Small-N Designs: Studying Individuals - PDF

Chapter 13 pt 2: SMALL-N DESIGNS: STUDYING ONLY A FEW INDIVIDUALS -​ Small-N design= a study where researchers only gather data from 1 person or animal -​ Each participant is treated separately -​ almost always repeated-measures designs, in which researchers observe how the...

Chapter 13 pt 2: SMALL-N DESIGNS: STUDYING ONLY A FEW INDIVIDUALS -​ Small-N design= a study where researchers only gather data from 1 person or animal -​ Each participant is treated separately -​ almost always repeated-measures designs, in which researchers observe how the person or animal responds to several systematically designed conditions -​ Data for each individual are presented -​ enable us to compare each individual during treatment periods and control periods -​ designs are often used in therapeutic settings, to confirm that a treatment works for an individual person -​ Large-N design -​ Participants are grouped -​ data from an individual participant are not of interest in themselves; data from all participants in each group are combined and studied together -​ Data are represented as group averages -​ Large samples enable group averages to be estimated more precisely. -​ These studies are used for both basic and applied research. -​ Balancing priorities -​ Experimental Control -​ Case studies can effectively advance our knowledge when researchers use careful research designs -​ Studying Special Cases -​ take advantage of special medical cases -​ Ex: testing the man with part of his brain removed (this is very rare) -​ Disadvantages -​ Internal validity -​ In the case of the specific brain studies, there may be some other factors contributing to the results (ie other parts of his brain were disturbed) -​ external validity -​ may not represent the general population very well -​ any patient who undergoes such surgery usually has health problems not found in the general population -​ , we cannot be sure whether results from studies on surgery patients would apply to people with no history of epilepsy or schizophrenia -​ How to fix= triangulate, meaning to compare a case study’s results to research using other methods -​ Power of the small-N design -​ In educational, clinical, and work settings, practitioners can use small-N designs to learn whether their interventions work -​ frequently used in behavior analysis, a technique in which practitioners use reinforcement principles to improve a client’s behavior  -​ Carefully designed small-N or single-N studies can help practitioners decide whether changes are caused by their interventions or by some other influence -​ Stable baseline design= a study in which a practitioner or researcher observes behavior for an extended baseline period before beginning a treatment or other intervention -​ multiple-baseline design= researchers stagger their introduction of an intervention across a variety of individuals, times, or situations to rule out alternative explanations -​ reversal design= the other two small-N designs (stable baseline and multiple baseline), researchers observe a problem behavior both with and without treatment, but take the treatment away for a while (the reversal period) to see whether the problem behavior returns (reverses -​ By observing how the behavior changes as the treatment is removed and reintroduced, the researchers can test for internal validity and make a causal statement -​ If the treatment is really working, behavior should improve only when the treatment is applied -​ Reversal designs are appropriate mainly for situations in which a treatment may not cause lasting change -​ it may be considered harmful and unethical to withdraw an effective treatment from a patient or client. -​ Evaluating the Four Validities in Small-N Designs -​ External validity -​ steps to maximize the external validity of their findings -​ 1. triangulate by combining the results of single-N studies with other studies on animals or other groups -​ 2. researchers can specify the population to which they want to generalize, and they rarely intend to generalize to everyone -​ 3. sometimes researchers are not concerned about generalizing at all (In such cases, even if the causal statement applies only to one person, it is still useful) -​ construct validity -​ they should use multiple observers and check for interrater reliability, in case one observer is biased or the behavior is difficult to identify -​ statistical validity -​ provide enough quantitative evidence