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Articles 1 - 10 of 10
Full-Text Articles in Psychology
Measuring The Outliers: An Introduction To Out-Of-Level Testing With High-Achieving Students, Karen Rambo-Hernandez, Russell 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.
Exploring The Various Interpretations Of "Test Bias", Russell Warne, Myeongsun Yoon, Chris Price
Exploring The Various Interpretations Of "Test Bias", Russell Warne, Myeongsun Yoon, Chris Price
Russell T Warne
Test bias is a hotly debated topic in society, especially as it relates to diverse groups of examinees who often score low on standardized tests. However, the phrase “test bias” has a multitude of interpretations that many people are not aware of. In this article, we explain five different meanings of “test bias” and summarize the empirical and theoretical evidence related to each interpretation. The five meanings are as follows: (a) mean group differences, (b) differential predictive validity, (c) differential item functioning, (d) differing factor structures of tests, and (e) unequal consequences of test use for various groups. We explain …
Using Above-Level Testing To Track Growth In Academic Achievement In Gifted Students, Russell Warne
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 …
Statistical Methods Used In Gifted Education Journals, 2006-2010, Russell Warne, Maria Lazo, Tami Ramos, Nicola Ritter
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.
Managing Clustered Data Using Hierarchical Linear Modeling, Russell Warne
Managing Clustered Data Using Hierarchical Linear Modeling, Russell Warne
Russell T Warne
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence assumption and lead to correct analysis of data, yet it is rarely used in nutrition research. The purpose of this viewpoint is to illustrate the benefits of hierarchical linear modeling within a nutrition research context.
An Introduction To Item Response Theory For Health Behavior Researchers, Russell Warne
An Introduction To Item Response Theory For Health Behavior Researchers, Russell Warne
Russell T Warne
OBJECTIVE:
To introduce item response theory (IRT) to health behavior researchers by contrasting it with classical test theory and providing an example of IRT in health behavior.
METHOD:
Demonstrate IRT by fitting the 2PL model to substance-use survey data from the Adolescent Health Risk Behavior questionnaire (n=1343 adolescents).
RESULTS:
An IRT 2PL model can produce viable substance use scores that differentiate different levels of substance use, resulting in improved precision and specificity at the respondent level.
CONCLUSION:
IRT is a viable option for health researchers who want to produce high-quality scores for unidimensional constructs. The results from our example-although not …
A Reliability Generalization Of The Overexcitability Questionnaire-Two (Oeqii), Russell Warne
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
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
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.
Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne
Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne
Russell T Warne
Exploratory factor analysis (EFA) has become a common procedure in educational and psychological research. In the course of performing an EFA, researchers often base the decision of how many factors to retain on the eigenvalues for the factors. However, many researchers do not realize that eigenvalues, like all sample statistics, are subject to sampling error, which means that confidence intervals (CIs) can be estimated for each eigenvalue. In the present article, we demonstrate two methods of estimating CIs for eigenvalues: one based on the mathematical properties of the central limit theorem, and the other based on bootstrapping. References to appropriate …