Handling missing data in the association-marginal model through longitudinal data analysis : a simulation study
Other Title(s)
التعامل مع البيانات المفقودة في نموذج الاقتران الهامشي من خلال تحليل البيانات الطولية : دراسة محاكاة
Joint Authors
Muhammad, Imtithal Ali
al-Zayyat, Mahi Muhsin
Halawah, Adil
al-Attar, Labibah
Source
Journal of Faculty of Commerce for Scientific Research
Issue
Vol. 56, Issue 3 (31 Jul. 2019), pp.187-212, 26 p.
Publisher
Alexandria University Faculty of Commerce
Publication Date
2019-07-31
Country of Publication
Egypt
No. of Pages
26
Main Subjects
Topics
- Researchers
- Data analysis
- English language
- Distribution(Probability theory)
- Linear models(Statistics)
- Longitudinal method
Abstract EN
Missing data can frequently occur in a longitudinal data analysis, where repeated measurements are taken over time.
Unfortunately, missing data can lead to large standard errors in parameter estimates because nonresponse is compounded across times of data collection to produce small longitudinal sample sizes.
Also, the problems of survey nonresponse(i.
e.
, reduction in statistical power and threat of parameter bias) are a particularly salient challenge for longitudinal researchers.
Thus, the main goal of this paper is to introduce a new idea that describes simultaneously the association structure (A) with the marginal distributions (M) of the responses for longitudinal data in the presence of missing data (MS), through a composite link.
This new idea (AM-MS) is of great importance where it is applicable for large and sparse tables.
In addition, it can also be used for fitting log linear models to contingency tables with missing data (MS) and fitting models with various assumptions about the missing data mechanisms either MCAR, MAR or NMAR.
A simulation study will be performed to apply this new idea, under various situations including (missing mechanisms, missing rates and five methods for handling missing data).
The goodness-of-fit test statistics and the number of adjusted residuals greater than 2 are used as evaluation criteria.
American Psychological Association (APA)
al-Zayyat, Mahi Muhsin& Muhammad, Imtithal Ali& Halawah, Adil& al-Attar, Labibah. 2019. Handling missing data in the association-marginal model through longitudinal data analysis : a simulation study. Journal of Faculty of Commerce for Scientific Research،Vol. 56, no. 3, pp.187-212.
https://search.emarefa.net/detail/BIM-994648
Modern Language Association (MLA)
al-Zayyat, Mahi Muhsin…[et al.]. Handling missing data in the association-marginal model through longitudinal data analysis : a simulation study. Journal of Faculty of Commerce for Scientific Research Vol. 56, no. 3 (Jul. 2019), pp.187-212.
https://search.emarefa.net/detail/BIM-994648
American Medical Association (AMA)
al-Zayyat, Mahi Muhsin& Muhammad, Imtithal Ali& Halawah, Adil& al-Attar, Labibah. Handling missing data in the association-marginal model through longitudinal data analysis : a simulation study. Journal of Faculty of Commerce for Scientific Research. 2019. Vol. 56, no. 3, pp.187-212.
https://search.emarefa.net/detail/BIM-994648
Data Type
Journal Articles
Language
English
Notes
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Record ID
BIM-994648