Alberto Maydeu‐olivare
Biographic Data
| ID | 3600963 |
|---|---|
| NAME | Alberto Maydeu‐olivare |
| GIVEN NAMES | Alberto |
| FAMILY NAME | Maydeu‐olivare |
| SIGNATURE | OLIVARE A M |
| AFFILIATIONS | University of South Carolina |
| ORCID | 0000-0001-5790-392X |
| VERIFIED | Yes |
| TOTAL WORKS | 36 |
| TOTAL CITATIONS | 4 |
| AUTHOR COUNT | 36 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1999 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Opportunity to learn and widening gaps
Paraguay’s National System for the Evaluation of Educational Process (SNEPE) conducted census-based Grade-3 mathematics tests in 2015 and 2018, revealing a sharp overall drop in scores. We used these open datasets (n = 182,016) to investigate the source of this decline. We estimated two-level random-intercept models with standardised predictors, first separately for each cohort and then pooled across years with a random slope for year. In both co…
Using Mixture Factor Analysis to Counter Faking
Self-reports (SRs) of typical behavior are often the only existing feasible method to gather data on important drivers of human performance. In applications such as personnel selection, SRs are vul
Assessing Cutoff Values of SEM Fit Indices
Holding model misspecification constant, the behavior of fit indices depends on factors such as the number of variables being modeled (model size), and the average observed correlation (magnitude of factor loadings or measurement quality). We examine by simulation the interplay of these factors with sample size in CFA models. When a biased estimator of the fit index is used (CFI, TLI, or GFI), the behavior of the sample indices depends on sample …
Asymptotic is Better than Bollen-Stine Bootstrapping to Assess Model Fit
Previous research on bootstrapped p-values for the chi-square test of model fit has been limited to small models (around 10 variables), revealing that these p-values are accurate provided the sample size is not too small. For small sample sizes (N < 100), usual p-values, obtained using asymptotic methods, are more accurate. However, as the number of variables increases asymptotic p-values incorrectly suggest that models fit poorly. We investigate…
Evaluating SEM Model Fit with Small Degrees of Freedom
and to rely more on SRMR and CFI
Using the Standardized Root Mean Squared Residual (SRMR) to Assess Exact Fit in Structural Equation Models
We examine the accuracy of p values obtained using the asymptotic mean and variance (MV) correction to the distribution of the sample standardized root mean squared residual (SRMR) proposed by Maydeu-Olivares to assess the exact fit of SEM models. In a simulation study, we found that under normality, the MV-corrected SRMR statistic provides reasonably accurate Type I errors even in small samples and for large models, clearly outperforming the cur…
Assessing Fit in Ordinal Factor Analysis Models
This study introduces the statistical theory of using the Standardized Root Mean Squared Error (SRMR) to test close fit in ordinal factor analysis. We also compare the accuracy of confidence intervals (CIs) and tests of close fit based on the SRMR with those obtained based on the Root Mean Squared Error of Approximation (RMSEA). The current (biased) implementation for the RMSEA never rejects that a model fits closely when data are binary and almo…
Chi-square Difference Tests for Comparing Nested Models
The relative fit of two nested models can be evaluated using a chi-square difference statistic. We evaluate the performance of five robust chi-square difference statistics in the context of confirmatory factor analysis with non-normal continuous outcomes. The mean and variance corrected difference statistics performed adequately across all conditions investigated. In contrast, the mean corrected difference statistics required larger samples for t…
Fitting Ordinal Factor Analysis Models With Missing Data
This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a wide range of conditions, including number of response categories, sample size, percent of missingness,…
The Effect of Estimation Methods on SEM Fit Indices
We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). We considered different types and levels of misspecification in factor analysis models: miss…
Understanding the Model Size Effect on SEM Fit Indices
This study investigated the effect the number of observed variables ( p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, t…
Evaluating Factorial Invariance
In this study, we introduce an interval estimation approach based on Bayesian structural equation modeling to evaluate factorial invariance. For each tested parameter, the size of noninvariance with an uncertainty interval (i.e. highest density interval [HDI]) is assessed via Bayesian parameter estimation. By comparing the most credible values (i.e. 95% HDI) with a region of practical equivalence (ROPE), the Bayesian approach allows researchers t…
Assessing Fit in Structural Equation Models
We compare the accuracy of confidence intervals (CIs) and tests of close fit based on the root mean square error of approximation (RMSEA) with those based on the standardized root mean square residual (SRMR). Investigations used normal and nonnormal data with models ranging from p = 10 to 60 observed variables. CIs and tests of close fit based on the SRMR are generally accurate across all conditions (even at p = 60 with nonnormal data). In contra…
The Relationship Between the Standardized Root Mean Square Residual and Model Misspecification in Factor Analysis Models
We argue that the definition of close fitting models should embody the notion of substantially ignorable misspecifications (SIM). A SIM model is a misspecified model that might be selected, based on parsimony, over the true model should knowledge of the true model be available. Because in applications the true model (i.e., the data generating mechanism) is unknown, we investigate the relationship between the population standardized root mean squa…
Assessing the Size of Model Misfit in Structural Equation Models
When a statistically significant mean difference is found, the magnitude of the difference is judged qualitatively using an effect size such as Cohen’s d . In contrast, in a structural equation model (SEM), the result of the statistical test of model fit is often disregarded if significant, and inferences are drawn using “close” models retained based on point estimates of sample statistics (goodness-of-fit indices). However, when a SEM cannot be …
Maximum Likelihood Estimation of Structural Equation Models for Continuous Data
Classical accounts of maximum likelihood (ML) estimation of structural equation models for continuous outcomes involve normality assumptions: standard errors (SEs) are obtained using the expected information matrix and the goodness of fit of the model is tested using the likelihood ratio (LR) statistic. Satorra and Bentler (1994) introduced SEs and mean adjustments or mean and variance adjustments to the LR statistic (involving also the expected …
Confirmatory Factor Analytic Structure and Measurement Invariance of the Brief Multidimensional Students’ Life Satisfaction Scale (BMSLSS) in a Longitudinal Sample of Adolescents
Social problem solving in chronic pain
Despite several models of coping have been proposed in chronic pain, research is not integrative and has not yet identified a reliable set of beneficial coping strategies. We intend to offer a comprehensive view of coping using the social problem-solving model. Participants were 369 chronic pain patients (63.78% women; mean age 58.89 years; standard deviation = 15.12 years). Correlation analyses and the structural equation model for mental health…
Assessing Approximate Fit in Categorical Data Analysis
A family of Root Mean Square Error of Approximation (RMSEA) statistics is proposed for assessing the goodness of approximation in discrete multivariate analysis with applications to item response theory (IRT) models. The family includes RMSEAs to assess the approximation up to any level of association of the discrete variables. Two members of this family are RMSEA2, which uses up to bivariate moments, and the full information RMSEAn. The RMSEA2 i…
Identifying the Source of Misfit in Item Response Theory Models
When an item response theory model fails to fit adequately, the items for which the model provides a good fit and those for which it does not must be determined. To this end, we compare the performance of several fit statistics for item pairs with known asymptotic distributions under maximum likelihood estimation of the item parameters: (a) a mean and variance adjustment to bivariate Pearson's X(2), (b) a bivariate subtable analog to Reiser's (19…
Local Dependence Diagnostics in IRT Modeling of Binary Data
Local dependence (LD) for binary IRT models can be diagnosed using Chen and Thissen’s bivariate X 2 statistic and the score test statistics proposed by Glas and Suárez-Falcón, and Liu and Thissen. Alternatively, LD can be assessed using general purpose statistics such as bivariate residuals or Maydeu-Olivares and Joe’s M r statistic. The authors introduce a new general statistic for assessing the source of model misfit, R 2 , and compare its perf…
Environmental risk and protective factors of adolescents’ and youths’ mental health
Item Response Modeling of Forced-Choice Questionnaires
Multidimensional forced-choice formats can significantly reduce the impact of numerous response biases typically associated with rating scales. However, if scored with classical methodology, these questionnaires produce ipsative data, which lead to distorted scale relationships and make comparisons between individuals problematic. This research demonstrates how item response theory (IRT) modeling may be applied to overcome these problems. A multi…
Target Rotations and Assessing the Impact of Model Violations on the Parameters of Unidimensional Item Response Theory Models
Reise, Cook, and Moore proposed a “comparison modeling” approach to assess the distortion in item parameter estimates when a unidimensional item response theory (IRT) model is imposed on multidimensional data. Central to their approach is the comparison of item slope parameter estimates from a unidimensional IRT model (a restricted model), with the item slope parameter estimates from the general factor in an exploratory bifactor IRT model (the un…
Item Response Modeling of Paired Comparison and Ranking Data
The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally presented as scaling models, that is, stimuli-centered models, they can also be used as person-centered …
Limited information estimation and testing of Thurstonian models for preference data
Social problem solving in chronic pain
Despite several models of coping have been proposed in chronic pain, research is not integrative and has not yet identified a reliable set of beneficial coping strategies. We intend to offer a comprehensive view of coping using the social problem-solving model. Participants were 369 chronic pain patients (63.78% women; mean age 58.89 years; standard deviation = 15.12 years). Correlation analyses and the structural equation model for mental health…
Using Graphical Methods in Assessing Measurement Invariance in Inventory Data
Most measurements of psychological constructs are performed using inventories or tests in which it is assumed that the observed interdependencies among the item responses are accounted for by a set of unobserved variables representing the psychological constructs being measured. Using test scores, researchers often attempt to detect and describe differences among groups to draw inferences about actual differences in the psychological constructs m…
Limited information estimation and testing of Thurstonian models for preference data
Further Empirical Results on Parametric Versus Non-Parametric IRT Modeling of Likert-Type Personality Data
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in their data. To verify this conjecture, we compare the fit of these models to the Social Problem Solving …
Limited Information Goodness-of-fit Testing in Multidimensional Contingency Tables
We introduce a family of goodness-of-fit statistics for testing composite null hypotheses in multidimensional contingency tables. These statistics are quadratic forms in marginal residuals up to order r . They are asymptotically chi-square under the null hypothesis when parameters are estimated using any asymptotically normal consistent estimator. For a widely used item response model, when r is small and multidimensional tables are sparse, the p…
A Cautionary Note on Using G 2 (dif) to Assess Relative Model Fit in Categorical Data Analysis
The likelihood ratio test statistic G(2)(dif) is widely used for comparing the fit of nested models in categorical data analysis. In large samples, this statistic is distributed as a chi-square with degrees of freedom equal to the difference in degrees of freedom between the tested models, but only if the least restrictive model is correctly specified. Yet, this statistic is often used in applications without assessing the adequacy of the least r…
A Multidimensional Ideal Point Item Response Theory Model for Binary Data
We introduce a multidimensional item response theory (IRT) model for binary data based on a proximity response mechanism. Under the model, a respondent at the mode of the item response function (IRF) endorses the item with probability one. The mode of the IRF is the ideal point, or in the multidimensional case, an ideal hyperplane. The model yields closed form expressions for the cell probabilities. We estimate and test the goodness of fit of the…
Identification and Small Sample Estimation of Thurstone's Unrestricted Model for Paired Comparisons Data
The interpretation of a Thurstonian model for paired comparisons where the utilities' covariance matrix is unrestricted proved to be difficult due to the comparative nature of the data. We show that under a suitable constraint the utilities' correlation matrix can be estimated, yielding a readily interpretable solution. This set of identification constraints can recover any true utilities' covariance matrix, but it is not unique. Indeed, we show …
Modeling Subjective Health Outcomes
From the *Faculty of Psychology, University of Barcelona, Barcelona, Spain; and †Faculty of Management, McGill University, Montreal, Canada. Reprints: Alberto Maydeu-Olivares, PhD, Faculty of Psychology, University of Barcelona, P. Valle de Hebrón, 171, 08035 Barcelona, Spain. E-mail: [email protected]
The Sage Handbook of Quantitative Methods in Psychology
Factor Analysis with Ordinal Indicators
Factor analysis models with ordinal indicators are often estimated using a 3-stage procedure where the last stage involves obtaining parameter estimates by least squares from the sample polychoric correlations. A simulation study involving 324 conditions (1,000 replications per condition) was performed to compare the performance of diagonally weighted least squares (DWLS) and unweighted least squares (ULS) in the procedure's third stage. Overall,…
Estimation of IRT graded response models
The performance of parameter estimates and standard errors in estimating F. Samejima's graded response model was examined across 324 conditions. Full information maximum likelihood (FIML) was compared with a 3-stage estimator for categorical item factor analysis (CIFA) when the unweighted least squares method was used in CIFA's third stage. CIFA is much faster in estimating multidimensional models, particularly with correlated dimensions. Overall…
Item Response Modeling of Paired Comparison and Ranking Data
The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally presented as scaling models, that is, stimuli-centered models, they can also be used as person-centered …
Item Response Modeling of Forced-Choice Questionnaires
Multidimensional forced-choice formats can significantly reduce the impact of numerous response biases typically associated with rating scales. However, if scored with classical methodology, these questionnaires produce ipsative data, which lead to distorted scale relationships and make comparisons between individuals problematic. This research demonstrates how item response theory (IRT) modeling may be applied to overcome these problems. A multi…
Target Rotations and Assessing the Impact of Model Violations on the Parameters of Unidimensional Item Response Theory Models
Reise, Cook, and Moore proposed a “comparison modeling” approach to assess the distortion in item parameter estimates when a unidimensional item response theory (IRT) model is imposed on multidimensional data. Central to their approach is the comparison of item slope parameter estimates from a unidimensional IRT model (a restricted model), with the item slope parameter estimates from the general factor in an exploratory bifactor IRT model (the un…
Environmental risk and protective factors of adolescents’ and youths’ mental health
Local Dependence Diagnostics in IRT Modeling of Binary Data
Local dependence (LD) for binary IRT models can be diagnosed using Chen and Thissen’s bivariate X 2 statistic and the score test statistics proposed by Glas and Suárez-Falcón, and Liu and Thissen. Alternatively, LD can be assessed using general purpose statistics such as bivariate residuals or Maydeu-Olivares and Joe’s M r statistic. The authors introduce a new general statistic for assessing the source of model misfit, R 2 , and compare its perf…
Assessing Approximate Fit in Categorical Data Analysis
A family of Root Mean Square Error of Approximation (RMSEA) statistics is proposed for assessing the goodness of approximation in discrete multivariate analysis with applications to item response theory (IRT) models. The family includes RMSEAs to assess the approximation up to any level of association of the discrete variables. Two members of this family are RMSEA2, which uses up to bivariate moments, and the full information RMSEAn. The RMSEA2 i…
Identifying the Source of Misfit in Item Response Theory Models
When an item response theory model fails to fit adequately, the items for which the model provides a good fit and those for which it does not must be determined. To this end, we compare the performance of several fit statistics for item pairs with known asymptotic distributions under maximum likelihood estimation of the item parameters: (a) a mean and variance adjustment to bivariate Pearson's X(2), (b) a bivariate subtable analog to Reiser's (19…
Social problem solving in chronic pain
Despite several models of coping have been proposed in chronic pain, research is not integrative and has not yet identified a reliable set of beneficial coping strategies. We intend to offer a comprehensive view of coping using the social problem-solving model. Participants were 369 chronic pain patients (63.78% women; mean age 58.89 years; standard deviation = 15.12 years). Correlation analyses and the structural equation model for mental health…
Assessing the Size of Model Misfit in Structural Equation Models
When a statistically significant mean difference is found, the magnitude of the difference is judged qualitatively using an effect size such as Cohen’s d . In contrast, in a structural equation model (SEM), the result of the statistical test of model fit is often disregarded if significant, and inferences are drawn using “close” models retained based on point estimates of sample statistics (goodness-of-fit indices). However, when a SEM cannot be …
Maximum Likelihood Estimation of Structural Equation Models for Continuous Data
Classical accounts of maximum likelihood (ML) estimation of structural equation models for continuous outcomes involve normality assumptions: standard errors (SEs) are obtained using the expected information matrix and the goodness of fit of the model is tested using the likelihood ratio (LR) statistic. Satorra and Bentler (1994) introduced SEs and mean adjustments or mean and variance adjustments to the LR statistic (involving also the expected …
Confirmatory Factor Analytic Structure and Measurement Invariance of the Brief Multidimensional Students’ Life Satisfaction Scale (BMSLSS) in a Longitudinal Sample of Adolescents
Assessing Fit in Structural Equation Models
We compare the accuracy of confidence intervals (CIs) and tests of close fit based on the root mean square error of approximation (RMSEA) with those based on the standardized root mean square residual (SRMR). Investigations used normal and nonnormal data with models ranging from p = 10 to 60 observed variables. CIs and tests of close fit based on the SRMR are generally accurate across all conditions (even at p = 60 with nonnormal data). In contra…
The Relationship Between the Standardized Root Mean Square Residual and Model Misspecification in Factor Analysis Models
We argue that the definition of close fitting models should embody the notion of substantially ignorable misspecifications (SIM). A SIM model is a misspecified model that might be selected, based on parsimony, over the true model should knowledge of the true model be available. Because in applications the true model (i.e., the data generating mechanism) is unknown, we investigate the relationship between the population standardized root mean squa…
Understanding the Model Size Effect on SEM Fit Indices
This study investigated the effect the number of observed variables ( p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, t…
Mathematics (32 works) · Statistics (32 works) · Econometrics (24 works) · Psychometric Methodologies and Testing (24 works) · Advanced Statistical Modeling Techniques (16 works) · Structural equation modeling (16 works) · Computer Science (15 works) · Sample size determination (12 works) · Goodness of fit (11 works) · Psychology (11 works)