Observed Score Linear Equating with Covariates

2011 ◽  
Vol 48 (4) ◽  
pp. 419-440 ◽  
Author(s):  
Kenny Bränberg ◽  
Marie Wiberg
Keyword(s):  
Observed Score ◽  
Linear Equating

A Unified Approach to Linear Equating for the Nonequivalent Groups Design

2005 ◽  
Vol 30 (3) ◽  
pp. 313-342 ◽  
Author(s):  
Alina A. von Davier ◽  
Nan Kong
Keyword(s):  
General Formula ◽  
Standard Error ◽  
Unified Approach ◽  
Unified Framework ◽  
Neat Design ◽  
Nonequivalent Groups ◽  
Standard Error Of Equating ◽  
Observed Score ◽  
Special Case ◽  
Linear Equating

This article describes a new, unified framework for linear equating in a non-equivalent groups anchor test (NEAT) design. The authors focus on three methods for linear equating in the NEAT design—Tucker, Levine observed-score, and chain—and develop a common parameterization that shows that each particular equating method is a special case of the linear equating function in the NEAT design. A new concept, the method function, is used to distinguish among the linear equating functions, in general, and among the three equating methods, in particular. This approach leads to a general formula for the standard error of equating for all linear equating functions in the NEAT design. A new tool, the standard error of equating difference , is presented to investigate if the observed difference in the equating functions is statistically significant.

Derivations of Observed Score Linear Equating Methods Based on Test Score Models for the Common Item Nonequivalent Populations Design

1986 ◽  
Vol 11 (4) ◽  
pp. 245-257 ◽  
Author(s):  
David Woodruff
Keyword(s):  
Test Score ◽  
Common Item ◽  
The Common ◽  
Observed Score ◽  
Linear Equating

Linear equating methods for the common item nonequivalent populations design are derived from explicitly stated test score models. The models are congeneric type models for tests administered in two distinct populations. Though the data collected in the design are incomplete, procedures for estimating all parameters necessary for equating are derived. The equating methods developed are compared with previously developed methods to which they are similar. By developing explicit models for the problem, insight is gained into the assumptions required for its solution.

Tolerance Intervals for True Scores

1985 ◽  
Vol 10 (1) ◽  
pp. 1-17 ◽  
Author(s):  
David Jarjoura
Keyword(s):  
Confidence Intervals ◽  
Practical Implication ◽  
Educational Measurement ◽  
True Score ◽  
Tolerance Intervals ◽  
Observed Score ◽  
True Scores

Issues regarding tolerance and confidence intervals are discussed within the context of educational measurement and conceptual distinctions are drawn between these two types of intervals. Points are raised about the advantages of tolerance intervals when the focus is on a particular observed score rather than a particular examinee. Because tolerance intervals depend on strong true score models, a practical implication of the study is that true score tolerance intervals are fairly insensitive to differences in assumptions among the five models studied.

Interpretation of the Standard Error of Measurement when True Scores and Error Scores on Mental Tests are Not Independent

1966 ◽  
Vol 19 (2) ◽  
pp. 611-617 ◽  
Author(s):  
Donald W. Zimmerman ◽  
Richard H. Williams
Keyword(s):  
Standard Deviation ◽  
Standard Error ◽  
Test Score ◽  
Test Reliability ◽  
True Score ◽  
Observed Score ◽  
Standard Error Of Measurement ◽  
True Scores ◽  
Modified Equation ◽  
Error Of Measurement

It is shown that for the case of non-independence of true scores and error scores interpretation of the standard error of measurement is modified in two ways. First, the standard deviation of the distribution of error scores is given by a modified equation. Second, the confidence interval for true score varies with the individual's observed score. It is shown that the equation, so=√[(N−O/a]+[so2(roō−roo)/roō]̄, where N is the number of items, O is the individual's observed score, a is the number of choices per item, so2 is observed variance, roo is test reliability as empirically determined, and roō is reliability for the case where only non-independent error is present, provides a more accurate interpretation of the test score of an individual.

Observed-Score Equating: An Overview

Psychometrika ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. 605-623 ◽  
Author(s):  
Alina A. von Davier
Keyword(s):  
Observed Score
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