NotesFAQContact Us
Collection
Advanced
Search Tips
What Works Clearinghouse Rating
Does not meet standards4
Showing 316 to 330 of 1,110 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Sheehan, Rory; Kimona, Kate; Giles, Audrey; Cooper, Vivien; Hassiotis, Angela – British Journal of Learning Disabilities, 2018
Background: There is relatively little published data that report the experiences and views of family carers of people with intellectual disabilities who display challenging behaviour who are prescribed psychotropic medication. Materials and methods: An online structured questionnaire was created by the Challenging Behaviour Foundation, a UK…
Descriptors: Online Surveys, Caregivers, Intellectual Disability, Drug Therapy
Peer reviewed Peer reviewed
Direct linkDirect link
Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2021
Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Statistical Inference
Zhang, Zhiyong; Zhang, Danyang – Grantee Submission, 2021
Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These…
Descriptors: Statistics Education, Definitions, Course Descriptions, Computer Science Education
Lockwood, J. R.; Castellano, Katherine E.; Shear, Benjamin R. – Journal of Educational and Behavioral Statistics, 2018
This article proposes a flexible extension of the Fay--Herriot model for making inferences from coarsened, group-level achievement data, for example, school-level data consisting of numbers of students falling into various ordinal performance categories. The model builds on the heteroskedastic ordered probit (HETOP) framework advocated by Reardon,…
Descriptors: Bayesian Statistics, Mathematical Models, Statistical Inference, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
König, Christoph; van de Schoot, Rens – Educational Review, 2018
The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline,…
Descriptors: Bayesian Statistics, Educational Research, Educational Practices, Data Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
D'Souza, Kelwyn A.; Siegfeldt, Denise V. – Decision Sciences Journal of Innovative Education, 2017
Selecting the right methodology to use for detecting cheating in online exams requires considerable time and effort due to a wide variety of scholarly publications on academic dishonesty in online education. This article offers a cheating detection framework that can serve as a guideline for conducting cheating studies. The necessary theories and…
Descriptors: Identification, Cheating, Computer Assisted Testing, Testing Problems
Peer reviewed Peer reviewed
Direct linkDirect link
Newman, Isadore; Hitchcock, John H.; Nastasi, Bonnie K. – Research in the Schools, 2017
Any attempt to influence behavior by sharing a research finding that makes a probabilistic statement (e.g., a p value) should necessarily entail consideration of how consumers of the information might interpret this information. Such consideration can be informed, at least in part, by applying phenomenological principles of inquiry. This does not…
Descriptors: Qualitative Research, Phenomenology, Stakeholders, Mixed Methods Research
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Guerra, Melissa G.; Kupczynski, Lori P.; Mundy, Marie-Ann; Garza, Christopher – International Journal of Special Education, 2017
The role that educational diagnosticians play in the educational process can vary across settings. In Texas, the Texas Education Agency oversees the State Board of Educator Certification (SBEC). SBEC certification standards outline knowledge and skills that are required for educational diagnosticians to practice in Texas. The Texas required…
Descriptors: Educational Diagnosis, Special Education, Diagnostic Tests, Specialists
Peer reviewed Peer reviewed
Direct linkDirect link
Guerra-Peña, Kiero; Steinley, Douglas – Educational and Psychological Measurement, 2016
Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This…
Descriptors: Growth Models, Bayesian Statistics, Sampling, Statistical Inference
Peer reviewed Peer reviewed
Direct linkDirect link
Bai, Haiyan; Sivo, Stephen A.; Pan, Wei; Fan, Xitao – International Journal of Research & Method in Education, 2016
Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method…
Descriptors: Sampling, Structural Equation Models, Statistical Inference, Comparative Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Kaimi, Irene – Teaching Statistics: An International Journal for Teachers, 2015
This articles argues in favour of a recently introduced approach to statistical inference which focuses on understanding the data generating process. A comprehensive example supports the discussion.
Descriptors: Statistical Inference, Statistical Data, Data Collection, Probability
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Nicholson, James; Ridgway, Jim – Statistics Education Research Journal, 2017
White and Gorard make important and relevant criticisms of some of the methods commonly used in social science research, but go further by criticising the logical basis for inferential statistical tests. This paper comments briefly on matters we broadly agree on with them and more fully on matters where we disagree. We agree that too little…
Descriptors: Statistical Inference, Statistics, Teaching Methods, Criticism
Peer reviewed Peer reviewed
Direct linkDirect link
Aridor, Keren; Ben-Zvi, Dani – Statistics Education Research Journal, 2017
This article examines how two processes--reasoning with statistical modelling of a real phenomenon and aggregate reasoning--can co-emerge. We focus in this case study on the emergent reasoning of two fifth graders (aged 10) involved in statistical data analysis, informal inference, and modelling activities using TinkerPlots™. We describe nine…
Descriptors: Foreign Countries, Models, Logical Thinking, Statistics
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Lehrer, Richard – Statistics Education Research Journal, 2017
Grade 6 (modal age 11) students invented and revised models of the variability generated as each measured the perimeter of a table in their classroom. To construct models, students represented variability as a linear composite of true measure (signal) and multiple sources of random error. Students revised models by developing sampling…
Descriptors: Models, Statistics, Statistical Inference, Mathematics Instruction
Pages: 1  |  ...  |  18  |  19  |  20  |  21  |  22  |  23  |  24  |  25  |  26  |  ...  |  74