The article summarizes the results of an evaluation of the accuracy of statewide industry and occupational employment projections for 20 states. The authors provide some recommendations, based on evaluation results, to improve subsequent rounds of statewide projections. (CH)

This paper describes a PASCAL program that computes both different types of transitions and learning statistics suitable for learning experiments in which a two-stage Markov model is used. The frequency counts of the different transitions are used for estimating the parameters of the two-stage Markov model. (Author/LMO)

Variance stabilizing transformations yield statistics whose standard errors are only influenced by the number of observations on which they are based. A solution that can be extended to inference on all of these parameters is presented and illustrated via three examples. (TJH)

A computer program simulated guessing on multiple-choice test items and calculated deviation IQ's from observed scores which contained a guessing component. Extensive variability in deviation IQ's due entirely to chance was found. (Author/LMO)

Causal models often omit variables that should be included, use variables that are measured fallibly, and ignore time lags. Such practices can lead to severely biased estimates of effects. The discussion explains these biases and shows how to take them into account. (Author)

Prior standardization of scores when an aggregate score is formed has been criticized. This article presents a demonstration of the effects of differential weighting of aggregate components that clarifies the need for prior standardization. The role of standardization in statistics and the use of aggregate scores in research are discussed.…

This paper describes a BASIC computer program that computes power for any combination of effect size, degrees of freedom for hypothesis, degrees of freedom for error, and alpha level. As a consequence of the algorithm, an approximation to the critical value of the Bonferroni F-test is also computed. (Author/JAZ)

The detrimental effects of nonresponse bias are particularly significant given the widespread use of the survey data collection method in educational surveys. Current methods for remediating nonresponse bias in educational surveys are explored and critiqued. (Author/LMO)

A method is described of deriving two alternative measures of divergent thinking ability as a single entity (avoiding the spurious effects of fluency) from student responses to open-ended questions traditionally used in tests of divergent thinking ability. (PN)

This paper discusses the sample size problem and four factors affecting its solution: significance level, statistical power, analysis procedure, and effect size. The interrelationship between these factors is discussed and demonstrated by calculating minimal sample size requirements for a variety of research conditions. (Author)

Traditionally, error in equating observed scores on two versions of a test is defined as the difference between the transformations that equate the quantiles of their distributions in the sample and in the population of examinees. This definition underlies, for example, the well-known approximation to the standard error of equating by Lord (1982).…

National studies of students, and studies that compare institutions, have identified many predictors of students' graduation rates, including socioeconomic status and admission selectivity. When there predictive variables are applied to data from an individual school, it may be found that they have less predictive power. This paper presents a…

The analytically derived expected asymptotic standard errors (SEs) of maximum likelihood (ML) item estimates can be predicted by a mathematical function without examinees' responses to test items. The empirically determined SEs of marginal maximum likelihood estimation/Bayesian item estimates can be obtained when the same set of items is…

Commenting on an article by D. Scott, in Mathematics Teaching 52 (Autumn 1970), He present author shows that it is important to know the distribution of error and not just the maximum error. (MM)