Analyzing Single System Design Data
Single system, or single case, design studies are a convenient method for evaluating practice, allowing professionals to track clients' response to treatment and change over time. They also allow researchers to gather data where it might be difficult to conduct a study involving treatment and control groups; in a school setting, or a community mental health agency, for example, random assignment may be impossible, whereas individual student or client progress across time can be more easily monitored. This pocket guide reviews a wide range of techniques for analyzing single system design data, including visual analysis methods, graphical methods, and statistical methods. From basic visual observation to complex ARIMA statistical models for use with interrupted time series designs, numerous data analysis methods are described and illustrated in this unique and handy book. The author frankly describes limitations and strengths of the data analysis methods so that readers can select an appropriate method and use the results responsibly in order to improve practice and client well-being. This accessible yet in-depth introduction will serve as a highly practical resource for doctoral students and researchers alike.
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agoraphobia analysis of single Applied Behavior Analysis ARIMA models Ascher Autocorrelation function ACF autocorrelation structure autoregressive baseline and treatment baseline data baseline phase data baseline trend change in level Chapter client Coefficient Confidence interval limit data from Biglan decision rules dependent variable design data design phase deviation bands extended baseline group comparison hierarchical linear models Huitema illustrated inferences internal validity interquartile range interrupted time series Kazdin McCain & McCleary mean trend line methodology moving average Nugent number of data OLS trend line ordinary least squares Ottenbacher parameter Partial autocorrelation function phase data points phase mean line phase transition phase trend Prineville data random variation regression model regression-discontinuity model replication series representing residuals response profiles short time series shown in Figure sigma unit single case design small numbers statistical model statistical process control statistically significant three sigma bands treatment phase data trend-based visual analysis Wagenaar