Attachment and alcohol : testing a motivational model of problem drinking /

The purpose of this study was to replicate and extend a motivational model of problem drinking (Cooper, Frone, Russel, & Mudar, 1995; Read, Wood, Kahler, Maddock & Tibor, 2003), testing the notion that attachment is a common antecedent for both the affective and social paths to problem drinking. The model was tested with data from three samples, first-year university students (N=679), students about to graduate from university (N=206), and first-time clients at an addiction treatment facility (N=21 1). Participants completed a battery of questionnaires assessing alcohol use, alcohol-related consequences, drinking motives, peer models of alcohol use, positive and negative affect, attachment anxiety and attachment avoidance. Results underscored the importance of the affective path to problem drinking, while putting the social path to problem drinking into question. While drinking to cope was most prominent among the clinical sample, coping motives served as a risk factor for problem drinking for both individuals identified as problem drinkers and university students. Moreover, drinking for enhancement purposes appeared to be the strongest overall predictor of alcohol use. Results of the present study also supported the notion that attachment anxiety and avoidance are antecedents for the affective path to problem drinking, such that those with higher levels of attachment anxiety and avoidance were more vulnerable to experiencing adverse consequences related to their drinking, explained in terms of diminished affect regulation. Evidence that nonsecure attachment is a potent predictor of problem drinking was also demonstrated by the finding that attachment anxiety was directly related to alcohol-related consequences over and above its indirect relationship through affect regulation. However, results failed to show that attachment anxiety or attachment avoidance increased the risk of problem drinking via social influence.

Search engine advertising in web retailing : an efficiency analysis

This study examines the efficiency of search engine advertising strategies employed by firms. The research setting is the online retailing industry, which is characterized by extensive use of Web technologies and high competition for market share and profitability. For Internet retailers, search engines are increasingly serving as an information gateway for many decision-making tasks. In particular, Search engine advertising (SEA) has opened a new marketing channel for retailers to attract new customers and improve their performance. In addition to natural (organic) search marketing strategies, search engine advertisers compete for top advertisement slots provided by search brokers such as Google and Yahoo! through keyword auctions. The rationale being that greater visibility on a search engine during a keyword search will capture customers' interest in a business and its product or service offerings. Search engines account for most online activities today. Compared with the slow growth of traditional marketing channels, online search volumes continue to grow at a steady rate. According to the Search Engine Marketing Professional Organization, spending on search engine marketing by North American firms in 2008 was estimated at $13.5 billion. Despite the significant role SEA plays in Web retailing, scholarly research on the topic is limited. Prior studies in SEA have focused on search engine auction mechanism design. In contrast, research on the business value of SEA has been limited by the lack of empirical data on search advertising practices. Recent advances in search and retail technologies have created datarich environments that enable new research opportunities at the interface of marketing and information technology. This research uses extensive data from Web retailing and Google-based search advertising and evaluates Web retailers' use of resources, search advertising techniques, and other relevant factors that contribute to business performance across different metrics. The methods used include Data Envelopment Analysis (DEA), data mining, and multivariate statistics. This research contributes to empirical research by analyzing several Web retail firms in different industry sectors and product categories. One of the key findings is that the dynamics of sponsored search advertising vary between multi-channel and Web-only retailers. While the key performance metrics for multi-channel retailers include measures such as online sales, conversion rate (CR), c1ick-through-rate (CTR), and impressions, the key performance metrics for Web-only retailers focus on organic and sponsored ad ranks. These results provide a useful contribution to our organizational level understanding of search engine advertising strategies, both for multi-channel and Web-only retailers. These results also contribute to current knowledge in technology-driven marketing strategies and provide managers with a better understanding of sponsored search advertising and its impact on various performance metrics in Web retailing.

The development of norm-based coding and category-specific face prototypes : an examination of 5- and 8-year-olds' face space

The present set of experiments was designed to investigate the organization and refmement of young children's face space. Past research has demonstrated that adults encode individual faces in reference to a distinct face prototype that represents the average of all faces ever encountered. The prototype is not a static abstracted norm but rather a malleable face average that is continuously updated by experience (Valentine, 1991); for example, following prolonged viewing of faces with compressed features (a technique referred to as adaptation), adults rate similarly distorted faces as more normal and more attractive (simple attractiveness aftereffects). Recent studies have shown that adults possess category-specific face prototypes (e.g., based on race, sex). After viewing faces from two categories (e.g., Caucasian/Chinese) that are distorted in opposite directions, adults' attractiveness ratings simultaneously shift in opposite directions (opposing aftereffects). The current series of studies used a child-friendly method to examine whether, like adults, 5- and 8-year-old children show evidence for category-contingent opposing aftereffects. Participants were shown a computerized storybook in which Caucasian and Chinese children's faces were distorted in opposite directions (expanded and compressed). Both before and after adaptation (i.e., reading the storybook), participants judged the normality/attractiveness of a small number of expanded, compressed, and undistorted Caucasian and Chinese faces. The method was first validated by testing adults (Experiment I ) and was then refined in order to test 8- (Experiment 2) and 5-yearold (Experiment 4a) children. Five-year-olds (our youngest age group) were also tested in a simple aftereffects paradigm (Experiment 3) and with male and female faces distorted in opposite directions (Experiment 4b). The current research is the first to demonstrate evidence for simple attractiveness aftereffects in children as young as 5, thereby indicating that similar to adults, 5-year-olds utilize norm-based coding. Furthermore, this research provides evidence for racecontingent opposing aftereffects in both 5- and 8-year-olds; however, the opposing aftereffects demonstrated by 5-year-olds were driven largely by simple aftereffects for Caucasian faces. The lack of simple aftereffects for Chinese faces in 5-year-olds may be reflective of young children's limited experience with other-race faces and suggests that children's face space undergoes a period of increasing differentiation over time with respect to race. Lastly, we found no evidence for sex -contingent opposing aftereffects in 5-year-olds, which suggests that young children do not rely on a fully adult-like face space even for highly salient face categories (i.e., male/female) with which they have comparable levels of experience.

Keyword Competition and Determinants of Ad Position in Sponsored Search Advertising

Given the significant growth of the Internet in recent years, marketers have been striving for new techniques and strategies to prosper in the online world. Statistically, search engines have been the most dominant channels of Internet marketing in recent years. However, the mechanics of advertising in such a market place has created a challenging environment for marketers to position their ads among their competitors. This study uses a unique cross-sectional dataset of the top 500 Internet retailers in North America and hierarchical multiple regression analysis to empirically investigate the effect of keyword competition on the relationship between ad position and its determinants in the sponsored search market. To this end, the study utilizes the literature in consumer search behavior, keyword auction mechanism design, and search advertising performance as the theoretical foundation. This study is the first of its kind to examine the sponsored search market characteristics in a cross-sectional setting where the level of keyword competition is explicitly captured in terms of the number of Internet retailers competing for similar keywords. Internet retailing provides an appropriate setting for this study given the high-stake battle for market share and intense competition for keywords in the sponsored search market place. The findings of this study indicate that bid values and ad relevancy metrics as well as their interaction affect the position of ads on the search engine result pages (SERPs). These results confirm some of the findings from previous studies that examined sponsored search advertising performance at a keyword level. Furthermore, the study finds that the position of ads for web-only retailers is dependent on bid values and ad relevancy metrics, whereas, multi-channel retailers are more reliant on their bid values. This difference between web-only and multi-channel retailers is also observed in the moderating effect of keyword competition on the relationships between ad position and its key determinants. Specifically, this study finds that keyword competition has significant moderating effects only for multi-channel retailers.

The Effect of Trainer Muscularity and Expertise on Self-Presentational Concerns, Body Image, and Performance in College Men during One-Repetition Maximum Testing

This study attempted to manipulate self-presentational efficacy to examine the effect on social anxiety, social physique anxiety, drive for muscularity, and maximal strength performance during a one-repetition maximum (1-RM) chest press and leg press test. Ninety-nine college men with a minimum of six months of previous weight training experience were randomly assigned to complete a 1-RM protocol with either a muscular male trainer described as an expert or a lean male trainer described as a novice. Participants completed measures of self-presentation and body image prior to meeting their respective trainer, and following the completion of the 1-RM tests. Although the self-presentational efficacy manipulation was not successful, the trainers were perceived significantly differently on musculature and expertise. The group with the muscular, expert trainer reported higher social anxiety and attained higher 1-RM scores for the chest and leg press. Thus, trainer characteristics can affect strength performance and self-presentational concerns in this population.

The time-course of lexical activation in Japanese morphographic word recognition: Evidence for a character-driven processing model

This lexical decision study with eye tracking of Japanese two-kanji-character words investigated the order in which a whole two-character word and its morphographic constituents are activated in the course of lexical access, the relative contributions of the left and the right characters in lexical decision, the depth to which semantic radicals are processed, and how nonlinguistic factors affect lexical processes. Mixed-effects regression analyses of response times and subgaze durations (i.e., first-pass fixation time spent on each of the two characters) revealed joint contributions of morphographic units at all levels of the linguistic structure with the magnitude and the direction of the lexical effects modulated by readers’ locus of attention in a left-to-right preferred processing path. During the early time frame, character effects were larger in magnitude and more robust than radical and whole-word effects, regardless of the font size and the type of nonwords. Extending previous radical-based and character-based models, we propose a task/decision-sensitive character-driven processing model with a level-skipping assumption: Connections from the feature level bypass the lower radical level and link up directly to the higher character level.

Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.

List of [timber railway] piles delivered and driven

List of [timber railway] piles delivered and driven (1 page, handwritten). This is signed by Alex Morrison, July 1856.

Sketch of the height of the piles driven for the bridge across Chippewa Creek at Port Robinson

Sketch of the height of the piles driven for the bridge across Chippewa Creek at Port Robinson, Jan. 3, 1856.

Stress testing a complement of value-a-risk.

Rapport de stage (maîtrise en finance mathématique et computationnelle)

The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purching Power Parity.

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors. By adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by feasible generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. Simulation results suggest that this new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of purchasing power parity (PPP) among the G-7 countries. The test based on the efficient estimates rejects the PPP hypothesis for most countries.

Testing Normality : A GMM Approach

In this paper, we consider testing marginal normal distributional assumptions. More precisely, we propose tests based on moment conditions implied by normality. These moment conditions are known as the Stein (1972) equations. They coincide with the first class of moment conditions derived by Hansen and Scheinkman (1995) when the random variable of interest is a scalar diffusion. Among other examples, Stein equation implies that the mean of Hermite polynomials is zero. The GMM approach we adopted is well suited for two reasons. It allows us to study in detail the parameter uncertainty problem, i.e., when the tests depend on unknown parameters that have to be estimated. In particular, we characterize the moment conditions that are robust against parameter uncertainty and show that Hermite polynomials are special examples. This is the main contribution of the paper. The second reason for using GMM is that our tests are also valid for time series. In this case, we adopt a Heteroskedastic-Autocorrelation-Consistent approach to estimate the weighting matrix when the dependence of the data is unspecified. We also make a theoretical comparison of our tests with Jarque and Bera (1980) and OPG regression tests of Davidson and MacKinnon (1993). Finite sample properties of our tests are derived through a comprehensive Monte Carlo study. Finally, three applications to GARCH and realized volatility models are presented.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Testing for a Unit Root in Panels with Dynamic Factors

This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.