Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…

While conflict event data sets are increasingly used in contemporary conflict research, important concerns persist regarding the quality of the collected data. Such concerns are not necessarily new. Yet, because the methodological debate and evidence on potential errors remains scattered across different subdisciplines of social sciences, there is…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…

Most quantitative studies in the social sciences suffer from missing data. However, despite the large availability of documents and software to treat such data, it appears that many social scientists do not apply good practices regarding missing data. We analyzed quantitative papers published in 2017 in six top-level social science journals.…

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner…

Purpose of the Study: Missing data are a common problem encountered while implementing measurement instruments. Yet the extent to which reliability, validity, average discrimination and difficulty of the test results are affected by the missing data has not been studied much. Since it is inevitable that missing data have an impact on the…

Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from…

Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the…

This study aims to investigate the effect of methods to deal with missing data on item difficulty estimations under different test length conditions and sampling sizes. In this line, a data set including 10, 20 and 40 items with 100 and 5000 sampling size was prepared. Deletion process was applied at the rates of 5%, 10% and 20% under conditions…

Objectives: The purpose of the study was to provide empirical evidence about the reporting of methodology to address missing outcome data and the acknowledgement of their impact in Cochrane systematic reviews in the mental health field. Methods: Systematic reviews published in the Cochrane Database of Systematic Reviews after January 1, 2009 by…

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…

This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…

This paper discusses reasons why manuscripts are not accepted for publication in "Ethiopian Journal of Education" ("EJE"). It intends to promote publication by domestic and/or international authors in "EJE" by analyzing the reasons for rejection of manuscripts. To gather the relevant data, a total of 101 rejected…