By Craig K. Enders PhD
Strolling readers step-by-step via complicated suggestions, this publication interprets lacking information strategies into anything that utilized researchers and graduate scholars can comprehend and make the most of of their personal learn. Enders explains the reason and procedural info for max chance estimation, Bayesian estimation, a number of imputation, and versions for dealing with lacking now not at random (MNAR) facts. Easy-to-follow examples and small simulated info units illustrate the ideas and make clear the underlying rules. The spouse site (www.appliedmissingdata.com) contains info records and syntax for the examples within the e-book in addition to up to date details on software program. The publication is out there to substantial researchers whereas delivering a degree of aspect that might fulfill quantitative experts.
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Additional resources for Applied Missing Data Analysis (Methodology In The Social Sciences)
Of course, the problem with this scenario is that there is no way to estimate φ. , φ). It ends up that these conditions depend on how you analyze the data. Rubin showed that likelihood-based analyses such as maximum likelihood estimation and multiple imputation do not require information about φ if the data are MCAR or MAR. For this reason, the missing data literature often describes the MAR mechanism as ignorable missingness because there is no need to estimate the parameters of the missing data distribution when performing analyses.
A check mark denotes complete data. 5. Missing Data Pattern for a Three-Form Design with Eight Questionnaires Item Sets X A B C Form Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 1 2 3 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ — ✓ ✓ — ✓ — ✓ ✓ — ✓ — ✓ ✓ — ✓ ✓ Items 10 10 10 10 10 10 10 10 Note. A check mark denotes complete data. X and the remaining five items to set A). Graham et al. (1996) used a computer simulation study to investigate this issue and found that splitting the questionnaire across multiple item sets reduced the standard errors from a regression analysis.
Furthermore, finding evidence for or against MCAR does not change the recommendation to use maximum likelihood or multiple imputation. However, identifying individual variables that are not MCAR is potentially useful because there may be a relationship between these variables and the probability of missingness. , 2001; Rubin, 1996; Schafer, 1997; Schafer & Graham, 2002). 18 APPLIED MISSING DATA ANALYSIS To illustrate how you might use the information from an MCAR test, suppose that a psychologist is studying quality of life in a group of cancer patients and finds that patients who refused the quality of life questionnaire have a higher average age and a lower average education than the patients who completed the survey.