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Showing 1 to 15 of 77 results Save | Export
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Gajendra Vishwakarma – Measurement: Interdisciplinary Research and Perspectives, 2025
In sample designs, it is commonly recognized that using auxiliary information significantly increases an estimator's precision. This manuscript introduces an weighted strategy for computing the finite population mean using auxiliary information in sample surveys. The equations for the mean squared error ("MSE") of the proposed estimator…
Descriptors: Sampling, Surveys, Computation, Efficiency
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Muhammad Aslam – Measurement: Interdisciplinary Research and Perspectives, 2025
The existing algorithm employing the log-normal distribution lacks applicability in generating imprecise data. This paper addresses this limitation by first introducing the log-normal distribution as a means to handle imprecise data. Subsequently, we leverage the neutrosophic log-normal distribution to devise an algorithm specifically tailored for…
Descriptors: Statistical Distributions, Algorithms, Sampling
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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
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Judith Glaesser – Field Methods, 2025
In qualitative comparative analysis, as with all methods, there is a question about how many cases are needed to make an analysis robust. In deciding on the number of cases, a key consideration is the number of conditions to be analyzed. I suggest that adding cases is preferable to dropping conditions if there are too many conditions relative to…
Descriptors: Comparative Analysis, Robustness (Statistics), Sampling, Case Studies
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Katie Marie Q. Magnone; Ellen J. Yezierski – Journal of Chemical Education, 2024
When designing a study, the sampling method for selecting research participants is an important decision with a host of considerations. When designing a professional development (PD) program with a limited number of spaces, the method of choosing participants from the applicants is also important. When research and professional development are…
Descriptors: Sampling, Faculty Development, Chemistry, Educational Research
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Matthew Forte; Elizabeth Tipton – Society for Research on Educational Effectiveness, 2024
Background/Context: Over the past twenty plus years, the What Works Clearinghouse (WWC) has reviewed over 1,700 studies, cataloging effect sizes for 189 interventions. Some 56% of these interventions include results from multiple, independent studies; on average, these include results of [approximately]3 studies, though some include as many as 32…
Descriptors: Meta Analysis, Sampling, Effect Size, Models
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Gabriela Perez-Garcia; Andrea Gomez Barillas; Renata Mendizábal-Cabrera; Danilo Alvarez; Brooke M. Ramay; Nikolina Walas; Jay P. Graham – Field Methods, 2025
In many countries, soiled toilet paper is placed in trash bins rather than flushed down the toilet. We investigated the use of soiled toilet paper in Guatemalan markets to surveil for pathogenic sequence types (STs) of "E. coli" and third generation cephalosporin-resistant "E. coli" (3GCR-EC). We collected used toilet paper…
Descriptors: Sanitation, Diseases, Pathology, Sanitary Facilities
Weihao Wang – ProQuest LLC, 2024
In this work, we introduce a novel oversampling technique, the theory of inheritance and Gower distance-based oversampling (TIGO) method, designed to address class imbalance issues in mixed categorical and continuous variables data set. Drawing inspiration from genetic inheritance principles, TIGO synthesizes new minority class data,…
Descriptors: Sampling, Statistics Education, Data Analysis, Prediction
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Abdul Haq; Muhammad Usman; Manzoor Khan – Measurement: Interdisciplinary Research and Perspectives, 2024
Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined…
Descriptors: Sampling, Error of Measurement, Evaluation Methods, Statistical Bias
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J. S. Allison; L. Santana; I. J. H. Visagie – Teaching Statistics: An International Journal for Teachers, 2025
Given sample data, how do you calculate the value of a parameter? While this question is impossible to answer, it is frequently encountered in statistics classes when students are introduced to the distinction between a sample and a population (or between a statistic and a parameter). It is not uncommon for teachers of statistics to also confuse…
Descriptors: Statistics Education, Teaching Methods, Computation, Sampling
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Juan F. Muñoz; Pablo J. Moya-Fernández; Encarnación Álvarez-Verdejo – Sociological Methods & Research, 2025
The Gini index is probably the most commonly used indicator to measure inequality. For continuous distributions, the Gini index can be computed using several equivalent formulations. However, this is not the case with discrete distributions, where controversy remains regarding the expression to be used to estimate the Gini index. We attempt to…
Descriptors: Bias, Educational Indicators, Equal Education, Monte Carlo Methods
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Yan Xia; Xinchang Zhou – Educational and Psychological Measurement, 2025
Parallel analysis has been considered one of the most accurate methods for determining the number of factors in factor analysis. One major advantage of parallel analysis over traditional factor retention methods (e.g., Kaiser's rule) is that it addresses the sampling variability of eigenvalues obtained from the identity matrix, representing the…
Descriptors: Factor Analysis, Statistical Analysis, Evaluation Methods, Sampling
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Lianne Bakkum; Célinde Paalman; Annelieke Müller; Agnies van Eeghen; Carlo Schuengel – Journal of Applied Research in Intellectual Disabilities, 2024
Background: Experience sampling may be useful for mental health research with people with intellectual disability, and evidence of the potential benefits is starting to emerge. This multiple-method study identified potential avenues for tailoring this method to the needs of people with intellectual disability. Method: A scoping review was…
Descriptors: Mental Health, Research Methodology, Intellectual Disability, Adults
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Beth Chance; Karen McGaughey; Sophia Chung; Alex Goodman; Soma Roy; Nathan Tintle – Journal of Statistics and Data Science Education, 2025
"Simulation-based inference" is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this article, we highlight for teachers…
Descriptors: Simulation, Sampling, Randomized Controlled Trials, Hypothesis Testing
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Hsin-Yun Lee; You-Lin Chen; Li-Jen Weng – Journal of Experimental Education, 2024
The second version of Kaiser's Measure of Sampling Adequacy (MSA[subscript 2]) has been widely applied to assess the factorability of data in psychological research. The MSA[subscript 2] is developed in the population and little is known about its behavior in finite samples. If estimated MSA[subscript 2]s are biased due to sampling errors,…
Descriptors: Error of Measurement, Reliability, Sampling, Statistical Bias
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