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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Santa Barbara Electronic Theses and Dissertations (2022)

Research has shown that Asian American and Pacific Islander (AAPI) students face higher education challenges that go unnoticed due to the Model Minority Myth (Chang et al., 2007; Chang, 2011; Maramba, 2008a; Maramba, 2008b; Maramba & Palmer, 2014; Museus, 2009; Museus & Chang, 2009; Museus & Kiang, 2009; Museus & Maramba, 2011; Suzuki, 1989, 2002). These obstacles are now being exacerbated by a pandemic which has been accompanied by an increase in racial tensions, a recession, and adverse health outcomes (AAPI Equity Alliance, 2020; 2022; Mar & Ong, 2020; Pew Research Center, 2020). Moreover, while there has been recent momentum on researching AAPIs in higher education, most studies focus on AAPIs who are struggling against traditional measures of achievement (Poon et. al., 2016). This, unintentionally, reinforces White hegemonic ideology by promoting deficit-modeling thinking (Poon et. al., 2016 This study expands on the current research of AAPIs in higher education by recruiting diverse AAPI undergraduates in respect to ethnicity, class, and first-generation status to avoid reinforcing White hegemonic ideology thought deficit-modeling thinking.This study utilized an exploratory sequential design to uncover diverse AAPI experiences as they navigate college during a time of high stress and financial instability. Findings from the qualitative phase revealed that AAPIs are experiencing COVID-19 related racism on and off campus, and that this discrimination is conducive to essentializing this population of students. Moreover, results reveal nuances in how AAPIs are perceiving institutional and home communities’ support while they pursue a degree during a pandemic. Challenges in maintaining student-run ethnic organizations, low academic motivation, increased family responsibilities, and low perceived institutional support during a time of heightened marginalization are contributing to a poor campus climate for these students. Findings from the interview data were used to inform the development of a survey. After conducting an exploratory and confirmatory factor analysis on 62 survey items, a final instrument was created with 34 questions. This survey was tested quantitatively with a larger sample to see if the initial qualitative findings could be generalized. Survey results indicated that AAPIs of diverse ethnicities experienced COVID-19 related racism despite previous research suggesting that AAPIs have differential racial experiences based on their ethnic backgrounds (CARE, 2008; Museus & Iftikar, 2013). The homogenization of AAPIs is not new in U.S. history and continues to be a tool of white supremacy to best serve white hegemonic interests in U.S. colleges (Museus & Iftikar, 2013; Iftikar & Museus, 2018). In addition, some survey findings generally aligned with interview results, with some exceptions. Implications for how higher education institutions and faculty can best support AAPI undergraduates are shared in the discussion, as well as future directions for research.

    Cover page: A Mixed Methods Inquiry into AAPIs’ Experiences as they Navigate Higher Education During COVID-19
    • Thesis
    • Peer Reviewed
    UC Santa Barbara Electronic Theses and Dissertations (2024)

    The current study aims to examine the process of one school district’s (Mountain District) effort to detrack its English language arts (ELA) program. This is done by looking at how teachers and administrators understood the inception and goals of the program, their experiences implementing the program, and how communities of practice influenced their interactions with the detracking program. Qualitative data collection consisted of semi-structured interviews with high school teachers and administrators. The data was analyzed using organizational theories, which are theories that focus on the mechanics of an organization (such as its values, culture, processes, and underlying beliefs) and how that influences the perspectives and experiences of actors within the organization (Bolman & Deal, 2021; Gonzales et al., 2018; Kezar, 2017; Manning, 2018; Weick et al., 2005).

      Cover page: Understanding the Organizational Reform of a School District through a Detracking Program
      • Article
      • Peer Reviewed
      UC Riverside Previously Published Works (2022)
        Cover page: Do learning communities increase first year college retention? Evidence from a randomized control trialCreative Commons 'BY-SA' version 4.0 license
        • Article
        • Peer Reviewed
        UC Riverside Previously Published Works (2021)
        Opportunities for large numbers of undergraduates to engage in authentic research experiences are limited in many large public institutions. These large public institutions serve the vast majority of students who are historically underrepresented in STEM fields, such as first-generation, low-income students of color. Although a course-based undergraduate research experience (CURE) is one scalable approach to providing such opportunities, there is limited evidence about the impact of participation, particularly for students historically underrepresented in science. This study provides evidence of the influence of student participation in a CURE on undergraduate science course grades using an experimental design and multiple years of data from students at a Hispanic-serving institution. Course grades were compared for five different science courses across five cohorts of students participating in a CURE (n = 935) and a similar group of students who did not participate in the CURE (n = 1,144). CURE students had significantly higher overall grades in a lecture course directly related to the CURE even after statistically adjusting for demographic and academic characteristics. Implications for CUREs as a model for improving science knowledge and achievement for students typically underrepresented in STEM fields are discussed.
          Cover page: Participation in a Course-Based Undergraduate Research Experience Results in Higher Grades in the Companion Lecture Course
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2024)

          Background

          Researchers have many options for web-based survey data collection, ranging from access to curated probability-based panels, where individuals are selectively invited to join based on their membership in a representative population, to convenience panels, which are open for anyone to join. The mix of respondents available also varies greatly regarding representation of a population of interest and in motivation to provide thoughtful and accurate responses. Despite the additional dataset-building labor required of the researcher, convenience panels are much less expensive than probability-based panels. However, it is important to understand what may be given up regarding data quality for those cost savings.

          Objective

          This study examined the relative costs and data quality of fielding equivalent surveys on Amazon's Mechanical Turk (MTurk), a convenience panel, and KnowledgePanel, a nationally representative probability-based panel.

          Methods

          We administered the same survey measures to MTurk (in 2021) and KnowledgePanel (in 2022) members. We applied several recommended quality assurance steps to enhance the data quality achieved using MTurk. Ipsos, the owner of KnowledgePanel, followed their usual (industry standard) protocols. The survey was designed to support psychometric analyses and included >60 items from the Patient-Reported Outcomes Measurement Information System (PROMIS), demographics, and a list of health conditions. We used 2 fake conditions ("syndomitis" and "chekalism") to identify those more likely to be honest respondents. We examined the quality of each platform's data using several recommended metrics (eg, consistency, reliability, representativeness, missing data, and correlations) including and excluding those respondents who had endorsed a fake condition and examined the impact of weighting on representativeness.

          Results

          We found that prescreening in the MTurk sample (removing those who endorsed a fake health condition) improved data quality but KnowledgePanel data quality generally remained superior. While MTurk's unweighted point estimates for demographics exhibited the usual mismatch with national averages (younger, better educated, and lower income), weighted MTurk data matched national estimates. KnowledgePanel's point estimates better matched national benchmarks even before poststratification weighting. Correlations between PROMIS measures and age and income were similar in MTurk and KnowledgePanel; the mean absolute value of the difference between each platform's 137 correlations was 0.06, and 92% were <0.15. However, correlations between PROMIS measures and educational level were dramatically different; the mean absolute value of the difference across these 17 correlation pairs was 0.15, the largest difference was 0.29, and the direction of more than half of these relationships in the MTurk sample was the opposite from that expected from theory. Therefore, caution is needed if using MTurk for studies where educational level is a key variable.

          Conclusions

          The data quality of our MTurk sample was often inferior to that of the KnowledgePanel sample but possibly not so much as to negate the benefits of its cost savings for some uses.

          International registered report identifier (irrid)

          RR2-10.1186/s12891-020-03696-2.
            Cover page: Comparing Health Survey Data Cost and Quality Between Amazon’s Mechanical Turk and Ipsos’ KnowledgePanel: Observational Study
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