Missing data techniques for structural equation modeling.

By P. Allison. Journal of Abnormal Psychology, 2003

1,356 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6c22369598059d7c191db52006d697ef7d07c352

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In Management Science and Operations Research

1. Ruth A. Baer
2. Varun Grover
3. K. Loya
4. C. Zygmont
5. H. Ismail
6. O. Siu
7. C. Kamau
8. A. D. De Hoogh

In Statistics and Probability

1. Paul T. von Hippel
2. D. Betsy McCoach
3. Anne C. Black
4. H. Ro
5. K. Loya
6. C. Zygmont
7. H. Ismail
8. O. Siu

Citations in the corpus

• Self-assessment in the process of self-regulated learning and its relationship with academic achievement (2020, methodology): “Hence, full information maximum likelihood estimation, which is generally favored in structural equation modeling (SEM) under the MCAR assumption (Allison 2003), was adopted to deal with missing data.”
• The Influence of Childhood (2012, methodology): “Data were incomplete and deemed missing at random (Allison, 2003; Schafer & Graham, 2002), showing mild departures from normality, with absolute univariate skewness and kurtosis values slightly different from zero (Enders, 2001). Consequently, an expectationmaximization (EM) covariance matrix was es”
• Employee engagement and job performance in Lebanon: the mediating role of creativity (2019, methodology): “05), listwise deletion of missing data was employed since it does not present bias into the data (Allison, 2003).”
• The Empathy Assessment Index (EAI): A Confirmatory Factor Analysis of a Multidimensional Model of Empathy (2011, methodology): “Following recent recommendations (Allison, 2003; Peugh & Enders, 2004) that listwise deletion method is prone to biased estimates, we used the expectation maximization algorithm (Schafer & Graham, 2002) to impute the missing values.”
• Motivation, Gender, and Possible Selves (2013, background): “This procedure yields unbiased estimates under relatively moderate assumptions, which in the current study are fully satisfied (Allison, 2003; Schafer & Graham, 2002).”
• The impact of visual factors on noise annoyance among people living in the vicinity of wind turbines (2008, methodology): “Missing values in the SEM were handled using the full information maximum likelihood (FIML) estimator (see, e.g., Allison, 2003).”
• A Brief Guide to Structural Equation Modeling (2006, methodology): “Allison’s (2003) review of techniques such as listwise deletion, pairwise deletion, maximum likelihood (ML), and multiple imputation is quite informative for readers interested in details beyond the scope of this review.”
• Work–life balance: a longitudinal evaluation of a new measure across Australia and New Zealand workers (2014, result): “…most noticeably the full information maximum likelihood (FIML), when conducting SEM has been noted to be preferable by some authors (e.g. Graham 2009; Schlomer, Bauman and Card 2010), although any differences in results when using FIML compared to EM are typically noted to be small (Allison 2003).”
• Understanding students’ motivation towards proactive career behaviours through goal-setting theory and the job demands–resources model (2018, methodology): “Missing data were dealt with using maximum likelihood estimation in line with methodology by Allison (2003).”
• Ethical and Passive Leadership and Their Joint Relationships with Burnout via Role Clarity and Role Overload (2018, methodology): “Studies have shown that this method generally outperforms other methods to deal with missing data (Allison 2003; Kline 2005).”
• Attrition in developmental psychology (2017, methodology): “Consequently, FIML, unlike pairwise deletion, uses the appropriate sample size for standard error estimates (Allison, 2003; Enders, 2001) and is less likely to produce a non-positive definite matrix (Wothke, 1993).”