When “model minorities” become “yellow peril”—Othering and the racialization of Asian Americans in the COVID-19 pandemic

Using the ongoing coronavirus disease 2019 (COVID-19) pandemic as a case study, this paper engages with debates on the assimilation of Asian Americans into the US mainstream. While a burgeoning scholarship holds that Asians are “entering into the dominant group” or becoming “White,” the prevalent practices of othering Asians and surging anti-Asian discrimination since the pandemic outbreak present a challenge to the assimilation thesis. This paper explains how anger against China quickly expands to Asian American population more broadly. Our explanation focuses on different forms of othering practices, deep-seated stereotypes of Asians, and the role of politicians and media in activating or exacerbating anti-Asian hatred. Through this scrutiny, this paper augments the theses that Asian Americans are still treated as “forever foreigners” and race is still a prominent factor in the assimilation of Asians in the United States. This paper also sheds light on the limitations of current measures of assimilation. More broadly, the paper questions the notion of color-blindness or post-racial America.     

AN EVALUATION OF TWO MODELS OF UNDERGRADUATE STATISTICS

The paper reports on a large scale evaluation of two alternative models of undergraduate statistics courses. The traditional course orientation was compared with a problem-solving orientation in which the emphasis was on real world problem-solving, during the winter and spring quarters of 1973–74. The evaluation battery included behavioral and problem-solving skill measures. The findings indicate that improvements in statistics programs can be made.     

Bootstrap Methods for Markov Processes

The block bootstrap is the best known bootstrap method for time‐series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first‐order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition density nonparametrically. Bootstrap samples are obtained by sampling the process implied by the estimated transition density. Conditions are given under which the errors made by the Markov bootstrap converge to zero more rapidly than those made by the block bootstrap.     

Increasing the Effectiveness of Student Evaluation of Instructor Data through a Factor Score Comparative Report

Effective student evaluation of instructor instruments must provide norm, or comparative, data to aid interpretation. This note highlights an innovative factor score comparative report. Rather than merely reporting comparative data on individual questions items, the innovative report provides comparative percentile data on the six factors underlying our thirty-three item questionnaire. The faculty indicated overwhelming support for the new report. The note also briefly mentions three other activities that are essential for student evaluation of instructor data to improve teacher effectiveness. Our goal is to stimulate other colleges to adopt the innovative norming practice once they have validated their local evaluation instruments. College-wide improvement of teaching will occur through such do-it-yourself efforts and only decision science faculty are technically qualified to lead such efforts.