Dahlberg formula: a novel approach for its evaluation

INTRODUCTION: The accurate evaluation of error of measurement (EM) is extremely important as in growth studies as in clinical research, since there are usually quantitatively small changes. In any study it is important to evaluate the EM to validate the results and, consequently, the conclusions. Be...

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Main Authors: Maria Christina de Souza Galvão, João Ricardo Sato, Edvaldo Capobiango Coelho
Format: Article
Language:English
Published: Dental Press Editora 2012-02-01
Series:Dental Press Journal of Orthodontics
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2176-94512012000100015
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spelling doaj-a3bfce6765ff4859ba4580b2ae8b89692020-11-24T23:38:12ZengDental Press Editora Dental Press Journal of Orthodontics 2176-94512177-67092012-02-0117111512410.1590/S2176-94512012000100015Dahlberg formula: a novel approach for its evaluationMaria Christina de Souza GalvãoJoão Ricardo SatoEdvaldo Capobiango CoelhoINTRODUCTION: The accurate evaluation of error of measurement (EM) is extremely important as in growth studies as in clinical research, since there are usually quantitatively small changes. In any study it is important to evaluate the EM to validate the results and, consequently, the conclusions. Because of its extreme simplicity, the Dahlberg formula is largely used worldwide, mainly in cephalometric studies. OBJECTIVES: (I) To elucidate the formula proposed by Dahlberg in 1940, evaluating it by comparison with linear regression analysis; (II) To propose a simple methodology to analyze the results, which provides statistical elements to assist researchers in obtaining a consistent evaluation of the EM. METHODS: We applied linear regression analysis, hypothesis tests on its parameters and a formula involving the standard deviation of error of measurement and the measured values. RESULTS AND CONCLUSION: we introduced an error coefficient, which is a proportion related to the scale of observed values. This provides new parameters to facilitate the evaluation of the impact of random errors in the research final results.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2176-94512012000100015BiostatisticsDahlberg errorMethod errorLinear regression analysis
collection DOAJ
language English
format Article
sources DOAJ
author Maria Christina de Souza Galvão
João Ricardo Sato
Edvaldo Capobiango Coelho
spellingShingle Maria Christina de Souza Galvão
João Ricardo Sato
Edvaldo Capobiango Coelho
Dahlberg formula: a novel approach for its evaluation
Dental Press Journal of Orthodontics
Biostatistics
Dahlberg error
Method error
Linear regression analysis
author_facet Maria Christina de Souza Galvão
João Ricardo Sato
Edvaldo Capobiango Coelho
author_sort Maria Christina de Souza Galvão
title Dahlberg formula: a novel approach for its evaluation
title_short Dahlberg formula: a novel approach for its evaluation
title_full Dahlberg formula: a novel approach for its evaluation
title_fullStr Dahlberg formula: a novel approach for its evaluation
title_full_unstemmed Dahlberg formula: a novel approach for its evaluation
title_sort dahlberg formula: a novel approach for its evaluation
publisher Dental Press Editora
series Dental Press Journal of Orthodontics
issn 2176-9451
2177-6709
publishDate 2012-02-01
description INTRODUCTION: The accurate evaluation of error of measurement (EM) is extremely important as in growth studies as in clinical research, since there are usually quantitatively small changes. In any study it is important to evaluate the EM to validate the results and, consequently, the conclusions. Because of its extreme simplicity, the Dahlberg formula is largely used worldwide, mainly in cephalometric studies. OBJECTIVES: (I) To elucidate the formula proposed by Dahlberg in 1940, evaluating it by comparison with linear regression analysis; (II) To propose a simple methodology to analyze the results, which provides statistical elements to assist researchers in obtaining a consistent evaluation of the EM. METHODS: We applied linear regression analysis, hypothesis tests on its parameters and a formula involving the standard deviation of error of measurement and the measured values. RESULTS AND CONCLUSION: we introduced an error coefficient, which is a proportion related to the scale of observed values. This provides new parameters to facilitate the evaluation of the impact of random errors in the research final results.
topic Biostatistics
Dahlberg error
Method error
Linear regression analysis
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2176-94512012000100015
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