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Luo, Yong; Jiao, Hong – Educational and Psychological Measurement, 2018
Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the…
Descriptors: Bayesian Statistics, Item Response Theory, Probability, Computer Software
Ramon Barrada, Juan; Veldkamp, Bernard P.; Olea, Julio – Applied Psychological Measurement, 2009
Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees' trait level. The most common way of reducing this risk is to impose a…
Descriptors: Item Banks, Adaptive Testing, Item Analysis, Psychometrics
Turton, Roger W. – Mathematics Teacher, 2007
This article describes several methods from discrete mathematics used to simulate and solve an interesting problem occurring at a holiday gift exchange. What is the probability that two people will select each other's names in a random drawing, and how does this result vary with the total number of participants? (Contains 5 figures.)
Descriptors: Probability, Algebra, Problem Solving, Monte Carlo Methods
Velasco, S.; Roman, F. L.; Gonzalez, A.; White, J. A. – International Journal of Mathematical Education in Science & Technology, 2006
In the nineteenth century many people tried to seek a value for the most famous irrational number, [pi], by means of an experiment known as Buffon's needle, consisting of throwing randomly a needle onto a surface ruled with straight parallel lines. Here we propose to extend this experiment in order to evaluate other irrational numbers, such as…
Descriptors: Geometric Concepts, Probability, Computer Simulation, Monte Carlo Methods
Ogden, Philip M. – 1973
A computer program to perform a Monte Carlo simulation of counting experiments was written. The program was based on a mathematical derivation which started with counts in a time interval. The time interval was subdivided to form a binomial distribution with no two counts in the same subinterval. Then the number of subintervals was extended to…
Descriptors: Computation, Computer Programs, Computer Science, Experiments

Snijders, Tom A. B. – Psychometrika, 1991
A complete enumeration method and a Monte Carlo method are presented to calculate the probability distribution of arbitrary statistics of adjacency matrices when these matrices have the uniform distribution conditional on given row and column sums, and possibly on a given set of structural zeros. (SLD)
Descriptors: Computer Simulation, Equations (Mathematics), Mathematical Models, Matrices
Klockars, Alan J.; Hancock, Gregory R. – 1993
The challenge of multiple comparisons is to maximize the power for answering specific research questions, while still maintaining control over the rate of Type I error. Several multiple comparison procedures have been suggested to meet this challenge. The stagewise protected procedure (SPP) of A. J. Klockars and G. R. Hancock tests null hypotheses…
Descriptors: Comparative Analysis, Computer Simulation, Hypothesis Testing, Mathematical Models
Martin, Gerald R. – 1976
Through Monte Carlo procedures, three different techniques for estimating the parameter theta (proportion of the "shocks" remaining in the system) in the Integrated Moving Average (0,1,1) time-series model are compared in terms of (1) the accuracy of the estimates, (2) the independence of the estimates from the true value of theta, and…
Descriptors: Comparative Analysis, Computer Programs, Data Analysis, Mathematical Models

Houser, Larry L. – Mathematics Teacher, 1981
Monte Carlo methods are used to simulate activities in baseball such as a team's "hot streak" and a hitter's "batting slump." Student participation in such simulations is viewed as a useful method of giving pupils a better understanding of the probability concepts involved. (MP)
Descriptors: Baseball, Mathematical Models, Mathematics Instruction, Models
Elashoff, Janet Dixon; Elashoff, Robert M. – 1971
The problem of comparing proportions when some data are missing is investigated, and determination is made of what statistical techniques are appropriate under each of several probability models describing the observations likely to be missing. Monte Carlo methods were used to investigate the properties of standard estimators under each of the…
Descriptors: Comparative Analysis, Data Analysis, Educational Research, Evaluation Methods
Neel, John H. – 1993
Induced probabilities have been largely ignored by educational researchers. Simply stated, if a new or random variable is defined in terms of a first random variable, then induced probability is the probability or density of the new random variable that can be found by summation or integration over the appropriate domains of the original random…
Descriptors: Educational Research, Elementary Secondary Education, Equations (Mathematics), Mathematical Models

Travers, Kenneth J.; Gray, Kenneth G. – Mathematics Teacher, 1981
Some activities designed around the Monte Carlo method of solving probability problems are described. The instructional applications of this method involve physical models or simple BASIC computer programs. (MP)
Descriptors: Computer Programs, Mathematical Applications, Mathematical Models, Mathematics Instruction

Bhaj, Dinesh S.; Snijders, Tom A. B. – Psychometrika, 1986
Two easily computed test statistics are proposed for testing the equality of two correlated proportions when some observations are missing on both responses. The performance of these tests in terms of size and power is compared with other tests by means of Monte Carlo simulations. (Author/BS)
Descriptors: Correlation, Expectancy Tables, Hypothesis Testing, Mathematical Models
Owston, Ronald D. – 1979
The development of a probabilistic model for validating Gange's learning hierarchies is described. Learning hierarchies are defined as paired networks of intellectual tasks arranged so that a substantial amount of positive transfer occurs from tasks in a lower position to connected ones in a higher position. This probabilistic validation technique…
Descriptors: Associative Learning, Classification, Difficulty Level, Mathematical Models

Abrahamowicz, Michal; Ramsay, James O. – Psychometrika, 1992
A nonparametric multicategorical model for multiple-choice data is proposed as an extension of the binary spline model of J. O. Ramsay and M. Abrahamowicz (1989). Results of two Monte Carlo studies illustrate the model, which approximates probability functions by rational splines. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Graphs, Item Analysis
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