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Multiple baseline studies are often categorized as either concurrent or nonconcurrent. [1] [2] [3] Concurrent designs are the traditional approach to multiple baseline studies, where baseline measurements of all participants start at (roughly) the same moment in real time.
Multiple baseline design involves simultaneous baseline measurement begins on two or more behaviours, settings, or participants. The IV is implemented on one behaviour, setting, or participant, while baseline continues for all others. Variations include the multiple probe design and delayed multiple baseline design. [1]
The reversal design is the most powerful of the single-subject research designs showing a strong reversal from baseline ("A") to treatment ("B") and back again. If the variable returns to baseline measure without a treatment then resumes its effects when reapplied, the researcher can have greater confidence in the efficacy of that treatment.
Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1] For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
Participants would experience each level of the repeated variables but only one level of the between-subjects variable. Andy Field (2009) [ 1 ] provided an example of a mixed-design ANOVA in which he wants to investigate whether personality or attractiveness is the most important quality for individuals seeking a partner.
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One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time (i.e. growth curve modeling for longitudinal designs); however, it may also be used for repeated measures data in which time is not a factor.
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