This study investigated sex differences and the relationship between need for approval and liberal-conservative attitudes regarding sex. The test measures used were the Marlowe-Crowne Social Desirability Scale (M-C SDS) and a questionnaire measuring liberal-conservative sexual attitudes taken from a research survey published in Psychology Today.

The present study will be concerned with the reliability of the Shipley-Hartford Abstraction Scale as an instrument for diagnosis of schizophrenia and personality disorders.

This study considers the relationship between scores on the "Experimental Draw-A-Group Projective Technique for Measuring Interpersonal Responsivenesss" (DAG), and self-concept as indicated by scores on the Tennessee Self Concept Scale (TSCS). The study assumes a significantly positive relationship between interpersonal responsiveness and self-concept. The study further seeks to establish sound empirical data to justify the use of the DAG scale in the research of self-concept.

The purpose of this study is to examine the relationship between manifest anxiety and orality as related to smokers and non-smokers as indicated on the Taylor Manifest Anxiety Scale and the Blaky Pitres Test (4). From the above theoretical background, the following relationships are hypothesized: Hypothesis I: Smokers will show more anxiety than nonsmokers. hypothesis 2: Female smokers will show more anxiety than male smokers. Hypothesis 3: Among the high anxiety group smokers will show more orality than non-smokers.

This study will attempt to verify the Hullian drive theory, E = f(HxD), as others have done before, but with one exception. The H, or habit strength, will be held to be neutral so that the E, or excitatory potentials, will be a function of drive alone. Without any habit to reinforce, any increase in excitatory potential can be related directly to increase in drive. Four hypotheses were investigated: The first hypothesis was that the HA, or high-anxiety groups, will also be the high-drive groups, and this will follow for the NA and LA groups, to be determined by the performance on the digit-span test. The second hypothesis was that the high-drive groups will perform better on the digit-span tests than the low-drive groups. The third hypothesis stressed that in accordance with Hullian theory, with increased stress being introduced with a single habit tendency, the low-drive groups will be outperformed by the high-drive groups. The fourth hypothesis presumed that verification of the first three hypotheses will show the "Taylor Manifest Anxiety Scale" to be capable of differentiating between high and low manifest anxiety groups and will verify the Taylor-Spence hypothesis based on Hullian theory that the HA's will outperform the LA's in a stress situation.

Gravitational lensing studies the distortions of a distant galaxy’s observed size, shape or flux due to the tidal bending of photons by matter between the source and observer. Such distortions can be used to infer knowledge on the mass distribution of the intervening matter, such as the dark matter halos in which clusters of individual galaxies may reside, or on cosmology through the statistics of the matter density of large scale structure and geometrical factors. In particular, gravitational lensing has the advantage that it is insensitive to the nature of the lensing matter. However, contamination of the signal by correlations between galaxy shape or size and local environment complicate a lensing analysis. Further, measurement of traditional lensing estimators is made more difficult by limitations on observations, in the form of atmospheric distortions or optical limits of the telescope itself. As a result, there has been a large effort within the lensing community to develop methods to either reduce or remove these contaminants, motivated largely by stringent science requirements for current and forthcoming surveys such as CFHTLenS, DES, LSST, HSC, Euclid and others. With the wealth of data from these wide-field surveys, it is more important than ever to understand the full range of independent probes of cosmology at our disposal. In particular, it is desirable to understand how each probe may be used, individually and in conjunction, to maximise the information of a lensing analysis and minimise or mitigate the systematics of each. With this in mind, I investigate the use of galaxy clustering measurements using photometric redshift information, including a contribution from flux magnification, as a probe of cosmology. I present cosmological forecasts when clustering data alone are used, and when clustering is combined with a cosmic shear analysis. I consider two types of clustering analysis: firstly, clustering with only redshift auto-correlations in tomographic redshift bins; secondly, clustering using all available redshift bin correlations. Finally, I consider how inferred cosmological parameters may be biased using each analysis when flux magnification is neglected. Results are presented for a Stage–III ground-based survey, and a Stage–IV space-based survey modelled with photometric redshift errors, and values for the slope of the luminosity function inferred from CFHTLenS catalogues. I find that combining clustering information with shear gives significant improvement on cosmological parameter constraints, with the largest improvement found when all redshift bins are included in the analysis. The addition of galaxy-galaxy lensing gives further improvement, with a full combined analysis improving constraints on dark energy parameters by a factor of > 3. The presence of flux magnification in a clustering analysis does not significantly affect the precision of cosmological constraints when combined with cosmic shear and galaxy-galaxy lensing. However if magnification is neglected, inferred cosmological parameter values are biased, with biases in some cosmological parameters found to be larger than statistical errors. We find that a combination of clustering, cosmic shear and galaxy-galaxy lensing can provide a significant reduction in statistical errors from each analysis individually, however care must be taken to measure and model flux magnification. Finally, I consider how measurements of galaxy size and flux may be used to constrain the dark matter profile of a foreground lens, such as galaxy- or galaxy-cluster-dark matter halos. I present a method of constructing probability distributions for halo profile free parameters using Bayes’ Theorem, provided the intrinsic size-magnitude distribution may be measured from data. I investigate the use of this method on mock clusters, with an aim of investigating the precision and accuracy of returned parameter constraints under certain conditions. As part of this analysis, I quantify the size and significance of inaccuracies in the dark matter reconstruction as a result of limitations in the data from which the sample and size-magnitude distribution is obtained. This method is applied to public data from the Space Telescope A901/902 Galaxy Evolution Survey (STAGES), and results are presented for the four STAGES clusters using measurements of source galaxy size and magnitude, and a combination of both. I find consistent results with existing shear measurements using measurements of galaxy magnitudes, but interesting inconsistent results when galaxy size measurements are used. The simplifying assumptions and limitations of the analysis are discussed, and extensions to the method presented.

This commentary accompanies a portfolio of nine compositions written between October 2006 and June 2009. This commentary traces the development of a range of compositional ideas throughout the portfolio. These revolve around the creation of multilayered textures where all the material and all subtle variations thereof are audible, leading to an investigation of rhythmical block durations and the role of memory. The context in which these ideas arose is provided through discussion of specific existing work that closely relates to the portfolio, in particular by John Cage, Morton Feldman, György Ligeti and Giacinto Scelsi.

Flexible pipes are essential components in the subsea oil and gas industry, where they are used to convey fluids under conditions of extreme external pressure and (often) axial load, while retaining low bending stiffness. This is made possible by their complex internal structure, consisting of unbonded components that are, to a certain extent, free to move internally relative to each other. Due to the product's high value and high cost of testing facilities, much e ort has been invested in the development of analytical and numerical models for simulating flexible pipe behaviour, which includes bulk response to various loading actions, calculation of component stresses and use of this data for component fatigue calculations. In this work, it is proposed that the multi-scale methods currently in widespread use for the modelling of composite materials can be applied to the modelling of flexible pipe. This allows the large-scale dynamics of an installed pipe (often several kilometers in length) to be related to the behaviour of its internal components (with characteristic lengths in millimeters). To do this, a formal framework is developed for an extension of the computational homogenisation procedure that allows multiscale models to be constructed in which models at both the large and small scales are composed of different structural elements. Within this framework, a large-scale flexible pipe model is created, using a two-dimensional corotational beam formulation with a constitutive model representative of flexible pipe bulk behaviour, which was obtained by further development of a recently proposed formulation inspired by the analogy between the flexible pipe structural behaviour and that of plastic materials with non-associative flow rules. A three-dimensional corotational formulation is also developed. The model is shown to perform adequately for practical analyses. Next, a detailed finite element (FE) model of a flexible pipe was created, using shell finite elements, generalised periodic boundary conditions and an implicit solution method. This model is tested against two analytical flexible pipe models for several basic load cases. Finally, the two models are used to carry out a sequential multi-scale analysis, in which a set of simulations using the detailed FE model is carried out in order to find the most appropriate coefficients for the large-scale model.

The current study used a three-phase mixed-methods design to produce a new self-report measure of the strategies that college students use to cope with the death of a loved-one. College students are commonly bereaved and may be in the process of undergoing important developmental tasks related to emerging adulthood. However, the application of grief-specific stress-and-coping theories (i.e., the Dual Process Model of Coping with Bereavement) to this population has been hampered by measurement issues. The current study aimed to address the flaws asserted above through the use of a mixed-methods scale development design. To this end, the researcher made use of the discussion component of a bereavement-focused special topics course to refine a focus-group facilitation guide and generate a preliminary list of content domains. In Study 1, three bereaved students participated in a formal focus-group. Three graduate-level bereavement researchers drew from the qualitative data available from the Pilot Study and Study 1 to develop a pool of 192 items for use in quantitative analysis. In Study 2, these items were administered to a sample of 700 bereaved undergraduates. Exploratory and Confirmatory factor analyses suggested that a 5-factor model was the best fit for the data. Results suggest that bereaved students use a variety of strategies when coping with bereavement, including using drugs and alcohol, seeking support from others, accessing religious faith, exploring new relationships and identities, and experiencing depression symptoms. Preliminary support was provided for the validity of a 26-item coping strategies measure with five subscales named the GCOPE.

Studies have found that the level of association between an area-level covariate and an outcome can vary depending on the spatial scale (SS) of a particular covariate. However, covariates used in regression models are customarily modeled at the same spatial unit. In this dissertation, we developed four SS model selection algorithms that select the best spatial scale for each area-level covariate. The SS forward stepwise, SS incremental forward stagewise, SS least angle regression (LARS), and SS lasso algorithms allow for the selection of different area-level covariates at different spatial scales, while constraining each covariate to enter at most one spatial scale. We applied our methods to two real applications with area-level covariates available at multiple scales to model variation in the following outcomes: 1) nitrate concentrations in private wells in Iowa and 2) body mass index z-scores of pediatric patients of the Virginia Commonwealth University Medical Center. In both applications, our SS algorithms selected covariates at different spatial scales, producing a better goodness of fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. We evaluated our methods using simulation studies to examine the performance of the SS algorithms and found that the SS algorithms generally outperformed the conventional modeling approaches. These findings underscore the importance of considering spatial scale when performing model selection.