EuroMed Journal of BusinessApproaches in researching human measurement: MMU model of utilising anthropometricdata to create size chartsRose Otieno

Article information:To cite this document:Rose Otieno, (2008),"Approaches in researching human measurement", EuroMed Journal of Business, Vol.3 Iss 1 pp. 63 - 82Permanent link to this document:http://dx.doi.org/10.1108/14502190810873821

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Users who downloaded this article also downloaded:Karla P. Simmons, Cynthia L. Istook, (2003),"Body measurement techniques: Comparing 3D body-scanningand anthropometric methods for apparel applications", Journal of Fashion Marketing and Management: AnInternational Journal, Vol. 7 Iss 3 pp. 306-332 http://dx.doi.org/10.1108/13612020310484852Susan P. Ashdown, (1998),"An investigation of the structure of sizing systems: A comparison of threemultidimensional optimized sizing systems generated from anthropometric data with the ASTM standardD5585-94", International Journal of Clothing Science and Technology, Vol. 10 Iss 5 pp. 324-341 http://dx.doi.org/10.1108/09556229810239324Alison Beazley, (1998),"Size and fit: Formulation of body measurement tables and sizing systems — Part2", Journal of Fashion Marketing and Management: An International Journal, Vol. 2 Iss 3 pp. 260-284 http://dx.doi.org/10.1108/eb022534

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Approaches in researchinghuman measurement

MMUmodel of utilising anthropometric data tocreate size charts

Rose OtienoManchester Metropolitan University, Manchester, UK

Abstract

Purpose – The purpose of this paper is to present a model of researching clothing anthropometrics at theManchester Metropolitan University in the UK (MMU model), to demonstrate steps in devising size chartsby analysing raw data, to relate key aspects of size charts to raw data, and to generate debate on suchmethods that impinge on the disseminated knowledge in this specialised area. Although sizing is importantto consumers, retailers and manufacturers, this area has received scarce attention in the literature.

Design/methodology/approach – The MMU model presents step-by-step processes in generatingsize charts. Data from 150 women generated descriptive statistics (mean, standard deviation,percentiles); these were utilised to devise seven sizes of a body measurements table. Correlations wereused to determine relationships, resulting in size charts with a defined size range and gradingincrements that are relatable to utilisation by consumers, retailers and manufacturers.

Findings – A step-by -step model of analysing raw data is presented. A verifiable size chart, codes,grading increments and size limits relatable to data are generated. The usefulness of size charts istherefore contextualised.

Research limitations/implications – This paper discusses only one model of researching clothinganthropometrics and provides a related conceptual framework; this could be the basis for futureresearch and debate in this area.

Practical implications – For competitiveness, efficient sizing is useful for marketing, especially increating niches, targeting customers and facilitating consumer satisfaction.

Originality/value – The MMU model provides an initial conceptual framework at one institution, abenchmark for similar practice in academia and industry and subsequent debate in literature.

Keywords Clothing, Measurement, Data analysis

Paper type Conceptual paper

The essence of human measurement in sizing provisionAnthropometry has been defined variously as the measurement of the human body inorder to determine its average dimensions, and the proportion of its parts amongdifferent ages and races or classes (Roebuck, 1995; Pheasant, 1984). Sizing informationis important to consumers, retailers and manufacturers; poor sizing could lead toconsumer dissatisfaction (Otieno et al., 2005). Designers and manufacturers useanthropometric data to define appropriate fit dimensions in the manufacture offurniture, domestic appliances, clothing, automobiles and aircraft artefacts. Bye et al.(2006) present a detailed evaluation of various methods used:

. linear (tape measure, callipers);

. multiple probe (adapted tape measure, anthropometers, somatography, planar,computer integrated apparel manufacture, photography); and

. body form (draping, casting, scanning).

The current issue and full text archive of this journal is available at

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pp. 63-82q Emerald Group Publishing Limited

1450-2194DOI 10.1108/14502190810873821

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National and international standards are based on such data. For example, the recentEuropean Standard EN 34021 parts 1-4 has been adopted for various nationalstandards including the UK’s BS EN 34201 Parts 1-3 (British Standards Institution,2001, 2002, 2003). Clothing anthropometrics deals with the physical measurement ofhumans, with the intention of interpreting data and creating appropriate size charts(Otieno, 1999). Based on the measurement of the average population, clothingmanufacturers and retailers then devise and provide consumers with size codeinformation on fit. Measuring the body is a precursor to the provision of garments thatfit adequately (Bye et al., 2006) and therefore of great interest to retailers andmanufacturers worldwide.

Although ergonomics has been recognised as an independent area of study since the1940s, clothing anthropometrics as a specialised, distinct and autonomous area ofstudy is a fairly recent phenomenon in the UK, with its influence coming to thelimelight in the last decade of the twentieth century when scanning technology becamethe centre stage of clothing surveys. This subject area is usually taught as a part ofpattern or product development, with few specialist anthropometrists even withininstitutions of higher education. It is observable that limited literature exists, especiallyregarding studies with accessible data that have been analysed and their methodssubstantiated. Teaching and research methods regarding anthropometrics are rarelydiscussed in the academic domain, although many learners are expected tocomprehend the impact of anthropometric data in generating sizing systems,patterns and their effect on the fit of garments. Many companies employ graduatesfrom clothing and fashion courses to oversee quality control issues related to garmentsizing. It is usually assumed that such graduates are conversant with issues related tosizing provision and development. Today, efficient sizing and fit is viewed as amarketing tool, for example in the creation of niches, targeting customers, and increating and preserving consumer satisfaction. The learning and teaching ofanthropometrics therefore needs special attention, particularly the content, methodsand underpinning analysis of data. The teaching and learning of garment sizingknowledge is therefore paramount to the clothing industry. The purpose of this paperis to present a model of researching clothing anthropometrics at the ManchesterMetropolitan University in the UK (MMU model) and to generate debate on suchmethods as these impinge on the disseminated knowledge in this specialised area.

Human measurement dates back to the Renaissance period, and several countriesworldwide have conducted surveys and developed efficient size charts for national ortargeted needs. The validity of sizing provision, however, relies on the accurate andcurrent measurement of relevant populations and the subsequent development of suchcharts for pattern and garment development. Many countries such as Germany, theUK, Croatia and the USA have recognised the need for efficient sizing for theirpopulations and utilise both traditional and new technologies to meet this need (e.g.Bougourd et al., 2000; DOB Verband, 1994; Ujevic et al., 2006; Newcomb and Istook,2004). Although Levitt (1983) suggested that the world is becoming a global village,this may not fully apply to the clothing industry. Fashion trends are global but sizecharts are local (Otieno, 1999; Vronti, 2005). As evidenced in a recent Croatian study,size charts are important in the provision of efficient and satisfactory selection ofgarments for local population (Ujevic et al., 2006). Vrontis and Vronti (2004) identify theadvantages accrued from adapted size charts in different countries, i.e. food fit and

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competitiveness. The SizeUK and SizeUSA surveys conducted in 2001 and 2004,respectively, sought to create and revise sizing systems that are pertinent to thosepopulations (Treleaven, 2003; Newcomb and Istook, 2004). Usually, surveys are acollaborative effort between organisations (Bougourd et al., 2000; Ujevic et al., 2006).Companies consider such researched size charts as proprietary, and hence suchinformation is not easily accessible in the public domain.

Aim and methodologyThe aim of this paper is to present a model of researching and analysing rawanthropometric data and generating size charts whose efficacy can be verified throughsize ranges, size coding and grading. Since the objective was not to create size charts,only a small data set of a sample of 150 subjects was utilised to present succinctly theprocedures utilised in this model. These procedures would normally be used in alarge-scale survey and analysis. Firstly, a small survey was conducted where aconvenience sample of 150 female students aged 19-35 years were measured atHollings campus at the Manchester Metropolitan University in the UK in 2001. Thesample consisted of willing participants who were recruited from the postgraduatecohorts through announcements in anthropometrics classes. Two trained researchassistants measured all the subjects; this ensured reliability and validity (Cameron,1982; Otieno, 1999). Subjects were measured at an agreed time, usually after theanthropometrics class. Manual measurement technologies utilising an anthropometer(height), balance scales (weight) and a tape measure (circumferential girths) wereemployed. Landmarks (marks made on the body to denote the beginning and end of ameasurement, usually around a bony structure) were identified on the body: neck point,shoulder point, the seventh cervicale bone, waist, hip, bust and knee prior tomeasurement. Subjects were measured in their bras and briefs in a secure and privatecubicle by two measurers. Various ethical aspects relating to privacy, nature ofclothing when being measured, health and safety and right of veto of the subject wereall discussed with the subjects prior to the exercise. Data were input in the SPSSpackage for analysis; 21 body measurements for 150 subjects were generated andsummary descriptive statistics utilised (Table I). Mean, median, standard deviation,minimum and maximum and percentiles were calculated. A table of bodymeasurements (Table II) was generated and used to determine the size range, sizecodes and grading increments by using the standard deviation and mean (Beazley,1998; Otieno, 1999, Vronti, 2005). Pearson’s correlation coefficients were calculated inorder to determine relationships between dimensions (Table III) and resulting sizechart (Table IV). Further, grading increments, upper and lower limits were determined(Tables V and VI).

Approaches in teaching and analysing anthropometric dataPinset (1962, p. 51) defined teaching as a “complex process of cooperation andinter-communication between the teacher and learner, whose aim is to produceprogressive series of changes in behaviour and experience”. For anthropometricresearch, practical aspects relating to methods and instruments are usually taught asprinciples and practices through demonstration methods. Important aspects such assubject selection, recruitment, identification of landmarks, ethical consideration andprocedures must be taught in training prior to actual measurement. Training enables

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Valid Weight Stature Neckbase Cerv2gdb Cerv2gdf Insidleg

n 100 99 100 100 100 100Mean 62.252 1,582.6667 37.0590 142.0770 155.7490 71.1680Median 60.300 1,590.0000 37.0000 141.8000 153.2500 70.6500SD 11.3960 65.33024 2.28434 5.81697 27.20546 3.86473Minimum 41.4 1,390.00 32.50 130.50 138.80 64.70Maximum 111.0 1,696.00 44.60 157.00 418.00 78.80Percentiles 25 54.300 1,547.0000 35.5000 138.3500 148.7250 67.8500

50 60.300 1,590.0000 37.0000 141.8000 153.2500 70.650075 67.800 1,630.0000 38.5000 146.7500 157.6000 74.300080 70.080 1,641.0000 39.0000 147.2000 159.0000 74.6800

Valid Sidw2hip Sidw2kn Sidewa2g Acro2wrist Shouleng Shoulwid

n 100 100 100 100 100 100Mean 6.7680 57.5880 100.5180 57.2540 15.5220 41.0710Median 6.7500 57.000 100.2500 57.0000 14.0000 41.0000SD 1.38629 6.15898 7.45871 3.49717 14.15850 2.70425Minimum 3.00 31.50 48.90 49.00 11.00 34.00Maximum 15.00 90.50 112.50 65.50 15.00 48.00Percentiles 25 6.0000 55.5000 97.8500 55.0000 13.0000 39.0500

50 6.7500 57.0000 100.2500 57.0000 14.0000 41.000075 7.3750 60.0000 105.2000 60.2000 15.0000 42.500080 8.0000 60.9000 106.3200 60.5800 15.0000 43.0000

Valid Acroback Acrofron Bustgirth Waistgirth Hipgirth Kneegirth

n 100 100 100 100 100 100Mean 35.3370 36.0380 92.2480 79.3800 94.0910 38.7300Median 35.000 36.1000 90.8000 77.0000 92.3500 38.6500SD 4.18085 3.53587 9.20813 10.60884 11.19342 3.34182Minimum 28.00 24.00 78.00 64.00 73.00 33.00Maximum 53.00 45.00 130.00 120.20 140.00 48.00Percentiles 25 32.6250 34.0000 85.7000 71.3250 86.7000 36.2000

50 35.0000 36.1000 90.8000 77.0000 92.3500 38.650075 37.4250 38.5000 98.0000 83.9250 100.8000 40.375080 38.7000 39.0000 99.4000 88.8000 102.9600 41.0000

Valid Anklegirth Wristgirth Armscgirth

n 100 100 100Mean 24.2300 16.1320 45.3110Median 24.0000 16.0000 45.0000SD 2.30745 1.20687 5.43422Minimum 20.00 14.00 34.30Maximum 33.50 21.30 60.60Percentiles 25 23.0000 15.2250 41.5000

50 24.0000 16.0000 45.000075 25.1500 17.0000 48.875080 25.8000 17.0000 49.1600

Table I.Summary statistics forbody measurements forwomen

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measurers to practise key procedures and contributes to validity and reliability ofmethods. The benchmarks for practice are the international standards (e.g. ISO 8559),European standards (e.g. EN 13402) and British standards (e.g. BS 7231). TheEuropean standards EN 13402 (Parts 1, 2 and 3) were developed based on the ISOstandards with the purpose of unifying body measurement and developing a commondesignation system across Europe (British Standards Institution, 2001, 2002, 2003).The teaching and learning environment for clothing anthropometrics is therefore acrucial component in generating sizing knowledge. Sampath et al. (1981, p. 15)suggested that the following figures are valid regarding learning:

We learn: 1% through taste, 1.5% through touch, 3.5% through hearing and 83% throughsight. We remember 20% of what we hear, 30% of what we see, 50% of what we see and hear,80% of what we say and 90% of what we say and do.

Oa 22.5SD 22SD 21SD M þ1SD þ2SD þ2.5SD Oa SD

6 8 (S) 10 12 (M) 14 16 (L) 18 11.4Weight 0 31.8 37.5 48.9 60.3 71.7 83.1 88.8 4 65.3Stature 3 1426.8 1459.4 1524.7 1590 1655 1720.6 1753.2 2 2.3Neck 0 31.3 32.4 347 37.0 39.3 41.6 42.7 1 5.8Cerv2gb 0 127.3 13.2 136.0 141.8 147.6 153.4 156.3 0 6.2Cerv2gf 0 137.5 140.6 146.8 153.0 159.2 165.4 168.5 0 3.8Inside leg 0 61.1 63.0 66.8 70.6 74.4 78.4 80.1 0 1.4Sid2hip 1 3.2 3.9 5.3 6.7 8.1 9.5 10.2 1 6.1Sid2knee 1 41.8 44.8 50.9 57.0 63.1 69.2 72.2 1 7.4Sid2ground 1 81.7 85.4 92.8 100.2 107.6 115.0 118.7 0 3.5Acr2wrist 0 48.3 50.0 53.5 57.0 60.5 64.0 65.7 0 1.4Sh/length 0 10.5 11.2 12.6 14.0 15.4 16.8 17.5 1 2.7Sh/width 0 34.3 35.6 38.3 41.0 43.7 46.4 47.7 0 4.2Acback 0 24.5 26.6 3.8 35.0 39.2 43.4 45.5 1 3.5Acfront 1 27.4 291 32.6 36.1 39.6 43.1 44.8 0 9.2Bust girth 0 67.8 72.4 81.6 90.8 100.0 109.2 113.8 1 10.6Waist girth 0 50.5 55.8 66.4 77.0 87.6 98.2 103.5 2 3.3Hip girth 0 64.3 69.9 81.1 92.3 103.5 114.7 120.3 2 11.2Knee girth 0 30.4 32 35.3 38.6 41.9 45.2 46.8 2 2.3Ankle girth 0 18.3 19.4 21.8 24.0 26.3 28.6 29.7 3 1.2Wrist girth 0 13.0 13.6 14.8 16.0 17.2 18.4 19.0 1 5.3Armsygirth 0 31.8 34.4 39.7 45.0 50.3 55.6 58.2 2 11.2

Notes: aO, outliers (i.e. extreme values)

Table II.Table of body

measurements showingthe initial size steps based

on standard deviationsand their rounded values

Across back Bust girth Waist girth Hip girth Stature

Weight (0.76) Weight (0.86) Weight (0.85) Weight (0.90) Cervical to groundback (0.83)

Bust girth (0.80) Across back (0.80) Across back (0.80) Across back (0.77) Bust girth (0.71)Waist girth (0.81) Hip girth (0.90) Waist (0.90)Hip girth (0.77)

Table III.Table of correlation

coefficients for possiblekey dimensions

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Anthropometrics as a pedagogical and specialised area of study, especially theanalysis of raw data into size charts, has had little or sporadic attention in the academicarena. Without access to raw data, the core area of anthropometrics continues to betaught as a small sub-section of pattern-making technology, and yet the industrydemands knowledgeable graduates. However, with the explosion of new technologiesand their incorporation in 3D and manual measurement procedures for clothing sizingin the second half of the twentieth century, this is set to change tremendously acrossworldwide. Many countries continue to utilise manual methods of data collection andeven when new technology is used, some data are measured manually.

Although various anthropometric studies have been conducted worldwide, thereexists a gap in literature regarding the analyses of such data into sizing systems.Although there is mention of methods used, rarely are these substantiated for criticalreview. Measuring equipment and tools vary and range from simple to complex andusing different levels of technology. Bye et al. (2006) have comprehensively analysedthe measurement systems used for apparel – linear, multiple probe and body formmethods. An evaluative comparison of such methods including those used in teachingand researching clothing anthropometrics, especially analysis of data, is scarce.Usually methods ranging from descriptive (e.g. means, standard deviations,percentiles) to complex multivariate analyses with mathematical formulae have beenutilised (Roebuck, 1995). Salusso (1982) utilised female data using the principalcomponent sizing system where 15 key dimensions were identified leading to lateraland linear components categories for describing body types. Tryfos (1986) developedan integer programming system to generate maximum size charts and reduce fitdiscomfort, thereby developing artificial groupings. Ashdown (1998) investigated the

Size codes 6 8 (S) 10 12 (M) 14 16 (L) 18

Key dimensionsStature 1,427 1,459 1,525 1,590 1,655 1,721 1,753Inside leg 61 63 67 71 74 78 80Bust girth 67 73 82 91 100 109 114Waist girth 50 56 66 77 88 98 103Hip girth 64 70 81 92 103 115 120

Weight 32 37 49 60 72 83 89Neck girth 31 32 35 37 39 42 43Cervical to ground at back 127 131 136 142 148 153 156Cervical to ground at front 137 141 147 153 159 165 168Side waist to hip 3 4 5 7 8 9 10Side waist to knee 42 45 51 57 63 69 72Side waist to ground 82 85 93 100 108 115 119Acromion to wrist 48 50 53 57 60 64 66Shoulder length 10 11 13 14 15 17 17Shoulder width 34 36 39 41 44 46 48Across back 24 27 31 35 39 43 45Across front 27 29 33 36 40 43 45Knee girth 30 32 35 39 42 45 47Ankle girth 18 19 22 24 26 29 30Wrist girth 13 14 15 16 17 18 19Armscye girth 32 34 40 45 50 56 58

Table IV.Final size charts (withoutease or design allowance)

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Size codes 6 8 (S) 10 12 (M) 14 16 (L) 18

Key dimensionsStature 1,411.1 1,443 1,492 1,558 1,623 1,688.1 1,737.1

1,427 1,459 1,525 1,590 1,655 1,721 1,7531,442.9 1,491.9 1,557.9 1,622.9 1,688 1,737 1,768.9

Inside leg 59.1 63 65.1 69.1 73 76.1 79.161 63 67 71 74 78 8062.9 65 69 72.9 76 79 80.9

Bust girth 64.1 70 78 86 96 105 11267 73 82 91 100 109 11469.9 77.9 85.9 95.9 104.9 111.9 116

Waist girth 47.1 53 61 72 83 93 10150 56 66 77 88 98 10352.9 60.9 71.9 82.9 92.9 100.9 104

Hip girth 61.1 67 75 87 98 109 11864 70 81 92 103 115 12066.9 74.9 86.9 97.9 108.9 117.9 122

Weight 29.1 35 48 55 66 78 8632 37 49 60 72 83 8934.9 47.9 54.9 65.9 77.9 85.9 92

Neck girth 30.1 32 34 36 38 41 4331 32 35 37 39 42 4331.9 33.9 35.9 37.9 40.9 42.9 43.9

Cervical to ground at back 125.1 129 134 139 145 151 155127 131 136 142 148 153 156128.9 133.9 138.9 144.9 150.9 154.9 157

Cervical to ground at front 135.1 139 144 150 156 162 167137 141 147 153 159 165 168138.9 143.9 149.9 154.9 161.9 166.9 169

Side waist to hip 2.5 3.5 4.5 6 7.5 8.5 9.53 4 5 7 8 9 103.4 4.4 5.9 7.4 8.4 9.4 10.5

Side waist to knee 40.1 44 48 54 60 66 7142 45 51 57 63 69 7243.9 47.9 53.9 59.9 65.9 70.9 73

Side waist to ground 80.1 84 89 97 104 112 11782 85 93 100 108 115 11983.9 88.9 96.9 103.9 111.9 116.9 121

Acromion to wrist 47.1 49 56 55 59 62 6548 50 53 57 60 64 6648.9 55.9 54.9 58.9 61.9 64.9 67

Shoulder length 10.1 10.5 12 13.5 14.5 16 17.510 11 13 14 15 17 1810.4 11.9 13.4 14.4 16.9 17.4 18.5

Shoulder width 34.1 35 38 40 43 45 4734 36 39 41 44 46 4834.9 37.9 39.9 42.9 44.9 46.9 49

Across back 24.1 26 29 33 37 41 4224 27 31 35 39 43 4525.9 28.9 32.9 36.9 40.9 41.9 48

(continued )

Table V.Lower and upper limits

for sizes 6 to 18

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structure of sizing systems utilising the goodness of fit analysis, and concluded thatsize assignment and ability to cater for outliers resulted in improved fit in existingsystems. Yoon and Jasper (1996) developed a size labelling system for ready-to-wearwomen’s garments and suggested that key dimensions should present strongcorrelations with other dimensions. Gupta and Gangadhar (2004) utilised a

Size codes 6 8 (S) 10 12 (M) 14 16 (L) 18

Across front 27.1 28 31 35 38 42 4427 29 33 36 40 43 4527.9 29.9 34.9 37.9 41.9 43.9 46

Knee girth 30.1 31 34 37 41 44 4630 32 35 39 42 45 4730.9 33.9 36.9 40.9 43.9 45.9 48

Ankle girth 18.1 18.5 20.5 23 25 27.5 29.518 19 22 24 26 29 3018.4 20.4 22.9 24.9 27.4 29.4 30.4

Wrist girth 13.1 13.5 14.5 15.5 16.5 17.5 18.513 14 15 16 17 18 1913.4 14.4 15.4 16.4 17.4 18.4 19.5

Armscye girth 32.1 33 37 43 48 53 5732 34 40 45 50 56 5832.9 36.9 42.9 47.9 52.9 56.9 59

Notes: Lower limits are shown in bold; upper limits are shown in italics; mid-points for sizes areshown in romanTable V.

Size codes 6-8 8-10 10-12 12-14 14-16 16-18

Key dimensionsStature 32 70 65 65 66 32Inside leg 2 4 4 3 4 2Bust girth 5 9 9 9 9 5Waist girth 6 10 10 11 10 5Hip girth 6 11 11 11 12 6

Weight 5 12 11 12 11 6Neck girth 1 3 2 2 3 1Cervical to ground at back 4 5 6 6 5 3Cervical to ground at front 4 6 6 6 6 3Side waist to hip 1 1 2 1 1 1Side waist to knee 3 6 6 6 6 3Side waist to ground 3 8 7 8 7 3Acromion to wrist 2 3 4 3 4 2Shoulder length 1 2 1 1 2 0Shoulder width 2 3 2 3 2 2Across back 3 4 4 4 4 2Across front 2 4 3 4 3 2Knee girth 2 3 4 3 3 2Ankle girth 1 3 2 2 3 1Wrist girth 1 1 1 1 1 1Armscye girth 2 6 5 5 6 2

Table VI.Grading increments forsize range 6-18

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multivariate statistical model in developing 11 size charts for India with bust and hipgirths as key dimensions. Using the principal component analysis, linear orthogonaldimensions were evaluated using means, standard deviations and correlationcoefficients to generate short, medium, and tall body categories.

One reason for the non-availability of detailed analyses is that most surveys andresulting data are considered proprietary. For surveys where scanning has been used,the software and formulae configurations are proprietary. In practice, most reports inthe public domain usually present the general outcome of the study, for example, bothSizeUK and SizeUSA reported changes in general body shapes citing only the keydimensions. For academic purposes, however, there is need to establish and evaluateanthropometric methods. In a recent study for example, Chi and Kennon (2006)validated and compared manual and scanning methods. They found that while bothmethods were suitable for static measurement, there was significant variation betweenthem regarding dynamic poses. Scanning technology has the advantages of generatingvast body measurements discreetly and fast in 3D formats and has revolutionisedcustomisation (Istook and Hwang, 2001). However, there is inconsistency intechnologies and also lack of standardisation (Simmons and Istook, 2003) andcurrent scanners fail to measure dynamic poses suitable for the design of highperformance clothing (Chi and Kennon, 2006). Treleaven (2003) decried the variation inquality of both software and hardware. Roebuck (1995) had earlier pointed out thedifficulty in analysing anthropometric data. Because these technologies areproprietary, validation, especially of analysis, is not possible and is usually obscure.

A method of analysing manual anthropometric data – the MMU modelGenerating raw dataAccessing raw data using manual methods could be slow, expensive, complicated andis subject to variation. Although the potential of 3D optical body scanners and 2D datacapture systems promise greater speed, accuracy and reproducibility (Bougourd et al.,2000, p. 164), the cost of these technologies is still prohibitive and therefore yet to bewidely used by institutions of higher learning in the UK. Knowledge about processes ofsurvey planning, principles and practices, utilisation of technology, data collectionstrategies, data analysis and translation of these data into usable facilities form thecontent area of clothing anthropometrics. There is still limited human resources andknowledge about collating and analysing such data, let alone teaching and researchingthis area. Incorporating small scale and international surveys, such content is the focusof study and research at the Manchester Metropolitan University (MMU). MMU was acollaborator for data collection during the SizeUK national sizing survey in 2001,contributing as data collection centre in the North West UK (Taylor, 2001; Webster,2001). This experience presented an opportunity for training for staff and studentsusing state-of-the-art scanning technology. Both small-scale and international surveysare developed and analysed. Firstly, the purpose and objectives of the study aredetermined. Secondly, methods of recruitment and measurement are discerned.Thirdly, resources including people and equipment are identified. Fourthly, proceduresand training needs as well as methods of analysis are decided upon. Finally,procedures for pattern development, testing and verification of fit are determined, andsize charts confirmed.

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Since the early 1990s, various surveys have been conducted and incorporated in thepedagogical processes of clothing anthropometrics at MMU. The cornerstone of theMMU model is Beazley’s study (1997, 1998, 1999) of 100 female students aged 18-28years. This research presented procedures, collation and analysis of data thatgenerated body measurement tables, size charts, patterns and basic blocks. Theobjectives were to obtain body measurements, statistically analyse data and develop asizing system and size charts for use in teaching and learning. This study confirmedthe difficulty of obtaining measurements, selecting key dimensions and labellingdecisions. This study resulted in body measurement tables and size charts that haveformed the basis of block patterns and 3D models that are currently used at MMU.Subsequent surveys have focused on both national and international samples that havegenerated size charts for related populations. A precedent anthropometric study of 618Kenyan children devised size charts for 3-6 year olds and generated a conceptualframework on the role of sizing in the marketing of garments (Otieno, 1999). Chen(1999) studied the relationship between body size and a sewing workstation layout andits impact on operator’s efficiency. Body measurement data were collected using 60Chinese and British operators. This study concluded that for maximum efficiency in aclothing workstation, overall layout must be flexible enough to accommodate productdesign, process design, schedule design and production planning.

Mlauli (2003) conducted an anthropometric study using black women’s data fromSouth Africa. The purpose of this baseline national study was to measure andcategorise figure types, create new size charts, evaluate sizing provision amongretailers and develop a conceptual framework on body cathexis among women. Eighthundred and thirteen black women aged 20-54 years from all nine provinces of SouthAfrica were measured, and this resulted in the development of body measurementtables. Size charts were also analysed for their impact on manufacture and marketingof clothing. A conceptual framework that relates these size charts and garment fit tooverall consumer satisfaction with clothing sizing and body sites was developed basedon interview data, thereby analysing body cathexis. There is still confusion about sizecharts, codes, and labelling systems in South Africa as there is globally (Mlauli, 2003;Vronti, 2005). This study resulted in the development of original body measurementtables and new size charts. Five height measurements were developed and resultsconfirmed that most of women in the study had a large hip measurement. This studyproposed bust, waist and hip as key dimensions and generated a conceptualframework that relates garment fit and satisfaction with clothing sizing. Data from theinterviews revealed that size charts and body cathexis are key factors in clothingconsumption among these women. These findings have major implications formanufacture and marketing of women’s clothing in South Africa and globally in themarketing strategy for exporters, determination of suitable size charts and adoption ofclothing sizing for mass production especially in satisfying local needs and demands.Another precedent women’s study in Cyprus focused on developing size charts and aconceptual framework on the role of sizing in the marketing of women’s wear (Vronti,2005). Vronti (2005) measured 815 Cypriot women developed new size charts and aconceptual framework that facilitates better understanding of fit and drape incompetitive markets. This baseline study has great implications for the Cypriotclothing industry, marketing of garments by local and foreign companies, provision ofquality garments of good fit, consumer satisfaction among female consumers, future

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research for males and children and future use of new scanning technologies. Thecontribution will not only enable the Cypriot manufacturers to develop patterns andgarments that are consistent with the current anthropometric characteristics, but alsoany global ones who wish to trade quality garments in this market. From this study itwas established that a drop of 10.4 cm, calculated by the difference between the meanof the hip (103.2 cm) and the mean of the bust (92.8 cm) could impact the size charts inthis population. It is imperative that a “Mediterranean size chart” is developed in orderto meet the needs of many countries in this region.

Body measurement procedures and database creationBody measurements are selected and procedures devised based on precedentguidelines on body measurement (International Organization for Standardization,1989; Beazley and Bond, 2003, pp. 1-4), objectives of the study and MMU guidelines onethical research. The latter defines all ethical issues as informed by the MMUuniversity ethical framework, for example size and efficacy of samples, permissionsand practices regarding measurement. Measurements are usually selected based on theobjectives of the study in relation to size chart or garment generation. Beazley andBond (2003, p. 8) suggest five stages of size chart development:

(1) obtaining raw data;

(2) analysis;

(3) adding ease allowances;

(4) formulating size charts; and

(5) conducting fitting trials.

Kunick (1984) had advised that statistics alone cannot solve sizing problems becausethe influence of fashion has to be considered; and hence the need for specialistknowledge. Bye et al. (2006) suggest that “it is necessary to go beyond the bodymeasurements to determine the ideal relationship between the body and the garment”(p. 76).

Processes in initial evaluation of dataRaw data are collated and cleaned by evaluating outliers, missing or spurious values ofeach variable using SPSS (Statistical Package for the Social Sciences) (Cameron, 1982;Cramer, 1998). Selected descriptive statistics (frequency tables, percentiles, standarddeviations, mean, minimum, maximum, kurtosis, skewness) provide a medium forinitial evaluation of data generated from MMU database of 100 women. Table I showsthe summary statistics of a sample of women’s data (MMU data set, 2006). Suchanalyses have been the cornerstone of developing body measurement tables that arethen transformed into size charts (Beazley, 1999; Beazley and Bond, 2003; Otieno, 1999;Mlauli, 2003).

Generating body measurements tables and size rangesProcedures for size chart development start with the utilisation of frequency tables,means and standard deviations of all variables under study. Percentiles are thenselected, for example mean values are used to identify the 50th percentile, which forclothing purposes serves as the base size. Selection of percentiles is important because

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it has implications for the base size; the same data can therefore generate variations ina single size. A larger percentile will therefore produce a bigger base size (larger fit),while a smaller percentile would present a smaller base size (smaller fit). Table I showsthe variations of base size when the 25th, 50th, 75th or 85th percentiles were selected. Itis observable that the 50th percentile is also the median value. For this discussion, the50th percentile will be utilised. At this stage, the anthropometrist can discern thepercentiles that best cater for varying sizing groups and therefore those that meettarget markets for manufacturers and retailers.

In the UK, the base size is equivalent to size code 12. Using this as the base size forthe 50th percentile, the standard deviation value is utilised to create size steps innotations of 1, 2 and 2.5 to the right and left of the base size value. According to Cramer(1998), the entire sample is statistically catered for using five standard deviationdivisions. In this case 2.5 divisions are catered for on each side of the median. Bysubtracting one standard deviation value from the mean, the next small size (size 10) isdetermined, and by subtracting the value of two standard deviations from the median,the subsequent small size (size 8) is determined. Starting from size 12, the process isrepeated in the reverse, thereby determining values for size 14 and 16.

Statistically only 2.5 per cent coverage is left above size 16 and 2.5 per cent belowsize 8. Sizes 18 and 6 could be demarcated by adding or subtracting 2.5 standarddeviation values to the median, respectively. The specific number of remaining casescan be determined from the frequency tables (Beazley, 1999; Otieno, 1999; Mlauli, 2003).Based on the values for the sizes 6, 8, 10, 12, 14, 16 and 18 for all the variables, a bodymeasurements table can be developed, thereby creating a size range. Table II shows thederived values for the body measurements tables, their rounded values and size codes.

Determining key dimensionsAn important consideration is the determination of key dimensions, size widths anddesignation that will be used by manufacturers and consumers in recognising andselecting a size. Key dimensions are measurements used to denote a garment size andthese could be primary, secondary or tertiary ones (Beazley, 1997; British StandardsInstitution, 2001, 2002, 2003).

Using the correlation coefficients, relationships between variables can bedetermined (Otieno, 2000; Gupta and Gangadhar, 2004). Such values can be assessedbased on the strength of their relationship (Cramer, 1998; British Standards Institution,1990; Kemsley, 1957): below 0.5 indicates no relationship, 0.6-0.75 indicates a mildrelationship and 0.76 and above indicates a strong relationship. Key dimensions arethus selected based on the strength of the relationship and the number of correlationswith most other variables. From the table of correlation coefficients generated in SPSS,five dimensions (bust, waist, hip, across back and stature) have the highest number ofstrong correlations with other variables at the significance level of 0.01, as shown inTable III.

According to Yoon and Jasper (1996), Beazley (1997) and Kemsley (1957), keydimensions should have strong multiple correlations with other dimensions related tothe garment, must be a good predictor of size for other body parts, and should be easyto measure. Bust strongly correlates with weight (0.86), across back (0.80) and hip(0.90). Waist strongly correlates with weight (0.85), across back (0.80) and hip (0.90).Hip strongly correlates with weight (0.90), across back (0.77) and waist (0.90). Stature

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correlates with a vertical measurement, cervicale to ground at the back (0.83) andinside leg, a lower torso measurement at 0.71). It is therefore logical that bust, waist,hip and inside leg qualify to be considered as key dimensions for this sample. Whilebust and waist could be used for upper torso garments, inside leg, waist and hip can beselected for lower torso garments. Industry practice propagates the use of stature forfull body garments and also categorisation of sizes. Height could therefore used alongwith any girth measurement in determining height. Although across back wasstrongly correlated with waist, hip and bust, and is utilised in pattern development, itis usually not utilised as a key dimension. Also since the other key dimensions (bustand waist) have been selected, it is logical to deduce that its influence on sizing willhave been catered for. In selecting the size range and codes, consideration is made tothe industry practice. Size widths are determined by demarcating the boundariesbetween sizes (Beazley, 1998, 1999; Otieno, 1999). Grades are determined firstly, by thevalues between sizes and secondly by considering realistic increments that themanufacturer selects. For example as in Table IV, the derived grades are variablewithin each dimension. One option is to calculate the average grade; the other is toselect the most common within the dimension. Some manufacturers opt to havevarying grades; for example to maintain a grade of 4 cm for chest, hip and waist forsizes 6 to 16 and then utilise a smaller grade of 2 cm for larger sizes 18-22. Gradingincrements can be evaluated and verified through fitting trials. The resulting sizecharts are presented in Table IV; these would have ease and design allowances addedbefore being used for pattern generation.

Garment size charts are developed based on the addition of ease allowances forvariables (Beazley, 1998; Gupta and Gangadhar, 2004). There is variation in theamount of ease allowances utilised for specific measurements; these amounts are notusually comparable between manufacturers with the decision to use them apparentlyarbitrary. The MMU model uses allowances that were utilised by Beazley (1998, 1999)and tested on various pattern blocks before adoption. These are bust (6 cm), waist(4 cm) and neck (2 cm), Patterns are then developed (MMU size charts, 1997). Trialgarments are created and fitting trials conducted using virtual and live models anddummies, thereby confirming the size charts. Since all apparel companies have theirown fit models, variation in sizing and fit are still prevalent. For Gupta and Gangadhar(2004) and McCulloch et al. (1998), validation of size charts is conducted using ameasure, the aggregate loss of fit based on a formula.

It is observable that various relationships exist between measurements and thesizes and different grading increments (deduced as the standard deviation value ofeach dimension) could be determined between various sizes within the size range 6-18(Table IV). Further, size boundaries can be determined by discerning upper and lowerlimits of each size from the mid point values shown in Table IV. For example, theboundary for size 12 for stature can be obtained by (1,427 þ 1,459) then dividing by 2,giving 1,443 (joint boundary for sizes 6 and 8). This can further be demarcated todetermine the upper limit for size 6 (1,442.9) and lower limit for size 8 (1,443.0).Determining lower and upper limits is key in defining size width and can impact thegrading increments used (Otieno, 1999). An example of the relationships between themidpoints, lower limit and upper limits and boundary demarcation values for staturefor all sizes is shown in Figure 1. The lower limits and upper limits and boundarydemarcation for all dimensions and sizes are shown in Table V.

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Utilising 3D data for pattern development and testing fitTwo-dimensional data are translated into 3D by digitising data for the verification offit. The quest to obtain garments of the right size and that fit well, feel comfortable andallow the body to move naturally has always remained elusive. This is even moreapparent in mass-produced garments where pattern-making models may not reflectmeasurements of current figure sizes and shapes. Figure 2 shows 3D evaluation of fit.

With the clothing industry evolving from mass production to mass customisation, thereis the need to verify existing pattern making models in order to test their effectiveness.CAD/CAM technologies could provide a medium for effective evaluation of fit and theefficacy of size charts. Carr (2001) assessed anthropometric data and procedures of threepattern-making models and evaluated fit using 3D CAD technology. The compatibility ofthese models with CAD technology in the assessment of fit was evaluated. The resultsrevealed inefficiency and variation in fit. Brownbridge (2002), in studying 3D clothingtechniques in relation to pattern-making and testing fit, concluded that new technology hasthe capability of creating a paradigm shift in the clothing industry.

Apeagyei (2002) studied 3D clothing technology and its impact on the provision ofefficient fit of garments for mass customisation. The concept of CAD technology hasbecome a focal point which has given rise to improvement of the areas that constitutethe sale and assembly of clothes by mass customisation. This study suggests the needfor a paradigm shift from “pushing existing standardised products” to that of

Figure 1.Stature: determining lowerand upper limits for sizes6-18

Figure 2.Verifying fit using 3Dtechnology

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clarifying needs, and then designing the tailored product. In the industry fit models areusually used to identify the base configuration for current industry patterns. Thesemodels are individuals chosen based on their body size and proportions to match themarket that the designer is targeting. Apeagyei (2002) focused on providing adequatefit for an individual consumer. Kiptanui (2001) studied the potential of 3D technologyin testing garment fit. Both manual and virtual fitting were conducted and fit criteriaestablished. Three elements of fit (grain, ease and balance) were evaluated. Resultsfrom this study confirmed that there is a lot of potential to efficiently evaluate fit byusing 2D and 3D CAD pattern systems with modifications.

The MMU model for researching clothing anthropometrics: body dataanalysis and size chart developmentThis paper has discussed a method and procedures of collating and analysing bodymeasurement data. Seven steps are utilised:

(1) planning and preparation;

(2) identifying resources;

(3) discerning measurements and procedures;

(4) data collection;

(5) analysis and development of size charts;

(6) fitting trials; and

(7) confirmation of charts.

These procedures have been utilised since the 1990s and have involved researchconducted in the UK and other populations such as Cyprus, Malaysia, South Africa,Kenya, Ghana and China. With the new scanning technologies, it is anticipated thatsimilar procedures will be utilised with variation in the analysis of mathematicalformulae and configurations for size chart development. Figure 3 shows the MMUmodel for researching clothing anthropometrics.

ConclusionsClothing anthropometrics is still developing as an autonomous area of study. There isneed to integrate content, methodology and practice especially in the pedagogicalprocess. Body sizing information is important to consumers, retailers andmanufacturers. Conducting surveys that are contextualised in learning and teachingis core to the growth of this subject. Methods of researching and teachinganthropometrics are still obscure especially analysis of data and their translation intoverifiable sizing systems. This study highlighted the use of raw data frommeasurement of subjects as a source of sizing information. With proper planning andorganisation, clothing companies can develop research-based size charts based on theirtarget markets. Body measurement data can be analysed statistically to develop sizecharts, size ranges, grading increments and designations for size widths (Tables I-VI)without resorting to the trial-and-error approaches that evidenced earlier practices inthe industry. Collaboration between industry and academe could generate reliablemethods for analysis and testing of fit. There is need for more literature in the publicdomain regarding this area. With the development and adoption of new scanningtechnologies and the availability of such systems to industry and academics, and the

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continuing use of traditional manual approaches worldwide, it is paramount that thesemethods are critically evaluated.

From the perspective of consumers, retailers and manufacturers, size charts areindispensable to the clothing industry: standardisation, labelling of garments andstock management are core marketing assets, proprietary information for retailers andmanufacturers, customer satisfaction, creation of niches and garment identification byconsumers. The model presented in this study discusses procedures that substantiatethe use of verifiable data in determining key aspects of a sizing system such as keydimensions, size codes and labelling, size designation, size ranges and gradingincrements. Normally commercial decisions are made based on the viability of sizing

Figure 3.The MMU model forresearching clothinganthropometrics

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systems; having a clearer understanding of the nature and role of these systems istherefore important to clothing marketers.

Although consumer satisfaction is a focus for retailers and manufacturers, finding agarment that fits properly and looks stylish continues to be a dilemma and frustrationto many consumers (Otieno et al., 2005). From body measurement to purchase, sizingissues pervade the supply chain processes. Today, many people not only live thedifficulties of finding clothes that fit but also suffer the subsequent confusion aboutgarment sizing. And with variability in size, proportion and shape, consumers, retailersand manufacturers are especially interested in determining body size to attainconsumer satisfaction and national standards (Bougourd et al., 2000; Newcomb andIstook, 2004). Consumer satisfaction is beneficial to retailers and manufacturers, andcould lead to patronage, repeat buying and positive word of mouth. Evaluation andachievement of garment fit is evasive.

From the perspective of marketers, sizing is a commercial asset with variousbenefits and could lead to improvements in standards of manufactured clothing,making clothes (lay planning and styling), proprietary size charts, understanding ofthe human factors for target markets, and data management and analysis which leadto the development of patterns. Anthropometric data are the cornerstone of sizingsystems and as such, are considered proprietary and marketing tools for retailers andmanufacturers and are used for size discernment and communication by consumers.Surveys are historical benchmarks for industry standards, and today, managementand manipulation of body data is the focus of modern non-contact technologies. Usingvarious methods (LaBat and DeLong, 1990), one principal aim has been to generatedata for designing garments that provide comfort and appropriate fit (Roebuck, 1995).Clothing design requires the accurate measurement of the body in order to generatebetter fitting garments for consumers. Clothing anthropometrics deals with data thatare used for clothing sizing and pattern development, size coding by retailers andmanufacturers and communication of sizing information to consumers. At the core ofclothing anthropometrics are therefore three key issues:

(1) how to measure the body adequately;

(2) how to analyse the vast data into efficient size charts; and

(3) how to use the size charts in marketing in order to create customer satisfactionwith clothing.

The processes of gathering body measurement data, their critical analysis and logicalinterpretation into size charts is the first step towards this goal.

Size charts are important in the clothing industry: they are used for standardisation,labelling of garments, stock management, size identification by consumers, asproprietary information for retailers and manufacturers, for creating consumersatisfaction, as a marketing tool and in creating niches. These are related to keydimensions, size codes, size ranges, grading increments, allowances and internationalstandardisation. Although statistics alone cannot solve the sizing problems, theutilisation of verifiable body measurement data can generate reliable procedures thatimpinge on the efficacy of size charts in the clothing industry.

This paper has discussed one model utilised for anthropometric research andteaching in one higher education institute, incorporating measurement procedures,analysis and step-by-step development of size charts. To evaluate comparative

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practice, other anthropometric practices will need to be discussed, especially thoserelated to academic practice in the area. As clothing anthropometric gains autonomyand wide specialism, evaluation of its methods is paramount. Further research intolevels and spread regarding the teaching and researching of anthropometrics in the UKis long overdue. This discussion could advise anthropometric research and practice.

This paper presents a model at one higher education institute focusing mainly onmanual approaches used in researching clothing anthropometrics. The experiencesgained from participation in SizeUK in 2001 with the utilisation of scanning technologyare valuable to the research environment. Further, evaluation of data analysis in newtechnologies is still necessary and the debate on methods of anthropometric methodsespecially data analysis are relevant for the future of the subject. Future debate couldfocus on comparative analysis in different locations, methods used and moreimportantly, validation of scanning analyses. For the clothing industry, knowledgeabout sizing and its utilisation is of paramount importance as it could serve as acompetitive edge.

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Further reading

Apeagyei, P. and Otieno, R. (2007), “Usability of pattern customising technology in theachievement and testing of fit for mass customisation”, Journal of Fashion Marketing andManagement, Vol. 11 No. 3, pp. 349-65.

Apeagyei, P., Otieno, R. and Tyler, D. (2007), “Ethical practice and methodological considerationin researching body cathexis for fashion products”, Journal of Fashion Marketing andManagement, Vol. 11 No. 3, pp. 332-48.

Corresponding authorRose Otieno can be contacted at: r.otieno@mmu.ac.uk

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This article has been cited by:

1. Josephine Kasambala, Elizabeth Kempen, Reena Pandarum. 2016. Determining female consumers’perceptions of garment fit, personal values and emotions when considering garment sizing. InternationalJournal of Consumer Studies 40:10.1111/ijcs.2015.40.issue-2, 143-151. [CrossRef]

2. Simeon Gill. 2015. A review of research and innovation in garment sizing, prototyping and fitting. TextileProgress 47, 1-85. [CrossRef]

3. K. KennedyPattern construction 205-220. [CrossRef]

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