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By Craig K. Enders PhD
Strolling readers step-by-step via complicated suggestions, this publication interprets lacking information ideas into anything that utilized researchers and graduate scholars can comprehend and make the most of of their personal examine. Enders explains the reason and procedural info for max chance estimation, Bayesian estimation, a number of imputation, and versions for dealing with lacking no longer at random (MNAR) facts. Easy-to-follow examples and small simulated facts units illustrate the recommendations and make clear the underlying ideas. The better half site (www.appliedmissingdata.com) contains information records and syntax for the examples within the e-book in addition to updated details on software program. The ebook is obtainable to sizeable researchers whereas offering a degree of aspect that might fulfill quantitative experts.
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Extra info for Applied Missing Data Analysis (Methodology In The Social Sciences)
Example text
00. These variables had complete data, so this power estimate serves as a useful benchmark for assessing the impact of planned missingness. 8. 29 — commensurate with overall reduction in sample size. For example, consider the correlation between Q1 and Q3. A 33% missing data rate on Q3 produced a 1% drop in power. The correlation between Q3 and Q5 is even more remarkable because one-third of the sample had complete data on this variable pair, yet power decreased by only 10%. The fact that power did not decrease dramatically is largely a by-product of maximum likelihood estimation.
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.
Every research study involves compromises, so you have to decide whether collecting additional variables offsets the resulting loss of power. Of course, increasing the sample size will An Introduction to Missing Data 23 always improve power, but this may not be feasible. In any case, planned missing data designs are highly useful and underutilized tools that will undoubtedly increase in popularity as researchers become familiar with their benefits. 11 THE THREE-FORM DESIGN Researchers in many disciplines use multiple-item questionnaires to measure complex constructs.