Assumptions Of Statistical Tests Research Articles

Role of Public Expenditure in Economic Growth: Econometric Evidence from Kosovo 2002–2015

Abstract The aim of the study is to examine the impact of public expenditure on economic growth of Kosovo. Time series data span for the period of time 2002-2015. The structure of the econometric model is built on Keynesian theories and endogenous growth model. The model estimation is performed only after implementing the Augmented Dickey-Fuller (ADF) Unit Root test to estimate if time series are stationary. Several tests have been implemented to determine model validity. The model has met all the assumptions of statistical tests: error term residuals have a normal distribution (Jarque-Bera test), there is no auto-correlation between variables (Breusch-Godfrey Serial test), and error variances are constant, known as the principle of homoscedasticity (Breusch-Pagan-Godfrey test). Gross domestic product is used as a dependent variable in the model, while public expenditure (G), foreign direct investment (FDI), export (EXP) and total budget revenue (TrTax) are used as the endogenous variables. The study results have revealed that there is a positive and statistically significant effect of public expenditures and exports on economic growth. Total budget revenue has a positive impact on economic growth but this has not been proved to be statistically significant. The authors of the research have also found out that FDI is negative and statistically insignificant.

Assumptions of Statistical Tests: What Lies Beneath

We have discussed many statistical tests and tools in this series of commentaries, and while we have mentioned the underlying assumptions of the tests, we have not explored them in detail. We stop to look at some of the assumptions of the t-test and linear regression, justify and explain them, mention what can go wrong when the assumptions are not met, and suggest some solutions in this case.

Medical Statistics: A Guide to Data Analysis and Critical Appraisal

This basic medical statistics book aims to, in the authors' own words, ‘provide the ‘know how’ and make statistics analysis and critical appraisal easy for all researchers and health-care professionals’. I don't think medical statistics is easy even for medical statisticians but this book is certainly a useful addition to the field. The two main strengths of this book are the chapter-specific notes for critical appraisal and the time devoted to explaining the assumptions of statistical testing. These two related topics are usually not considered in other books (or only to a limited degree) yet are important factors for improving the quality of statistics in medical research. Users of the statistical software SPSS will get the most out of this book as it includes the commands for carrying out the statistical tests but I would not hesitate in recommending it to users of other software as well. However, I felt that more emphasis on encouraging researchers to save files of the underlying syntax should have been given. The advantages of such are that an exact record of what analyses were done is documented and that new or updated analyses can be done at the press of a button. Although the authors introduce the ‘paste’ command in chapter 1, they do not provide a more detailed example of its use in the remainder of the book. It might have been useful to write ‘click OK or paste’ rather than just ‘click OK’ in the boxes of SPSS commands that are found throughout the book. The book has good coverage as it includes t-tests (and their non-parametric equivalents), chi-squared tests, analysis of variance, regression (linear and logistic), tests of agreement, diagnostic tests and Kaplan–Meier survival analysis. Chapter 1 is devoted to data management; the authors are to be congratulated for this as it is a subject not often addressed yet many medical researchers collect and record data in a sloppy manner resulting in data sets not primed for statistical analysis. The standard error is a notoriously difficult concept to get across when introducing statistical testing (chapters 3 and 4). To this end, I think these chapters ought to have included sampling distributions. A positive point is that these chapters detail the difference between statistical and clinical significance; many clinicians make the mistake of declaring that a statistically significant result indicates an important finding. Chapters 5 and 6 are excellent introductions to methods for continuous outcome variables with good sections on post-hoc tests, collinearity, interactions and model building. Measuring repeatability and the utility of diagnostic tests, for both categorical and continuous variables, are the subject matter of chapters 9 and 10. The inclusion of both topics is most welcome as they are important and not always covered in basic medical statistics books. These chapters also contain a useful general point that p-values sometimes have little meaning even though statistical software automatically churns them out. I also enjoyed the quotations that start each chapter, the clearly presented and labelled tables and figures, and the glossary of terms. In conclusion, a very good book.

Quality of antidepressant drugs research articles published in Indian medical journals.

Aim and Objective:Quality of methodological and statistical parameters published in various Indian Medical Journals are usually debated in terms of appropriateness. Aim of this manuscript is to analyze and validate efficacy studies on antidepressant drugs published in Indian Medical Journals regarding quality of various methodological and statistical parameters used.Materials and Methods:An in-depth review of Efficacy studies on Antidepressant published in Indian Medical Journals was conducted. Articles were retrieved from all possible sources like Published Journals, downloaded through Pubmed, MedInd, Opengate, Medknow, and Cochrane. These studies were analyzed and validated for various methodological and statistical parameters. Descriptive statistics for various parameters with confidence interval (CI) is also reported herewith.Results:Of 32 articles reviewed, primary and secondary endpoints and sample size calculation was reported in only one article (3.1%; 95% CI, 0.5 to 15.7%). Power and CI was not reported in any of the reviewed articles. Inclusion and exclusion criteria were reported in 9 (28.3%; 95% CI, 15.5 to 45.3%) articles. Information regarding randomization was mentioned in 12 (37.5%; 95% CI, 22.9 to 54.7%) articles, whereas reporting of blinding or open label status of the study was mentioned in 21 (65.6%; 95% CI, 48.3 to 79.5%) articles. Assumptions of statistical tests were not reported in any of the reviewed articles. Statistical tests were used in only 14 (43.7%; 95% CI, 28.1 to 60.6%) articles of which 70% of were inappropriate. All studies were underpowered for small and medium effect size.Conclusion:Quality of reporting of methodological and statistical aspects of antidepressant efficacy studies published in Indian medical journals are poor and hence makes difficult to conclude on issues related to validity of these studies.

Quality of reporting of descriptive and inferential statistics in negative studies published in Indian medical journals

Aims and Objectives : It is observed that negative studies published in medical journals are underpowered to detect the actual difference between the groups, but no data are available on the quality of the statistics reported in these studies. Therefore, this study was carried out with aim of evaluating negative studies published in Indian medical journals for adequacy of reporting of descriptive and inferential statistics. Materials and Methods : All the original articles published in 14 Pubmed-indexed Indian medical journals were analyzed to determine whether the study was negative or positive. All the negative studies were analyzed for correctness of the descriptive statistics and inferential statistics. The types of data and statistical methods were also noted down. Descriptive statistics was used and values were expressed as frequency, percentages and confidence interval. Results : Incorrect descriptive statistics was mentioned in 15 (28.8%, 95% CI 18.3-42.2%) studies. Information related to assumptions of statistical tests were mentioned in only two (3.8%, 95% CI 1.0-12.9%) articles. Inappropriate/incorrect statistical tests was used in 22 (42.3%, 95% CI 29.8-55.8%) studies. The most common reason for inappropriate reporting of descriptive statistics was use of mean and SD for description of ordinal data. The most common reason for incorrect statistical test was use of parametric test for ordinal data. The most common statistical test was the t-test. Conclusion : Negative studies published in prominent Indian medical journals are statistically weak, and readers critically analyze these studies before making any opinion based on them.