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Zachary K. Collier; Joshua Sukumar; Roghayeh Barmaki – Practical Assessment, Research & Evaluation, 2024
This article introduces researchers in the science concerned with developing and studying research methods, measurement, and evaluation (RMME) to the educational data mining (EDM) community. It assumes that the audience is familiar with traditional priorities of statistical analyses, such as accurately estimating model parameters and inferences…
Descriptors: Educational Indicators, School Statistics, Data Analysis, Information Retrieval
Siegel, Lianne; Chu, Haitao – Research Synthesis Methods, 2023
Reference intervals, or reference ranges, aid medical decision-making by containing a pre-specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta-analysis based on a random effects model: a frequentist approach, a Bayesian…
Descriptors: Bayesian Statistics, Meta Analysis, Intervals, Decision Making
Thompson, Yutian T.; Song, Hairong; Shi, Dexin; Liu, Zhengkui – Educational and Psychological Measurement, 2021
Conventional approaches for selecting a reference indicator (RI) could lead to misleading results in testing for measurement invariance (MI). Several newer quantitative methods have been available for more rigorous RI selection. However, it is still unknown how well these methods perform in terms of correctly identifying a truly invariant item to…
Descriptors: Measurement, Statistical Analysis, Selection, Comparative Analysis
Zhang, Xue; Tao, Jian; Wang, Chun; Shi, Ning-Zhong – Journal of Educational Measurement, 2019
Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item…
Descriptors: Bayesian Statistics, Item Response Theory, Measurement, Models
Zhang, Xue; Tao, Jian; Wang, Chun; Shi, Ning-Zhong – Grantee Submission, 2019
Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item…
Descriptors: Bayesian Statistics, Item Response Theory, Measurement, Models
Pavel Chernyavskiy; Traci S. Kutaka; Carson Keeter; Julie Sarama; Douglas Clements – Grantee Submission, 2025
When researchers code behavior that is undetectable or falls outside of the validated ordinal scale, the resultant outcomes often suffer from informative missingness. Incorrect analysis of such data can lead to biased arguments around efficacy and effectiveness in the context of experimental and intervention research. Here, we detail a new…
Descriptors: Bayesian Statistics, Mathematics Instruction, Learning Trajectories, Item Response Theory
Claassen, Christopher; Traunmüller, Richard – Sociological Methods & Research, 2020
Religious group size, demographic composition, and the dynamics thereof are of interest in many areas of social science including migration, social cohesion, parties and voting, and violent conflict. Existing estimates however are of varying and perhaps poor quality because many countries do not collect official data on religious identity. We…
Descriptors: Religious Cultural Groups, Muslims, Jews, Census Figures
Su, Shu-Ching; Sedory, Stephen A.; Singh, Sarjinder – Sociological Methods & Research, 2015
In this article, we adjust the Kuk randomized response model for collecting information on a sensitive characteristic for increased protection and efficiency by making use of forced "yes" and forced "no" responses. We first describe Kuk's model and then the proposed adjustment to Kuk's model. Next, by means of a simulation…
Descriptors: Data Collection, Models, Responses, Efficiency
Demir, Ergül – Educational Sciences: Theory and Practice, 2017
In this study, the aim was to construct a significant structural measurement model comparing students' affective characteristics with their mathematic achievement. According to this model, the aim was to test the measurement invariances between gender sub-groups hierarchically. This study was conducted as basic and descriptive research. Secondary…
Descriptors: Foreign Countries, Measurement, Student Characteristics, Comparative Analysis
Teker, Gulsen Tasdelen; Guler, Nese; Uyanik, Gulden Kaya – Educational Sciences: Theory and Practice, 2015
Generalizability theory (G theory) provides a broad conceptual framework for social sciences such as psychology and education, and a comprehensive construct for numerous measurement events by using analysis of variance, a strong statistical method. G theory, as an extension of both classical test theory and analysis of variance, is a model which…
Descriptors: Guidelines, Generalizability Theory, Computer Software, Statistical Analysis
Exploring Variation in Measurement as a Foundation for Statistical Thinking in the Elementary School
English, Lyn D.; Watson, Jane M. – International Journal of STEM Education, 2015
Background: This study was based on the premise that variation is the foundation of statistics and statistical investigations. The study followed the development of fourth-grade students' understanding of variation through participation in a sequence of two lessons based on measurement. In the first lesson all students measured the arm span of one…
Descriptors: Elementary School Students, Grade 4, Statistics, Measurement
Taskin, Cengiz – International Education Studies, 2016
The purpose of this study was to determine the effect of core training program on speed, acceleration, vertical jump, and standing long jump in female soccer players. A total of 40 female soccer players volunteered to participate in this study. They were divided randomly into 1 of 2 groups: core training group (CTG; n = 20) and control group (CG;…
Descriptors: Females, Athletes, Team Sports, Training
Andrade, Alejandro; Danish, Joshua A.; Maltese, Adam V. – Journal of Learning Analytics, 2017
Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a…
Descriptors: Measurement, Interaction, Models, Educational Environment
Green, Daniel; Kearney, Thomas – Mathematics Teaching in the Middle School, 2015
Emperor penguins, the largest of all the penguin species, attain heights of nearly four feet and weigh up to 99 pounds. Many students are not motivated to learn mathematics when textbook examples contain largely nonexistent contexts or when the math is not used to solve significant problems found in real life. This article's project explores how…
Descriptors: Mathematics Instruction, Animals, Foreign Countries, Measurement
Jeon, Minjeong – ProQuest LLC, 2012
Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…
Descriptors: Hierarchical Linear Modeling, Computation, Measurement, Maximum Likelihood Statistics

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