The effect of sexist attitudes on the perception of visual artists by community college and university students

This study compared the effects of sexist labeling on the perceptions of visual artists by the community college and university students and determined their sex role orientation. The 370 students were shown five slides of an artist's works and were given six versions of an artist's biography. It contained embedded sexual labeling (woman, girl, person/ she, man, guy, person/he). The Artist Evaluation Questionnaire was administered to the female and male community college and university students that required the students to evaluate the female and male artists on several aspects of affective and cognitive measures. The questionnaire consisted of 9 items that had to be rated by the participants. In addition, the students filled out the Demographic Questionnaire and the BEM Sex Role Inventory, titled the Attitude Questionnaire. The Analysis of Variance testing procedures were administered to analyze the responses. The results disclosed gender differences in students' ratings. The female artist's work, when the artist was referred to by the neutral sexual label, "person", received significantly higher ratings from the female students. The male students gave the female artist her highest ratings when she was referred to by the low status sexual label, "girl". Both sexes did not express statistically significant preferences for any of the male sexual labels. Gender difference became apparent when it was found that female students rated both sexes equally, and their ratings were lower than those of the male students. The male students rated the female artist's work higher than the work of the male artist. The analysis of the sex role inventory questionnaire revealed the absence of the feminine (expressive) and masculine (instrumental) personalities among the students. The personalities of almost all the students were androgynous, with a few within the range of the near feminine, and a few within the range of the near masculine. The study reveals that there are differences in perception of sexual labels among the community college and university students.

Use of conjoint analysis to examine beliefs related to the practice of early literacy instruction

The purpose of the study was to examine the relationship between teacher beliefs and actual classroom practice in early literacy instruction. Conjoint analysis was used to measure teachers' beliefs on four early literacy factors—phonological awareness, print awareness, graphophonic awareness, and structural awareness. A collective case study format was then used to measure the correspondence of teachers' beliefs with their actual classroom practice. ^ Ninety Project READS participants were given twelve cards in an orthogonal experimental design describing students that either met or did not meet criteria on the four early literacy factors. Conjoint measurements of whether the student is an efficient reader were taken. These measurements provided relative importance scores for each respondent. Based on the relative important scores, four teachers were chosen to participate in a collective case study. ^ The conjoint results enabled the clustering of teachers into four distinct groups, each aligned with one of the four early literacy factors. K-means cluster analysis of the relative importance measurements showed commonalities among the ninety respondents' beliefs. The collective case study results were mixed. Implications for researchers and practitioners include the use of conjoint analysis in measuring teacher beliefs on the four early literacy factors. Further, the understanding of teacher preferences on these beliefs may assist in the development of curriculum design and therefore increase educational effectiveness. Finally, comparisons between teachers' beliefs on the four early literacy factors and actual instructional practices may facilitate teacher self-reflection thus encouraging positive teacher change. ^

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.