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What (a) Difference a Degree Makes: The Evaluation of the New Social Work Degree in England 总被引:1,自引:0,他引:1
Orme Joan; MacIntyre Gillian; Green Lister Pam; Cavanagh Kate; Crisp Beth R.; Hussein Shereen; Manthorpe Jill; Moriarty Jo; Sharpe Endellion; Stevens Martin 《British Journal of Social Work》2009,39(1):161-178
After many years of debate in the UK about the need for a degree-levelqualification in social work, the arguments for a minimum degree-levelqualification were accepted. The requirements for the degreein England were developed drawing on work from a number of sources,including a benchmark statement for undergraduate degrees insocial work and focus groups with stakeholders. The new degreein England, launched in 2003, involves one extra yearsstudy; improvements in the qualifying standard for social work;and specific curriculum and entrance requirements. At the timeof launching the degree, the government department responsiblefor funding (Department of Health) commissioned a three-yearevaluation of the implementation of the new degree to establishwhether the new qualifying level leads to improvements in thequalified workforce. The aim of the evaluation is to describethe experiences of those undertaking the degree, collect theviews of the various stakeholders about the effectiveness ofthe degree and measure the impact of a degree-level qualificationon those entering the workforce. This article, written by theteam undertaking the evaluation of the England degree, exploresthe reasons for the methodological approach adopted and theissues that have arisen in setting up the research. 相似文献
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Chris Orme 《Econometric Reviews》1989,8(2):217-222
In this short note it is demonstrated that although the log-likelihood function for the truncated normal regression model may not be globally concave, it will possess a unique maximum if one exists. This is because the hessian matrix is negative semi-definite when evaluated at any possible solution to the likelihood equations. Since this rules out any saddle points or local minima, more than two local maxima occuring is impossible. 相似文献
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Several tests for heteroskedasticity in linear regression models are examined. Asymptoticrobustness to heterokurticity, nonnormality and skewness is discussed. The finite sample eliability of asymptotically valid tests is investigated using Monte Carlo experiments. It is found that asymptotic critical values cannot, in general. be relied upon to give good agreement between nominal and actual finite sample significance levels. The use of the bootstrap overcomes this problem for general approaches that lead to asymptotically pivotal test statistics. Power comparisons are made for bootstrap tests and modified Glejser and Koenker tests are recommended. 相似文献
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ABSTRACT This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993) is severe, (i) the t-test based on the Heckman–Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996) that the standard regression-based t-test (Heckman, 1979) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982), are robust to nonnormality but have very little power. 相似文献