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A multidisciplinary systematic review of the use of diagrams as a means of collecting data from research subjects: application, benefits and recommendations

Background In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of diagrams in earlier stages of the research process to collect data. Despite
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  RESEARCH ARTICLE Open Access A multidisciplinary systematic review of the useof diagrams as a means of collecting data fromresearch subjects: application, benefits andrecommendations Muriah J Umoquit 1 , Peggy Tso 1,2 , Helen ED Burchett 3 , Mark J Dobrow 1,2* Abstract Background: In research, diagrams are most commonly used in the analysis of data and visual presentation of results. However there has been a substantial growth in the use of diagrams in earlier stages of the researchprocess to collect data. Despite this growth, guidance on this technique is often isolated within disciplines. Methods: A multidisciplinary systematic review was performed, which included 13 traditional healthcare and non-health-focused indexes, non-indexed searches and contacting experts in the field. English-language articles thatused diagrams as a data collection tool and reflected on the process were included in the review, with norestriction on publication date. Results: The search identified 2690 documents, of which 80 were included in the final analysis. The choice to usediagrams for data collection is often determined by requirements of the research topic, such as the need tounderstand research subjects ’ knowledge or cognitive structure, to overcome cultural and linguistic differences, or tounderstand highly complex subject matter. How diagrams were used for data collection varied by the degrees of instruction for, and freedom in, diagram creation, the number of diagrams created or edited and the use of diagramsin conjunction with other data collection methods. Depending on how data collection is structured, a variety of options for qualitative and quantitative analysis are available to the researcher. The review identified a number of benefits to using diagrams in data collection, including the ease with which the method can be adapted tocomplement other data collection methods and its ability to focus discussion. However it is clear that the benefitsand challenges of diagramming depend on the nature of its application and the type of diagrams used. Discussion/Conclusion: The results of this multidisciplinary systematic review examine the application of diagramsin data collection and the methods for analyzing the unique datasets elicited. Three recommendations arepresented. Firstly, the diagrammatic approach should be chosen based on the type of data needed. Secondly,appropriate instructions will depend on the approach chosen. And thirdly, the final results should presentexamples of srcinal or recreated diagrams. This review also highlighted the need for a standardized terminologyof the method and a supporting theoretical framework. Background Diagrams are graphic representations used to explain therelationships and connections between the parts it illus-trates. There are many subcategories of the broader term ‘ diagram ’ , which are distinguished by the elements they incorporate or their overall topic. Two dominant subca-tegories include ‘ concept maps ’ and ‘ mind maps ’ [1]. Dia-grams are typically brought into the research process inlater stages of data analysis or when summarizing andpresenting final results. It is commonplace to see a dia-gram illustrating how concepts or themes relate to eachother or to explain how the research data relates to an * Correspondence: 1 Cancer Services & Policy Research Unit, Cancer Care Ontario, (620 UniversityAve), Toronto, (M5G 2L7), CanadaFull list of author information is available at the end of the article Umoquit et al  . BMC Medical Research Methodology  2011, 11 :11 © 2011 Umoquit et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (, which permits unrestricted use, distribution, andreproduction in any medium, provided the srcinal work is properly cited.  underlying theory. These diagrams can be developedthrough the researchers ’ inductive reasoning of the datacollected or may be assisted by computer software [2].The use of diagrams in earlier stages of the researchprocess (i.e. to collect data) is a relatively new methodand is not a common data collection approach at pre-sent. However, their use is developing in multiple disci-plines, including healthcare research. Diagrams havebeen used to collect data from research subjects by ask-ing them to either draw a diagram themselves or modify a prototypic diagram supplied by the researcher. Theuse of diagrams in data collection has been viewedfavorably in helping to gather rich data on healthcaretopics. These research topics are widely varied andinclude collecting information to improve patient safety with medication [3], understanding neighborhood char-acteristics related to mental well-being [4], mapping outhealthcare networks [5], evaluating patient educationalprograms [6,7], understanding how different populations  view microbial illnesses [8], diagramming as part of nur-sing education that is evidence-based [9] and involvescritical thinking [10,11], to engage youth in healthcare consultations [12], and to gain insights on physicianprofessional growth [13] and their accountability rela-tionships [14].Despite the increasing use of diagrams in data collec-tion, there lacks a strong “ supportive structure ” (pg.343) for researchers choosing this method [15]. The useof diagrams in data collection has developed indepen-dently in multiple disciplines under a number of differ-ent names, making knowledge transfer regarding thistechnique difficult. For example, little has been pub-lished on process mapping outside of the organizationalliterature until fairly recently [5,16,17]. This has limited the exchange of best practices between disciplines.Researchers are often starting from scratch whendesigning their diagramming data collection approachesand their analysis of the unique data collected [15].By conducting a multidisciplinary systematic review, asdefined in the PRISMA statement [18], we hope to con-solidate lessons learned and offer recommendations forresearchers in healthcare and other disciplines abouthow diagrams may be incorporated into their data col-lection process. The questions that guided our searchfor relevant studies were:(1) What drives the selection of a diagrammingapproach for data collection?(2) What are the different approaches to diagrammingfor data collection?(3) What are the different approaches to analyzingdata collected with diagramming?(4) What are the benefits and challenges of using dia-gramming for data collection? Methods Diagramming techniques used for data collection inthe research process is a challenging area to review,given the variable terminology across, and even within,fields. A preliminary survey of the literature helpedidentify some key terminology used in different disci-plines (e.g. “ graphic elicitation ” or “ participatory dia-gramming ” ). The terms used in the titles and abstractsof the preliminary articles identified, as well as the key-words used to index them in databases, formed thebasis of our multidisciplinary search strategy. We com-bined these specific terms with general ‘ diagram ’ termsand with general ‘ data collection ’ and/or ‘ analysis ’ terms.In December 2009, we electronically searched 13indexed sources, including traditional health carerelated indexes and non-health focused indexes(EMBASE; HealthSTAR; Medline; Cumulative Index toNursing and Allied Health Literature; GEOBASE; Info-Trac Environmental Issues & Policy eCollection; Pro-QUEST Dissertations; Design and Applied Arts Index;Education Resources Information Center; InternationalBibliography of the Social Sciences; PsychINFO; PublicAffairs Information Services; and Social Science Cita-tion Index). To ensure that all appropriate referenceswere identified and to limit publication bias, non-indexed sources were also searched via general searchtools (i.e. Google Scholar and Google Books) touncover any additional publications. To supplementthe search, 35 experts, identified by our searches, werecontacted and asked to identify additional relevant arti-cles and grey literature.Reference Manager 11 was used to support the review.Following the removal of duplicates, articles werescreened based on their title and abstract. The full-textwas then screened for articles not excluded based ontheir title/abstract. Articles were excluded if they werenot written in English, did not use diagramming techni-ques in the data collection process (i.e. research subjectsdid not create or edit diagrams) or were not evaluativeor reflective about the data collection process and/oranalysis of data collected from diagramming methods.No publication date or publication type restrictionswere imposed; research studies, theoretical articles,method articles and opinion pieces were included if they met the above criteria.The screening was undertaken by two authors (MJU,PT). Double screening was done at regular intervals toensure inter-rater reliability. Further, the two researchersmet weekly during the screening and data extractionphases to discuss the nuances of the articles and toresolve differences by deliberation until consensus wasreached. Umoquit et al  . BMC Medical Research Methodology  2011, 11 :11 2 of 10  Results A total of 2690 references were identified, after theremoval of duplicates. Given our search had no publica-tion date restrictions and included dissertations, full-textarticles were sometimes difficult to retrieve. Authorswere contacted when the article could not be foundonline or through the University of Toronto ’ s library system. While 4 articles were retrieved in this manner,27 full-text articles still could not be found and wereultimately excluded. In total, 233 full-text articles werescreened and a total of 80 articles were included in thestudy  ’ s review. Figure1 presents a flow diagram of oursearch and screening. Data was extracted on the generalcharacteristics of the articles and the four objectivesdetailed earlier (see Table 1). General characteristics Of the 80 articles included in our review, 53 were pub-lished studies [1,4-15,19-58], 19 were dissertations [59-77], 2 were books [78,79] and 6 represented grey lit- erature [3,80-84], including unpublished working papers submitted by key experts and reports available on theinternet. These articles were published between 1986 and2010, with the majority published after 2000 and a sub-stantial increase after 2006. This suggests that interest inthese techniques has been increasing in recent years.The most common discipline, determined by the leadauthor ’ s affiliation and/or publication title, was from theeducation field. Other disciplines included healthcare,engineering, environmental science, geography, indus-trial design, psychology, and social science. The majority of articles clearly specified the study sample size, whichaveraged 36 research subjects, with a range of 2 to 243.Diagramming methods were used with a wide variety of research subjects, including students (elementary tograduate school), farmers, nurses, physicians, engineers,administrators and graphic designers. What drives the selection of a diagramming approach fordata collection? The majority of articles specified at least one explicitreason why a form of diagramming was selected fordata collection. These reasons fall into two broad cate-gories: requirements or challenges of the research topicand the unique dataset that results from using diagrams.The specific research topic examined was the mostcommon reason for researchers choosing a diagrammingtechnique for data collection. For some research topics,past studies have validated diagramming data collectiontechniques as a useful way to collect data. For example,research has established the usefulness of diagrams incollecting data about research subjects ’ knowledge orcognitive structures [19-22,59,80]. Diagrams in data col- lection have also been validated as a means of measuring changes over time [6,10,20,22-24,60,85] and differences between participant groups [20,25,61,81]. Diagramming methods were also sought out whenresearch topics were not conducive to the more com-mon qualitative data collection methods, such as inter- views alone. These reasons include a research topic thatdeals with a population with linguistic, cultural, social orintuitional barriers the researcher wants to overcome[12,14,15,26-29,79] or with highly complex subject mat- ter [12,14,25,30-32,85]. Examples of highly complex sub-  ject matters include the abstract nature of the researchtopic of  ‘ pedagogical constructs ’ [25] and the multifa-ceted and diverse nature of  ‘ clinical accountability rela-tionships ’ [14].Secondly, researchers sought out diagramming datacollection methods because of the benefits previous stu-dies found regarding the quality and uniqueness of thecollected dataset. When research subjects drew diagramswithout prompts, previous studies concluded that itminimized the influence of the researcher on the partici-pant and their responses [1,33-35,61,62,81,82]. Studies have also found that diagramming is a reflective tool forthe research subjects [28,29,36,63]. Since diagrams can represent both concrete and theoretical notions [37],diagramming offers a more holistic coverage of the topic[29,38,61], with more uncensored and unique data gath- ered [1,24,28,35,39,40,58] than more traditional qualita- tive data collection methods. What are the different approaches to diagramming fordata collection? A range of applications were identified, which variedwidely based on the degree of instruction, degree of freedom in diagram creation, the number of diagramscreated or edited and the use of diagram in conjunctionwith other data collection methods.Half of the studies did not report the details of theinstructions provided to the research subjects, except fordescribing the basic request to create a diagram. Onestudy explicitly observed that specific instructions areneeded to ensure the research participants create a dia-gram and not another form of written material [32],such as a drawing or table. Simple or short instructionswere often given to research subjects when the diagramsought by the researchers did not have to conform to arigid structure, such as life-cycle [13] and professionalpractice diagrams [32].When the researcher sought highly structured dia-grams, the degree of instruction provided to theresearch subjects ranged from the preferred method of giving specific and detailed instructions on what ele-ments should be included (e.g. hierarchies, arrows) toshowing an example of the type of diagram theresearcher would like the participant to create. For Umoquit et al  . BMC Medical Research Methodology  2011, 11 :11 3 of 10  example, in comparison to other diagramming techni-ques, concept maps have a fairly rigid definition and a very specific set of elements that the end diagram shouldcontain. Such diagrams may require more detailedinstructions. One study had associate nursing degree stu-dents draw their own concept maps after a 20-minuteintroductory tutorial, presentation of a sample diagram,discussion and question period, and instructions listingall elements to be included (e.g. arrangement of items,hierarchal order, linking concepts with arrow, labelingpropositions, identifying cross-links/relationships) [9]. Insome instances, research subjects were given the oppor-tunity to practice the diagramming method and receivecorrective feedback prior to data collection [19,23,41,64]. Final keyinformantsarticlesn=31Key informants articlesn=310 duplicates removedFinal healthdatabasesn=820Final non-healthdatabasesn=1571 Advanced Googlesearches (Scholar andBooks)n=49153 duplicates removedMaster databasen=2860Non-health databasesn=1698Health databasesn=1639127 duplicates removed819 duplicates removedFinal Googlesearchesn=438Full-text reviewedn=233 Articles includedn=80Excludes n=2457(94 not in English, 2336 did not usediagrammatic methods for datacollection process, 27 full-text article notretrievable)170 duplicatesremoved All titles & abstractsreviewedn=2690Excludes n= 153(2 not in English, 121 did not usediagrammatic methods for datacollection process, 30 were notevaluative or reflective about the datacollection and/or analysis) Figure 1 Flow of articles through the systematic review . Umoquit et al  . BMC Medical Research Methodology  2011, 11 :11 4 of 10  It was most common for research subjects to createan srcinal diagram on their own, in groups or a combi-nation of both. Alternatively, some studies had the parti-cipant edit either a designated diagram provided by theresearcher [3,14,15,42] or a researcher-created diagram generated by the researcher during the interview withparticipant input [4,5,7,43]. A few studies also chose the middle ground between srcinal and prepared diagrams.For example, some provided a central concept or wordto create the diagram around [14,44], or included some words or shapes to fill in on a prepared diagram [45]and others gave research subjects a list of words to usein the creation of their diagram [65,80]. Half of the studies used diagrams at multiple timeswithin the data collection process as a means of com-parison. A subset used a pre-/post- (or time series)approach to data collection, allowing researchers totrack changes before, after, and sometimes during anintervention. This was found primarily within the disci-pline of education. For example, Rios asked a sample of teachers to create concept maps at multiple time inter- vals in order to identify their conceptual models andexamine the impact of student interactions on the tea-chers ’ subject matter structure or vice versa [66].Some researchers explicitly expressed the idea thatdiagrams alone would not capture complete perspectivesfrom the research subjects [57,77], suggesting that diagrams should be used in combination with other datacollection methods. The majority of the studies did usediagramming data collection techniques in addition toother methods. In some cases the additional data collec-tion methods were used explicitly in conjunction withthe diagramming techniques, such as creating the dia-gram within interviews or discussion of the diagrams inlater focus groups or interviews [8,23,39,59,60,67]. What are the different approaches to analyzing datacollected with diagramming? The majority of the articles reported details about theanalysis of diagram data. The use of only quantitative orqualitative analysis, or a mixture of both types of analy-sis was fairly equally distributed among the articles.Within each of these three categories of analysis, therewere a variety of different techniques used that arebriefly outlined below.The majority of studies comparing diagrams acrosstime or across research subjects chose either quantita-tive techniques only or a mix of qualitative and quanti-tative techniques for their analysis. Quantitative analysistechniques included counting (e.g. number of conceptsidentified, number of links between concepts, number of examples given, levels in hierarchy) and scoring. Thetwo most common scoring methods were structuralscoring and relational scoring [22]. Structural scoring Table 1 Characteristics of reviewed studies using diagramming data collection approaches General characteristics of reviewed articles • Range between 1986 and 2010, with increasing popularity: substantial increase after 2006 • Wide range of disciplines use diagramming data collection approaches: used in the educationfield most commonly • Number of research subjects varied: average was 36 with a range of 2 to 243 research subjects • Wide range of research participant characteristics: included students, professionals,administrators and laypersons Reasons for choosing diagrams for datacollection • Requirements and challenges of the research topic: e.g., to capture cognitive structure, changesover time and/or differences between groups; to overcome linguistic, cultural, social or intuitionalbarriers; to collect data on highly complex subject matter • Unique dataset: diagrams seen as a reflective tool, providing holistic coverage throughuncensored and unique data Approaches for instruction and creation of diagrams • Instruction given to research subjects important in shaping end product: ranged from basicrequests to create a diagram to specific instructions on what elements to include and practicesessions with feedback  • Degree of freedom in diagram creation: data collected by srcinal diagram creation in groupsor individually, or through editing a presented diagram or through researcher creation with real-time input • Number of diagrams created or edited: multiple diagrams can be used to track changes overtime • Use of other data collection methods: other methods for collecting data were commonly usedalongside of diagramming approaches Approaches to analysis of diagrams createdin data collection process • Highly structured diagrams were conducive for quantitative analysis: e.g., counting of elementsand/or scoring based on weights assigned to elements of the diagram • Less structured diagrams were conducive for qualitative analysis: e.g., thematic and contentanalysis • Additional data analysis: diagrams can guide additional data analysis of data collected withother means, provide validation and/or visual representation to illustrate conclusions Benefits and challenges of using diagrams tocollect data • Complementary to other data collection techniques • Helps research subjects to focus and reflect on topic(s) • Benefits/challenges dependent on the application and type of diagram used Umoquit et al  . BMC Medical Research Methodology  2011, 11 :11 5 of 10
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