We make the following recommendations. Ensure that all relevant factors are sampled, and that the sampling meets the theory's assumption that the conditions represent a random and representative sample of the factor's 'universe'. Research evaluations will require large samples of each factor if they are to generalise adequately. Where feasible, conduct 2 separate studies (pilot and evaluation, or Generalisability and Decision studies). For unbalanced data, use either urgenova, or 1 of the procedures minimum norm quadratic unbiased estimator, (minque), maximum likelihood (ml) or restricted maximum likelihood (reml) in spss or sas if the data are too complex. State which mathematical procedure was used and the degrees of freedom (d.f.) of the effect estimates. If the procedure does not report d.f., re-analyse with type III sum of squares anova (anova ss III) and report these d.f. Describe and justify the regression model used. Present the raw variance components. Describe the effects that they represent in plain, non-statistical language. If standard error of measurement (SEM) or Reliability coefficients are presented, give the equations used to calculate them. Make sure that the method of reporting reliability (precision or discrimination) is appropriate to the purpose of the assessment. This will usually demand a precision indicator such as SEM. Consider a graphical presentation to combine precision and discrimination.
We concluded that norm referencing is preferable to criterion referencing, negative marking preferable to number-right marking, a discontinuous scale preferable to a continuous scale and that grades should be weighted to favour the most recent outcomes, although there should still be a degree of persistence (earlier grades should not disappear all together). Grade boundaries should be established with regard to rules on remediation and progression.
Nurse education has been transformed over the last decade and continuing change is likely. Nurse educators are responsible for meeting the quality assurance standards of local stakeholders and student retention and progress are important aspects of this process. As part of a monitoring exercise, an enquiry was set up to review pre-registration selection and recruitment strategies and to establish if there were any significant relationships between the characteristics of pre-registration diploma entrants and their academic achievement or completion rates. A multi-factorial tree-based technique was used for this purpose. This is one of the first British studies to consider both academic performance and completion rates for pre-registration diploma students. Four cohorts (N = 355) were studied. There was marked heterogeneity in student characteristics with a wide age distribution, a significant proportion of mature entrants with previous care experience, and considerable diversity in terms of education. Education and age were significant predictors of academic achievement: entrants with a minimum of two A levels and mature women with recent study experience did particularly well. Younger recruits with modest educational qualifications on entry performed less well in their assessments of theoretical knowledge. Younger students tended to leave more regularly, and well-qualified entrants showed a greater tendency to complete, although these relationships were not statistically significant. Multi-factorial analysis demonstrated that organisational and course characteristics have a conjoint influence on course outcomes. Although the study is concerned with Project 2000 in the United Kingdom, there are lessons to be drawn concerning the selection and support of non-traditional recruits into nursing.
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