Simulation combines the values of cooperative education and the project method for providing a greater number of the facets of learning. The three basic types of simulation are: physical, social, and environmental. All can be applied to distributive education. Research is being conducted to determine when to use which type. (AG)

This article describes scenario development and suggests plans for the use of scenarios in simulations and games. (BB)

An outline of a basic debriefing structure which the author considers applicable to most simulation games in the classroom. (AK)

The author suggests that simulation should be a combination of computerized dynamic environments and rich qualitative humanistic environments.'' (Author/AK)

A review of a simulation on urban processes. (MM)

Describes incorporating the use of memorandums as a technique of training engineering managers. Training Technology is a quarterly supplement to Educational Technology Magazine. (GR)

This article deals with teacher training methods designed to enable student teachers to practise decision-making. (AA)

It is demonstrated that tests of homogeneity of independent correlation coefficients based on the simple forms of the normal and t approximations to the distribution of the correlation coefficients are comparable in terms of robustness, size and power. (Author)

The validity of asymptotic results for the distribution of unrotated and rotated factor loadings is investigated via simulation techniques. In particular, principal component extraction and quartimax rotation are examined on a problem with thirteen variables. The asymptotic results appear to be quite good. (Author/JKS)

A practical overview of things to keep in mind when planning and implementing educational games. (BD)

Investigated the approximate confidence intervals for effect sizes developed by K. Bird (2002) and proposed a more accurate method developed through simulation studies. The average coverage probability for the new method was 0.959. (SLD)

Developed a procedure for overcoming the computational and technological limitations of analyzing data with missing observations by extracting data from a sparsely filled data matrix into analyzable smaller subsets of data. Demonstrated the validity of this subdividing method using a Monte Carlo simulation. (SLD)

Studied the problem of missing data at the second level of a two-level hierarchal data structure using data generated to simulate the 1982 High School and Beyond data set with five different missing data treatments: listwise deletion, overall mean substitution, group mean substitution, the EM algorithm, and multiple imputation. (SLD)