Education Commons^™

Above-Level Test Item Functioning Across Examinee Age Groups, Russell Warne, Kristine Doty, Anne Marie Malbica, Victor Angeles, Scott Innes, Jared Hall, Kelli Masterson-Nixon

Russell T Warne

Five Reasons To Put The G Back Into Giftedness: An Argument For Applying The Cattell–Horn–Carroll Theory Of Intelligence To Gifted Education Research And Practice, Russell Warne

Russell T Warne

The Advanced Placement Program's Impact On Academic Achievement, Russell T. Warne, Braydon Anderson

Russell T Warne

Measuring The Outliers: An Introduction To Out-Of-Level Testing With High-Achieving Students, Karen Rambo-Hernandez, Russell Warne

Russell T Warne

Out-of-level testing is an underused strategy for addressing the needs of students who score in the extremes, and when used wisely, it could provide educators with a much more accurate picture of what students know. Out-of-level testing has been shown to be an effective assessment strategy with high-achieving students; however, out-of-level testing has not been shown to work well with low-achieving students. This article provides a brief history of out-of-level testing, along with guidelines for using it.

Using Above-Level Testing To Track Growth In Academic Achievement In Gifted Students, Russell Warne

Russell T Warne

Above-level testing is the practice of administering aptitude or academic achievement tests that are designed for typical students in higher grades or older age-groups to gifted or high-achieving students. Although widely accepted in gifted education, above-level testing has not been subject to careful psychometric scrutiny. In this study, I examine reliability data, growth trajectories, distributions, and group differences of above-level test scores obtained from the Iowa Tests of Basic Skills and Iowa Tests of Educational Development. Two hundred twenty-four middle school students participated in this study. All participants were tested at least 1 time for an overall total of 435 …

Evaluating A Proposed Modification Of The Guttman Rule For Determining The Number Of Factors In An Exploratory Factor Analysis, Russell T. Warne, Ross Larsen

Russell T Warne

What The Joint Admission Medical Program (Jamp) Can Do For Texas Physicians; What Texas Physicians Can Do For Jamp - See More At: Http://Www.Texmed.Org/Aug12journal/#Sthash.M6pv8cjh.Dpuf, Alan Podawiltz, James Richardson, Wallace Gleason, Kathleen Fallon, David Jones, Elizabeth Peck, Jeffrey Rabek, Manuel Schydlower, William Thomson, Russell Warne, Budge Mabry, Paul Hermesmeyer, Quentin Smith

Russell T Warne

Texas faces health challenges requiring a physician workforce with understanding of a broad range of issues – including the role of culture, income level, and health beliefs – that affect the health of individuals and communities. Building on previous successful physician workforce "pipeline" efforts, Texas established the Joint Admission Medical Program (JAMP), a first-of-its-kind program to encourage access to medical education by Texans who are economically disadvantaged. The program benefits those from racial and ethnic minority groups and involves all 31 public and 34 private Texas undergraduate colleges and universities offering life science degrees, as well as all 9 medical …

Statistical Methods Used In Gifted Education Journals, 2006-2010, Russell Warne, Maria Lazo, Tami Ramos, Nicola Ritter

Russell T Warne

This article describes the statistical methods used in quantitative and mixed methods articles between 2006 and 2010 in five gifted education research journals. Results indicate that the most commonly used statistical methods are means (85.9% of articles), standard deviations (77.8%), Pearson’s r (47.8%), χ2 (32.2%), ANOVA (30.7%), t tests (30.0%), and MANOVA (23.0%). Approximately half (53.3%) of the articles included reliability reports for the data at hand; Cronbach’s alpha was the most commonly reported measure of reliability (41.5%). Some discussions of best statistical practice and implications for the field of gifted education are included.

A Reliability Generalization Of The Overexcitability Questionnaire-Two (Oeqii), Russell Warne

Russell T Warne

Reliability generalization (RG) is a meta-analysis that combines and synthesizes reliability coefficients from different studies to ascertain the average observed reliability across studies. An RG study was conducted on previously reported data from 16 samples of the Overexcitability Questionnaire–Two (OEQII) with a combined N of 5,275. Cronbach’s alpha was found to be consistently higher on all OEQII subscales when scale variance was high and the sample consisted of adults. Sample size, gender composition of the sample, number of items from the subscale used, and location of sample (United States or a different county) had varying effects on observed alpha levels …

Beyond Multiple Regression: Using Commonality Analysis To Better Understand R2 Results, Russell Warne

Russell T Warne

Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated independent variables. Commonality analysis—heretofore rarely used in gifted education research—is a statistical method that partitions the explained variance of a dependent variable into nonoverlapping parts according to the independent variable(s) that are related to each portion. This Methodological Brief includes an example of commonality analysis and equations for researchers who wish to conduct their …

An Investigation Of Measurement Invariance Across Genders On The Overexcitability Questionnaire-Two (Oeqii), Russell Warne

Russell T Warne

The Overexcitability Questionnaire–Two (OEQII) is a quantitative instrument for assessing overexcitabilities as they are described in Dabrowski’s theory of positive disintegration. This article uses multigroup confirmatory factor analysis to examine the measurement invariance of OEQII scores across genders. Results indicate that raw OEQII scores cannot be compared across genders. Caution should be used in interpreting OEQII scores.