The Fear of COVID-19 Scale: A Reliability Generalization Meta-Analysis

A reliability generalization meta-analysis was carried out to estimate the average reliability of the seven-item, 5-point Likert-type Fear of COVID-19 Scale (FCV-19S), one of the most widespread scales developed around the COVID-19 pandemic. Different reliability coefficients from classical test theory and the Rasch Measurement Model were meta-analyzed, heterogeneity among the most reported reliability estimates was examined by searching for moderators, and a predictive model to estimate the expected reliability was proposed. At least one reliability estimate was available for a total of 44 independent samples out of 42 studies, being that Cronbach’s alpha was most frequently reported. The coefficients exhibited pooled estimates ranging from .85 to .90. The moderator analyses led to a predictive model in which the standard deviation of scores explained 36.7% of the total variability among alpha coefficients. The FCV-19S has been shown to be consistently reliable regardless of the moderator variables examined.2020-2

Robustness of different estimators of the between-study variance in random effects meta-analysis: A Monte-Carlo simulation

Robustness of different estimators of the between-study variance in random effects meta-analysis: A Monte-Carlo simulation2020-2

The Fear of COVID-19 Scale: A meta-Analytic structural equation modeling approach

The widespread administration and multiple validations of the Fear of Covid-19 Scale (FCV-19S) in different languages have highlighted the controversy over its underlying structure and the resulting reliability index. In the present study, a meta-analysis based on structural equation modeling (MASEM) was conducted to assess the internal structure of the 7-item, 5-point Likert-type FCV-19S version, estimate an overall reliability index from the underlying model that best reflected the internal structure (one τ-equivalent factor, one congeneric factor, or two-factor models), and perform moderator analyses for the model-implied inter item correlations and estimated factor loadings. A Pearson inter-item correlation matrix was obtained for 48 independent studies, from which a pooled matrix was calculated following a random-effects multivariate meta-analysis. The results from the one-stage MASEM analysis showed that the two-factor model properly fitted the pooled matrix, while the τ-equivalent and congeneric one-factor models did not. Even though, the use of a bifactor model exhibited the predominance of the general factor over the domain-specific ones. High omega coefficients were obtained for the entire scale (.91) and the psychological (.83) and physiological (.83) symptoms subscales. Moderator analyses evidenced an increase in the estimated factor loadings, as well as in the reliability of the FCV-19S, when the standard deviation of the total scores increased and when the FCV-19S was administered to specific (vs. general) populations. The FCV-19S can be therefore considered as a highly related two-factor scale whose reliability makes it suitable for applied and research purposes.2022-2

Reproducibility of Published Meta-Analyses on Clinical-Psychological Interventions

Meta-analysis is one of the most useful research approaches, the relevance of which relies on its credibility. Reproducibility of scientific results could be considered as the minimal threshold of this credibility. We assessed the reproducibility of a sample of meta-analyses published between 2000 and 2020. From a random sample of 100 articles reporting results of meta-analyses of interventions in clinical psychology, 217 meta-analyses were selected. We first tried to retrieve the original data by recovering a data file, recoding the data from document files, or requesting it from original authors. Second, through a multistage workflow, we tried to reproduce the main results of each meta-analysis. The original data were retrieved for 67% (146/217) of meta-analyses. Although this rate showed an improvement over the years, in only 5% of these cases was it possible to retrieve a data file ready for reuse. Of these 146, 52 showed a discrepancy larger than 5% in the main results in the first stage. For 10 meta-analyses, this discrepancy was solved after fixing a coding error of our data-retrieval process, and for 15 of them, it was considered approximately reproduced in a qualitative assessment. In the remaining meta-analyses (18%, 27/146), different issues were identified in an in-depth review, such as reporting inconsistencies, lack of data, or transcription errors. Nevertheless, the numerical discrepancies were mostly minor and had little or no impact on the conclusions. Overall, one of the biggest threats to the reproducibility of meta-analysis is related to data availability and current data-sharing practices in meta-analysis.This research was funded with a grant from the Spanish Ministry of Science and Innovation (Project PID2019-104080GB-I00/AEI/10.13039/501100011033; PID2019-104033GA-I00/MCIN/AEI/10.13039/501100011033, FEDER funds), by the Spanish Ministry of Universities (predoctoral grant: FPU18/04805), and by the Regional Program for the Promotion of Scientific and Technical Research of Excellence (Action Plan 2022) of the Seneca Foundation - Science and Technology Agency of the Region of Murcia (Grant 22064/PI/22)

A systematic review and network meta-analysis on the effectiveness of exercise-based interventions for reducing the injury incidence in youth team-sport players. Part 2: An analysis by movement patterns

Objectives: The objectives of this network meta-analysis were: a) to estimate and compare the pooled effects of some injury prevention programs (IPPs) whose exercise-based components were categorized using a movement pattern-specific taxonomy on reducing overall and some specific body regions (lower extremity, thigh, knee, and ankle) injury incidences in youth team sport athletes, and b) to explore the individual effects of these components on the injury incidence rates (IIRs) previously mentioned. Materials and Methods: Searches were performed in PubMed, Web of Science, SPORTDiscus, and Cochrane Library. Eligible criteria were: exercise-based interventions comprised of exercises involving athletic motor skill competencies and evaluated against a control group, overall IIRs were reported, and youth (≤ 19 years old) team sport players. For the current analysis, a taxonomy based on movement patterns was employed for exercise component identification (upper body pushing and pulling; lower body concentric and eccentric; core; mechanics; acceleration; and lower body stability). Pooled effects were calculated by Frequentist random effects pairwise and network meta-analyses. Results: Nineteen studies were included. Most of the IPPs exhibit risk reduction when compared to their control groups on overall, lower extremity, and ankle injuries. Interventions comprised of lower body concentric and eccentric, core, mechanics, and lower body stability exercises were the most effective measures for reducing these injuries. None of the IPPs demonstrated to be effective for reducing thigh injuries, and contradictory results were found for knee injuries. Individual analysis at component level revealed that the lower body (bilateral and unilateral, concentric, and eccentric) component was the only one associated with a significant reduction on overall injuries. Conclusions: Indirect evidence suggests that interventions incorporating lower body concentric and eccentric, core, mechanics, and lower body stability exercises might be the most effective for reducing overall, lower extremity, and ankle injuries in youth team sports

Advances in the study of heterogeneity in Meta-Analysis

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología. Departamento de Psicología Social y Metodología. Fecha de Lectura: 25-04-2023Esta Tesis tiene embargado el acceso al texto completo hasta el 25-10-2024La heterogeneidad en metaanálisis se entiende como la variabilidad encontrada en la distribución paramétrica a la que pertenecen los efectos estimados por un grupo de estudios y es, por tanto, tan importante como el tamaño del efecto total para comprender la pregunta de investigación. El parámetro de heterogeneidad también está implicado en el proceso de estimación del efecto total, el cálculo de sus intervalos de confianza y predicción, así como de índices como I2. Además, la estimación del parámetro de heterogeneidad es crucial, ya que una heterogeneidad positiva suele dar lugar a análisis adicionales destinados a explicar las fuentes de dicha heterogeneidad. Esta tesis presenta nuevos avances en el estudio estadístico de la heterogeneidad en metaanálisis y consta de dos estudios, cada uno con objetivos diferentes. El primer estudio examina cómo se comportan los estimadores puntuales del parámetro de heterogeneidad más conocidos, así como algunos más novedosos, cuando no se cumple el supuesto de normalidad subyacente a la población de efectos verdaderos en un modelo de efectos aleatorios. En concreto, se trata de un estudio de simulación Monte Carlo que compara 21 procedimientos frecuentistas y 24 bayesianos en términos de sesgo absoluto, varianza de muestreo, y error cuadrático medio en meta-análisis de diferencias de medias estandarizadas que presentan diversos grados de desviación de la normalidad de los efectos aleatorios. Asimismo, se pone a disposición de los meta-analistas aplicados la función tau2() en R para que puedan actuar con cautela en la interpretación de los resultados obtenidos a partir de un modelo de efectos aleatorios. El último estudio continúa con el tratamiento estadístico de la heterogeneidad, pero en lugar de desde una perspectiva de estimación como el primer estudio, este segundo estudio se centra en la predicción del parámetro de heterogeneidad. Los modelos de localización y escala se han extendido recientemente al campo del meta-análisis, permitiendo estudiar simultáneamente el efecto de variables predictoras sobre la media y la varianza de la distribución de los efectos verdaderos. Sin embargo, la mayor complejidad de estos modelos puede dificultar su ajuste y aún no se han examinado las propiedades estadísticas de los procedimientos implicados. El segundo estudio es una simulación Monte Carlo cuyo objetivo es comparar los métodos de estimación, las pruebas de significación, y los intervalos de confianza disponibles para los coeficientes de escala de dichos modelos en un contexto meta-analítico, así como examinar las tasas de no convergencia de algunos de estos procedimientos que pueden afectar al ajuste de los modelos especificado

Performance of Location-Scale Models in Meta-Analysis: A Simulation Study

With the present work, we examine the performance of currently available procedures in the metafor R package for fitting location-scale models in meta-analyses from a frequentist approach. More specifically, the present work consists of a Monte Carlo simulation study in which estimation and inference procedures concerning the coefficients of the scale part of the model (involving the effect of a predictor for the heterogeneity variance component of a meta-analysis) are tested and compared. This work highlights the potential of recently extended location-scale models in the meta-analytical practice and shows their performance under conditions in which many researchers in the health and social sciences would be able to include their research

Reformulating the Meta-analytical Random Effects Model as a Mixture Model

The “traditional” random-effects model (REM) in meta-analysis was formulated as a variance-components model, based on the work of Cochrane and Hedges. It has been accepted routinely although it is known that has several defects, because it is conventionally accepted that the impact of those flaws is negligible. It is especially important in effect size indices whose conditional sampling variance is a function of the effect size itself. We describe an alternative formulation of the REM, as a mixture model. We have derive formulas for the expected value, the variance, and the skewness of the marginal distribution of g. They can be used to improve meta analytical techniques, as they provide very accurate predictions. Our main conclusion is that the mixture model is a more correct, flexible, and elegant way to formulate the REM