This article introduces a new measure, the skewness of the standardized deviates, to quantify publication bias in meta-analysis. Publication bias is a significant issue in systematic reviews and meta-analyses, affecting the validity and generalization of conclusions. Current methods for detecting publication bias include selection models and funnel-plot-based methods. However, measures to quantify publication bias are rarely studied. The proposed skewness measure describes the asymmetry of the collected studies' distribution and can serve as a test statistic. It is based on the standardized deviates of the effect sizes and is studied for its large sample properties. The article also presents a new test for publication bias derived from the skewness. The performance of the skewness-based test is evaluated using simulations and three case studies. The skewness-based test is shown to have high power in many cases, particularly when the effect sizes are unimodal. However, it may have lower power in scenarios with weak or moderate heterogeneity. The combined test, which uses both the regression intercept and the skewness-based test, maintains high power in most settings. The proposed skewness measure is intuitive and can be used to quantify publication bias. It is also a test statistic, and its large sample properties have been studied. The article also discusses the limitations of the skewness measure, including its performance in small meta-analyses and its interpretation in multi-modal distributions. The skewness-based test is based on the asymmetry of the funnel plot, which can be caused by sources other than publication bias, such as reference bias or study quality. Therefore, researchers should carefully examine whether the asymmetry is due to publication bias or other sources of bias. The article concludes that the skewness-based test is a promising measure for quantifying publication bias in meta-analysis.This article introduces a new measure, the skewness of the standardized deviates, to quantify publication bias in meta-analysis. Publication bias is a significant issue in systematic reviews and meta-analyses, affecting the validity and generalization of conclusions. Current methods for detecting publication bias include selection models and funnel-plot-based methods. However, measures to quantify publication bias are rarely studied. The proposed skewness measure describes the asymmetry of the collected studies' distribution and can serve as a test statistic. It is based on the standardized deviates of the effect sizes and is studied for its large sample properties. The article also presents a new test for publication bias derived from the skewness. The performance of the skewness-based test is evaluated using simulations and three case studies. The skewness-based test is shown to have high power in many cases, particularly when the effect sizes are unimodal. However, it may have lower power in scenarios with weak or moderate heterogeneity. The combined test, which uses both the regression intercept and the skewness-based test, maintains high power in most settings. The proposed skewness measure is intuitive and can be used to quantify publication bias. It is also a test statistic, and its large sample properties have been studied. The article also discusses the limitations of the skewness measure, including its performance in small meta-analyses and its interpretation in multi-modal distributions. The skewness-based test is based on the asymmetry of the funnel plot, which can be caused by sources other than publication bias, such as reference bias or study quality. Therefore, researchers should carefully examine whether the asymmetry is due to publication bias or other sources of bias. The article concludes that the skewness-based test is a promising measure for quantifying publication bias in meta-analysis.