Dataset Information

Implementing Automated Nonparametric Statistical Analysis on Functional Analysis Data: A Guide for Practitioners and Researchers.

ABSTRACT: Functional analysis (FA) is an integral component of behavioral assessment and treatment given that clinicians design behavioral treatments based on FA results. Unfortunately, the interrater reliability of FA data interpretation by visual analysis can be inconsistent, potentially leading to ineffective treatment implementation. Hall et al. (2020) recently developed automated nonparametric statistical analysis (ANSA) to facilitate the interpretation of FA data and Kranak et al. (2021) subsequently extended and validated ANSA by applying it to unpublished clinical data. The results of both Hall et al. and Kranak et al. support ANSA as an emerging statistical supplement for interpreting FA data. In the present article, we show how ANSA can be applied to interpret FA data collected in clinical settings in multielement and pairwise designs. We provide a detailed overview of the calculations involved, how to use ANSA in practice, and recommendations for its implementation. A free web-based application is available at https://ansa.shinyapps.io/ansa/.

Supplementary information

The online version contains supplementary material available at 10.1007/s40614-021-00290-2.

SUBMITTER: Kranak MP

PROVIDER: S-EPMC8894515 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Implementing Automated Nonparametric Statistical Analysis on Functional Analysis Data: A Guide for Practitioners and Researchers.

Kranak Michael P MP Hall Scott S SS

Perspectives on behavior science 20210524 1

Functional analysis (FA) is an integral component of behavioral assessment and treatment given that clinicians design behavioral treatments based on FA results. Unfortunately, the interrater reliability of FA data interpretation by visual analysis can be inconsistent, potentially leading to ineffective treatment implementation. Hall et al. (2020) recently developed automated nonparametric statistical analysis (ANSA) to facilitate the interpretation of FA data and Kranak et al. (2021) subsequentl ...[more]

PMID: 35342875

Similar Datasets

Project description:AbstractThe most common statistical procedure with a sample of circular data is to test the null hypothesis that points are spread uniformly around the circle without a preferred direction. An array of tests for this has been developed. However, these tests were designed for continuously distributed data, whereas often (e.g. due to limited precision of measurement techniques) collected data is aggregated into a set of discrete values (e.g. rounded to the nearest degree). This disparity can cause an uncontrolled increase in type I error rate, an effect that is particularly problematic for tests that are based on the distribution of arc lengths between adjacent points (such as the Rao spacing test). Here, we demonstrate that an easy-to-apply modification can correct this problem, and we recommend this modification when using any test, other than the Rayleigh test, of circular uniformity on aggregated data. We provide R functions for this modification for several commonly used tests. In addition, we tested the power of a recently proposed test, the Gini test. However, we concluded that it lacks sufficient increase in power to replace any of the tests already in common use. In conclusion, using any of the standard circular tests (except the Rayleigh test) without modifications on rounded/aggregated data, especially with larger sample sizes, will increase the proportion of false-positive results-but we demonstrate that a simple and general modification avoids this problem.Significance statementCircular data are widespread across biological disciplines, e.g. in orientation studies or circadian rhythms. Often these data are rounded to the nearest 1-10 degrees. We have shown previously that this leads to an inflation of false-positive results when testing whether the data is significantly different from a random distribution using the Rao test. Here we present a modification that avoids this increase in false-positives for rounded data while retaining statistical power for a variety of tests. In sum, we provide comprehensive guidance on how best to test for departure from uniformity in non-continuous data.

Dataset Information

Implementing Automated Nonparametric Statistical Analysis on Functional Analysis Data: A Guide for Practitioners and Researchers.

Supplementary information

Publications

Implementing Automated Nonparametric Statistical Analysis on Functional Analysis Data: A Guide for Practitioners and Researchers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets