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2 results

  • Optimization of Blocked Designs in FMRI Studies

  • Bärbel Maus, Gerard J. P. van Breukelen, Rainer Goebel, Martijn P. F. Berger
  • Journal:
    Psychometrika / Volume 75 / Issue 2 / June 2010
    Published online by Cambridge University Press:
    01 January 2025, pp. 373-390

  • Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is characterized by three factors: the length of blocks, i.e., number of trials per blocks, the ordering of task and rest blocks, and the time between trials within one block. Optimal design theory was applied to find the optimal combination of these three design factors. Furthermore, different error structures were used within a general linear model for the analysis of fMRI data, and the maximin criterion was applied to find designs which are robust against misspecification of model parameters.

  • 4 - Designing a Behavioural Study

  • Melissa Bateson, Newcastle University, Paul Martin
  • Book:
    Measuring Behaviour
    Published online:
    07 May 2021
    Print publication:
    06 May 2021, pp 41-62

  • Summary

    Study design is fundamental to good science. A poorly designed study will waste time and resources and could produce misleading or uninterpretable results. A good study design aims to minimise random variation and eliminate confounding variables. Correlational studies make use of natural variation in the variables of interest, whereas experimental studies manipulate variables to understand their causal effects on behaviour. There are advantages and disadvantages to both types of study, but experiments uniquely allow inferences about causation. A good experimental design requires subjects to be randomly allocated to experimental groups. Randomisation ensures the generalisability of results and eliminates confounds in experimental studies. Measurements should ideally be made blind to group membership. Blinding minimises biases caused by the conscious or unconscious expectations of the experimenter or subjects. Careful consideration should be given to when behaviour is measured, as time can affect behaviour. Power calculations can be used to determine the appropriate sample size.