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Pigeons were exposed to fixed‐interval schedules of food reinforcement with durations of 300 sec, 100 sec, or 40 sec. A range of doses of d‐amphetamine was administered to each pigeon, and the resulting behavior was analyzed at several levels of detail. Average rates in different portions of the intervals predicted the magnitude of the drug's effect, but a finer analysis showed that average rates did not adequately characterize the behavior in some parts of the intervals. The probability of responding in different parts of an interval without drug was also a good predictor of the magnitude of the effect of d‐amphetamine, and at the same time was more descriptive of the interval‐to‐interval performance. Analyses of the control performance indicated that responding in individual intervals could be described as consisting of two parts: a very low, or zero, rate at the beginning of the interval followed by an abrupt transition to a slightly, but reliably, positively accelerated rate maintained until reinforcement.

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Three pigeons were trained on two‐link chained fixed‐interval fixed‐interval schedules. Numbers of responses, time spent responding, and the total time spent in each component were measured. The data were analyzed according to the matching law for multiple and concurrent schedules. In most conditions, the ratio of response rates in the two links was a constant proportion of the ratio that would be predicted in a multiple schedule with the same components. Data on pauses during the interval schedules showed that, in most conditions, the pause duration was a linear function of the interval length, and greater in the initial link than in the terminal link. The experiment thus demonstrated a quantitative functional analysis of performance on a chained schedule.

It is argued that the analysis‐of‐variance model is inappropriate for assessing treatment effects in single‐subject designs. In particular, such designs are demonstrated to violate the crucial assumption concerning the statistical independence of observations. Alternative methods of data analysis are suggested.

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Ten students in a personalized university course were given target dates for completing each of 26 lessons. The lessons could be completed before those dates, but not after. The first two failures to complete a lesson by the target date led to “warnings”; the next failure required the student to withdraw from the course. When each student's rate of lesson completion was compared with and without target dates, it was found that students completed an average of 1.0 lesson a day with the target‐date contingency and 0.3 without it. Individual data indicated that most students did few or no lessons without the contingency. It was concluded that a target‐date contingency is an effective method for maintaining student progress in personalized university courses.

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Interrupted time‐series design and quasi‐experimental logic were employed to assess the behavioral effects of a prison disciplinary intervention on a population of approximately 2000 adult male inmates. The uncontrolled nature of the prison environment prevented experimental analysis of intervention effects. The quasi‐experimental methodology applied in this study exemplifies how data, otherwise lost, can be retrieved. In addition, the process by which qualified conclusions can be drawn from the retrieved data is demonstrated.

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