Standard Errors of Equipercentile Equating for the Common Item Nonequivalent Populations Design

1985 ◽  
Vol 10 (2) ◽  
pp. 143 ◽  
Author(s):  
David Jarjoura ◽  
Michael J. Kolen
Keyword(s):  
Standard Errors ◽  
Equipercentile Equating ◽  
Common Item ◽  
The Common

Standard Errors of Equipercentile Equating for the Common Item Nonequivalent Populations Design

1985 ◽  
Vol 10 (2) ◽  
pp. 143-160 ◽  
Author(s):  
David Jarjoura ◽  
Michael J. Kolen
Keyword(s):  
Real Data ◽  
Standard Errors ◽  
Equipercentile Equating ◽  
Large Samples ◽  
Common Item ◽  
Complex Procedure ◽  
Different Populations ◽  
The Common

An equating design in which two groups of examinees from slightly different populations are administered different test forms that have a subset of items in common is widely used. A procedure for equipercentile equating under this design has been previously outlined, but standard errors for this rather complex procedure have not been provided. This paper provides these standard errors and a simulation that verifies the equations for large samples. A real data example is provided for considering issues involved in using these procedures.

Analytic smoothing for equipercentile equating under the common item nonequivalent populations design

Psychometrika ◽  
1987 ◽  
Vol 52 (1) ◽  
pp. 43-59 ◽  
Author(s):  
Michael J. Kolen ◽  
David Jarjoura
Keyword(s):  
Equipercentile Equating ◽  
Common Item ◽  
The Common

A Comparison of Bootstrap Standard Errors of IRT Equating Methods for the Common-Item Nonequivalent Groups Design

2001 ◽  
Vol 14 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Tsung-Hsun Tsai ◽  
Bradley A. Hanson ◽  
Michael J. Kolen ◽  
and Robert A. Forsyth
Keyword(s):  
Standard Errors ◽  
Common Item ◽  
Nonequivalent Groups ◽  
The Common ◽  
Irt Equating

Standard Errors of the Kernel Equating Methods Under the Common-Item Design

1997 ◽  
Vol 21 (4) ◽  
pp. 349-369 ◽  
Author(s):  
Michelle Liou ◽  
Philip E. Cheng ◽  
Eugene G. Johnson
Keyword(s):  
Standard Errors ◽  
Common Item ◽  
Kernel Equating ◽  
The Common

scholarly journals STANDARD ERRORS OF THE KERNEL EQUATING METHODS UNDER THE COMMON-ITEM DESIGN

1996 ◽  
Vol 1996 (1) ◽  
pp. i-36 ◽  
Author(s):  
Michelle Liou ◽  
Philip E. Cheng ◽  
Eugene G. Johnson
Keyword(s):  
Standard Errors ◽  
Common Item ◽  
Kernel Equating ◽  
The Common

scholarly journals Regression Discontinuity and Heteroskedasticity Robust Standard Errors: Evidence from a Fixed-Bandwidth Approximation

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Otávio Bartalotti
Keyword(s):  
Bias Correction ◽  
Asymptotic Theory ◽  
Regression Discontinuity ◽  
Traditional Approach ◽  
Standard Errors ◽  
Test Coverage ◽  
Regression Discontinuity Designs ◽  
The Common ◽  
Theoretical Results ◽  
Robust Standard Errors

AbstractIn regression discontinuity designs (RD), for a given bandwidth, researchers can estimate standard errors based on different variance formulas obtained under different asymptotic frameworks. In the traditional approach the bandwidth shrinks to zero as sample size increases; alternatively, the bandwidth could be treated as fixed. The main theoretical results for RD rely on the former, while most applications in the literature treat the estimates as parametric, implementing the usual heteroskedasticity-robust standard errors. This paper develops the “fixed-bandwidth” alternative asymptotic theory for RD designs, which sheds light on the connection between both approaches. I provide alternative formulas (approximations) for the bias and variance of common RD estimators, and conditions under which both approximations are equivalent. Simulations document the improvements in test coverage that fixed-bandwidth approximations achieve relative to traditional approximations, especially when there is local heteroskedasticity. Feasible estimators of fixed-bandwidth standard errors are easy to implement and are akin to treating RD estimators aslocallyparametric, validating the common empirical practice of using heteroskedasticity-robust standard errors in RD settings. Bias mitigation approaches are discussed and a novel bootstrap higher-order bias correction procedure based on the fixed bandwidth asymptotics is suggested.

A Comparison of Two Equipercentile Equating Methods for Common Item Equating

1990 ◽  
Vol 50 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Deborah J. Harris ◽  
Michael J. Kolen
Keyword(s):  
Equipercentile Equating ◽  
Common Item

scholarly journals Inference in Regression Discontinuity Designs with a Discrete Running Variable

2018 ◽  
Vol 108 (8) ◽  
pp. 2277-2304 ◽  
Author(s):  
Michal Kolesár ◽  
Christoph Rothe
Keyword(s):  
Confidence Intervals ◽  
Conditional Expectation ◽  
Regression Discontinuity ◽  
Model Misspecification ◽  
Standard Errors ◽  
Moderate Number ◽  
Regression Discontinuity Designs ◽  
The Common ◽  
Present Simulation ◽  
Theoretical Results

We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values. In particular, we study the common practice of using confidence intervals (CIs) based on standard errors that are clustered by the running variable as a means to make inference robust to model misspecification (Lee and Card 2008). We derive theoretical results and present simulation and empirical evidence showing that these CIs do not guard against model misspecification, and that they have poor coverage properties. We therefore recommend against using these CIs in practice. We instead propose two alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function. (JEL C13, C51, J13, J31, J64, J65)

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.

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