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Takeda, Kazuya; Tanabe, Shigeo; Koyama, Soichiro; Nagai, Tomoko; Sakurai, Hiroaki; Kanada, Yoshikiyo; Shomoto, Koji – Measurement in Physical Education and Exercise Science, 2018
The aim of this study was to clarify the intra- and inter-rater reliability of the rate of force development in hip abductor muscle force measurements using a hand-held dynamometer. Thirty healthy adults were separately assessed by two independent raters on two separate days. Rate of force development was calculated from the slope of the…
Descriptors: Interrater Reliability, Human Body, Measurement Equipment, Handheld Devices
Zakszeski, Brittany N.; Hojnoski, Robin L.; Wood, Brenna K. – Topics in Early Childhood Special Education, 2017
Classroom engagement is important to young children's academic and social development. Accurate methods of capturing this behavior are needed to inform and evaluate intervention efforts. This study compared the accuracy of interval durations (i.e., 5 s, 10 s, 15 s, 20 s, 30 s, and 60 s) of momentary time sampling (MTS) in approximating the…
Descriptors: Intervals, Time, Sampling, Learner Engagement
Wood, Brenna K.; Hojnoski, Robin L.; Laracy, Seth D.; Olson, Christopher L. – Topics in Early Childhood Special Education, 2016
Although, collectively, results of earlier direct observation studies suggest momentary time sampling (MTS) may offer certain technical advantages over whole-interval (WIR) and partial-interval (PIR) recording, no study has compared these methods for measuring engagement in young children in naturalistic environments. This study compared direct…
Descriptors: Young Children, Research Methodology, Observation, Intervals
Stamey, James D.; Beavers, Daniel P.; Sherr, Michael E. – Sociological Methods & Research, 2017
Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is…
Descriptors: Bayesian Statistics, Classification, Models, Correlation
Li, Xin; Beretvas, S. Natasha – Structural Equation Modeling: A Multidisciplinary Journal, 2013
This simulation study investigated use of the multilevel structural equation model (MLSEM) for handling measurement error in both mediator and outcome variables ("M" and "Y") in an upper level multilevel mediation model. Mediation and outcome variable indicators were generated with measurement error. Parameter and standard…
Descriptors: Sample Size, Structural Equation Models, Simulation, Multivariate Analysis
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
Lee, C. Matthew; Gorelick, Mark – Measurement in Physical Education and Exercise Science, 2011
The purpose of this study was to examine the validity of the Smarthealth watch (Salutron, Inc., Fremont, California, USA), a heart rate monitor that includes a wristwatch without an accompanying chest strap. Twenty-five individuals participated in 3-min periods of standing, 2.0 mph walking, 3.5 mph walking, 4.5 mph jogging, and 6.0 mph running.…
Descriptors: Metabolism, Intervals, Physical Activities, Validity
Laenen, Annouschka; Alonso, Ariel; Molenberghs, Geert; Vangeneugden, Tony; Mallinckrodt, Craig H. – Applied Psychological Measurement, 2010
Longitudinal studies are permeating clinical trials in psychiatry. Therefore, it is of utmost importance to study the psychometric properties of rating scales, frequently used in these trials, within a longitudinal framework. However, intrasubject serial correlation and memory effects are problematic issues often encountered in longitudinal data.…
Descriptors: Psychiatry, Rating Scales, Memory, Psychometrics
Chan, Wai – Educational and Psychological Measurement, 2009
A typical question in multiple regression analysis is to determine if a set of predictors gives the same degree of predictor power in two different populations. Olkin and Finn (1995) proposed two asymptotic-based methods for testing the equality of two population squared multiple correlations, [rho][superscript 2][subscript 1] and…
Descriptors: Multiple Regression Analysis, Intervals, Correlation, Computation
Woods, Carol M. – Psychological Methods, 2007
This research focused on confidence intervals (CIs) for 10 measures of monotonic association between ordinal variables. Standard errors (SEs) were also reviewed because more than 1 formula was available per index. For 5 indices, an element of the formula used to compute an SE is given that is apparently new. CIs computed with different SEs were…
Descriptors: Intervals, Computation, Measurement Techniques, Error of Measurement
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Bonett, Douglas G.; Price, Robert M. – Journal of Educational and Behavioral Statistics, 2005
The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral…
Descriptors: Correlation, Predictor Variables, Measures (Individuals), Error of Measurement