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Chapter 9

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Nussbaum D

CHAPTER 9 Single-subject designs (also known as single-case designs) give an alternative to group designs.  The focus is on N = 1, a single subject. However, this can be referred to as „small-N design‟ and be used for 1-9 subjects.  Useful for research on interventions in behaviour analysis and clinical practice Small-N design: in-depth study of a single or relatively few subjects under tightly controlled experimental conditions in which the independent variable(s) is repeatedly manipulated over successive trials or conditions and in which the dependent variable(s) is repeatedly measured Small-N and single-subject designs usually have four components: 1. Repeated measurement of the dependent variable 2. Baseline phase 3. Treatment phase(s) with all subjects exposed to each phase 4. Graphic display, perhaps supplemented by statistical analysis Repeated Measurement In ideal situation, measurements are taken before intervention then continuing through the intervention. Sometimes, measurements cannot be taken before the intervention. You may be able to collect data from client records or by asking about previous experiences.  Client records only have information that is available and even that may have missing details  Behaviours and actions are easier to remember than moods or feelings  Recollections should be limited for the previous month or else they may become inaccurate Baseline Phase Def.: period in which the intervention to be evaluated is not offered to the client  This is abbreviated by the letter A  During baseline phase, repeated measurements of the dependent variable are taken or reconstructed.  These measures reflect the status of the client on the dependent variable before the intervention Repeated baseline measures allow for some of the threats to internal validity Patterns In baseline stage, measurements are taken until a pattern emerges Stable line: little variability in the scores. This score is desirable because changes can be easily detected and there will be little problems in testing, instrumentation, statistical regression, and maturation in the data. Trend: when the scores may be either increasing or decreasing during the baseline period.  Curvlinear: rate of change is accelerating over time with regular increases and decreases  Cyclical: A pattern is good when you can accurately predict the next data point Internal Validity When measurement during baseline phase is taken from existing data or recollection, the threat to internal validity is greater. Repeated measures during the baseline phase help rule out threats to validity  Validity threats should appear in the baseline  Will not control for an extraneous event (history) that occurs between the last baseline measurement and the first intervention measurement When baseline is a stable line, threats may be ruled out. This is more difficult when the baseline is a trend, especially if it is moving in the desired direction. Treatment Phase Def.: Represents the time period during which the intervention is implemented  Signified by letter B The patterns of the data from the treatment phase are compared with the data of the baseline phase to see if a change has occurred. Graphing Y-axis  represents the scores of the dependent variable X-axis  represents a unit of time Measuring Targets of Intervention The dependent variable in a single-subject design is the concern or issue that is the focus of the intervention. The target for change may be one specific problem or different aspects of the problem. Once the target of the intervention has been identified, the method of operalization for the outcome must be decided. Operalization occurs before the beginning of the study. Measures of behaviours, status, or functioning are often characterized in four ways:  Frequency: counting the number of times a behaviour occurs or the number of times people experience different feelings within a particular time period  Duration: how long behavior lasts  Interval: time between episodes  Magnitude: intensity of behavioral event When measuring behaviour, researchers must make sure that it is not too difficult or time consuming to measure it. As well, they must be careful of reactivity. The process of measurement might change a subject‟s behaviour. The choice of measurements must also be sensitive enough to detect changes.  Too global, it may be impossible to detect incremental/small changes  Whatever is measured must occur often enough that measuring it will have any effect Analyzing Small-N and Single-Subject Designs Common techniques:  Visual examination of the graph  Statistical technique Assessing practical (clinical) significance is of primary importance Practical (or clinical significance): has the intervention made any meaningful difference in the well-being of the subject? There are several principles for determining this:  Setting criteria: establishing with the client or community the criteria for success. If the intervention reaches that point, it is meaningful  Cut-off scores: whether the intervention has reduced the problem the problem to a level below a clinical cut-off score.  Costs and benefits Visual Analysis Def.: process of looking at a graph of the data points to determine whether the intervention has altered the subject‟s preintervention pattern of scores. Three concepts to guide this:  Level: magnitude of the target variable; typically used when the observations fall along relatively stable lines  Trend: direction in the pattern of the data points  Variability: how different or divergent the scores are within a baseline or intervention phase o Widely different scores in the baseline and intervention stages make assessment more difficult. o One way to deal with this is to draw range lines. Types of Small-N and Single-Subject Designs Basic-Design (A-B) This is the basic single-subject design. Includes a baseline phase with repeated measurements and an intervention phase continuing the same meas
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