Browsing by Author "Nickodem, Kyle"
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Item Analyzing & Reporting Achievement Gaps: Guidance for Minnesota Schools(2016-01) Rodriguez, Michael C.; Nickodem, Kyle; Palma, Jose; Stanke, LukeItem Comprehensive Partitioning of Student Achievement Variance to Inform Equitable Policy Design(2018-04) Rodriguez, Michael C.; Nickodem, KyleItem Do LGB Students Feel Safe and Why Does it Matter?(2018-04) Lamm, Rik; Do, Tai; Vue, Kory; Nickodem, Kyle; Rodriguez, Michael C.Item Effects of Participation in School Sports on Academic and Social Outcome Variables(2015-04) Bulut, Okan; Van Boekel, Martin; Stanke, Luke; Palma, Jose R.; Nickodem, Kyle; Vue, Kory; Chang, Yu-Feng; Latterell, Nicholas; Rodriguez, Michael C.Item Examining Noneffortful Responding in Survey Responses and SEL Measures(2024) Lamm, Rik; Abulela, Mohammed A.A; Nickodem, Kyle; Rodriguez, Michael CItem Interaction Among Gender, Race/Ethnicity, and School Sports Participation in Youth Development(2017-04) Kang, Youngsoon; Van Boekel, Martin; Nickodem, Kyle; Palma, Jose R.; Vue, Kory; Jang, Yoojeong; Rodriguez, Michael C.; Bulut, OkanItem Investigating the Role of Support in Athletes’ and Non-Athletes’ Academic Achievement: An SEM Approach(2017-04) Nickodem, Kyle; Van Boekel, Martin; Vue, Kory; Rodriguez, Michael C.; Palma, Jose R.; Kang, Youngsoon; Yoojeong, JangItem Investigating “Books at Home” as an Indicator of Student Socioeconomic Status(2024) Ihlenfeldt, Sam; Nickodem, Kyle; Rodriguez, Michael CItem LGB Students and School Sports: A Positive Youth Development Approach(2016-04) Nickodem, Kyle; Van Boekel, Martin; Stanke, Luke; Palma, Jose R.; Vue, Kory; Bulut, Okan; Kang, Youngsoon; Chang, Yu-Feng; Rodriguez, Michael C.Item A measure of diversity(2020) Vue, Kory; Nickodem, Kyle; Rodriguez, Michael C.Item Measurement Invariance of Social and Emotional Learning Measures across Four Administrations: Conventional Fit Statistics Versus RMSEAD(2024) Abulela, Mohammed A.A; Nickodem, Kyle; Rodriguz, Michael CItem Measuring Ethnic Diversity in Schools(2015-04) Vue, Kory; Van Boekel, Martin; Chang, Yu-Feng; Rodriguez, Michael C.; Palma, Jose C.; Stanke, Luke; Latterell, Nicholas; Nickodem, KyleItem Multilevel Reliability for Social and Emotional Learning Measures(2020) Chavez, Carlos; Rodriguez, Michael C.; Nickodem, Kyle; Vue, Kory; Do, TaiItem The Role of Out-of-School-Time Positive Experiences on Risky Behaviors(2018-04) Lamm, Rik; Do, Tai; Vue, Kory; Nickodem, Kyle; Rodriguez, Michael C.Item School and Community Sports Participation and Positive Youth Development: A Multilevel Analysis(2018-04) Nickodem, Kyle; Van Boekel, Martin; Kang, Youngsoon; Chavez, Carlos; Rodriguez, Michael C.Item Social-Emotional Learning ICCs and Associations with School Composition and Achievement(2019-04) Nickodem, Kyle; Rodriguez, Michael C; Lamm, RikItem Use of Aggregated Covariates In Propensity Score Analysis of Clustered Data(2020-06) Nickodem, KylePropensity score methods can be used to reduce selection bias and improve causal inferencing with nonrandomized data. However, there is little guidance for implementing a propensity score analysis when treatment exposure is a property of clusters rather than subjects. For example, education policies and practices are often implemented by school or district rather than by individual student. The three studies in this dissertation strive to clarify procedural quandaries for a propensity score analysis with cluster-level treatment exposure and subject-level outcomes. Additionally, omission of a true confounder from a propensity score analysis can bias treatment effect estimation. My dissertation also explores the utility of aggregated covariates as replacements for missing true cluster-level confounders. The first simulation study compared four procedures for generating aggregated covariates. The results highlight that: 1) researchers need to verify the comparability of generated samples to real world contexts; 2) a propensity score analysis with cluster-level treatment exposure requires at least 60 clusters. The second simulation compared covariate balance and treatment effect estimation when appraising treatment exposure by subjects or by clusters and including aggregated covariates of varying quality. Treatment appraisal by subjects outperformed appraisal by clusters under certain conditions. When highly correlated (r = .92 - .98) with the missing true confounders, aggregated covariates were viable replacements. The last study applied the guidance from the simulations to statewide survey data. The investigation found no association between the presence of a school resource officer and students’ social-emotional well-being and academic performance. A critical caveat is the results may not generalize to student populations that have historically been targeted by discrimination and school violence.Item Using Developmental Skills & Supports to Improve Youth Outcomes(2016-06) Rodriguez, Michael C.; Palma, Jose; Van Boekel, Martin; Bulut, Okan; Nickodem, Kyle; Kang, Youngsoon; Vue, Kory; Jang, Yoo Jeong