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Abstract Background Advantages of meta-analysis depend on the assumptions underlying the statistical procedures used being met. One of the main assumptions that is usually taken for granted is the normality underlying the population of true effects in a random-effects model, even though the available evidence suggests that this assumption is often not met. This paper examines how 21 frequentist and 24 Bayesian methods, including several novel procedures, for computing a point estimate of the heterogeneity parameter ( $${\tau }^{2}$$ τ 2 ) perform when the distribution of random effects departs from normality compared to normal scenarios in meta-analysis of standardized mean differences. Methods A Monte Carlo simulation was carried out using the R software, generating data for meta-analyses using the standardized mean difference. The simulation factors were the number and average sample size of primary studies, the amount of heterogeneity, as well as the shape of the random-effects distribution. The point estimators were compared in terms of absolute bias and variance, although results regarding mean squared error were also discussed. Results Although not all the estimators were affected to the same extent, there was a general tendency to obtain lower and more variable $${\tau }^{2}$$ τ 2 estimates as the random-effects distribution departed from normality. However, the estimators ranking in terms of their absolute bias and variance did not change: Those estimators that obtained lower bias also showed greater variance. Finally, a large number and sample size of primary studies acted as a bias-protective factor against a lack of normality for several procedures, whereas only a high number of studies was a variance-protective factor for most of the estimators analyzed. Conclusions Although the estimation and inference of the combined effect have proven to be sufficiently robust, our work highlights the role that the deviation from normality may be playing in the meta-analytic conclusions from the simulation results and the numerical examples included in this work. With the aim to exercise caution in the interpretation of the results obtained from random-effects models, the tau2() R function is made available for obtaining the range of $${\tau }^{2}$$ τ 2 values computed from the 45 estimators analyzed in this work, as well as to assess how the pooled effect, its confidence and prediction intervals vary according to the estimator chosen.

This study conducted a meta-analysis to identify the primary risk and protective factors associated with the revictimization in intimate partner violence against women (IPVAW). Out of 2382 studies initially identified in eight databases, 22 studies met the inclusion criteria and provided the necessary data for calculating pooled effect sizes. The analysis focused on non-manipulative quantitative studies examining revictimization in heterosexual women of legal age. Separate statistical analyses were performed for prospective and retrospective studies, resulting in findings related to 14 variables. The Metafor package in RStudio was used with a random-effects model. The meta-analysis revealed that childhood abuse was the most strongly associated risk factor for revictimization, while belonging to a white ethnicity was the most prominent protective factor. Other significant risk factors included alcohol and drug use, recent physical violence, severity of violence, and PTSD symptomatology. The study also found that older age was a protective factor in prospective studies. The consistency of results across different study designs and sensitivity analyses further supported the robustness of the findings. It is important to note that the existing literature on revictimization in women facing intimate partner violence is limited and exhibits significant heterogeneity in terms of methodology and conceptual frameworks.

The aim of the study is to determine the association between Behavioral Lifestyles (regular physical activity, healthy diet, sleeping, and weight control) and longevity in the elderly. A search strategy was conducted in the PsycInfo, Medline, PubMed, Web of Science (WoS), and Scopus databases. The primary outcome was mortality/survival. Four variables (mean of participant's age at the baseline of the study, follow-up years of the study, gender, and year of publication) were analyzed to evaluate the role of potential moderators. Ninety-three articles, totaling more than 2,800,000 people, were included in the meta-analysis. We found that the lifestyles analyzed predict greater survival. Specifically, doing regular physical activity, engaging in leisure activities, sleeping 7–8 h a day, and staying outside the BMI ranges considered as underweight or obesity are habits that each separately has a greater probability associated with survival after a period of several years.

Assessing significant change (or reliable change) in a person often involve comparing the responses of that person in two administrations of a test or scale. Several procedures have been proposed to determine if a difference between two observed scores is statistically significant or rather is within the range of mere random fluctuations due to measurement error. Application of those procedures involve some knowledge of the test properties. But sometimes those procedures cannot be employed because the properties are unknown or are not trustworthy. In this paper we propose the bootstrap of items procedure to create confidence intervals of the individual's scores without using any known psychometric properties of the test. Six databases containing the responses of several groups to one or more subscales have been analyzed using two methods: bootstrap of items and a classical procedure based on confidence intervals to estimate the true score. The rates of significant change obtained were very similar, suggesting that item bootstrapping is a promising solution when other methods cannot be applied.

Copyright of Educación XX1 is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anales de Psicología is the property of Servicio de Publicaciones de la Universidad de Murcia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study conducted a meta-analysis to identify the primary risk and protective factors associated with the revictimization in intimate partner violence against women (IPVAW). Out of 2382 studies initially identified in eight databases, 22 studies met the inclusion criteria and provided the necessary data for calculating pooled effect sizes. The analysis focused on non-manipulative quantitative studies examining revictimization in heterosexual women of legal age. Separate statistical analyses were performed for prospective and retrospective studies, resulting in findings related to 14 variables. The Metafor package in RStudio was used with a random-effects model. The meta-analysis revealed that childhood abuse was the most strongly associated risk factor for revictimization, while belonging to a white ethnicity was the most prominent protective factor. Other significant risk factors included alcohol and drug use, recent physical violence, severity of violence, and PTSD symptomatology. The study also found that older age was a protective factor in prospective studies. The consistency of results across different study designs and sensitivity analyses further supported the robustness of the findings. It is important to note that the existing literature on revictimization in women facing intimate partner violence is limited and exhibits significant heterogeneity in terms of methodology and conceptual frameworks. [ABSTRACT FROM AUTHOR]

A wealth of consumer research has proposed an experiential advantage : consumers yield greater happiness from purchasing experiences compared to material possessions. While this research stream has undoubtedly influenced consumer research, few have questioned its limitations, explored moderators, or investigated filedrawer effects. This has left marketing managers, consumers, and researchers questioning the relevance of the experiential advantage. To address these questions, the authors develop a model of consumer happiness and well-being based on psychological needs (i.e. autonomy, relatedness, self-esteem, and meaningfulness), and conduct an experiential advantage meta-analysis to test this model. Collecting 360 effect sizes from 141 studies, the meta-analysis supports the experiential advantage (d  =   0.383, 95% CI [0.336, 0.430]), of which approximately a third of the effect may be attributable to publication bias. The analysis finds differential effects depending on the type of dependent measure, suggesting that the experiential advantage may be more tied to relatedness than to happiness and willingness to pay. The experiential advantage is reduced for negative experiences, for solitary experiences, for lower socioeconomic status consumers, and when experiences provide a similar level of utilitarian benefits relative to material goods. Finally, results suggest future studies in this literature should use larger sample sizes than current practice. [ABSTRACT FROM AUTHOR]

The concept of intelligence has been extensively studied, undergoing an evolution from a unitary concept to a more elaborate and complex multidimensional one. In addition, several research studies have focused their efforts for decades on the study of intelligence as a predictor of academic performance of students at different educational stages, being a stable and highly relevant predictor along with other variables such as executive functions, social context, culture or parental guardianship. Thus, the present study, based on a systematic review and meta-analysis, includes 27 studies with a total sample of 42,061 individuals. The main objective was to analyse the relationship between intelligence and academic performance using different predictive models that include moderating variables such as country of origin, type of intelligence, gender and age. The findings of this research highlight the significant, positive and moderate relationship between intelligence and academic performance (r = 0.367; p < 0.001), highlighting the predictive capacity on school performance when the type of intelligence (general and implicit; 35%) or the country of origin (45%) is taken as a moderating variable, with the explanatory models on age or sex not being significant. Therefore, it can be concluded that intelligence, in addition to being a good predictor of academic performance, is influenced depending on the type of intelligence or theoretical model taken as a reference, and also depending on the country or culture of origin. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The interest in the value relevance of marketing investments has given rise to numerous studies on the marketing-finance interface. This study integrates extant research findings and establishes empirical generalizations on marketing's impact on firm value. Specifically, the authors conduct a meta-analysis of prior econometric elasticity estimates of the stock market impact of marketing actions and marketing assets. Analyses based on 488 elasticities drawn from 83 studies reveal a mean elasticity of .04 for advertising expenditure variables and of .54 for marketing asset variables. Among marketing assets, customer-related assets show a higher mean elasticity of .72, compared with .33 for brand-related assets. Further analyses show that advertising elasticities are lower in more concentrated industries and that marketing asset elasticities are higher during recession times. Researchers should also be aware that characteristics of the research design (e.g., the type of firm value metric used, the omission of control variables, or not accounting for endogeneity) may affect the estimation results. [ABSTRACT FROM AUTHOR]

The aim of the study is to determine the association between Behavioral Lifestyles (regular physical activity, healthy diet, sleeping, and weight control) and longevity in the elderly. A search strategy was conducted in the PsycInfo, Medline, PubMed, Web of Science (WoS), and Scopus databases. The primary outcome was mortality/survival. Four variables (mean of participant's age at the baseline of the study, follow-up years of the study, gender, and year of publication) were analyzed to evaluate the role of potential moderators. Ninety-three articles, totaling more than 2,800,000 people, were included in the meta-analysis. We found that the lifestyles analyzed predict greater survival. Specifically, doing regular physical activity, engaging in leisure activities, sleeping 7–8 h a day, and staying outside the BMI ranges considered as underweight or obesity are habits that each separately has a greater probability associated with survival after a period of several years. [ABSTRACT FROM AUTHOR]

Reliability generalization (RG) is a meta‐analytic approach that aims to characterize how reliability estimates from the same test vary across different applications of the instrument. With this purpose RG meta‐analyses typically focus on a particular test and intend to obtain an overall reliability of test scores and to investigate how the composition and variability of the samples affect reliability. Although several guidelines have been proposed in the meta‐analytic literature to help authors improve the reporting quality of meta‐analyses, none of them were devised for RG meta‐analyses. The purpose of this investigation was to develop REGEMA (REliability GEneralization Meta‐Analysis), a 30‐item checklist (plus a flow chart) adapted to the specific issues that the reporting of an RG meta‐analysis must take into account. Based on previous checklists and guidelines proposed in the meta‐analytic arena, a first version was elaborated by applying the nominal group methodology. The resulting instrument was submitted to a list of independent meta‐analysis experts and, after discussion, the final version of the REGEMA checklist was reached. In a pilot study, four pairs of coders applied REGEMA to a random sample of 40 RG meta‐analyses in Psychology, and results showed satisfactory inter‐coder reliability. REGEMA can be used by: (a) meta‐analysts conducting or reporting an RG meta‐analysis and aiming to improve its reporting quality; (b) consumers of RG meta‐analyses who want to make informed critical appraisals of their reporting quality, and (c) reviewers and editors of journals who are considering submissions where an RG meta‐analysis was reported for potential publication. [ABSTRACT FROM AUTHOR]

Food marketing is facing increasing challenges in using portion size (e.g., “supersizing”) as a marketing tool. Marketers have used portion size to attract customers and encourage purchase, but social agencies are expressing concern that larger portion sizes encourage greater consumption, which can cause excessive consumption and obesity. This article addresses two questions that are central to this debate: (1) How much effect does portion size have on consumption? and (2) Are there limits to this effect? A meta-analytic review reveals that, for a doubling of portion size, consumption increases by 35% on average. However, the effect has limits. An extended analysis shows that the effect of portion size is curvilinear: as portions become increasingly larger, the effect diminishes. In addition, although the portion-size effect is widespread and robust across a range of individual and environmental factors, the analysis shows that it is weaker among children, women, and overweight individuals, as well as for nonsnack food items and in contexts in which more attention is given to the food being eaten. [ABSTRACT FROM AUTHOR]

Copyright of Anales de Psicología is the property of Servicio de Publicaciones de la Universidad de Murcia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Whether in meta-analysis or single experiments, selecting results based on statistical significance leads to overestimated effect sizes, impeding falsification. We critique a quantitative synthesis that used significance to score and select previously published effects for situation awareness-performance associations (Endsley, 2019). How much does selection using statistical significance quantitatively impact results in a meta-analytic context? We evaluate and compare results using significance-filtered effects versus analyses with all effects as-reported. Endsley reported high predictiveness scores and large positive mean correlations but used atypical methods: the hypothesis was used to select papers and effects. Papers were assigned the maximum predictiveness scores if they contained at-least-one significant effect, yet most papers reported multiple effects, and the number of non-significant effects did not impact the score. Thus, the predictiveness score was rarely less than the maximum. In addition, only significant effects were included in Endsley's quantitative synthesis. Filtering excluded half of all reported effects, with guaranteed minimum effect sizes based on sample size. Results for filtered compared to as-reported effects clearly diverged. Compared to the mean of as-reported effects, the filtered mean was overestimated by 56%. Furthermore, 92% (or 222 out of 241) of the as-reported effects were below the mean of filtered effects. We conclude that outcome-dependent selection of effects is circular, predetermining results and running contrary to the purpose of meta-analysis. Instead of using significance to score and filter effects, meta-analyses should follow established research practices. [ABSTRACT FROM AUTHOR]

Mixed-effects models can be used to examine the association between a categorical moderator and the magnitude of the effect size. Two approaches are available to estimate the residual between-studies variance, τ r e s 2 —namely, separate estimation within each category of the moderator versus pooled estimation across all categories. We examine, by means of a Monte Carlo simulation study, both approaches for τ r e s 2 estimation in combination with two methods, the Wald-type χ 2 and F tests, to test the statistical significance of the moderator. Results suggest that the F test using a pooled estimate of τ r e s 2 across categories is the best option in most conditions, although the F test using separate estimates of τ r e s 2 is preferable if the residual heterogeneity variances are heteroscedastic. [ABSTRACT FROM AUTHOR]

Sometimes the reports of primary studies that are potentially analyzable within the signal detection theory framework do not report sample statistics for its main indexes, especially the sample variance of d'. We describe a procedure for estimating the variance of d' from other sample statistics (specifically, the mean and variance of the observed rates of hit and false alarm). The procedure acknowledges that individuals can be heterogeneous in their sensitivity and/or decision criteria, and it does not adopt unjustifiable or needlessly complex assumptions. In two simulation studies reported here, we show that the procedure produces certain biases, but, when used in meta-analysis, it produces very reasonable results. Specifically, the weighted estimate of the mean sensitivity is very accurate, and the coverage of the confidence interval is very close to the nominal confidence level. We applied the procedure to 20 experimental groups or conditions from seven articles (employing recognition memory or attention tasks) that reported statistics for both the hit and false alarm rates, as well as for d'. In most of these studies the assumption of homogeneity was untenable. The variances estimated by our method, based on the hit and false alarm rates, approximate reasonably to the variances in d' reported in those articles. The method is useful for estimating unreported variances of d', so that the associated studies can be retained for meta-analyses. [ABSTRACT FROM AUTHOR]

Copyright of Anales de Psicología is the property of Servicio de Publicaciones de la Universidad de Murcia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Self-esteem has been deemed an important variable in many theories over the past 100 years, and several interventions are aimed at improving self-esteem in order to prevent criminal and delinquent behavior. However, no systematic attempt to quantify the collective results of this variable’s effect on criminal and delinquent behavior has been undertaken, and only one narrative literature review has taken place. This meta-analysis seeks to inform the research by focusing on 48 effect sizes from 42 studies spanning 25 years (1990-2015) and 71,130 individuals. Our findings indicate that self-esteem has a small, negative but significant effect on crime and delinquency. [ABSTRACT FROM AUTHOR]

The psychosocial impact concept improves the understanding of the effects of disasters on people and communities. However, its definition is not clear. This work explores consistencies between studies (k = 21) that refer to the psychosocial impact of disasters by way of a meta-analytic synthesis. This synthesis indicates that people are more prone to illness when they are exposed to a disaster, and less when they are protected (OR = 2.737). Please check the change conveys the intended meaning or amend Nevertheless, there are no differences in healthy responses, regardless of how protected or exposed they are (OR = 1.053). Finally, a model is proposed to explain four types of psychosocial impact: resilient, traumatic, sensitive, witness. [ABSTRACT FROM AUTHOR]

Assessing significant change (or reliable change) in a person often involve comparing the responses of that person in two administrations of a test or scale. Several procedures have been proposed to determine if a difference between two observed scores is statistically significant or rather is within the range of mere random fluctuations due to measurement error. Application of those procedures involve some knowledge of the test properties. But sometimes those procedures cannot be employed because the properties are unknown or are not trustworthy. In this paper we propose the bootstrap of items procedure to create confidence intervals of the individual's scores without using any known psychometric properties of the test. Six databases containing the responses of several groups to one or more subscales have been analyzed using two methods: bootstrap of items and a classical procedure based on confidence intervals to estimate the true score. The rates of significant change obtained were very similar, suggesting that item bootstrapping is a promising solution when other methods cannot be applied. [ABSTRACT FROM AUTHOR]

Subgroup analyses allow us to examine the influence of a categorical moderator on the effect size in meta-analysis. We conducted a simulation study using a dichotomous moderator, and compared the impact of pooled versus separate estimates of the residual between-studies variance on the statistical performance of the QB (P) and QB (S) tests for subgroup analyses assuming a mixed-effects model. Our results suggested that similar performance can be expected as long as there are at least 20 studies and these are approximately balanced across categories. Conversely, when subgroups were unbalanced, the practical consequences of having heterogeneous residual between-studies variances were more evident, with both tests leading to the wrong statistical conclusion more often than in the conditions with balanced subgroups. A pooled estimate should be preferred for most scenarios, unless the residual between-studies variances are clearly different and there are enough studies in each category to obtain precise separate estimates. [ABSTRACT FROM AUTHOR]

The authors discuss the re-analysis of datasets from metanalyses of ego-depletion effect using the p-uniform method. They state that p-uniform test with method-of-the-moments and minimal Kolmogorov-Smirnov test were used to estimate the parameter. They mention that consideration of ego-depletion effect as statistical or experimental artifact might have been premature based on the analyses.

Sequential stopping rules allow hypotheses to be tested using smaller sample sizes than are possible under conventional methods, while controlling the Type I and II error rates. However, the consequences of using such procedures when combining studies in a meta-analysis have rarely been discussed. For a primary study to be included in a meta-analysis, it must provide an estimate of the effect size, and it must be possible to calculate the variance of this estimate, which is used for weighting the study. It is therefore crucial to know whether the use of sequential stopping rules introduces any bias in the estimate of the effect size and/or modifies the variance of the estimate. In the present research, both aspects were studied for the CLAST rule, as applied to testing the difference between two means from paired samples, in a variety of scenarios of sample size and population effect size. The results show that although the bias is small, but still larger than that for the fixed-sample rule, the variance of the estimate is much higher with the CLAST sequential stopping rule. The implications of these results for the incorporation of such studies into meta-analyses are discussed. It is recommended to incorporate such studies into meta-analyses by taking only the information conveyed in the initial sample. The authors of primary studies employing sequential rules should report that information when publishing their results. [ABSTRACT FROM AUTHOR]

In the context of the evidence-based practices movement, the emphasis on computing effect sizes and combining them via meta-analysis does not preclude the demonstration of functional relations. For the latter aim, we propose to augment the visual analysis to add consistency to the decisions made on the existence of a functional relation without losing sight of the need for a methodological evaluation of what stimuli and reinforcement or punishment are used to control the behavior. Four options for quantification are reviewed, illustrated, and tested with simulated data. These quantifications include comparing the projected baseline with the actual treatment measurements, on the basis of either parametric or nonparametric statistics. The simulated data used to test the quantifications include nine data patterns in terms of the presence and type of effect and comprise ABAB and multiple-baseline designs. Although none of the techniques is completely flawless in terms of detecting a functional relation only when it is present but not when it is absent, an option based on projecting split-middle trend and considering data variability as in exploratory data analysis proves to be the best performer for most data patterns. We suggest that the information on whether a functional relation has been demonstrated should be included in meta-analyses. It is also possible to use as a weight the inverse of the data variability measure used in the quantification for assessing the functional relation. We offer an easy to use code for open-source software for implementing some of the quantifications. [ABSTRACT FROM PUBLISHER]

Since heterogeneity between reliability coefficients is usually found in reliability generalization studies, moderator analyses constitute a crucial step for that meta-analytic approach. In this study, different procedures for conducting mixed-effects meta-regression analyses were compared. Specifically, four transformation methods for the reliability coefficients, two estimators of the residual between-studies variance, and two methods for testing regression coefficients significance were combined in a Monte Carlo simulation study. The different methods were compared in terms of bias and mean square error (MSE) of the slope estimates, and Type I error and statistical power rates for the slope statistical tests. The results of the simulation study did not vary as a function of the residual variance estimator. All transformation methods provided negatively biased estimates, but both bias and MSE were reasonably small in all cases. In contrast, important differences were found regarding statistical tests, with the method proposed by Knapp and Hartung showing a better adjustment to the nominal significance level and higher power rates than the standard method. [ABSTRACT FROM AUTHOR]