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Cross, Rod – Physics Teacher, 2021
A common procedure when conducting physics experiments is to repeat a measurement several times to calculate the mean and standard deviation. That might be the only instruction we give to students as a means to minimize random errors. However, that technique does not guarantee that the answer will be correct. It might give the same wrong answer…
Descriptors: Physics, Science Experiments, Computation, Error of Measurement
Nicewander, W. Alan – Educational and Psychological Measurement, 2018
Spearman's correction for attenuation (measurement error) corrects a correlation coefficient for measurement errors in either-or-both of two variables, and follows from the assumptions of classical test theory. Spearman's equation removes all measurement error from a correlation coefficient which translates into "increasing the reliability of…
Descriptors: Error of Measurement, Correlation, Sample Size, Computation
Rutkowski, Leslie; Zhou, Yan – Journal of Educational Measurement, 2015
Given the importance of large-scale assessments to educational policy conversations, it is critical that subpopulation achievement is estimated reliably and with sufficient precision. Despite this importance, biased subpopulation estimates have been found to occur when variables in the conditioning model side of a latent regression model contain…
Descriptors: Error of Measurement, Error Correction, Regression (Statistics), Computation
Cheema, Jehanzeb R. – Review of Educational Research, 2014
Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those…
Descriptors: Educational Research, Data, Data Collection, Data Processing
Citkowicz, Martyna; Hedges, Larry V. – Society for Research on Educational Effectiveness, 2013
In some instances, intentionally or not, study designs are such that there is clustering in one group but not in the other. This paper describes methods for computing effect size estimates and their variances when there is clustering in only one group and the analysis has not taken that clustering into account. The authors provide the effect size…
Descriptors: Multivariate Analysis, Effect Size, Sampling, Sample Size
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
Battauz, Michela; Bellio, Ruggero; Gori, Enrico – Psychometrika, 2008
The achievement level is a variable measured with error, that can be estimated by means of the Rasch model. Teacher grades also measure the achievement level but they are expressed on a different scale. This paper proposes a method for combining these two scores to obtain a synthetic measure of the achievement level based on the theory developed…
Descriptors: Academic Achievement, Measurement, Error of Measurement, Computation
Sullivan, Paul – Journal of Human Resources, 2009
This paper develops an empirical occupational choice model that corrects for misclassification in occupational choices and measurement error in occupation-specific work experience. The model is used to estimate the extent of measurement error in occupation data and quantify the bias that results from ignoring measurement error in occupation codes…
Descriptors: Computation, Models, Career Choice, Error Correction
Raju, Nambury S.; Lezotte, Daniel V.; Fearing, Benjamin K.; Oshima, T. C. – Applied Psychological Measurement, 2006
This note describes a procedure for estimating the range restriction component used in correcting correlations for unreliability and range restriction when an estimate of the reliability of a predictor is not readily available for the unrestricted sample. This procedure is illustrated with a few examples. (Contains 1 table.)
Descriptors: Correlation, Reliability, Predictor Variables, Error Correction