There were two major goals of this study - to examine validity of scores for the Boholst Life Position Scale and to examine potential associations between life positions and affective traits. Two hundred seventy-seven students enrolled in undergraduate psychology classes at a large university volunteered for the study. Concurrent validity of scores for the life position scale was supported based on two compared instruments. Pearson product-moment correlations for the comparisons were -.765 and .617, both statistically significant at the p < .001 level. Factor analysis demonstrated that the scale could accurately be conceptualized as consisting of two factors - an "I" factor and a "You" factor. MANOVA, ANOVA, multiple linear regression, and canonical correlation analysis were used to examine associations between life positions and the affective traits of angry, sad, glad, social anxiety, loneliness, and satisfaction with life. Subjects were catagorized into four groups representing their life position: "I'm OK, you're OK," "I'm OK, you're not OK," "I'm not OK, you're OK," and "I'm not OK, you're not OK." A MANOVA employing life position as the independent variable with four levels and the six affective traits as the dependent variables demonstrated statistical significance (p < .001 level) and h2 was .505. All six separate ANOVAs, with life position as the independent variable and each separate affective trait as the dependent variable, revealed statistical significance (p < .001) and h2 varied from a high of .396 for the sadness variable to a low of .116 for social anxiety. Six separate multiple linear regression equations using two independent variables, a measure of self-esteem and a measure of the perceived OK-ness of others, and each separate affective trait as the dependent variable, showed statistical significance (p < .001). The average Adjusted R2 was .475. Both canonical correlation functions were statistically significant (Rc12 = .77 and Rc22 = .21). In summary, life positions were strongly associated with specific affective traits.

This dissertation: (a) investigated the degree to which the squared canonical correlation coefficient is biased in multivariate nonnormal distributions and (b) identified formulae that adjust the squared canonical correlation coefficient (Rc2) such that it most closely approximates the true population effect under normal and nonnormal data conditions. Five conditions were manipulated in a fully-crossed design to determine the degree of bias associated with Rc2: distribution shape, variable sets, sample size to variable ratios, and within- and between-set correlations. Very few of the condition combinations produced acceptable amounts of bias in Rc2, but those that did were all found with first function results. The sample size to variable ratio (n:v)was determined to have the greatest impact on the bias associated with the Rc2 for the first, second, and third functions. The variable set condition also affected the accuracy of Rc2, but for the second and third functions only. The kurtosis levels of the marginal distributions (b2), and the between- and within-set correlations demonstrated little or no impact on the bias associated with Rc2. Therefore, it is recommended that researchers use n:v ratios of at least 10:1 in canonical analyses, although greater n:v ratios have the potential to produce even less bias. Furthermore,because it was determined that b2 did not impact the accuracy of Rc2, one can be somewhat confident that, with marginal distributions possessing homogenous kurtosis levels ranging anywhere from -1 to 8, Rc2 will likely be as accurate as that resulting from a normal distribution. Because the majority of Rc2 estimates were extremely biased, it is recommended that all Rc2 effects, regardless of which function from which they result, be adjusted using an appropriate adjustment formula. If no rationale exists for the use of another formula, the Rozeboom-2 would likely be a safe choice given that it produced the greatest number of unbiased Rc2 estimates for the greatest number of condition combinations in this study.

The complex human motor function of speech presents a scientifically interesting, yet relatively unexplored, means to study brain-behavior relationships. Fortunately, magnetic resonance imaging (MRI), which has been proven to characterize soft tissue excellently, has recently become a promising technique for the study of speech. MRI\'s contributions in speech research could lead to new and individualized treatment for speech disorders. Although many studies have shown that MRI can capture information about speech, this project sought to determine what covert information could be disclosed from MRI movies through multivariate analysis. The articulation of phoneme pairs was imaged using a novel sequence, and simultaneously recorded. The data were then analyzed using support vector machine (SVM) analysis and canonical correlation analysis (CCA). Determination of classification accuracy through SVM analysis revealed that phoneme pairs were distinguishable from one another consistently over 90% of the time using information found from MRI movie clips of the speech. Additionally, study of the SVM weights demonstrated that SVM could identify regions of the vocal tract that are used to form auditory distinctions between the phonemes. Finally, CCA revealed relationships between images and the frequencies in corresponding audio waveforms; once again, the speech articulators were identified as lending maximum correlation to the sound profile. These promising results demonstrate that multivariate analysis can uncover information that is known to be true concerning speech production. These analyses may perhaps even contribute to existing knowledge and thus provide a platform from which to advance the treatment of speech dysfunction. / Master of Science

The standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number of variables, a stepwise procedure is usually implemented. The stepwise procedure of model selection was designed for its computational efficiency and is not guaranteed to find the best model with respect to any optimality criteria. While the model selected may not be the best possible of those in the model space, commonly it is almost as good as the best model. Many times there will be several models that exist that may be competitors of the best model in terms of the selection criterion, but classical model building dictates that a single model be chosen to the exclusion of all others. An alternative to this is Bayesian model averaging (BMA), which uses the information from all models based on how well each is supported by the data. Using BMA allows a variance component due to the uncertainty of the model selection process to be estimated. The variance of any statistic of interest is conditional on the model selected so if there is model uncertainty then variance estimates should reflect this. BMA methodology can also be used for variable assessment since the probability that a given variable is active is readily obtained from the individual model posterior probabilities. The multivariate methods considered in this research are principal components analysis (PCA), canonical variate analysis (CVA), and canonical correlation analysis (CCA). Each method is viewed as a particular multivariate extension of univariate multiple regression. The marginal likelihood of a univariate multiple regression model has been approximated using the Bayes information criteria (BIC), hence the marginal likelihood for these multivariate extensions also makes use of this approximation. One of the main criticisms of multivariate techniques in general is that they are difficult to interpret. To aid interpretation, BMA methodology is used to assess the contribution of each variable to the methods investigated. A second issue that is addressed is displaying of results of an analysis graphically. The goal here is to effectively convey the germane elements of an analysis when BMA is used in order to obtain a clearer picture of what conclusions should be drawn. Finally, the model uncertainty variance component can be estimated using BMA. The variance due to model uncertainty is ignored when the standard model building tenets are used giving overly optimistic variance estimates. Even though the model attained via standard techniques may be adequate, in general, it would be difficult to argue that the chosen model is in fact the correct model. It seems more appropriate to incorporate the information from all plausible models that are well supported by the data to make decisions and to use variance estimates that account for the uncertainty in the model estimation as well as model selection. / Ph. D.

Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.

Central America is a region regularly affected by natural disasters, with most of them having a hydro-meteorological origin. Therefore, the understanding of annual changes of precipitation upon the region is relevant for planning and mitigation of natural disasters. This thesis focuses on studying the precipitation variability at annual scales in Central America within the framework of the Swedish Centre for Natural Disaster Science. The aims of this thesis are: i) to establish the main climate variability sources during the boreal winter, spring and summer by using different statistical techniques, and ii) to study the connection of sea surface temperature anomalies of the neighbouring oceans with extreme precipitation events in the region. Composites analysis is used to establish the variability sources during winter. Canonical correlation analysis is employed to explore the connection between the SST anomalies and extreme rainfall events during May-June and August-October. In addition, a global circulation model is used to replicate the results found with canonical correlation analysis, but also to study the relationship between the Caribbean Sea surface temperature and the Caribbean low-level jet. The results show that during winter both El Niño Southern Oscillation and the Pacific Decadal Oscillation, are associated with changes of the sea level pressure near the North Atlantic Subtropical High and the Aleutian low. In addition, the El Niño Southern Oscillation signal is intensified (destroyed) when El Niño and the Pacific Decadal Oscillation have the same (opposite) sign. Sea surface temperature anomalies have been related to changes in both the amount and temporal distribution of rainfall. Precipitation anomalies during May-June are associated with sea surface temperature anomalies over the Tropical North Atlantic region. Whereas, precipitation anomalies during August-September-October are associated with the sea surface temperature anomalies contrast between the Pacific Ocean and the Tropical North Atlantic region. Model outputs show no association between sea surface temperature gradients and the Caribbean low-level jet intensification. Canonical correlation analysis shows potential for prediction of extreme precipitation events, however, forecast validation shows that socio-economic variables must be included for more comprehensive natural disaster assessments.

This study starts from the perspective of a future increase availability of water quality data at the water treatment facility at Lovön and aims to use the existing data to identify a pattern in the role of the different sub-basin that constitute the Mälaren basin. The data are analyzed with the graphical tool of the scatterplot and a Non-linear Canonical Correlation Analysis, a variation of the classical multivariate method, that by using a neural network model is able to handle not linear relationships. From the data analysis, it is possible to identify that different areas have different contribution in shaping the water quality at the facility of Lovön, but also that this pattern of contribution is strongly affected by the season inside the analyzed year.

Genome-wide associations studies (GWASs) have identified hundreds of loci that are strongly associated with coronary artery disease and its risk factors. However, the causal variants and genes remain unknown for the vast majority of the identified loci. Zebrafish model systems coupled with clustered regularly interspaced short palindromic repeats-C–associated 9 (CRISPR Cas-9) mutagenesis have enabled the possibility to systematically characterize candidate genes in GWAS-identified loci. In this thesis, canonical correlation analysis (CCA) was used to identify putative causal genes in multiplexed genetic screens for atherogenic traits in zebrafish larvae in an efficient manner. The two datasets used in this thesis contained genes and phenotypes obtained through sequencing and high-throughput imaging of fish larvae. Dataset 1 contained (7 genes, 11 phenotypes, n = 384) and dataset 2 (4 genes, 11 phenotypes, n = 384). CCA’s multiple genes vs. multiple phenotype analysis in dataset 1 identified the genes met, pepd, timd4 and vegfa to have an association with the total cholesterol, triglycerides, glucose, corrected lipid disposition, as well as co- localization of (macrophage and lipid deposition,) (neutrophils and lipid deposition) and (macrophage and neutrophils). In dataset 2, CCA found previously reported correlation of genes apobb1 and apoea with total cholesterol, low-density lipoprotein and triglycerides as well as co localization of neutrophils and lipids. In comparison with hierarchical linear model, CCA represents a powerful and promising tool to identify causal genes for cardiovascular diseases in data from zebrafish model systems.

The use of conversational agents has increased across the world. From providing automated support for companies to being virtual psychologists they have moved from an academic curiosity to an application with real world relevance. While many researchers have focused on the content of the dialogue and synthetic speech to give the agents a voice, more recently animating these characters has become a topic of interest. An additional use for character animation technology is in the film and video game industry where having characters animated without needing to pay for expensive labour would save tremendous costs. When animating characters there are many aspects to consider, for example the way they walk. However, to truly assist with communication automated animation needs to duplicate the body language used when speaking. In particular conversational agents are often only an animation of the upper parts of the body, so head motion is one of the keys to a believable agent. While certain linguistic features are obvious, such as nodding to indicate agreement, research has shown that head motion also aids understanding of speech. Additionally head motion often contains emotional cues, prosodic information, and other paralinguistic information. In this thesis we will present our research into synthesising head motion using only recorded speech as input. During this research we collected a large dataset of head motion synchronised with speech, examined evaluation methodology, and developed a synthesis system. Our dataset is one of the larger ones available. From it we present some statistics about head motion in general. Including differences between read speech and story telling speech, and differences between speakers. From this we are able to draw some conclusions as to what type of source data will be the most interesting in head motion research, and if speaker-dependent models are needed for synthesis. In our examination of head motion evaluation methodology we introduce Forced Canonical Correlation Analysis (FCCA). FCCA shows the difference between head motion shaped noise and motion capture better than standard methods for objective evaluation used in the literature. We have shown that for subjective testing it is best practice to use a variation of MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) based testing, adapted for head motion. Through experimentation we have developed guidelines for the implementation of the test, and the constraints on the length. Finally we present a new system for head motion synthesis. We make use of simple templates of motion, automatically extracted from source data, that are warped to suit the speech features. Our system uses clustering to pick the small motion units, and a combined HMM and GMM based approach for determining the values of warping parameters at synthesis time. This results in highly natural looking motion that outperforms other state of the art systems. Our system requires minimal human intervention and produces believable motion. The key innovates were the new methods for segmenting head motion and creating a process similar to language modelling for synthesising head motion.

The counseling profession seeks to support and enrich the quality of life of the general public by providing effective clinical services. Many counselors struggle with practicing self-care regularly, increasing the risk of burnout. When counselors provide services while experiencing burnout, they risk harming clients being served. The conservation of resources theory suggests that there is an increased risk of maladaptive coping and burnout when there is a decrease in resources used to protect someone from experiencing stress. A quantitative survey research study using a nonprobability convenience sampling was used to explore the relationship between counselor burnout, life balance, and self-compassion among fully licensed and provisionally licensed counselors throughout the United States with at least 2 years of experience (N = 331). Two canonical correlation analyses were conducted to determine (a) if there was any significant relationship between the subscales of the Juhnke-Balkin Life Balance Inventory, measuring life balance, and the Counselor Burnout Inventory (CBI), measuring burnout, and (b) if there was a significant relationship between the subscales of the CBI, measuring burnout, and the Self-Compassion Scale, measuring self-compassion. Both canonical correlation analyses indicated a statistically significant relationship. Particularly, professional counselors are experiencing poor work-life balance, decreased attention in their personal life, decreased quality of their relationships, negative work environment, and lower levels of self-compassion. The potential social change impact from this research study is that a better understanding of how to mitigate and/or prevent experiences of burnout in counselors may improve counselor’s quality of life, mitigate turnover, counselor burnout, reduce client harm, and increase the quality of clinical services.