120 research outputs found
Are track recommendations dependent on schools and school boards? A study of trends in the level of track recommendations, number of double recommendations and reconsiderations in Dutch urban and rural areas
Track recommendations provided to students in the final grade of primary education lead the allocation to specific school tracks in secondary education in the Netherlands. Where the results of a standardised test indicate that students are able to go to a higher track level, primary schools are required to reconsider and potentially adjust the track recommendation to a higher level. The current research aimed to (1) investigate trends in the level of track recommendations, double track recommendations and reconsiderations over the years 2014–2015 to 2018–2019, (2) explore the variation in (trends of) track recommendations between Dutch primary schools and their school boards, and (3) assess the association between track recommendations and the school level variables degree of urbanisation and type of primary education. We used multilevel growth curve modelling for continuous and count data based on publicly available school-level population data regarding track recommendations and school leavers tests from 2014–2015 to 2018–2019. The number of double track recommendations has increased over the cohorts, with a slightly decreasing gap between schools in rural and urban areas. The number of reconsiderations first decreased and then increased. The differences in reconsiderations between rural and urban areas are increasing over time. An initial trend towards higher average recommendations stabilising in the later cohorts appeared with no clear pattern for degree of urbanisation. The current study adds to the existing knowledge by assessing longitudinal trends instead of cross-sectional analyses and including multiple stakeholders and factors simultaneously.</p
Some common errors of experimental design, interpretation and inference in agreement studies
We signal and discuss common methodological errors in agreement studies and the use of kappa indices, as found in publications in the medical and behavioural sciences. Our analysis is based on a proposed statistical model that is in line with the typical models employed in metrology and measurement theory. A first cluster of errors is related to nonrandom sampling, which results in a potentially substantial bias in the estimated agreement. Second, when class prevalences are strongly nonuniform, the use of the kappa index becomes precarious, as its large partial derivatives result in typically large standard errors of the estimates. In addition, the index reflects rather one-sidedly in such cases the consistency of the most prevalent class, or the class prevalences themselves. A final cluster of errors concerns interpretation pitfalls, which may lead to incorrect conclusions based on agreement studies. These interpretation issues are clarified on the basis of the proposed statistical modelling. The signalled errors are illustrated from actual studies published in prestigious journals. The analysis results in a number of guidelines and recommendations for agreement studies, including the recommendation to use alternatives to the kappa index in certain situations
School motivation profiles of Dutch 9th graders
The aim of this study was to identify school motivation profiles of Dutch 9th grade students in a four-dimensional motivation space, including mastery, performance, social and extrinsic motivation. Multiple clustering methods (K-means, K-medoids, restricted latent profile analysis) and multiple indices for selecting the optimal number of clusters were applied. The statistical selection methods did not completely concur on the optimal number of clusters, but a clear common denominator was provided by the Calinski-Harabasz index and the minimum and mean Silhouette values. All three indices indicated two clusters as the optimal number, regardless of the clustering method used: one cluster of 9th graders with high average scores on all dimensions and one cluster with low mean scores on all dimensions. In addition, we explored the substantive interpretation of multiple cluster solutions. It was discovered that most students are in clusters that can be classified into one of three profile types that may differ in level: (1) approximately equal mean scores on all dimensions, (2) relative high mean scores on mastery and social motivation, and (3) a relatively low mean score on performance motivation. The latter profile type is believed to be a new discovery
The predictive power of track recommendations in Dutch secondary education
In the transition from Dutch primary to secondary education, two indicators are used to place students in the right track: primary school teachers' track recommendations (TTR) and standardized achievement tests (SATs) at the end of primary school. Which indicator is better for placing students is a long-standing issue among educational researchers and professionals. Since 2015, the SAT is administered after the TTR has been given; previously, SAT was administered first. In the current study, it was investigated to what extent TTR and a commonly used SAT predict students' educational attainment after three years of secondary education for multiple cohorts before and after 2015. The results were compared for educational tracks and for different socio-economic status (SES) groups, using multiple samples approaching population data. For all educational tracks and SES groups the results show that TTR is a better predictor of educational attainment than SAT. Furthermore, large differential effects for SES were found. The change of administrative sequence in 2015 had no effect on the overall predictive accuracy: TTR remained the better predictor. The results give new insights into the predictive value of both TTR and SAT before and after the change in administration sequence
Lassoing and corraling rooted phylogenetic trees
The construction of a dendogram on a set of individuals is a key component of
a genomewide association study. However even with modern sequencing
technologies the distances on the individuals required for the construction of
such a structure may not always be reliable making it tempting to exclude them
from an analysis. This, in turn, results in an input set for dendogram
construction that consists of only partial distance information which raises
the following fundamental question. For what subset of its leaf set can we
reconstruct uniquely the dendogram from the distances that it induces on that
subset. By formalizing a dendogram in terms of an edge-weighted, rooted
phylogenetic tree on a pre-given finite set X with |X|>2 whose edge-weighting
is equidistant and a set of partial distances on X in terms of a set L of
2-subsets of X, we investigate this problem in terms of when such a tree is
lassoed, that is, uniquely determined by the elements in L. For this we
consider four different formalizations of the idea of "uniquely determining"
giving rise to four distinct types of lassos. We present characterizations for
all of them in terms of the child-edge graphs of the interior vertices of such
a tree. Our characterizations imply in particular that in case the tree in
question is binary then all four types of lasso must coincide
Interactions Between Friction Modifiers and Dispersants in Lubricants: The Case of Glycerol Monooleate and Polyisobutylsuccinimide-Polyamine
Recognizing Treelike k-Dissimilarities
A k-dissimilarity D on a finite set X, |X| >= k, is a map from the set of
size k subsets of X to the real numbers. Such maps naturally arise from
edge-weighted trees T with leaf-set X: Given a subset Y of X of size k, D(Y) is
defined to be the total length of the smallest subtree of T with leaf-set Y .
In case k = 2, it is well-known that 2-dissimilarities arising in this way can
be characterized by the so-called "4-point condition". However, in case k > 2
Pachter and Speyer recently posed the following question: Given an arbitrary
k-dissimilarity, how do we test whether this map comes from a tree? In this
paper, we provide an answer to this question, showing that for k >= 3 a
k-dissimilarity on a set X arises from a tree if and only if its restriction to
every 2k-element subset of X arises from some tree, and that 2k is the least
possible subset size to ensure that this is the case. As a corollary, we show
that there exists a polynomial-time algorithm to determine when a
k-dissimilarity arises from a tree. We also give a 6-point condition for
determining when a 3-dissimilarity arises from a tree, that is similar to the
aforementioned 4-point condition.Comment: 18 pages, 4 figure
- …