Situations Where It Is Appropriate to Use Frequency Estimation Equipercentile Equating

2013 ◽  
Vol 50 (3) ◽  
pp. 338-354
Author(s):  
Hongwen Guo ◽  
Hyeonjoo J. Oh ◽  
Daniel Eignor
1995 ◽  
Vol 20 (3) ◽  
pp. 259-286 ◽  
Author(s):  
Michelle Liou ◽  
Philip E. Cheng

We propose simplified formulas for computing the standard errors of equiper-centile equating for continuous and discrete test scores. The suggested formulas are conceptually simple and easily extended to more complicated equating designs such as chained equipercentile equating, smoothed equipercentile equating, and equating using the frequency estimation method. Results from an empirical study indicate that the derived formulas work reasonably well for samples with moderate sizes (e.g., 1,000 examinees).


Author(s):  
Xushan CHEN ◽  
Jibin YANG ◽  
Meng SUN ◽  
Jianfeng LI

2020 ◽  
Vol 65 (1) ◽  
pp. 115-122
Author(s):  
Andrea Amalia Minda

In this paper we propose a procedure to correct Jain's algorithm, which in certain situations fails in correctly estimating the frequency by indicating frequency values that are very far from the real frequency. It happens because the two points considered for the method proposed by Jain are not on the same lobe. Thus, a method is proposed according to which these points are chosen so that the results are improved.


2011 ◽  
Vol 30 (4) ◽  
pp. 831-835
Author(s):  
Yu-chun Huang ◽  
Zai-lu Huang ◽  
Ben-xiong Huang ◽  
Shu-hua Xu

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