Download Applied Missing Data Analysis (Methodology In The Social by Craig K. Enders PhD PDF

By Craig K. Enders PhD

Strolling readers step-by-step via complicated strategies, this booklet interprets lacking facts concepts into whatever 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 optimum chance estimation, Bayesian estimation, a number of imputation, and versions for dealing with lacking no longer at random (MNAR) info. Easy-to-follow examples and small simulated information units illustrate the innovations and make clear the underlying rules. The spouse site ( contains info documents and syntax for the examples within the e-book in addition to updated details on software program. The ebook is on the market to substantial researchers whereas supplying a degree of element that may fulfill quantitative experts.

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Extra resources for Applied Missing Data Analysis (Methodology In The Social Sciences)

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7 Note. A check mark denotes complete data. 11. 2 Note. A check mark denotes complete data. more powerful than a complete-data design that used the same number of data points. This has important implications for designing a longitudinal study. , data point) costs $50 to administer and your grant budget allows you to collect 1,000 assessments. ’s simulation results suggest that collecting complete data from N participants will actually yield less power than collecting incomplete data from a larger number of respondents.

This makes it very difficult to get accurate power estimates from standard analysis techniques. , 2007). Monte Carlo computer simulations are a useful alternative that you can use to estimate power for virtually any analysis. This section describes how to use computer simulations to estimate power for the three-form design, but the basic approach generalizes to any number of power analyses, with or without missing data. Paxton, Curran, Bollen, Kirby, and Chen (2001) give a more detailed overview of Monte Carlo methodology, and Muthén and Muthén (2002) illustrate Monte Carlo power simulations.

Many statistical software packages have built-in data generation routines that do not require much programming, so it is relatively straightforward to perform power simulations. Structural equation modeling packages are particularly useful because they offer a variety of different data generation and analysis options. com. An Introduction to Missing Data 31 The first step of a computer simulation is to specify the population parameters. 30. This is a very straightforward data generation model, but specifying the population parameters is typically the most difficult aspect of a computer simulation.

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