Liqun Hu, Tonghui Wang, David Trafimow, S.T. Boris Choy, Xiangfei Chen, Cong Wang and Tingting Tong
The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance…
Abstract
Purpose
The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.
Design/methodology/approach
Based on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.
Findings
The exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.
Originality/value
In this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.
Details
Keywords
Xiangfei Chen, David Trafimow, Tonghui Wang, Tingting Tong and Cong Wang
The authors derive the necessary mathematics, provide computer simulations, provide links to free and user-friendly computer programs, and analyze real data sets.
Abstract
Purpose
The authors derive the necessary mathematics, provide computer simulations, provide links to free and user-friendly computer programs, and analyze real data sets.
Design/methodology/approach
Cohen's d, which indexes the difference in means in standard deviation units, is the most popular effect size measure in the social sciences and economics. Not surprisingly, researchers have developed statistical procedures for estimating sample sizes needed to have a desirable probability of rejecting the null hypothesis given assumed values for Cohen's d, or for estimating sample sizes needed to have a desirable probability of obtaining a confidence interval of a specified width. However, for researchers interested in using the sample Cohen's d to estimate the population value, these are insufficient. Therefore, it would be useful to have a procedure for obtaining sample sizes needed to be confident that the sample. Cohen's d to be obtained is close to the population parameter the researcher wishes to estimate, an expansion of the a priori procedure (APP). The authors derive the necessary mathematics, provide computer simulations and links to free and user-friendly computer programs, and analyze real data sets for illustration of our main results.
Findings
In this paper, the authors answered the following two questions: The precision question: How close do I want my sample Cohen's d to be to the population value? The confidence question: What probability do I want to have of being within the specified distance?
Originality/value
To the best of the authors’ knowledge, this is the first paper for estimating Cohen's effect size, using the APP method. It is convenient for researchers and practitioners to use the online computing packages.
Details
Keywords
David Trafimow, Ziyuan Wang, Tingting Tong and Tonghui Wang
The purpose of this article is to show the gains that can be made if researchers were to use gain-probability (G-P) diagrams.
Abstract
Purpose
The purpose of this article is to show the gains that can be made if researchers were to use gain-probability (G-P) diagrams.
Design/methodology/approach
The authors present relevant mathematical equations, invented examples and real data examples.
Findings
G-P diagrams provide a more nuanced understanding of the data than typical summary statistics, effect sizes or significance tests.
Practical implications
Gain-probability diagrams provided a much better basis for making decisions than typical summary statistics, effect sizes or significance tests.
Originality/value
G-P diagrams provide a completely new way to traverse the distance from data to decision-making implications.