1,720,968 research outputs found
Know your HIV epidemic (KYE) report: review of the HIV epidemic in South Africa.
No full textIn order to update and consolidate South Africa’s evidence base for HIV-prevention interventions, it was decided by the Government of South Africa to commission a synthesis of the available data on the epidemiology of prevalent and incident HIV infections, and the wider epidemic context of these infections. This know your epidemic (KYE) approach has been successfully implemented in a number of sub-Saharan African countries.2 The process involves a desk review and secondary analysis of existing biological, behavioural and socio-demographic data in order to determine the epidemiology of new HIV infections. KYE reports present key findings and policy and programme recommendations which are grounded in local evidence and aim to support decision-making and improve HIV-prevention results. In 2010, South Africa also conducted a know your response (KYR) review, which critically assessed HIV-prevention policies, programmes and resource allocations. The overall results of this HIV epidemic review and the KYR review will be published in a separate, national KYE/KYR synthesis report
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Assessment of variability in on-farm trials : a Uganda case.
Full text linkThesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002.On-farm trials techniques have become an integral part of research aimed at improving agricultural production especially in subsistence farming. The poor performance of certain technologies on the farmers' fields known to have performed well on stations have been of concern. Traditionally, on-farm trials are meant to address such discrepancies. The main problems associated with on-farm trials in most developing countries are high
variability and inappropriate application of statistical knowledge known to work on station to on-farm situation. Characterisation of various on-farm variability and orientation of existing statistical methods may lead to improved agricultural research. Characterization of the various forms of variability in on-farm trials was conducted. Based on these forms of variability, estimation procedures and their strength have been assessed. Special analytical tools for handling non-replicated experiments known to be
common to on-farm trials are presented. The above stated procedures have been illustrated through a review of Uganda case. To understand on-farm variability require grouping of sources of variability into agronomic, animal and socioeconomic components. This led to a deeper understanding of levels of variability and appropriate estimation procedures. The mixed model, modified stability analysis and additive main effects and multiplicative interaction methods have been found to play a role in on-farm
trials. Proper approach to on-farm trials and application of appropriate statistical tools will lead to efficient results that will subsequently enhance agricultural production especially under subsistence farming.Rockefeller Foundation and Makerere University
Spatial analysis and efficiency of systematic designs in intercropping experiments.
Full text linkThesis (M.Sc.) - University of Natal, Pietermaritzburg, 2002In studies involving intercropping plant populations, the main interest is to locate the position of the maximum response or to study the response pattern. Such studies normally require many plant population levels. Thus, designs such as spacing systematic designs that minimise experimental land area are desired. Randomised block designs may not perform well as they allow few population levels which may not span the maximum or enable exploration of other features of the response surface. However, lack of complete randomisation in systematic designs may imply spatial variability (largescale and small-scale variations i.e. trend and spatial dependence) in observations. There is no correct statistical method laid out for data analysis from such designs. Given that spacing systematic designs are not well explored in literature, the main thrusts of this study are two fold; namely, to explore the use of spatial modelling techniques in analysing and modelling data from systematic designs, and to evaluate the efficiency of systematic designs used in intercropping experiments. Three classes of models for trend and error modelling are explored/introduced. These include spatial linear mixed models, semi-parametric mixed models and beta-hat models incorporating spatial variability. The reliability and precision of these methods are demonstrated. Relative efficiency of systematic designs to completely randomised design are evaluated. The analysis of data from systematic designs is shown be easily implemented. Measures of efficiency that include <pp directed measures (A and E criteria), D1 and DB efficiencies for regression parameters, and power are used. Systematic designs are shown to be efficient; on average 72% for A and E- efficiencies and 93% for D1 and DB efficiencies. Overall, these results suggest that systematic designs are suitable and reliable for intercropping plant population studies
Proposed statistical techniques for combining parameter estimates: a case of food production in sub-saharan Africa
Full text linkSummary in EnglishThe underperforming agricultural sector in Sub-Saharan Africa (SSA) has left African countries with
insufficient food production in the face of challenges related to climate change, diseases and
increasing population growth. The agricultural sector is the main source of food, generates income,
employs a large portion of the population, and produces raw materials for agribusinesses. The
improvement of agricultural food production contributes to food security, poverty alleviation, the
development of trade, and a country's economy. The challenges facing the SSA countries include
ineffective farming system, loss of soil fertility, limited access to land, climate change, water scarcity,
outdated production technology that needs to change, restricted market access due to poor
infrastructure, and high transaction costs among others. To address these challenges, the combination
of multiple nutrients was proposed to increase grain yield of crop simply because of the contribution
of each nutrient rather than the use of a single fertiliser.
Research conducted in SSA with the aim of improving food production miss the opportunity to share
the findings across the various sectors. This points out the lack of appropriate statistical techniques to
address the challenges. We can understand better the real situation on food production by developing
a comprehensive scientific and statistical approach that can gather all published single information to
a unified finding. The process of collecting and combining research outputs require the use of meta analysis (MA) to provide precise estimates on various parameters associated with food production.
Various factors can be considered in making significant contribution to agricultural food production
such as fertiliser, access to market, energy use, trade, etc. To establish the diverse set of relationships
that can be developed among the factors, structural equation model (SEM) statistical technique is
used. In some conditions, this procedure can be more restrictive and inflexible since the approach
requires the specification of latent variables in the mix of a huge diversity of sets of variables. In the body of this work, we propose a more suitable, flexible and accurate approach in determining the
number of linear regressions based on the observed data in a clear and precise manner through factor
analysis and principal component analysis (PCA). In addition, to test the large number of variables or
factors of the parameters obtained in SEM, we propose to synthesise all this information by integrating
MA into SEM. The incorporation of MA into SEM allows us to account simultaneously all effects of
factors of the food production in a single model. In MA, the effect sizes are assumed independent
from each study and univariate MA is used. A single study could involve multiple tests of the same
hypothesis, resulting in reporting multiple outcomes (MOs). In such situation, the researcher
developed MOs approach to determine the multiple linear regression model that tested and analysed
the relations between the factors of interests in the food production.
The results of MA were expressed in terms of fixed- and random-effects. The fixed-effects models
were more appropriate simply because of the presence of homogenous effects in the studies. The
random effect models helped to control unobserved heterogeneity when the between-studies variance
was large. It was more productive to apply the combined inorganic fertilizer by the raisin yield grain
of maize. The findings of SEM provide efficient results in the evaluation of the relations among
variables and for testing a statistical theoretical model. The findings from the integration approach of
MA into SEM permitted to combine parameter estimates within a single model. Researchers in
agricultural and related field can use these techniques positively.
We hope that many researchers can benefit from the methodological approach to estimate and draw
inference in addressing the food production situation. The outcomes of this work contribute to science
by providing scientifically comprehensive statistical approaches to evaluate and synthesise the more
suitable results. The benefit can be extended to the development of suitable food production.Ph.D. (Statistics)Statistic
Statistical modelling of return on capital employed of individual units
Full text linkReturn on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done.
The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with.
To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with.
Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with.
Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE.M. Sc. (Statistics)Mathematical Science
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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