A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations.

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of 'missingness'.

Association between panic disorder and childhood adversities: a systematic review and meta-analysis.

BACKGROUND: Adverse childhood experiences (ACEs) increase the risk of mental health difficulties in general, but the link to panic disorder (PD) has received comparatively little attention. There are no data for the magnitudes between ACEs and PD. This systematic review and meta-analysis estimated the overall, as well as the subgroups, odds ratio of having PD in adults who report ACEs, compared to adults who do not. METHODS: The study was pre-registered on PROSPERO [CRD42018111506] and the database was searched in June 2021. In order to overcome the violation of independent assumptions due to multiple estimations from the same samples, we utilized a robust variance estimation model that supports meta-analysis for clustered estimations. Accordingly, an advanced method relaxing the distributional and asymptotic assumptions was used to assess publication bias and sensitivity. RESULTS: The literature search and screening returned 34 final studies, comprising 192,182 participants. Ninety-six estimations of 20 types of ACEs were extracted. Pooled ORs are: overall 2.2, CI (1.82-2.58), sexual abuse 1.92, CI (1.37-2.46), physical abuse 1.71, CI (1.37-2.05), emotional abuse 1.61, CI (0.868-2.35), emotional neglect 1.53, CI (0.756-2.31), parental alcoholism 1.83, CI (1.24-2.43), and parental separation/loss 1.82, CI (1.14-2.50). No between-group difference was identified by either sociolegal classification (abuse, neglect, household dysfunction) or threat-deprivation dimensions (high on threat, high on deprivation and mixed). CONCLUSIONS: There are links of mild to medium strength between overall ACEs and PD as well as individual ACEs. The homogeneous effect sizes across ACEs either suggest the effects of ACEs on PD are comparable, or raised the question whether the categorical or dimensional approaches to classifying ACEs are the definitive ways to conceptualize the impact of ACEs on later mental health.

Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control.

BACKGROUND: The effectiveness of malaria vector control interventions is often evaluated using cluster randomized trials (CRT) with outcomes assessed using repeated cross-sectional surveys. A key requirement for appropriate design and analysis of longitudinal CRTs is accounting for the intra-cluster correlation coefficient (ICC). In addition to exchangeable correlation (constant ICC over time), correlation structures proposed for longitudinal CRT are block exchangeable (allows a different within- and between-period ICC) and exponential decay (allows between-period ICC to decay exponentially). More flexible correlation structures are available in statistical software packages and, although not formally proposed for longitudinal CRTs, may offer some advantages. Our objectives were to empirically explore the impact of these correlation structures on treatment effect inferences, identify gaps in the methodological literature, and make practical recommendations. METHODS: We obtained data from a parallel-arm CRT conducted in Tanzania to compare four different types of insecticide-treated bed-nets. Malaria prevalence was assessed in cross-sectional surveys of 45 households in each of 84 villages at baseline, 12-, 18- and 24-months post-randomization. We re-analyzed the data using mixed-effects logistic regression according to a prespecified analysis plan but under five different correlation structures as well as a robust variance estimator under exchangeable correlation and compared the estimated correlations and treatment effects. A proof-of-concept simulation was conducted to explore general conclusions. RESULTS: The estimated correlation structures varied substantially across different models. The unstructured model was the best-fitting model based on information criteria. Although point estimates and confidence intervals for the treatment effect were similar, allowing for more flexible correlation structures led to different conclusions based on statistical significance. Use of robust variance estimators generally led to wider confidence intervals. Simulation results showed that under-specification can lead to coverage probabilities much lower than nominal levels, but over-specification is more likely to maintain nominal coverage. CONCLUSION: More flexible correlation structures should not be ruled out in longitudinal CRTs. This may be particularly important in malaria trials where outcomes may fluctuate over time. In the absence of robust methods for selecting the best-fitting correlation structure, researchers should examine sensitivity of results to different assumptions about the ICC and consider robust variance estimators.

Consistent and robust inference in hazard probability and odds models with discrete-time survival data.

For discrete-time survival data, conditional likelihood inference in Cox's hazard odds model is theoretically desirable but exact calculation is numerical intractable with a moderate to large number of tied events. Unconditional maximum likelihood estimation over both regression coefficients and baseline hazard probabilities can be problematic with a large number of time intervals. We develop new methods and theory using numerically simple estimating functions, along with model-based and model-robust variance estimation, in hazard probability and odds models. For the probability hazard model, we derive as a consistent estimator the Breslow-Peto estimator, previously known as an approximation to the conditional likelihood estimator in the hazard odds model. For the hazard odds model, we propose a weighted Mantel-Haenszel estimator, which satisfies conditional unbiasedness given the numbers of events in addition to the risk sets and covariates, similarly to the conditional likelihood estimator. Our methods are expected to perform satisfactorily in a broad range of settings, with small or large numbers of tied events corresponding to a large or small number of time intervals. The methods are implemented in the R package dSurvival.

Estimating outcome-specific effects in meta-analyses of multiple outcomes: A simulation study.

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented.

Synthesizing effects for multiple outcomes per study using robust variance estimation versus the three-level model.

Primary studies increasingly report information that can be used to provide multiple effect sizes. Of interest in this study, primary studies might compare a treatment and a control group on multiple related outcomes that result in multiple dependent effect sizes to be synthesized. There are a number of ways to handle the resulting within-study "multiple-outcome" dependency. The present study focuses on use of the multilevel meta-analysis model (Van den Noortgate, López-López, Marín-Martínez, & Sánchez-Meca, 2013) and robust variance estimation (Hedges, Tipton, & Johnson, 2010) for handling this dependency, as well as for estimating outcome-specific mean effect sizes. We assessed these two approaches under various conditions that differed from each other in within-study sample size; the number of effect sizes per outcome; the number of outcomes per study; the number of studies per meta-analysis; the ratio of variances at Levels 1, 2, and 3; and the true correlation between pairs of effect sizes at the between-study level. Limitations and directions for future research are discussed.

A framework for the meta-analysis of Bland-Altman studies based on a limits of agreement approach.

Bland-Altman method comparison studies are common in the medical sciences and are used to compare a new measure to a gold-standard (often costlier or more invasive) measure. The distribution of these differences is summarized by two statistics, the 'bias' and standard deviation, and these measures are combined to provide estimates of the limits of agreement (LoA). When these LoA are within the bounds of clinically insignificant differences, the new non-invasive measure is preferred. Very often, multiple Bland-Altman studies have been conducted comparing the same two measures, and random-effects meta-analysis provides a means to pool these estimates. We provide a framework for the meta-analysis of Bland-Altman studies, including methods for estimating the LoA and measures of uncertainty (i.e., confidence intervals). Importantly, these LoA are likely to be wider than those typically reported in Bland-Altman meta-analyses. Frequently, Bland-Altman studies report results based on repeated measures designs but do not properly adjust for this design in the analysis. Meta-analyses of Bland-Altman studies frequently exclude these studies for this reason. We provide a meta-analytic approach that allows inclusion of estimates from these studies. This includes adjustments to the estimate of the standard deviation and a method for pooling the estimates based upon robust variance estimation. An example is included based on a previously published meta-analysis. Copyright © 2017 John Wiley & Sons, Ltd.

A comparison of the variance estimation methods for heteroscedastic nonlinear models.

Heteroscedasticity is commonly encountered when fitting nonlinear regression models in practice. We discuss eight different variance estimation methods for nonlinear regression models with heterogeneous response variances, and present a simulation study to compare the performance of the eight methods in terms of estimating the standard errors of the fitted model parameters. The simulation study suggests that when the true variance is a function of the mean model, the power of the mean variance function estimation method and the transform-both-sides method are the best choices for estimating the standard errors of the estimated model parameters. In general, the wild bootstrap estimator and two modified versions of the standard sandwich variance estimator are reasonably accurate with relatively small bias, especially when the heterogeneity is nonsystematic across values of the covariate. Furthermore, we note that the two modified sandwich estimators are appealing choices in practice, considering the computational advantage of these two estimation methods relative to the variance function estimation method and the transform-both-sides approach. Copyright © 2016 John Wiley & Sons, Ltd.

Analysis of counts with two latent classes, with application to risk assessment based on physician-visit records of cancer survivors.

Motivated by a cancer survivorship program, this paper explores event counts from two categories of individuals with unobservable membership. We formulate the counts using a latent class model and consider two likelihood-based inference procedures, the maximum likelihood estimation (MLE) and a pseudo-MLE procedure. The pseudo-MLE utilizes additional information on one of the latent classes. It yields reduced computational intensity and potentially increased estimation efficiency. We establish the consistency and asymptotic normality of the proposed pseudo-MLE, and we present an extended Huber sandwich estimator as a robust variance estimator for the pseudo-MLE. The finite-sample properties of the two-parameter estimators along with their variance estimators are examined by simulation. The proposed methodology is illustrated by physician-claim data from the cancer program.

Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales.

Environmental heterogeneity is regarded as one of the most important factors governing species richness gradients. An increase in available niche space, provision of refuges and opportunities for isolation and divergent adaptation are thought to enhance species coexistence, persistence and diversification. However, the extent and generality of positive heterogeneity-richness relationships are still debated. Apart from widespread evidence supporting positive relationships, negative and hump-shaped relationships have also been reported. In a meta-analysis of 1148 data points from 192 studies worldwide, we examine the strength and direction of the relationship between spatial environmental heterogeneity and species richness of terrestrial plants and animals. We find that separate effects of heterogeneity in land cover, vegetation, climate, soil and topography are significantly positive, with vegetation and topographic heterogeneity showing particularly strong associations with species richness. The use of equal-area study units, spatial grain and spatial extent emerge as key factors influencing the strength of heterogeneity-richness relationships, highlighting the pervasive influence of spatial scale in heterogeneity-richness studies. We provide the first quantitative support for the generality of positive heterogeneity-richness relationships across heterogeneity components, habitat types, taxa and spatial scales from landscape to global extents, and identify specific needs for future comparative heterogeneity-richness research.

Accuracy and precision of fixed and random effects in meta-analyses of randomized control trials for continuous outcomes.

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that imputing a fixed correlation 0.8 or adopting a multivariate meta-regression with robust variance estimation work well for estimating the pooled effect but lead to slightly distorted between-study heterogeneity estimates. In contrast, three-level meta-regressions resulted in largely unbiased fixed effects but more inconsistent prediction intervals. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.

Simplified methods for variance estimation in microbiome abundance count data analysis.

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.

Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta-analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta-analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions.

Adding Chinese herbal medicine bath therapy to conventional therapies for psoriasis vulgaris: A systematic review with meta-analysis of randomised controlled trials.

BACKGROUND: Chinese herbal medicine (CHM) bath is commonly used in China as an adjuvant therapy for managing psoriasis vulgaris. Previous systematic reviews showed that CHM bath therapy was effective and safe for psoriasis vulgaris, however, without exploration of the specifics of CHM bath therapy such as the optimal temperature, duration of each session, and the total treatment duration. PURPOSE: To evaluate the add-on effects of CHM bath therapy to conventional therapies for adult psoriasis vulgaris. METHODS: We conducted a comprehensive search in nine medical databases from inception to September 2022 to identify relevant randomised controlled trials (RCTs) published in Chinese or English. The included studies compared the combination of CHM bath therapy and conventional therapies to conventional therapies alone for adult psoriasis vulgaris. Methodological quality assessment of the included RCTs was performed using the Cochrane risk-of-bias tool 2 (RoB 2). Statistical analysis was carried out using RevMan 5.4, R 4.2.3 and Stata 12.0 software. The certainty of evidence of outcome measures was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation Working Group (GRADE) system. RESULTS: A total of 23 RCTs involving 2,183 participants were included in this systematic review. Findings suggested that the combination of CHM bath therapy and conventional therapies was more effective in reducing Psoriasis Area and Severity Index (PASI), Dermatology Life Quality Index (DLQI) and itch visual analogue scale, compared to using conventional therapies alone. These enhanced effects were notably observed when the CHM bath was set above 38 °C and had a duration of 20 and 30 min, as assessed by DLQI. Moreover, an eight-week treatment duration resulted in better effects for PASI compared to shorter durations. Additionally, the top ten frequently used herbs in the included studies were identified. Despite the findings, the certainty of evidence was rated as 'low' or 'moderate' based on the GRADE assessment, and significant heterogeneity was detected in subgroup and sensitivity analyses. CONCLUSION: The CHM bath therapy combined with conventional therapies is more effective and safer than conventional therapies alone for adult psoriasis vulgaris. The results suggest a potential correlation between treatment effects and factors such as extended treatment duration, increased bath temperature, and longer bath sessions. However, the certainty of evidence was downgraded due to methodological limitations of the included studies. To confirm the findings of this systematic review, a double-blinded, placebo-controlled RCT is needed in the future.

Variance estimation for the average treatment effects on the treated and on the controls.

Common causal estimands include the average treatment effect, the average treatment effect of the treated, and the average treatment effect on the controls. Using augmented inverse probability weighting methods, parametric models are judiciously leveraged to yield doubly robust estimators, that is, estimators that are consistent when at least one the parametric models is correctly specified. Three sources of uncertainty are associated when we evaluate these estimators and their variances, that is, when we estimate the treatment and outcome regression models as well as the desired treatment effect. In this article, we propose methods to calculate the variance of the normalized, doubly robust average treatment effect of the treated and average treatment effect on the controls estimators and investigate their finite sample properties. We consider both the asymptotic sandwich variance estimation, the standard bootstrap as well as two wild bootstrap methods. For the asymptotic approximations, we incorporate the aforementioned uncertainties via estimating equations. Moreover, unlike the standard bootstrap procedures, the proposed wild bootstrap methods use perturbations of the influence functions of the estimators through independently distributed random variables. We conduct an extensive simulation study where we vary the heterogeneity of the treatment effect as well as the proportion of participants assigned to the active treatment group. We illustrate the methods using an observational study of critical ill patients on the use of right heart catherization.

Quantitative evidence synthesis: a practical guide on meta-analysis, meta-regression, and publication bias tests for environmental sciences.

Meta-analysis is a quantitative way of synthesizing results from multiple studies to obtain reliable evidence of an intervention or phenomenon. Indeed, an increasing number of meta-analyses are conducted in environmental sciences, and resulting meta-analytic evidence is often used in environmental policies and decision-making. We conducted a survey of recent meta-analyses in environmental sciences and found poor standards of current meta-analytic practice and reporting. For example, only ~ 40% of the 73 reviewed meta-analyses reported heterogeneity (variation among effect sizes beyond sampling error), and publication bias was assessed in fewer than half. Furthermore, although almost all the meta-analyses had multiple effect sizes originating from the same studies, non-independence among effect sizes was considered in only half of the meta-analyses. To improve the implementation of meta-analysis in environmental sciences, we here outline practical guidance for conducting a meta-analysis in environmental sciences. We describe the key concepts of effect size and meta-analysis and detail procedures for fitting multilevel meta-analysis and meta-regression models and performing associated publication bias tests. We demonstrate a clear need for environmental scientists to embrace multilevel meta-analytic models, which explicitly model dependence among effect sizes, rather than the commonly used random-effects models. Further, we discuss how reporting and visual presentations of meta-analytic results can be much improved by following reporting guidelines such as PRISMA-EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Ecology and Evolutionary Biology). This paper, along with the accompanying online tutorial, serves as a practical guide on conducting a complete set of meta-analytic procedures (i.e., meta-analysis, heterogeneity quantification, meta-regression, publication bias tests and sensitivity analysis) and also as a gateway to more advanced, yet appropriate, methods.

Cluster wild bootstrapping to handle dependent effect sizes in meta-analysis with a small number of studies.

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small-sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

Efficacy and Safety of Oral Chinese Herbal Medicine for Migraine: A Systematic Review and Meta-Analyses Using Robust Variance Estimation Model.

Background: Migraine is a prevalent headache disorder with significant impacts on patients' quality of life and economic burden. Chinese herbal medicine (CHM) is commonly prescribed for migraine in China. This review aimed to provide a rigorous evaluation of evidence on the efficacy of oral CHM for migraine and explore the correlation between its effect size and treatment duration. Methods: We searched nine digital databases (PubMed, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, AMED, BioMedical Literature, CNKI, CQVIP, and Wanfang Data) from their inceptions to May 2021, with the language being restricted to Chinese and English. Randomized, placebo-controlled trials using oral CHM to treat adult migraine were included. Data screening and extraction were conducted by two independent reviewers. The methodological quality of randomized controlled trials (RCTs) was assessed using the Cochrane Risk of Bias tool. Meta-analyses were conducted to estimate the effect size using a random effect model, and a robust variance estimation (RVE) model was constructed to explore the correlation between treatment effects and treatment duration. The certainty of the evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation. Publication bias was tested using a funnel plot and Egger's test. Results: A total of 18 RCTs involving 3,015 participants were included. Results of the meta-analyses showed that, at the end of the treatment phase, CHM was more efficacious than placebo in reducing migraine frequency, migraine days, and pain severity, and increasing response rate. Additionally, CHM showed superior effects to placebo in lowering migraine frequency and pain severity at the end of the 4-week follow-up. The RVE model suggested that the benefits of CHM for migraine frequency and pain intensity increased as treatment duration extended. The number of adverse events reported by the CHM and placebo groups was comparable. The certainty of the evidence was graded as "moderate." No publication bias was detected. Conclusion: Oral CHM appeared to be more efficacious than placebo for reducing migraine frequency and pain severity. Greater treatment effects were associated with longer treatment duration. The oral CHM was well tolerated. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/#recordDetails, identifier: CRD42021270719.

An assessment of statistical methods for nonindependent data in ecological meta-analyses.

In ecological meta-analyses, nonindependence among observed effect sizes from the same source paper is common. If not accounted for, nonindependence can seriously undermine inferences. We compared the performance of four meta-analysis methods that attempt to address such nonindependence and the standard random-effect model that ignores nonindependence. We simulated data with various types of within-paper nonindependence, and assessed the standard deviation of the estimated mean effect size and Type I error rate of each method. Although all four methods performed substantially better than the standard random-effects model that assumes independence, there were differences in performance among the methods. A two-step method that first summarizes the multiple observed effect sizes per paper using a weighted mean and then analyzes the reduced data in a standard random-effects model, and a robust variance estimation method performed consistently well. A hierarchical model with both random paper and study effects gave precise estimates but had a higher Type I error rates, possibly reflecting limitations of currently available meta-analysis software. Overall, we advocate the use of the two-step method with a weighted paper mean and the robust variance estimation method as reliable ways to handle within-paper nonindependence in ecological meta-analyses.

Are people more prejudiced towards groups that are perceived as coherent? A meta-analysis of the relationship between out-group entitativity and prejudice.

A meta-analysis was conducted to examine the relationship between out-group entitativity and prejudice. A quantitative analysis of 85 effect sizes from 33 independent samples showed a significant positive relationship between entitativity and prejudice (Fisher's z = .414, 95% CI [0.272, 0.557], p < .0001). Three possible moderators of the relationship between entitativity and prejudice were tested: conceptualization of the entitativity (essence-based entitativity scale, agency-based entitativity scale, common entitativity scale), the target of the prejudice, and the measures of prejudice (attitudes, emotions, behaviour towards out-group). Results demonstrated that out-group entitativity correlated with prejudice only when entitativity was conceptualized as an essence-based or common-based scale, and prejudice was measured as the attitude to the out-group. The target of prejudice does not moderate the relationship between entitativity and prejudice.