A Probability Based Framework for Testing the Missing Data Mechanism

Autor/in	Lin, Johnny Cheng-Han
Titel	A Probability Based Framework for Testing the Missing Data Mechanism
Quelle	(2013), (131 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of California, Los Angeles
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3031-3305-3
Schlagwörter	Hochschulschrift; Dissertation; Data Analysis; Statistical Inference; Error of Measurement; Probability; Multivariate Analysis; Research Problems; Goodness of Fit; Longitudinal Studies; Hypothesis Testing; Intramural Athletics; African American Students + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Auswertung; Inferential statistics; Schließende Statistik; Messfehler; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Multivariate Analyse; Forschungskritik; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Hypothesenprüfung; Hypothesentest; African Americans; Student; Students; Afroamerikaner; Schüler; Schülerin; Studentin
Abstract	Many methods exist for imputing missing data but fewer methods have been proposed to test the missing data mechanism. Little (1988) introduced a multivariate chi-square test for the missing completely at random data mechanism (MCAR) that compares observed means for each pattern with expectation-maximization (EM) estimated means. As an alternative, this manuscript proposed two new ways of testing MCAR that use estimated parameters from missingness indicators rather than moment information from observed scores. The first statistic in the probability-based (PBB) family, PBB-MCAR I, is a chi-square test of independence that tests the assumption that missingness indicators are independent among all grouping patterns. The second statistic, PBB-MCAR II, is a chi-square goodness of fit statistic that tests differences of observed versus expected probabilities conditional on ranked values of a suspect variable that drives missingness dependencies. A simulation study showed that although Little's test consistently maintained optimal Type I error rates, the empirical power of PBB-MCAR II to detect violations of MCAR was on par with Little's test under most conditions, whereas PBB-MCAR I had lower power to detect aberrations of MCAR because it tests a more restricted set of independence assumptions. These newly-developed test statistics were demonstrated in two education-based applications, a) as a way of testing the missing data mechanism when creating longitudinal trajectories of intramural sports participation among African American students, and b) as a tool to detect departures from completely at random test-taking. Future work will involve creating an R package to promote the use of these missing data tests among education researchers, extending PBB-MCAR II to incorporate auxiliary variables, and resolving the problem of sparse missing data patterns by adopting the limited information goodness of fit test proposed by Maydeu-Olivares and Joe (2005). [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
Anmerkungen	ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01