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Shashi Bhushan; Anoop Kumar – Measurement: Interdisciplinary Research and Perspectives, 2024
The data we encounter in real life often contain missing values. In sampling methods, missing value imputation is done with different methods. This article proposes novel logarithmic type imputation methods for estimating the population mean in the presence of missing data under ranked set sampling (RSS). According to the determined theoretical…
Descriptors: Research Problems, Sampling, Computation, Mathematical Formulas
Sohaib Ahmad; Javid Shabbir – Measurement: Interdisciplinary Research and Perspectives, 2025
This study aims to suggest a generalized class of estimators for population proportion under simple random sampling, which uses auxiliary attributes. The bias and MSEs are considered derived to the first degree approximation. The validity of the suggested and existing estimators is assessed via an empirical investigation. The performance of…
Descriptors: Computation, Sampling, Data Collection, Data Analysis
Abdul Haq – Measurement: Interdisciplinary Research and Perspectives, 2024
This article introduces an innovative sampling scheme, the median sampling (MS), utilizing individual observations over time to efficiently estimate the mean of a process characterized by a symmetric (non-uniform) probability distribution. The mean estimator based on MS is not only unbiased but also boasts enhanced precision compared to its simple…
Descriptors: Sampling, Innovation, Computation, Probability
Stanley, T. D.; Doucouliagos, Hristos – Research Synthesis Methods, 2023
Partial correlation coefficients are often used as effect sizes in the meta-analysis and systematic review of multiple regression analysis research results. There are two well-known formulas for the variance and thereby for the standard error (SE) of partial correlation coefficients (PCC). One is considered the "correct" variance in the…
Descriptors: Correlation, Statistical Bias, Error Patterns, Error Correction
Poom, Leo; af Wåhlberg, Anders – Research Synthesis Methods, 2022
In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the…
Descriptors: Meta Analysis, Effect Size, Mathematical Formulas, Monte Carlo Methods
Sarkar, Jyotirmoy; Rashid, Mamunur – Educational Research Quarterly, 2017
The standard deviation (SD) of a random sample is defined as the square-root of the sample variance, which is the "mean" squared deviation of the sample observations from the sample mean. Here, we interpret the sample SD as the square-root of twice the mean square of all pairwise half deviations between any two sample observations. This…
Descriptors: Sample Size, Sampling, Visualization, Geometric Concepts
Qian, Jiahe – ETS Research Report Series, 2017
The variance formula derived for a two-stage sampling design without replacement employs the joint inclusion probabilities in the first-stage selection of clusters. One of the difficulties encountered in data analysis is the lack of information about such joint inclusion probabilities. One way to solve this issue is by applying Hájek's…
Descriptors: Mathematical Formulas, Computation, Sampling, Research Design
Suero, Manuel; Privado, Jesús; Botella, Juan – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
A simulation study is presented to evaluate and compare three methods to estimate the variance of the estimates of the parameters d and "C" of the signal detection theory (SDT). Several methods have been proposed to calculate the variance of their estimators, "d'" and "c." Those methods have been mostly assessed by…
Descriptors: Evaluation Methods, Theories, Simulation, Statistical Analysis
Hayes, Kevin – Teaching Statistics: An International Journal for Teachers, 2012
This article provides an easy derivation of updating formulae for the sample covariance and, by extension, for the Pearson product-moment correlation coefficient.
Descriptors: Correlation, Statistical Analysis, Mathematical Formulas, Sampling
Lim, Ik Soo; Leek, E. Charles – Psychological Review, 2012
Previous empirical studies have shown that information along visual contours is known to be concentrated in regions of high magnitude of curvature, and, for closed contours, segments of negative curvature (i.e., concave segments) carry greater perceptual relevance than corresponding regions of positive curvature (i.e., convex segments). Lately,…
Descriptors: Visual Perception, Mathematical Formulas, Theories, Sampling
Zaidin, M. Arifin – Journal of Education and Practice, 2015
The purpose of this study is to assess the correlation between aspects of tutor and the students' basic writing outcomes of the Elementary School Teacher Education at the Distance Learning Program Unit, Open University of Palu. This is ex post facto correlation with the population research of 387 people and the total sample of 100 people. This…
Descriptors: Tutors, Writing Ability, Correlation, Distance Education
Calzada, Maria E.; Gardner, Holly – Mathematics and Computer Education, 2011
The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…
Descriptors: Intervals, Effect Size, Simulation, Undergraduate Students
Peer reviewedHornick, Chris W.; Overall, John E. – Journal of Educational Statistics, 1980
Estimates of sample size requirements for 2 X 2 tables, derived from three approximate formulae, were evaluated by comparison with exact calculations. Although different, the estimates derived from all three formulae were judged adequate for most practical purposes, with the most accurate results being obtained from Cohen's formula. (Author/JKS)
Descriptors: Expectancy Tables, Mathematical Formulas, Nonparametric Statistics, Sampling
Peer reviewedFindeisen, Peter – Psychometrika, 1979
Guttman's assumption underlying his definition of "total images" is rejected. Partial images are not generally convergent everywhere. Even divergence everywhere is shown to be possible. The convergence type always found on partial images is convergence in quadratic mean; hence, total images are redefined as quadratic mean-limits.…
Descriptors: Factor Analysis, Mathematical Formulas, Multiple Regression Analysis, Sampling
Peer reviewedEgghe, L. – Journal of the American Society for Information Science and Technology, 2002
Studies concentration aspects of bibliographies, particularly the impact of incompleteness on its concentration values (i.e. its degree of inequality of production of its sources). Models incompleteness by different sampling techniques and discusses implications for the measurement of production inequality in incomplete bibliographies. (Author/LRW)
Descriptors: Bibliographies, Inequality (Mathematics), Mathematical Formulas, Measurement Techniques
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