A comparison of imputation strategies for ordinal missing data on Likert scale variables
This article compares a variety of imputation strategies for ordinal missing data on Likert
scale variables (number of categories= 2, 3, 5, or 7) in recovering reliability coefficients …
scale variables (number of categories= 2, 3, 5, or 7) in recovering reliability coefficients …
Evaluating the performances of missing data handling methods in ability estimation from sparse data
Large amounts of missing data could distort item parameter estimation and lead to biased
ability estimates in educational assessments. Therefore, missing responses should be …
ability estimates in educational assessments. Therefore, missing responses should be …
[PDF][PDF] Imputation methods for missing categorical questionnaire data: A comparison of approaches
WH Finch - Journal of Data Science, 2010 - researchgate.net
Missing data are a common problem for researchers working with surveys and other types of
questionnaires. Often, respondents do not respond to one or more items, making the …
questionnaires. Often, respondents do not respond to one or more items, making the …
Missing data and multiple imputation in the context of multivariate analysis of variance
WH Finch - The Journal of Experimental Education, 2016 - Taylor & Francis
Multivariate analysis of variance (MANOVA) is widely used in educational research to
compare means on multiple dependent variables across groups. Researchers faced with the …
compare means on multiple dependent variables across groups. Researchers faced with the …
On the use of student evaluation of teaching: a longitudinal analysis combining measurement issues and implications of the exercise
Multi item questionnaires are widely used to collect students' evaluation of teaching at
university. This article makes an attempt to analyse students' evaluation on a broad …
university. This article makes an attempt to analyse students' evaluation on a broad …
Handling missing data in item response theory. Assessing the accuracy of a multiple imputation procedure based on latent class analysis
A critical issue in analyzing multi-item scales is missing data treatment. Previous studies on
this topic in the framework of item response theory have shown that imputation procedures …
this topic in the framework of item response theory have shown that imputation procedures …
Missing data and attrition
The study of change rests on the assumption that observed differences in measurements
over time reflect true change in the construct being measured. If measurement properties …
over time reflect true change in the construct being measured. If measurement properties …
Understanding the intention-to-behaviour relationship for adolescents: an application of the multi-process action control model
The multi-process action control model (M-PAC) is an integrative model specifically
designed to evaluate intention-behaviour gaps. To date, however, the processes through …
designed to evaluate intention-behaviour gaps. To date, however, the processes through …
The impact of missing data on the detection of nonuniform differential item functioning
WH Finch - Educational and Psychological Measurement, 2011 - journals.sagepub.com
Missing information is a ubiquitous aspect of data analysis, including responses to items on
cognitive and affective instruments. Although the broader statistical literature describes …
cognitive and affective instruments. Although the broader statistical literature describes …
[图书][B] The performance of multiple imputation and full information maximum likelihood for missing ordinal data in structural equation models
ED Teman - 2012 - search.proquest.com
Monte Carlo simulation techniques were used to compare the performance of full
information maximum likelihood (FIML), multiple imputation, and listwise deletion to handle …
information maximum likelihood (FIML), multiple imputation, and listwise deletion to handle …