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...
Main Authors: | , , |
---|---|
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 |
id |
doaj-a3bfce6765ff4859ba4580b2ae8b8969 |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT mariachristinadesouzagalvao dahlbergformulaanovelapproachforitsevaluation AT joaoricardosato dahlbergformulaanovelapproachforitsevaluation AT edvaldocapobiangocoelho dahlbergformulaanovelapproachforitsevaluation |
_version_ |
1725517607156580352 |