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Full-Text Articles in Physical Sciences and Mathematics
Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever
Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever
HCA Healthcare Journal of Medicine
This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.
Parametric And Non-Parametric Tests For The Comparison Of Two Samples Which Both Include Paired And Unpaired Observations, Ben Derrick, Paul White, Deirdre Toher
Parametric And Non-Parametric Tests For The Comparison Of Two Samples Which Both Include Paired And Unpaired Observations, Ben Derrick, Paul White, Deirdre Toher
Journal of Modern Applied Statistical Methods
Samples that include both independent and paired observations cause a dilemma for researchers that covers the full breadth of empirical research. Parametric approaches for the comparison of two samples using all available observations are considered, under normality and non-normality. These approaches are compared to naive and newly proposed non-parametric alternatives.
A Simulation Study On The Size And Power Properties Of Some Ridge Regression Tests, B. M. Golam Kibria, Shipra Banik
A Simulation Study On The Size And Power Properties Of Some Ridge Regression Tests, B. M. Golam Kibria, Shipra Banik
Applications and Applied Mathematics: An International Journal (AAM)
Ridge regression techniques have been extensively used to solve the multicollinearity problem for both linear and non-linear regression models since its inception. This paper studied different ridge regression t-type tests of the individual coefficients of a linear regression model. A simulation study has been conducted to evaluate the performance of the proposed tests with respect to their sizes and powers under different settings of the linear regression model. Our simulation results demonstrated that most of the proposed tests have sizes close to the 5% nominal level and all tests except tAKS, tkM2 and tkM9 have considerable gain in powers over …
Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser
Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser
Journal of Modern Applied Statistical Methods
Nguyen et al. (2016) offered advice to researchers in the commonly-encountered situation where they are interested in testing for a difference in central tendency between two populations. Their data and the available literature support very simple advice that strikes the best balance between ease of implementation, power and reliability. Specifically, apply Satterthwaite’s test, with preliminary ranking of the data if a strong deviation from normality is expected, or is suggested by visual inspection of the data. This simple guideline will serve well except when dealing with small samples of discrete data, when more sophisticated treatment may be required.
'Parallel Universe' Or 'Proven Future'? The Language Of Dependent Means T-Test Interpretations, Anthony M. Gould, Jean-Etienne Joullié
'Parallel Universe' Or 'Proven Future'? The Language Of Dependent Means T-Test Interpretations, Anthony M. Gould, Jean-Etienne Joullié
Journal of Modern Applied Statistical Methods
Of the three kinds of two-mean comparisons which judge a test statistic against a critical value taken from a Student t-distribution, one – the repeated measures or dependent-means application – is distinctive because it is meant to assess the value of a parameter which is not part of the natural order. This absence forces a choice between two interpretations of a significant test result and the meaning of the test hypothesis. The parallel universe view advances a conditional, backward-looking conclusion. The more practical proven future interpretation is a non-conditional proposition about what will happen if an intervention is (now) applied …
An Empirical Demonstration Of The Need For Exact Tests, Vance W. Berger
An Empirical Demonstration Of The Need For Exact Tests, Vance W. Berger
Journal of Modern Applied Statistical Methods
The robustness of parametric analyses is rarely questioned or qualified. Robustness, generally understood, means the exact and approximate p-values will lie on the same side of alpha for any reasonable data set; and 1) any data set would qualify as reasonable and 2) robustness holds universally, for all alpha levels and approximations. For this to be true, the approximation would need to be perfect all of the time. Any discrepancy between the approximation and the exact p-value, for any combination of alpha level and data set, would constitute a violation. Clearly, this is not true, and when confronted with this …
Type I Error Rates Of The Two-Sample Pseudo-Median Procedure, Nor Aishah Ahad, Abdul Rahman Othman, Sharipah Soaad Syed Yahaya
Type I Error Rates Of The Two-Sample Pseudo-Median Procedure, Nor Aishah Ahad, Abdul Rahman Othman, Sharipah Soaad Syed Yahaya
Journal of Modern Applied Statistical Methods
The performance of the pseudo-median based procedure is examined in terms of controlling Type I error for a two independent groups test. The procedure is a modification of the one-sample Wilcoxon statistic using the pseudo-median of differences between group values as the central measure of location. The proposed procedure was shown to have good control of Type I error rates under the study conditions regardless of distribution type.
The Effect Of Different Degrees Of Freedom Of The Chi-Square Distribution On The Statistical Power Of The T, Permutation T, And Wilcoxon Tests, Michèle Weber
Journal of Modern Applied Statistical Methods
The Chi-square distribution is used quite often in Monte Carlo studies to examine statistical power of competing statistics. The power spectrum of the t-test, Wilcoxon test, and permutation t test are compared under various degrees of freedom for this distribution. The two t tests have similar power, which is generally less than the Wilcoxon.