This study measures brand loyalty of banking clients in South Africa. To do so, the study employs the newly developed brand loyalty conceptual framework of Moolla (2010) from the fast–moving consumer good industry as point of departure, and firstly, test its applicability to banking clients, secondly, adapt the framework where needed, and thirdly, used the adapted framework to measure the brand loyalty levels of the banking clients. The results show that the Moolla model could be used with minor adaptations in the banking industry, and that the reliability as measured by Cronbach alpha coefficients are acceptable. In measuring the brand loyalty levels, it is clear banking clients are not very loyal, scoring low on all the brand loyalty influences except customer satisfaction (which falls in the fair to good margin). / Thesis (M.B.A.)--North-West University, Potchefstroom Campus, 2012.

Type 2 diabetes (T2DM) is a growing concern worldwide, particularly among Aboriginal Canadians. Diet has been associated with diabetes risk, and dietary pattern analysis (DPA) provides a method in which whole dietary patterns may be explored in relation to disease. Factor analysis (FA) and reduced rank regression (RRR) of data from the Sandy Lake Health and Diabetes Project identified patterns associated with incident T2DM at follow-up. A RRR-derived pattern characterized by tea, hot cereal, and peas, and low intake of high-sugar foods and beef was positively associated with diabetes; however, the relationship was attenuated with adjustment for age and other covariates. A FA-derived pattern characterized by processed foods was positively associated with incident T2DM in a multivariate model (OR=1.38; CIs: 1.02, 1.86 per unit), suggesting intake of processed foods may predict T2DM risk.

In the James Bay Lowlands of northern Ontario, kimberlite pipes are concealed by peat, thick layers of till, and Tyrell sea sediments. Studies have shown that buried ore bodies produce geochemical signatures in surface media. This thesis explores the geochemistry of near-surface groundwater above concealed kimberlite pipes using factor analysis to determine whether (1) a factor analysis can reveal an underlying structure (factors) in a multivariate groundwater geochemical dataset, and whether (2) those factors are related to the presence of concealed kimberlite. Factor analysis was performed on two datasets of nearsurface groundwater, collected at 0.2 m and 1.1 m below ground surface in peat. Results revealed that (1) there is a significant difference in the behaviour of elements in groundwater near the surface compared to those in deeper groundwater, which is sheltered from the effects of the atmosphere; (2) for both datasets, the first factor is dominated by elements known to be enriched in kimberlite, notably rare earth elements (REE), U, Th, Ti – the composition of factor one is consistent with their derivation from kimberlite in a limestone background where such elements are in very low concentration; (3) high-valence and lowvalence kimberlite indicator elements (KIE) are found separated into distinct factors suggesting that once released from the kimberlite after weathering, KIE are subjected to various geochemical processes to be differentiated as they migrate upward to the surface; and (4) Fe and Mn load on a factor distinct from other metals, suggesting that in this environment Fe-Mn-O-OH is not a significant controller of metal mobility in groundwater. Overall, this research has further highlighted the multivariate nature of geochemical processes in groundwater. Compared with previous work in geochemical exploration where often only univariate or bivariate statistics or single element profiles over concealed ore bodies were used, this thesis has shown that factor analysis, as a multivariate data analysis technique, is a robust exploration tool, able to shed light on relevant geochemical processes hidden within geochemical datasets. This thesis shows that high-valence KIE, notably U,V, Th, Ti and the REE, as a group, are better indicators of the presence of kimberlites than other well-known KIE. Single element concentration profiles such as Ni or Cr (known KIE) show similar anomalies over a concealed kimberlite as a factor score profile for factor one (U, V, Th, Ti, REE, Ni) would; however, it is the peculiar assemblage of elements in factor one that makes it unique to kimberlites, a feature that can be used in future exploration work for concealed kimberlites in similar surficial environments, such as the Siberian wetlands. The results suggest that future geochemical exploration work involving groundwater should focus on the more stable groundwater located below the zone of oxidation, sheltered from the effects of the atmosphere.

This research explores factor analysis applied to data from skewed distributions for the general skew model, the selection-elliptical model, the selection-normal model, the skew-elliptical model and the skew-normal model for finite sample sizes. In terms of asymptotics, or large sample sizes, quasi-maximum likelihood methods are broached numerically. The skewed models are formed using selection distribution theory, which is based on Rao’s weighted distribution theory. The models assume the observed variable of the factor model is from a skewed distribution by defining the distribution of the unobserved common factors skewed and the unobserved unique factors symmetric. Numerical examples are provided using maximum likelihood selection skew-normal factor analysis. The numerical examples, such as maximum likelihood parameter estimation with the resolution of the “sign switching” problem and model fitting using likelihood methods, illustrate that the selection skew-normal factor analysis model better fits skew-normal data than does the normal factor analysis model.

The relationships between and among questions from a survey in three different areas, namely food and food related health, food safety and hygiene, and information gathering concerning food, with the main focus on the first two areas, are investigated. Data from older people have been analyzed. The questions are both about knowledge and behavior in relation to the areas mentioned above. Earlier studies have shown that people in general do not know about or act according to dietary advises about health and hygiene. The results from this study show that there are fairly strong correlations for all groups but men when it comes to knowledge and behavior about fruit and vegetable consumption. The results also show a significant association between fruit and vegetable consumption and gender and that women consume more than men. A factor model estimating the relationships between health, hygiene and information shows that the fitted factor model can be considered having an acceptable fit, with at least two variables, knowledge about the recommended amount of fruit and vegetables to eat per day and trust for grocery stores concerning food and health, being classified as good indicators.

This thesis presents the results of research into the use of factor models for stationary economic time series. Two basic scenarios are considered. The first is a situation where a large number of observations are available on a relatively small number variables, and a dynamic factor model is specified. It is shown that a dynamic factor model may be derived as a representation of a VARMA model of reduced spectral rank observed subject to measurement error. In some cases the resulting factor model corresponds to a minimal state-space representation of the VARMA plus noise model. Identification is discussed and proved for a fairly general class of dynamic factor model, and a frequency domain estimation procedure is proposed which has the advantage of generalising easily to models with rich dynamic structures. The second scenario is one where both the number of variables and the number of observations jointly diverge to infinity. The principal components estimator is considered in this case, and consistency is proved under assumptions which allow for much more error cross-correlation than the previously published theorems. Ancillary results include finite sample/variables bounds linking population principal components to population factors, and consistency results for principal components in a dual limit framework under a `gap' condition on the eigenvalues. A new factor model, named the Grouped Variable Approximate Factor Model, is introduced. This factor model allows for arbitrarily strong correlation between some of the errors, provided that the variables corresponding to the strongly correlated errors may be arranged into groups. An approximate instrumental variables estimator is proposed for the model and consistency is proved.

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008. / Title from first page of PDF file (viewed June 16, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 84-94).

Thesis (Ph. D.) -- University of Texas Southwestern Medical Center at Dallas, 2005. / Vita. Bibliography: 87-97.

Title from title page of PDF (University of Missouri--St. Louis, viewed March 23, 2010). Includes bibliographical references (p. 352-361).

Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves [72]-79). Also available on the Internet.