Bootstrap Interval Estimation of Reliability via Coefficient Omega

Three different bootstrap confidence intervals (CIs) for coefficient omega were investigated. The CIs were assessed through a simulation study with conditions not previously investigated. All methods performed well; however, the normal theory bootstrap (NTB) CI had the best performance because it had more consistent acceptable coverage under the simulation conditions investigated

Type I Error Rates of the Kenward-Roger Adjusted Degree of Freedom F-test for a Split-Plot Design with Missing Values

The Type I error rate of the Kenward-Roger (KR) test, implemented by PROC MIXED in SAS, was assessed through a simulation study for a one between- and one within-subjects factor split-plot design with ignorable missing values and covariance heterogeneity. The KR test controlled the Type I error well under all of the simulation factors, with all estimated Type I error rates between .040 and .075. The best control was for testing the between-subjects main effect (error rates between .041 and .057) and the worst control was for the between-by-within interaction (.040 to .075). The simulated factors had very small effects on the Type I error rates, with simple effects in two-way tables no larger than .01

Type I Error Rates For A One Factor Within-Subjects Design With Missing Values

Missing data are a common problem in educational research. A promising technique, that can be implemented in SAS PROC MIXED and is therefore widely available, is to use maximum likelihood to estimate model parameters and base hypothesis tests on these estimates. However, it is not clear which test statistic in PROC MIXED performs better with missing data. The performance of the Hotelling- Lawley-McKeon and Kenward-Roger omnibus test statistics on the means for a single factor withinsubject ANOVA are compared. The results indicate that the Kenward-Roger statistic performed better in terms of keeping the Type I error close to the nominal alpha level

Type I Error Rates of the Kenward-Roger \u3cem\u3eF\u3c/em\u3e-test for a Split-Plot Design with Missing Values and Non-Normal Data

The Type I error of the Kenward-Roger (KR) F-test was assessed through a simulation study for a between- by within-subjects split-plot design with non-normal ignorable missing data. The KR-test for the between- and within-subjects main effect was robust under all simulation variables investigated and when the data were missing completely at random (MCAR). This continued to hold for the between-subjects main effect when data were missing at random (MAR). For the interaction, the KR F-test performed fairly well at controlling Type I under MCAR and the simulation variables investigated. However, under MAR, the KR F-test for the interaction only provided acceptable Type I error when the within-subjects factor was set at 3 and 5% missing data

Confidence Intervals for the Coefficient Alpha Difference from Two Independent Samples (Groups)

Four different bootstrap methods for estimating confidence intervals (CIs) for a coefficient alpha difference from two independent samples (groups) were examined. These four CIs were compared to the most promising non-bootstrap CI alternatives in the literature. All CIs were assessed with a Monte Carlo simulation with conditions similar to previous research. The results indicate that there is a clear order in coverage performance of the CIs. The bootstrapped highest density interval had the best coverage performance across all simulation conditions. Yet, it was impacted by unequal sample sizes when one of the groups had the smallest sample size investigated of 50, or when items came from a compound symmetric correlation matrix with $\rho = 0.64$. Regardless of the simulation condition, the percentile bootstrap is a good alternative as long as both group sample sizes were 200 or more

Developing a Measure of Psychological Aggression: Stage 2

Current research indicates that psychological aggression can lead to physical aggression. Thus, accurate measures of psychological aggression can be used to quell future physical aggression. However, unsound psychometric properties and fragmented definitions have diminished the accuracy of current psychological aggression scales. The purpose here is to create a sound psychological aggression scale. This part of the study focused on pilot testing preliminary items written to capture behaviors that constitute psychological aggression. An analysis revealed that some preliminary items required removal because of their abnormal distributions. The next step is to field test the items to establish the factor structure of the new scale

Multiple Imputation to Correct for Measurement Error in Admixture Estimates in Genetic Structured Association Testing

Objectives: Structured association tests ( SAT), like any statistical model, assumes that all variables are measured without error. Measurement error can bias parameter estimates and confound residual variance in linear models. It has been shown that admixture estimates can be contaminated with measurement error causing SAT models to suffer from the same afflictions. Multiple imputation (MI) is presented as a viable tool for correcting measurement error problems in SAT linear models with emphasis on correcting measurement error contaminated admixture estimates. Methods: Several MI methods are presented and compared, via simulation, in terms of controlling Type I error rates for both non-additive and additive genotype coding. Results: Results indicate that MI using the Rubin or Cole method can be used to correct for measurement error in admixture estimates in SAT linear models. Conclusion: Although MI can be used to correct for admixture measurement error in SAT linear models, the data should be of reasonable quality, in terms of marker informativeness, because the method uses the existing data to borrow information in which to make the measurement error corrections. If the data are of poor quality there is little information to borrow to make measurement error corrections. Copyright © 2009 S. Karger AG, Base

Reflexive Self-Ethnography on Functional Diversity. A three voices story

La diversidad funcional es una experiencia, es la condición vital de una persona y, como tal, le afecta a ella y a su entorno y relaciones. Esta experiencia puede ser vivida y analizada de manera simultánea, rechazando la dicotomía sujeto/ objeto que presupone una distancia neutralizante entre el investigador y su objeto de estudio. Aquí se ofrece, desde esa condición dual, la de investigadora y madre, una narrativa autoetnográfica de la diversidad funcional, la diversidad funcional de Carmen, la niña de ojos grandes y hermosos.Functional diversity is an experience, is the living condition of a person and, as that, effects herr and her environment and relations. This experience can be simultaneously lived and analysed, rejecting the subject/object dichotomy that states a neutralizing distance between researcher and her study object. Here is offered, from this dual condition, researcher and mother, a self-ethnographic narrative of the functional diversity, Carmen, the girl of big and beautiful eyes, functional diversity.Humanidade

Autoetnografía reflexiva sobre la diversidad funcional: Un relato a tres voces

Functional diversity is an experience, is the living condition of a person and, as that, effects herr and her environment and relations. This experience can be simultaneously lived and analysed, rejecting the subject/ object dichotomy that states a neutralizing distance between researcher and her study object. Here is offered, from this dual condition, researcher and mother, a self-ethnographic narrative of the functional diversity, Carmen, the girl of big and beautiful eyes, functional diversity.La diversidad funcional es una experiencia, es la condición vital de una persona y, como tal, le afecta a ella y a su entorno y relaciones. Esta experiencia puede ser vivida y analizada de manera simultánea, rechazando la dicotomía sujeto/ objeto que presupone una distancia neutralizante entre el investigador y su objeto de estudio. Aquí se ofrece, desde esa condición dual, la de investigadora y madre, una narrativa autoetnográfica de la diversidad funcional, la diversidad funcional de Carmen, la niña de ojos grandes y hermosos