Comparative evaluation of the reliability and validity of three data extraction programs: UnGraph, GraphClick, and DigitizeIt

被引:58
作者
Rakap, Salih [1 ]
Rakap, Serife [2 ]
Evran, Derya [3 ]
Cig, Oguzcan [4 ]
机构
[1] Ondokuz Mayis Univ, Dept Special Educ, Coll Educ, Samsun, Turkey
[2] Dumlupinar Primary Sch, Carsamba, Samsun, Turkey
[3] UCL, Inst Educ, Quantitat Social Sci, London WC1E 6BT, England
[4] Florida State Univ, Dept Early Childhood Educ, Coll Educ, Tallahassee, FL 32306 USA
关键词
Data extraction programs; Reliability and validity; Single-subject experimental research designs; UnGraph; GraphClick; DigitizeIt;
D O I
10.1016/j.chb.2015.09.008
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
The use of evidence-based practices in education has been gaining a lot of attention in recent years. Researchers often use meta-analyses to identify evidence-based practices. To conduct meta-analyses of studies employing single-subject experimental research (SSER) designs for the purpose of identifying evidence base for a practice, a necessary step is to obtain raw data from published graphs. One method for obtaining raw data from published SSER graphs is the use of computer programs specifically designed to extract data from graphs. Purpose of the present study was to examine the reliability and validity of three data extraction programs, Ungraph, GraphClick, and DigitizeIt, using 60 graphs obtained from 15 SSER studies focused on a practice. Three coders extracted data from the graphs using the three programs. Values extracted by each coder were compared to (a) each other (reliability) and (b) values reported in the original articles in which the graphs were obtained from (validity). Results showed that raw data from SSER graphs can be obtained reliably using all three data extraction programs and values obtained using the three programs are highly valid. These results suggest that researchers can use data extracted using these programs with a high level of confidences while concluding meta-analyses of studies employing SSER designs. Authors make recommendations for improving the accuracy of data extraction using the three programs. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:159 / 166
页数:8
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