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small N single case - trend seen over baseline Behaviour...

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Single-Case Research Designs Use only one case or one group to investigate a specific phenomenon. Not the same as a case study. Uses a time-series design To overcome limitations of what is essentially a one-group (case) before-after design, you take multiple pre and post-treatment measures. Still just a quasi-experimental design. Still cannot exclude natural history as an explanation for the effect. Changing one variable at a time Cardinal rule – only one variable can change from one phase to the next. Length of phases Ideally, stability in the response needs to be established before moving to next phase Cross-over effects can be a problem Short treatment phases may minimizes cross-over but not always possible. Cyclic variations Difficulty in establishing a stable baseline Variability in baseline data makes it hard to know when baseline trend has stabilized. Interpretation of the treatment effect is made easier if the treatment response is opposite to the
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Unformatted text preview: trend seen over baseline. Behaviour may be reactive to measurement and thus impossible to obtain a valid measurement 111111111111111111111111111111111111111111111111111111111111111111111111 Disadvantages of Small-N Designs Hard to demonstrate causality No controls in most cases Lack of statistics Can’t really look at interaction effects Counterbalancing is a problem Dependent variable usually limited to response rates Problem of external validity Advantage of Small-N Design Avoiding problems with the group mean Can examine participants from hard to find populations Can deal explicitly with individual (not group) behaviour. Results are easy to interpret (often no stats) Avoids small, unimportant effects Flexibility Can focus on helping one (few) participant(s)...
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