scholarly journals Influence of Sample Size, Estimation Method and Normality on Fit Indices in Confirmatory Factor Analysis

2015 ◽  
Vol 9 (2) ◽  
pp. 1822-1833
Author(s):  
Murat DoÄŸan
Keyword(s):  
Monte Carlo Simulation ◽  
Sample Size ◽  
Simulation Study ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Sample Size Estimation ◽  
Fit Indices ◽  
Size Estimation ◽  
Confirmatory Factor

In this study, Monte Carlo simulation is used to evaluate the characteristics of CFA fit indices under different conditions (such as sample size, estimation method and distributional conditions). The simulation study was performed using seven different samples where sample has a different sample size such as 50, 100, 200, 400, 800, 1600, 4000, four different estimation methods (Maximum Likelihood, Generalized Least Square, Least Square and Weighted Least Square) and three distribution conditions (normal, slightly non-normal and moderately non-normal). A simulation study was conducted with EQS software to examine the effect of these conditions on the most common eleven fit indices that are studied in CFA and SEM. As a result of this study, all of the factors studied are shown to have an influence on the fit indices.

scholarly journals Optimal sample size calculation for null hypothesis significance tests

2019 ◽  
Author(s):  
Joseph F. Mudge ◽  
Jeffrey E. Houlahan
Keyword(s):  
Sample Size ◽  
Estimation Methods ◽  
Sample Size Estimation ◽  
Type I ◽  
Type Ii ◽  
Size Estimation ◽  
Sample Sizes ◽  
Optimal Sample Size ◽  
Critical Effect ◽  
Optimal Sample

AbstractTraditional study design tools for estimating appropriate sample sizes are not consistently used in ecology and can lead to low statistical power to detect biologically relevant effects. We have developed a new approach to estimating optimal sample sizes, requiring only three parameters; a maximum acceptable average of α and β, a critical effect size of minimum biological relevance, and an estimate of the relative costs of Type I vs. Type II errors.This approach can be used to show the general circumstances under which different combinations of critical effect sizes and maximum acceptable combinations of α and β are attainable for different statistical tests. The optimal α sample size estimation approach can require fewer samples than traditional sample size estimation methods when costs of Type I and II errors are assumed to be equal but recommends comparatively more samples for increasingly unequal Type I vs. Type II errors costs. When sampling costs and absolute costs of Type I and II errors are known, optimal sample size estimation can be used to determine the smallest sample size at which the cost of an additional sample outweighs its associated reduction in errors. Optimal sample size estimation constitutes a more flexible and intuitive tool than traditional sample size estimation approaches, given the constraints and unknowns commonly faced by ecologists during study.

scholarly journals Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation

2015 ◽  
Vol 48 (20) ◽  
pp. 273-278 ◽  
Author(s):  
Yeong Shiong Chiew ◽  
Christopher Pretty ◽  
Elena Moltchanova ◽  
Carl Scarrott ◽  
Daniel Redmond ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Trial Design ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Estimation Trial

scholarly journals A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Moses M. Ngari ◽  
Susanne Schmitz ◽  
Christopher Maronga ◽  
Lazarus K. Mramba ◽  
Michel Vaillant
Keyword(s):  
Systematic Review ◽  
Sample Size ◽  
Survival Function ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Survival Curves ◽  
Survival Analyses ◽  
Survival Regression

Abstract Background Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context. Methods Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria. Results Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively. Conclusion The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

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