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Miguel A. García-Pérez – Educational and Psychological Measurement, 2024
A recurring question regarding Likert items is whether the discrete steps that this response format allows represent constant increments along the underlying continuum. This question appears unsolvable because Likert responses carry no direct information to this effect. Yet, any item administered in Likert format can identically be administered…
Descriptors: Likert Scales, Test Construction, Test Items, Item Analysis
Lei, Pui-Wa; Wu, Qiong; DiPerna, James C.; Morgan, Paul L. – Educational and Psychological Measurement, 2009
Currently, few measures are available to monitor young children's progress in acquiring key early academic skills. In response to this need, the authors have begun developing measures (i.e., the Early Arithmetic, Reading and Learning Indicators, or EARLI) of preschoolers' numeracy skills. To accurately and efficiently monitor acquisition of early…
Descriptors: Preschool Children, Measures (Individuals), Numeracy, Emergent Literacy
Kim, Do-Hong; Huynh, Huynh – Educational and Psychological Measurement, 2008
The current study compared student performance between paper-and-pencil testing (PPT) and computer-based testing (CBT) on a large-scale statewide end-of-course English examination. Analyses were conducted at both the item and test levels. The overall results suggest that scores obtained from PPT and CBT were comparable. However, at the content…
Descriptors: Reading Comprehension, Computer Assisted Testing, Factor Analysis, Comparative Testing

Vegelius, Jan – Educational and Psychological Measurement, 1979
A new measure of similarity between persons applicable in Q-analysis is proposed. It allows assumptions of non-orthogonality between the items, across which the similarity is computed. The similarity measure may also be applied in an R-analysis. (Author/JKS)
Descriptors: Correlation, Item Analysis, Q Methodology, Test Construction

Schroeder, Lee L. – Educational and Psychological Measurement, 1975
Describes a program system which was developed for the purpose of making estimates about the nature of the distribution of test scores which will result from an administration of a test composed of items of which estimates of item-difficulty and discrimination are available. (Author/RC)
Descriptors: Computer Programs, Item Analysis, Pretesting, Simulation

Neill, John A.; Jackson, Douglas N. – Educational and Psychological Measurement, 1976
Illustrates a multivariate approach to item analysis. Previous formulation is extended by investigating techniques simultaneously taking into account scale variance with the goal of reducing the average correlation between scales. Study examines problems in determining optimum values for combinations of item parameters selected for personality…
Descriptors: Correlation, Factor Structure, Item Analysis, Personality Measures

Berk, Ronald A. – Educational and Psychological Measurement, 1978
Three formulae developed to correct item-total correlations for spuriousness were evaluated. Relationships among corrected, uncorrected, and item-remainder correlations were determined by computing sets of mean, minimum, and maximum deviation coefficients and Spearman rank correlations for nine test lengths. (Author/JKS)
Descriptors: Correlation, Intermediate Grades, Item Analysis, Test Construction

Andrich, David – Educational and Psychological Measurement, 1978
A generalization of the Rasch model is employed to quantify statements on a scale in the Thurstone tradition, as well as to measure a person's attitudes in the Likert tradition. An illustration of the technique is provided. (JKS)
Descriptors: Attitude Measures, Item Analysis, Measurement Techniques, Psychometrics

Devito, Anthony J.; Kubis, Joseph F. – Educational and Psychological Measurement, 1983
Alternate forms of the state anxiety (A-State) and trait anxiety (A-Trait) scales of the State-Trait Anxiety Inventory (STAI) were constructed by dividing the 20 items of each scale into two briefer forms having 10 items each. The alternate forms and item statistics are presented. (Author/BW)
Descriptors: Anxiety, Higher Education, Item Analysis, Personality Measures

Gati, Itamar – Educational and Psychological Measurement, 1981
This paper examines the properties of the Item Efficiency Index proposed by Neill and Jackson (1976; EJ 137 077) for minimum redundancy item analysis. (Author/BW)
Descriptors: Correlation, Factor Structure, Item Analysis, Mathematical Models

Vegelius, Jan – Educational and Psychological Measurement, 1979
The G index is a measure of similarity between pairs of dichotomized items. The G index is generalized here to the case where items are trichotomized. (JKS)
Descriptors: Correlation, Item Analysis, Nonparametric Statistics, Technical Reports

Myerberg, N. James – Educational and Psychological Measurement, 1979
The effect of stratified sampling of items based on item difficulty and/or interitem correlations on the estimation of test score distribution parameters using multiple matrix sampling was studied. Results indicated that stratification did not consistently improve the stability of parameter estimation. (Author/JKS)
Descriptors: Item Analysis, Item Sampling, Matrices, Technical Reports

Hashway, Robert M. – Educational and Psychological Measurement, 1978
ANALYSIS, a FORTRAN computer program developed as an aid to investigation of and instruction in the psychometric properties of test items, is described. The program utilizes both correlational and analysis of variance approaches to classical item analysis. (Author/JKS)
Descriptors: Analysis of Variance, Computer Programs, Correlation, Item Analysis

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

Rogers, Paul W. – Educational and Psychological Measurement, 1978
Two procedures for the display of item analysis statistics are described. One procedure allows for investigation of difficulty; the second plots item difficulty against item discrimination. (Author/JKS)
Descriptors: Difficulty Level, Graphs, Guidelines, Item Analysis