Using the dual-criteria methods to supplement visual inspection: An analysis of nonsimulated data
| Autor: | Sarah C. Huxley, Marc J. Lanovaz, Marie-Michèle Dufour |
|---|---|
| Přispěvatelé: | Université de Montréal. Faculté des arts et des sciences. École de psychoéducation |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
050103 clinical psychology
Sociology and Political Science False positives and false negatives Data analysis Datasets as Topic computer.software_genre False positive paradox Humans 0501 psychology and cognitive sciences False Positive Reactions False positive Dual-criteria method Applied Psychology 05 social sciences Single-case designs nutritional and metabolic diseases Dual (category theory) nervous system diseases Visual inspection Philosophy Data point Data Interpretation Statistical Type I error Data mining Psychology computer 050104 developmental & child psychology Type I and type II errors |
| DOI: | 10.1002/jaba.394 |
| Popis: | The purpose of our study was to examine the probability of observing false positives in nonsimulated data using the dual-criteria methods. We extracted data from published studies to produce a series of 16,927 datasets and then assessed the proportion of false positives for various phase lengths. Our results indicate that collecting at least three data points in the first phase (Phase A) and at least five data points in the second phase (Phase B) is generally sufficient to produce acceptable levels of false positives. |
| Databáze: | OpenAIRE |
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