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Yu, Chong Ho; Douglas, Samantha; Lee, Anna; An, Min – Practical Assessment, Research & Evaluation, 2016
This paper aims to illustrate how data visualization could be utilized to identify errors prior to modeling, using an example with multi-dimensional item response theory (MIRT). MIRT combines item response theory and factor analysis to identify a psychometric model that investigates two or more latent traits. While it may seem convenient to…
Descriptors: Visualization, Item Response Theory, Sample Size, Correlation
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Kogar, Esin Yilmaz; Kelecioglu, Hülya – Journal of Education and Learning, 2017
The purpose of this research is to first estimate the item and ability parameters and the standard error values related to those parameters obtained from Unidimensional Item Response Theory (UIRT), bifactor (BIF) and Testlet Response Theory models (TRT) in the tests including testlets, when the number of testlets, number of independent items, and…
Descriptors: Item Response Theory, Models, Mathematics Tests, Test Items
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Bensley, D. Alan; Lilienfeld, Scott O. – Teaching of Psychology, 2015
Studies of psychological misconceptions have often used tests with methodological shortcomings, unknown psychometric properties, and ad hoc methods for identifying misconceptions, creating problems for estimating frequencies of specific misconceptions. To address these problems, we developed a new test, the Test of Psychological Knowledge and…
Descriptors: Misconceptions, Psychological Patterns, Psychometrics, Attitude Measures
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Inoue, Chihiro – Language Learning Journal, 2016
The constructs of complexity, accuracy and fluency (CAF) have been used extensively to investigate learner performance on second language tasks. However, a serious concern is that the variables used to measure these constructs are sometimes used conventionally without any empirical justification. It is crucial for researchers to understand how…
Descriptors: Comparative Analysis, Syntax, Accuracy, Task Analysis
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Socha, Alan; DeMars, Christine E. – Educational and Psychological Measurement, 2013
Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for…
Descriptors: Sample Size, Test Length, Correlation, Test Format
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Li, Ying; Rupp, Andre A. – Educational and Psychological Measurement, 2011
This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…
Descriptors: Test Length, Item Response Theory, Statistical Analysis, Error Patterns
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Christ, Theodore J.; Zopluoglu, Cengiz; Monaghen, Barbara D.; Van Norman, Ethan R. – Journal of School Psychology, 2013
Curriculum-Based Measurement of Oral Reading (CBM-R) is used to collect time series data, estimate the rate of student achievement, and evaluate program effectiveness. A series of 5 studies were carried out to evaluate the validity, reliability, precision, and diagnostic accuracy of progress monitoring across a variety of progress monitoring…
Descriptors: Reading Skills, Response to Intervention, Curriculum Based Assessment, Oral Reading
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection