Model Assumptions, Alignment, Missing Data, and Dropout
The third module includes a wide variety of topics related to power and sample size analysis. First, we examine multivariate and mixed models, their assumptions, and how these assumptions impact power. Afterwards, we focus on aligning the features of data analysis and power analysis as well as the consequences of misalignment. Then we focus on missing data from sources like participant drop-out, machine failures or data entry errors; and how to account for missing data by adjusting your sample size. This module highlights several important features to consider in power and sample size analysis. To conclude the module, you will walk through an exercise problem to solve for power for a multilevel study independently.
- Compare and contrast univariate, multivariate, and mixed linear models.
- Describe how model assumptions impact power analysis.
- Examine the features that must be aligned to compute power and sample size correctly.
- Give examples of what problems can happen if power and data analysis are not aligned.
- Define and describe the types of missing data.
- Predict missing data in a given research design.
- Describe the assumptions of the multivariate linear and reversible mixed models.
- Describe the sources for predicting missing data in a design.
- Define and identify continuous, binary, and Poisson outcomes.
- Demonstrate how to increase sample size to account for data missing at random.
- 3.0 Model Assumptions
- 3.1 Alignment of Power and Data Analysis
- 3.2 Predicting Missing Data and Dropout
- 3.3 Accounting for Missing Data and Dropout
- 3.4 Continuous, Binary and Poisson Outcomes
- 3.5 Module Three Activities