Table Of Content
- Social Media
- The effect of increasing N on the individual- and group-level analyses
- It's Time To Redefine Home
- Elevating Your Brand
- Newey to leave Red Bull over Horner allegations
- Implications of the simulation study for research practice
- Psychophysical methods in cognitive and mathematical psychology
- Small Sample Research Designs for Evidence-based Rehabilitation: Issues and Methods
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Social Media
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The effect of increasing N on the individual- and group-level analyses
Collectively, these methods represent an established and sophisticated way of doing scientific psychology that is very different in style and substance to the one targeted by the OSC’s replication study and characterized as inherently flawed by the scientific and popular press. When the goal is to estimate population parameters, when the phenomenon of interest is highly reactive (i.e., changed by observation), and when only limited data can be collected from individuals, then the recommendation to increase sample size at the participant level is an appropriate one. To foreshadow the contents of the rest of the article, our primary aim is not to denigrate current research practices, but, rather, to provide a sharper and more balanced appraisal of what we believe are the often-overlooked merits of small-N designs. These designs concentrate their experimental power at the individual participant level and provide high-powered tests of effects at that level. As a result, they are in a sense automatically “self-replicating” (Little & Smith, 2018), as we discuss more fully later. In addition to their self-replicating properties, small-N studies often embody a number of hallmarks of good scientific practice, particularly as they pertain to precise measurement, effective experimental control, and quantitatively exact theory.
It's Time To Redefine Home
We resimulated the set of N participants 100 times allowing δ to equal 1 with a probability of [.10,.25,.50,.75,.90]. That is, 20 new simulated data sets were taken at each level of δ and then bootstrapped with the individual and group level analyses applied to each bootstrapped sample. The bootstrapping procedure was repeated 1000 times per set of simulated participants. In situations of this kind, the logic of small-N and large-N scientific discovery will unfold very differently.
Elevating Your Brand
Lest one think it problematic that the near-zero results are significant some proportion of the time for the individual analysis, recall that the individual analysis provides an estimate of the interaction for each participant. Consequently, one can examine the value of the estimate to determine its importance rather than relying on a null hypothesis test to decide whether it is or is not actually zero. In small-N designs the individual participant is the unit of analysis with each person serving as his or her own control.
Newey to leave Red Bull over Horner allegations
For the individuals sampled with a positive interaction, the individual analysis is very sensitive with the average power greater than .9 even at the lowest levels of the effect. The individual-level analysis is also sensitive even to small effects near zero, from individuals sampled with a null interaction. Effectively, the analysis is sensitive enough to occasionally detect that the interaction effect is different from zero even when that effect is rather small.
Implications of the simulation study for research practice
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Small Sample Research Designs for Evidence-based Rehabilitation: Issues and Methods
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These might include issues such as intensity or duration or combining multiple components of the intervention and testing them across various patients and settings. In making these claims for findings like Fechher’s and Ebbinghaus’s laws, we are of course not attempting to suggest that all of the historical studies carried out using small-N or single-participant designs yielded enduring and reliable knowledge. That would be like claiming that all the best songs were written in the sixties because the songs from the sixties that continue to be played today are better than the songs being written now—and would embody the same kind of logical error. Rather, it is to claim that these examples show that use of single-participant designs is in no way inimical to the discovery of precise quantitative relationships of enduring psychological significance. Indeed, it might have been much more difficult for Fechner and Ebbinghaus to have discovered their laws had they worked with large-N designs.
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Clinically-derived evidence can serve as the catalyst for investigators to design high-quality research that is relevant to clinical practice (see article by Whyte and others in this issue). Small-N designs represent one approach that is available to practitioners and that may allow them to contribute to the advancement of rehabilitation science and practice. Since randomly selecting a representative sample from a large target population is not a component of small-N designs, replication is the alternative strategy used to establish the generalizability of small-N research findings. Barlow and Hersen34 describe three strategies for establishing generalizability in small-N research. The first form of generalizability involves the accumulation of a number of direct replications of the specific treatment effect on one well-defined outcome measure within a defined clinical setting.
One recommendation to remedy the replication crisis is to collect larger samples of participants. We argue that this recommendation misses a critical point, which is that increasing sample size will not remedy psychology’s lack of strong measurement, lack of strong theories and models, and lack of effective experimental control over error variance. In contrast, there is a long history of research in psychology employing small-N designs that treats the individual participant as the replication unit, which addresses each of these failings, and which produces results that are robust and readily replicated. We illustrate the properties of small-N and large-N designs using a simulated paradigm investigating the stage structure of response times. Our simulations highlight the high power and inferential validity of the small-N design, in contrast to the lower power and inferential indeterminacy of the large-N design.
According to this paradigm, the goal of experimentation is to draw inferences about the properties of an underlying population or populations from measurements made on samples drawn from those populations. Expressed in model-comparison terms (Maxwell & Delaney, 1990), the goal of inference is to decide between two models of the psychological phenomenon under investigation, or more precisely, to decide between two models of the data-generating process that gave rise to the observed experimental outcomes. One model is a null model, in which the value of a parameter in the data-generating process is zero; the other is an alternative model, in which the value of the parameter is nonzero. This parameter is conceptualized as the numerical value that would be obtained if a population-wide census could feasibly be undertaken. The additive factors method originated as a way to determine the presence or not of sequential or serial stages of processing in response time (RT) tasks (Sternberg, 1969). Two factors, varied factorially, which influence different processes, will have additive effects on response time under the assumption that the processes are arrayed sequentially.
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