Suchergebnisse

Bayes factors (BFs) play an important role in comparing the fit of statistical models. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using exact BFs. Instead, it is approximated, often using the Bayesian Information Criterion (BIC) or a variant of BIC. The authors provide a comparison of several BF approximations, including two new approximations, the Scaled Unit Information Prior Bayesian Information Criterion (SPBIC) and Information matrix-based Bayesian Information Criterion (IBIC). The SPBIC uses a scaled unit information prior that is more general than the BIC's unit information prior, and the IBIC utilizes more terms of approximation than the BIC. Through simulation, the authors show that several measures perform well in large samples, that performance declines in smaller samples, and that SPBIC and IBIC provide improvement to existing measures under some conditions, including small sample sizes. The authors illustrate the use of the fit measures with the crime data of Ehrlich and then conclude with recommendations for researchers.

Even though theories of democracy place emphasis on universal suffrage, little attempt has been made to measure the degree of suffrage in a broad range of countries. This article uses qualitative descriptions of suffrage to develop quantitative measures of suffrage for most countries of the world. We present annual suffrage measures from 1950 to 2000, provide basic descriptive statistics, & document trends in suffrage across the world. We also compare our measure of suffrage to two other measures available for 1968 & 1985. 7 Figures, 2 Appendixes, 37 References. Adapted from the source document.

Im Gegensatz zu Europa werden sozialwissenschaftliche Primärdaten in den USA selten auf öffentlich zugängliche Weise archiviert - selbst wenn die Datenerhebung öffentlich finanziert wurde. Dies gefährdet die künftige Verfügbarkeit der Daten. Der Beitrag erläutert aktuelle Versuche und Maßnahmen, Archivierungsnormen für die USA zu entwickeln, die Erhaltung von Daten zu sichern sowie diese Daten auch in Zukunft öffentlich zugänglich zu machen. ; Social science data collected in the United States, both historically and at present, have often not been placed in any public archive - even when the data collection was supported by government grants. The availability of the data for future use is, therefore, in jeopardy. Enforcing archiving norms may be the only way to increase data preservation and availability in the future.

'Im Gegensatz zu Europa werden sozialwissenschaftliche Primärdaten in den USA selten auf öffentlich zugängliche Weise archiviert - selbst wenn die Datenerhebung öffentlich finanziert wurde. Dies gefährdet die künftige Verfügbarkeit der Daten. Der Beitrag erläutert aktuelle Versuche und Maßnahmen, Archivierungsnormen für die USA zu entwickeln, die Erhaltung von Daten zu sichern sowie diese Daten auch in Zukunft öffentlich zugänglich zu machen.' (Autorenreferat)

This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it jointly with its related confidence interval. The results of the study demonstrate that there is little empirical support for the use of .05 or any other value as universal cutoff values to determine adequate model fit, regardless of whether the point estimate is used alone or jointly with the confidence interval. The authors' analyses suggest that to achieve a certain level of power or Type I error rate, the choice of cutoff values depends on model specifications, degrees of freedom, and sample size.

A key advantage of the root mean square error of approximation (RMSEA) is that under certain assumptions, the sample estimate has a known sampling distribution that allows for the computation of confidence intervals. However, little is known about the finite sampling behaviors of this measure under violations of these ideal asymptotic conditions. This information is critical for developing optimal criteria for using the RMSEA to evaluate model fit in practice. Using data generated from a computer simulation study, the authors empirically tested a set of theoretically generated research hypotheses about the sampling characteristics of the RMSEA under conditions commonly encountered in applied social science research. The results suggest that both the sample estimates and confidence intervals are accurate for sample sizes of n = 200 and higher, but caution is warranted in the use of these measures at smaller sample sizes, at least for the types of models considered here.

In this article, the authors examine the most common type of improper solutions: zero or negative error variances. They address the causes of, consequences of, and strategies to handle these issues. Several hypotheses are evaluated using Monte Carlo simulation models, including two structural equation models with several misspecifications of each model. Results suggested several unique findings. First, increasing numbers of omitted paths in the measurement model were associated with decreasing numbers of improper solutions. Second, bias in the parameter estimates was higher in samples with improper solutions than in samples including only proper solutions. Third, investigations of the consequences of using constrained estimates in the presence of improper solutions indicated that inequality constraints helped some samples achieve convergence. Finally, the use of confidence intervals as well as four other proposed tests yielded similar results when testing whether the error variance was greater than or equal to zero.

This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of instrumental variables performs similarly to ML when models are correctly specified. Under correct specification, both estimators have little bias except at the smallest sample sizes and are approximately equally efficient. As predicted, when models are incorrectly specified, 2SLS generally performs better, with less bias and more accurate hypothesis tests. Unless a researcher has tremendous confidence in the correctness of his or her model, these results suggest that a 2SLS estimator should be considered.