An Analysis of Multiple Measures to Reduce Disproportionality in Accelerated Program Identification
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Low-income students and students of color are disproportionately identified at lower rates for accelerated programs in K-12 education compared to their peers in the United States. A lack of equitable identification processes that provide access to accelerated programs can be linked to issues of social inequity (Cao et al., 2017). An analysis of information from tools utilized in a Pacific northwest school district’s identification process for accelerated programs was conducted. Cognitive assessment and academic measures used for universal screening processes at the second-grade level were examined to determine if they contributed to disproportionate outcomes. Information about accelerated programs in K-12 education, how students are identified for participation in these programs, and potential biases of the tools used for identification are provided in chapters one and two. Cultural mobility framework, three-stratum theory, and educational test theory were used to understand identification practices and tools that can lead to disproportionate outcomes in identification. Answering the research questions required testing the significance of between-group differences in assessment results for sub-groups of students, analyzing the relationships that exist between independent race and socioeconomic status variables and dependent assessment variables, and learning how identified between-group differences or bias could be counteracted to impact more equitable identification practices for accelerated programs. A quantitative methodology that applied a causal-comparative research design using structural equation modeling was able to identify the significance of between-group differences in assessments used for accelerated program identification in a K-12 school district. The population included close to the total population of second-grade students attending a Pacific northwest K-12 school district. The results of this study demonstrate that a statistical analysis can be used to identify unintentional bias in assessments and the identified differences can be used to mitigate the bias. School leaders may use these results to review identification policies and processes to more equitably identify low-income students and students of color for accelerated programs.