Abstract
This chapter introduces a cluster sampling model and then adapts that model to develop generalizations of split plot models. Split plot models are among the simplest of the mixed models considered in ALM-III in that they involve only two independent error terms (or, equivalently, two variance components). The chapter closes with a discussion of issues related to properly identifying the existence of two random error terms
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Christensen, R. (2015). Analysis of variance, design, and regression: Linear modeling for unbalanced data (2nd ed.). Boca Raton, FL: Chapman and Hall/CRC Pres.
Christensen, R. (1984). A note on ordinary least squares methods for two-stage sampling. Journal of the American Statistical Association, 79, 720–721.
Christensen, R. (1987). The analysis of two-stage sampling data by ordinary least squares. Journal of the American Statistical Association, 82, 492–498.
Cornell, J. A. (1988). Analyzing mixture experiments containing process variables. A split plot approach. Journal of Quality Technology, 20, 2–23.
Fisher, R. A. (1935). The design of experiments, (9th ed., 1971). New York: Hafner Press.
Mathew, T., & Sinha, B. K. (1992). Exact and optimum tests in unbalanced split-plot designs under mixed and random models. Journal of the American Statistical Association, 87, 192–200.
Monlezun, C. J., & Blouin, D. C. (1988). A general nested split-plot analysis of covariance. Journal of the American Statistical Association, 83, 818–823.
Skinner, C. J., Holt, D., & Smith, T. M. F. (1989). Analysis of complex surveys. New York: Wiley.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Christensen, R. (2020). Split Plot Models. In: Plane Answers to Complex Questions. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-32097-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-32097-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32096-6
Online ISBN: 978-3-030-32097-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)