Estimating slope and level change in N = 1 designs.
The current study proposes a new procedure for separately estimating slope change and level change between two adjacent phases in single-case designs. The procedure eliminates baseline trend from the whole data series before assessing treatment effectiveness. The steps necessary to obtain the estimates are presented in detail, explained, and illustrated. A simulation study is carried out to explore the bias and precision of the estimators and compare them to an analytical procedure matching the data simulation model. The experimental conditions include 2 data generation models, several degrees of serial dependence, trend, and level and/or slope change. The results suggest that the level and slope change estimates provided by the procedure are unbiased for all levels of serial dependence tested and trend is effectively controlled for. The efficiency of the slope change estimator is acceptable, whereas the variance of the level change estimator may be problematic for highly negatively autocorrelated data series.
Faculty of Psychology, Universitat de Barcelona, Spain.
Antonio
A
Solanas
Rumen
R
Manolov
Patrick
P
Onghena
D000704:Analysis of Variance; D001521:Behavior Therapy / Q000706:statistics & numerical data*; D015982:Bias; D003196:Computer Graphics; D003198:Computer Simulation; D003625:Data Collection / Q000706:statistics & numerical data*; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D009010:Monte Carlo Method; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D012984:Software