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Fellers, Pamela S.; Kuiper, Shonda – Journal of Statistics Education, 2020
Increasingly students, particularly those in the social sciences, work with survey data collected through a more complex sampling method than a simple random sample. Failing to understand how to properly approach survey data can lead to inaccurate results. In this article, we describe a series of online data visualization applications and…
Descriptors: Statistics, Introductory Courses, Teaching Methods, Concept Formation
Guerrero, Tricia A.; Griffin, Thomas D.; Wiley, Jennifer – Grantee Submission, 2020
The Predict-Observe-Explain (POE) learning cycle improves understanding of the connection between empirical results and theoretical concepts when students engage in hands-on experimentation. This study explored whether training students to use a POE strategy when learning from social science texts that describe theories and experimental results…
Descriptors: Prediction, Observation, Reading Comprehension, Correlation
Dogan, C. Deha – Eurasian Journal of Educational Research, 2017
Background: Most of the studies in academic journals use p values to represent statistical significance. However, this is not a good indicator of practical significance. Although confidence intervals provide information about the precision of point estimation, they are, unfortunately, rarely used. The infrequent use of confidence intervals might…
Descriptors: Sampling, Statistical Inference, Periodicals, Intervals
Arzumanyan, George; Halcoussis, Dennis; Phillips, G. Michael – American Journal of Business Education, 2015
This paper presents the Agresti & Coull "Adjusted Wald" method for computing confidence intervals and margins of error for common proportion estimates. The presented method is easily implementable by business students and practitioners and provides more accurate estimates of proportions particularly in extreme samples and small…
Descriptors: Business Administration Education, Error of Measurement, Error Patterns, Intervals
Watkins, Ann E.; Bargagliotti, Anna; Franklin, Christine – Journal of Statistics Education, 2014
Although the use of simulation to teach the sampling distribution of the mean is meant to provide students with sound conceptual understanding, it may lead them astray. We discuss a misunderstanding that can be introduced or reinforced when students who intuitively understand that "bigger samples are better" conduct a simulation to…
Descriptors: Simulation, Sampling, Sample Size, Misconceptions
Vannest, Kimberly J.; Parker, Richard I. – Journal of Special Education, 2010
Instructional time use is an intervention without equal. The measure of such has clear and important implications for special education practice and research. Although exhortations to maximize instruction and thereby student engagement exist throughout the literature, few studies discuss how special education teachers use their time, and none…
Descriptors: School Schedules, Error of Measurement, Sampling, Special Education Teachers