Tweet
Thanks to all of those who replied to my post with suggested journal
articles, many happy nights were spent wading through statistical
formulas!
I have summarised the useful papers on single subject designs and
have added a few of my thoughts on the subject.
I believe that performing standard tests for normality, variance and
dependence will help guide the researcher to the appropriate test.
The debate surrounding repeated measures is somewhat of a moot
point. If the data fits the assumptions and the most logical test is
chosen, then the analysis will be valid. Furthermore, those with
advanced statistical knowledge or access to a statistician could
consider a mixed modelling approach (see the referenced website).
Analysing multiple individuals in a single subject design, can overcome
limitations regards generalising results.
There is little research on regression techniques and single subject
design, most articles deal with tests for comparison. Although, if the
ratio of predictor variables to criterion measures is low, bootstrapping
may be a useful tool when only within subject variability is encountered.
Nick Flyger
Assistant Lecturer and Masters Candidate
Otago University School of Physical Education
Dunedin, New Zealand
03 4799117
Backman, C. L., & Harris, S. R. (1999). Case studies, single-
subject research and N of 1 randomized trials. American Journal
of Physical Medicine and Rehabilitation, 78(2), 170-176.
Backman, C. L., & Harris, S. R. (1999). Case studies, single-
subject research, and N of 1 randomized trials. American journal
of physical medicine and rehabilitation, 78(2), 170-176.
Bates, B. T. (1996). Single-subject methodology: an alternative
approach. Medicine & Science in Sports & Exercise., 28(5), 631-
638.
Bates, B. T., James, C. R., & Dufek, J. S. (2004). Single Subject
Analysis. In N. Stergiou (Ed.), Innovative analysis of human
movement. Champaign, Ill: Human Kinetics.
Boneau, C. A. (1960). The effects of violations of assumptions
underlying the t-test. Psychological Bulletin, 57, 49-64.
Dufek, J. S., Bates, B. T., Stergiou, N., & James, C. R. (1995).
Interactive effects between group and single-subject response
patterns. Human Movement Science, 14(3), 301-323.
Gagnon, F. A., Susak, L. E., Phillips, N., Wing, P. C., & Tsang, I.
K. Y. (1993). Study designs for microgravity human physiology
experiments. Aviation, Space, and Environmental Medicine, 64,
153-157.
Hopkins, W. G. (2004). A new view of statistics. Retrieved
19/10/2004, 2004, from http://www.sportsci.org/resource/stats/
Horne, G. P., Yang, M. C. K., & Ware, W. B. (1982). Time series
analysis for single-subject designs. Psychological Bulletin, 91(1),
178-189.
James, C. R., & Bates, B. T. (1997). Experimental and statistical
design issues in human movement research. Measurement in
Physical Education and Exercise Science, 1(1), 55-69.
Johannessen, T., Fosstvedt, D., & Petersen, H. (1990). Statistical
aspects of controlled single subject trials. Family Practice, 7(4),
325-328.
Kenny, D. A., & Judd, C. M. (1986). Consequences of violating
the independence assumption in analysis of variance. Psychological
Bulletin, 99(3), 422-431.
Mullineaux, D. R., Bartlett, R. M., & Bennett, S. (2001). Research
design and statistics in biomechanics and motor control. Journal of
Sports Sciences, 19, 739-760.
Phillips, J. P. N. (1983). Serially correlated errors in some single-
subject designs. British Journal of Mathematical and Statistical
Psychology, 36, 269-280.
Reboussin, D. M., & Morgan, T. M. (1996). Statistical
considerations in the use and analysis of single-subject designs.
Medicine & Science in Sports & Exercise., 28(5), 639-644.
Rochon. (1990). A statistical model for the "N-of-1" study.
Journal of Clinical Epidemiology, 43(5), 499-508.
-----------------------------------------------------------------
To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl
For information and archives: http://isb.ri.ccf.org/biomch-l
-----------------------------------------------------------------
articles, many happy nights were spent wading through statistical
formulas!
I have summarised the useful papers on single subject designs and
have added a few of my thoughts on the subject.
I believe that performing standard tests for normality, variance and
dependence will help guide the researcher to the appropriate test.
The debate surrounding repeated measures is somewhat of a moot
point. If the data fits the assumptions and the most logical test is
chosen, then the analysis will be valid. Furthermore, those with
advanced statistical knowledge or access to a statistician could
consider a mixed modelling approach (see the referenced website).
Analysing multiple individuals in a single subject design, can overcome
limitations regards generalising results.
There is little research on regression techniques and single subject
design, most articles deal with tests for comparison. Although, if the
ratio of predictor variables to criterion measures is low, bootstrapping
may be a useful tool when only within subject variability is encountered.
Nick Flyger
Assistant Lecturer and Masters Candidate
Otago University School of Physical Education
Dunedin, New Zealand
03 4799117
Backman, C. L., & Harris, S. R. (1999). Case studies, single-
subject research and N of 1 randomized trials. American Journal
of Physical Medicine and Rehabilitation, 78(2), 170-176.
Backman, C. L., & Harris, S. R. (1999). Case studies, single-
subject research, and N of 1 randomized trials. American journal
of physical medicine and rehabilitation, 78(2), 170-176.
Bates, B. T. (1996). Single-subject methodology: an alternative
approach. Medicine & Science in Sports & Exercise., 28(5), 631-
638.
Bates, B. T., James, C. R., & Dufek, J. S. (2004). Single Subject
Analysis. In N. Stergiou (Ed.), Innovative analysis of human
movement. Champaign, Ill: Human Kinetics.
Boneau, C. A. (1960). The effects of violations of assumptions
underlying the t-test. Psychological Bulletin, 57, 49-64.
Dufek, J. S., Bates, B. T., Stergiou, N., & James, C. R. (1995).
Interactive effects between group and single-subject response
patterns. Human Movement Science, 14(3), 301-323.
Gagnon, F. A., Susak, L. E., Phillips, N., Wing, P. C., & Tsang, I.
K. Y. (1993). Study designs for microgravity human physiology
experiments. Aviation, Space, and Environmental Medicine, 64,
153-157.
Hopkins, W. G. (2004). A new view of statistics. Retrieved
19/10/2004, 2004, from http://www.sportsci.org/resource/stats/
Horne, G. P., Yang, M. C. K., & Ware, W. B. (1982). Time series
analysis for single-subject designs. Psychological Bulletin, 91(1),
178-189.
James, C. R., & Bates, B. T. (1997). Experimental and statistical
design issues in human movement research. Measurement in
Physical Education and Exercise Science, 1(1), 55-69.
Johannessen, T., Fosstvedt, D., & Petersen, H. (1990). Statistical
aspects of controlled single subject trials. Family Practice, 7(4),
325-328.
Kenny, D. A., & Judd, C. M. (1986). Consequences of violating
the independence assumption in analysis of variance. Psychological
Bulletin, 99(3), 422-431.
Mullineaux, D. R., Bartlett, R. M., & Bennett, S. (2001). Research
design and statistics in biomechanics and motor control. Journal of
Sports Sciences, 19, 739-760.
Phillips, J. P. N. (1983). Serially correlated errors in some single-
subject designs. British Journal of Mathematical and Statistical
Psychology, 36, 269-280.
Reboussin, D. M., & Morgan, T. M. (1996). Statistical
considerations in the use and analysis of single-subject designs.
Medicine & Science in Sports & Exercise., 28(5), 639-644.
Rochon. (1990). A statistical model for the "N-of-1" study.
Journal of Clinical Epidemiology, 43(5), 499-508.
-----------------------------------------------------------------
To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl
For information and archives: http://isb.ri.ccf.org/biomch-l
-----------------------------------------------------------------