Emotional intelligence and racial identity\u27s impact on academic achievement in American multiracial high school students

Minorities reached 50.4% of the American population, representing a majority for the first time, and those self-reporting as multiracial grew by a larger percentage than those reporting a single race (U.S. Census Bureau, 2011). Millennials born after 1980 are the most racially diverse generation (Pew Research Center, 2014). This study investigated how racial identity and emotional intelligence might impact academic achievement among U.S. Millennial multiracial adolescents of African descent. Research suggests a student\u27s racial identification has a significant impact on academic performance (Herman, 2009) and minority youth struggle academically (Annie E. Casey Foundation, 2014). The theoretical framework included the construct of racial identity for youth of African descent, who experience high levels of actual or perceived discrimination (Parham, 2002; Sellers, Linder, Martin & Lewis, 2006; Sellers & Shelton, 2000). This secondary analysis consisted of existing survey responses and standardized academic achievement scores for 32 California high school students who self-reported as multiracial of African descent. Data included responses from the Emotional Quotient (EQ-i: YV[s]) survey, which measures emotional competencies; the Cross Racial Identity survey, which measures racial identity attitudes for those of African descent; and the California Standards Test Scores in English-Language Arts. Research questions asked whether relationships exist among emotional intelligence competencies, racial identity attitudes, and academic achievement. Findings revealed a statistically significant relationship between the Emotional Intelligence scale score of adaptability and academic achievement (Pearson product-moment correlation coefficient r = .378, n = 32, p = .033; Spearman\u27s rank correlation coefficient ρ = .368, n = 32, p = .038). A second statistically significant relationship was found between the racial identity attitude and emotional intelligence scale scores (Pearson product-moment correlation coefficient r = .413, n = 32, p = .014). Findings support research suggesting adaptability is important for multiracial youth, involving cultural, ethnic, nationality, language, and socioeconomic issues, and a relationship exists between racial identity and academic achievement. Multiracial students represent a demographic, which should be recognized as distinct and varied, and multiracial students are at-risk. Recommendations include expanded research to inform classroom practice, enlightened educational policies, and greater social investment to support an increasingly diverse student population

The Composition and Dynamics of the Soil Algal Flora in a Western South Dakota Grassland

Forest, Miller and Raizen (1963) and Hutcheson and Olson (1967) have conducted two of the only investigations on soil algae in a prairie ecosystem. The present investigation dealt first with identification of genera of algae present in the soil and an evaluation of their numbers. Secondarily a relationship was established between the numbers of algae in the soil (by species) and their environment, by the correlation coefficients. The variation in the environment was then used to predict the occurrence of algae in the soil using stepwise additive multiple regression analysis. Finally several environmental factors were evaluated under controlled conditions to determine their effect on soil algal growth. Changes in Algal biomass were determined spectrophotometrically from chlorophyll extraction

Can a Work Organization Have an Attitude Problem? The Impact of Workplaces on Employee Attitudes and Economic Outcomes

Using the employee opinion survey responses from several thousand employees working in 193 branches of a major U.S. bank, we consider whether there is a distinctive workplace component to employee attitudes despite the common set of corporate human resource management practices that cover all the branches. Several different empirical tests consistently point to the existence of a systematic branch-specific component to employee attitudes. “Branch effects” can also explain why a significant positive cross-sectional correlation between branch-level employee attitudes and branch sales performance is not observed in longitudinal fixed-effects sales models. The results of our empirical tests concerning the determinants of employee attitudes and the determinants of branch sales are consistent with an interpretation that workplace-specific factors lead to better outcomes for both employees and the bank, and that these factors are more likely to be some aspect of the branches’ internal operations rather than some characteristic of the external market of the branch.

Prototype selection for parameter estimation in complex models

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy's stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org