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Lenhard, Wolfgang; Lenhard, Alexandra – Educational and Psychological Measurement, 2021
The interpretation of psychometric test results is usually based on norm scores. We compared semiparametric continuous norming (SPCN) with conventional norming methods by simulating results for test scales with different item numbers and difficulties via an item response theory approach. Subsequently, we modeled the norm scores based on random…
Descriptors: Test Norms, Scores, Regression (Statistics), Test Items
Is the Factor Observed in Investigations on the Item-Position Effect Actually the Difficulty Factor?
Schweizer, Karl; Troche, Stefan – Educational and Psychological Measurement, 2018
In confirmatory factor analysis quite similar models of measurement serve the detection of the difficulty factor and the factor due to the item-position effect. The item-position effect refers to the increasing dependency among the responses to successively presented items of a test whereas the difficulty factor is ascribed to the wide range of…
Descriptors: Investigations, Difficulty Level, Factor Analysis, Models
Choi, In-Hee; Wilson, Mark – Educational and Psychological Measurement, 2015
An essential feature of the linear logistic test model (LLTM) is that item difficulties are explained using item design properties. By taking advantage of this explanatory aspect of the LLTM, in a mixture extension of the LLTM, the meaning of latent classes is specified by how item properties affect item difficulties within each class. To improve…
Descriptors: Classification, Test Items, Difficulty Level, Statistical Analysis
Andrich, David; Marais, Ida; Humphry, Stephen Mark – Educational and Psychological Measurement, 2016
Recent research has shown how the statistical bias in Rasch model difficulty estimates induced by guessing in multiple-choice items can be eliminated. Using vertical scaling of a high-profile national reading test, it is shown that the dominant effect of removing such bias is a nonlinear change in the unit of scale across the continuum. The…
Descriptors: Guessing (Tests), Statistical Bias, Item Response Theory, Multiple Choice Tests
Finch, Holmes; Edwards, Julianne M. – Educational and Psychological Measurement, 2016
Standard approaches for estimating item response theory (IRT) model parameters generally work under the assumption that the latent trait being measured by a set of items follows the normal distribution. Estimation of IRT parameters in the presence of nonnormal latent traits has been shown to generate biased person and item parameter estimates. A…
Descriptors: Item Response Theory, Computation, Nonparametric Statistics, Bayesian Statistics
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
Wolkowitz, Amanda A.; Skorupski, William P. – Educational and Psychological Measurement, 2013
When missing values are present in item response data, there are a number of ways one might impute a correct or incorrect response to a multiple-choice item. There are significantly fewer methods for imputing the actual response option an examinee may have provided if he or she had not omitted the item either purposely or accidentally. This…
Descriptors: Multiple Choice Tests, Statistical Analysis, Models, Accuracy
Santelices, Maria Veronica; Wilson, Mark – Educational and Psychological Measurement, 2012
The relationship between differential item functioning (DIF) and item difficulty on the SAT is such that more difficult items tended to exhibit DIF in favor of the focal group (usually minority groups). These results were reported by Kulick and Hu, and Freedle and have been enthusiastically discussed by more recent literature. Examining the…
Descriptors: Test Bias, Test Items, Difficulty Level, Statistical Analysis

Ivens, Stephen H. – Educational and Psychological Measurement, 1971
Descriptors: Difficulty Level, Item Analysis, Nonparametric Statistics, Statistical Analysis

Lu, K. H. – Educational and Psychological Measurement, 1971
Descriptors: Difficulty Level, Statistical Analysis, Statistical Significance, Test Items

Shoemaker, David M. – Educational and Psychological Measurement, 1972
Descriptors: Difficulty Level, Error of Measurement, Item Sampling, Simulation

Reynolds, Thomas J. – Educational and Psychological Measurement, 1981
Cliff's Index "c" derived from an item dominance matrix is utilized in a clustering approach, termed extracting Reliable Guttman Orders (ERGO), to isolate Guttman-type item hierarchies. A comparison of factor analysis to the ERGO is made on social distance data involving multiple ethnic groups. (Author/BW)
Descriptors: Cluster Analysis, Difficulty Level, Factor Analysis, Item Analysis

Green, Kathy – Educational and Psychological Measurement, 1985
Five sets of paired comparison judgments were made concerning test item difficulty, in order to identify the most probable source of intrasensitivity in the data. The paired comparisons method was useful in providing information about sensitivity to stimulus differences, but less useful for assessing dimensionality of judgment criteria.…
Descriptors: Adults, Difficulty Level, Evaluative Thinking, Higher Education