Questionnaire design improvement and missing item scores estimation for rapid and efficient decision making

2011 ◽  
Vol 197 (1) ◽  
pp. 5-23 ◽  
Author(s):  
Daji Ergu ◽  
Gang Kou
Keyword(s):  
Decision Making ◽  
Questionnaire Design ◽  
Design Improvement ◽  
Item Scores ◽  
Missing Item

Investigation and Treatment of Missing Item Scores in Test and Questionnaire Data

2003 ◽  
Vol 38 (4) ◽  
pp. 505-528 ◽  
Author(s):  
Klaas Sijtsma ◽  
L. Andries van der Ark
Keyword(s):  
Questionnaire Data ◽  
Item Scores ◽  
Missing Item

Evolutionary learning of novel grammars for design improvement

1994 ◽  
Vol 8 (2) ◽  
pp. 83-94 ◽  
Author(s):  
John S. Gero ◽  
Sushil J. Louis ◽  
Sourav Kundu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
State Space ◽  
Shape Grammar ◽  
Evolutionary Learning ◽  
State Spaces ◽  
Design Improvement ◽  
Beam Section ◽  
Improved Characteristics

AbstractThis paper focuses on that form of learning that relates to exploration, rather than generalization. It uses the notion of exploration as the modification of state spaces within which search and decision making occur. It demonstrates that the genetic algorithm formalism provides a computational construct to carry out this learning. The process is exemplified using a shape grammar for a beam section. A new shape grammar is learned that produces a new state space for the problem. This new state space has improved characteristics.

IBMM for Questionnaire Design Improvement

2012 ◽  
pp. 77-91
Author(s):  
Gang Kou ◽  
Daji Ergu ◽  
Yi Peng ◽  
Yong Shi
Keyword(s):  
Questionnaire Design ◽  
Design Improvement

Estimating Probabilities of Passing for Examinees With Incomplete Data in Mastery Tests

2021 ◽  
pp. 001316442110237
Author(s):  
Sandip Sinharay
Keyword(s):  
Item Response ◽  
Incomplete Data ◽  
Classical Test Theory ◽  
Test Theory ◽  
Power Outage ◽  
Medical Licensing ◽  
Misclassification Rates ◽  
Item Scores ◽  
Missing Item ◽  
Mastery Tests

Administrative problems such as computer malfunction and power outage occasionally lead to missing item scores and hence to incomplete data on mastery tests such as the AP and U.S. Medical Licensing examinations. Investigators are often interested in estimating the probabilities of passing of the examinees with incomplete data on mastery tests. However, there is a lack of research on this estimation problem. The goal of this article is to suggest two new approaches—one each based on classical test theory and item response theory—for estimating the probabilities of passing of the examinees with incomplete data on mastery tests. The two approaches are demonstrated to have high accuracy and negligible misclassification rates.

scholarly journals Incidence of Missing Item Scores in Personality Measurement, and Simple Item-Score Imputation

Methodology ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Joost R. van Ginkel ◽  
Klaas Sijtsma ◽  
L. Andries van der Ark ◽  
Jeroen K. Vermunt
Keyword(s):  
Missing Data ◽  
Statistical Tests ◽  
Parameter Estimates ◽  
Item Score ◽  
Unit Nonresponse ◽  
Listwise Deletion ◽  
Personality Research ◽  
Planned Missingness ◽  
Item Scores ◽  
Missing Item

The focus of this study was the incidence of different kinds of missing-data problems in personality research and the handling of these problems. Missing-data problems were reported in approximately half of more than 800 articles published in three leading personality journals. In these articles, unit nonresponse, attrition, and planned missingness were distinguished but missing item scores in trait measurement were reported most frequently. Listwise deletion was the most frequently used method for handling all missing-data problems. Listwise deletion is known to reduce the accuracy of parameter estimates and the power of statistical tests and often to produce biased statistical analysis results. This study proposes a simple alternative method for handling missing item scores, known as two-way imputation, which leaves the sample size intact and has been shown to produce almost unbiased results based on multi-item questionnaire data.

scholarly journals Simple imputation methods versus direct likelihood analysis for missing item scores in multilevel educational data

2011 ◽  
Vol 44 (2) ◽  
pp. 516-531 ◽  
Author(s):  
Damazo T. Kadengye ◽  
Wilfried Cools ◽  
Eva Ceulemans ◽  
Wim Van den Noortgate
Keyword(s):  
Imputation Methods ◽  
Likelihood Analysis ◽  
Simple Imputation ◽  
Item Scores ◽  
Missing Item
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