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Articles 1 - 23 of 23
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.
A Study Verifying The Dimensioning Of A Multivariate Dichotomized Sample In Exploratory Factor Analysis, Rosilei S. Novak, Jair M. Marques
A Study Verifying The Dimensioning Of A Multivariate Dichotomized Sample In Exploratory Factor Analysis, Rosilei S. Novak, Jair M. Marques
Journal of Modern Applied Statistical Methods
The sample size dichotomized was related to the measure of sampling adequacy, considering the explanations provided by factors and commonalities. Monte Carlo simulation generated multivariate normal samples and varying the number of observations, the factor analysis was applied in each sample dichotomized. Results were modeled by polynomial regression based on the sample sizing.
Power Analysis On A Pilot Study Of The Caloric Intake Of Children Helping Prepare Meals Versus Children Not, Danielle Clifford
Power Analysis On A Pilot Study Of The Caloric Intake Of Children Helping Prepare Meals Versus Children Not, Danielle Clifford
Student Research Poster Presentations 2020
The purpose of this analysis is to determine the sample size needed for a study that will be used to discover if there is a difference in the caloric intake of children who help with meal preparation and children who do not help with meal preparation.
The Impact Of Sample Size In Cross-Classified Multiple Membership Multilevel Models, Hyewon Chung, Jiseon Kim, Ryoungsun Park, Hyeonjeong Jean
The Impact Of Sample Size In Cross-Classified Multiple Membership Multilevel Models, Hyewon Chung, Jiseon Kim, Ryoungsun Park, Hyeonjeong Jean
Journal of Modern Applied Statistical Methods
A simulation study was conducted to examine parameter recovery in a cross-classified multiple membership multilevel model. No substantial relative bias was identified for the fixed effect or level-one variance component estimates. However, the level-two cross-classification multiple membership factor variance components were substantially biased with relatively fewer groups.
Sample Size For Non-Inferiority Tests For One Proportion: A Simulation Study, Özlem Güllü, Mustafa Agah Tekindal
Sample Size For Non-Inferiority Tests For One Proportion: A Simulation Study, Özlem Güllü, Mustafa Agah Tekindal
Journal of Modern Applied Statistical Methods
The objective of non-inferiority trials is to demonstrate the efficiency of a novel treatment whether it is acceptably less or more efficient than a control or active (existing) treatment. They are employed in situations where, when compared to the active treatment, the novel treatment is to be advantageous with higher rates of reliability, compatibility, cost-efficiency, etc. Odds ratio is the most significant measure used in investigating the size of efficiency of treatments relative to one another. The purpose of the study is to calculate and evaluate the sample size under different scenarios based on three different test statistics in non-inferiority …
A Monte Carlo Study Of The Effects Of Variability And Outliers On The Linear Correlation Coefficient, Hussein Yousif Eledum
A Monte Carlo Study Of The Effects Of Variability And Outliers On The Linear Correlation Coefficient, Hussein Yousif Eledum
Journal of Modern Applied Statistical Methods
Monte Carlo simulations are used to investigate the effect of two factors, the amount of variability and an outlier, on the size of the Pearson correlation coefficient. Some simulation algorithms are developed, and two theorems for increasing or decreasing the amount of variability are suggested.
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Journal of Modern Applied Statistical Methods
Composite endpoints are a popular outcome in controlled studies. However, the required sample size is not easily obtained due to the assortment of outcomes, correlations between them and the way in which the composite is constructed. Data simulations are required. A macro is developed that enables sample size and power estimation.
A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran
A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran
Journal of Modern Applied Statistical Methods
In most empirical studies (clinical, network modeling, and survey-based and aeronautical studies, etc.), sample observations are drawn from population to analyze and draw inferences about the population. Such analysis is done with reference to a measurable quality characteristic of a product or process of interest. However, fixing a sample size is an important task that has to be decided by the experimenter. One of the means in deciding an appropriate sample size is the fixation of error limit and the associated confidence level. This implies that the analysis based on the sample used must guarantee the prefixed error and confidence …
Strategies For Reducing Control Group Size In Experiments Using Live Animals, Matthew Kramer, Enrique Font
Strategies For Reducing Control Group Size In Experiments Using Live Animals, Matthew Kramer, Enrique Font
Conference on Applied Statistics in Agriculture
Reducing the number of animal subjects used in biomedical experiments is desirable for both ethical and practical reasons. Previous suggestions for reducing sample sizes in these experiments have focused on improving experimental designs and methods of statistical analysis; reducing the number of controls (thus, the number of overall animals used) is rarely mentioned. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. animals used as controls in similar previous experiments. Using example data from the literature, we describe how to incorporate information from historical controls …
Comparison Of Model Fit Indices Used In Structural Equation Modeling Under Multivariate Normality, Sengul Cangur, Ilker Ercan
Comparison Of Model Fit Indices Used In Structural Equation Modeling Under Multivariate Normality, Sengul Cangur, Ilker Ercan
Journal of Modern Applied Statistical Methods
The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.
Spss Programs For Addressing Two Forms Of Power For Multiple Regression Coefficients, Christopher Aberson
Spss Programs For Addressing Two Forms Of Power For Multiple Regression Coefficients, Christopher Aberson
Journal of Modern Applied Statistical Methods
This paper presents power analysis tools for multiple regression. The first takes input of correlations between variables and sample size and outputs power for multiple predictors. The second addresses power to detect significant effects for all of the predictors in the model. Both employ user-friendly SPSS Custom Dialogs.
Jmasm31: Manova Procedure For Power Calculations (Spss), Alan Taylor
Jmasm31: Manova Procedure For Power Calculations (Spss), Alan Taylor
Journal of Modern Applied Statistical Methods
D’Amico, Neilands & Zambarano (2001) showed how the SPSS MANOVA procedure can be used to conduct power calculations for research designs. This article demonstrates a simple way of entering data required for power calculations into SPSS and provides examples that supplement those given by D’Amico, Neilands & Zambarano.
Sample Size Considerations For Multiple Comparison Procedures In Anova, Gordon P. Brooks, George A. Johanson
Sample Size Considerations For Multiple Comparison Procedures In Anova, Gordon P. Brooks, George A. Johanson
Journal of Modern Applied Statistical Methods
Adequate sample sizes for omnibus ANOVA tests do not necessarily provide sufficient statistical power for post hoc multiple comparisons typically performed following a significant omnibus F test. Results reported support a comparison-of-most-interest approach for sample size determination in ANOVA based on effect sizes for multiple comparisons.
Recommended Sample Size For Conducting Exploratory Factor Analysis On Dichotomous Data, Robert H. Pearson, Daniel J. Mundform
Recommended Sample Size For Conducting Exploratory Factor Analysis On Dichotomous Data, Robert H. Pearson, Daniel J. Mundform
Journal of Modern Applied Statistical Methods
Minimum sample sizes are recommended for conducting exploratory factor analysis on dichotomous data. A Monte Carlo simulation was conducted, varying the level of communalities, number of factors, variable-to-factor ratio and dichotomization threshold. Sample sizes were identified based on congruence between rotated population and sample factor loadings.
Notes On Hypothesis Testing Under A Single-Stage Design In Phase Ii Trial, Kung-Jong Lui
Notes On Hypothesis Testing Under A Single-Stage Design In Phase Ii Trial, Kung-Jong Lui
Journal of Modern Applied Statistical Methods
A primary objective of a phase II trial is to determine future development is warranted for a new treatment based on whether it has sufficient activity against a specified type of tumor. Limitations exist in the commonly-used hypothesis setting and the standard test procedure for a phase II trial. This study reformats the hypothesis setting to mirror the clinical decision process in practice. Under the proposed hypothesis setting, the critical points and the minimum required sample size for a desired power of finding a superior treatment at a given α -level are presented. An example is provided to illustrate how …
Tests For 2 X 2 Tables In Clinical Trials, Vic Hasselblad, Yulia Lokhnygina
Tests For 2 X 2 Tables In Clinical Trials, Vic Hasselblad, Yulia Lokhnygina
Journal of Modern Applied Statistical Methods
Five standard tests are compared: chi-squared, Fisher's exact, Yates’ correction, Fisher’s exact mid-p, and Barnard’s. Yates’ is always inferior to Fisher’s exact. Fisher’s exact is so conservative that one should look for alternatives. For certain sample sizes, Fisher’s mid-p or Barnard’s test maintain the nominal alpha and have superior power.
Examining Cronbach Alpha, Theta, Omega Reliability Coefficients According To Sample Size, Ilker Ercan, Berna Yazici, Deniz Sigirli, Bulent Ediz, Ismet Kan
Examining Cronbach Alpha, Theta, Omega Reliability Coefficients According To Sample Size, Ilker Ercan, Berna Yazici, Deniz Sigirli, Bulent Ediz, Ismet Kan
Journal of Modern Applied Statistical Methods
Differentiations according to the sample size of different reliability coefficients are examined. It is concluded that the estimates obtained by Cronbach alpha and teta coefficients are not related with the sample size, even the estimates obtained from the small samples can represent the population parameter. However, the Omega coefficient requires large sample sizes.
Sample Size Determination In Animal Health Studies, Zhanglin Cui, Alan G. Zimmermann, Daniel H. Mowrey
Sample Size Determination In Animal Health Studies, Zhanglin Cui, Alan G. Zimmermann, Daniel H. Mowrey
Conference on Applied Statistics in Agriculture
Oftentimes in animal health studies, a treatment group is randomly assigned to a pen of animals, and the pen of animals as a whole is treated (fed the same medicated feed or water) together. In this scenario, the pen of animals is the experimental unit and the individual animal may be an observational unit. In addition to having the pen as the experimental unit, if multiple sites are used and site is treated as a random factor, this adds complexity to the study. To properly design the study, it is necessary to determine the number of animals in a pen, …
Sample Size Selection For Pair-Wise Comparisons Using Information Criteria, Xuemei Pan, C. Mitchell Dayton
Sample Size Selection For Pair-Wise Comparisons Using Information Criteria, Xuemei Pan, C. Mitchell Dayton
Journal of Modern Applied Statistical Methods
This article provides results for rates of correct identifications of paired-comparison information criteria and Tukey HSD as functions of the pattern of mean differences and of sample size. Therefore, the tables provided are useful for selecting sample sizes in real world applications.
On The Power Function Of Bayesian Tests With Application To Design Of Clinical Trials: The Fixed-Sample Case, Lyle Broemeling, Dongfeng Wu
On The Power Function Of Bayesian Tests With Application To Design Of Clinical Trials: The Fixed-Sample Case, Lyle Broemeling, Dongfeng Wu
Journal of Modern Applied Statistical Methods
Using a Bayesian approach to clinical trial design is becoming more common. For example, at the MD Anderson Cancer Center, Bayesian techniques are routinely employed in the design and analysis of Phase I and II trials. It is important that the operating characteristics of these procedures be determined as part of the process when establishing a stopping rule for a clinical trial. This study determines the power function for some common fixed-sample procedures in hypothesis testing, namely the one and two-sample tests involving the binomial and normal distributions. Also considered is a Bayesian test for multi-response (response and toxicity) in …
A Different Future For Social And Behavioral Science Research, Shlomo S. Sawilowsky
A Different Future For Social And Behavioral Science Research, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
The dissemination of intervention and treatment outcomes as effect sizes bounded by conf idence intervals in order to think meta-analytically was promoted in a recent article in Educational Researcher. I raise concerns with unfettered reporting of effect sizes, point out the con in confidence interval, and caution against thinking meta-analytically. Instead, cataloging effect sizes is recommended for sample size estimation and power analysis to improve social and behavioral science research.
Twenty Nonparametric Statistics And Their Large Sample Approximations, Gail F. Fahoome
Twenty Nonparametric Statistics And Their Large Sample Approximations, Gail F. Fahoome
Journal of Modern Applied Statistical Methods
Nonparametric procedures are often more powerful than classical tests for real world data which are rarely normally distributed. However, there are difficulties in using these tests. Computational formulas are scattered throughout the literature, and there is a lack of availability of tables and critical values. The computational formulas for twenty commonly employed nonparametric tests that have large-sample approximations for the critical value are brought together. Because there is no generally agreed upon lower limit for the sample size, Monte Carlo methods were used to determine the smallest sample size that can be used with the respective large-sample approximation. The statistics …
Determining Sample Size To Bound The Probability Of Classifying A Sample Into The Wrong One Of Two Multinomially Distributed Populations, C. Philip Cox
Conference on Applied Statistics in Agriculture
The problem considered is that of choosing between the two specifications . . . of known multinomial probabilities on the basis of sample values x j, the observed counts in the j = 1,... ,k, classes, with . . . The particular question examined is 'how large should N be to achieve reliable differentiation?'. It is shown how to find N such that the probability of misc1assification does not exceed a prescribable value. The method is exemplified in a genetic context.