Qualitative comparative analysis (QCA) is an influential methodological approach motivated by set theory and boolean logic. QCA proponents have developed algorithms to analyze quantitative data, in a bid to uncover necessary and sufficient conditions where causal relationships are complex, conditional, or asymmetric. This article uses computer…

This computer simulation study evaluates the robustness of the nonparametric Levene test of equal variances (Nordstokke & Zumbo, 2010) when sampling from populations with unequal (and unknown) means. Testing for population mean differences when population variances are unknown and possibly unequal is often referred to as the Behrens-Fisher…

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with…

Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)

Canonical correlation analysis is a powerful statistical method subsuming other parametric significance tests as special cases, and which can often best honor the complex reality to which most researchers wish to generalize. However, it has been suggested that the canonical correlation coefficient is positively biased. A Monte Carlo study…

Describes item response and information functions for the Zinnes and Griggs paired comparison item response theory (IRT) model (1974) and presents procedures for estimating stimulus and person parameters. Monte Carlo simulations show that at least 400 ratings are required to obtain reasonably accurate estimates of the stimulus parameters and their…

Functional and structural relationship alternatives to the standard "F"-test for analysis of covariance (ANCOVA) are discussed for cases when the covariate is measured with error. An approximate statistical test based on the functional relationship approach is preferred on the basis of Monte Carlo simulation results. (SLD)

W. M. K. Trochim and others defend the record of the regression-discontinuity (RD) design and blur the statistical tests for treatment effect. Their Monte Carlo results show the problematic nature of RD and its potential bias. New testing strategies and restrictions for the application of RD are proposed. (SLD)

The use of stepwise regression has been criticized for both interpretive misunderstandings and statistical aberrations. A major statistical problem with stepwise regression and other procedures that involve multiple significance tests is the inflation of the Type I error rate. General approaches to control the family-wise error rate such as the…

In order to evaluate standard setting procedures, apart from the more commonly applied approach of simply comparing the derived standards or failure rates across various techniques, this study investigated the errors of classification associated with the contrasting groups procedures. Monte Carlo simulations were employed to produce…

The regression-discontinuity design involving a treatment interaction effect (TIE), pretest-posttest functional form specification, and choice of point-of-estimation of the TIE are examined. Formulas for controlling the magnitude of TIE in simulations can be used for simulating the randomized experimental case where estimation is not at the…

Simulated data typical of those from meta analyses are used to evaluate the reliability, Type I and Type II errors, bias, and standard error of the meta-analytic procedures of Schmidt and Hunter (1977). Concerns about power, reliability, and Type I errors are presented. (SLD)

In an experiment manipulating artificial data in a factorial design, model misspecification and varying levels of error in measurement and in model structure are shown to have significant effects on LISREL parameter estimates in a modified peer influence model. (Author/LMO)

The ability of 3-way INDSCAL and ALSCAL models to recover true structure in proximity data based on 2-dimensional configurations varying in number of subjects (15 and 20) and stimuli, amount of error, and monotonic transformation is examined. INDSCAL outperformed metric and nonmetric ALSCAL in all conditions. (SLD)

A conceptual approach and a set of computer simulations are presented to demonstrate that random measurement error in the pretest does not bias the estimate of the treatment effect in the regression-discontinuity design. Focus is on the case of no interaction between pretest and treatment on posttest. (SLD)