Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern

2011 ◽  
Vol 12 (2) ◽  
pp. 276-284 ◽  
Author(s):  
ERIN L. LANDGUTH ◽  
BRADLEY C. FEDY ◽  
SARA J. OYLER‐McCANCE ◽  
ANDREW L. GAREY ◽  
SARAH L. EMEL ◽  
...  
Keyword(s):  
Sample Size ◽  
Allelic Richness ◽  
Size Number ◽  
Genetic Pattern ◽  
Spatial Genetic Pattern

Influences on the Mantel-Haenszel Chi-Square in Detection of Differential Item Functioning under Rasch Conditions

1995 ◽  
Vol 80 (3_suppl) ◽  
pp. 1071-1074 ◽  
Author(s):  
Thomas Uttaro
Keyword(s):  
Sample Size ◽  
Differential Item Functioning ◽  
Item Bias ◽  
Reference Groups ◽  
Chi Square ◽  
Item Functioning ◽  
Size Number ◽  
Gender Based ◽  
And Gender ◽  
Present Simulation

The Mantel-Haenszel chi-square (χ2MH) is widely used to detect differential item functioning (item bias) between ethnic and gender-based subgroups on educational and psychological tests. The empirical behavior of χ2MH has been incompletely understood; previous research is inconclusive. The present simulation study explored the effects of sample size, number of items, and trait distributions on the power of χ2MH to detect modeled differential item functioning. A significant effect was obtained for sample size with unacceptably low power for 250 subjects each in the focal and reference groups. The discussion supports the 1990 recommendations of Swaminathan and Rogers, opposes the 1993 view of Zieky that a sample size of 250 for each group is adequate.

scholarly journals Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

2019 ◽  
Vol 111 ◽  
pp. 80-102 ◽  
Author(s):  
Andrew T. Hendrickson ◽  
Amy Perfors ◽  
Danielle J. Navarro ◽  
Keith Ransom
Keyword(s):  
Sample Size ◽  
Size Number

Aggregation of Consumer Ratings of Online Products: Applying Social Choice Theory to Investigate the Cordorcet Efficiency of Mean Rule

2007 ◽  
Vol 51 (13) ◽  
pp. 837-840
Author(s):  
Xianjun Sam Zheng
Keyword(s):  
Sample Size ◽  
Social Choice ◽  
Majority Rule ◽  
Choice Theory ◽  
Social Choice Theory ◽  
Sample Mean ◽  
Condorcet Efficiency ◽  
Size Number ◽  
Consumer Ratings ◽  
The Mean

Mean rule has been popularly used to aggregate consumer ratings of online products. This study applied social choice theory to evaluate the Condorcet efficiency of the mean rule, and to investigate the effect of sample size (number of voters) on the agreement or disagreement between the mean and majority rules. The American National Election Survey data (1968) were used, where three candidates competed for the presidency, and the numerical thermometer scores were provided for each candidate. Random sampling data with varied sample sizes were drew from the survey, and then were aggregated according to the majority rule, the mean rule, and other social choice rules. The results show that the sample winner of the mean rule agrees with the sample majority winner very well; as sample size increases, the sample mean rule even converges faster to the correct population majority winner and ordering than does the sample majority rule. The implications for using aggregation rules for online product rating were also discussed.

scholarly journals Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

2019 ◽  
Author(s):  
Andrew T Hendrickson ◽  
Amy Perfors ◽  
Danielle Navarro ◽  
Keith Ransom
Keyword(s):  
Sample Size ◽  
Computational Models ◽  
Inference Problems ◽  
Size Number ◽  
New Item ◽  
Category Frequency

Categorization and generalization are fundamentally related inference problems. Yet leading computational models of categorization (as exemplified by, e.g., Nosofsky, 1986) and generalization (as exemplified by, e.g., Tenenbaum & Griffiths, 2001) make qualitatively different predictions about how inference should change as a function of the number of items. Assuming all else is equal, categorization models predict that increasing the number of items in a category increases the chance of assigning a new item to that category; generalization models predict a decrease, or category tightening with additional exemplars. This paper investigates this discrepancy, showing that people do indeed perform qualitatively differently in categorization and generalization tasks even when all superficial elements of the task are kept constant. Furthermore, the effect of category frequency on generalization is moderated by assumptions about how the items are sampled. We show that neither model naturally accounts for the pattern of behavior across both categorization and generalization tasks, and discuss theoretical extensions of these frameworks to account for the importance of category frequency and sampling assumptions.

scholarly journals Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

2021 ◽  
Vol 73 (6) ◽  
pp. 1391-1402
Author(s):  
S. Genç ◽  
M. Mendeş
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Regression Tree ◽  
Linear Modeling ◽  
Experimental Conditions ◽  
Monte Carlo Simulation Study ◽  
Optimum Sample Size ◽  
Size Number ◽  
Explained Variation ◽  
Optimum Sample

ABSTRACT This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance-covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling.

scholarly journals Reference sample size for multiple regression in corn

2020 ◽  
Vol 55 ◽  
Author(s):  
Alberto Cargnelutti Filho ◽  
Marcos Toebe
Keyword(s):  
Grain Yield ◽  
Sample Size ◽  
Multiple Regression ◽  
Reference Sample ◽  
Multiple Regression Model ◽  
Coefficient Of Determination ◽  
Size Number ◽  
Corn Grain ◽  
Double Cross ◽  
Corn Grain Yield

Abstract: The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y = β0 + β1X1 + β2X2. The Y, X1, and X2 traits were measured in 361, 373, and 416 plants, respectively, of single-, three-way, and double-cross hybrids in the 2008/2009 crop year; and in 1,777, 1,693, and 1,720 plants, respectively, of single-, three-way, and double-cross hybrids in the 2009/2010 crop year, totaling 6,340 plants. Descriptive statistics were calculated, and frequency histograms and scatterplots were created. The sample size (number of plants) for the estimate of the β0, β1, and β2 parameters, of the residual standard error, the coefficient of determination, the variance inflation factor, and the condition number between the explanatory traits of the model (X1 and X2) were determined by resampling with replacement. Measuring 260 plants is sufficient to adjust precise multiple regression models of corn grain yield as a function of ear length and ear diameter. The Y = -229.76 + 0.54X1 + 6.16X2 model is a reference for estimating corn grain yield.

scholarly journals Impact of different conditions on accuracy of five rules for principal components retention

Psihologija ◽  
2013 ◽  
Vol 46 (3) ◽  
pp. 331-347
Author(s):  
Aleksandar Zoric ◽  
Goran Opacic
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Principal Components ◽  
Poor Performance ◽  
Error Variance ◽  
Parallel Analysis ◽  
Monte Carlo Experiment ◽  
Size Number ◽  
The Impact ◽  
New Criteria

Polemics about criteria for nontrivial principal components are still present in the literature. Finding of a lot of papers, is that the most frequently used Guttman Kaiser?s criterion has very poor performance. In the last three years some new criteria were proposed. In this Monte Carlo experiment we aimed to investigate the impact that sample size, number of analyzed variables, number of supposed factors and proportion of error variance have on the accuracy of analyzed criteria for principal components retention. We compared the following criteria: Bartlett?s ?2 test, Horn?s Parallel Analysis, Guttman-Kaiser?s eigenvalue over one, Velicer?s MAP and CHull originally proposed by Ceulemans & Kiers. Factors were systematically combined resulting in 690 different combinations. A total of 138,000 simulations were performed. Novelty in this research is systematic variation of the error variance. Performed simulations showed that, in favorable research conditions, all analyzed criteria work properly. Bartlett?s and Horns criterion expressed the robustness in most of analyzed situations. Velicer?s MAP had the best accuracy in situations with small number of subjects and high number of variables. Results confirm earlier findings of Guttman-Kaiser?s criterion having the worse performance.

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