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What Works Clearinghouse Rating
Meier, Kimberly M.; Blair, Mark R. – Cognition, 2013
The current study investigates the relative extent to which information utility and planning efficiency guide information-sampling strategies in a classification task. Prior research has pointed to the importance of probability gain, the degree to which sampling a feature reduces the chance of error, in contexts where participants are restricted…
Descriptors: Sampling, Probability, Experiments, Eye Movements
Inzunsa Cazares, Santiago – North American Chapter of the International Group for the Psychology of Mathematics Education, 2016
This article presents the results of a qualitative research with a group of 15 university students of social sciences on informal inferential reasoning developed in a computer environment on concepts involved in the confidence intervals. The results indicate that students developed a correct reasoning about sampling variability and visualized…
Descriptors: Qualitative Research, College Students, Inferences, Logical Thinking
What Works Clearinghouse, 2016
This document provides step-by-step instructions on how to complete the Study Review Guide (SRG, Version S3, V2) for single-case designs (SCDs). Reviewers will complete an SRG for every What Works Clearinghouse (WWC) review. A completed SRG should be a reviewer's independent assessment of the study, relative to the criteria specified in the review…
Descriptors: Guides, Research Design, Research Methodology, Program Evaluation
Koran, Jennifer – Measurement and Evaluation in Counseling and Development, 2016
Proactive preliminary minimum sample size determination can be useful for the early planning stages of a latent variable modeling study to set a realistic scope, long before the model and population are finalized. This study examined existing methods and proposed a new method for proactive preliminary minimum sample size determination.
Descriptors: Factor Analysis, Sample Size, Models, Sampling
Weiss, Michael J.; Lockwood, J. R.; McCaffrey, Daniel F. – Journal of Research on Educational Effectiveness, 2016
In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but then within each arm, are grouped together (e.g., within classrooms/schools, through shared case managers, in group therapy sessions, through shared doctors, etc.) to receive…
Descriptors: Randomized Controlled Trials, Error of Measurement, Control Groups, Experimental Groups
Konstantopoulos, Spyros – Educational and Psychological Measurement, 2013
Large-scale experiments that involve nested structures may assign treatment conditions either to subgroups such as classrooms or to individuals such as students within subgroups. Key aspects of the design of such experiments include knowledge of the variance structure in higher levels and the sample sizes necessary to reach sufficient power to…
Descriptors: Statistical Analysis, Research Design, Correlation, Computation
Lehrer, Richard; Schauble, Leona – Science Education, 2017
This study describes how students' intuitions about sampling are informed by extended experiences in investigating local ecosystems. Elementary students in a rural/suburban district in the upper midwest spent a year conducting first-hand comparative field studies of nearby ponds, prairies, and forests. At the close of the term, we conducted…
Descriptors: Knowledge Level, Sampling, Ecology, Intuition
Giovenco, Daniel P.; Gundersen, Daniel A.; Delnevo, Cristine D. – Journal of American College Health, 2016
Objective: To explore the feasibility of a random-digit dial (RDD) cellular phone survey in order to reach a national and representative sample of college students. Methods: Demographic distributions from the 2011 National Young Adult Health Survey (NYAHS) were benchmarked against enrollment numbers from the Integrated Postsecondary Education…
Descriptors: College Students, Sampling, Sample Size, Comparative Analysis
McNeish, Daniel – Review of Educational Research, 2017
In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…
Descriptors: Models, Statistical Analysis, Sampling, Sample Size
Bennett, Kimberley Ann – Teaching Statistics: An International Journal for Teachers, 2015
Students may need explicit training in informal statistical reasoning in order to design experiments or use formal statistical tests effectively. By using scientific scandals and media misinterpretation, we can explore the need for good experimental design in an informal way. This article describes the use of a paper that reviews the measles mumps…
Descriptors: Statistical Analysis, Thinking Skills, Research Design, Data Interpretation
Pettus-Davis, Carrie; Howard, Matthew Owen; Dunnigan, Allison; Scheyett, Anna M.; Roberts-Lewis, Amelia – Research on Social Work Practice, 2016
Randomized controlled trials (RCTs) are rarely used to evaluate social and behavioral interventions designed for releasing prisoners. Objective: We use a pilot RCT of a social support intervention (Support Matters) as a case example to discuss obstacles and strategies for conducting RCT intervention evaluations that span prison and community…
Descriptors: Institutionalized Persons, Correctional Institutions, Intervention, Sampling
Miciak, Jeremy; Taylor, W. Pat; Stuebing, Karla K.; Fletcher, Jack M.; Vaughn, Sharon – Journal of Research on Educational Effectiveness, 2016
An appropriate estimate of statistical power is critical for the design of intervention studies. Although the inclusion of a pretest covariate in the test of the primary outcome can increase statistical power, samples selected on the basis of pretest performance may demonstrate range restriction on the selection measure and other correlated…
Descriptors: Educational Research, Research Design, Intervention, Statistical Analysis
Worrell, Kelly; Shaw, Michele R.; Postma, Julie; Katz, Janet R. – Journal of School Nursing, 2015
Asthma is a major cause of illness, missed school days, and hospitalization in children. One type of asthma common in children is exercise-induced asthma (EIA). EIA causes airway narrowing with symptoms of cough and shortness of breath during exercise. The purpose of this article is to review the literature relevant to screening children and…
Descriptors: Literature Reviews, Screening Tests, School Health Services, Exercise Physiology
Oshima, T. C.; Wright, Keith; White, Nick – International Journal of Testing, 2015
Raju, van der Linden, and Fleer (1995) introduced a framework for differential functioning of items and tests (DFIT) for unidimensional dichotomous models. Since then, DFIT has been shown to be a quite versatile framework as it can handle polytomous as well as multidimensional models both at the item and test levels. However, DFIT is still limited…
Descriptors: Test Bias, Item Response Theory, Test Items, Simulation
Lee, Hollylynne S.; Starling, Tina T.; Gonzalez, Marggie D. – Mathematics Teacher, 2014
Research shows that students often struggle with understanding empirical sampling distributions. Using hands-on and technology models and simulations of problems generated by real data help students begin to make connections between repeated sampling, sample size, distribution, variation, and center. A task to assist teachers in implementing…
Descriptors: Sampling, Sample Size, Statistical Distributions, Simulation

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