Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation

The behavioral, educational, and social sciences are undergoing a paradigmatic shift in methodology, from disciplines that focus on the dichotomous outcome of null hypothesis significance tests to disciplines that report and interpret effect sizes and their corresponding confidence intervals. Due to the arbitrariness of many measurement instruments used in the behavioral, educational, and social sciences, some of the most widely reported effect sizes are standardized. Although forming confidence intervals for standardized effect sizes can be very beneficial, such confidence interval procedures are generally difficult to implement because they depend on noncentral t, F, and x^2 distributions. At present, no main-stream statistical package provides exact confidence intervals for standardized effects without the use of specialized programming scripts. Methods for the Behavioral, Educational, and Social Sciences (MBESS) is an R package that has routines for calculating confidence intervals for noncentral t, F, and x^2 distributions, which are then used in the calculation of exact confidence intervals for standardized effect sizes by using the confidence interval transformation and inversion principles. The present article discusses the way in which confidence intervals are formed for standardized effect sizes and illustrates how such confidence intervals can be easily formed using MBESS in R.

Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation

The behavioral, educational, and social sciences are undergoing a paradigmatic shift in methodology, from disciplines that focus on the dichotomous outcome of null hypothesis significance tests to disciplines that report and interpret effect sizes and their corresponding confidence intervals. Due to the arbitrariness of many measurement instruments used in the behavioral, educational, and social sciences, some of the most widely reported effect sizes are standardized. Although forming confidence intervals for standardized effect sizes can be very beneficial, such confidence interval procedures are generally difficult to implement because they depend on noncentral t, F, and x2 distributions. At present, no main-stream statistical package provides exact confidence intervals for standardized effects without the use of specialized programming scripts. Methods for the Behavioral, Educational, and Social Sciences (MBESS) is an R package that has routines for calculating confidence intervals for noncentral t, F, and x2 distributions, which are then used in the calculation of exact confidence intervals for standardized effect sizes by using the confidence interval transformation and inversion principles. The present article discusses the way in which confidence intervals are formed for standardized effect sizes and illustrates how such confidence intervals can be easily formed using MBESS in R

"If We Don't Produce, Bring Another:" Work Organization and Tomato Worker Health.

Objectives: Specific work processes and management structures that contribute to high rates of occupational illness and injury in agricultural industries are not well described in academic literature. This qualitative study of work organization in the U.S. fresh tomato industry investigates how work processes and management structures impact tomato workers' occupational health. Methods: After conducting literature review and key informant interviews, semi-structured interviews and focus groups were conducted with 36 individuals with experience working in the U.S. fresh tomato industry. Interviews and focus groups were audio-recorded, transcribed, coded, and analyzed using a modified grounded theory approach. Results: These data indicate that participants endured income insecurity and hazardous supervisory practices, including wage theft, retaliation, intimidation, and humiliation, that put them at risk of preventable illness and injury. Support from workers' organizations and health-conscious supervisory practices helped mitigate some of these occupational hazards. Conclusion: Participants' adverse work experiences may be considered sequelae of workers' lack of job control and positions of socioeconomic structural vulnerability. Other aspects of tomato work organization, including health-conscious supervisory practices and the involvement of workers' organizations, indicate that modifying work organization to better safeguard health is possible. Such modifications present compelling opportunities for employers, employees, organizations, community and government leaders, and health care professionals to help create healthier occupational environments for tomato workers

A new reference genome assembly for the microcrustacean Daphnia pulex

Comparing genomes of closely related genotypes from populations with distinct demographic histories can help reveal the impact of effective population size on genome evolution. For this purpose, we present a high quality genome assembly of Daphnia pulex (PA42), and compare this with the first sequenced genome of this species (TCO), which was derived from an isolate from a population with >90% reduction in nucleotide diversity. PA42 has numerous similarities to TCO at the gene level, with an average amino acid sequence identity of 98.8 and >60% of orthologous proteins identical. Nonetheless, there is a highly elevated number of genes in the TCO genome annotation, with similar to 7000 excess genes appearing to be false positives. This view is supported by the high GC content, lack of introns, and short length of these suspicious gene annotations. Consistent with the view that reduced effective population size can facilitate the accumulation of slightly deleterious genomic features, we observe more proliferation of transposable elements (TEs) and a higher frequency of gained introns in the TCO genome

Guanosine diphosphatase is required for protein and sphingolipid glycosylation in the Golgi lumen of Saccharomyces cerevisiae

Current models for nucleotide sugar use in the Golgi apparatus predict a critical role for the lumenal nucleoside diphosphatase. After transfer of sugars to endogenous macromolecular acceptors, the enzyme converts nucleoside diphosphates to nucleoside monophosphates which in turn exit the Golgi lumen in a coupled antiporter reaction, allowing entry of additional nucleotide sugar from the cytosol. To test this model, we cloned the gene for the S. cerevisiae guanosine diphosphatase and constructed a null mutation. This mutation should reduce the concentrations of GDP-mannose and GMP and increase the concentration of GDP in the Golgi lumen. The alterations should in turn decrease mannosylation of proteins and lipids in this compartment. In fact, we found a partial block in O- and N-glycosylation of proteins such as chitinase and carboxypeptidase Y and underglycosylation of invertase. In addition, mannosylinositolphosphorylceramide levels were drastically reduced

Development and Utilization of Space Fission Power and Propulsion Systems

No abstract availabl

Quantum-Dot-Based Solid-State Lighting With Electric-Field-Tunable Chromaticity

Solid-state lighting is currently based on blue light-emitting diodes combined with wavelength downconversion via phosphors. Replacing the phosphors with quantum dots has a number of potential advantages, including narrowband and size-tailorable emission spectra. Here, we point out another advantage: the ability to perform real-time tuning of chromaticity of solid-state lighting by altering quantum dot absorption or emission wavelengths and oscillator strengths using electric fields. We discuss a possible architecture for such a solid-state lamp, and the chromaticity ranges that could be obtained for given ranges of absorption or emission wavelength and oscillator strength changes

Sample Size under Inverse Negative Binomial Group Testing for Accuracy in Parameter Estimation

Background:The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or absence), group testing is efficient when the prevalence is low, so that estimation, detection, and sample size methods have been developed under the binomial model. However, when the event is rare (low prevalence Methodology/Principal Findings: This research proposes three sample size procedures (two computational and one analytic) for estimating prevalence using group testing under inverse (negative) binomial sampling. These methods provide the required number of positive pools (rm), given a pool size (k), for estimating the proportion of AP plants using the Dorfman model and inverse (negative) binomial sampling. We give real and simulated examples to show how to apply these methods and the proposed sample-size formula. The Monte Carlo method was used to study the coverage and level of assurance achieved by the proposed sample sizes. An R program to create other scenarios is given in Appendix S2. Conclusions: The three methods ensure precision in the estimated proportion of AP because they guarantee that the width (W) of the confidence interval (CI) will be equal to, or narrower than, the desired width (v), with a probability of c. With the Monte Carlo study we found that the computational Wald procedure (method 2) produces the more precise sample size (with coverage and assurance levels very close to nominal values) and that the samples size based on the Clopper-Pearson CI (method 1) is conservative (overestimates the sample size); the analytic Wald sample size method we developed (method 3) sometimes underestimated the optimum number of pools

Composition for personal growth : program design and evaluation.

<p>Two categories were used for cross-validation of the model, either type 1 or type 2. Clinical isolates were treated as an unknown class and cross-validated sensitivity, specificity, and class error were based on their classification prediction score with their respective reference strain control class. CV, cross-validated.</p><p>Cross-validated PLS-DA modeling statistics for the prediction performance for NA-SERS typing of individual type 1 and 2 <i>M</i>. <i>pneumoniae</i> clinical isolates.</p