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Wiberg, Marie – Practical Assessment, Research & Evaluation, 2021
The overall aim was to examine the equated values when using different linkage plans and different observed-score equipercentile equating methods with the equivalent groups (EG) design and the nonequivalent groups with anchor test (NEAT) design. Both real data from a college admissions test and simulated data were used with frequency estimation,…
Descriptors: Equated Scores, Test Items, Methods, College Entrance Examinations
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Chiu, Loren Z. F.; Daehlin, Torstein E. – Measurement in Physical Education and Exercise Science, 2020
Males (n = 29) and females (n = 34) performed vertical jumps. Jump height was estimated from force platform data using five numerical methods and compared using intraclass correlation ([rho]), and linear and rank regression standard error of estimate ("SEE"). Take-off velocity plus center of mass height at take-off and mechanical work…
Descriptors: Physical Activities, Scientific Concepts, Computation, Motion
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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
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Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2016
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
Descriptors: Causal Models, Markov Processes, Longitudinal Studies, Probability
Dorko, Allison; Speer, Natasha – Investigations in Mathematics Learning, 2015
Units of measure are critical in many scientific fields. While instructors often note that students struggle with units, little research has been conducted about the nature and extent of these difficulties or why they exist. We investigated calculus students' unit use in area and volume computations. Seventy-three percent of students gave…
Descriptors: Undergraduate Students, College Mathematics, Calculus, Geometric Concepts
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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
Woodruff, David; Traynor, Anne; Cui, Zhongmin; Fang, Yu – ACT, Inc., 2013
Professional standards for educational testing recommend that both the overall standard error of measurement and the conditional standard error of measurement (CSEM) be computed on the score scale used to report scores to examinees. Several methods have been developed to compute scale score CSEMs. This paper compares three methods, based on…
Descriptors: Comparative Analysis, Error of Measurement, Scores, Scaling
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Min, Shangchao; He, Lianzhen – Language Testing, 2014
This study examined the relative effectiveness of the multidimensional bi-factor model and multidimensional testlet response theory (TRT) model in accommodating local dependence in testlet-based reading assessment with both dichotomously and polytomously scored items. The data used were 14,089 test-takers' item-level responses to the testlet-based…
Descriptors: Foreign Countries, Item Response Theory, Reading Tests, Test Items
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Cox, Bradley E.; McIntosh, Kadian; Reason, Robert D.; Terenzini, Patrick T. – Review of Higher Education, 2014
Nearly all quantitative analyses in higher education draw from incomplete datasets-a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across…
Descriptors: Data Analysis, Statistical Inference, Research Problems, Computation
Cheema, Jehanzeb – ProQuest LLC, 2012
This study looked at the effect of a number of factors such as the choice of analytical method, the handling method for missing data, sample size, and proportion of missing data, in order to evaluate the effect of missing data treatment on accuracy of estimation. In order to accomplish this a methodological approach involving simulated data was…
Descriptors: Educational Research, Educational Researchers, Statistical Analysis, Sample Size
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Pan, Tianshu; Yin, Yue – Psychological Methods, 2012
In the discussion of mean square difference (MSD) and standard error of measurement (SEM), Barchard (2012) concluded that the MSD between 2 sets of test scores is greater than 2(SEM)[superscript 2] and SEM underestimates the score difference between 2 tests when the 2 tests are not parallel. This conclusion has limitations for 2 reasons. First,…
Descriptors: Error of Measurement, Geometric Concepts, Tests, Structural Equation Models
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Battauz, Michela; Bellio, Ruggero – Psychometrika, 2011
This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable…
Descriptors: Error of Measurement, Structural Equation Models, Computation, Item Response Theory
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Gilliland, Dennis; Melfi, Vince – Journal of Statistics Education, 2010
Confidence interval estimation is a fundamental technique in statistical inference. Margin of error is used to delimit the error in estimation. Dispelling misinterpretations that teachers and students give to these terms is important. In this note, we give examples of the confusion that can arise in regard to confidence interval estimation and…
Descriptors: Statistical Inference, Surveys, Intervals, Sample Size
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Dimoliatis, Ioannis D. K.; Jelastopulu, Eleni – Universal Journal of Educational Research, 2013
The surgical theatre educational environment measures STEEM, OREEM and mini-STEEM for students (student-STEEM) comprise an up to now disregarded systematic overestimation (OE) due to inaccurate percentage calculation. The aim of the present study was to investigate the magnitude of and suggest a correction for this systematic bias. After an…
Descriptors: Educational Environment, Scores, Grade Prediction, Academic Standards
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Lee, Chun-Ting; Zhang, Guangjian; Edwards, Michael C. – Multivariate Behavioral Research, 2012
Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…
Descriptors: Personality Traits, Intervals, Monte Carlo Methods, Factor Analysis
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