A Multidimensional Perspective of College Readiness: Relating Student and School Characteristics to Performance on the ACT®. ACT Research Report Series 2015 (6)

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Autor/inn/en	McNeish, Daniel M.; Radunzel, Justine; Sanchez, Edgar
Institution	ACT, Inc.
Titel	A Multidimensional Perspective of College Readiness: Relating Student and School Characteristics to Performance on the ACT®. ACT Research Report Series 2015 (6)
Quelle	(2015), (56 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	College Readiness; Student Characteristics; Institutional Characteristics; College Entrance Examinations; High Schools; High School Seniors; Questionnaires; Regression (Statistics); Statistical Analysis; Factor Analysis; Scores; Student Experience; Study Habits; Parent Participation; Academic Aspiration; Occupational Aspiration; College Credits; ACT Assessment + Suchen Sie Ihr Suchwort? Aufnahmeprüfung; High school; Oberschule; Fragebogen; Regression; Regressionsanalyse; Statistische Analyse; Faktorenanalyse; Studienerfahrung; Study behavior; Study behaviour; Studienverhalten; Elternmitwirkung; Berufsneigung; Berufsziel; College; Colleges; Achievement; Performance; Anrechnung; Hochschule; Fachhochschule; Leistung; Assessment; Eignungsprüfung; Eignungstest; Hochschulzulassung
Abstract	This study examined the contributions of students' noncognitive characteristics toward explaining performance on the ACT® test, over and above traditional predictors such as high school grade point average (HSGPA), coursework taken, and school characteristics. The sample consisted of 6,440 high school seniors from 4,541 schools who took the ACT in the fall of 2012 and completed an online questionnaire about their high school experience, study and work habits, parental involvement, educational and occupational plans and goals, and college courses taken and/or credits earned in high school. Twelve percent of the total sample responded and met the study inclusion criteria. A blockwise regression model with cluster-robust standard errors was used to assess the relationships between cognitive and noncognitive characteristics with ACT scores. The total variance in ACT scores accounted for by available student and school characteristics ranged from 44% (reading) to 61% (Composite). HSGPA explained the most variance (20% to 31%), high school coursework taken explained an additional 8% (reading) to 17% (mathematics), and high school characteristics accounted for an additional 7% to 9% of the variance in ACT scores. After accounting for these traditional predictors, students' noncognitive factors explained between 4% and 7% of the variance in ACT scores. These noncognitive characteristics included indicators of needing help in improving subject-related academic skills, educational plans, parental involvement, perceptions of education, and taking the ACT test prior to senior year. Socioeconomic status and other demographic characteristics accounted for less variance in ACT scores (4% or below), after adjusting for other student and school characteristics. Moreover, adjusted mean score differences among racial/ethnic, family income, and parental education groups were substantially reduced, compared to unadjusted group differences. Students' noncognitive characteristics were also highly related to HSGPA. Study findings suggest that noncognitive characteristics affect ACT scores directly as well as through their impact on HSGPA. In light of the growing interest for evaluating both cognitive and noncognitive measures of college readiness, the results from this study may help to provide better context and guidance for the interpretation of college readiness measures. These findings also contribute to a more holistic understanding of college readiness. This perspective is important in order to better understand the multidimensional nature of college and career readiness and subsequent success. Three appendices are included: (1) Items that loaded with magnitude above 0.40 on components (table); (2) Additional Detail on Model-Based and Design-Based Methods to Account for Clustering; and (3) More Detail on Least Angle Regression (LARS). (As Provided).
Anmerkungen	ACT, Inc. 500 ACT Drive, P.O. Box 168, Iowa City, IA 52243-0168. Tel: 319-337-1270; Web site: http://www.act.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01