2018
DOI: 10.1177/0013164418805532
|View full text |Cite
|
Sign up to set email alerts
|

Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing

Abstract: Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated multiple missing data methods in computerized adaptive multistage testing, including two imputation techniques, the use of full information maximum likelihood and the… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
13
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 9 publications
(13 citation statements)
references
References 33 publications
0
13
0
Order By: Relevance
“…Extensive reviews of the literature on missing data analysis were performed by Graham (2009), Graham (2012), Schafer (1997), Schafer and Graham (2002), Sinharay, Stern, and Russell (2001), and Vriens and Sinharay (2006). In addition, researchers have considered a wide variety of problems regarding missing data in educational or psychological measurement (e.g., Cetin‐Berber, Sari, & Huggins‐Manley, 2019; De Ayala, Plake, & Impara, 2001; Finch, 2008; Holman & Glas, 2005; Huisman & Molenaar, 2001; Smits, Mellenbergh, & Vorst, 2002; Sijtsma & van der Ark, 2003; Xiao & Bulut, 2020). A brief summary of most of these studies is included in the first subsection of the Supporting Information.…”
Section: Introductionmentioning
confidence: 99%
“…Extensive reviews of the literature on missing data analysis were performed by Graham (2009), Graham (2012), Schafer (1997), Schafer and Graham (2002), Sinharay, Stern, and Russell (2001), and Vriens and Sinharay (2006). In addition, researchers have considered a wide variety of problems regarding missing data in educational or psychological measurement (e.g., Cetin‐Berber, Sari, & Huggins‐Manley, 2019; De Ayala, Plake, & Impara, 2001; Finch, 2008; Holman & Glas, 2005; Huisman & Molenaar, 2001; Smits, Mellenbergh, & Vorst, 2002; Sijtsma & van der Ark, 2003; Xiao & Bulut, 2020). A brief summary of most of these studies is included in the first subsection of the Supporting Information.…”
Section: Introductionmentioning
confidence: 99%
“…As the item selection procedure in CAT is dependent on examinees’ observed performance on administered items, the missingness of responses is often seen as missing at random (Han & Guo, 2014; Mislevy, 2016). The mean substitution method is one of the appropriate imputation techniques for missing at random (Huisman, 2000; Cetin‐Berber et al., 2019). Item mean substitution uses the average of an item's observed scores to replace every missing score of this item; person mean substitution uses the average of an examinee's observed scores to replace all of this examinee's missing scores.…”
Section: A New Item Selection Algorithmmentioning
confidence: 99%
“…For MNAR data, the likelihood of a missing response cannot be explained by any measurable variables but is caused by unmeasured variable(s) (e.g., missingness depends on the individual’s ability). Missing data handling approaches have shown to perform differently based on the type of missing mechanisms (e.g., Cetin-Berber et al, 2019; Finch, 2010; Robitzsch & Rupp, 2009). Mislevy and Wu (1996) suggested considering different missing data mechanisms when estimating parameters for obtaining more accurate results.…”
Section: Missing Mechanismsmentioning
confidence: 99%