Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal's fail-safe number. Although Rosenthal's estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal's fail-safe number.This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal's estimator.Comment: Published in the International Scholarly Research Notices in December 201

A meta-analysis of the reliabilty of young’s internet addiction test

AIM: Currently there are at least 5 scales being used most frequently in studies for diagnosing Internet addiction or problematic Internet use. Moreover there are various studies using each scale mentioning differing reliability coefficients. The most frequently used questionnaire until now is Young’s Internet addiction test (YIAT20). The aim of this study is to produce an overall value for the reliability YIAT20, drawing from a large sample of studies. METHODS: A systematic search of the databases PsycINFO, Medline, EMBASE, Pubmed/Medline, and Google Scholar revealed 20 studies using out of which 11 gave reliability measures. We performed a meta-analysis of the values of Cronbach’s α values mentioned in each study, noted various moderates including sample subgroup (college or pre-college students), sample mean age, sample male percentage, online or offline answering of the questionnaire, and continent of the author’s study. Difference were sought between reliability in various categories while we performed a weighted least squares general linear model to find predictive factors of the variance in YIAT20’s reliability. All analyses were done in PASW 18.0. RESULTS: Eleven studies comprising of a total of 6821 participants were included in the final analysis. The overall Cronbach’s alpha computed from the studies was 0.889 (95% CI 0.884-0.895). The standard deviation of α was low: 0.049. Cronbach’s α was significantly lower in pre-college students [Mean Difference: -0.045 (95% CI-0.057, -0.032)], while Asian studies produced a higher value of Cronbach’s α not reaching significance [Mean Difference: -0.015 (95% CI-0.032, 0.002)]. The weighted least squares general linear model was adequate with R-Squared = 0.504 (Adjusted R-Squared = 0.292). The variable continent proved out to be a significant predictor of the value of reliablity. CONCLUSION: YIAT20 is a frequently used scale to measure Internet addiction. Mean differences showed that it is more reliable in college students and probably in Asia. A general linear model showed that the continent of the study affects significantly the outcome of reliability of the study with reliability decreasing when the continent is Europe. More studies are required to examine which scale is more reliable in pre-college students and in other continents

On the Greek economic crisis of 2009–2012: Fundamental causes and effects; future prospects for Greece and Eurozone

The aim of this paper is to present the economic, social, political and moral causes and effects of the Greek debt crisis. The debt and deficit figures for Greece are presented. The causes of the crisis are: (a) corruption, (b) tax evasion and deposit of the stolen money in tax heavens (c) inefficiency of the public sector, (d) absence of transparency practices in the system of justice, in the areas of health, education, defense and labor, (e) excessive consumption habits of the Greeks, (f) lack of entrepreneurial and innovative spirit among the Greeks, (g) excessive debt and non-permissible budget deficit. The consequences are: (a) unemployment, (b) poverty, (c) social exclusion, (d) rising inequalities as it is shown by original research on Gini coefficient, (e) homelessness, (f) health problems, (g) rising suicide rate. Future prospects are: possible exclusion of Greece from Eurozone because of inability to repay debt and to reduce deficit

Assessment of Performance of Correlation Estimates in Discrete Bivariate Distributions using Bootstrap Methodology

Little attention has been given to the correlation coefficient when data come from discrete or continuous non-normal populations. In this article, we consider the efficiency of two correlation coefficients which are from the same family, Pearson's and Spearman's estimators. Two discrete bivariate distributions were examined: the Poisson and the Negative Binomial. The comparison between these two estimators took place using classical and bootstrap techniques for the construction of confidence intervals. Thus, these techniques are also subject to comparison. Simulation studies were also used for the relative efficiency and bias of the two estimators. Pearson's estimator performed slightly better than Spearman's

Assessment of Performance of Correlation Estimates in Discrete Bivariate Distributions using Bootstrap Methodology

Little attention has been given to the correlation coefficient when data come from discrete or continuous non-normal populations. In this article, we consider the efficiency of two correlation coefficients which are from the same family, Pearson's and Spearman's estimators. Two discrete bivariate distributions were examined: the Poisson and the Negative Binomial. The comparison between these two estimators took place using classical and bootstrap techniques for the construction of confidence intervals. Thus, these techniques are also subject to comparison. Simulation studies were also used for the relative efficiency and bias of the two estimators. Pearson's estimator performed slightly better than Spearman's

Traumatic injury in the United States: In-patient epidemiology 2000–2011

Background Trauma is a leading cause of death and disability in the United States (US). This analysis describes trends and annual changes in in-hospital trauma morbidity and mortality; evaluates changes in age and gender specific outcomes, diagnoses, causes of injury, injury severity and surgical procedures performed; and examines the role of teaching hospitals and Level 1 trauma centres in the care of severely injured patients. Methods We conducted a retrospective descriptive and analytic epidemiologic study of an inpatient database representing 20,659,684 traumatic injury discharges from US hospitals between 2000 and 2011. The main outcomes and measures were survey-adjusted counts, proportions, means, standard errors, and 95% confidence intervals. We plotted time series of yearly data with overlying loess smoothing, created tables of proportions of common injuries and surgical procedures, and conducted survey-adjusted logistic regression analysis for the effect of year on the odds of in-hospital death with control variables for age, gender, weekday vs. weekend admission, trauma-centre status, teaching-hospital status, injury severity and Charlson index score. Results The mean age of a person discharged from a US hospital with a trauma diagnosis increased from 54.08 (s.e. = 0.71) in 2000 to 59.58 (s.e. = 0.79) in 2011. Persons age 45–64 were the only age group to experience increasing rates of hospital discharges for trauma. The proportion of trauma discharges with a Charlson Comorbidity Index score greater than or equal to 3 nearly tripled from 0.048 (s.e. = 0.0015) of all traumatic injury discharges in 2000 to 0.139 (s.e. = 0.005) in 2011. The proportion of patients with traumatic injury classified as severe increased from 22% of all trauma discharges in 2000 (95% CI 21, 24) to 28% in 2011 (95% CI 26, 30). Level 1 trauma centres accounted for approximately 3.3% of hospitals. The proportion of severely injured trauma discharges from Level 1 trauma centres was 39.4% (95% CI 36.8, 42.1). Falls, followed by motor-vehicle crashes, were the most common causes of all injuries. The total cost of trauma-related inpatient care between 2001 and 2011 in the US was $240.7 billion (95$12.0 billion (95% CI 10.5, 13.4) in 2001 to 29.1 billion (95% CI 25.2, 32.9) in 2011. Conclusions Trauma, which has traditionally been viewed as a predicament of the young, is increasingly a disease of the old. The strain of managing the progressively complex and costly care associated with this shift rests with a small number of trauma centres. Optimal care of injured patients requires a reappraisal of the resources required to effectively provide it given a mounting burden