Luís Eduardo Garrido
Biographic Data
| ID | 6529969 |
|---|---|
| NAME | Luís Eduardo Garrido |
| GIVEN NAMES | Luís Eduardo |
| FAMILY NAME | Garrido |
| SIGNATURE | GARRIDO L E |
| AFFILIATIONS | Pontificia Universidad Católica Madre y Maestra |
| ORCID | 0000-0001-8932-6063 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 15 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Dimensionality Assessment in Forced-Choice Questionnaires: First Steps Toward an Exploratory Framework
Forced-choice (FC) questionnaires have gained increasing attention as a strategy to reduce social desirability in self-reports, supported by advancements in confirmatory models that address the ipsativity of FC test scores. However, these models assume a known dimensionality and structure, which can be overly restrictive or fail to fit the data adequately. Consequently, exploratory models can be required, with accurate dimensionality assessment a…
The International Work Addiction Scale (Iwas): A screening tool for clinical and organizational applications validated in 85 cultures from six continents
A Systematic Evaluation of Wording Effects Modeling Under the Exploratory Structural Equation Modeling Framework
Wording effects, the systematic method variance arising from the inconsistent responding to positively and negatively worded items of the same construct, are pervasive in the behavioral and health sciences. Although several factor modeling strategies have been proposed to mitigate their adverse effects, there is limited systematic research assessing their performance with exploratory structural equation models (ESEM). The present study evaluated …
Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling
To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection…
Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence
The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locall…
Exploratory Bi-factor Analysis with Multiple General Factors
Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multi…
Cross-Cultural Validation of a Spanish-Language Version of the Composite Abuse Scale (Revised) – Short Form (Casr-SF)
Heartache in Young Adulthood: Cross-Cultural Adaptation and Validation of a Spanish Version of the Breakup Distress Scale
Romantic breakups are considered one of the most stressful events experienced by young adults. Although the Breakup Distress Scale (BDS) is one of the most widely used instruments to measure breakup distress, there is limited evidence regarding its psychometric properties. Thus, we sought to adapt and validate a Spanish version of the BDS. The sample consisted of 179 Dominican young adults (78% female, 87% heterosexual, and 94% currently single),…
Construyendo test adaptativos de elección forzosa "on the fly" para la medición de la personalidad
"Los nuevos desarrollos metodológicos y tecnológicos de la última década permiten resolver, o al menos atenuar, los problemas psicométricos de los test de elección forzosa (EF) para la medición de la personalidad. En estas pruebas, a la persona evaluada se le muestran bloques de dos o más frases de parecida deseabilidad social, entre las que debe elegir aquella que le represente mejor. De esta manera, los test de EF buscan reducir los sesgos de r…
Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables
The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analyt…
On Omega Hierarchical Estimation: A Comparison of Exploratory Bi-Factor Analysis Algorithms
As general factor modeling continues to grow in popularity, researchers have become interested in assessing how reliable general factor scores are. Even though omega hierarchical estimation has been suggested as a useful tool in this context, little is known about how to approximate it using modern bi-factor exploratory factor analysis methods. This study is the first to compare how omega hierarchical estimates were recovered by six alternative a…
Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial.
Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to tradition…
Iteration of Partially Specified Target Matrices: Application to the Bi-Factor Case
The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-load…
A new look at Horn’s parallel analysis with ordinal variables.
Previous research evaluating the performance of Horn's parallel analysis (PA) factor retention method with ordinal variables has produced unexpected findings. Specifically, PA with Pearson correlations has performed as well as or better than PA with the more theoretically appropriate polychoric correlations. Seeking to clarify these findings, the current study employed a more comprehensive simulation study that included the systematic manipulatio…
Performance of Velicer’s Minimum Average Partial Factor Retention Method With Categorical Variables
Despite strong evidence supporting the use of Velicer’s minimum average partial (MAP) method to establish the dimensionality of continuous variables, little is known about its performance with categorical data. Seeking to fill this void, the current study takes an in-depth look at the performance of the MAP procedure in the presence of ordinal-level measurement. Using Monte Carlo methods, seven factors related to the data (sample size, factor loa…
Performance of Velicer’s Minimum Average Partial Factor Retention Method With Categorical Variables
Despite strong evidence supporting the use of Velicer’s minimum average partial (MAP) method to establish the dimensionality of continuous variables, little is known about its performance with categorical data. Seeking to fill this void, the current study takes an in-depth look at the performance of the MAP procedure in the presence of ordinal-level measurement. Using Monte Carlo methods, seven factors related to the data (sample size, factor loa…
A new look at Horn’s parallel analysis with ordinal variables.
Previous research evaluating the performance of Horn's parallel analysis (PA) factor retention method with ordinal variables has produced unexpected findings. Specifically, PA with Pearson correlations has performed as well as or better than PA with the more theoretically appropriate polychoric correlations. Seeking to clarify these findings, the current study employed a more comprehensive simulation study that included the systematic manipulatio…
Iteration of Partially Specified Target Matrices: Application to the Bi-Factor Case
The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-load…
Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial.
Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to tradition…
Construyendo test adaptativos de elección forzosa "on the fly" para la medición de la personalidad
"Los nuevos desarrollos metodológicos y tecnológicos de la última década permiten resolver, o al menos atenuar, los problemas psicométricos de los test de elección forzosa (EF) para la medición de la personalidad. En estas pruebas, a la persona evaluada se le muestran bloques de dos o más frases de parecida deseabilidad social, entre las que debe elegir aquella que le represente mejor. De esta manera, los test de EF buscan reducir los sesgos de r…
Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables
The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analyt…
On Omega Hierarchical Estimation: A Comparison of Exploratory Bi-Factor Analysis Algorithms
As general factor modeling continues to grow in popularity, researchers have become interested in assessing how reliable general factor scores are. Even though omega hierarchical estimation has been suggested as a useful tool in this context, little is known about how to approximate it using modern bi-factor exploratory factor analysis methods. This study is the first to compare how omega hierarchical estimates were recovered by six alternative a…
Heartache in Young Adulthood: Cross-Cultural Adaptation and Validation of a Spanish Version of the Breakup Distress Scale
Romantic breakups are considered one of the most stressful events experienced by young adults. Although the Breakup Distress Scale (BDS) is one of the most widely used instruments to measure breakup distress, there is limited evidence regarding its psychometric properties. Thus, we sought to adapt and validate a Spanish version of the BDS. The sample consisted of 179 Dominican young adults (78% female, 87% heterosexual, and 94% currently single),…
Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence
The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locall…
Exploratory Bi-factor Analysis with Multiple General Factors
Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multi…
Cross-Cultural Validation of a Spanish-Language Version of the Composite Abuse Scale (Revised) – Short Form (Casr-SF)
The International Work Addiction Scale (Iwas): A screening tool for clinical and organizational applications validated in 85 cultures from six continents
A Systematic Evaluation of Wording Effects Modeling Under the Exploratory Structural Equation Modeling Framework
Wording effects, the systematic method variance arising from the inconsistent responding to positively and negatively worded items of the same construct, are pervasive in the behavioral and health sciences. Although several factor modeling strategies have been proposed to mitigate their adverse effects, there is limited systematic research assessing their performance with exploratory structural equation models (ESEM). The present study evaluated …
Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling
To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection…
Dimensionality Assessment in Forced-Choice Questionnaires: First Steps Toward an Exploratory Framework
Forced-choice (FC) questionnaires have gained increasing attention as a strategy to reduce social desirability in self-reports, supported by advancements in confirmatory models that address the ipsativity of FC test scores. However, these models assume a known dimensionality and structure, which can be overly restrictive or fail to fit the data adequately. Consequently, exploratory models can be required, with accurate dimensionality assessment a…
Statistics (12 works) · Mathematics (10 works) · Computer Science (7 works) · Structural equation modeling (7 works) · Factor analysis (6 works) · Mental Health Research Topics (6 works) · Monte Carlo method (6 works) · Functional Brain Connectivity Studies (5 works) · Psychology (5 works) · Confirmatory factor analysis (4 works)