Table Of Content
- Implications of the simulation study for research practice
- Psychophysical methods in cognitive and mathematical psychology
- What makes a good letter N logo?
- model
- Methodology
- Small-N Designs: Overview and Examples
- It all starts with a N logo
- Individual- versus group-level inference: a simulation using additive factors
Small-N designs usually allow researchers to observe within-person variability and relate environmental or physical characteristics to patient performance. Repeated observations permit a systematic analysis of the course of treatment and may suggest useful modifications as the study progresses. The researcher using a small-N design can take into account and analyze the impact of day-to-day contingencies and related events that affect patient behaviors and performances. This approach is well-established in education and the behavioral sciences and is increasingly present in the clinical literature.6–8 The textbook by Bloom et al.4 is an excellent resource for using small-N designs to evaluate and inform clinical practice. This text addresses many of the issues (e.g., statistical versus visual analysis) that are not adequately covered in this article due to space limitations. Today, we’re taking a major step toward our vision for a more open computing platform for the metaverse.
Implications of the simulation study for research practice
Experimental evolution lines and design of the heatwave experiment. (a)... - ResearchGate
Experimental evolution lines and design of the heatwave experiment. (a)....
Posted: Thu, 18 Jan 2024 10:42:05 GMT [source]
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Psychophysical methods in cognitive and mathematical psychology
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What makes a good letter N logo?
Recent articles by Grice et al., (2017) and Normand (2016) make similar points to those we make here. Like us, Normand stresses the importance of repeated measures on single individuals as a reliable and in many ways, preferable, approach for understanding psychological phenomena. Grice et al. highlight the errors in inference that can arise when individual-level hypotheses are tested at the group level, especially when the population to which the group belongs is not clearly specified. Our critique also recalls elements of the now-classic papers by Meehl (1967, 1990), particularly in our distinction between process-oriented and phenomenon-oriented research and our emphasis on parameter estimation and prediction as an alternative to significance testing. The efficacy of a given treatment in a traditional RCT is usually assessed only after the treatment is completed (post-test). The typical pre-test, post-test RCT design precludes the continuous assessment and analysis of the patient’s performance during treatment.
model
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We could have made similar points with reference to other sensory sciences, like auditory science, or animal learning. Among the primary outlets for research in vision are journals like Vision Research,Journal of the Optical Society of America, A, and more recently, Journal of Vision, in which the small-N design has tended to be the dominant paradigm. Published studies in these journals often report data from as few as two or three participants (typically styled “observers”), often members of the laboratory, and consequently far from naive. Our reading is that vision science shows no evidence of being in the grip of the kind of replication crisis that is currently afflicting cognitive and social psychology.
They noted that the foundational studies of animal learning by Pavlov and Thorndike, some decades before Fisher’s book, were carried using small samples and that this practice continued in the work of B. F. Skinner (1938)—who provided us with the epigraph for this article—and his contemporaries, and that the small-N design remains the mainstay of animal learning studies to this day. They also noted that the historical shift from small-N to large-N designs was traced by Boring (1954). He analyzed the studies published in the Journal of Experimental Psychology in the years 1916, 1933, and 1951 and reported that in 1916 not a single study used a large-sample design, but by 1951 the number had risen to over 50%. Until the current replication crisis, this history might reasonably have been read as one of psychology gradually learning to use more appropriate and reliable methods—a process that was largely complete by the time of the cognitive revolution of the 1960s.
It all starts with a N logo
Cohen’s hypothetical study has in fact been carried out numerous times, apparently first by Bruner and Goodman (1947) who found that poor children do overestimate coins sizes compared to rich children even when the coins are physically present as a comparison! Changing the focus from whether or not some behavior occurs to the process underlying it allows a progressively more refined picture to be built up of the conditions under which the behavior occurs and the likely mechanisms that give rise to it. In the remainder of the article, we argue that adopting a small-N approach to testing process-based predictions allows for much stronger inferences than the corresponding large-N approach. What these areas have in common is that theory testing is carried out by fitting (or otherwise evaluating) explicit mathematical models and that the relevant model properties are most directly expressed at the individual participant level. Of course, this kind of research is only possible with experimental manipulations that do not produce strong carry-over effects (Keppel 1982, p. 371), because model-testing at the individual level requires a sufficient number of experimental trials to produce an adequate level of within-condition measurement precision. The requirement that experimental manipulations not produce strong carry-over effects is needed in order to allow consecutive trials to be treated as statistically independent.
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Although there are unsolved problems, unanswered questions, and ongoing controversies in this area as in any other, vision science provides us, overall, with a highly coherent picture of how stimuli are coded and represented in the visual system. Vision science has undoubtedly benefited from the close theoretical link between behavior and physiology; but even with this qualification, there seems to be no evidence that its habitual use of small samples of participants has led to a replication crisis of a particularly virulent kind. Since the cognitive revolution of the 1960s, the dominant paradigm for inference from data in scientific psychology has been a null-hypothesis significance-testing one.
The parameters of each item distribution were linked via a linear equation in which the interaction effect was either present or absent. This implies that a sufficient number of trials must be collected at the individual level in order for such verification to be meaningful. Whether averaging or aggregation leads to artifacts that can distort inference depends wholly on the domain and the models in question and it is difficult to give a priori, domain-independent prescriptions.
In these situations, the value of averaging is primarily the pragmatic one of data smoothing, rather than the inferential one of estimating population parameters. The results for the weighted least squares analysis and the maximum likelihood method are qualitatively similar, and we make no further distinction between them except to note that the maximum likelihood estimation appears to be more sensitive to small differences from the null interaction. The next thing to note is that the individual analysis picks up the difference in individuals sampled from the null interaction and the positive interaction quite clearly, with the two distinct regions reflecting the separation between individuals.
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