Alien Registration- Demerchant, Flora B. (Houlton, Aroostook County)

https://digitalmaine.com/alien_docs/36121/thumbnail.jp

First Year Perceptions: How Does Starfish Retention Solutions™ help With Student Engagement?

This research investigation examined the legitimacy of Starfish Retention Solutions™ interactive early warning system impact on first year student’s academic engagement. The goal of the study was to diagnose first year student’s perceptual opinion of the early warning systems capacity to motivate students to seek academic resources and evaluate their level of educational satisfaction. State legislators have been habitually cutting appropriation funding from the state budget in protest to the escalating college drop–out rates and declining graduation rates. The action of public officials has lead colleges and university administers to compensate for education costs by increasing college tuition. In response, education leadership throughout the nation have poured over academic research studies that substantiates reasons for student retention/attrition issues and suggests strategies that would combat student retention. In recent years technological software was designed to encourage student engagement, connect students with instructors, motivate students to inquire about academic advising and encourage students to seek academic resources. One particular software technology that has gained interest from academic practitioners is Starfish Retention Solutions™. This study was based on research expert Vincent Tinto’s four conditions of student engagement and theory of social integration. The purpose of this inquiry is to investigate if there is perceptual evidence that Starfish Retention Solutions™ early warning system has an impact on student engagement and educational satisfaction. The study was a cross-sectional research design featuring open-ended response questions. A volunteer self-reported online survey was administered through Qualtrics, a cloud based research platform, to (N = 9, 255) participants attending Bluegrass Community Technical College campuses. A cross-tabulation data analysis was conducted to analyze student opinions for gender and race/ethnicity demographics. This body of research will be a source of supporting information for those in institutional leadership who are contemplating implementation of Starfish Retention Solutions™, in addition this research study will be a foundational platform for future research studies. Conclusively this research study will add to the limited collection of published research that is currently available

Alien Registration- Perkins, Flora B. (Hallowell, Kennebec County)

https://digitalmaine.com/alien_docs/17268/thumbnail.jp

Say-on-Pay Votes:The Role of the Media

We investigate the association between the media coverage of firms' CEO pay packages and subsequent shareholder voting on say-on-pay resolutions, and find that negative media coverage is able to predict shareholder discontent over say on pay. When we divide media coverage into coverage in the financial and business press versus coverage in the general press, we find that shareholder voting on say-on-pay resolutions is mainly associated with the articles from the financial and business press. This suggests that the media cannot be considered a homogeneous information source that is equally able to predict shareholders' voting behaviors. As such, our findings have important implications for studies on the role of the media in corporate governance.</p

Old and New Ideas for Data Screening and Assumption Testing for Exploratory and Confirmatory Factor Analysis

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables

Predictions of Native American Population Structure Using Linguistic Covariates in a Hidden Regression Framework

International audienceBACKGROUND: The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations. METHODOLOGY/PRINCIPAL FINDINGS: Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg's and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg's classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters. CONCLUSIONS/SIGNIFICANCE: The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations