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Doctoral education, a key component of higher education in the United States, is performing well. It educates future professors, researchers, innovators, and entrepreneurs. It attracts students and scholars from all over the world and is being emulated globally. This success, however, should not engender complacency. A Data-Based Assessment of Research-Doctorate Programs in the United States provides an unparalleled dataset that can be used to assess the quality and effectiveness of doctoral programs based on measures important to faculty, students, administrators, funders, and other stakeholders. This report features analysis of selected findings across six broad fields: agricultural sciences, biological and health sciences, engineering, physical and mathematical sciences, social and behavioral sciences, and humanities, as well as a discussion of trends in doctoral education since the last assessment in 1995, and suggested uses of the data. It also includes a detailed explanation of the methodology used to collect data and calculate ranges of illustrative rankings.

This paper checks if the auditors in an emergent context, where the fair value (FV) concept, its implementation and audit are relatively new, are aware of the estimation risk induced by the valuation process (the FV provider and FV disclosure), depending on the quality of internal control (IC). An experiment was applied to a group of auditors and master students, using two elements pertaining to FV reporting: “Valuation attributes and sensitivity of data”, respectively “Methods, assumptions and model”. This experiment revealed that: (1) FV audit risk is lower when the estimation is made by an external, instead of an internal valuator; (2) the master’s students, compared to more experienced professional auditors, manifest an overconfidence in the external Valuation Report in terms of valuation attributes, data availability and solutions adopted to test the sensitivity of value; (3) the audit risk is lower when the valuator is external and hence the auditors verify in detail the information provided in the Valuation Report as inputs and methods applied; (4) when IC is strong as quality, the verification of methods, assumptions and model induces for auditors a higher risk than the other FV disclosed component, valuation attributes and sensitivity of data, in the case of management estimation.

Whether you are evaluating the effectiveness of a drug, a medical device, a behavioral intervention, a community mobilization, or even a new law, this is the book for you. Written in plain language, it simplifies the process of designing interventions, analyzing the data, and publishing the results. Because the choice of research design depends on the nature of the intervention, the book covers randomized and nonrandomized designs, prospective and retrospective studies, planned clinical trials and observational studies. In addition to reviewing standard statistical analysis, the book has easy-to-follow explanations of cutting edge techniques for evaluating interventions, including propensity score analysis, instrumental variable analysis, interrupted time series analysis and sensitivity analysis. All techniques are illustrated with up-to-date examples from medical and public health literature. This will be essential reading for a wide range of healthcare professionals involved in research as well as those more specifically interested in public health issues and epidemiology.

Reliable information on how health service strategies affect the poor is in short supply. In an attempt to redress the imbalance, 'Improving Health Service Delivery in Developing Countries' presents evidence on strategies for strengthening health service delivery, based on systematic reviews of the literature, quantitative and qualitative analyses of existing data, and seven country case studies. The authors also explore how changes in coverage of different health services affect each other on the national level. Finally, the authors explain why setting international targets for health services has been not been successful and offer an alternative approach based on a specific country's experience. The book's findings are clear and hopeful: There are many ways to improve health services. Measuring change and using information to guide decisions and inform stakeholders are critically important for successful implementation. Asking difficult questions, using information intelligently, and involving key stakeholders and institutions are central to the \"learning and doing\" practices that underlie successful health service delivery.

Interprofessional teamwork and collaborative practice are emerging as key elements of efficient and productive work in promoting health and treating patients. The vision for these collaborations is one where different health and/or social professionals share a team identity and work closely together to solve problems and improve delivery of care. Although the value of interprofessional education (IPE) has been embraced around the world - particularly for its impact on learning - many in leadership positions have questioned how IPE affects patent, population, and health system outcomes. This question cannot be fully answered without well-designed studies, and these studies cannot be conducted without an understanding of the methods and measurements needed to conduct such an analysis. This Institute of Medicine report examines ways to measure the impacts of IPE on collaborative practice and health and system outcomes. According to this report, it is possible to link the learning process with downstream person or population directed outcomes through thoughtful, well-designed studies of the association between IPE and collaborative behavior. Measuring the Impact of Interprofessional Education on Collaborative Practice and Patient Outcomes describes the research needed to strengthen the evidence base for IPE outcomes. Additionally, this report presents a conceptual model for evaluating IPE that could be adapted to particular settings in which it is applied. Measuring the Impact of Interprofessional Education on Collaborative Practice and Patient Outcomes addresses the current lack of broadly applicable measures of collaborative behavior and makes recommendations for resource commitments from interprofessional stakeholders, funders, and policy makers to advance the study of IPE.

The purpose of economic evaluation is to inform decisions intended to improve healthcare. The new edition of Methods for the Economic Evaluation of Health Care Programmes equips the reader with the essential hands-on experience to undertake evaluations, providing a 'tool kit' based on the authors own experience of undertaking economic evaluations.

The National Institute on Disability and Rehabilitation Research (NIDRR) is the principal federal agency supporting applied research, training, and development to improve the lives of individuals with disabilities. NIDRR's mission is to generate new knowledge and promote its effective use in improving the ability of persons with disabilities to perform activities of their choice in the community, as well as to expand society's capacity to provide full opportunities and accommodations for its citizens with disabilities. NIDRR prides itself on being proactive in establishing program performance measures and developing accountability data systems to track the progress of its grantees. An electronic annual reporting system is used to collect data from grantees on many aspects of grant operation and outputs. Various formative and summative evaluation approaches have been used to assess the quality of the performance and results of the agency's research portfolio and its grantees. Prompted by the need to provide more data on its program results, in 2009 NIDRR requested that the National Research Council (NRC) conduct an external evaluation of some of the agency's key processes and assess the quality of outputs produced by NIDRR grantees (National Institute on Disability and Rehabilitation Research, 2009a). Review of Disability and Rehabilitation Research presents the results of that evaluation.

The American Time Use Survey (ATUS), conducted by the Bureau of Labor Statistics, included a subjective well-being (SWB) module in 2010 and 2012. The module, funded by the National Institute on Aging (NIA), is being considered for inclusion in the ATUS for 2013. The National Research Council was asked to evaluate measures of self-reported well-being and offer guidance about their adoption in official government surveys. The charge for the study included an interim report to consider the usefulness of the ATUS SWB module, specifically the value of continuing it for at least one more wave. Among the key points raised in this report are the value, methodological benefits, and cost and effects on the ATUS and new opportunities. Research on subjective or self-reported well-being has been ongoing for several decades, with the past few years seeing an increased interest by some countries in using SWB measures to evaluate government policies and provide a broader assessment of the health of a society than is provided by such standard economic measures as gross domestic product. NIA asked the panel to prepare an interim report on the usefulness of the SWB module of the ATUS, with a view as to the utility of continuing the module in 2013. The Subjective Well-Being Module of the American Time Use Survey is intended to fulfill only one narrow aspect of the panel's broader task. It provides an overview of the ATUS and the SWB module, a brief discussion of research applications to date, and a preliminary assessment of the value of SWB module data. The panel's final report will address issues of whether research has advanced to the point that SWB measures-and which kinds of measures-should be regularly included in major surveys of official statistical agencies to help inform government economic and social policies.

There are numerous factors that influence the price of a farm and some of these factors are not monetary related. This makes the task of the valuer complex and increases the possibility of large differences in the estimated market value determined and the actual selling price. This article reports the results of a study that analysed the unique and distinctive attributes of farms, in order to determine whether it is possible to develop a linear multiple regression model for the valuation of farms (which satisfies accuracy requirements) with reasonably available data. The improvement of accuracy levels of Multiple Regression Analysis (MRA) models as well as the limitations of using these MRA models during farm evaluations was also studied. By following a stepwise regression approach, 60 farms, primarily located in the eco-zone “mixed bushveld” western area of the Limpopo province, were analysed using ten independent variables. Three models have been developed. The results showed that a fairly accurate regression model could be developed. However, a model that achieves a high level of accuracy could not be developed, due to multifaceted reasons, including non-farm factors and the size of the geographical areas. Accurate MRA valuation estimates will be to the advantage of individual farm owners regarding their municipal tax assessments. It will lead to a wider use of MRAs for the valuation of farms, but great circumspect should be taken when using MRA models in farm valuations. This is due to the possibility that the MRA models do not satisfy minimum accuracy requirements. It is difficult, but possible, to develop a fairly accurate MRA model for the valuation of farms. Therefore, if currently used MRA models are not fairly accurate for municipal valuation purposes, it should be possible to improve the accuracy. Further research is recommended in the use of other regression techniques such as non-linear, geographic weighted regression and quantile regression. These other techniques would, however, require a larger data sample, in order to provide meaningful results. Daar is veelvuldige faktore wat die prys van ’n plaas beïnvloed, wat nie noodwendig suiwer finansieël van aard is nie. Dit maak die taak van die waardeerder moeilik en kompleks, wat weer veroorsaak dat die moontlikheid bestaan dat daar groot verskille tussen die gewaardeerde waardes en die verkooppryse van plase voorkom. Hierdie artikel rapporteer die resultate van ’n studie wat die unieke en onderskeidende eienskappe van plase ontleed ten einde te bepaal of dit moontlik is om ’n liniêre regressie-analiese model te ontwikkel (wat aan minimum akkuraatheidsvlakke voldoen) met redelik beskikbare inligting, vir die waardasie van plase. Die verbetering van die akkuraatheidsvlakke van MRAmodelle sowel as die beperkings van hierdie MRA-modelle, vir gebruik in plaas waardasies, is ook nagevors. Deur middel van ’n stapsgewyse regressie-analiese metode, is 60 plase wat hoofsaaklik in die “gemengde bosveld” ekosone, in die westelike gedeelte van die Limpopo-provinsie geleë is, ontleed deur van 10 onafhanklike veranderlikes gebruik te maak. Drie modelle is ontwikkel. Die resultate het aangetoon dat ’n regressiemodel ontwikkel kan word wat redelik akkuraat is, maar ’n model met ’n hoë mate van akkuraatheid kon nie ontwikkel word nie as gevolg van veelvuldige redes, insluitend redes wat nie direk verwant is aan plaasfaktore nie en die grootte van die geografiese gebiede. Alhoewel akkurate MRA-waardasies tot voordeel sal wees van plaas eienaars vir die bepaling van munisipale belastingwaardes en dit sal lei tot ’n wyer en meer algemene gebruik van MRA’s vir plaaswaardasies, moet groot versigtigheid aan die dag gelê word met die gebruik van MRA-modelle in plaaswaardasies omdat dit waarskynlik is dat die MRA-modelle nie aan minimum akkuraatheidvereistes voldoen nie. Dit is moeilik, tog moontlik, om ’n MRA-model vir plaaswaardasies te ontwikkel wat redelik akkuraat is. Dus, waar MRA-modelle vir munisipale waardasies gebruik word, wat nie redelik akkuraat is nie, behoort die akkuraatheid verhoog te kan word. ’n MRA-modelwaardasie sal egter nooit die waardasie van ’n ervare en kundige professionele waardeerder kan vervang, wanneer ’n maksimale akkurate waardasie benodig word nie.