In this study, we examine the predictive power of Morningstar’s new ratings for mutual funds’ future performance and compare its predictive power with four competing predictors. We also examine Morningstar’s new ratings’ predictive power in bull and bear periods. Furthermore, we compare the predictive power of the new and old star-ratings. We perform all these tests for both U.S. and Canadian equity funds. We use a regression model and non-parametric tests in this study. The results suggest Morningstar’s new ratings accurately rank funds and predict out-of-sample performance of only five-star rated complete funds for short- and medium-terms for U.S., and for medium-term only for Canada. Also, predictive power of Morningstar’s new ratings is low compared to four alternative predictors for both countries. Further, the new star ratings accurately predicts for bear period for both markets. The old ratings (new ratings), however relatively predict better for U.S. funds (Canadian funds). / ix, 184 leaves ; 29 cm

This paper introduces a model framework for dynamic credit rating processes. Our framework aggregates ordinal rating information stemming from a variety of rating sources. The dynamic of the consensus rating captures systematic as well as idiosyncratic changes. In addition, our framework allows to validate the different rating sources by analyzing the mean/variance structure of the rating deviations. In an empirical study for the iTraxx Europe companies rated by the big three external rating agencies we use Bayesian techniques to estimate the consensus ratings for these companies. The advantages are illustrated by comparing our dynamic rating model to a naive benchmark model. (authors' abstract)

De flesta forskare inom området är överens om att det finns kopplingar mellan luktsinnet och känslor. Däremot finns det mindre forskning och bevis kring hur människors inre tillstånd påverkar olfaktorisk perception, dvs. uppfattningen av dofter. Föreliggande studie hade avsikt att studera sambandet mellan sinnesstämning och uppskattning av behagliga dofter. Den aktuella sinnesstämningen mättes med hjälp av Mood Adjective Checklist (Sjöberg et.al., 1979), ett tillförlitligt och känsligt instrument. För att mäta doftuppskattning användes fem olika dofter på doftstickor. Dofterna valdes ut systematiskt, testades i förväg och bekräftades som behagliga. Resultaten kunde inte visa något signifikant samband mellan sinnesstämning och doftuppskattning (p = .612). Ingen predicerande effekt i sinnesstämning och i de olika dimensionerna av sinnesstämningen på doftuppskattning kunde påvisas (p varierar mellan .293 och .862). Resultaten kan ha påverkats av metodologiska brister och utformningen av dofttestningsinstrumentet som diskuterats.

Classroom observations have been increasingly used for teacher evaluations, and thus it is important to examine the measurement quality and the use of observation ratings. When a teacher is observed in multiple classrooms, his or her observation ratings may vary across classrooms. In that case, using ratings from one classroom per teacher may not be adequate to represent a teacher’s quality of instruction. However, the fact that classrooms are nested within teachers is usually not considered while classroom observation data is analyzed. Drawing on the Measures of Effective Teaching dataset, this dissertation examined the variation of a teacher’s classroom observation ratings across his or her multiple classrooms. In order to account for the teacher-level, school-level, and rater-level variation, a cross-classified random effects model was used for the analysis. Two research questions were addressed: (1) What is the variation of a teacher’s classroom observation ratings across multiple classrooms? (2) To what extent is the classroom-level variation within teachers explained by observable classroom characteristics? The results suggested that the math classrooms shared 4.9% to 14.7% of the variance in the classroom observation ratings and English Language and Arts classrooms shared 6.7% to 15.5% of the variance in the ratings. The results also showed that the classroom characteristics (i.e., class size, percent of minority students, percent of male students, percent of English language learners, percent of students eligible for free or reduced lunch, and percent of students with disabilities) had limited contributions to explaining the classroom-level variation in the ratings. The results of this dissertation indicate that teachers’ multiple classrooms should be taken into consideration when classroom observation ratings are used to evaluate teachers in high-stakes settings. In addition, other classroom-level factors that could contribute to explaining the classroom-level variation in classroom observation ratings should be investigated in future research.

The problem was to determine the significance of various relationships between job-performance ratings and selected factors associated with the college curricula of junior executive trainees. Job-performance ratings were made by personnel directors and immediate supervisors of college graduates enrolled as participants in junior executive training programs in department stores in Texas.

The purpose of this study was to determine if leniency and halo error in student ratings could be reduced by training the student raters and by using a Behaviorally Anchored Rating Scale (BARS) rather than a Likert scale. Two hypotheses were proposed. First, the ratings collected from the trained raters would contain less halo and leniency error than those collected from the untrained raters. Second, within the group of trained raters the BARS would contain less halo and leniency error than the Likert instrument.

Absenteeism, lateness, and work performance on the job were investigated. Pre-entry attitudes and self-reported behaviors in the three areas were assessed via RELY, a self-report instrument developed by Kurt Helm (1980). Subjects (N=282) were entry-level stock, bag and clerical personnel for a large grocery store chain. They were 91% Caucasian and 62% male. Results showed significant correlation between three empirically derived scales and criteria: total days absent, total occurrences of lateness, and supervisory performance ratings. However, these findings were considerably weaker under cross-validation. The findings indicate absence-proneness as a tenable concept. Further investigation may find a considerable amount of the variance in attendance to be the result of pre-entry attitudes.

This study predicted that individuals diagnosed as having higher levels of stress, based upon DSM-III-R, Axis IV ratings, would also be diagnosed as having more severe forms of mental illness. Conversely, it predicted that individuals with higher premorbid health ratings, according to DSM-III-R, Axis V, would be diagnosed as having less severe forms of mental illness. Highly significant correlations were found between stress ratings and severity of disorder. Significant inverse relationships were also found between Axis V ratings and disorder severity. Additionally, several other demographic variables were significantly correlated with severity of disorder.

Abstrakt_anglicky _Diplomova_prace_Ptackova_Renata The diploma thesis "Pošta pro Tebe - reality TV and social motivation of participants" focus on the use of social television. It describes in detail the reality TV genre, its brief history of broadcasting, focusing on the motivational elements of the program participants Pošta pro Tebe. It investigates the impact and effect of media and mediated relationship to the contents of a reality. It analysis the audience base, explores reasons for visiting the TV studio spectators, why people looking for this TV show and why they watch it by questionnaires, interviews. It's looking for the credibility of the program and asking whether the audience would become program participants. Further, the work captures the audience ratings and popularity in each year of its broadcast. Finally, describes the creation of the show and behind the scenes. The diploma thesis is divided into 4 chapters: Theoretical introduction thesis (clarifying the concepts that are crucial for the work related), Ratings (analyzing rating charts of the first channel of Czech Television and the demographic composition of the audience), Behind the Scenes program (described shooting of, the role of interested participants, technical background of the show), Audience (defines who is the audience and...

Master of Science / Department of Computing and Information Sciences / Doina Caragea / Over the last decade, the Internet has become an involving medium and user-generated content is continuously growing. Recommender systems that exploit user feedback are widely used in e-commerce and quite necessary for business enhancement. To make use of such user feedback, we propose a new content/collaborative hybrid approach, which is built on top of the recently released hetrec2011-movielens-2k dataset and is an extension of a previously proposed approach, called Weighted Tag Recommender (WTR). The WTR approach makes use of tag information available in hetrec2011-movielens-2k, but it does not use explicit ratings. As opposed to WTR, our modified approach can make use of ratings to capture collaborative filtering and either user-tags, available in the hetrec2011-movielens-2k, or movie keywords retrieved from IMDB, to capture movie content information. We call the two versions of our approach Weighted Tag Rating Recommender (WTRR) and Weighted Keyword Rating Recommender (WKRR), respectively. Movie keywords (which are not user specific) allow us to use all ratings available in hetrec2011-movielens-2k, as WKKR associates the content information from movies with the users, based on their ratings. On the other hand, tags provide more specific information for a user, but limit the usage of the data to the user-movie pairs that have tags (significantly smaller number compared with all pairs that have ratings). Both our keyword and tag representations of users can help alleviate the noise and semantic ambiguity problems inherent in information contributed by users of social networks. Experiments using the WTRR approach on a subset of the dataset (which contains both ratings and tags) show that it slightly outperforms the WKRR approach. However, WKRR can be applied to the whole hetrec2011-movielens-2k dataset and results show that the information from keywords can help build a movie recommender system competitive with other neighborhood based approaches and even with more sophisticated state-of-the-art approaches.