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Konold, Cliff; Harradine, Anthony; Kazak, Sibel – International Journal of Computers for Mathematical Learning, 2007
In current curriculum materials for middle school students in the US, data and chance are considered as separate topics. They are then ideally brought together in the minds of high school or university students when they learn about statistical inference. In recent studies we have been attempting to build connections between data and chance in the…
Descriptors: Middle School Students, Computer Software, Statistical Inference, Statistical Distributions

Chant, David; Dalgleish, Lenard I. – Multivariate Behavioral Research, 1992
A Statistical Analysis System (SAS) macro procedure for performing a jackknife analysis on structure coefficients in discriminant analysis is described together with issues and caveats about its use in multivariate methods. An example of use of the SAS macro is provided. (SLD)
Descriptors: Computer Software, Correlation, Discriminant Analysis, Error of Measurement
Kish, Leslie – 1989
A brief, practical overview of "design effects" (DEFFs) is presented for users of the results of sample surveys. The overview is intended to help such users to determine how and when to use DEFFs and to compute them correctly. DEFFs are needed only for inferential statistics, not for descriptive statistics. When the selections for…
Descriptors: Computer Software, Error of Measurement, Mathematical Models, Research Design
Baldwin, Beatrice; Lomax, Richard – 1990
This LISREL study examines the robustness of the maximum likelihood estimates under varying degrees of measurement model misspecification. A true model containing five latent variables (two endogenous and three exogenous) and two indicator variables per latent variable was used. Measurement model misspecification considered included errors of…
Descriptors: Computer Software, Error of Measurement, Item Response Theory, Mathematical Models
Sinex, Scott A; Gage, Barbara A.; Beck, Peggy J. – AMATYC Review, 2007
A simple, guided-inquiry investigation using stacked sandwich cookies is employed to develop a simple linear mathematical model and to explore measurement error by incorporating errors as part of the investigation. Both random and systematic errors are presented. The model and errors are then investigated further by engaging with an interactive…
Descriptors: Mathematical Models, Measurement, Error of Measurement, Science Process Skills

Kaplan, David – Multivariate Behavioral Research, 1988
The impact of misspecification on the estimation, testing, and improvement of structural equation models was assessed via a population study in which a prototypical latent variable model was misspecified. Results provide insights into the maximum likelihood estimator versus a limited two-stage least squares estimator in LISREL. (TJH)
Descriptors: Computer Simulation, Computer Software, Demography, Error of Measurement
Millsap, Roger E. – 1986
A component analytic method for analyzing multivariate longitudinal data is presented that does not make strong assumptions about the structure of the data. Central to the method are the facts that components are derived as linear composites of the observed or manifest variables and that the components must provide an adequate representation of…
Descriptors: Comparative Analysis, Computer Software, Cross Sectional Studies, Error of Measurement
Phillips, Gary W. – 1983
Ways in which the Statistical Package for the Social Sciences (SPSS) can be used to perform some Rasch analyses are described in detail. It is shown how SPSS and a set of item calibrations can be used to estimate person abilities, standard errors of measurement, test characteristic curve, test information curve, classification consistency on a…
Descriptors: Classification, Computer Software, Error of Measurement, Estimation (Mathematics)
Baldwin, Beatrice – 1986
LISREL-type structural equation modeling is a powerful statistical technique that seems appropriate for social science variables which are complex and difficult to measure. The literature on the specification, estimation, and testing of such models is voluminous. The greatest proportion of this literature, however, focuses on the technical aspects…
Descriptors: Analysis of Covariance, Computer Software, Equations (Mathematics), Error of Measurement

Wolfle, Lee M.; Ethington, Corinna A. – Educational and Psychological Measurement, 1986
Using data from High School and Beyond, this study empirically investigated the extent of within-variable, between-occasion error covariances among variables included in educational achievement models. Little evidence was found to support the statement that reliability estimates for social background variables are inflated because of correlated…
Descriptors: Academic Achievement, Computer Software, Correlation, Equations (Mathematics)
Zwick, Rebecca; Sklar, Jeffrey C. – Journal of Educational and Behavioral Statistics, 2005
Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that…
Descriptors: Error of Measurement, Regression (Statistics), Computer Software, Predictor Variables
Marsh, Herbert W.; Hocevar, Dennis – 1986
The advantages of applying confirmatory factor analysis (CFA) to multitrait-multimethod (MTMM) data are widely recognized. However, because CFA as traditionally applied to MTMM data incorporates single indicators of each scale (i.e., each trait/method combination), important weaknesses are the failure to: (1) correct appropriately for measurement…
Descriptors: Computer Software, Construct Validity, Correlation, Error of Measurement
Tatsuoka, Kikumi K.; Tatsuoka, Maurice M. – 1985
The study examines the rule space model, a probabilistic model capable of measuring cognitive skill acquisition and of diagnosing erroneous rules of operation in a procedural domain. The model involves two important components: (1) determination of a set of bug distributions (bug density functions representing clusters around the rules); and (2)…
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Assisted Testing, Computer Software