
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TL;DR: The establishment of measurement invariance across groups is a logical prerequisite to conducting substantive cross-group comparisons (e.g., tests of group mean differences, invariance of structura, etc.).
Abstract: The establishment of measurement invariance across groups is a logical prerequisite to conducting substantive cross-group comparisons (e.g., tests of group mean differences, invariance of structura...
6,086 citations