Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Educational Measurement and Research

Major Professor

Ferron John, Ph.D.

Committee Member

Robert Dedrick, Ph.D.

Committee Member

Yi-hsin Chen, Ph.D.

Committee Member

Tony Tan, Ed.D.


extraneous variables, Single-case experiment design, treatment effect, SCD


The effect of time-varying extraneous variables has been studied in other statistical analyses such as using Kaplan–Meier or Cox regression analysis in survival analyses. Nonetheless, the effect of modeling versus not modeling individual specific time varying extraneous variables has not been explored in multiple-baseline single case designs through Monte Carlo simulation studies. Therefore, in my dissertation, I used simulation methods to explore for a variety of conditions (varying in the number of participants, number of observations per participant, type of extraneous variable effect, size of the true intervention effect) the impact of extraneous variables on bias and standard error of treatment effect estimates, as well as confidence interval coverage. I examined the degree to which bias, standard error, and confidence interval coverage are affected by including measures of the extraneous variables in the multilevel model used to estimate the average treatment effect. Results showed that not modeling the extraneous variable effects led to substantial biases in the treatment effect estimates and 95% confidence intervals with coverage rates less than 50%. Modeling the extraneous variables led to unbiased effect estimates and confidence intervals for the treatment effect with 95% coverage rates. Several limitation and implications are discussed in this dissertation. The simulation conditions as well as the outcomes could be expanded in future research. Also, different extraneous variable distributions can be modeled and tested after reviewing more single case design literature to identify other types of extraneous variable effects. Finally, methods for identifying and tracking changes in extraneous variables need to be developed and studied, so that it is feasible to include these variables in the multilevel model used to estimate treatment effects in multiple-baseline studies.