Meta-analyses of partial correlations are biased: Detection and solutions.

The prevalence and effect of publication bias in orthopaedic meta-analyses

The purpose of the study: to consider the features of statistical analysis in psychological and medical-psychological research. Results. One of the features of psychological research is the process of measuring the results of the experiment. Unlike medical research, which typically uses physical units of measurement, most variables in psychological research are not unambiguous or easy to measure. To describe the procedures of psychological measurement in psychological research four types of measurement scales are used: nominative, ordinal, interval, and scale of equal relations. Statistical analysis of the results of medical and psychological research depends on the type of scale in which the studied trait was measured. The nominative scale is a scale that classifies by name. Conjugacy tables are used to describe and analyze nominative scales. An ordinal scale is a scale that classifies on the principle of more or less. Statistical analysis of ordinal scales is performed using non-parametric criteria. The interval scale is a scale according to which each of the possible values ​​of the feature is at the same distance from the other value. The scale of equal relations has all the properties of nominative, ordinal, and interval scales. To analyze the results of the study, which were measured in interval scales or in scales of equal relations, one uses parametric or nonparametric criteria depending on the distribution of a random variable. Conclusions. The choice of method of statistical analysis of the results of psychological research depends on the type of scale in which the data were measured.

This paper addresses two central problems in simulation methodology: the generation of confidence intervals for the steady state means of the output sequences and the sequential use of these confidence intervals to control the run length. The variance of the sample mean of a covariance stationary process is given approximately by p(0)/N, where p(f) is the spectral density at frequency f and N is the sample size. In an earlier paper we developed a method of confidence interval generation based on the estimation of p(0) through the least squares fit of a quadratic to the logarithm of the periodogram. This method was applied in a run length control procedure to a sequence of batched means. As the run length increased the batch means were rebatched into larger batch sizes so as to limit storage requirements. In this rebatching the shape of the spectral density changes, gradually becoming flat as N increases. Quadratics were chosen as a compromise between small sample bias and large sample stability. In this paper we consider smoothing techniques which adapt to the changing spectral shape in an attempt to improve both the small and large sample behavior of the method. The techniques considered are polynomial smoothing with the degree selected sequentially using standard regression statistics, polynomial smoothing with the degree selected by cross validation, and smoothing splines with the amount of smoothing determined by cross validation. These techniques were empirically evaluated both for fixed sample sizes and when incorporated into the sequential run length control procedure. For fixed sample sizes they did not improve the small sample behavior and only marginally improved the large sample behavior when compared with the quadratic method. Their performance in the sequential procedure was unsatisfactory. Hence, the straightforward quadratic technique recommended in the earlier paper is still recommended as an effective, practical technique for simulation confidence interval generation and run length control.

Recently the attention of school administrators has been called to the possible uses of partial and multiple correlations.1 And in a similar manner Dr. B. R. Buckingham, in an editorial in the April number of the Journal of Educational Research, not only discusses the possible uses of partial correlations, but offers as well a simple illustrative solution, based on hypothetical data, and points out some ways and means of shortening the processes. Several specific studies have made use of these coefficients. Dr. Mallory, in his study of the relation of physical defects to achievement in the elementary school,2 makes use of partial correlations to free the total correlations found from the effects of certain factors which might affect the relationship found between certain physical defects and achievements. Dr. Ivy, in his study of Mississippi teachers,3 uses partial correlations to determine the relation between pay and training when a third factor, experience, is controlled. By use of multiple correlations he seeks to answer the question: Do these items constitute an appreciable number of the factors that enter into the amount of salary paid ? Dr. Knight, in his study of qualities related to teaching success,4 used partial correlations to keep constant the factors, intelligence and normal school scholarship, in determining the relation between to teach and ability to pass a professional test. There have been several articles which have described the number of operations by which both the partial and multiple coefficients may

Medical science, medical services and medical education.

Researchers have looked at comparisons between medical epidemiological research and psychological research using effect size r in an effort to compare relative effects. Often the outcomes of such efforts have demonstrated comparatively low effects for medical epidemiology research in comparison with effect sizes seen in psychology. The conclusion has often been that relatively small effects seen in psychology research are as strong as those found in important epidemiological medical research. The author suggests that many of the calculated effect sizes from medical epidemiological research on which this conclusion has been based are flawed. Specifically, rather than calculating effect sizes for treatment, many results have been for a Treatment Effect × Disease Effect interaction that was irrelevant to the main study hypothesis. A technique for developing a “hypothesis-relevant” effect size r is proposed.

Is IPO Underperformance a Peso Problem?

Estimation of Correlation Coefficient to Correct Selection Bias in Small Samples

Analyzing Experimental Data Using Regression: When is Bias a Practical Concern?

In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. As a consequence, the generalized method of moments (GMM) estimator exhibits a large bias in small samples, especially when the autoregressive parameter is close to unity. To address this issue, we propose a regularized version of the one-step GMM estimator using three regularization schemes based on three different ways of inverting the covariance matrix of the instruments. Under double asymptotics, we show that our regularized estimators are consistent and asymptotically normal. These regularization schemes involve a tuning or regularization parameter which needs to be chosen. We derive a data-driven selection of this regularization parameter based on an approximation of the higher-order mean square error and show its optimality. As an empirical application, we estimate a model of income dynamics.

Azerbaijan is a modern, rapidly developing democratic country at the crossroads of Europe and Asia. The country is currently harmonizing its national legislation with international norms, and reforming its national scientific and medical. Higher standards of medical research and education will enhance public health and protect human rights to life and health that are specified in Azerbaijan Constitution. In order to raise its medical research and education to international standards, Azerbaijani scientists and authorities are studying the experience of other countries and taking measures to implement international standards and norms in the country's national legislation. Cooperation with the WHO, UNESCO and other international and foreign organizations, both on regional and global level is creating steps to achieve this goal. These steps include, for example, creation of the Azerbaijan unit of the UNESCO Chair in Bioethics and teaching bioethics based on UNESCO's Bioethics Core Curriculum. Another step is providing research fellowship for young Azerbaijani professionals to study at leading medical research and educational centers around the world including Harvard Medical School and Boston Children's Hospital in the USA, and Koc University in Turkey. A complementary step is the development of local bioethical research, including its legal, ethical and scientific foundations. Adherence to ethical principles in different spheres of life is currently one of the most challenging social and professional issues, especially, this is true with the development of new medical technologies in recent decades and the development of new ethical and legal standards, issues involving different areas of health and medicine and their relation to human rights. Bioethics in Azerbaijan is developing as an important field that deals with universal moral principles within the context of both national laws and the UNESCO Universal Declaration on Bioethics and Human Rights.

Fraud and Deceit in Medical Research

This paper considers estimation of the parameters for the fractionally integrated class of processes known as ARFIMA. We consider the small sample properties of a conditional sum-of-squares estimator that is asymptotically equivalent to MLE. This estimator has the advantage of being relatively simple and can estimate all the parameters, including the mean, simultaneously. The simulation evidence we present indicates that estimation of the mean can make a considerable difference to the small sample bias and MSE of the other parameter estimates.

Adjusted quasi-maximum likelihood estimator for mixed regressive, spatial autoregressive model and its small sample bias

FRI0613-HPR Assessing Reporting Bias in Trials of Exercise Interventions for Pain Management in Rheumatic Diseases: An Application of the Test for Excess Significance