Improving test efficiency for a grid multidimensional computerized classification test by the application of a conditional latent-trait distribution to a sequential probability ratio test

2021 ◽  
Author(s):  
Tien-Hsiang Liu ◽  
Cheng-Te Chen ◽  
Chung-Ping Cheng ◽  
Ching-Lin Shih
Keyword(s):  
Latent Trait ◽  
Sequential Probability Ratio Test ◽  
Ratio Test ◽  
Probability Ratio ◽  
Trait Distribution ◽  
Test Efficiency ◽  
Sequential Probability ◽  
Computerized Classification Test ◽  
Latent Trait Distribution ◽  
Classification Test

scholarly journals The VPRT: A Sequential Testing Procedure Dominating the SPRT

1993 ◽  
Vol 9 (3) ◽  
pp. 431-450 ◽  
Author(s):  
Noel Cressie ◽  
Peter B. Morgan
Keyword(s):  
Sample Size ◽  
Decision Problem ◽  
Sequential Analysis ◽  
Testing Procedure ◽  
Sequential Probability Ratio Test ◽  
Ratio Test ◽  
Sequential Decision ◽  
Probability Ratio ◽  
Sampling Cost ◽  
Sequential Probability

Under more general assumptions than those usually made in the sequential analysis literature, a variable-sample-size-sequential probability ratio test (VPRT) of two simple hypotheses is found that maximizes the expected net gain over all sequential decision procedures. In contrast, Wald and Wolfowitz [25] developed the sequential probability ratio test (SPRT) to minimize expected sample size, but their assumptions on the parameters of the decision problem were restrictive. In this article we show that the expected net-gain-maximizing VPRT also minimizes the expected (with respect to both data and prior) total sampling cost and that, under slightly more general conditions than those imposed by Wald and Wolfowitz, it reduces to the one-observation-at-a-time sequential probability ratio test (SPRT). The ways in which the size and power of the VPRT depend upon the parameters of the decision problem are also examined.

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