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Wilcox, Rand R. – 1979
In the past, several latent structure models have been proposed for handling problems associated with measuring the achievement of examinees. Typically, however, these models describe a specific examinee in terms of an item domain or they describe a few items in terms of a population of examinees. In this paper, a model is proposed which allows a…
Descriptors: Achievement Tests, Guessing (Tests), Mathematical Models, Multiple Choice Tests
Peer reviewed Peer reviewed
Drasgow, Fritz; And Others – Applied Psychological Measurement, 1989
Multilinear formula scoring (MFS) is reviewed, with emphasis on estimating option characteristic curves (OCSs). MFS was used to estimate OCSs for the arithmetic reasoning subtest of the Armed Services Vocational Aptitude Battery for 2,978 examinees. A second analysis obtained OCSs for simulated data. The use of MFS is discussed. (SLD)
Descriptors: Estimation (Mathematics), Mathematical Models, Multiple Choice Tests, Scores
Peer reviewed Peer reviewed
Burton, Richard F. – Assessment & Evaluation in Higher Education, 2001
Describes four measures of test unreliability that quantify effects of question selection and guessing, both separately and together--three chosen for immediacy and one for greater mathematical elegance. Quantifies their dependence on test length and number of answer options per question. Concludes that many multiple choice tests are unreliable…
Descriptors: Guessing (Tests), Mathematical Models, Multiple Choice Tests, Objective Tests
Peer reviewed Peer reviewed
Divgi, D. R. – Journal of Educational Measurement, 1986
This paper discusses various issues involved in using the Rasch Model with multiple-choice tests and questions the suitability of this model for multiple-choice items. Results of some past studies supporting the model are shown to be irrelevant. The effects of the model's misfit on test equating are demonstrated. (Author JAZ)
Descriptors: Equated Scores, Goodness of Fit, Latent Trait Theory, Mathematical Models
Hutchinson, T. P. – 1984
One means of learning about the processes operating in a multiple choice test is to include some test items, called nonsense items, which have no correct answer. This paper compares two versions of a mathematical model of test performance to interpret test data that includes both genuine and nonsense items. One formula is based on the usual…
Descriptors: Foreign Countries, Guessing (Tests), Mathematical Models, Multiple Choice Tests
Peer reviewed Peer reviewed
Hutchinson, T. P. – Contemporary Educational Psychology, 1986
Qualitative evidence for the operation of partial knowledge is given by two findings. First, performance when second and subsequent choices are made is above the chance level. Second, it is positively related to first choice performance. A number of theories incorporating partial knowledge are compared quantitatively. (Author/LMO)
Descriptors: Difficulty Level, Feedback, Goodness of Fit, Mathematical Models
Wilcox, Rand R. – 1981
These studies in test adequacy focus on two problems: procedures for estimating reliability, and techniques for identifying ineffective distractors. Fourteen papers are presented on recent advances in measuring achievement (a response to Molenaar); "an extension of the Dirichlet-multinomial model that allows true score and guessing to be…
Descriptors: Achievement Tests, Criterion Referenced Tests, Guessing (Tests), Mathematical Models
Powell, J. C. – 1980
A multi-faceted model for the selection of answers for multiple-choice tests was developed from the findings of a series of exploratory studies. This model implies that answer selection should be curvilinear. A series of models were tested for fit using the chi square procedure. Data were collected from 359 elementary school students ages 9-12.…
Descriptors: Elementary Education, Foreign Countries, Goodness of Fit, Guessing (Tests)
Ryan, Joseph P.; Hamm, Debra W. – 1976
A procedure is described for increasing the reliability of tests after they have been given and for developing shorter but more reliable tests. Eight tests administered to 200 graduate students studying educational research are analyzed. The analysis considers the original tests, the items loading on the first factor of the test, and the items…
Descriptors: Career Development, Factor Analysis, Factor Structure, Item Analysis
Jannarone, Robert J. – 1986
A variety of locally dependent models are introduced having individual difference parameters that may be interpreted as reflecting effective learning abilities. One version is a univariate extension of the Rasch model with a Markov property: the probability that a given individual will pass an item depends on previous items only through the…
Descriptors: Academic Aptitude, Bayesian Statistics, Cognitive Ability, Estimation (Mathematics)
Livingston, Samuel A. – 1986
This paper deals with test fairness regarding a test consisting of two parts: (1) a "common" section, taken by all students; and (2) a "variable" section, in which some students may answer a different set of questions from other students. For example, a test taken by several thousand students each year contains a common multiple-choice portion and…
Descriptors: Difficulty Level, Error of Measurement, Essay Tests, Mathematical Models
Yen, Wendy M. – 1979
Three test-analysis models were used to analyze three types of simulated test score data plus the results of eight achievement tests. Chi-square goodness-of-fit statistics were used to evaluate the appropriateness of the models to the four kinds of data. Data were generated to simulate the responses of 1,000 students to 36 pseudo-items by…
Descriptors: Achievement Tests, Correlation, Goodness of Fit, Item Analysis
Levine, Michael V.; Drasgow, Fritz – 1984
Some examinees' test-taking behavior may be so idiosyncratic that their scores are not comparable to the scores of more typical examinees. Appropriateness indices, which provide quantitative measures of response-pattern atypicality, can be viewed as statistics for testing a null hypothesis of normal test-taking behavior against an alternative…
Descriptors: Cheating, College Entrance Examinations, Computer Simulation, Estimation (Mathematics)