Abstract
There are settings where students are placed in a particular environment for an extended period, for instance 6 or 12 months, such as internships and longitudinal clerkships. With appropriate and repeated, carefully planned assessments, we can obtain series of measurements of the same performance of interest, which help us to understand how performance changes over time, and how that change over time changes with specific events such as training or difficult situations. Even though numbers of students may be small in such settings, and it may come down to single cases in some settings, with sufficient numbers of carefully placed measurements we can use time series models to understand change. Equally, SCEDs are very popular in some fields where finding participants can be challenging, such as in Special Needs Education. Different types of models for small samples and case studies are discussed in this chapter with their relative pros and cons.
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References
Allison, D. B., & Gorman, B. S. (1993). Calculating effect sizes for meta-analysis: The case of the single case. Behaviour Research and Therapy, 31(6), 621–631. https://doi.org/10.1016/0005-7967(93)90115-B.
Allison, D. B., & Gorman, B. S. (1994). Make things as simple as possible, but no simpler: A rejoinder to Scruggs and Mastropieri. Behaviour Research and Therapy, 32(8), 885–890. https://doi.org/10.1016/0005-7967(94)90170-8.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
Box, G. E. P., & Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70(349), 70–79. https://doi.org/10.1080/01621459.1975.10480264.
Brockwell, P. J., & Davis, R. A. (2009). Time series: Theory and methods (2nd ed.). New York: Springer.
Hannan, E. J. (1970). Multiple time series. New York: Wiley.
Leppink, J. (2019). Statistical methods for experimental research in education and psychology. Cham: Springer. https://doi.org/10.1007/978-3-030-21241-4.
Leppink, J. (2020). In God we trust, all others bring data: A Bayesian approach to standard setting. Health Professions Education, https://doi.org/10.1016/j.hpe.2020.01.003.
Lindley, D. (1972). Bayesian statistics: A review. London: SIAM.
Ljung, G., M., & Box, G. E. P. (1978). On a measure of a lack of fit in time series models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297.
Michiels, B., Heyvaert, M., Meulders, A., & Onghena, P. (2017). Confidence intervals for single-case effect size measures based on randomization test inversion. Behavior Research Methods, 49(1), 363–381. https://doi.org/10.3758/s13428-016-0714-4.
Parker, R. I., Hagan-Burke, S., & Vannest, K. J. (2007). Percentage of all non-overlapping data (PAND): An alternative to PND. The Journal of Special Education, 40(4), 194–204. https://doi.org/10.1177/00224669070400040101.
Parker, R. I., Vannest, K. J., & Davis, J. L. (2011). Effect size in single-case research: A review of nine nonoverlap techniques. Behavior Modification, 35(4), 303–322. https://doi.org/10.1177/0145445511399147.
Scruggs, T. E., & Mastropieri, M. A. (1994). The utility of the PND statistic: A reply to Allison and Gorman. Behaviour Research and Therapy, 32(8), 879–883. https://doi.org/10.1016/0005-7967(94)90169-4.
Scruggs, T. E., & Mastropieri, M. A. (1998). Summarizing single-subject research: Issues and applications. Behavior Modification, 22(3), 221–242. https://doi.org/10.1177/01454455980223001.
Scruggs, T. E., & Mastropieri, M. A. (2001). How to summarize single-participant research: Ideas and applications. Exceptionality, 9(4), 227–244. https://doi.org/10.1207/S15327035EX0904_5.
Scruggs, T. E., Mastropieri, M. A., & Casto, G. (1987). The quantitative synthesis of single subject research: Methdology and validation. Remedial and Special Education, 8(2), 24–33. https://doi.org/10.1177/074193258700800206.
Tanious, R., & De, T, K., & Onghena, P. (2019). A multiple randomization testing procedure for level, trend, variability, overlap, immediacy, and consistency in single-case phase designs. Behaviour Research and Therapy, 119, 103414. https://doi.org/10.1016/j.brat.2019.103414.
White, O. R. (1987). Some comments concerning: “The quantitative synthesis of single-subject research”. Remedial and Special Education, 8(2), 34–39. https://doi.org/10.1177/074193258700800207.
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Leppink, J. (2020). Studies with Small Samples or Individuals. In: The Art of Modelling the Learning Process. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-43082-5_16
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