Publication rates from biomedical and behavioral and social science R01s funded by the National Institutes of Health.

Prior research has shown a serious lack of research transparency resulting from the failure to publish study results in a timely manner. The National Institutes of Health (NIH) has increased its use of publication rate and time to publication as metrics for grant productivity. In this study, we analyze the publications associated with all R01 and U01 grants funded from 2008 through 2014, providing sufficient time for these grants to publish their findings, and identify predictors of time to publication based on a number of variables, including if a grant was coded as a behavioral and social sciences research (BSSR) grant or not. Overall, 2.4% of the 27,016 R01 and U01 grants did not have a publication associated with the grant within 60 months of the project start date, and this rate of zero publications was higher for BSSR grants (4.6%) than for non-BSSR grants (1.9%). Mean time in months to first publication was 15.2 months, longer for BSSR grants (22.4 months) than non-BSSR grants (13.6 months). Survival curves showed a more rapid reduction of risk to publish from non-BSSR vs BSSR grants. Cox regression models showed that human research (vs. animal, neither, or both) and clinical trials research (vs. not) are the strongest predictors of time to publication and failure to publish, but even after accounting for these and other predictors, BSSR grants continued to show longer times to first publication and greater risk of no publications than non-BSSR grants. These findings indicate that even with liberal criteria for publication (any publication associated with a grant), a small percentage of R01 and U01 grantees fail to publish in a timely manner, and that a number of factors, including human research, clinical trial research, child research, not being an early stage investigator, and conducting behavioral and social sciences research increase the risk of time to first publication.

A Robust Effect Size Index.

Effect size indices are useful tools in study design and reporting because they are unitless measures of association strength that do not depend on sample size. Existing effect size indices are developed for particular parametric models or population parameters. Here, we propose a robust effect size index based on M-estimators. This approach yields an index that is very generalizable because it is unitless across a wide range of models. We demonstrate that the new index is a function of Cohen's d, [Formula: see text], and standardized log odds ratio when each of the parametric models is correctly specified. We show that existing effect size estimators are biased when the parametric models are incorrect (e.g., under unknown heteroskedasticity). We provide simple formulas to compute power and sample size and use simulations to assess the bias and standard error of the effect size estimator in finite samples. Because the new index is invariant across models, it has the potential to make communication and comprehension of effect size uniform across the behavioral sciences.

Specification issues in nonlinear sem: the moderation that wasn't / Problemas de especificación en SEM no lineal: la moderación que no lo era

BACKGROUND: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifies interactions only, misleading results may be obtained. METHOD: This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. RESULTS: Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. CONCLUSIONS: Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers

ANTECEDENTES: el análisis de efectos de interacción o moderación entre variables latentes es común en ciencias sociales. Sin embargo, cuando los predictores están correlacionados, los efectos de interacción y cuadráticos se vuelven parecidos, haciendo difícil su distinción. Así, cuando los datos provienen de un modelo de cuadrático a nivel poblacional y el modelo de análisis solo especifica interacciones, se pueden obtener resultados engañosos. MÉTODO: este artículo aborda las consecuencias de diferentes tipos de errores de especificación en modelos de ecuaciones estructurales no lineales utilizando un estudio de Monte Carlo. RESULTADOS: los resultados muestran que estimar un modelo con interacciones cuando en la población hay efectos cuadráticos conducirá a una detección equivocada de efectos de moderación con casi plena seguridad, y lo mismo ocurrirá en el escenario opuesto. La estimación simultánea de interacciones y efectos cuadráticos en el modelo conduce a resultados correctos. CONCLUSIONES: la estimación simultánea de efectos de interacción y cuadráticos permite evitar detectar efectos no lineales espurios o engañosos. Los resultados se discuten para ofrecer recomendaciones a los investigadores aplicados

Specification issues in nonlinear SEM: The moderation that wasn't.

BACKGROUND: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifies interactions only, misleading results may be obtained. METHOD: This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. RESULTS: Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. CONCLUSIONS: Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers.

Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application.

BACKGROUND: Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. PURPOSES: By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). METHODS: The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. RESULTS: Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. CONCLUSIONS: AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

Multilevel Factor Score Regression.

Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE.

The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability.

Propensity score (PS) methods are implemented by researchers to balance the differences between participants in control and treatment groups that exist in observational studies using a set of baseline covariates. Propensity scores are most commonly calculated using baseline covariates in a logistic regression model to predict the binary grouping variable (control versus treatment). Low reliability associated with the covariates can adversely impact the calculation of treatment effects in propensity score models. The incorporation of latent variables when calculating propensity scores has been suggested to offset the negative impact of covariate unreliability. Simulation studies were conducted to compare the performance of latent variable methods with traditional propensity score methods when estimating the treatment effect under conditions of covariate unreliability. The results indicated that using factor scores or composite variables to compute propensity scores resulted in biased estimates and inflated Type I error rates as compared to using latent factors to compute propensity scores in certain conditions. This was largely dependent upon the number of infallible covariates also included in the PS model and the outcome analysis model analyzed. Implications of the findings are discussed.

Modeling agreement on bounded scales.

Agreement is an important concept in medical and behavioral sciences, in particular in clinical decision making where disagreements possibly imply a different patient management. The concordance correlation coefficient is an appropriate measure to quantify agreement between two scorers on a quantitative scale. However, this measure is based on the first two moments, which could poorly summarize the shape of the score distribution on bounded scales. Bounded outcome scores are common in medical and behavioral sciences. Typical examples are scores obtained on visual analog scales and scores derived as the number of positive items on a questionnaire. These kinds of scores often show a non-standard distribution, like a J- or U-shape, questioning the usefulness of the concordance correlation coefficient as agreement measure. The logit-normal distribution has shown to be successful in modeling bounded outcome scores of two types: (1) when the bounded score is a coarsened version of a latent score with a logit-normal distribution on the [0,1] interval and (2) when the bounded score is a proportion with the true probability having a logit-normal distribution. In the present work, a model-based approach, based on a bivariate generalization of the logit-normal distribution, is developed in a Bayesian framework to assess the agreement on bounded scales. This method permits to directly study the impact of predictors on the concordance correlation coefficient and can be simply implemented in standard Bayesian softwares, like JAGS and WinBUGS. The performances of the new method are compared to the classical approach using simulations. Finally, the methodology is used in two different medical domains: cardiology and rheumatology.

Assessing Individual Intellectual Output in Scientific Research: Mexico's National System for Evaluating Scholars Performance in the Humanities and the Behavioral Sciences.

Assessing the research of individual scholars is currently a matter of serious concern and worldwide debate. In order to gauge the long-term efficacy and efficiency of this practice, we carried out a limited survey of the operation and outcome of Mexico's 30-year old National System of Investigators or SNI, the country's main instrument for stimulating competitive research in science and technology. A statistical random sample of researchers listed in the area of Humanities and Behavioral Sciences-one of SNI's first and better consolidated academic divisions comprising a wide range of research disciplines, from philosophy to pedagogy to archaeology to experimental brain research-was screened comparing individual ranks or "Levels of distinction" to actual compliance with the SNI's own evaluation criteria, as reflected in major public databases of scholarly production. The same analysis was applied to members of a recent Review Committee, integrated by top-level researchers belonging to that general area of knowledge, who have been in charge of assessing and ranking their colleagues. Our results for both sets of scholars show wide disparity of individual productivity within the same SNI Level, according to all key indicators officially required (books issued by prestigious publishers, research articles appeared in indexed journals, and formation of new scientists), as well as in impact estimated by numbers of citations. Statistical calculation from the data indicates that 36% of members in the Review Committee and 53% of researchers in the random sample do not satisfy the official criteria requested for their appointed SNI Levels. The findings are discussed in terms of possible methodological errors in our study, of relevance for the SNI at large in relation to independent appraisals, of the cost-benefit balance of the organization as a research policy tool, and of possible alternatives for its thorough restructuring. As it currently stands SNI is not a model for efficient and effectual national systems of research assessment.

Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores / Imputación múltiple de valores perdidos en el análisis factorial exploratorio de escalas multidimensionales: estimación de las puntuaciones de rasgos latentes

Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants’ scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputations of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data

Imputación múltiple de valores perdidos en el análisis factorial exploratorio de escalas multidimensionales: estimación de las puntuaciones de rasgos latentes. Resumen: Los investigadores con frecuencia se enfrentan a la difícil tarea de analizar las escalas en las que algunos de los participantes no han respondido a todos los ítems. En este artículo nos centramos en el análisis factorial exploratorio de escalas multidimensionales (es decir, escalas que constan de varias de subescalas), donde cada subescala se compone de una serie de ítems de tipo Likert, y el objetivo del análisis es estimar las puntuaciones de los participantes en los rasgos latentes correspondientes. En este contexto, se propone un nuevo enfoque para hacer frente a las respuestas faltantes que se basa en (1) la imputación múltiple de las respuestas faltantes y (2) la rotación simultánea de las muestras de datos imputados. Se ha aplicado el método en una muestra de datos reales en que las respuestas que faltantes fueron introducidas artificialmente siguiendo un patrón real de respuestas faltantes, y un estudio de simulación basado en conjuntos de datos artificiales. Los resultados muestran que nuestro enfoque (en concreto, Hot-Deck de imputación múltiple seguido de rotación Consensus Promin) es capaz de calcular correctamente la puntuación factorial estimada incluso para los participantes que tienen valores perdidos

Modelling partially cross-classified multilevel data.

This article proposes an approach to modelling partially cross-classified multilevel data where some of the level-1 observations are nested in one random factor and some are cross-classified by two random factors. Comparisons between a proposed approach to two other commonly used approaches which treat the partially cross-classified data as either fully nested or fully cross-classified are completed with a simulation study. Results show that the proposed approach demonstrates desirable performance in terms of parameter estimates and statistical inferences. Both the fully nested model and the fully cross-classified model suffer from biased estimates of some variance components and statistical inferences of some fixed effects. Results also indicate that the proposed model is robust against cluster size imbalance.

Dental school vacant budgeted faculty positions, academic years 2008-09 to 2010-11.

The annual turnover of dental school faculty creates a varying number of vacant budgeted positions from year to year. The American Dental Education Association (ADEA) conducts an annual survey to determine the status and characteristics of these vacant faculty positions. The number of vacant budgeted faculty positions in U.S. dental schools increased throughout the 1990s, with a peak of 417 positions in 2005-06. Since that time, there has been a decrease in the number of estimated vacancies, falling to 227 in 2010-11. The 2008-09 to 2010-11 faculty vacancy surveys explored these decreases, along with information relevant to the number and characteristics of dental faculty vacancies, including data on the distribution of full-time, part-time, and volunteer faculty, reasons for faculty separations, and sources of new faculty.

Addicted to constructs: science in reverse?

Advancement in science requires clarity of constructs.Like other fields in behavioral science, addiction research is being held back by researchers' use of different terms to mean similar things (synonymy) and the same term to mean different things (polysemy). Journals can help researchers to stay focused on novel and significant research questions by challenging new terms introduced without adequate justification and requiring authors to be parsimonious in their use of terms. To support construct lucidity, new modes of thinking about research integration are needed to keep up with the aggregate of relevant research.

Modeling actor and partner effects in dyadic data when outcomes are categorical.

When 2 people interact in a relationship, the outcome of each person can be affected by both his or her own inputs and his or her partner's inputs. For Gaussian dyadic outcomes, linear mixed models taking into account the correlation within dyads are frequently used to estimate actor's and partner's effects based on the actor-partner interdependence model. In this article, we explore the potential of generalized linear mixed models (GLMMs) for the analysis of non- Gaussian dyadic outcomes. Several approximation techniques that are available in standard software packages for these GLMMs are investigated. Despite the different modeling options related to these different techniques, none of these have an overall satisfactory performance in estimating actor and partner effects and the within-dyad correlation, especially when the latter is negative and/or the number of dyads is small. An approach based on generalized estimating equations for the analysis of non-Gaussian dyadic data turns out to be an interesting alternative.

Mixture class recovery in GMM under varying degrees of class separation: frequentist versus Bayesian estimation.

Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories.

On using a pilot sample variance for sample size determination in the detection of differences between two means: power consideration

The a priori determination of a proper sample size necessary to achieve some specified power is an important problem encountered frequently in practical studies. To establish the needed sample size for a two-sample t test, researchers may conduct the power analysis by specifying scientifically important values as the underlying population means while using a variance estimate obtained from related research or pilot study. In order to take account of the variability of sample variance, this article considers two approaches to sample size determinations. One provides the sample size required to guarantee with a given assurance probability that the actual power exceeds the planned power. The other gives the necessary sample size such that the expected power attains the designated power level. The suggested paradigm of adjusted sample variance combines the existing procedures into one unified framework. Numerical results are presented to illustrate the usefulness and advantages of the proposed approaches that accommodate the stochastic nature of the sample variance. More importantly, supplementary computer programs are developed to aid the usefulness and implementation of the suggested techniques. The exposition helps to clarify discrepancy in the previous demonstration and to extend the development of sample size methodology(AU)

The roles of constraint-based and dedication-based influences on user’s continued online shopping behavior

The objective of this study was to determine empirically the role of constraint-based and dedicationbased influences as drivers of the intention to continue using online shopping websites. Constraintbased influences consist of two variables: trust and perceived switching costs. Dedication-based influences consist of three variables: satisfaction, perceived usefulness, and trust. The current results indicate that both constraint-based and dedication-based influences are important drivers of the intention to continue using online shopping websites. The data also shows that trust has the strongest total effect on online shoppers’ intention to continue using online shopping websites. In addition, the results indicate that the antecedents of constraint-based influences, technical bonds (e.g., perceived operational competence and perceived website interactivity) and social bonds (e.g., perceived relationship investment, community building, and intimacy) have indirect positive effects on the intention to continue using online shopping websites. Based on these findings, this research suggests that online shopping websites should build constraint-based and dedication-based influences to enhance user's continued online shopping behaviors simultaneously (AU)

El objetivo de este trabajo fue comprobar experimentalmente el papel que juegan las técnicas basadas en la coacción o en la dedicación como impulsoras del uso de páginas web de compra on-line. Las influencias basadas en la coacción presentan dos variables: confianza y cambio de coste percibido. Las influencias basadas en la dedicación muestran tres variables: satisfacción, utilidad percibida y confianza. Los resultados indicaron que ambas influencias son importantes impulsores de la intención de continuar usando páginas web de compra on-line. Los datos mostraron que la confianza era la variable más influyente en la intención del uso de estas páginas web. Adicionalmente, los resultados indicaron que los antecedentes de las influencias basadas en la restricción, en cadenas técnicas (v.gr., competencia operacional percibida e interactividad percibida del sitio web) y las redes sociales (v.gr., inversión en relaciones percibida, comunidad e intimidad) tuvieron efectos indirectos positivos en la intención de continuar usando páginas web de compra on-line. De acuerdo con estos resultados, se sugiere que dichas páginas web deberían usar tanto influencias basadas en la restricción como en la dedicación para incrementar el uso continuado de la conducta de compra on-line (AU)

Development and validation of the theory of planned behavior questionnaire in physical activity

The Theory of Planned Behavior (TPB) is one of the main theoretical models in the study of the different variables, which influence in the practise of physical activity. The aim in this study was to develop a questionnaire based on TPB in physical activity context providing evidence for the validity of the obtained measures. The instrumental project included three independent studies. The first study entailed the construction and qualitative assessment of the items. In the second study, the analysis of factorial structure was performed by means of exploratory measures, and it showed that the reliability of measures was adequate. The third study provided evidence on the dimensionality of the scale. The confirmatory factorial analysis guaranteed the stability of factorial structure proposed by the TPB and provided evidence for the internal validity of the inventory. Moreover, this study provided evidence of its external validity (AU)

La Teoría de la Conducta Planeada (TCP) es uno de los modelos teóricos más empleados para conocer las diferentes variables que influyen en la realización de ejercicio físico. El objetivo en este trabajo fue elaborar un cuestionario basado en la TCP en el contexto del ejercicio físico y aportar evidencias sobre la validez de sus mediciones. El trabajo instrumental incluyó tres estudios independientes. El primer estudio consistió en la elaboración y evaluación cualitativa de los ítems. En el segundo estudio se analizó la estructura factorial mediante procedimientos exploratorios y se mostró que la fiabilidad de las mediciones era adecuada. El tercer estudio aportó evidencias sobre la dimensionalidad del inventario. El análisis factorial confirmatorio avaló la estabilidad de la estructura factorial propuesta por la TCP, así como aportó evidencias de validez interna del instrumento. También se aportan evidencias de su validez externa (AU)

Clusterwise HICLAS: a generic modeling strategy to trace similarities and differences in multiblock binary data.

In many areas of the behavioral sciences, different groups of objects are measured on the same set of binary variables, resulting in coupled binary object × variable data blocks. Take, as an example, success/failure scores for different samples of testees, with each sample belonging to a different country, regarding a set of test items. When dealing with such data, a key challenge consists of uncovering the differences and similarities between the structural mechanisms that underlie the different blocks. To tackle this challenge for the case of a single data block, one may rely on HICLAS, in which the variables are reduced to a limited set of binary bundles that represent the underlying structural mechanisms, and the objects are given scores for these bundles. In the case of multiple binary data blocks, one may perform HICLAS on each data block separately. However, such an analysis strategy obscures the similarities and, in the case of many data blocks, also the differences between the blocks. To resolve this problem, we proposed the new Clusterwise HICLAS generic modeling strategy. In this strategy, the different data blocks are assumed to form a set of mutually exclusive clusters. For each cluster, different bundles are derived. As such, blocks belonging to the same cluster have the same bundles, whereas blocks of different clusters are modeled with different bundles. Furthermore, we evaluated the performance of Clusterwise HICLAS by means of an extensive simulation study and by applying the strategy to coupled binary data regarding emotion differentiation and regulation.

Productividad de los programas de doctorado en Psicología con Mención de Calidad en artículos de revistas incluidas en el Journal Citation Reports / Productivity of doctoral programs in Psychology with Quality Mention in journal articles included in Journal Citation Reports

El presente estudio tiene como objetivo principal clasificar los programas de doctorado en Psicología con Mención de Calidad, en función de su productividad científica, a través del número de artículos publicados por los profesores de dichos programas y recogidos en la base de datos Web of Science. Además, también se analizan cuáles son las revistas científicas donde más publican esos profesores y la evolución en el número de artículos publicados hasta 2009. Los resultados obtenidos muestran que el programa de doctorado con mayor productividad es el de Neurociencias, de la Universidad de Oviedo, con una proporción de cuarenta artículos publicados en revistas del Journal Citation Reports por cada profesor, en contraste con otros programas que no alcanzan la proporción de diez artículos por profesor. En cuanto a las revistas donde más publican estos profesores, se obtuvo que de las veinte más frecuentes, nueve de ellas son hispanoamericanas y se observa un incremento gradual en el número de artículos publicados. Finalmente, se discuten los resultados y las implicaciones más relevantes de la evaluación de la calidad (AU)

The main objective of the present study was to classify doctoral programs with Quality Mention in Psychology based on their scientific productivity. For this purpose, articles in the Web of Science published by professors teaching in these doctoral programs were analyzed. In addition, we analyzed scientific journals in which these professors tend to publish more papers and the evolution in the number of papers published until 2009. Results showed that the most productive doctoral program was the Neurosciences program at the University of Oviedo. This program showed a ratio of 40 articles -published in journals included in Journal Citation Reports- by each professor. In contrast, other programs did not reach a ratio of 10 articles per professor. Regarding journals, results showed that 9 out of the 20 most popular journals are Hispanic and a gradual increase in the number of published papers was also observed. Lastly, results and implications for quality assessment are discussed (AU)