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Research in personality trait change has largely relied on mean‐level and rank‐order change across the lifespan. The current research expanded the literature in several ways: analyzing four types of change and correlated change patterns, obtaining multi‐informant reports, including lower‐order personality traits, and collecting multiple assessments during a short yet important time for college‐attending emerging adults (baseline N = 259, Mage = 18.79). There was little evidence for mean‐level change, yet participants showed significant individual differences such that rank‐ordering and ipsative profiles were much more dynamic than mean score patterns. Informant‐reports from close others demonstrated largely similar patterns: little to no mean‐level change, significant increase in rank‐ordering, and about half of participants reporting configural change mostly in elevation and scatter rather than in profile shapes. Interestingly, there was no correlated change between self and other‐reports. This indicated that close others do not share individuals' perception of their own personality trait change, at least not in the demographic group studied. By examining individual‐level, sample‐level, and multi‐informant perspectives, our thorough investigation provided useful benchmarks for future research to examine the source of variability in change trajectories. [ABSTRACT FROM AUTHOR]

In the current account, we present an analysis of a non-discriminating criterion under simple additive weighting synthesis, considering a deep decision hierarchy. A non-discriminating criterion describes a criterion where all decision alternatives under consideration perform equally. We question eliminating such a criterion from the decision hierarchy in search of simpler problem representation and computational efficiency. Yet, we prove such an approach may result in order misrepresentations between decision alternatives. This analysis is performed in the form of four research questions that relate to the detection of certain conditions under which such distortions in the order integrity of decision alternatives will occur, calculating the change in their final performances, distinguishing the alternatives whose performances are consistent, and examining the role of the normalization procedure adopted in averting such distortions when the non-discriminating criterion is ignored. Along these lines, this study provides clear inferences which are of interest to researchers and decision makers, using simple additive weighting and similar methods that rely on additive synthesis. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis. Results: A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent "trendlines" for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the "trendline" profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65–70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11–20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15. Conclusions: This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis. [ABSTRACT FROM AUTHOR]

The article reviews the use of statistical tests to establish a prima facie case that an exam has a disparate impact on minorities. Two common scenarios are discussed. The first is that promotions are made in accordance with the ‘rank-order’ of the exam scores or a composite of the exam scores and some other factors. Courts have used several statistical tests in this situation, which may lead to conflicting conclusions from similar data. It will be shown that when the spreads of the exam scores in both groups are close to each other, the modified Wilcoxon test has desirable statistical properties. When the spreads of the exam scores of the two groups are noticeably different, a two-test procedure is proposed and shown to have higher power, especially when the spread of the minority scores is less than that of the majority. The second situation occurs when once an applicant passes the exam, s/he is eligible for further consideration and the actual exam score no longer matters. Courts may need to consider both the practical and statistical significance of the difference in pass rates. Small, unimportant differences may reach statistically significance when the numbers of applicants are large. In contrast, large differences in pass rates may not be detected as statistically significant in small samples. Two tables are provided to assist courts in reaching more consistent decisions when statistical and practical significance may not agree. [ABSTRACT FROM AUTHOR]

Abstract: A reliable and efficient transportation system contributes immensely to growth and prosperity of a nation. Nigeria, with its vast import traffic cannot afford to transport hinterland bound cargo in a manner not consistent with reliable practices. This study seeks to evaluate the rank-order and magnitude of the operational constraints associated with cargo haulage from Nigerian seaports to the hinterland. Data were obtained from primary sources through systematic administration of a questionnaire to truck drivers at the point of waiting in the seaport terminal. In all, 302 respondents were sampled. The results of the study reveals that ‘harassment’ from law enforcement agencies ranked first (Mean=2.08). The results also shows that the association between nine pairs of the constraints were significant with the strongest negative correlation being that of delays and armed robbery, r =−0.599, p <0.001. These results are fundamental for the prioritization of palliative measures to improve the operational problems of trucking in Nigeria. [ABSTRACT FROM AUTHOR]

Many years of breeding experiments, germplasm screening, and molecular biologic experimentation have generated volumes of sequence, genotype, and phenotype information that have been stored in public data repositories. These resources afford genetic and genomic researchers the opportunity to handle and analyze raw data from multiple laboratories and study groups whose research interests revolve around a common or closely related trait. However, although such data sets are widely available for secondary analysis, their heterogeneous nature often precludes their direct combination and joint exploration. Integration of phenotype information across multiple studies and databases is challenging due to variations in the measurement instruments, endpoint classifications, and biological material employed by each investigator. In the present work, we demonstrate how Rasch measurement model can surmount these problems. The model allows incorporating data sets with partially overlapping variables, large numbers of missing data points and dissimilar ratings of phenotypic endpoints. The model also enables quantifying the extent of heterogeneity between data sets. Biologists can use the model in a data-mining process to obtain combined ratings from various databases and other sources. Subsequently, these ratings can be used for selecting desirable material or (in combination with genotypic information) for mapping genes involved in the particular trait. The model is not limited to genetics and breeding and can be applied in many other areas of biology and agriculture. [ABSTRACT FROM AUTHOR]

• Personality traits are considered to be more stable than mental health symptoms. • The degree that personality is more stable than symptoms is rarely tested. • Mental health symptoms were found to be quite stable. • Personality domains and facets were more stable, but stability varied widely. • At longer follow-ups some aspects of personality were symptom-like. Few investigations have directly compared personality and internalizing symptoms stability within the same sample and have not included personality facets. This study examined rank-order stability and mean-level change of Big Five domains, facets of neuroticism and extraversion, and internalizing symptoms in a sample of 550 adolescent females. Personality and symptoms were assessed every nine months for three years. Three year rank-order stability was higher for personality domains and facets compared to symptoms. Notable exceptions included lower stability of depressivity and positive emotionality facets. Facets and symptoms showed similar mean level change. Overall, we observed modest and variable temporal differences between symptoms and traits; symptoms exhibited high rank-order stability and low mean-level change, but domains and facets were generally more stable. [ABSTRACT FROM AUTHOR]

Landsat data have been widely used for change detection studies of forest ecosystems. Technical issues related to the longevity and quality of the Landsat-5 and -7 instruments prompted this investigation into how data from other sensors may be integrated with the existing Landsat image archive. Change maps indicating the location and extent of stand replacing disturbances occurring between 1999 and 2004 were developed using a rank-order change detection approach. The near-infrared (NIR) band from an image representing initial stand conditions (T1: Landsat-7 ETM+), and the NIR band of images acquired on subsequent dates and with different sensors (T2: ASTER, SPOT-4, and Landsat-5 TM) were selected, essentially acting as three different T2 images. Pair-wise comparisons between the T1 image and each of the T2 images required the pixel values to be sorted, ranked, and differenced; a threshold was then applied to the difference values to identify the stand replacing disturbances. The rank-order change detection approach precluded the need for an additional image normalization process. When compared to a manually interpreted map of change events, the output from the ASTER, SPOT-4, and Landsat-5 TM data were all equally effective in identifying all of the stand replacing disturbances that occurred between 1999 and the year of T2 image acquisition, and errors of commission were minimal. Important logistical limitations to cross-sensor change do exist however and include the lack of spatially or temporally extensive image archives for sensors other than Landsat, incompatible image footprints, and data cost and policy. This rank-order change detection approach is suitable for applications involving multi-temporal datasets where problems may exist due to image normalization, cross-sensor radiometric calibration, or unavailability of a desired sensor type. [Copyright &y& Elsevier]

• We compare the performance of a set of representative cross-efficiency methods. • We study the concept of cross-efficiency from a productivity perspective. • We apply these methods to a real-life data set and account for ordinal variables. • Based on a multidimensional metric we rank the methods. • Newly proposed more sophisticated methods do not outperform classic ones. Cross-efficiency measurement is an extension of Data Envelopment Analysis that allows for tie-breaking ranking of the Decision Making Units (DMUs) using all the peer evaluations. In this article we examine the theory of cross-efficiency measurement by comparing a selection of methods popular in the literature. These methods are applied to performance measurement of European warehouses. We develop a cross-efficiency method based on a rank-order DEA model to accommodate the ordinal nature of some key variables characterizing warehouse performance. This is one of the first comparisons of methods on a real-life dataset and the first time that a model allowing for qualitative variables is included in such a comparison. Our results show that the choice of model matters, as one obtains statistically different rankings from each one of them. This holds in particular for the multiplicative and game-theoretic methods whose results diverge from the classic method. From a managerial perspective, focused on the applicability of the methods, we evaluate them through a multidimensional metric which considers their capability to rank DMUs, their ease of implementation, and their robustness to sensitivity analyses. We conclude that standard weight-restriction methods, as initiated by Sexton et al. [48] , perform as well as recently introduced, more sophisticated alternatives. [ABSTRACT FROM AUTHOR]