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Iannario, Maria; Tarantola, Claudia – Sociological Methods & Research, 2023
This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the…
Descriptors: Data Analysis, Data Interpretation, Probability, Models
Jordan P. Beck; Diane M. Miller – Journal of Chemical Education, 2022
A version of the classic rotationally resolved infrared (IR) spectrum of a diatomic molecule experiment has been developed using the POGIL framework to more fully engage students in the collection, modeling, analysis, and interpretation of the data. An analysis of the experimental protocol reveals that the POGIL approach actively engages students…
Descriptors: Learner Engagement, Chemistry, Science Instruction, Laboratory Experiments
Masnick, Amy M.; Morris, Bradley J. – Education Sciences, 2022
Data reasoning is an essential component of scientific reasoning, as a component of evidence evaluation. In this paper, we outline a model of scientific data reasoning that describes how data sensemaking underlies data reasoning. Data sensemaking, a relatively automatic process rooted in perceptual mechanisms that summarize large quantities of…
Descriptors: Models, Science Process Skills, Data Interpretation, Cognitive Processes
Mattox, Stephen R.; Duda, Stephanie – Science Teacher, 2022
Permafrost is any soil or surface deposit in an Arctic or alpine region at some depth below the surface at which the temperature has remained below zero degrees Celsius (32 degrees Fahrenheit) continuously for a long period of time. The amount of carbon dioxide and methane stored in permafrost is nearly twice the amount in the atmosphere and, as…
Descriptors: Models, Scientific Concepts, Earth Science, Science Activities
Ferguson, Sarah L.; Moore, E. Whitney G.; Hull, Darrell M. – International Journal of Behavioral Development, 2020
The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable modeling but not LPA specifically. A general procedure for conducting LPA is provided in six steps: (a) data inspection, (b) iterative evaluation of models,…
Descriptors: Statistical Analysis, Computer Software, Data Analysis, Goodness of Fit
Elizabeth Talbott; Andres De Los Reyes; Devin M. Kearns; Jeannette Mancilla-Martinez; Mo Wang – Exceptional Children, 2023
Evidence-based assessment (EBA) requires that investigators employ scientific theories and research findings to guide decisions about what domains to measure, how and when to measure them, and how to make decisions and interpret results. To implement EBA, investigators need high-quality assessment tools along with evidence-based processes. We…
Descriptors: Evidence Based Practice, Evaluation Methods, Special Education, Educational Research
Bradshaw, Laine; Levy, Roy – Educational Measurement: Issues and Practice, 2019
Although much research has been conducted on the psychometric properties of cognitive diagnostic models, they are only recently being used in operational settings to provide results to examinees and other stakeholders. Using this newer class of models in practice comes with a fresh challenge for diagnostic assessment developers: effectively…
Descriptors: Data Interpretation, Probability, Classification, Diagnostic Tests
Ames, Allison; Myers, Aaron – Educational Measurement: Issues and Practice, 2019
Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…
Descriptors: Bayesian Statistics, Psychometrics, Models, Predictive Measurement
Forster, Michelle; Bestelmeyer, Stephanie; Baez-Rodriguez, Noelia; Berkowitz, Alan; Caplan, Bess; Esposito, Rhea; Grace, Elizabeth; McGee, Steven – Science Teacher, 2018
Thousands of students around the country have participated in activities using the Data Jam model, creating poetry, songs, videos, or sculpture to improve their data literacy, gain knowledge of local science research, and creatively express their findings. This article introduces the Data Jam model and describes how teachers can use it in…
Descriptors: Information Literacy, Scientific Literacy, Science Activities, Science Instruction
Wise, Alyssa Friend; Shaffer, David Williamson – Journal of Learning Analytics, 2015
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…
Descriptors: Learning Theories, Predictor Variables, Data, Data Analysis
Elouazizi, Noureddine – Journal of Learning Analytics, 2014
This paper identifies some of the main challenges of data governance modelling in the context of learning analytics for higher education institutions, and discusses the critical factors for designing data governance models for learning analytics. It identifies three fundamental common challenges that cut across any learning analytics data…
Descriptors: Data, Governance, Data Analysis, Influences
Tutz, Gerhard; Berger, Moritz – Journal of Educational and Behavioral Statistics, 2016
Heterogeneity in response styles can affect the conclusions drawn from rating scale data. In particular, biased estimates can be expected if one ignores a tendency to middle categories or to extreme categories. An adjacent categories model is proposed that simultaneously models the content-related effects and the heterogeneity in response styles.…
Descriptors: Response Style (Tests), Rating Scales, Data Interpretation, Statistical Bias
Brookhart, Susan M. – ASCD, 2015
In this book, best-selling author Susan M. Brookhart helps teachers and administrators understand the critical elements and nuances of assessment data and how that information can best be used to inform improvement efforts in the school or district. Readers will learn: (1) What different kinds of data can--and cannot--tell us about student…
Descriptors: Data, Decision Making, Student Evaluation, Data Analysis
Podeschi, R. J. – Information Systems Education Journal, 2015
This paper reports on the use of QlikView business intelligence software for use in a Business Intelligence (BI) course within an undergraduate information systems program. The course provides students with concepts related to data warehousing, data mining, visualizations, and software tools to provide business intelligence solutions for decision…
Descriptors: Experiential Learning, Business, Intelligence, Computer Software
Adler, Jill; Ronda, Erlina – African Journal of Research in Mathematics, Science and Technology Education, 2015
We describe and use an analytical framework to document mathematics discourse in instruction (MDI), and interpret differences in mathematics teaching. MDI is characterised by four interacting components in the teaching of a mathematics lesson: exemplification (occurring through a sequence of examples and related tasks), explanatory talk (talk that…
Descriptors: Mathematics Instruction, Discourse Analysis, Models, Teaching Models