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Table of Contents   
LETTER TO EDITOR  
Year : 2012  |  Volume : 15  |  Issue : 4  |  Page : 400-401
Analyzing association and repeated measures data


1 Deptartment of Dental Materials, Manipal College of Dental Sciences, Light House Hill Road, Mangalore, Karnataka, India
2 Deptartment of Oral Pathology, Manipal College of Dental Sciences, Light House Hill Road, Mangalore, Karnataka, India

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Date of Submission18-Aug-2012
Date of Decision01-Sep-2012
Date of Acceptance11-Sep-2012
Date of Web Publication3-Oct-2012
 

How to cite this article:
Madhaystha PS, Srikant N. Analyzing association and repeated measures data. J Conserv Dent 2012;15:400-1

How to cite this URL:
Madhaystha PS, Srikant N. Analyzing association and repeated measures data. J Conserv Dent [serial online] 2012 [cited 2019 Jun 17];15:400-1. Available from: http://www.jcd.org.in/text.asp?2012/15/4/400/101933
Sir,

We read with great interest, the series on research methodology in dentistry [1],[2] by Krithikadatta J, Valarmathi S. We congratulate the authors on the detailed description given on the how's and whys of the research technique and also the common errors that are observed in the representation of the data.

We would like to add to their wonderful article a few key analysis techniques that are commonly encountered in a dental research. In cases where a patient is assessed more than twice for a variable, for example the flow of saliva is assessed at various stages of radiotherapy, Repeated Measures ANOVA or a General Linear Model of analysis is advocated. This kind of data is also obtained when a treatment has multiple stages, for example leukoplakia, oral sub mucous fibrosis etc [Figure 1]. [3]
Figure 1: Test for Repeated Measures

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In cases where we are looking for analysis of association of two parameters, correlation is commonly used, although they do not relate the cause to effect of the parameters concerned. For normally distributed data, Pearson's correlation is used, and for skewed data or ordered categorical data, Spearman's correlation is used. This gives the correlation coefficient "r," which gives the level of correlation, and "r 2" value gives the percentage of cases, which are showing the association, both of which has to be reported [Figure 2]. This gives a one-sight indication of the confounding and interaction between the parameters. [3]
Figure 2: Tests for correlation

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The importance of the graph cannot be over looked. Normality is represented by a histogram, data can be represented by various types of bar chart, box plot, line, error chart, scatter diagram etc., which reduce the number of tables and also enhance the understanding of the outcome of a study at a glance [Figure 3]. [3]
Figure 3: Pictorial representations of data

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   References Top

1.Krithikadatta J. Research methodology in Dentistry: Part I - The essentials and relevance of research. J Conserv Dent 2012;15:5-11.  Back to cited text no. 1
[PUBMED]  Medknow Journal  
2.Krithikadatta J, Valarmathi S. Research methodology in dentistry: Part II - The relevance of statistics in research. J Conserv Dent 2012;15:206-13.  Back to cited text no. 2
[PUBMED]  Medknow Journal  
3.Bland M. An introduction to medical statistics. 3 rd Ed. New york: Oxford University press; 2000.  Back to cited text no. 3
    

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Correspondence Address:
Prashanthi S Madhaystha
Manipal College of Dental Sciences, Light House Hill Road, Mangalore, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0972-0707.101933

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    Figures

  [Figure 1], [Figure 2], [Figure 3]



 

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