ISSN 2043-8087
Journal of Experimental Psychopathology
 Volume 2, Issue 2, 139-169, 2011
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How to conduct and statistically analyze case-based time series studies, one patient at a time

Michael R. Nash, Ph.D. (a), Jeffrey J. Borckardt, Ph.D. (b), Amineh Abbasa (a), Erin Gray (a)
(a) University of Tennessee
(b) Medical University of South Carolina

Volume 2, Issue 2, 2011, Pages 139-169

We describe how to conduct case-based time-series studies in a practice setting. First we offer a sampler of clinical research questions that can be addressed by case-based studies. Second we construct a hypothetical case that illustrates the structure of a time-series project now being conducted in a university-based outpatient psychotherapy clinic. This case also familiarizes the reader with the data array of a time-series study. Third, we present two actual case studies, each carried out in a different outpatient setting. Fourth, we move to the logistics of how a time-series study is efficiently conducted in an applied setting. Finally we provide a step-by-step description of Simulation Modeling Analysis (SMA) for time-series data and how the practitioner can use freely-available software to analyze his or her real-world clinical practice data (i.e., relatively short streams of time-series data).

Table of Contents
I. The domain of clinical research questions addressed by case-based time-series studies
  Questions of Improvement: Looking for an Effect for Phase
    Time-series improvement studies bridge the gap between practice and the laboratory.
  Questions of Change-Process: Looking for Patterns of Change During Treatment
  How Time-series Process-Change Studies Help Bridge the Gap Between Practice and the Laboratory.
II. A real practice-based time-series project illustrated by an imaginary case
  What is Autocorrelation?
  How do we calculate Autocorrelation?
  Why is Autocorrelation Important?
  Applying SMA to the Hypothetical Case
  Case 1: An Improvement Study in an EAP Setting: Looking for an Effect of Phase.
  Case 2: Private-practice setting: looking for patterns of change during treatment.
    Process-Change: The Cross-Lag Correlation.
    Time, Cause, inference, and Aristotle.
  Creating a Data Stream
    Source and Content of the Observations.
    Sample Evenly and Frequently.
    Supplementing with a Standardized Outcome Measure.
  Maintaining the Datastream.
  Ethical Considerations
  Data Fluctuation in Group Designs
  Data Fluctuation in Time-series Studies
  Autocorrelation as Nuisance and Raison d'Etre
    The Nuisance.
    The Promise.
  Simulation Modeling Analysis for Time-Series (SMA: Step by Step) Conclusion
Appendix: Comparison of SMA to ITSACORR and Explanation of Simulation Modeling Calculations Using an Example Data Set
  Simulation Modeling Explained with an Example:

Correspondence to
Michael R. Nash, Professor of Psychology, Psychology Department, 307 Austin Peay Building, University of Tennessee, Knoxville, TN 37996-0900.

time-series, statistics psychotherapy outcome

Received 26 Jul 2010; Revised 6 Dec 2010; Accepted 14 Dec 2010; In Press 5 May 2011

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