ACT research report series: Relationships between the noncognitive characteristics, high school course work and grades, and test scores for ACT-tested students

Noble, Julie; Davenport, Mark; Schiel, Jeff; Pommerich, Mary

doi:10.1037/e427722008-001

Cited by 12 publications

(30 citation statements)

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“…Earlier studies by Willingham (1985), Young and Barrett (1992), Cabrera, Nora and Castaneda (1993), Mouw and Kkanna (1993), Eimers and Pike (1997) and Noble et al (1999) provide evidence that intellectual variables can be useful predictors of overall academic success for college students (not necessarily business majors).The intellectual variables include ACT and SAT scores, high school grades, and various measures of writing, quantitative and technology skills. Studies that focus on the intellectual variables of business majors include Brookshire and Palocsay (2005) and Smith and Schumacher (2006).…”

Section: Introductionmentioning

confidence: 99%

Does the Performance on Principles of Economics Courses Affect the Overall Academic Success of Undergraduate Business Majors?

Rhodd¹,

Schrouder²,

Allen³

2009

International Review of Economics Education

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Using a sample of 1,339 graduates from an accredited business school and the maximum likelihood technique, this paper explores the relationship between overall academic success and performance on the Principles of Economics courses.The estimated model, which also includes some demographic variables, shows that the rank of professors teaching the course, age of students, and the number of credits earned do not influence business majors' overall academic success. However, the grades earned on the Principles of Economics courses, gender, ethnicity, the major in which the student is enrolled, the number of years the student takes to graduate, as well as whether or not the student is completing a minor significantly affect the overall academic success or the final GPA of business majors.

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Section: Introductionmentioning

confidence: 99%

Does the Performance on Principles of Economics Courses Affect the Overall Academic Success of Undergraduate Business Majors?

Rhodd¹,

Schrouder²,

Allen³

2009

International Review of Economics Education

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“…The inclusion of variables from the educational institution (institution effect), as well as the use of hierarchical linear models, increases the variance accounted for but not beyond 6-7% (Pike & Saupe). The inclusion of noncognitive factors (motivation, study habits) does not improve the percentage of explained variance (Mouw & Khanna, 1993), presumably due to the strong relation between psychosocial variables and prior achievement (Noble, Davenport, Schiel, & Pommerich, 1999). In fact, in a study in which previous achievement was not included in the models, Clifton et al (2004) practically only explained 13.8% of the variance of mean grades from the demographic variables and pedagogical factors.…”

Section: Linear Models Versus Logistic Modelsmentioning

confidence: 99%

Prediction of University Students' Academic Achievement by Linear and Logistic Models

Ayán

García

2008

Span. J. Psychol.

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University students' academic achievement measured by means of academic progress is modeled through linear and logistic regression, employing prior achievement and demographic factors as predictors. The main aim of the present paper is to compare results yielded by both statistical procedures, in order to identify the most suitable approach in terms of goodness of fit and predictive power. Grades awarded in basic scientific courses and demographic variables were entered into the models at the first step. Two hypotheses are proposed: (a) Grades in basic courses as well as demographic factors are directly related to academic progress, and (b) Logistic regression is more appropriate than linear regression due to its higher predictive power. Results partially confirm the first prediction, as grades are positively related to progress. However, not all demographic factors considered proved to be good predictors. With regard to the second hypothesis, logistic regression was shown to be a better approach than linear regression, yielding more stable estimates with regard to the presence of ill-fitting patterns. Keywords: logistic versus linear regression, prediction, credits, academic achievement, advance in careerSe estudia el efecto de dos tipos de factores sobre el rendimiento de estudiantes universitarios: variables académicas de rendimiento previo y variables demográficas, mediante modelos lineales y logísticos. El principal objetivo del trabajo es comparar los resultados obtenidos con ambas técnicas estadísticas, para determinar cuál de ellos es más adecuado en términos de ajuste y capacidad predictiva cuando se pretende explicar y predecir el rendimiento académico, en función de variables de rendimiento previo y factores sociodemográficos. Como medida del rendimiento a predecir se empleó el avance en la carrera. Las hipótesis planteadas son: 1) El avance está directamente relacionado con las calificaciones en materias básicas de primer año y con variables demográficas y 2) Los modelos logísticos son más adecuados que los modelos lineales, ya que presentan mayor capacidad predictiva. Los resultados permiten confirmar la primera hipótesis en su primera parte, ya que el rendimiento previo está directa y significativamente asociado al avance en la carrera. Pero se cumple de forma parcial por lo que se refiere al efecto factores demográficos. Con respecto a la segunda hipótesis, la regresión logística mostró ser más adecuada que la lineal, pues arroja estimaciones más estables en relación con la presencia de patrones de mal ajuste. Palabras clave: regresión logística versus regresión lineal, predicción, créditos, rendimiento académico, avance en la carrera

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“…Students who earned higher grades in the recommended coursework also produced higher ACT scores; hence, higher student GPAs are associated with higher ACT scores (ACT, 2014;Noble et al, 1999aNoble et al, , 1999bNoble & McNabb, 1989).…”

Section: Jcpsmentioning

confidence: 99%

“…Students who completed or indicated they would be completing the minimal suggested coursework scored 2-3 scale points higher than students who did not complete the recommended coursework (ACT, 2014). Additionally, ACT (2014) Noble, Davenport, Schiel, and Pommerich (1999b) established that the variances in ACT exam scores were mostly explained by coursework completed and student GPA (R² = .29 to .52). More specifically, completion of high school mathematics and science courses is highly associated to an increase in ACT scores (ACT, 2014;Noble, Davenport, Schiel, & Pommerich, 1999a, 1999bNoble & McNabb, 1989;Schiel, Pommerich, & Noble, 1996).…”

Section: Jcpsmentioning

confidence: 99%

“…Additionally, ACT (2014) Noble, Davenport, Schiel, and Pommerich (1999b) established that the variances in ACT exam scores were mostly explained by coursework completed and student GPA (R² = .29 to .52). More specifically, completion of high school mathematics and science courses is highly associated to an increase in ACT scores (ACT, 2014;Noble, Davenport, Schiel, & Pommerich, 1999a, 1999bNoble & McNabb, 1989;Schiel, Pommerich, & Noble, 1996). Each high school mathematics course completed was associated with a 2-point gain in ACT mathematics scores, and each additional science course was associated with gains of 1.26-1.58 in science (Noble & McNabb, 1989).…”

Section: Jcpsmentioning

confidence: 99%

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The influence of student, neighborhood, and school factors on college readiness : an examination of factors combined as a result of implementing a race- and socioeconomically-based student assignment plan.

Herberger¹

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The influence of student, neighborhood, and school factors on college readiness : an examination of factors combined as a result of implementing a race-and socioeconomically-based student assignment plan. Gregory Paul Herberger University of LouisvilleFollow this and additional works at: https://ir.library.louisville.edu/etd Part of the Educational Assessment, Evaluation, and Research Commons, and the Educational Leadership CommonsThis Doctoral Dissertation is brought to you for free and open access by ThinkIR: The University of Louisville's Institutional Repository. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of ThinkIR: The University of Louisville's Institutional Repository. This title appears here courtesy of the author, who has retained all other copyrights. For more information, please contact thinkir@louisville.edu. Recommended CitationHerberger, Gregory Paul, "The influence of student, neighborhood, and school factors on college readiness : an examination of factors combined as a result of implementing a race-and socioeconomically-based student assignment plan." (2016). the district's race-based student assignment and implemented a race-and socioeconomicbased student assignment plan. The purpose of this study was to examine students' backgrounds and school composition factors within a race-and socioeconomic-based

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ACT research report series: Relationships between the noncognitive characteristics, high school course work and grades, and test scores for ACT-tested students

Cited by 12 publications

References 0 publications

Does the Performance on Principles of Economics Courses Affect the Overall Academic Success of Undergraduate Business Majors?

Does the Performance on Principles of Economics Courses Affect the Overall Academic Success of Undergraduate Business Majors?

Prediction of University Students' Academic Achievement by Linear and Logistic Models

The influence of student, neighborhood, and school factors on college readiness : an examination of factors combined as a result of implementing a race- and socioeconomically-based student assignment plan.

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