Anxiety sensitivity's facets in relation to anxious and depressive symptoms in youth

Anxiety sensitivity is a multifaceted cognitive risk factor currently being examined in relation to anxiety and depression. The paucity of research on the relative contribution of the facets of anxiety sensitivity to anxiety and depression, coupled with variations in existing findings, indicate that the relations remain inadequately understood. In the present study, the relations between the facets of anxiety sensitivity, anxiety, and depression were examined in 730 Hispanic-Latino and European-American youth referred to an anxiety specialty clinic. Youth completed the Childhood Anxiety Sensitivity Index, the Revised Children’s Manifest Anxiety Scale, and the Children’s Depression Inventory. The factor structure of the Childhood Anxiety Sensitivity Index was examined using ordered-categorical confirmatory factor analytic techniques. Goodness-of-fit criteria indicated that a two-factor model fit the data best. The identified facets of anxiety sensitivity included Physical/Mental Concerns and Social Concerns. Support was also found for cross-ethnic equivalence of the two-factor model across Hispanic-Latino and European-American youth. Structural equation modeling was used to examine models involving anxiety sensitivity, anxiety, and depression. Results indicated that an overall measure of anxiety sensitivity was positively associated with both anxiety and depression, while the facets of anxiety sensitivity showed differential relations to anxiety and depression symptoms. Both facets of anxiety sensitivity were related to overall anxiety and its symptom dimensions, with the exception being that Social Concerns was not related to physiological anxiety symptoms. Physical/Mental Concerns were strongly associated with overall depression and with all depression symptom dimensions. Social Concerns was not significantly associated with depression or its symptom dimensions. These findings highlight that anxiety sensitivity’s relations to youth psychiatric symptoms are complex. Results suggest that focusing on anxiety sensitivity’s facets is important to fully understand its role in psychopathology. Clinicians may want to target all facets of anxiety sensitivity when treating anxious youth. However, in the context of depression, it might be sufficient for clinicians to target Physical/Mental Incapacitation Concerns.

Contemporary aspects of dividends: Before and during the financial crisis

The number of dividend paying firms has been on the decline since the popularity of stock repurchases in the 1980s, and the recent financial crisis has brought about a wave of dividend reductions and omissions. This dissertation examined the U.S. firms and American Depository Receipts that are listed on the U.S. equity exchanges according to their dividend paying history in the previous twelve quarters. While accounting for the state of the economy, the firm’s size, profitability, earned equity, and growth opportunities, it determines whether or not the firm will pay a dividend in the next quarter. It also examined the likelihood of a dividend change. Further, returns of firms were examined according to their dividend paying history and the state of the economy using the Fama-French three-factor model. Using forward, backward, and step-wise selection logistic regressions, the results show that firms with a history of regular and uninterrupted dividend payments are likely to continue to pay dividends, while firms that do not have a history of regular dividend payments are not likely to begin to pay dividends or continue to do so. The results of a set of generalized polytomous logistic regressions imply that dividend paying firms are more likely to reduce dividend payments during economic expansions, as opposed to recessions. Also the analysis of returns using the Fama-French three factor model reveals that dividend paying firms are earning significant abnormal positive returns. As a special case, a similar analysis of dividend payment and dividend change was applied to American Depository Receipts that trade on the NYSE, NASDAQ, and AMEX exchanges and are issued by the Bank of New York Mellon. Returns of American Depository Receipts were examined using the Fama-French two-factor model for international firms. The results of the generalized polytomous logistic regression analyses indicate that dividend paying status and economic conditions are also important for dividend level change of American Depository Receipts, and Fama-French two-factor regressions alone do not adequately explain returns for these securities.

The effect of the business cycle on the performance of socially responsible equity mutual funds

The current study applies a two-state switching regression model to examine the behavior of a hypothetical portfolio of ten socially responsible (SRI) equity mutual funds during the expansion and contraction phases of US business cycles between April 1991 and June 2009, based on the Carhart four-factor model, using monthly data. The model identified a business cycle effect on the performance of SRI equity mutual funds. Fund returns were less volatile during expansion/peaks than during contraction/troughs, as indicated by the standard deviation of returns. During contraction/troughs, fund excess returns were explained by the differential in returns between small and large companies, the difference between the returns on stocks trading at high and low Book-to-Market Value, the market excess return over the risk-free rate, and fund objective. During contraction/troughs, smaller companies offered higher returns than larger companies (ci = 0.26, p = 0.01), undervalued stocks out-performed high growth stocks (h i = 0.39, p <0.0001), and funds with growth objectives out-performed funds with other objectives (oi = 0.01, p = 0.02). The hypothetical SRI portfolio was less risky than the market (bi = 0.74, p <0.0001). During expansion/peaks, fund excess returns were explained by the market excess return over the risk-free rate, and fund objective. Funds with other objectives, such as balanced funds and income funds out-performed funds with growth objectives (oi = −0.01, p = 0.03). The hypothetical SRI portfolio exhibited similar risk as the market (bi = 0.93, p <0.0001). The SRI investor adds a third criterion to the risk and return trade-off of traditional portfolio theory. This constraint is social performance. The research suggests that managers of SRI equity mutual funds may diminish value by using social and ethical criteria to select stocks, but add value by superior stock selection. The result is that the performance of SRI mutual funds is very similar to that of the market. There was no difference in the value added among secular SRI, religious SRI, and vice screens.

A Critical Review of the Implied Cost of Equity: A New Way to Estimate the Expected Return

For the last three decades, the Capital Asset Pricing Model (CAPM) has been a dominant model to calculate expected return. In early 1990% Fama and French (1992) developed the Fama and French Three Factor model by adding two additional factors to the CAPM. However even with these present models, it has been found that estimates of the expected return are not accurate (Elton, 1999; Fama &French, 1997). Botosan (1997) introduced a new approach to estimate the expected return. This approach employs an equity valuation model to calculate the internal rate of return (IRR) which is often called, 'implied cost of equity capital" as a proxy of the expected return. This approach has been gaining in popularity among researchers. A critical review of the literature will help inform hospitality researchers regarding the issue and encourage them to implement the new approach into their own studies.

Risk -taking propensity of faculty, administrators, and academic department heads at a large, urban, multi -campus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky).^ Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree.^ Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64.^ The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 x 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category.^ The homogeneity of the study sample and the effect of a risk-encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change. ^

Ethnic and American identity development: A developmental systems approach

Given the role ethnic identity has as a protective factor against the effects of marginalization and discrimination (Umaña-Taylor, 2011), research longitudinally examining ethnic identity has become of increased importance. However, successful identity development must incorporate elements from both one's ethnic group and from the United States (Berry, 1980). Despite this, relatively few studies have jointly evaluated ethnic and American identity (Schwartz et al., 2012). The current dissertation, guided by three objectives, sought to address this and several other gaps in the literature. First, psychometric properties of the Multigroup Ethnic Identity Measure (MEIM) and the American Identity Measure (AIM) were evaluated. Secondly, the dissertation examined growth trends in recently immigrated Hispanic adolescents' and their caregivers' ethnic and American identity. Lastly, the relationship between adolescents' and caregivers' ethnic and American identity was evaluated. The study used an archival sample consisting of 301 recently immigrated Hispanic families collected from Miami (N = 151) and Los Angeles (N = 150). Consistent with previous research, results in Study 1 indicated a two-factor model reliably provided better fit than a one-factor model and established longitudinal invariance for the MEIM and the AIM. Results from Study 2 found significant growth in adolescents' American identity. While some differences were found across site and nationality, evidence suggested recently immigrated Hispanic adolescents were becoming more bicultural. Counterintuitively, results found a significant decline in caregivers' ethnic identity which future studies should further examine. Finally, results from Study 3, found several significant positive relationships between adolescents' and their caregivers' ethnic and American identity. Findings provided preliminary evidence for the importance of examining identity development within a systemic lens. Despite several limitations, these three studies represented a step forward in addressing the current gaps in the cultural identity literature. Implications for future investigation are discussed.

Risk-taking propensity of faculty, administrators and academic departament heads at large, urban, multicampus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky). Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree. Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64. The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 X 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category. The homogeneity of the study sample and the effect of a risk encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change.

Diatom abundance in surface sediments of the northern North Pacific

In order to map the modern distribution of diatoms and to establish a reliable reference data set for paleoenvironmental reconstruction in the northern North Pacific, a new data set including the relative abundance of diatom species preserved in a total of 422 surface sediments was generated, which covers a broad range of environmental variables characteristic of the subarctic North Pacific, the Sea of Okhotsk and the Bering Sea between 30° and 70°N. The biogeographic distribution patterns as well as the preferences in sea surface temperature of 38 diatom species and species groups are documented. A Q-mode factor analysis yields a three-factor model representing assemblages associated with the Arctic, Subarctic and Subtropical water mass, indicating a close relationship between the diatom composition and the sea surface temperatures. The relative abundance pattern of 38 diatom species and species groups was statistically compared with nine environmental variables, i.e. the summer sea surface temperature and salinity, annual surface nutrient concentration (nitrate, phosphate, silicate), summer and winter mixed layer depth and summer and winter sea ice concentrations. Canonical Correspondence Analysis (CCA) indicates 32 species and species groups have strong correspondence with the pattern of summer sea surface temperature. In addition, the total diatom flux data compiled from ten sediment traps reveal that the seasonal signals preserved in the surface sediments are mostly from spring through autumn. This close relationship between diatom composition and the summer sea surface temperature will be useful in deriving a transfer function in the subarctic North Pacific for the quantitative paleoceanographic and paleoenvironmental studies. The relative abundance of the sea-ice indicator diatoms Fragilariopsis cylindrus and F. oceanica of >20% in the diatom composition is used to represent the winter sea ice edge in the Bering Sea. The northern boundary of the distribution of F. doliolus in the open ocean is suggested to be an indicator of the Subarctic Front, while the abundance of Chaetoceros resting spores may indicate iron input from nearby continents and shelves and induced productivity events in the study area.

Diatom abundance in surface sediments of the Southern Ocean

Based on the quantitative analysis of diatom assemblages preserved in 274 surface sediment samples recovered in the Pacific, Atlantic and western Indian sectors of the Southern Ocean we have defined a new reference database for quantitative estimation of late-middle Pleistocene Antarctic sea ice fields using the transfer function technique. The Detrended Canonical Analysis (DCA) of the diatom data set points to a unimodal distribution of the diatom assemblages. Canonical Correspondence Analysis (CCA) indicates that winter sea ice (WSI) but also summer sea surface temperature (SSST) represent the most prominent environmental variables that control the spatial species distribution. To test the applicability of transfer functions for sea ice reconstruction in terms of concentration and occurrence probability we applied four different methods, the Imbrie and Kipp Method (IKM), the Modern Analog Technique (MAT), Weighted Averaging (WA), and Weighted Averaging Partial Least Squares (WAPLS), using logarithm-transformed diatom data and satellite-derived (1981-2010) sea ice data as a reference. The best performance for IKM results was obtained using a subset of 172 samples with 28 diatom taxa/taxa groups, quadratic regression and a three-factor model (IKM-D172/28/3q) resulting in root mean square errors of prediction (RMSEP) of 7.27% and 11.4% for WSI and summer sea ice (SSI) concentration, respectively. MAT estimates were calculated with different numbers of analogs (4, 6) using a 274-sample/28-taxa reference data set (MAT-D274/28/4an, -6an) resulting in RMSEP's ranging from 5.52% (4an) to 5.91% (6an) for WSI as well as 8.93% (4an) to 9.05% (6an) for SSI. WA and WAPLS performed less well with the D274 data set, compared to MAT, achieving WSI concentration RMSEP's of 9.91% with WA and 11.29% with WAPLS, recommending the use of IKM and MAT. The application of IKM and MAT to surface sediment data revealed strong relations to the satellite-derived winter and summer sea ice field. Sea ice reconstructions performed on an Atlantic- and a Pacific Southern Ocean sediment core, both documenting sea ice variability over the past 150,000 years (MIS 1 - MIS 6), resulted in similar glacial/interglacial trends of IKM and MAT-based sea-ice estimates. On the average, however, IKM estimates display smaller WSI and slightly higher SSI concentration and probability at lower variability in comparison with MAT. This pattern is a result of different estimation techniques with integration of WSI and SSI signals in one single factor assemblage by applying IKM and selecting specific single samples, thus keeping close to the original diatom database and included variability, by MAT. In contrast to the estimation of WSI, reconstructions of past SSI variability remains weaker. Combined with diatom-based estimates, the abundance and flux pattern of biogenic opal represents an additional indication for the WSI and SSI extent.

Characterization of urban aerosol in Cork city (Ireland) using aerosol mass spectrometry

Ambient wintertime background urban aerosol in Cork city, Ireland, was characterized using aerosol mass spectrometry. During the three-week measurement study in 2009, 93% of the ca. 1 350 000 single particles characterized by an Aerosol Time-of-Flight Mass Spectrometer (TSI ATOFMS) were classified into five organic-rich particle types, internally mixed to different proportions with elemental carbon (EC), sulphate and nitrate, while the remaining 7% was predominantly inorganic in nature. Non-refractory PM1 aerosol was characterized using a High Resolution Time-of-Flight Aerosol Mass Spectrometer (Aerodyne HR-ToF-AMS) and was also found to comprise organic aerosol as the most abundant species (62 %), followed by nitrate (15 %), sulphate (9 %) and ammonium (9 %), and chloride (5 %). Positive matrix factorization (PMF) was applied to the HR-ToF-AMS organic matrix, and a five-factor solution was found to describe the variance in the data well. Specifically, "hydrocarbon-like" organic aerosol (HOA) comprised 20% of the mass, "low-volatility" oxygenated organic aerosol (LV-OOA) comprised 18 %, "biomass burning" organic aerosol (BBOA) comprised 23 %, non-wood solid-fuel combustion "peat and coal" organic aerosol (PCOA) comprised 21 %, and finally a species type characterized by primary m/z peaks at 41 and 55, similar to previously reported "cooking" organic aerosol (COA), but possessing different diurnal variations to what would be expected for cooking activities, contributed 18 %. Correlations between the different particle types obtained by the two aerosol mass spectrometers are also discussed. Despite wood, coal and peat being minor fuel types used for domestic space heating in urban areas, their relatively low combustion efficiencies result in a significant contribution to PM1 aerosol mass (44% and 28% of the total organic aerosol mass and non-refractory total PM1, respectively).Ambient wintertime background urban aerosol in Cork city, Ireland, was characterized using aerosol mass spectrometry. During the three-week measurement study in 2009, 93% of the ca. 1 350 000 single particles characterized by an Aerosol Time-of-Flight Mass Spectrometer (TSI ATOFMS) were classified into five organic-rich particle types, internally mixed to different proportions with elemental carbon (EC), sulphate and nitrate, while the remaining 7% was predominantly inorganic in nature. Non-refractory PM1 aerosol was characterized using a High Resolution Time-of-Flight Aerosol Mass Spectrometer (Aerodyne HR-ToF-AMS) and was also found to comprise organic aerosol as the most abundant species (62 %), followed by nitrate (15 %), sulphate (9 %) and ammonium (9 %), and chloride (5 %). Positive matrix factorization (PMF) was applied to the HR-ToF-AMS organic matrix, and a five-factor solution was found to describe the variance in the data well. Specifically, "hydrocarbon-like" organic aerosol (HOA) comprised 20% of the mass, "low-volatility" oxygenated organic aerosol (LV-OOA) comprised 18 %, "biomass burning" organic aerosol (BBOA) comprised 23 %, non-wood solid-fuel combustion "peat and coal" organic aerosol (PCOA) comprised 21 %, and finally a species type characterized by primary m/z peaks at 41 and 55, similar to previously reported "cooking" organic aerosol (COA), but possessing different diurnal variations to what would be expected for cooking activities, contributed 18 %. Correlations between the different particle types obtained by the two aerosol mass spectrometers are also discussed. Despite wood, coal and peat being minor fuel types used for domestic space heating in urban areas, their relatively low combustion efficiencies result in a significant contribution to PM1 aerosol mass (44% and 28% of the total organic aerosol mass and non-refractory total PM1, respectively).

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Essays in the Economics of Education

This dissertation is comprised of three essays in the economics of education. In the first essay, I examine how college students' major choice and major switching behavior responds to major-specific labor market shocks. The second essay explores the incidence and persistence of overeducation for workers in the United States. The final essay examines the role that students' cognitive and non-cognitive skills play in their transition from secondary to postsecondary education, and how the effect of these skills are moderated by race, gender, and socioeconomic status.

Multi-View Weighted Network

Extensive investigation has been conducted on network data, especially weighted network in the form of symmetric matrices with discrete count entries. Motivated by statistical inference on multi-view weighted network structure, this paper proposes a Poisson-Gamma latent factor model, not only separating view-shared and view-specific spaces but also achieving reduced dimensionality. A multiplicative gamma process shrinkage prior is implemented to avoid over parameterization and efficient full conditional conjugate posterior for Gibbs sampling is accomplished. By the accommodating of view-shared and view-specific parameters, flexible adaptability is provided according to the extents of similarity across view-specific space. Accuracy and efficiency are tested by simulated experiment. An application on real soccer network data is also proposed to illustrate the model.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

Multistabilities and symmetry-broken one-color and two-color states in closely coupled single-mode lasers

We theoretically investigate the dynamics of two mutually coupled, identical single-mode semi-conductor lasers. For small separation and large coupling between the lasers, symmetry-broken one-color states are shown to be stable. In this case the light outputs of the lasers have significantly different intensities while at the same time the lasers are locked to a single common frequency. For intermediate coupling we observe stable symmetry-broken two-color states, where both lasers lase simultaneously at two optical frequencies which are separated by up to 150 GHz. Using a five-dimensional model, we identify the bifurcation structure which is responsible for the appearance of symmetric and symmetry-broken one-color and two-color states. Several of these states give rise to multistabilities and therefore allow for the design of all-optical memory elements on the basis of two coupled single-mode lasers. The switching performance of selected designs of optical memory elements is studied numerically.