Descriptor

Mathematical Formulas | 7 |

Item Analysis | 6 |

Correlation | 4 |

Test Reliability | 4 |

Test Construction | 3 |

Analysis of Variance | 2 |

Error of Measurement | 2 |

Factor Analysis | 2 |

Predictor Variables | 2 |

Statistical Analysis | 2 |

Test Items | 2 |

More ▼ |

Source

Educational and Psychological… | 13 |

Author

Publication Type

Guides - Non-Classroom | 13 |

Journal Articles | 13 |

Reports - Research | 13 |

Opinion Papers | 1 |

Education Level

Audience

Location

Laws, Policies, & Programs

Assessments and Surveys

What Works Clearinghouse Rating

Peer reviewed

Reynolds, Thomas J.; Jackosfsky, Ellen F. – Educational and Psychological Measurement, 1981

The purpose of this paper is to outline the role of orthogonal rotation in canonical analysis, including the evaluative measures that need be reported and scrutinized upon application. (Author)

Descriptors: Attitude Measures, Multivariate Analysis, Orthogonal Rotation, Transformations (Mathematics)

Peer reviewed

Meir, Elchanan I.; Gati, Itamar – Educational and Psychological Measurement, 1981

In many personality and interest inventories, a score profile, rather than a single score, is attributed to each subject. Six applicable criteria are suggested for use in examining the adequacy of items in such inventories. These criteria relate to the items' response distributions, internal consistency, and discriminative value. (Author/BW)

Descriptors: Evaluation Criteria, Interest Inventories, Item Analysis, Personality Measures

Peer reviewed

Conger, Anthony J. – Educational and Psychological Measurement, 1980

Reliability maximizing weights are related to theoretically specified true score scaling weights to show a constant relationship that is invariant under separate linear tranformations on each variable in the system. Test theoretic relations should be derived for the most general model available and not for unnecessarily constrained models.…

Descriptors: Mathematical Formulas, Scaling, Test Reliability, Test Theory

Peer reviewed

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

Peer reviewed

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

Peer reviewed

Fleming, James S. – Educational and Psychological Measurement, 1981

The perfunctory use of factor scores in conjunction with regression analysis is inappropriate for many purposes. It is suggested that factoring methods are most suitable for independent variable sets when some consideration has been given to the nature of the domain, which is implied by the predictors. (Author/BW)

Descriptors: Factor Analysis, Multiple Regression Analysis, Predictor Variables, Research Problems

Peer reviewed

Hsu, Louis M. – Educational and Psychological Measurement, 1980

In two treatment-repeated measurements designs, the ratio between the unbiased variance of the differences and twice the variance of the errors of measurement can be used to test for interaction of subjects and treatments. The use of this statistic is illustrated. (Author/CP)

Descriptors: Analysis of Variance, Aptitude Tests, Error of Measurement, Mathematical Formulas

Peer reviewed

Harris, Chester W. – Educational and Psychological Measurement, 1980

Brennan's B statistic is a generalized upper-lower discrimination index which was first published in 1972: Peirce earlier introduced a statistic on the relation between a predictor and an outcome which has the same structure as Brennan's B. (Author/CP)

Descriptors: Discriminant Analysis, Item Analysis, Mathematical Formulas, Predictive Measurement

Peer reviewed

Greener, Jack M.; Osburn, H. G. – Educational and Psychological Measurement, 1980

Corrections for restriction in range due to explicit selection assume linearity of regression and homoscedastic array variances. A Monte Carlo study was conducted to examine the effects of some common forms of violation of these assumptions. (Author/CP)

Descriptors: Correlation, Error of Measurement, Predictor Variables, Statistical Bias

Peer reviewed

Willson, Victor L. – Educational and Psychological Measurement, 1980

Guilford's average interrater correlation coefficient is shown to be related to the Friedman Rank Sum statistic. Under the null hypothesis of zero correlation, the resultant distribution is known and the hypothesis can be tested. Large sample and tied score cases are also considered. An example from Guilford (1954) is presented. (Author)

Descriptors: Correlation, Hypothesis Testing, Mathematical Formulas, Reliability

Peer reviewed

Vegelius, Jan – Educational and Psychological Measurement, 1980

One argument against the G index is that, unlike phi, it is not a correlation coefficient; yet, G conforms to the Kendall and E-coefficient definitions. The G index is also equal to the Pearson product moment correlation coefficient obtained from double scoring. (Author/CP)

Descriptors: Correlation, Mathematical Formulas, Test Reliability

Peer reviewed

Silverstein, A. B. – Educational and Psychological Measurement, 1980

An alternative derivation was given of Gaylord's formulas showing the relationships among the average item intercorrelation, the average item-test correlation, and test reliability. Certain parallels were also noted in analysis of variance and principal component analysis. (Author)

Descriptors: Analysis of Variance, Item Analysis, Mathematical Formulas, Test Reliability

Peer reviewed

Gustafsson, Jan-Eric – Educational and Psychological Measurement, 1980

The statistically correct conditional maximum likelihood (CML) estimation method has not been used because of numerical problems. A solution is presented which allows a rapid computation of the CML esitmates also for long tests. CML has decisive advantages in the construction of statistical tests of goodness of fit. (Author/CP)

Descriptors: Goodness of Fit, Item Analysis, Latent Trait Theory, Mathematical Formulas