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Literature DB >> 10983457

Toward an explanation of the power law artifact: insights from response surface analysis.

Abstract

The power law (y = ax-b) has been shown to provide a good description of data collected in a wide range of fields in psychology. R. B. Anderson and Tweney (1997) suggested that the model's data-fitting success may in part be artifactual, caused by a number of factors, one of which is the use of improper data averaging methods. The present paper follows up on their work and explains causes of the power law artifact. A method for studying the geometric relations among responses generated by mathematical models is introduced that shows the artifact is a result of the combined contributions of three factors: arithmetic averaging of data that are generated from a nonlinear model in the presence of individual differences.

Mesh：

Year: 2000 PMID： 10983457 DOI： 10.3758/bf03198418

Source DB: PubMed Journal: Mem Cognit ISSN： 0090-502X

7 in total

26 in total

Toward an explanation of the power law artifact: insights from response surface analysis.

1. Short-term retention of individual verbal items.

2. The problem of inference from curves based on group data.

3. Mixed-effects Models in the Study of Individual Differences with Repeated Measures Data.

4. Artifactual power curves in forgetting.

5. Genuine power curves in forgetting: a quantitative analysis of individual subject forgetting functions.

6. Subtractive versus Ratio Model of "Fair" Allocation: Can the Group Level Analyses Be Misleading?

7. Neural events and the psychophysical law.

1. The power law repealed: the case for an exponential law of practice.

2. The power law as an emergent property.

3. Flexibility versus generalizability in model selection.

4. Bias in exponential and power function fits due to noise: comment on Myung, Kim, and Pitt.

5. A hierarchical model for estimating response time distributions.

6. Modeling individual differences in cognition.

7. Are item-level strategy shifts abrupt and collective? Age differences in cognitive skill acquisition.

8. Similarity, distance, and categorization: a discussion of Smith's (2006) warning about "colliding parameters".

9. Comparing time-accuracy curves: beyond goodness-of-fit measures.

10. Model evaluation using grouped or individual data.