By Craig K. Enders PhD
Jogging readers step-by-step via advanced techniques, this ebook interprets lacking info concepts into whatever that utilized researchers and graduate scholars can comprehend and make the most of of their personal study. Enders explains the reason and procedural information for optimum probability estimation, Bayesian estimation, a number of imputation, and types for dealing with lacking no longer at random (MNAR) facts. Easy-to-follow examples and small simulated facts units illustrate the thoughts and make clear the underlying rules. The better half site (www.appliedmissingdata.com) comprises info documents and syntax for the examples within the publication in addition to updated info on software program. The publication is on the market to great researchers whereas offering a degree of aspect that might fulfill quantitative experts.
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Extra resources for Applied Missing Data Analysis (Methodology In The Social Sciences)
1. To begin, I used the job performance scores to create a binary missing data indicator and subsequently used indepen- An Introduction to Missing Data 19 dent t tests to assess group mean differences on IQ and psychological well-being. 001. 19. Collectively, these tests suggest that the job performance ratings are not MCAR because the missing and observed cases systematically differ with respect to IQ. This conclusion is correct because I deleted job performance scores for the cases in the lower half of the IQ distribution.
The so-called cohort-sequential design (Duncan, Duncan, & Hops, 1996; Nesselroade & Baltes, 1979) is a common planned missing data design that is ideally suited for this type of research question. The basic idea behind the cohort-sequential design is to combine a number of shortterm longitudinal studies into a single longitudinal data analysis. You do this by sampling different age cohorts at the initial data collection wave and following each cohort over the same period. , 1996). , the 12-year-olds have missing data at ages 15, 16, and 17, the 13-year-olds have missing data at ages 12, 16, and 17, and so on).
1997, p. 354; Schafer & Graham, 2002, p. 152), but the only way to evaluate the MAR assumption is to collect follow-up data from the missing respondents. Of course, this is difficult or impossible in many situations. Sensitivity analyses are also useful for assessing the potential impact of MNAR data. Graham et al. (1997, pp. 354–358) provide a good illustration of a sensitivity analysis; I discuss these procedures in Chapter 10. 8 AN INCLUSIVE ANALYSIS STRATEGY The preceding section is overly simplistic because it suggests that the MAR assumption is automatically satisfied when the “cause” of missingness is a measured variable.
Applied Missing Data Analysis (Methodology In The Social Sciences) by Craig K. Enders PhD