Assessed the robustness of an estimation method for multilevel and path analysis with hierarchical data proposed by B. Muthen (1989) with unequal groups and small sample sizes and in the presence of a low or high intraclass correlation. Simulation results show the effects of varying these conditions on the within-group and between-groups part of…

Monte Carlo methods were used to evaluate an EM algorithm used for the correction of missing data in latent class analysis. Findings regarding bias in parameter estimates suggest practical limits for the utility of the EM algorithm in terms of sample size and nonresponse rate. (SLD)

Empirical results based on comparison with Monte Carlo estimates and using sample sizes of 200, 100, 50, and 20 suggested that the bootstrap technique provides less biased and more consistent results than the jackknife technique for a case of canonical correlation analysis. (SLD)

Proposes and illustrates a new method by which "what if" analyses can be conducted using estimated true population effects. Use of these "what if" methods may prevent authors with large sample sizes from overinterpreting their small effects once they see that the small effects would no longer have been statistically significant with only a…

Shows why the two-step approach proposed by D. Robinson and J. Levine (1997) is inappropriate for the evaluation of empirical results and reiterates the preferred approach of increased sample size and the computation of confidence intervals. (SLD)

Supports a two-step approach to the estimation and discussion of effect sizes, making a distinction between single-study decision-oriented research and multiple-study synthesis. Introduces and illustrates the concept of "conclusion coherence." (Author/SLD)

Derived simplified equations to compute the standard error of the frequency estimation method for equating score distributions that are continuized using a uniform or Gaussian kernel function. Results from two empirical studies indicate that these equations work reasonably well for moderate size samples. (SLD)

Investigated the relationship between sample size, latent trait distribution, and item parameter estimation with the nominal response model through simulation. Results suggest guidelines for reasonable item parameter estimation. (SLD)

Provides select power and sample size tables and interpolation strategies associated with the root mean square error of approximation test of not close fit under standard assumed conditions. The goal is to inform researchers conducting structural equation modeling about power limitations when testing a model. (SLD)

Introduces a computer package written for Mathematica, the purpose of which is to perform a number of difficult iterative functions with respect to the squared multiple correlation coefficient under the fixed and random models. These functions include computation of the confidence interval upper and lower bounds, power calculation, calculation of…

Focusing on a single sample obtained randomly with replacement from a single population, this article examines the regression of population on sample proportions and develops an unbiased estimator of the square of the correlation between them. This estimator turns out to be the regression coefficient. Use of the squared-correlation estimator as a…

Tables for selecting sample size in correlation studies are presented. Some of the tables allow selection of sample size so that r (or r[squared], depending on the statistic the researcher plans to interpret) will be within a target interval around the population parameter with probability 0.95. The intervals are [plus or minus] 0.05, [plus or…

The present simulation investigated the performance of parallel analysis for unidimensional binary data. Single-factor models with 8 and 20 indicators were examined, and sample size (50, 100, 200, 500, and 1,000), factor loading (.45, .70, and .90), response ratio on two categories (50/50, 60/40, 70/30, 80/20, and 90/10), and types of correlation…

Bifactor and second-order factor models are two alternative approaches for representing general constructs comprised of several highly related domains. Bifactor and second-order models were compared using a quality of life data set (N = 403). The bifactor model identified three, rather than the hypothesized four, domain specific factors beyond the…

The authors investigated 2 issues concerning the power of latent growth modeling (LGM) in detecting linear growth: the effect of the number of repeated measurements on LGM's power in detecting linear growth and the comparison between LGM and some other approaches in terms of power for detecting linear growth. A Monte Carlo simulation design was…