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In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

In panel data models and other regressions with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) to account for heteroscedasticity and un-modeled dependence among the errors. Although asymptotically consistent, CRVE can be biased downward when the number of clusters is small, leading to hypothesis tests with rejection rates that are too high. More accurate tests can be constructed using bias-reduced linearization (BRL), which corrects the CRVE based on a working model, in conjunction with a Satterthwaite approximation for t-tests. We propose a generalization of BRL that can be applied in models with arbitrary sets of fixed effects, where the original BRL method is undefined, and describe how to apply the method when the regression is estimated after absorbing the fixed effects. We also propose a small-sample test for multiple-parameter hypotheses, which generalizes the Satterthwaite approximation for t-tests. In simulations covering a wide range of scenarios, we find that the conventional cluster-robust Wald test can severely over-reject while the proposed small-sample test maintains Type I error close to nominal levels. The proposed methods are implemented in an R package called clubSandwich . This article has online supplementary materials.

The linear regression model is widely used in empirical work in economics, statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroscedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates is allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker-White heteroscedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroscedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroscedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is provided, and the proposed methods are also illustrated with an empirical application. Supplementary materials for this article are available online.

This study examined the nexus between urbanization and carbon emissions in West Africa. Second-generation econometric techniques that are robust to cross-sectional dependence and slope heterogeneity were used for the study. From the Pesaran–Yamagata homogeneity test, the slope coefficients were heterogeneous in nature. Also, the Breusch–Pagan LM test, the Pesaran scaled LM test, bias-corrected LM test, Pesaran CD test and the Friedman’s test confirmed the studied panels to be cross-sectionally dependent. Further, the CADF and the CIPS unit root tests established the variables to be first-differenced stationary. Additionally, the Westerlund and Edgerton bootstrap cointegration test and the Pedroni residual cointegration test affirmed the series to be cointegrated in the long run. The Driscoll–Kraay standard errors regression estimator was employed to examine the long-run equilibrium relationship amid the series, and from the results, urbanization had a significantly positive influence on CO 2 emissions in all the three panels. Also, economic growth had a materially positive effect on CO 2 emissions, while renewable energy consumption had a substantially negative impact on CO 2 emissions in all the panels. The causal connections amid the series were finally explored through the Dumitrescu–Hurlin panel causality test, and the discoveries were a bit varied across the various panels. Policy recommendations are further discussed.

Freedman [Adv. in Appl. Math. 40 (2008) 180—193; Ann. Appl. Stat. 2 (2008) 176—196] critiqued ordinary least squares regression adjustment of estimated treatment effects in randomized experiments, using Neyman's model for randomization inference. Contrary to conventional wisdom, he argued that adjustment can lead to worsened asymptotic precision, invalid measures of precision, and small-sample bias. This paper shows that in sufficiently large samples, those problems are either minor or easily fixed. OLS adjustment cannot hurt asymptotic precision when a full set of treatment—covariate interactions is included. Asymptotically valid confidence intervals can be constructed with the Huber—White sandwich standard error estimator. Checks on the asymptotic approximations are illustrated with data from Angrist, Lang, and Oreopoulos's [Am. Econ. J.: Appl. Econ. 1:1 (2009) 136—163] evaluation of strategies to improve college students' achievement. The strongest reasons to support Freedman's preference for unadjusted estimates are transparency and the dangers of specification search.

The conventional heteroskedasticity-robust (HR) variance matrix estimator for cross-sectional regression (with or without a degrees-of-freedom adjustment), applied to the fixed-effects estimator for panel data with serially uncorrelated errors, is incon- sistent if the number of time periods T is fixed (and greater than 2) as the number of entities n increases. We provide a bias-adjusted HR estimator that is$\\sqrt{nT}$-consistent under any sequences (n, T) in which n and/or T increase to ∞. This estimator can be extended to handle serial correlation of fixed order.

Purpose Identifying and understanding causal risk factors for crime over the life-course is a key area of inquiry in developmental criminology. Prospective longitudinal studies provide valuable information about the relationships between risk factors and later criminal offending. Meta-analyses that synthesize findings from these studies can summarize the predictive strength of different risk factors for crime, and offer unique opportunities for examining the developmental variability of risk factors. Complex data structures are common in such meta-analyses, whereby primary studies provide multiple (dependent) effect sizes. Methods This paper describes a recent innovative method for handling complex meta-analytic data structures arising due to dependent effect sizes: robust variance estimation (RVE). We first present a brief overview of the RVE method, describing the underlying models and estimation procedures and their applicability to meta-analyses of research in developmental criminology. We then present a tutorial on implementing these methods in the R statistical environment, using an example meta-analysis on risk factors for adolescent delinquency. Results The tutorial demonstrates how to estimate mean effect sizes and meta-regression models using the RVE method in R, with particular emphasis on exploring developmental variation in risk factors for crime and delinquency. The tutorial also illustrates hypothesis testing for meta-regression coefficients, including tests for overall model fit and incremental hypothesis tests. Conclusions The paper concludes by summarizing the benefits of using the RVE method with complex meta-analytic data structures, highlighting how this method can advance research syntheses in the field of developmental criminology.

Adsorption is considered to be one of the most effective technologies widely used in global environmental protection areas. Modeling of experimental adsorption isotherm data is an essential way for predicting the mechanisms of adsorption, which will lead to an improvement in the area of adsorption science. In this paper, we employed three isotherm models, namely: Langmuir, Freundlich, and Dubinin-Radushkevich to correlate four sets of experimental adsorption isotherm data, which were obtained by batch tests in lab. The linearized and non-linearized isotherm models were compared and discussed. In order to determine the best fit isotherm model, the correlation coefficient (r2) and standard errors (S.E.) for each parameter were used to evaluate the data. The modeling results showed that non-linear Langmuir model could fit the data better than others, with relatively higher r2 values and smaller S.E. The linear Langmuir model had the highest value of r2, however, the maximum adsorption capacities estimated from linear Langmuir model were deviated from the experimental data.

PurposeThis study aimed to investigate the validity and applicability of a non-exercise estimation of cardiorespiratory fitness using resting seismocardiography (SCG eV̇O2peak) in people with overweight and obesity before and after a 14-week lifestyle intervention.MethodsThe study was carried out at a Folk high school that offers 14-week courses on lifestyle changes where participants live at the school and voluntarily participate in daily lectures and activities. Sixty-seven men and women with age and body mass index between 18 and 70 years and 25–50 kg·m–2 were tested at baseline, and 52 had a follow-up test after 14 weeks. Testing included the determination of anthropometric variables, an SCG eV̇O2peak at supine rest, and a gold standard V̇O2peak test on a cycle ergometer until voluntary exhaustion.ResultsAgreement analysis for V̇O2peak at baseline (n = 67, SCG eV̇O2peak: 26.9 ± 1.9 ml·min–1·kg–1, V̇O2peak: 26.6 ± 1.6 ml·min–1·kg–1, mean ± 95% confidence interval) showed a bias of 0.3 ± 1.0 ml·min–1·kg–1 with 95% limits of agreement (LoA) ranging ± 9.8 ml·min–1·kg–1. A Pearson’s correlation of r = 0.78 (p < 0.0001) and a standard error of estimate (SEE) of 5.0 ml·min–1·kg–1 were found between methods. At follow-up (n = 52), body mass was reduced by 6.6 ± 1.4 kg (p < 0.0001). V̇O2peak increased by 3.3 ± 0.9 ml·min–1·kg–1 and 175 ± 78 ml·min–1 and SCG eV̇O2peak by 2.6 ± 0.8 ml·min–1·kg–1 and 93 ± 76 ml·min–1 (two-way ANOVA repeated measure: intervention p < 0.0001, method p = 0.939 and interaction p = 0.125, relative V̇O2peak). A Pearson’s correlation of r = 0.37 (p < 0.05) was found between changes in relative V̇O2peak but not for absolute V̇O2peak r = 0.10 (p = 0.402).ConclusionsThe SCG method is accurate for estimating V̇O2peak and appropriate for detecting group changes in both relative and absolute V̇O2peak following a lifestyle intervention in people with overweight and obesity. Furthermore, the method can detect individual changes in V̇O2peak but not independently of body mass changes. Yet, the applicability is still limited by the relatively large variation in LoA and SEE.

This study aims to determine the role of globalization, electronic government, financial development, concerning the moderation of institutional quality in reducing income inequality and poverty in One Belt One Road countries. The electronic government and regional integration of the economies of the One Belt One Road countries has increased globalization and can play a vital role in reducing income inequality and poverty. However, this globalization and digital transformation of government systems can only be beneficial in the presence of good institutional quality. The sample includes 64 One Belt One Road countries from 2003 to 2018. We employed a two-step system generalized method of moment (Sys-GMM) and a robustness check through Driscoll–Kraay standard errors regression. Our findings show that globalization, economic growth, e-government development, government expenditure, and inflation have a statistically significant and negative impact on income inequality and are key to eradicating income inequality and poverty. On the other hand, financial development, gross capital formation, and population size positively influence income inequality, which causes an increase in poverty and income inequality as financial development and population levels increase. Moderating variable institutional quality also positively impacts income inequality, which means that institutional quality in Belt and Road Countries is weak, as they are mostly developing countries that need to improve their systems. Moreover, the marginal effect also revealed that institutional quality has a corrective effect on the factors’ relationship with income inequality. Our findings endorse and conclude that globalization and e-government development improve economic growth and eradicate poverty and income inequality by boosting digitalization, investments, job creation, and wage increases for semi-skilled and unskilled human capital in Belt and Road countries. The sustainable utilization of financial and institutional resources plays a vital role in reducing income inequality and poverty in Belt and Road countries.