Mixed Effects Models on Test Science Research Document Library https://research.testscience.org/keywords/mixed-effects-models/ Recent content in Mixed Effects Models on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Tue, 01 Jan 2019 00:00:00 +0000 The Effect of Extremes in Small Sample Size on Simple Mixed Models- A Comparison of Level-1 and Level-2 Size https://research.testscience.org/post/2019-the-effect-of-extremes-in-small-sample-size-on-simple-mixed-models-a-comparison-of-level-1-and-level-2-size/ Tue, 01 Jan 2019 00:00:00 +0000 https://research.testscience.org/post/2019-the-effect-of-extremes-in-small-sample-size-on-simple-mixed-models-a-comparison-of-level-1-and-level-2-size/ We present a simulation study that examines the impact of small sample sizes in both observation and nesting levels of the model on the fixed effect bias, type I error, and the power of a simple mixed model analysis. Despite the need for adjustments to control for type I error inflation, our findings indicate that smaller samples than previously recognized can be used for mixed models under certain conditions prevalent in applied research. We present a simulation study that examines the impact of small sample sizes in both observation and nesting levels of the model on the fixed effect bias, type I error, and the power of a simple mixed model analysis. Despite the need for adjustments to control for type I error inflation, our findings indicate that smaller samples than previously recognized can be used for mixed models under certain conditions prevalent in applied research.

Suggested Citation

Carter, Kristina A, Heather M Wojton, and Stephanie T Lane. “The Effect of Extremes in Small Sample Size on Simple Mixed Models: A Comparison of Level-1 and Level-2 Size.” The ITEA Journal of Test and Evaluation 40, no. 1 (2019): 16–29.

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]]> The Purpose of Mixed-Effects Models in Test and Evaluation https://research.testscience.org/post/2019-the-purpose-of-mixed-effects-models-in-test-and-evaluation/ Tue, 01 Jan 2019 00:00:00 +0000 https://research.testscience.org/post/2019-the-purpose-of-mixed-effects-models-in-test-and-evaluation/ Mixed-effects models are the standard technique for analyzing data with grouping structure. In defense testing, these models are useful because they allow us to account for correlations between observations, a feature common in many operational tests. In this article, we describe the advantages of modeling data from a mixed-effects perspective and discuss an R package—ciTools—that equips the user with easy methods for presenting results from this type of model. Suggested Citation Haman, John, Matthew Avery, and Heather Wojton. Mixed-effects models are the standard technique for analyzing data with grouping structure. In defense testing, these models are useful because they allow us to account for correlations between observations, a feature common in many operational tests. In this article, we describe the advantages of modeling data from a mixed-effects perspective and discuss an R package—ciTools—that equips the user with easy methods for presenting results from this type of model.

Suggested Citation

Haman, John, Matthew Avery, and Heather Wojton. “The Purpose of Mixed-Effects Models in Test and Evaluation.” The ITEA Journal of Test and Evaluation 40, no. 4 (2019): 249–55.

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]]> Prediction Uncertainty for Autocorrelated Lognormal Data with Random Effects https://research.testscience.org/post/2017-prediction-uncertainty-for-autocorrelated-lognormal-data-with-random-effects/ Sun, 01 Jan 2017 00:00:00 +0000 https://research.testscience.org/post/2017-prediction-uncertainty-for-autocorrelated-lognormal-data-with-random-effects/ Accurately presenting model estimates with appropriate uncertainties is critical to the credibility and defensibility of anypiece of statistical analysis. When dealing with complex data that require hierarchical covariance structures, many of the standardapproaches for visualizing uncertainty are insufficient. One such case is data fit with log-linear autoregressive mixed effectsmodels. Data requiring such an approach have three exceptional characteristics.1. The data are sampled in “groups” that exhibit variation unexplained by other model factors. Accurately presenting model estimates with appropriate uncertainties is critical to the credibility and defensibility of anypiece of statistical analysis. When dealing with complex data that require hierarchical covariance structures, many of the standardapproaches for visualizing uncertainty are insufficient. One such case is data fit with log-linear autoregressive mixed effectsmodels. Data requiring such an approach have three exceptional characteristics.1. The data are sampled in “groups” that exhibit variation unexplained by other model factors.2. The data are sampled over time and exhibit autocorrelation.3. The data originate from a skewed distribution.These data are addressed using a log-linear autoregressive mixed model (LLARMM), which accounts for each of thesecharacteristics.

Suggested Citation

Freeman, Laura J, and Matthew R Avery. Lognormal Data with Random Effects. IDA Document NS D-8629. Alexandria, VA: Institute for Defense Analyses, 2017.

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