Meta-analyzing partial correlation coefficients using Fisher's <i>z</i> transformation

The partial correlation coefficient (PCC) is used to quantify the linear relationship between two variables while taking into account/controlling for other variables. Researchers frequently synthesize PCCs in a meta-analysis, but two of the assumptions of the common equal-effect and random-effects meta-analysis model are by definition violated. First, the sampling variance of the PCC cannot assumed to be known, because the sampling variance is a function of the PCC. Second, the sampling distribution of each primary study's PCC is not normal since PCCs are bounded between -1 and 1. I advocate applying the Fisher's z transformation analogous to applying Fisher's z transformation for Pearson correlation coefficients, because the Fisher's z transformed PCC is independent of the sampling variance and its sampling distribution more closely follows a normal distribution. Reproducing a simulation study by Stanley and Doucouliagos and adding meta-analyses based on Fisher's z transformed PCCs shows that the meta-analysis based on Fisher's z transformed PCCs had lower bias and root mean square error than meta-analyzing PCCs. Hence, meta-analyzing Fisher's z transformed PCCs is a viable alternative to meta-analyzing PCCs, and I recommend to accompany any meta-analysis based on PCCs with one using Fisher's z transformed PCCs to assess the robustness of the results

Analyzing data of a Multilab replication project with individual participant data meta-analysis:A tutorial

Multilab replication projects such as Registered Replication Reports (RRR) and Many Labs projects are used to replicate an effect in different tabs. Data of these projects are usually analyzed using conventional meta-analysis methods. This is certainly not the best approach because it does not make optimal use of the available data as a summary rather than participant data are analyzed. I propose to analyze data of multilab replication projects with individual participant data (IPD) meta-analysis where the participant data are analyzed directly. The prominent advantages of IPD meta-analysis are that it generally has larger statistical power to detect moderator effects and allows drawing conclusions at the participant and lab level. However, a disadvantage is that IPD meta-analysis is more complex than conventional meta-analysis. In this tutorial, I illustrate IPD meta-analysis using the RRR by McCarthy and colleagues, and 1 provide R code and recommendations to facilitate researchers to apply these methods

Atomic resolution mapping of phonon excitations in STEM-EELS experiments

Atomically resolved electron energy-loss spectroscopy experiments are commonplace in modern aberrationcorrected transmission electron microscopes. Energy resolution has also been increasing steadily with the continuous improvement of electron monochromators. Electronic excitations however are known to be delocalised due to the long range interaction of the charged accelerated electrons with the electrons in a sample. This has made several scientists question the value of combined high spatial and energy resolution for mapping interband transitions and possibly phonon excitation in crystals. In this paper we demonstrate experimentally that atomic resolution information is indeed available at very low energy losses around 100 meV expressed as a modulation of the broadening of the zero loss peak. Careful data analysis allows us to get a glimpse of what are likely phonon excitations with both an energy loss and gain part. These experiments confirm recent theoretical predictions on the strong localisation of phonon excitations as opposed to electronic excitations and show that a combination of atomic resolution and recent developments in increased energy resolution will offer great benefit for mapping phonon modes in real space

A critical reflection on computing the sampling variance of the partial correlation coefficient

The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis. The default inverse variance weights in standard meta-analysis models require researchers to compute not only the partial correlation coefficients of each study but also its corresponding sampling variance. The existing literature is diffuse on how to estimate this sampling variance, because two estimators exist that are both widely used. We critically reflect on both estimators, study their statistical properties, and provide recommendations for applied researchers. We also compute the sampling variances of studies using both estimators in a meta-analysis on the partial correlation between self-confidence and sports performance

Conducting meta-analyses based on p values:Reservations and recommendations for applying p-uniform and p-curve

Because of overwhelming evidence of publication bias in psychology, techniques to correct meta-analytic estimates for such bias are greatly needed. The methodology on which the p-uniform and p-curve methods are based has great promise for providing accurate meta-analytic estimates in the presence of publication bias. However, in this article, we show that in some situations, p-curve behaves erratically, whereas p-uniform may yield implausible estimates of negative effect size. Moreover, we show that (and explain why) p-curve and p-uniform result in overestimation of effect size under moderate-to-large heterogeneity and may yield unpredictable bias when researchers employ p-hacking. We offer hands-on recommendations on applying and interpreting results of meta-analyses in general and p-uniform and p-curve in particular. Both methods as well as traditional methods are applied to a meta-analysis on the effect of weight on judgments of importance. We offer guidance for applying p-uniform or p-curve using R and a user-friendly web application for applying p-uniform

Multicentric B-cell lymphoma in a pygmy goat

A six-year-old, male pygmy goat was referred with a sudden onset of peripheral lymphadenopathy, which initially started as enlarged inguinal lymph nodes. Clinical examination showed swollen retropharyngeal, prescapular and inguinal lymph nodes. Serologic testing for bovine leukemia, caprine arthritis-encephalitis virus and caseous lymphadenitis was negative. Fine needle aspirates of the prescapular lymph nodes were taken and revealed multiple, large lymphoblastic cells on cytology. Because of the poor prognosis and clinical deterioration, the animal was euthanized. Full necropsy was performed and showed generalized lymphadenopathy. Further histological and immunohistochemical investigation of the lymph nodes characterized this neoplasia as a multicentric large B-cell lymphoma