6.99 Epilogue: Assessing Client Performance

This chapter helps you use Therapy-Science’s iGraph™ to graph, visualize, and analyze your client’s performance for evidence of improvement over the course of therapy.

The first three sections in Chapter 6 focus on the fundamentals of single-case graphs including its graphical components and measurement basics (level, trend, variability/stability). The next four sections introduce single-case design concepts and provide different strategies to help you visualize and analyze client performance across different clinical scenarios. These include:

• investigating interventions when conventional baseline data is difficult or impossible to collect,
• using baseline alternatives such as reconstructed and retrospective baselines,
• adding a maintenance phase to the end of therapy to track self-management and independent use of the target skill,
• employing multiple phase-changes to handle changes in criteria and task difficulty, and
• including functional probe data to monitor generalization of skills acquired during an intervention.

The Multiple baseline / multipanel approach is explored at the end of the chapter. By displaying a series of interventions across several graphs, multiple baseline designs provide powerful, flexible and efficient ways to organize your intervention efforts.

It’s important to recognize that single-case clinical designs can be highly responsive to the needs as a critically thinking clinician. The designs described in this chapter provide a variety of examples to visually represent your client’s performance, evaluate improvement, and help check your own beliefs for personal bias and logical error related to the intervention (i.e., is your client really improving? What evidence can you provide that demonstrated the effectiveness of your therapy?). You should start to see that single-case designs and graphs are cognitive tools to aid in your evaluation and critical decision making about your client’s performance and progress.

Two important single-case components to manipulate for problem solving are data series and phases.

A data series represents the measurement of individual behavior that can be analyzed for its level, trend, and variability. These measures can help you determine the client’s average performance, their improvement over time, and whether the measurement is stable enough for you to be confident in the data you are collecting. A phase is the portion of a data series in which the treatment ingredients are relatively the same. A new phase line is usually added to a graph to mark the accomplishment of an interim goal when the intervention needs to be changed, or there is a new extrinsic event that needs to be noted because it could impact performance. The new phase line separates the data series into separate data paths within the data series. These phases can then be compared to one another to determine whether a noticeable change in the client’s performance has occurred, whether it’s important, due to the intervention, or marks the achievement of an important goal. Data paths from two different data series can be compared to determine whether one therapy is better than another or whether different interventions may be influencing one another.

Thought Questions and Exercises

To Baseline or Not to Baseline…. It’s not uncommon clinical practice to start therapy without collecting any baseline data, or just a single data point. Take a look at the examples below and consider the questions being asked about clinical progress in each situation. Here is the therapy data that you collected for your client’s artic problem so far:

When should your client achieve their goal? Here’s a simple data series of a client demonstrating improvement towards achieving their goal producing dysfluent speech in less than 5 percent of the 1-minute time-samples observed.

• Use a Therapy-Science analysis tool to help you predict what session your client may be expected to cross that 5% threshold?
• (bonus) Now use the boxplot feature of the trend line to visualize the performance variability of the Phase B data path. Using the interquartile range information, what session</wm range would you estimate that your client would most likely cross the 5% threshold?
  

  Bibliography

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  Bloom, M., & Britner, P. A. (2011). Client-Centered Evaluation: New Models for Helping Professionals. Pearson Higher Ed.

  Bloom, M., Fischer, J., & Orme, J. G. (2009). Evaluating Practice: Guidelines for the Accountable Professional (6th ed.). Pearson.

  Di Noia, J., & Tripodi, T. (2011). Single-case design for clinical social workers(Second edition). NASW Press Washington, DC.

  Orme, J, & Fischer,J. (2008). Single System designs. In Encyclopedia of Social Work/. Oxford University Press.

  Parsonson, B. S., & Baer, D. M. (1986). The graphic analysis of data. In A. Poling & W. Fuqua (Eds.), Research methods in applied behavior analysis (pp. 157–186). Springer.