Statistical Methods Employed in "American Educational Research Journal," "Educational Researcher," and "Review of Educational Research" from 1978 to 1987

Elmore, Patricia B.; Woehlke, Paula

doi:10.2307/1175043

Cited by 22 publications

(33 citation statements)

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“…We found much of this information encouraging because it indicated widespread professional agreement in the way that doctoral students majoring in quantitative methods are trained in statistics. For example, almost all of the surveyed programs emphasized traditional data-analytic procedures, a result that agrees with the work of Elmore andWoehlke (1988, 1996). There was also evidence that most respondents thought recent graduates were as well or better trained than earlier graduates.…”

Section: Discussionsupporting

confidence: 71%

“…A number of such content analyses have been performed over the years. Elmore andWoehlke (1988, 1996) Elmore and Woehlke (1996) found that the predominant statistical method used was ANOVA/ANCOVA, which was used in 64 out of 245 articles that they coded. The next most frequent procedures were multivariate procedures, which were used in 12% of the articles, followed by bivariate correlation (12%), multiple regression/correlation (11%), ttests (9%), structural equation modeling (9%), and nonparametrics (8%).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Training Doctoral Students in Educational Statistics in the United States: A National Survey

Curtis

Harwell

1998

Journal of Statistics Education

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Although numerous research studies have focused on issues related to the teaching of statistics, few studies have focused on the training of people who may become statistics teachers. The purpose of this study was to examine doctoral students' preparation in statistics in the field of education. A national survey was conducted of twenty-seven quantitative methods (QM) programs. One QM professor from each program was identified and asked to describe and evaluate the training of QM and non-QM doctoral students at his or her institution. The vast majority of professors indicated that most or all of the students in their QM programs received training in the "old standard" procedures --ANOVA, multiple regression, and traditional multivariate procedures, whereas fewer than half of the professors indicated that most or all of their QM students received training in more recent procedures such as bootstrapping and multilevel models. Professors were also asked to rate the skills of their QM students in areas such as mathematical statistics and computing on a scale from "Weak" to "Strong." Most professors gave high ratings to their QM students' skills with statistical packages, but gave much more mixed ratings of their QM students' training in mathematical statistics. Nearly half of the professors thought that most of their QM students could have benefited from one or two additional statistics courses. Results are discussed in terms of training future doctoral students.

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Section: Discussionsupporting

confidence: 71%

Section: Background and Literature Reviewmentioning

confidence: 99%

Training Doctoral Students in Educational Statistics in the United States: A National Survey

Curtis

Harwell

1998

Journal of Statistics Education

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“…Benzer şekilde bu bulgular Willson (1980) 'ın AERJ'de 1969'ın AERJ'de -1978 yılları arasında yayımlanan makaleleri incelediği ve ANOVA, ANCOVA, korelasyon, çoklu regresyon, diskriminant analizi ve MANOVA'nın sıklıkla kullanıldığını saptadığı çalışmanın sonuçları ile desteklenmektedir. Goodwin ve Goodwin (1985a, 1985b Yurt dışında eğitim bilimleri alanında yapılan bazı makale inceleme çalışmalarında (Elmore ve Woehlke, 1998;Goodwin ve Goodwin, 1985a;Hsu, 2005;Kieffer vd., 2001;Willson, 1980) seçilen dergilerle, bu çalışma kapsamında seçilen AERJ dergisinin ortak olması eğilimlerinde ortaya konulmasını sağlamaktadır. Bu bağlamda, sık kullanılan veri analiz yöntemlerindeki eğilimlerin 1970'lerden 2000'li yıllara kadar benzer olduğu görülmektedir.…”

Section: Tartışma Sonuç Ve öNerilerunclassified

“…Uluslararası alan yazında ise çalışma kapsamında en yüksek etki faktörüne sahip dergilerden bazılarının (Educational Researcher [ER] ve American Educational Research Journal [AERJ]) incelendiği çalışmalar vardır (Elmore ve Woehlke, 1998;Goodwin ve Goodwin, 1985a;Hsu, 2005;Kieffer vd., 2001;Willson, 1980). Ancak, bu araştırmalar dergilerin 1970-2000 yılları arasındaki yayınları ile sınırlıdır.…”

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Content Analysis of Papers Published in Educational Journals with High Impact Factors

Yalçın

Yavuz

Dibek

2016

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“…For instance, Goodwin and Goodwin (1985) reviewed the publications that appeared in the American Educational Research Journal (AERJ) during 1979-1983, and Elmore and Woehlke (1998 Psychology during 1988, Kieffer, Reese, and Thompson (2001 found a predominance of univariate techniques compared with multivariate techniques (a ratio of 3:1 in AERJ), observing that the use of each one of the techniques was stable during those 10 years.…”

Section: Technical Models and Statisticsmentioning

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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Statistical Methods Employed in "American Educational Research Journal," "Educational Researcher," and "Review of Educational Research" from 1978 to 1987

Cited by 22 publications

References 0 publications

Training Doctoral Students in Educational Statistics in the United States: A National Survey

Training Doctoral Students in Educational Statistics in the United States: A National Survey

Content Analysis of Papers Published in Educational Journals with High Impact Factors

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

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