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Sinharay, Sandip – Grantee Submission, 2018
Tatsuoka (1984) suggested several extended caution indices and their standardized versions that have been used as person-fit statistics by researchers such as Drasgow, Levine, and McLaughlin (1987), Glas and Meijer (2003), and Molenaar and Hoijtink (1990). However, these indices are only defined for tests with dichotomous items. This paper extends…
Descriptors: Test Format, Goodness of Fit, Item Response Theory, Error Patterns
Davison, Mark L.; Biancarosa, Gina; Carlson, Sarah E.; Seipel, Ben; Liu, Bowen – Assessment for Effective Intervention, 2018
The computer-administered Multiple-Choice Online Causal Comprehension Assessment (MOCCA) for Grades 3 to 5 has an innovative, 40-item multiple-choice structure in which each distractor corresponds to a comprehension process upon which poor comprehenders have been shown to rely. This structure requires revised thinking about measurement issues…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Pilot Projects, Measurement
Davison, Mark L.; Biancarosa, Gina; Carlson, Sarah E.; Seipel, Ben; Liu, Bowen – Grantee Submission, 2018
The computer-administered Multiple-Choice Online Causal Comprehension Assessment (MOCCA) for Grades 3 to 5 has an innovative, 40-item multiple-choice structure in which each distractor corresponds to a comprehension process upon which poor comprehenders have been shown to rely. This structure requires revised thinking about measurement issues…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Pilot Projects, Measurement
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
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
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
Yu, Lei; Moses, Tim; Puhan, Gautam; Dorans, Neil – ETS Research Report Series, 2008
All differential item functioning (DIF) methods require at least a moderate sample size for effective DIF detection. Samples that are less than 200 pose a challenge for DIF analysis. Smoothing can improve upon the estimation of the population distribution by preserving major features of an observed frequency distribution while eliminating the…
Descriptors: Test Bias, Item Response Theory, Sample Size, Evaluation Criteria

Meijer, Rob R. – Applied Psychological Measurement, 1994
Through simulation, the power of the U3 statistic was compared with the power of one of the simplest person-fit statistics, the sum of the number of Guttman errors. In most cases, a weighted version of the latter statistic performed as well as the U3 statistic. (SLD)
Descriptors: Error Patterns, Item Response Theory, Nonparametric Statistics, Power (Statistics)
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