Effectiveness of jackknife methods in reducing bias in estimation of the log-1 autocorrelation parameter p1 was evaluated through a Monte Carlo study using sample sizes ranging from 6 to 500. These estimates appear less biased in the small sample case than many that have been investigated recently. (SLD)

An investigation of the effects of randomly missing data in two-predictor regression analyses is described. The differences in the effectiveness of five common treatments of missing data on estimates of R-squared values and each of the two standardized regression weights is also investigated. Bootstrap sample sizes of 50, 100, and 200 were drawn…

The effects of variations in degree of range restriction and different subgroup sample sizes on the validity of several item bias detection procedures based on Item Response Theory (IRT) were investigated in a simulation study. The degree of range restriction for each of two subpopulations was varied by cutting the specified subpopulation ability…

A normally distributed data set of 1,000 values--ranging from 50 to 150, with a mean of 50 and a standard deviation of 20--was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from…

The effects of priors and amount of bias in the Bayesian approach to the estimation problem in item response models are examined using simulation studies. Different specifications of prior information have only modest effects on Bayesian estimates, which are less biased than joint maximum likelihood estimates for small samples. (TJH)

A two-stage procedure for estimating item bias was examined with six indices of item bias and the Mantel-Haenszel statistic. Results suggest that the two-stage procedure is not very useful when the number of biased items is small and bias magnitude is weak. (SLD)

Empirical evidence is presented of the relative efficiency of two potential linkage plans to be used when equivalent test forms are being administered. Equating is a process by which scores on one form of a test are converted to scores on another form of the same test. A Monte Carlo study was conducted to examine equating stability and statistical…

A Monte Carlo study was conducted to compare the statistical bias and standard errors of non-equivalent-groups linear test equating in small samples of examinees. One thousand samples of each size (15, 25, 50, and 100) were drawn with replacement from each of five archival data files from elementary school and secondary school teacher subject area…