The Impact of Missing Data on Sample Reliability Estimates
Implications for Reliability Reporting Practices
Bibliographic Data
| ID | 20284358 |
|---|---|
| Authors | Craig K Enders (0000-0002-7048-8369, University of Nebraska–Lincoln, corresponding author) |
| Year | 2004 |
| Volume | 64 |
| Issue | 3 |
| Pages | 419-436 |
| Publication date | 2004-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164403261050 |
| OpenAlex | W2152173046 |
| Language | EN |
| Citations received | 14 |
| References cited | 18 |
A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that the EMapproach yields (a) less bias in reliability estimates, (b) dramatically reduces cross-sample fluctuation of estimates, and (c) yields more accurate confidence intervals. Implications for reliability reporting practices are discussed, and the EM procedure is demonstrated using a heuristic data set
Confidence interval · Covariance matrix · Data set · Econometrics · Expectation–maximization algorithm · Heuristic · Mathematical optimization · Maximization · Maximum likelihood · Missing data · Reliability (semiconductor) · Sample (material) · Sample size determination · Standard error · Statistics · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Distribution Estimation and Applications · Statistical Methods and Bayesian Inference
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Development and Assessment of Short and Very Short Forms of the Infant Behavior Questionnaire–Revised
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Évaluation de l’efficacité des méthodes de traitement des valeurs manquantes dans le contexte d’échelles de mesure de type Likert
Do required fields in online surveys in the social sciences impair reliability
Comparison of the Various Methods Used in Solving Missing Data Problems
The Effect of Modeling Missing Data With IRTree Approach on Parameter Estimates Under Different Simulation Conditions
Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing
Evaluating the Performances of Missing Data Handling Methods in Ability Estimation From Sparse Data
Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data
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Overestimation of Internal Consistency by Coefficient Omega in Data Giving Rise to a Centroid-Like Factor Solution
Father Involvement in Infancy
Statistical analysis with missing data
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Using the Expectation Maximization Algorithm to Estimate Coefficient Alpha for Scales With Item-Level Missing Data.
A comparison of inclusive and restrictive strategies in modern missing data procedures.
On Structural Equation Modeling with Data that are not Missing Completely at Random
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The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models
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The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models With Missing Data
The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data
Reliability Generalization
Maximizing the Usefulness of Data Obtained with Planned Missing Value Patterns
Missing Data in Likert Ratings
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| Citations per year | 0.67 |
|---|---|
| Citation span | 2005 - 2025 (21) |
| Highly cited | No |