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Nájera, Pablo; Sorrel, Miguel A.; Abad, Francisco José – Educational and Psychological Measurement, 2019
Cognitive diagnosis models (CDMs) are latent class multidimensional statistical models that help classify people accurately by using a set of discrete latent variables, commonly referred to as attributes. These models require a Q-matrix that indicates the attributes involved in each item. A potential problem is that the Q-matrix construction…
Descriptors: Matrices, Statistical Analysis, Models, Classification
Raykov, Tenko; Marcoulides, George A.; Li, Tenglong – Educational and Psychological Measurement, 2016
A method for evaluating the validity of multicomponent measurement instruments in heterogeneous populations is discussed. The procedure can be used for point and interval estimation of criterion validity of linear composites in populations representing mixtures of an unknown number of latent classes. The approach permits also the evaluation of…
Descriptors: Validity, Measures (Individuals), Classification, Evaluation Methods
von Davier, Matthias; Tyack, Lillian; Khorramdel, Lale – Educational and Psychological Measurement, 2023
Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our…
Descriptors: Scoring, Networks, Artificial Intelligence, Elementary Secondary Education
Park, Ryoungsun; Kim, Jiseon; Chung, Hyewon; Dodd, Barbara G. – Educational and Psychological Measurement, 2017
The current study proposes novel methods to predict multistage testing (MST) performance without conducting simulations. This method, called MST test information, is based on analytic derivation of standard errors of ability estimates across theta levels. We compared standard errors derived analytically to the simulation results to demonstrate the…
Descriptors: Testing, Performance, Prediction, Error of Measurement
Nicole B. Kersting; Bruce L. Sherin; James W. Stigler – Educational and Psychological Measurement, 2014
In this study, we explored the potential for machine scoring of short written responses to the Classroom-Video-Analysis (CVA) assessment, which is designed to measure teachers' usable mathematics teaching knowledge. We created naïve Bayes classifiers for CVA scales assessing three different topic areas and compared computer-generated scores to…
Descriptors: Scoring, Automation, Video Technology, Teacher Evaluation
Gable, Robert K.; Ludlow, Larry H.; McCoach, D. Betsy; Kite, Stacey L. – Educational and Psychological Measurement, 2011
The development of the Survey of Knowledge of Internet Risk and Internet Behavior is described. A total of 1,366 Grades 7 and 8 male and female students from an urban, suburban, and rural school offered agree-disagree responses to 26 statements defining one Knowledge Scale and five behavior dimensions. Literature-based support is presented for…
Descriptors: Content Validity, Construct Validity, Risk, Measures (Individuals)
Choi, Namok; Fuqua, Dale R.; Newman, Jody L. – Educational and Psychological Measurement, 2008
Pedhazur and Tetenbaum speculated that factor structures from self-ratings of the Bem Sex-Role Inventory (BSRI) personality traits would be different from factor structures from desirability ratings of the same traits. To explore this hypothesis, both desirability ratings of BSRI traits (both "for a man" and "for a woman") and…
Descriptors: Personality Traits, Sex Role, Gender Discrimination, Self Evaluation (Individuals)

Schriesheim, Chester; Schriesheim, Janet – Educational and Psychological Measurement, 1974
Descriptors: Classification, Intervals, Questionnaires, Responses
Yang, Xiangdong – Educational and Psychological Measurement, 2007
This article investigates several methods of identifying individual guessers from their response data. Both the posterior probability method and the likelihood ratio method are based on the two-state mixture modeling approach to response times. The accuracy method is based on response accuracy data. Results from the simulation study showed that…
Descriptors: Probability, Simulation, Test Items, Models

Howell, Margaret A. – Educational and Psychological Measurement, 1971
Descriptors: Classification, Competitive Selection, Models, Norms

De Corte, Wilfried – Educational and Psychological Measurement, 2000
Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)
Descriptors: Classification, Correlation, Estimation (Mathematics), Multivariate Analysis

Brennan, Robert L.; Prediger, Dale J. – Educational and Psychological Measurement, 1981
This paper considers some appropriate and inappropriate uses of coefficient kappa and alternative kappa-like statistics. Discussion is restricted to the descriptive characteristics of these statistics for measuring agreement with categorical data in studies of reliability and validity. (Author)
Descriptors: Classification, Error of Measurement, Mathematical Models, Test Reliability

McQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1975
Develops and illustrates a method for clustering hierarchically the interrelationships between many persons, as represented in a matrix of a thousand by a thousand. (RC)
Descriptors: Classification, Cluster Grouping, Matrices, Measurement Techniques

Smith, I. Leon – Educational and Psychological Measurement, 1971
Descriptors: Academic Achievement, Classification, Cognitive Processes, Grade 11

Mcquitty, Louis L.; Frary, Jewel M. – Educational and Psychological Measurement, 1971
Discussion of a method of classification which attempts to use the particular set of indices of association which produce the most reliable and valid solution. (PR)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Criteria
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