Asymptotic standard errors of irt observed-score equating methods

Psychometrika ◽  
2003 ◽  
Vol 68 (2) ◽  
pp. 193-211 ◽  
Author(s):  
Haruhiko Ogasawara
Keyword(s):  
Standard Errors ◽  
Asymptotic Standard Errors ◽  
Observed Score

Item Response Theory True Score Equatings and Their Standard Errors

2001 ◽  
Vol 26 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Haruhiko Ogasawara
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Standard Errors ◽  
Simultaneous Estimation ◽  
True Score ◽  
Response Theory ◽  
Asymptotic Standard Errors ◽  
Item Parameters ◽  
Irt Equating ◽  
Equating Coefficients

The asymptotic standard errors of the estimates of the equated scores by several types of item response theory (IRT) true score equatings are provided. The first group of equatings do not use IRT equating coefficients. The second group of equatings use the IRT equating coefficients given by the moment or characteristic curve methods. The equating designs considered in this article cover those with internal or external common items and the methods with separate or simultaneous estimation of item parameters of associated tests. For the estimates of the asymptotic standard errors of the equated true scores, the method of marginal maximum likelihood estimation is employed for estimation of item parameters.

ASYMPTOTIC INFERENCE ON THE MOVING AVERAGE IMPACT MATRIX IN COINTEGRATED I (2) VAR SYSTEMS

2002 ◽  
Vol 18 (3) ◽  
pp. 673-690 ◽  
Author(s):  
Paolo Paruolo
Keyword(s):  
Moving Average ◽  
Impact Factors ◽  
Standard Errors ◽  
Linear Combinations ◽  
Asymptotic Standard Errors ◽  
Vector Autoregressive ◽  
Average Impact ◽  
The Common ◽  
Row Space ◽  
Asymptotic Variances

This paper provides asymptotic standard errors for the moving average (MA) impact matrix for the second differences of a vector autoregressive (VAR) process integrated of order 2, I(2). Standard errors of the row space of the MA impact matrix are also provided; bases of this row space define the common I(2) trends linear combinations. These standard errors are then used to formulate Wald-type tests. The MA impact matrix is shown to be linked to impact factors that measure the total effect of disequilibrium errors on the growth rate of the system. Most of the relevant limit distributions are Gaussian, and we report artificial regressions that can be used to calculate the estimators of the asymptotic variances. The use of the techniques proposed in the paper is illustrated on UK money data.

scholarly journals Accounting for standard errors of measurement when modeling change

2020 ◽  
Vol 45 (1) ◽  
pp. 11-18
Author(s):  
Kevin J. Grimm ◽  
Kimberly Fine ◽  
Gabriela Stegmann
Keyword(s):  
Structural Equation ◽  
Equation Modeling ◽  
Standard Errors ◽  
Model Parameters ◽  
Change Over Time ◽  
Item Response Modeling ◽  
Estimation Of Model Parameters ◽  
Observed Score ◽  
Over Time ◽  
Standard Errors Of Measurement

Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores can differ in terms of their precision—both within and across participants. We propose an approach to weight observed scores by their level of precision, which is estimated as the inverse of their standard error of measurement in the context of item response modeling. Thus, scores with lower standard errors of measurement have greater weight, and scores with higher standard errors of measurement are down weighted. We discuss the weighting approaches and illustrate how to apply this approach with commonly available software. We then compare this approach to modeling change without weighting based on standard errors of measurement.

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