A Review of Missing Data Handling Methods in Education Research
نویسنده
چکیده
Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those methods as a quick fix. This study reviews the current literature on missing data handling methods within the special context of education research to summarize the pros and cons of various methods and provides guidelines for future research in this area.
منابع مشابه
کاربرد جای گذاری چندگانه در تحقیقات پزشکی و اپیدمیولوژی
Data missing, which occurs for different reasons, is an unavoidable problem in epidemiological studies. It is quite widespread and, therefore, it is considered as a challenge in research design and data analysis by many methodologists. Complete case analysis is often used in studies with missing data however, this approach may result in inaccurate estimates and inferences due to bias associated...
متن کاملInvestigating the missing data effect on credit scoring rule based models: The case of an Iranian bank
Credit risk management is a process in which banks estimate probability of default (PD) for each loan applicant. Data sets of previous loan applicants are built by gathering their data, and these internal data sets are usually completed using external credit bureau’s data and finally used for estimating PD in banks. There is also a continuous interest for bank to use rule based classifiers to b...
متن کاملمقایسه روش الگوریتم EM و روشهای متداول جانهی دادههای گمشده: مطالعهروی پرسشنامه خوددرمانی بیماران دیابتی
Background and Objectives: Missing data is a big challenge in the research. According to the type of the study and of the variables, different ways have been proposed to work with these data. This study compared five popular imputation approaches in addressing missing data in the questionnaires. Methods: In this study, 500 questionnaires were used for self-medication in diabetic patients. Mi...
متن کاملDEA with Missing Data: An Interval Data Assignment Approach
In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the prop...
متن کاملMethods for Handling Missing Data in the Behavioral Neurosciences: Don’t Throw the Baby Rat out with the Bath Water
Missing data are a major problem in the behavioral neurosciences, particularly when data collection is costly. Often researchers exclude cases with missing data, which can result in biased estimates and reduced power. Trying to avoid the deletion of a case because of a missing data point can be conducted, but implementing a naïve missing data method can result in distorted estimates and incorre...
متن کامل