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Visual inspection is the primary data analysis technique in single-subject research. It entails plotting the data of individual participants, carefully examining these graphs, and making judgments regarding whether and to what extent the independent variable impacted the dependent variable. During this process, researchers primarily look for changes in the level, trend, and latency.

Visual Inspection

An example of visual inspection involves evaluating data from a single-subject study to determine treatment effectiveness based on level, trend, and latency. If a graph shows a high, increasing trend in the baseline condition that quickly changes to a lower, decreasing trend in the treatment condition, and then returns to a high level when baseline is reinstated, visual inspection strongly suggests the treatment is effective. Conversely, small changes in level and continuous trends across conditions suggest an ineffective treatment.

Visual Inspection Example

Advocates of group research raise several concerns regarding the visual inspection of data in single-subject designs. They argue that it may lack the sensitivity needed to detect weak treatment effects. Furthermore, visual inspection can be unreliable, as different researchers might interpret the same data differently. Finally, the overall judgments derived from visual inspection are difficult to efficiently summarize or compare across multiple studies.

Criticisms of Visual Inspection

When using visual inspection as the primary data analysis technique in single-subject research, what three key features of the graphed data do researchers primarily evaluate?

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