Predicting First Term Success in an Associates Degree Nursing Program Using Cognitive and Noncognitive Factors

Since the late 1990s the nursing field has experienced increased demand for RN’s as well as a number of internal and external factors that have worsened this problem. College admissions officers have struggled to identify those students who are most likely to persist in an associate degree nursing (ADN) program. Estimates of programmatic attrition vary, but fall somewhere between 25-50%. A great deal of research has been expended in an attempt to determine which preadmission variables are most likely to indicate programmatic success. Unfortunately, no “best set” of admissions variables has been identified. The purpose of this research was to identify cognitive and noncognitive predictors of success in an ADN program. These variables can then be used by nursing program administrators to help identify students during the admissions phase who are most likely to persist through the first term and potentially to degree completion. Bloom’s theory of school learning serves as the theoretical framework for this research. The participants in this study were 188 students (summer and fall cohorts) in the Associate of Science in Nursing (ASN) program at a large state college in the southeastern region of the United States. The research design was a quantitative, non-experimental, correlational design to predict the relationship between four input predictor variables and one criterion variable. The Health Education Systems Inc A2 assessment (HESI A2) and the Grit-S Scale were used to measure these input variables. Binary regression was used to analyze the resulting data. This research is critical in addressing nursing shortfalls, a pressing real world problem facing society at large, nursing in general, and college admissions departments for ADN programs in particular

Crystallization histories of the group IIF iron meteorites and Eagle Station pallasites

The group IIF iron meteorites and Eagle Station pallasites (PES) have highly siderophile element abundances (HSE; Re, Os, Ir, Ru, Pt, and Pd) of metal that are consistent with formation in planetesimal cores by fractional crystallization with minor to major solid metal–liquid metal mixing. Modeling of HSE abundances of the IIF irons indicates a complex formation history that included the mixing of primitive and evolved solid and liquid metals. By contrast, modeling of HSE abundances of PES metal suggests these meteorites formed mainly as equilibrium solids from a common liquid. Abundances of some of the siderophile elements in the IIF irons and PES are permissive of a common core origin; however, the abundances of W and Ni indicate the PES ultimately formed on a more oxidized body. The PES most likely formed by the injection of olivine present at the core–mantle boundary into a metallic core liquid as a result of impact. The core then crystallized inward, trapping the olivine.NASA Emerging Worlds grants NNX16AN07G and 80NSSC20K033

Statnote 9: The one-way analysis of variance (random effects model)

There is an alternative model of the 1-way ANOVA called the 'random effects' model or ‘nested’ design in which the objective is not to test specific effects but to estimate the degree of variation of a particular measurement and to compare different sources of variation that influence the measurement in space and/or time. The most important statistics from a random effects model are the components of variance which estimate the variance associated with each of the sources of variation influencing a measurement. The nested design is particularly useful in preliminary experiments designed to estimate different sources of variation and in the planning of appropriate sampling strategies

Statnote 5: Is one set of data more variable than another?

There may be circumstances where it is necessary for microbiologists to compare variances rather than means, e,g., in analysing data from experiments to determine whether a particular treatment alters the degree of variability or testing the assumption of homogeneity of variance prior to other statistical tests. All of the tests described in this Statnote have their limitations. Bartlett’s test may be too sensitive but Levene’s and the Brown-Forsythe tests also have problems. We would recommend the use of the variance-ratio test to compare two variances and the careful application of Bartlett’s test if there are more than two groups. Considering that these tests are not particularly robust, it should be remembered that the homogeneity of variance assumption is usually the least important of those considered when carrying out an ANOVA. If there is concern about this assumption and especially if the other assumptions of the analysis are also not likely to be met, e.g., lack of normality or non additivity of treatment effects then it may be better either to transform the data or to carry out a non-parametric test on the data

Statnote 8: statistical power and sample size

Statistical software is now commonly available to calculate Power (P') and sample size (N) for most experimental designs. In many circumstances, however, sample size is constrained by lack of time, cost, and in research involving human subjects, the problems of recruiting suitable individuals. In addition, the calculation of N is often based on erroneous assumptions about variability and therefore such estimates are often inaccurate. At best, we would suggest that such calculations provide only a very rough guide of how to proceed in an experiment. Nevertheless, calculation of P' is very useful especially in experiments that have failed to detect a difference which the experimenter thought was present. We would recommend that P' should always be calculated in these circumstances to determine whether the experiment was actually too small to test null hypotheses adequately

Statnote 7: chi-square contingency tables

When the data are counts or the frequencies of particular events and can be expressed as a contingency table, then they can be analysed using the chi-square distribution. When applied to a 2 x 2 table, the test is approximate and care needs to be taken in analysing tables when the expected frequencies are small either by applying Yate’s correction or by using Fisher’s exact test. Larger contingency tables can also be analysed using this method. Note that it is a serious statistical error to use any of these tests on measurement data

Statnote 6: post-hoc ANOVA tests

If data are analysed using ANOVA, and a significant F value obtained, a more detailed analysis of the differences between the treatment means will be required. The best option is to plan specific comparisons among the treatment means before the experiment is carried out and test them using ‘contrasts’. In some circumstances, post-hoc tests may be necessary and experimenters should think carefully which of the many tests available should be used. Different tests can lead to different conclusions and careful consideration as to the appropriate test should be given in each circumstance