Presentation on theme: "Lab K An exercise in experimental design and implementation."— Presentation transcript:
Lab K An exercise in experimental design and implementation
Between-subjects designWithin-subjects design -each person in the lab would be assigned to one of the four treatments using random allocation (eg. rolling a fair die and ignoring two of the faces, generating random numbers in minitab and ignoring all but digits 1, 2, 3 and 4, etc.) - Randomisation is used so that there are no systematic differences between the people allocated to different treatments – i.e. it attempts to control for potential confounding variables -each person in the lab would be asked to perform the experiment under each of the conditions, i.e. four times - randomization would be used to assign participants to a particular order of the treatments, thereby counterbalancing the design to minimize the potential order-effects acting as a confounding factor
Between-subjects designWithin-subjects design Advantages and disadvantages + only requires each participant to do the experiment once, thereby saving time + potential order effects (eg. fatigue, practice, crossover effects) are avoided and do not need to be controlled for in the design + the subsequent analysis is straight forward, as there are four independent groups to compare + the randomization is relatively straight forward - would require a larger number of participants to get adequate numbers in each treatment group Advantages and disadvantages -requires each participant to do the experiment four times, thereby needing a lot of time -introduces potential order effects (eg. fatigue, practice, crossover effects) which the design should carefully consider in order to minimize the potential effects (i.e. lag between experiments) -the subsequent analysis is complex, involving the analysis of repeated measures + more powerful to detect treatment differences, since effects of individuals are controlled + would require a relatively small number of participants to get reliable results for each treatment level
Suggested design and why Given our limited numbers in the lab group, we would favour the within-subjects design, notwithstanding the additional work involved in the randomization and the analysis.
To make a Latin square… 1.Create a random order of the levels, (e.g. BACD for a four-level IV) 2.Sequentially number the levels in this random order starting with 1. (e.g. B=1, A=2, C=3 and D=4) 3.To create the first order in the Latin square, put the last number/level in the 3rd position, (e.g. 1, 2, 4, 3). If we had more than four levels, N say, then every subsequent unevenly numbered position (e.g. 5, 7, and so on) would have one less than the previous unevenly numbered position as shown in the following table. Order number 12345678910 Level12N3N-14N-25N-36
Labeling of the treatment levels in the random order 1 = C 2 = D 3 = B4 = A Latin Square of labels Latin square of treatments 1243Order 1CDAB 2314Order 2DBCA 3421Order 3BADC 4132Order 4ACBD
Random allocation to orders 14112214312 21121221323 32134233331 42142243342 53153252351 63162264362 72174271373 84183281381 91194294391 103201301401
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