What the data tells us – quantitative analysis

] The program may be electronically recorded by The Joint Commission and is subject to the protection of the copyright laws of the US. No individual or entity other than The Joint Commission may electronically record any portion of these programs for any purpose without the written permission of The Joint

Fossum, 2003). Comparative inquiry also encourages administrators and educators to question educational systems and the role that education plays in individual and national development (Kelly, Altbach, & Arnove, 1982). In this article basic factual material such as enrolment and program distribution data and changes in these data are woven into an analysis of the conditions within the two nations that define their graduate education approaches or programs. As Altbach (1991) put it: “Facts do not speak for themselves; they need to be put into a context, to be explained.” The better we understand each other’s strengths and differences, the better chance we have to develop more vigorous graduate education systems that can adapt to the changing realities of a global, technologically infused and increasingly competitive marketplace for higher education.

As regards other determinants of regional specialization, our analysis shows that the introduction of spatial effects in the general regression model increases the number of significant explicative variables. The overall results of econometric analyses show that openness and market access positively affect regional specialization in most of the considered sectors. This outcome is in accordance with the findings from New Economic Geography. Physical and human capital have no clear-cut effect on specialization in the various models estimated. This may well be due to the unavailability of sectoral data for these factors. Income level and scale economies show an increasing positive effect when spatial interaction is accounted for.

 The AM PAC CAT web address is accessible from any computer on the Regional network  AM-PAC data can be combined with both clinical and administrative data in Health Connect to permit comparisons of functional improvement based on patient characteristics and service utilization

The incongruity in joke (5) does not result from a syntactical or semantic ambiguity at all, and yet it induces dissonance. The dissonance is not a result of compositionality, but due to the access of a whole linguistic structure, i.e. we recall the familiar proverb ‘You can lead a horse to water but you cannot make it drink’, and the deviation from the recognizable structure causes the viola- tion of our expectations. Thus, access is not re- stricted to the lexical level; we seem to store and access bigger units of discourse if encountered fre- quently enough. The only way to do justice to this joke would be to encode the entire sentential struc- ture directly into the lexicon. Our model will now also consider these larger chunks, whose meaning is specified atomically. The dissonance will now come from the semantic difference between the accessed expression and the one under analysis.

An important and interesting step in this direction was undertaken by Picciano [37] who related student perceptions of social presence to actual and perceived interactions and learning in an online, graduate level course in education. Picciano analyzed the relationships among survey data on students' perceived social presence, learning, and interaction and measures of their actual interactions in course discussions and performance scores on exams and a written assignment. He found that perceptions of social presence were correlated with perceptions of learning and interaction, and that perceived learning and perceived interactions were also correlated, but that perceived social presence was correlated with neither actual interactions nor performance. He did find, however, that, when learners were split by their interactivity into high, moderate, and low groupings, students in the highly interactive group significantly outperformed the others on the written assignment. When students were similarly grouped by perceptions of social presence, students experiencing the highest levels of social presence also scored significantly higher than the other students on the written assignment. There were no such differences in exam scores.

Cross, et al., 2014 http://oro.open.ac.uk/40593/ The roles, reception and use of badges as an assessment strategy are explored through data from two OU MOOCs that ran in 2013. There was variability in views about badges. Many were positive, citing them to be motivating or a means of evidencing their learning, but others felt that badges seemed childish. Transparency of the process of badge approval was seen as an important potential improvement in order to assure quality and avoid cheating. The paper argues that badges can provide formative guidance to learners and should not be seen simply as an award following summative assessment.

Currently available studies are mostly based on pattern de- scription, whereas the possible relationships between green shape characteristics and temporal trends in species extinc- tion remain largely unexplored. This is probably due to the assumed lack of data to reconstruct temporal trends. However, results based on the use of museum data from Rome (Fattorini, 2011a) suggest that a similar approach could be attempted in other cities for which old insect col- lections are available.

output prices. When we measure marginal cost based on inputs and factor prices, however, we do not find high marginal cost in booms, or countercyclical markups, because input prices are less procyclical than productivity. But when we allow for procyclical factor utilization that affects the shadow cost of labor, we find countercyclical markups; these countercyclical markups are then reflected in countercyclical optimal inventory holdings relative to expected sales. We find little reason for firms to engage in the standard production or cost-smoothing envisioned in conventional inventory models. Such intertemporal substitution requires forecastable changes in marginal cost relative to interest rates that we cannot find in the data. The last finding is important given that the linear-quadratic inventory model—by far the most commonly employed model of inventory behavior—imposes a constant target sales-stock relationship and requires that persistent deviations from that target be the result of intertemporal substitution.

Electronic Health Record (EHR). An EHR is generated by a health care provider to document patients’ medical and health information on a continuing basis. It may contain demographic data, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. The EHR can support clinical activities including evidence-based decision support, quality management, and outcomes reporting. It can automate and streamline the clinician’s workflow. An EHR is not directly accessed by patients, although certain data may be made available through a patient portal.

5.1.7 Hospice home care must be protected Perhaps one of the most interesting findings to emerge from this data mining study concerns hospice home care. Patient and family experiences of access to home care vary greatly across the country. In Cork and parts of the greater Dublin area (the Northern and South-Western areas of the former Eastern Regional Health Authority), where inpatient hospice units are in place, fewer patients are admitted to home care and waiting times are longer than in some areas where there is no hospice. Also, for patients with conditions other than cancer, admission to home care is below average in most LHOs within these areas. In comparison, in areas without an inpatient unit, such as the Midlands, there is greater access to hospice home care for all patients. This can be explained by the fact that in areas without a hospice, home care is often the only palliative care option available, and development funding allocated to specialist palliative care can only be invested in home care. However, we have also noted that a patient’s access to home care during the course of illness does not automatically mean that they will die at home. In the North East, for example, where there is no inpatient hospice, 27% of patients of home care services die in hospital, compared to 6% in the Mid West, which benefits from the full range of services that only the presence of an inpatient unit can provide.

In addition to the LAUS data from BLS, the U.S. Citizenship and Immigration Services (USCIS) confirms that unemployment data published by the U.S. Census Bureau’s (CB) through the American Community Survey (ACS) also qualifies as “reliable and verifiable” to demonstrate TEA qualification for an area when an EB-5 investor files his/her form I-526 petition. 1 However, the most current ACS data measures economic statistics in the U.S.

The length of stay on the cancer floor of Apolo Hospital were organized into a frequency distribution. The mean length of stay was 28 days, the medial 25 days and modal length is 23 days. The standard deviation was computed to be 4.2 days. Is the distribution symmetrical, or skewed? What is the coefficient of skewness? Interpret. Solution: Solve yours by using the formula.

In 2006, Cooper (2006b) provided a preliminary review of the evidence regarding counselling in UK secondary schools (revised and reprinted as Cooper, 2008a). However, the data reviewed for this study came from just five evaluation studies; no systematic method was used to identify, locate and retrieve research reports; and methods of analysis were relatively basic. The aim of the present analysis, therefore, is to expand this previous review: system- atically and exhaustively searching for, and analysing, data from audit and evaluation studies of counselling services in UK secondary schools. In doing so, the review aims to provide interested individuals such as school counsellors, pastoral care staff, headteachers, researchers, parents, funding authorities and students with a comprehensive picture of the outcomes, processes, and nature of counselling in secondary schools in the UK, as well as information about the kind of young people who attend these services. It also aims to provide benchmarks for practitioners and managers delivering or evaluating these services, and a platform on which subsequent research can be built.

Different spatial relations have different func- tional and geometric bias. The wide usage of neural language models in different areas in- cluding generation of image description moti- vates the study of what kind of knowledge is encoded in neural language models about in- dividual spatial relations. With the premise that the functional bias of relations is ex- pressed in their word distributions, we con- struct multi-word distributional vector repre- sentations and show that these representations perform well on intrinsic semantic reasoning tasks, thus confirming our premise. A compar- ison of our vector representations to human se- mantic judgments indicates that different bias (functional or geometric) is captured in differ- ent data collection tasks which suggests that the contribution of the two meaning modalities is dynamic, related to the context of the task.

Hemphill, Russell, and Schöpke – MPSA 2019 We introduce a computational model to measure the policy agendas expressed by individual MCs in their regular communication on Twitter. We then test individual and institutional influences on how legislators explain their work to constituents, journalists, and partisans. By using a public and widely adopted communication tool like Twitter, we aggregate information found in newsletters, press releases, and floor debates to provide a birds-eye view of a lawmaker’s diverse agenda. Members of Congress have sent millions of tweets in the last couple years, and in order to leverage this data efficiently, we trained a supervised machine learning classifier to categorize lawmaker tweets according to the U.S. Policy Agenda Project’s Policy Codebook. We used the results to examine the differential attention that policy topics receive from MCs. Just as some issues garner a disproportionate amount of attention in roll calls or legislative hearings, individual politicians also skew their attention online. We catalogue these policy patterns with a classifier that achieved an F1 score of 0.79 and a Cohen’s kappa with human labelers of 0.78, suggesting good performance for assessing the policy content among lawmaker tweets. Using this classifier, we labeled 1,485,834 original MC tweets

What Drives U.S. Congressional Members’ Policy Attention on Twitter? - ​ UNDER REVIEW explore additional designs. For instance, more experiments with different word embedding models may identify a better approach such as topic2vec ​ (Niu, Dai, Zhang, & Chen, 2015) ​ that can learn both words and topics. It may also be useful to include individual characteristics in the models themselves. Party, chamber, and gender have small influence on the topics discussed and so may be more analytically useful as model features—they may help the model assign topics when two categories have similar probabilities such as immigration and agriculture or energy and environment in the U.S. context. Researchers should also experiment with unsupervised approaches to detecting political topics. Our approach leverages manually labeled data to effectively classify documents, but an unsupervised approach, where the model identifies latent relationships, may generate results that are useful for different approaches to

Preprocessing In preparing text for use in a machine learning model, the text is divided into sequences of characters called ​ tokens ​ that are then used for analysis. Often, tokens are words, but in some cases they are multi-word phrases or parts of words such as word stems.

performance? It also prompted questions about the validity of PISA assessment itself. Secondly, and perhaps more importantly, it highlighted the significant information, data and analysis deficit that exists in Irish educational evaluation. The framework for school evaluation in Ireland, developed over the past decade and a half, is a self-evaluation-type model. Under the current framework, Looking at our Schools, schools are expected to consider their performance on an ongoing basis across five broad areas; quality of school management, planning, curriculum provision, teaching and learning, as well as pupil support. 21 This self-evaluation process also has a related external dimension; it forms the basis of a Whole School Evaluation, which includes a detailed school inspection by a visiting Inspectorate every 5 to 7 years. 22 While the evaluation process in Irish schools covers a total of 143 ‘themes for self-evaluation’ and results in considerable documentation- gathering, culminating in post-evaluation verbal and written reports, the analytic capacity of the system as currently configured and implemented is considered extremely limited. For example, the OECD background report for Ireland on improving school leadership notes that while references are made to quality, no objective evidence is provided in statistical form (OECD 2007: 13). Although the Department of Education and Science does make use of state examination data, state examinations are not standardised assessments and data is not used for evaluation purposes. 23 In a recent analysis of the Irish self-evaluation system and its implementation, McNamara et al., (2011) consider the views of inspectors, education leaders and teachers and highlight a number of weaknesses. They note the process is perceived as one that supports ‘impressionistic conclusions’ over

Several cross-sectional analyses have been performed on the baseline data obtained from the PFBHS to examine the association between fi ght exposure and various imaging measures. Repeated measures analysis of variance was employed to test the association between the outcome variables and fi ght exposure variables. Guided by the cutpoints (that is, tree branch splitting values) and deviance reduction values from the regres- sion trees, we defi ned and tested fi ght exposure as follows: linear eff ect of total number of professional fi ghts, linear eff ect of total number of years of profes- sional fi ghting, a threshold eff ect with brain volume reduction estimated separately for less than 5 years of professional fi ghting versus at least 5 years, and an exposure composite score as a function of number of professional fi ghts and number of professional fi ghts per year. In each model, we included the type of fi ghter (boxer or MMA fi ghter) and an interaction term for the type of fi ghter with the other exposure variable. Given the exploratory nature of this study, a signifi cance level of 0.05 was used to test the signifi cance of the regression coeffi cients of the exposure variables; no adjustments for multiplicity were applied. A secondary aim was to test for associations between imaging measures and cognitive test scores and between fi ght exposure and cognitive test scores. Generalized linear models were constructed with cognitive scores as the dependent variables and brain volume or fi ght exposure variables as the independent variables of interest. All analyses were adjusted for age (treated as a continuous variable), education (defi ned as no college-level versus some college-level), and race, which was defi ned as (a) Caucasian, (b) African- American, or (c) other (Asian, Pacifi c Islander, American Indian, or Alaskan Native).