A Monte Carlo Comparison of Robust MANOVA Test Statistics

Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test statistics with fifteen other test statistics across seven manipulated factors. These statistics were evaluated across 12,076 different conditions in terms of Type I error and power. Results suggest that when assumptions were met, the standard MANOVA test functioned well. However, when assumptions were violated, it performed poorly, whereas several of the alternatives performed better. Discussion focuses on advice for selecting alternatives in practice. This study’s focus on all these in one simulation and the 3 group case should be helpful to the practitioner making methodological sections

Factorial Invariance Testing under Different Levels of Partial Loading Invariance within a Multiple Group Confirmatory Factor Analysis Model

Scalar invariance in factor models is important for comparing latent means. Little work has focused on invariance testing for other model parameters under various conditions. This simulation study assesses how partial factorial invariance influences invariance testing for model parameters. Type I error inflation and parameter bias were observed

Using Exploratory Factor Analysis for Locating Invariant Referents in Factor Invariance Studies

Model identification in multi-group confirmatory factor analysis (MCFA) requires an equality constraint of referent variables across groups. Invariance assumption violations make it difficult to locate parameters that actually differ. Suggested procedures for locating invariant referents are cumbersome, complex, and provide imperfect results. Exploratory factor analysis (EFA) may be an alternative because of its ease of use, yet empirical evaluation of its effectiveness is lacking. EFAs accuracy for distinguishing invariant from non-invariant referents was examined

Comparing Factor Loadings in Exploratory Factor Analysis: A New Randomization Test

Factorial invariance testing requires a referent loading to be constrained equal across groups. This study introduces a randomization test for comparing group exploratory factor analysis loadings so as to identify an invariant referent. Results show that it maintains the Type I error rate while providing adequate power under most conditions

Parameter Estimation with Mixture Item Response Theory Models: A Monte Carlo Comparison of Maximum Likelihood and Bayesian Methods

The Mixture Item Response Theory (MixIRT) can be used to identify latent classes of examinees in data as well as to estimate item parameters such as difficulty and discrimination for each of the groups. Parameter estimation via maximum likelihood (MLE) and Bayesian estimation based on the Markov Chain Monte Carlo (MCMC) are compared for classification accuracy and parameter estimation bias for difficulty and discrimination. Standard error magnitude and coverage rates were compared across number of items, number of latent groups, group size ratio, total sample size and underlying item response model. Results show that MCMC provides more accurate group membership recovery across conditions and more accurate parameter estimates for smaller samples and fewer items. MLE produces narrower confidence intervals than MCMC and more accurate parameter estimates for larger samples and more items. Implications of these results for research and practice are discussed

Litigation Following a Cyber Attack: Possible Outcomes and Mitigation Strategies Utilizing the Safety Act

Liability for a cyber attack is not limited to the attackers. An attack may be foreseeable in some circumstances, and the failure of the target or the other entities to take steps to prevent the attack can constitute a breach of duty to injured victims. In the absence of the protections provided by the Support Anti-Terrorism By Fostering Effective Technologies (SAFETY) Act, a cyber attack on a chemical facility could give rise to a number of common-law tort and contract claims against the target of the attack and other entities, potentially including the target’s cyber security vendors. This article discusses claims that might arise in various cyber attack scenarios and the effect of the SAFETY Act on these potential claims. The SAFETY Act is a tort liability management statute that was passed as part of the Homeland Security Act of 2002. Under the SAFETY Act, entities that sell or otherwise deploy products that can be used to deter, defend against, respond to, mitigate, or otherwise combat “acts of terrorism” are eligible to receive liability protections. These liability protections can take the form of jurisdictional defenses, a cap on liability, or a presumption of immediate dismissal of third-party liability claims. This article reviews several scenarios to examine whether liability could be found against companies that make cyber security tools or against entities that purchase such tools. The article then examines how the SAFETY Act could be utilized to mitigate or eliminate such liability

Occurrence and habits of the Gambaga Flycatcher Muscicapa gambagae in Kenya, including the first description of its song

Historically, the Gambaga Flycatcher Muscicapa gambagae has been a relatively poorly known bird in Kenya. Following a review of all known records in Kenya, we show that breeding of presumed resident birds is known from three discreet areas, but that as many as 47% of all records, from the months of October to March, come from areas where breeding is not known. This finding indicates a migratory origin for these individuals, and the concurrent absence of northern, summer-breeding Gambaga Flycatchers from the mountainous regions of western Saudi Arabia, Yemen and northern Somalia point to that region as a likely origin of these winter visitors. Furthermore, records show that the frequency of occurrence of the Gambaga Flycatcher in Kenya is also increasing, with a rate of reporting since 2000 which is four times higher than during the period 1960–2000, likely representing a shift in range. Lastly, we also describe some habitat characteristics at preferred sites, and provide the first published sonograms and accompanying description of the song

Toward a Population Health Model of Segmented Assimilation: The Case of Low Birth Weight in Los Angeles

The authors adapt the segmented assimilation theory to a model population health, which posits that assimilation is actually harmful to migrants\u27 health. The authors also specify models of individual and contextual factors to indirectly test the theory of segmented assimilation - a theory that posits interactions between individual and residential circumstances. Using Year 2000 vital statistics data merged with 2000 U.S. census data from Los Angeles County, the authors model the probability of being born low birth weight among the native and foreign born. Results confirm an immigrant advantage at the individual level and protective effects of immigrant coresidence at the neighborhood level

Neighborhood Effects on Health: Concentrated Advantage and Disadvantage

We investigate an alternative conceptualization of neighborhood context and its association with health. Using an index that measures a continuum of concentrated advantage and disadvantage, we examine whether the relationship between neighborhood conditions and health varies by socio-economic status. Using NHANES III data geo-coded to census tracts, we find that while largely uneducated neighborhoods are universally deleterious, individuals with more education benefit from living in highly educated neighborhoods to a greater degree than individuals with lower levels of education