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Assessing the effect of imbalance correction through oversampling in the prediction of injury prevalence in distance runners
No full textDoctor EducationisPrediction of injury prevalence in distance runner
The statistical analysis of complex sampling data
Get PDF>Magister Scientiae - MScMost standard statistical techniques illustrated in text books assume that the data are collected from a simple random sample (SRS) and hence are independently and identically distributed (i.i.d.). In reality, data are often sourced through complex sampling (CS) designs, with a combination of stratification and clustering at different levels of the design. Consequently, the CS data are not i.i.d. and sampling weights that are developed over different stages, are calculated and included in the analysis of this data to account for the sampling design. Logistic regression is often employed in the modelling of survey data since the response under investigation typically has a dichotomous outcome. Furthermore, since the logistic regression model has no homogeneity or normality assumptions, it is appealing when modelling a dichotomous response from survey data.
This research considers the comparison of the estimates of the logistic regression model parameters when the CS design is accounted for, i.e. weighting is present, to when the data are modelled using an SRS design, i.e. no weighting. In addition, the standard errors of the estimators will be obtained using three different variance techniques, viz. Taylor series linearization, the jackknife and the bootstrap. The different estimated standard errors will be used in the calculation of the standard (asymptotic) interval which will be compared to the bootstrap percentile interval in terms of the interval coverage probability. A further level of comparison is obtained when using only design weights to those obtained using calibrated and integrated sampling weights. This simulation study is based on the Income and Expenditure Survey (IES) of 2005/2006. The results showed that generally when weighting was used the estimators performed better as opposed to when the design was ignored, i.e. under the assumption of SRS, with the results for the Taylor series linearization being more stable
The statistical analysis of complex sampling data
No full text>Magister Scientiae - MScMost standard statistical techniques illustrated in text books assume that the data are collected from a simple random sample (SRS) and hence are independently and identically distributed (i.i.d.). In reality, data are often sourced through complex sampling (CS) designs, with a combination of stratification and clustering at different levels of the design. Consequently, the CS data are not i.i.d. and sampling weights that are developed over different stages, are calculated and included in the analysis of this data to account for the sampling design. Logistic regression is often employed in the modelling of survey data since the response under investigation typically has a dichotomous outcome. Furthermore, since the logistic regression model has no homogeneity or normality assumptions, it is appealing when modelling a dichotomous response from survey data.
This research considers the comparison of the estimates of the logistic regression model parameters when the CS design is accounted for, i.e. weighting is present, to when the data are modelled using an SRS design, i.e. no weighting. In addition, the standard errors of the estimators will be obtained using three different variance techniques, viz. Taylor series linearization, the jackknife and the bootstrap. The different estimated standard errors will be used in the calculation of the standard (asymptotic) interval which will be compared to the bootstrap percentile interval in terms of the interval coverage probability. A further level of comparison is obtained when using only design weights to those obtained using calibrated and integrated sampling weights. This simulation study is based on the Income and Expenditure Survey (IES) of 2005/2006. The results showed that generally when weighting was used the estimators performed better as opposed to when the design was ignored, i.e. under the assumption of SRS, with the results for the Taylor series linearization being more stable
Statistical inference of the multiple regression analysis of complex survey data
Get PDFThesis (PhD)--Stellenbosch University, 2016.ENGLISH SUMMARY : The quality of the inferences and results put forward from any statistical analysis is directly dependent on the correct method used at the analysis stage. Most survey data analyzed in practice riginate from stratified multistage cluster samples or complex samples. In developed countries
the statistical analysis, for example linear modelling, of complex sampling (CS) data, otherwise known as survey-weighted least squares (SWLS) regression, has received some attention over time. In developing countries such as South Africa and the rest of Africa, SWLS regression is often confused with weighted least squares (WLS) regression or, in some extreme cases, the CS design is ignored and an ordinary least squares (OLS) model is fitted to the data. This is in contrast to what is found in the developed countries. Furthermore, especially in the developing countries, inference concerning the linear modelling of a continuous response is not as well documented as is the case for the inference of a categorical response, specifically in terms of a dichotomous response.
Hence, the decision was made to research the linear modelling of a continuous response under CS with the objective of illustrating how the results could differ if the statistician ignores the complex design of the data or naively applies WLS in comparison to the correct SWLS regression.
The complex sampling design leads to observations having unequal inclusion probabilities, the inverse of which is known as the design weight of an observation. Once adjusted for unit nonresponse and differential non-response, the sampling weights can have large variability that could have an adverse effect on the estimation precision. Weight trimming is cautiously recommended as a remedy for this, but could also increase the bias of an estimator which then affects the estimation precision once more. The effect of weight trimming on estimation precision is also investigated in this research.
Two important parts of regression analysis are researched here, namely the evaluation of the fitted model and the inference concerning the model parameters. The model evaluation part includes the adjustment of well-known prediction error estimation methods, viz. leave-one-out cross-validation, bootstrap estimation and .632 bootstrap estimation, for application to CS data. It also considers a number of outlier detection diagnostics such as the leverages and Cook's distance. The model parameter inference includes bootstrap variance estimation as well as the construction of bootstrap confidence intervals, viz. the percentile, bootstrap-t, and BCa confidence intervals. Two simulation studies are conducted in this thesis. For the first simulation study a model was developed and then used to simulate a hierarchical population such that stratified two-stage cluster samples can be selected from this population. The second simulation study makes use of stratified two-stage cluster samples that are sampled from real-world data, i.e. the Income and Expenditure Survey of 2005/2006 conducted by Statistics South Africa. Similar conclusions are made from both simulation studies. These conclusions include that the incorrect linear model applied to CS data could lead to wrong conclusions, that weight trimming, when conducted with care, further
improves estimation precision, and that linear modelling based on resampling methods such as the bootstrap, could outperform standard linear modelling methods, especially when applied to real-world data.AFRIKAANSE OPSOMMING : Die gehalte van die inferensie en resultate wat deur enige statistiese analise voortgebring word, is afhanklik daarvan dat die korrekte analise metode gebruik word. In praktyk is dit meestal so dat die data wat geanaliseer word, ingesamel is volgens 'n gestratifseerde meerstadium trossteekproef, wat ook bekendstaan as 'n komplekse steekproef (KS). Die statistiese analise, byvoorbeeld lineere modelering, van komplekse steekproewe, het in ontwikkelde lande reeds heelwat aandag ontvang. Veral in ontwikkelende lande, soos Suid-Afrika, is daar gevind dat navorsers dikwels hierdie tipe
lineere modelering verwar met geweegde kleinste kwadrate regressie of selfs sover gaan as om die komplekse ontwerp van die steekproef te ignoreer en 'n gewone kleinste kwadrate model te pas.
Daar is ook gevind dat inferensie oor die lineere modelering van 'n kontinue afhanklike veranderlike nie so goed gedokumenteer is in vergelyking met die literatuur wat bestaan vir die inferensie rondom 'n kategoriese afhanklike veranderlike nie. Dus is 'n besluit geneem om te illustreer hoe die afvoer van gewone en geweegde kleinste kwadrate modelle kan verskil van die korrekte lineere model wanneer 'n kontinue afhanklike veranderlike gemodeleer word.
Komplekse steekproefneming het gewoonlik ongelyke insluitingswaarskynlikhede tot gevolg.
Die inverse van hierdie insluitingswaarskynlikhede staan bekend as die ontwerpgewig van 'n waarneming. Die ontwerpgewigte word aangepas ten opsigte van eenheid nie-respons en differensiele nie-respons waarna hulle bekend staan as steekproefnemingsgewigte. Hierdie gewigte kan groot variasie toon wat 'n negatiewe invloed op die gehalte van die beraming kan he. 'n Moontlike oplossing hiervoor is om die gewigte versigtig te snoei en sodanig die variasie te verminder, maar hierdie aanpassing mag tot 'n toename in beramingsydigheid lei wat ook nie na wense is nie. Die effek van gewigsnoeiing op die gehalte van die inferensie word ook hier ondersoek. Twee belangrike dele in regressie word hier oor navorsing gedoen, naamlik die evaluering van
die gepaste model asook inferensie met betrekking tot die modelparameters. Die model evaluering gedeelte sluit onder andere die uitbreiding van bekende voorspellingsfoutberamingsmetodes, naamlik los-een-uit kruisgeldigheidsbepaling, bootstrap beraming en .632 bootstrap beraming, vir die toepassing in KS in. 'n Aantal uitskieter opsporings diagnostiese toetse soos die hefboom en Cook se afstand is ook beskou. Skoenlus variansieberaming en die berekening van vertrouensintervalle, naamlik die persentiel, bootstrap-t en BCa intervalle, vorm deel van die model parameter inferensie.
Daar is twee simulasie studies onderneem in hierdie tesis. Vir die eerste simulasie studie is 'n simulasie model ontwikkel en daarna gebruik vir die simulasie van 'n hierargiese populasie waaruit gestratifiseerde tweestadium trossteekproewe geneem kan word. Die tweede simulasie studie maak gebruik van gestratifiseerde tweestadium trossteekproewe wat geneem is vanuit werklike data, naamlik die Inkomste en Uitgawe Opname van 2005/2006, 'n opname gedoen deur Statistiek Suid-Afrika. Beide simulasie studies het soortgelyke gevolgtrekkings getoon. Hierdie gevolgtrekkings sluit onder andere in dat verkeerde gevolgtrekkings gemaak kan word indien die verkeerde lineere model op komplekse steekproefdata gepas word, dat die gewigsnoeiing, indien dit versigtig toegepas word, die beramingsgehalte kan verbeter en dat hersteekproefnemingsmetodes goed werk, veral as dit op werklike data toegepas word.Doctora
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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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