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The development of a model to indicate residual wear life in recycled clothing

Douthitt, Suzanne, Ph.D.

The Ohio State University, 1993

UMI 300 N. ZeebRd. Ann Arbor, MI 48106

THE DEVELOPMENT OF A MODEL TO INDICATE RESIDUAL WEAR LIFE IN RECYCLED CLOTHING

DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By

Suzanne Douthitt, B.S., M.S.

*****

The Ohio State University 1993

Dissertation Committee: Approved by: Dr. Kathryn A. Jakes Dr. Charles J. Noel j Advisor/ Dr. Hazel 0. Jackson College' of Human Ecology '' Graduate Program Dr. William I. Notz -Textiles an$K Clothing ACKNOWLEDGMENTS

I wish to express my appreciation to my advisor, Dr. Kathryn Jakes, for her guidance, encouragement, and commitment throughout my graduate studies and this research. I feel fortunate to have assembled such a talented and supportive committee. To Dr. Noel, I extend my sincere thanks for sharing his guidance and technical expertise (along with a great sense of humor) during this study and my coursework. Dr. Jackson' s enthusiasm and input have been invaluable. I am grateful to Dr. Notz for helping to make Statistics manageable to me. His interest in the project and hard work with the analysis are greatly appreciated. My appreciation to Dr. Nancy A. Rudd for providing me with confidence and motivation throughout the program. Her commitment to education and the students is evident. Special recognition and thanks are extended to Rose Burnette, Carol D'Ippolito, and Leta Hendricks for their time and expertise, so generously shared in this research. I am most grateful to Chuck Shearer, General Manager of Grossman Industries, for supplying the shirts and expressing a willingness to cooperate with this project. ii I would also like to acknowledge Fred Ruland for his consultation and genuine enthusiasm for research and teaching. My appreciation for the Helen Clarkson Fellowship and the Mary Lapitsky Graduate Award goes to the Department of Textiles and Clothing, The Ohio State University. The monies helped to defray the cost of this project and allowed me to devote a full-time schedule to this project. I wish to extend my appreciation to the other graduate students in the program for their friendship and encouragement. It certainly helped to make the process easier (and even fun at times) . Special mention to Laurie Crawford, whose optimism, support, and caring nature have been most welcome. My heartfelt thanks goes to my parents and sister, Laurie, for expressing their belief in my abilities and their constant encouragement. It is comforting to know that you are always only a phone call away. Most importantly, I want to extend my love and thanks to my husband, Craig, and Newfoundlands, Zevon, and Beasley, whose unconditional support and devotion throughout the process enabled me to fulfill my goals. The gratitude and recognition that is due for their help in this and all my endeavors can never be adequately expressed. I dedicate this work to them. iii VITA

1 9 8 1 ...... B.S. Long Island University Brooklyn, New York Physician's Assistant License and Certification 1981-1985...... Physician's Assistant Brooklyn, New York 1985-1990...... Research Assistant The Ohio State University 1990-1992...... Graduate Teaching Assistant Department of Textiles and Clothing, The Ohio State University 1992 ...... M.S. The Ohio State University Department of Textiles and Clothing

FIELDS OF STUDY Major Field: Human Ecology, Textiles and Clothing Minor Field: Statistics

iv TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... ii VITA ...... iv LIST OF F I G U R E S ...... viii LIST OF TABLES ...... ix CHAPTER PAGE I. INTRODUCTION...... 1 Problem Statement...... 4 P u r p o s e ...... 6 Assumptions...... 9 Limitations...... 9 Definitions...... 10 II. RELATED LITERATURE...... 12 Clothing Consumption as a Process ...... 12 Apparel Degradation...... 13 The Concept of Waste Applied to Clothing. . 16 The Economic Connection Using Full-Cost Accounting...... 18 Textile Product Serviceability...... 19 Aesthetic Appearance and the Concept of Serviceability...... 25 Textile Testing ...... 29 Laboratory Simulation Tests ...... 32 Review of Prediction Tests...... 34 Methods of Monitoring the Progressive Deterioration of Textile Materials. . . . 36 The Use of Degradative Curves and Simulation W o r k ...... 40 S u m m a r y ...... 41 Research Question ...... 44

v CHAPTER PAGE III. THEORETICAL FRAMEWORK...... 46 IV. METHODOLOGY...... 59 Selection and Training of Judges...... 60 Selection of Characteristics for Assessment...... 63 Part I: Assessment of the Judges...... 69 Data Collection Instrument...... 71 Method...... 72 Replicas...... 75 Data Analysis ...... 77 Part II: Destructive Tests...... 82 Method...... 85 Data Analysis ...... 88 V. PRESENTATION OF FINDINGS AND DISCUSSION OF PART I ...... 92 Pilot Study ...... 92 Part I - Assessment of Judges ...... 94 Classification of Shirts Based on Evidence of Discard ...... 97 Cluster Analysis...... 98 Factor Analysis ...... 99 Discussion...... 106 VI. PRESENTATIONS OF FINDINGS AND DISCUSSION OF PART II...... 112 Description...... 112 Comparison of Physical Testing to ASTM Specifications...... 113 Pearson's Correlations...... 114 Multiple Regressions...... 137 Description...... 139 Canonical Correlation Analysis...... 142

vi CHAPTER PAGE VII. INTERPRETATION OF FINDINGS AND CONCLUSIONS...... 153 Description of Objective#1 ...... 153 Description of Objective#2 ...... 157 Description of Objective#3 ...... 159 Description of Objective#4 ...... 165 Conclusion...... 172 VIII. SUMMARY AND RECOMMENDATIONS...... 175 Implications and Recommendations...... 183

APPENDICES 1 ; A. Definitions...... 187 B. Script for Training Judges...... 190 C. Worksheets for Visual Assessments...... 195 D. Diagram of Location of Wear inShirt ...... 197 LIST OF REFERENCES...... 199 LIST OF FIGURES

FIGURE PAGE 1. Fabric Categories...... 26 2. Criteria for Defining Aesthetic Concepts ...... 27 3. Hypothetical Curves Illustrating Different Types of Degradation P a t t e r n s ...... 39 4. Clothing Consumption Model with Discard Options...... 47 5. Disposition Decision Taxonomy. .... 50 6. Model for Life Cycle Analysis of a Textile Product ...... 52

viii LIST OF TABLES TABLE PAGE 1. Residential Solid Waste Composition . . . 14 2. Key for Snagging Resistance Replicas. . . 75 3. Key for Abrasion Resistance Replicas. . . 76 4. Wilcoxon Test for Interrater Consistency - Pilot Study ...... 79 5. Friedman Test for Interrater Consistency - Pilot Study ...... 81 6. List of Evaluative Tests for Recycled Shirts ...... 86 7. Numerical Rating for SnaggingResistance Laboratory T e s t ...... 87 8. Summary of Significant Friedman Tests . . 94 9. Wilcoxon Test for Interrater Consistency - Sanple D a t a ...... 95 10. Friedman Test for Interrater Consistency - Sanple D a t a ...... 96 11. Cluster Analysis...... 100 12. Factor Analysis ...... 103 13. Summary of Factor Analysis of the Assessment of Judges Variables...... 106 14 . Pearson Correlations...... 115 15. Interrelationship of Fabric Structure to Tensile and Tear Strength and Abrasion Resistance...... 123

ix TABLE PAGE

16. Multiple Regression - Fabric Thickness. . 127 17. Multiple Regression - Fabric Whiteness. . 129 18. Multiple Regression - Abrasion Resistance...... 131 19. Multiple Regression - Tearing Strength - Fill Direction. . . 133 20. Multiple Regression - Tearing Strength - Warp Direction. . . 134 21. Multiple Regression - Snagging Resistance...... 135 22. Multiple Regression - Flexural Rigidity...... 136 23. Canonical Correlation Analysis...... 147 24. Standardized Canonical Coefficients of Variables...... 149 25. Intervals for Determining Total Scores For Factors in Factor Analysis...... 155 26. Distribution of Sanple Shirts by Four Factors...... 156 27. Average of Judges Ratings of 65 Shirts for Variables Determined by CCA and Ratings Total...... 171

x CHAPTER 1 INTRODUCTION The production of a garment entails many steps from cultivation or formation of the textile fiber, removal of any irrpurities, conversion to yam and fabric, to chemical processing such as bleaching, dyeing, and finishing. The fabric is then constructed into the garment by cutting, stitching, and other assembly operations. All of these stages require the use of resources, time, and energy. There are many possible routes in the process of clothing consumption. Upon purchase, the predominant fate of a garment is use by a single owner followed by permanent discard. To some lesser degree, clothing items are passed on for use by a second owner. American consumers typically discard clothing before its physical utility has been depleted (Francis, 1991) , although there have been some efforts to recycle and reuse products as a means of conservation of money and resources. As early as 1939, the Wool Products Labelling Act made provisions for the labelling of recycled wool (Joseph, 1986) . More recently, efforts have been made by textile researchers to recycle fiber waste and fabric cuttings generated by textile and apparel manufacturing (Bandyopadhyay, 1990; Testore, 1990) . Although recycling of textiles is not new, it has gained considerable interest recently from textile manufacturers and consumers in our environmentally conscious society (Kalogeridis, 1990). this study focuses on generating a model that indicates continued performance in discarded clothing. A preface to the discussion of the definition of residual wear life is the specification of the intrinsic purposes that dress serves; adornment, utility/protection, and modesty (Storm, 1987) . Of these major theories of the first function of dress, adornment is the most widely accepted today since it is commonly assumed that humans first used dress to enhance their appearance. Adornment is a universal function of and motive for dress. Most m o d e m dress is worn, at least in part, for this purpose. Utility and protection from environmental elements includes both physical and psychological components. An example of physical protection through clothing is the special clothing worn by participants in sports or jobs such as football players and astronauts. Psychological protection that clothes can provide includes the use of clothing symbols that the wearer believes will bring luck or happiness. The role of clothing and modesty reflects the attitude that the human body is to be hidden. According to Storm, the role modesty played in early dress was probably minimal. For the purpose of this study, residual wear life is defined as "the performance of a garment in both functional and aesthetic aspects over a period of time after discard by the owner". A measure of functional performance consists of factors related to durability such as tensile strength, dimensional stability, and other factors which are directly related to the ability of a garment to serve as covering and protection. This may be distinguished from aesthetic performance which consists of such factors as color and pilling that are not related to the ability of the garment to serve as protection or covering. Rather these elements contribute to the psychological and social interface provided by clothing. Therefore, residual wear life is not limited to a garment with structural stability but encompasses all reasons for apparel discard. This multifaceted concept is based on the definition of quality as "fitness for use", but also takes into account a time component and the resultant changes that occur in the textile over its lifetime. The results of this research will be useful to the consumer as well as the merchandiser of used clothing in the determination of product pricing, expectations, and satisfaction. Furthermore, the ecologically responsible consumer may be interested in evaluating the remaining usefulness of clothing to responsibly dispose of used clothing and textiles that no longer fulfill their needs. This creates a feedback loop in the process of clothing consumption. The garment is not permanently discarded, but rather returned to use. Since reuse of the garment prevents it from contributing to the waste stream and conserves resources by materials reuse, it is desired. Problem Statement The United States is currently undergoing a "Green Revolution" . Consumers are encouraged to purchase many types of products that are labelled as environmentally friendly. However, relatively little attention has been given to possible methods of participation in this effort by the textile and apparel industries. For instance, alteration of clothing consumption practices of certain individuals can help to balance the environmental consequences of the production of textile and apparel items. This is due to the fact that about one-half of the fibers in the clothes that Americans wear are synthetic fibers derived from oil, which is a nonrenewable source (The Earth Works Group, 1990). In addition, most synthetic fibers are more resistant to degradation or disintegration than cellulosic fibers. Since their manufacture has already contributed to consumption of resources, it is advantageous to use the garments as long as they can last. This can be achieved by placing emphasis on clothing consumption paths which recycle textiles in the form of the original garments, textiles (e.g., cut into rags), or fibers. Recycling of textile products helps to conserve resources and prevent the alternative of depositing them in already overburdened landfills. It is anticipated that about one-half of the landfills operating in 1992 will be closed within five years and almost three quarters within the next 15 years (Blumberg, 1989). Several businesses currently exist that are involved in the redistribution of discarded textiles and clothing. One such company, Grossman Industries, is a textile recycling company located in Columbus, Ohio. The majority of their textile acquisitions are castoffs purchased from charitable organizations, which are sorted and shipped to third world countries for continued use as clothing. The company processed about 12 million pounds of recycled textiles over the past year (Shearer, 1992) . Though this may seem substantial, it is a relatively small percentage of the approximately 45 million pounds of textiles discarded in the midwest region of the United States. The items received at Grossman Industries are sorted and categorized as vintage, number ones, recyclers, and rags. These categories determine the final disposition of the textile item. The garments categorized as vintage clothing are from a past period. Number ones are those garments or textiles that are in very good condition. Recyclers are those items which are inferior in condition to number ones, but still may be reused for their original purpose. Rags are those items that are not acceptable for use in their original purpose, but may be cut into scraps for wiping rags. This company has been in business for over 30 years, decades prior to the recent emergence of niche markets for resale clothing (such as infant and baby clothes, formal and special occasion wear, vintage clothing and sports equipment) . Consumer acceptance of purchasing and wearing recycled clothes is facilitated by the changing attitudes regarding the choice of used clothing as it is becoming an acceptable and environmentally responsible way of enhancing a wardrobe, not to mention economically rewarding (Jacobs, 1992) . This is demonstrated by a comment made by a textile recycling dealer (Smith, 1992): "I used to be a rag dealer, the low man on the totem pole. Now I have the prestige of being on the leading edge, a major recycler, helping the environment." In 1988, the United States exported 135,000 tons of used clothing to Third World countries (The Earth Works Group, 1990) . By donating clothes to organizations or charities that redistribute them, their deposit in landfills is averted. Furthermore, they may be used to provide clothing for other community members through nonprofit thrift stores. Purpose The EPA estimates that around 11 percent of waste in the United States is currently being recycled. This translates to a total of 17 million tons per year (The Earth Works Group, 1990) . Therefore, measures that are taken to recycle specific components of municipal solid waste can help contribute to an increase in the amount of waste that is recycled. It is estimated that 8% of the municipal solid waste is composed of textiles, consisting predominantly of apparel, home furnishings, and industrial products (Ham, 1990). As a relatively significant contributor to the amount of solid waste, enhancing the reuse of textile products can make a positive impact on the environment. One step in addressing the solid waste problem specifically for textiles and clothing is the identification of a method of evaluating the condition of discarded garments that employs visual nondestructive assessment by untrained judges. Such a method may be readily employed by the general public or by graders in the recycling facility who have little or no textile training. The correlation of the results of these appraisals with laboratory performance test results will provide the ability to indicate residual wear life of discarded clothing relying only on the nondestructive assessments of untrained judges. The results of nondestructive assessment are valuable in the determination of an appropriate f o m of redistribution of discarded goods after the items are no longer wanted for their original use by the first owner. Additionally, from the perspective of the used clothing consumer, serviceability expectations may be different for used versus new clothing. To assist in their purchase decision, an evaluative model to indicate continued wear life in discarded clothing would be useful. This is also of interest to textile recycling companies who can apply this model to the quality ranking and pricing of their acquisitions that reflect a certain level of performance. The direction of this research is to determine if the assessments of judges, as described in Part I, give information about physical performance assessments, as described in Part II. There is no a priori model involved. Therefore, this research can be used to provide preliminary guides of the nature of any relationship between the two sets of variables. In addition, the research can be used to obtain a qualitative idea of the two sets of variables. The goal of the research is to develop a model that may be used to indicate wear in discarded clothes that have unknown histories of use. To achieve this goal, four objectives are delineated. These are: 1. To obtain a greater understanding of the condition of discarded clothing by employing an assessment of judges of selected sample garments. 2. To perfom an evaluation of interrater consistency to determine the effect of any psychological component in their visual evaluation. Interrater con­ sistency has implications on the ultimate application of this research by non­ trained individuals. 3. To compare the results of assessments of judges with laboratory performance testing in an effort to uncover a link between laboratory performance of the garments and their visual appearance. 4. To make inferences about residual wear life based on results of nondestructive and destructive tests. Assumptions The design of this research assumes that there is some relation between the performance of textiles in actual wear and the performance of textiles in accelerated physical tests. Since it is not possible to express a specific quantitative value of this relationship, a qualitative description of relative residual wear life is used. The choice to use a random selection of shirts from several truckloads of discarded shirts assumes that the sanple is representative of shirts discarded in the collection regions of the textile recycling conpany. Furthermore, the study design assumes that the choice of evaluative criteria is all-inclusive of those representing garment wear in woven dress shirts. Otherwise, the resultant model will be inconplete. Limitations This study is limited to the evaluation of woven cellulosic or cellulosic blend dress shirts. Difficulty would be encountered if all categories of garments were included in the sanple and would introduce potentially confounding variables due to the minor variations in the evaluation procedures for various garment types. Fiber content was selected to be consistent with the most common fabric for woven dress shirts. Fiber content was not determined, rather it was presumed in a manner conparable to that which is used by graders in a recycling facility, i.e., by hand of the fabric. Dress shirts were selected as the type of garment to 10 be examined in this work because dress shirts are worn by- women and men, and they are represented in relatively high proportion in the truckloads from which the samples were chosen. However, the methodology employed in this work should be easily adapted to other garment categories. Another limitation of this research is the lack of documentation in the literature of a reliable method to measure the relationship between performance of textiles in accelerated tests and in actual wear. In fact, Slater (Barnett and Slater, 1987) presented evidence that the correlation is neither well understood nor documented. Thus, the research has the potential to contribute to fundamental aspects of textile science. Definitions Terms used in this study are listed below. Other terms related to textiles and testing are in Appendix A. 1. residual wear life is "the performance of a garment in both functional and aesthetic aspects over a period of time after discard by the owner" . (Jakes and Douthitt, unpublished) 2. visible evidence of discard are "grossly visible signs of fabric wear such as tears, pilling, fabric attrition, etc. that are not easily repaired and affect the aesthetic and/or functional garment performance". (They render the garment tangibly useless.)(adapted from Slater, 1987) 3. functional evidence of discard is "the remaining category for sanple items without visible evidence of 11 discard". It is inferred that these garments no longer meet the aesthetic needs for satisfaction of the original owner which most likely resulted in the decision to discard, (adapted from Slater, 1986) 4. recycling - is "collecting, reprocessing, marketing, and using materials once considered trash". (Environmental Protection Agency, 1991) 5. municipal solid waste (msw) is "solid waste generated at residences, commercial establishments and institutions." (Draft of Franklin County Solid Waste District Solid Waste Management Plan. Sept 1992) 6. human ecosystem - is "the organism, its environment, and their interaction." (Journal of Home Economics, Spring 1979 p28) CHAPTER II RELATED LITERATURE Clothing Consumption as a Process The process of clothing consumption has been described as consisting of four phases; namely, acquisition, inventory, use, and discard (Winakor, 1969) . Even though they are interrelated as a chain of events, this research focuses specifically on aspects of the final phase, garment discard. Methods of garment disposal include handing down, throwing away, selling, exchanging, using for rags, or remaking. The physical condition of the item is not the only determinant in the decision to discard. For example, a garment may be rejected because it no longer fits or because of a change in fashion. These reasons can cause clothing items to be discarded prior to the depletion of their physical utility. However, the clothing life cycle does not necessarily end after the item is disposed. Options such as reconstruction or repairing may extend the wear life. Alternately, some fabric can be broken down into fibers that can be reprocessed into new fabric. Clothes that are no longer wanted by the original owner may be responsibly discarded by recycling or donating them to charities or organizations that redistribute them as apparel. To

12 illustrate the scope of this situation, in Washington, D.C. two million pounds of clothes are kept out of landfills every year by the Salvation A m y alone (The Earth Group, 1990) . The choice of recycling can provide a way to abandon last year's fashions without the guilt. It is estimated that about 8% of municipal solid waste in the United States is composed of textiles, which includes both apparel and non-clothing items (Franklin Associates, Ltd., 1988). In Cincinnati, Ohio, the proportion of clothing and home furnishings in residential waste alone has been approximated at 4.41% in 1990 (Table 1) . Furthermore, their figures indicate that the amount of residential solid waste generated is positively correlated with the per capita income of individual municipalities. Therefore, materials contained in the residential solid waste stream targeted for recycling include old clothing and textiles. Apparel Degradation It was inferred by Francis (1990) that the majority of discarded garments have some residual usefulness. That is to say, the item's total life expectancy is rarely realized by the original owner. Taking advantage of this fact by recycling these items could conserve landfill space as well as energy and economic resources. Apparel items deteriorate during their useful life by exposure to many mechanisms during use, storage, and care. The main factors contributing to textile degradation are

13 14

Table 1. Residential Solid Waste Composition

Material % by Weight Estimated Total (tpy)1 Secondary Fibers 8.60 38,636 OCC/Kraft 8.15 36,614 Newsprint 1.48 6,649 Magazine 14^M 66.648 Other Paper 33.11 148,747 Total Paper Plastic HDPE 0.54 2,426 PET 0.73 3,280 LDPE 3.43 15,409 Other Plastic 2lA3. 2 ,.754 Total Plastic 6.88 30,909 Glass Amber 1.39 6,245 Green 0.95 4,268 Clear 3.24 14,556 Nonrecyclable QJM. 359 Total Glass 5.66 25,428 Yard Debris Grass/Leaves 16.88 75,833 Prunings 2»6.8 12.040 Total Yard Debris 19.56 87,873 Nonferrous Metal Aluminum Beverage Cans 0.87 3,908 Aluminum Food Containers 0.13 584 Other Aluminum 0.25 1,123 Other Nonferrous IL51 2 ,.251 Total Nonferrous Metal 1.76 7,906 Ferrous Metal Food Containers 1.66 7,458 Other Ferrous 2^1 2-,JL2.Q. Total Ferrous Metal 3.69 16,578 15

Table.. 1 (Continued) .

Other Materials Food Waste 5.38 24,170 T e x tile s 4.41 19,812 Diapers 2.35 10,517 Wood 3 .21 14,421 Fines 2.03 9,120 Household Products 0.24 1, 078 Total Other Materials 17.62 78,158

Material .% by Weight Estimated Total (tpy)1 Miscellaneous Inorganics 2.77 12,444 Organics 4.29 19,273 Other Waste 4..66 20.935 Total Miscellaneous 11.72 56,652

TOTAL 100.00 449,250

Source: "residential solid waste composition, Cincinnati, Ohio" City of Cincinnati Public Works Department, 1989. 1 Estimate based upon total residential solid waste generation of 449,250 tons for the baseline year in Franklin County actinic energy, abrasion, laundering, or dry cleaning, weathering, chemical or microbiological agents, and stress (Slater, 1986). Physical changes which may occur over time include alterations in fabric dimension or fit, decrease in tensile strength, decrease in tearing or bursting strength, alteration of stiffness or elasticity, consequences of abrasion, changes in surface appearance and color. In addition, chemical exposure may change properties involving the fabric's permeability to air, water, moisture vapor or gas, resistance to heat, and static propensity. All of these changes are potentially important to the owner in determining whether a garment maintains its usefulness. Degradation which occurs during use normally results in the gradual loss of desirable properties, yielding a product exhibiting a shortened life expectancy due to damage. The Concept of Waste .Applied to Clothing Inefficient use of resources may result in waste. In the apparel industry, waste is actually promoted through fashion changes. Kelley, Geiger, and Bailey (1975) proposed that "clothes may offer an opportunity for acquisition and change of material goods when more expensive long term consumer goods such as housing and home furnishings are priced out of the family's budget". Every stage in the manufacture and consumption of clothing is a potential source of waste. The concept of waste is assumed to entail the waste of tangible objects such as natural resources or material goods. However, 17 intangible entities such as time or psychological satisfaction can be considered as resources or outputs (Stephens, 1985) and also can be "wasted". This concept of waste can be specifically applied to clothing. In Much Ado About Nothing. Shakespeare recognized the resulting waste due to fashion: "See'st thou not, I say, what a deformed thief this fashion is?". In 1949, Sherrill described economic waste in clothing as "the failure of consumers to receive the maximum satisfactory wear from their clothes". Lapitsky (1952) characterized the occurrence of clothing waste when "an item of wearing apparel is discarded which still has either years or seasons of usefulness and thus retains its physical utility". VeVerka (1974, pl4) classified clothing waste as present when "garments are discarded or placed in inactive storage before they are worn out". Finally, clothing waste has been said to exist when garments are "left hanging in the closet or discarded before they wear out" (Margerum, Walker & Kemaleguen, 1977) . Past studies indicated that as much as one-third of an entire wardrobe was worn infrequently (Bradlyn, 1965; Lapitsky, 1952). In 1967, Avery determined that 59% of the garments discarded by study participants had "much wear" left in them. Explanations for inactivity or disposal of garments have been provided in many studies (Boyle, 1965; Grieg, 1975; Lapitsky, 1952; VeVerka, 1974). These explanations have been summarized by Stephens (1985) as follows: 18 fashion obsolescence, garment damage unattractive appearance of garment, poor selection skills, excessive maintenance requirements, unacceptable garment source, wardrobe incompatibility, housing (storage) constraints, change in preference (dislike, grown tired of) , advanced age of garment, poor construction/repair skills, and few opportunities for wear. These reasons can be categorized as aesthetic and/or functional in origin. However, it should be realized that what is considered waste for one individual, i.e. the garment is discarded, may represent conservation of resources to another individual when that garment is redistributed. The literature, then shows that for many reasons clothing is discarded prior to depletion of its functional or aesthetic wear life. No study has measured the wear life which remains in discarded clothing items. Therefore, this research was initiated to provide a method to indicate residual wear life. The Economic Connection Using Full-Cost Accounting Under full-cost . accounting, natural resources are considered as assets of companies and are factored into the calculation of a country's gross domestic product (Popoff and Buzzelli, 1993). A product's cost reflects the environmental costs, including use, recycling, and disposal. Application of full-cost accounting espouses socially responsible consumer behavior. Such an emphasis on recycling of products and 19 packages can accommodate a reverse distribution of goods (Stephens, 1985). Life cycle analysis (LCA) is fundamental to the initiation of full-cost accounting. In the process of LCA., each step of a product's life is systematically identified and described, from raw materials to final disposal. However, LCA is still in its developmental stages and requires agreement among industry, government, academia, and the environmental community. Therefore, it may be controversial and difficult to implement. When this concept is applied to the textile and apparel industries, there is the potential of benefit through the balance of resources utilized in the manufacture, use and disposal of these products with those replenished. Textile Product Serviceability Textile Product Serviceability may be defined as "the performance of a textile product in use" (Merkel, 1991) . It is an important factor in the consumer's determination of the item's lifespan. There is no single criterion that can be used to measure this concept. The performance of a textile product is dependent on such factors as the component parts and the construction method. Performance is a consequence of the composite of the raw materials, design specifications, and manufacturing processes. Examples of components that may be varied include yam density, fiber type, strength, construction, weight, seam type, seam allowance and stitch 20 density. Furthermore, consumers view product performance from a different standpoint assigning performance with varying levels of importance and assessing performance with varying levels of skill. Serviceability implies fitness for purpose or the capability to be used, worn, cleaned, and easily repaired. Therefore, it also includes durability and the resistance to mechanical deterioration. Serviceability also involves the ability of the product to retain its initial smoothness, shape, dimensions, color (s), hand and other attributes through a reasonable period of use. Serviceability includes the concept of wear life, which starts with the brand new product and ends whenever the consumer decides that the product is no longer acceptable for its intended use. Ironically, the criterion of serviceability is the most elusive to evaluate at the point of purchase. Consequently, it is the most likely factor to lead to consumer dissatisfaction if the product turns out to be inadequate. In an effort to evaluate serviceability, the consumer may utilize information provided by the manufacturer such as fiber content and further descriptors of good performance such as "combed" or "Pima" cotton, "Merino" or "worsted" wool. Other aspects such as good workmanship in sewing are important since they are associated with long seam life. As previously stated, wear and care of the textile products bring about changes of the initial appearance and comfort. For example, colors fade, 21 fabrics stain and wrinkle, pills develop, the hand changes from crisp to limp. The period of serviceability ends when the deterioration in appearance and comfort exceeds the consumer's tolerance. This becomes one point at which the decision is made to discard the product. Quality is a concept which is closely related to serviceability. One part of garment quality is the skill of workmanship of its construction. However, the ability of a product to substantially retain its original quality characteristics through an expected wear life is also important. A serviceable product is one which fulfills its purpose for some "reasonable" period of time. Efforts to obtain high quality in products are frequently referred to as quality control, defined as "a system for verifying and maintaining a desired level of quality in a product or process by careful planning, use of proper equipment, continued inspection, and corrective action as required" (Merkel, 1992) . David Garvin, a recognized expert on quality (cited in Mehta, 1992), names eight dimensions of quality: performance, features, reliability, conformance, durability, serviceability, aesthetics, and perceived quality. Quality can also be defined as a combination of the characteristics or properties of a product in terms of whether the product is fit for use or not (Mehta, 1992) . Fitness for use is the most widely used concept of quality and should be judged from the consumers' point of view and not from either the 22 manufacturer's or seller's perspective. The fitness for use concept can be applied to garments. For a garment to be fit for use provided its style is acceptable (Time, March 1984) : 1. It must be free from defects such as stains, material (fabric) defects, open seams, loose hanging (untrimmed) threads, misaligned buttons and buttonholes, defective zippers, and so on. 2. It must fit properly for the labelled size. 3. It must perform satisfactorily in normal use, meaning that a garment must be able to withstand normal care procedures without color loss, shrinkage, seam failure, or tears.

Some of the factors that influence consumers' perception of quality are (Time, March 1984): 1. Price - consumers tend to associate quality with a higher price. There is some evidence that price is used by shoppers in quality estimates and m some, cases estimates of quality are affected by price. 2. Technology - this indicates factors such as fabric and seam strength, colorfastness, shrinkage, and other properties that are affected by the state of technology in the industry. 3. Psychology - a garment can be reasonably priced and the best that technology can offer, but if it does not meet the aesthetic requirements of the consumer, then it is not a quality garment to that consumer. 4. Time Orientation - this relates to durability, which varies with categories of garments. Exploratory research using focus groups revealed that the criteria used by consumers in assessing clothing quality fit into five dimensions (O'Neal, 1990) . They are: physical, performance, connotative, aesthetic, and affective attributes. The physical characteristics consist of details such as construction or workmanship, fiber content, fabric hand, garment cut and style. Aspects of the performance dimension include durability, laundering or care properties. The connotative attributes deal with the properties which signify or suggest quality such a brand, price, and store image. The affective dimension is concerned with subjective feelings and emotions common to individuals resulting from the use of the product. Finally, the aesthetic component deals with factors that contribute to the individual's perceptions of beauty. Therefore, it was concluded that consumer perceptions of clothing quality is a multidimensional concept. Furthermore, these dimensions vary in importance depending on such factors as the situation, personal values, and prior knowledge. O'Neal (1993) defines perceived quality as "the consumer's judgment of the overall ability of an item of clothing to perfom in a manner which meets or exceeds the expectations of what constitutes quality". Therefore, she concludes that the subjective nature of perceived clothing quality implies that responses differ between judges. Solinger (1980) describes two dimensions of apparel quality: (1) physical features, or what the gament is; and (2) performance features, or what the garment does. Brown (1992) elaborates that it is the physical features of a garment, such as construction and the fiber content and fabric construction, which determine its performance. Therefore, consumers purchase garments with specific physical features that they believe will fulfill their performance expectations. A garment's physical features are intrinsic attributes; they cannot be altered without changing the garment itself. A garment's performance features determine the standards it meets and how it benefits the consumer. Performance features can be further classified as the gament' s aesthetic performance features and functional performance features. Brown (1992) defines aesthetic performance as "attractiveness". This includes characteristics such as color, line, shape, texture, and fashionability. Functional performance includes performance features other than appearance, namely features which affect the gament's utility and durability. Utility refers to usefulness and includes fit, comfort, and care requirements of the gament. Durability refers to how well the gament retains its structure and appearance after wear and care. Examples include dimensional stability and seam stability. Aesthetic and functional performance occasionally overlap. For example, fit may be an aesthetic feature (attractive or unattractive) or a functional feature (comfortable or uncomfortable) . Therefore, rejection of a gament occurs when it no longer meets the owner's needs for any reason, whether aesthetic or functional. Since the definition of residual wear life fomulated for this study incorporates both aesthetic and 25 functional performance features, both explanations for apparel discard are included. A general guide for average reasonable service lives of textiles include (Slater, 1986) : carpets and upholstery should last for several years, and suits, jackets, and slacks should last a year or two if used frequently. Shirts and blouses should endure a season or two of regular wear. The International Fabricare Institute (cited in Brown, 1992) estimates the average life expectancy of a dress shirt at three years, with variations due to product variables and frequency of use. Expected service life depends on the product and strongly on the weight of the fabric. The more premature the failure and more expensive the item, the greater the likelihood of more intense disappointment by the purchaser. Aesthetic Appearance and the Concept of Serviceability Total fabric character may be arbitrarily divided into four categories: aesthetics, comfort, performance, and convertibility. In Figure 1, these are diagrammed as overlapping circles to show their interdependence. Fabric aesthetic character is defined as a relationship among a minimum of six concepts: style, body cover, surface texture, drape and resilience (Brand, 1964) . These concepts can be described in several ways (Figure 2) . This is because it is experienced not only at the sensual level, but is a harmonious blend of feelings and emotions, judgment and reasoning, 26

Convertibility Comfort (Tailorability)

Scratchy

'rinkli Aesthetics Performance

Source: R.H. Brand (Sept 1964): "Measurement of Fabric Aesthetics", Textile Research Journal, 791-804.

FIGURE 1. Fabric Categories Subconcepts (words)

Physical Subjective AESTHETIC Measures Measures Concepts (words)

TECHNOLOGY PSYCHOLOGY SUPPLANTS EVALUATES Sense data Sense data

Sensations Touch Sight Kinesthetics

Source: Brand, R.H. (Sept 1964): "Measurement o f Fabric Aesthetics" Textile Research Journal,791-804

FIGURE 2. Criteria for Defining Aesthetic Concepts motivation and personality. First there is subjective perception, which is represented by common words that express the psychological value of the sense data associated with the concept. Second, a concept may be described by a composite of subconcepts symbolized by more explicit words (such as the partitioning of cover into bottom and top cover) . Objective tests in the form of physical measurements attempt to quantify the concept and supplant sense data. Finally, common word pairs related to at least one of the three main physiological sensations, namely; visual, tactile or kinesthetic, may be used (e.g.: thick-thin, rough-smooth, etc.). Of all the concepts related to serviceability, aesthetic appearance is the most subjective (Belck, 1990) . The judgement of a gament' s appeal is dependent primarily on individual perspective or. preferences rather than laboratory measurements. Despite the amount of subjectivity or lack of precision when evaluating the aesthetic aspects of a textile, much information can be obtained through certain senses. They can be visual, tactile or a combination of both. Some properties of interest to the consumer include color, luster, and translucence, which are evaluated visually. Color is often rated as the most important quality in the selection process. Texture and drape may combine the visual and tactile; while body, hand, loft or bulk tend to be primarily tactile properties. The manner in which the fabric drapes is primarily dependent on the method of construction used in the 29 forming of fabric, and on the type of y a m structure used (Belck, 1990) . A fabric with a stiff hand tends to have less drapability than a fabric with a soft hand. Although the properties of body or hand and drape can be evaluated through both visual and tactile senses, drape is more often evaluated or determined by the visual senses, and body and hand by the tactile senses. Textile scientists define aesthetics as a function of measurable properties, each of which may be expressed with numerical values (Brand, 1964). Therefore, mechanical analogs such as bending length and drape projections have been enployed to measure aesthetic properties. Although mechanical measures and analogs are useful, they can be misleading when the analog is assumed as equivalent to the aesthetic property. A garment will often be rejected for aesthetic reasons before it becomes useless for functional reasons (Slater 1986) . An example of this concept is the formation of pills on the fabric surface that, although objectionable, can have only a minimal effect on functional performance of the garment. A textile item which is perceived as unattractive or unacceptable will not be considered serviceable regardless of other properties related to durability, comfort, and ease of care which the item possesses. Textile Testing Textile testing is carried out for several reasons. It provides information on serviceability, including both fitness 30 for purpose and durability (Merkel, 1991) . In addition, before a product is offered for sale, manufacturers and distributors may want to find out whether or not it is serviceable. Therefore, the producer or wholesale purchaser may test serviceability prior to its sale. A service test, also known as a wear test or lifetime test provides one means of measuring serviceability. Samples of the product are given to individuals that use it in the intended way until it is no longer serviceable while documenting the wearing time and cleaning. In actual practice, there are always questions raised on the reproducibility of these methods. For example, the number of products tested need to be adequate; the testers that are selected must be typical users of the product; the amount of time required to perform the test should not outlive the demand for the product. Probably one of the most important factors evaluated is the determination of when the product has reached the end of its service life. Service tests are usually very expensive and time- consuming. Consequently, such tests are performed infrequently. Examples of their appropriate use include when the product is not subject to rapid change in fashion or as an attempt to verify the results of more rapid laboratory tests. ASTM test method D3181 (ASIM, 1992) outlines the standard practice for conducting wear tests on textiles. It begins with garments that are new and are exposed to different wear 31 treatments, so that the exposure history is established and documented. In theory, it should be possible to speed up service tests to get faster results. For example, some characteristics of a product that would predict serviceability may be identified and then directly measured. However, accelerated service tests are rarely done for two reasons. They usually do not save enough time to be a plausible alterative. Also, the correlation of results of an accelerated test do not closely correspond to the normal conditions of wear, during which the product is used intermittently. An alternative to service testing is laboratory testing of individual characteristics of the products or the textile from which they are made. These tests are performed under the assumption that the results actually correspond to service. The assumption of a relationship between the laboratory test results and serviceability is still yet to be experimentally proven. Another assumption made in both laboratory and service testing is that the results of the tests are reproducible, meaning that repeated testing of samples of the same product gives approximately the same results. Reliability also implies that the results of testing of samples of a given product will be similar, even if different people in different laboratories use different instruments for measuring the characteristics of the product. 32 Generally, all laboratory testing can be related to the overall purpose of ensuring product serviceability. The results of tests provide facts to be used for decision-making. Many test methods are designed. to measure some specific characteristic of the product such as strength which is assumed to relate to performance characteristics in actual wear. Other laboratory tests are actually used to describe the product, such as fabric weight, length, width. In general, serviceability cannot be practically measured due to the amount of time required to observe a product in normal wear. Consequently, relatively rapid laboratory test results are used instead. If the results correlate well with service performance and provide complementary results, they can be used successfully to predict serviceability. Laboratory tests can also be used to verify conformance with specification requirements, for quality control in production, in certification programs, and to meet government requirements. They also find applications in product evaluation, failure analysis, research, development and forensic work. Laboratory Simulation Tests Simulation tests can be divided generally into four categories: the simulation of results obtained from testing a fabric on one machine by those from another of a similar type, the simulation of results of one laboratory test by those of a different one, the simulation of one set of conditions by another, and the simulation of results obtained in the field by those obtained in a laboratory test (Barnett and Slater, 1987). The simulation of field trial results by a laboratory test is difficult to achieve, mainly because the differences in test conditions in the two cases are so great (Barnett and Slater, 1987) . Laboratory tests are conducted to evaluate how a material performs under a set of predetermined conditions. Since these tests can be carefully controlled, they are more reliable indicators of life expectancy than field trials. In turn, these results are then used to predict behavior in service. Therefore, any technique that enables this type of a comparison to be made is of value in formulating predictions. The aim of simulation laboratory testing is to duplicate, in an accelerated manner, the conditions under which the specimen will be used in practice. In addition, the experimenter also seeks to eliminate all confounding factors so that the consequences of a change in selected variables can be investigated. Laboratory testing of this type has the advantage that conditions can be duplicated, so that variability and precision of results can be determined. In addition, it is usually less expensive than slower and less precise field trials. Disadvantages include the fact that the end result of a test often depends on a subjective opinion such as a decision about when damage is so great that the article can no longer be worn. The trial cannot provide this since it is objective by definition. In addition, the conditions are artificial and are deliberately designed to exclude some variables that occur in practical use. For example, literature supports the hypothesis that the fiber breakage mechanism in actual service is difficult to artificially duplicate. This is because it is predominantly due to oscillatory internal motions involving twisting, bending, flexing, etc. which destroy the fabric cohesion and result in breaks due to localized high stress concentrations (Weiner, 1963) . Thus, although they provide a useful means of comparing different fabrics, simulation trials are often flawed and show little relationship to the practical conditions they are assumed to predict. Despite the disadvantage, many attempts to use prediction techniques have been reported in the literature. Standard examples include the simulation of an objective test (such as wear) and a subjective one (such as hand) by laboratory testing. Review of Prediction Tests Schiefer and Wemtz (1952) compared two types of abraders (the Schiefer Tester using a flat sandpaper abradant and the Accelerotor using an impeller blade and sandpaper abradant) to predict fabric performance in practical situations. They used two identical sets of 16 fabrics representing different constructions on the different machines. Although the rankings of the 16 fabrics were not significantly different for the two abradants based upon the t-values computed from Kendall rank 35 coefficients, there was an absence of perfect rank correlation between the two sets of fabrics. This may be explained by the difference in type of the two abradants and the rate of abrasion, and the fact that a given change in any one construction factor does not always result in quantitatively comparable changes in the resistance to abrasion. This ambiguity can also be expected when results of laboratory tests are compared with those from service tests. Weiner (1963) carried out laboratory and field measurements of several different sources of wear. In addition, subjective evaluation by a wear-score system was described, using a combination of factors. The author proposed a series of tests to establish which method correlates best with the practical situation, but the results do not appear to have been published. Therefore, it is assumed that the investigation proved inconclusive. Sarma, Ranganathan and Chipalkatti (1969) outlined the problems of correlation of laboratory abrasion tests with wear life. They note that, even if the researcher could eliminate all instrumental variations and choose the optimum measures, it would be impossible to make allowances for the type, shape or construction of the article, environmental factors, conditions of use and individual idiosyncrasies of the user. Recognizing those limitations, the authors carried out service tests and concluded that the Stoll flex abrasion gives results closest to those obtained in the field trials compared to 36 other abrasion testers. Gooderson, Ramsay, and Trousdell (1976) assessed the performance of a fabric intended for combat use, by means of a worldwide troop trial and an accelerated wear trial. The authors claimed substantial agreement between the two methods of evaluation, but this only implies that the two gament types were ranked in the same order for the two tests. However, not enough quantitative data were supplied to enable a complete verification of their claim. Gaspar and Hargreaves (1978) reviewed literature discussing laboratory testing equipment for abrasion and accelerated wear trials of textiles. They appear inconclusive in stating that laboratory test results give no evidence of any predictability of field results, while also indicating that Martindale test rub values have been related to expected g a m e n t life. Despite the difference in conditions, they .claimed that the Martindale and Stoll test can roughly predict the likely wear life of fabrics. Methods of Monitoring the Progressive Deterioration of Textile Materials Slater (1986) derived a model that mathematically describes the changes in any property of a textile during the degradation process. The assumption was made that the value of the property being measured will decay from the initial level of 100% to the final level of 0%. He used a normalization procedure that involves expressing lifetime and 37 property values as percentages of the original while the fabric progresses from 0 to 100% of its total lifetime. He explains that in this way, the property value variations produced by individual differences will tend to be insignificant and a continuous curve is obtained when a large number of units are evaluated. This permits a relationship between two dissimilar properties to be assessed. Slater (1986) further defined two critical stages in degradation; the point at which a material becomes functionally unsatisfactory (when it no longer fulfills its original purpose) and the point at which it becomes tangibly useless (when it is visibly or otherwise obvious that it cannot fulfill its original purpose) . The example cited in that manuscript is a raincoat that may lose its ability to repel water, even though there is no evidence upon inspection to indicate this change. However, after prolonged wear, holes may appear in the garment which provide visual evidence that it has lost its protective ability. Chemical and physical property changes may occur in a series of non-uniform stages gradually over a period of time (Slater, 1986). This is explained by periods of gradual deterioration during mild use interspersed in an unpredictable way with more rapid decreases at times of more severe use. The individual pattern exhibited by a specimen is unique. This creates difficulties in measuring or predicting wear life. Theoretically, there are modes of deterioration where failure occurs almost instantaneously. Furthermore, in these instances the property value passes almost simultaneously through functional and visible deterioration (Figure 3) . In the first case, the property being measured may decrease practically to zero at the instant testing began and remained there subsequently for the entire lifetime of the material being tested. This exemplifies the most unsatisfactory performance curve obtainable. Another type of behavior that appears on the graph in Figure 3 (which appears on the following page) occurs when the original property value remained unchanged until the 100% lifetime was reached, at which time it decreased instantaneously to zero. This would be the ideal performance curve. Yet another type of behavior is represented by the direct route between the two 100% points, indicating a linear decrease in property which changes proportionally over time. Slater's mathematical formula (1986) for monitoring the progressive deterioration of textile materials is the following: P = 100(1-t/l00)k, where P = Percent of original property retained, t = time and k is a constant established for the property being measured. The plot of the property/lifetime curve is bounded by the rectangle with coordinates at a starting point in time (t) of 0%, property (p) = 100% and 39

Nrc*fi|*q« initial property ramming

(tl I I

10

«0 —

n —

i t 29 «o so i@ tte

life im m n p i r t O (%)

FIGURE 3. Hypothetical Curves Illustrating Different Types of Degradation Patterns 40 termination at t=100%, p = 0%. In the proportional curve, k is unity and the equation reduces to: P = 100(l-t/l00) . The equation can be modified to enable several properties that contribute jointly to the degradative process to be evaluated. This is achieved by establishing exponential constants (k) independently for each property. The only major difficulty noted in applying the technique is the establishment of total lifetime, which usually relies on some subjective measure of an endpoint. It is essential to the success of the entire procedure that the coordinates of the limiting rectangle be known. This may require several preliminary test runs. The Use of Degradation Curves in Simulation Work In attempting to simulate one set of conditions with another set of conditions, it is imperative to know the relationship between them. According to Slater (1986), the normalized degradation curves can be applied in this case. The major difficulty in obtaining a degradation curve is the need to establish the total lifetime of the product. For some cases, such as in chemical destruction or tensile stress, determination of total lifetime does not pose a problem since degradation continues until the fabric disintegrates or breaks. In many other cases, however, the material becomes unusable for testing before this final stage has been completed. To establish a limit to lifetime, one method assumes that as soon as the fabric is no longer structurally able to be 41 tested, its life has ended. Another method to define end of lifetime is establishing the limit when a particular property falls to a given proportion of its initial value. This requires measurement of the property before testing begins and then defining a percentage of the initial value of the property as the endpoint. The third method of defining lifetime makes the assumption that a theoretical endpoint is reached after the amount of degradation that would reduce the value of a property to zero has taken place, even though that point cannot be measured directly. Degradation curves can be used to define a unique comparison of the times at which two levels of degradation are reached. The value of the property at two preselected levels of degradation can be measured so that the k value can be derived from the known relation between the ratio of the value of the property at two preselected levels of percent degradation and the k value. The manner of expressing life is of most use when two dissimilar degradation paths are obtained. Summary Human Ecology involves the interaction of people with their near environment (Compton, 1972) . The near environment includes their housing, home furnishings, household equipment, clothing and textiles, food and family. Human Ecology focuses on the individual and his/her reciprocal relationships with other people and with technology in the settings most critical for human development: the family, home and community. Its 42 basic mission is to improve the quality of human life. Nature obtains stability by allowing energy to flow smoothly through the ecosystem, by retaining and recycling resources, and by encouraging diversity of species (Compton, 1972) . Both the species and the environment have remarkable ability to change. The continued well-being of an ecosystem depends upon keeping the natural recycling mechanisms intact. The individual needs to recognize that resources are finite and must be conserved. The maintenance of environmental quality and the development of attitudes and values necessary to solve problems within the macroenvironment are necessary. The process of clothing consumption has been described to include the stages of acquisition, inventory, use, and discard. Discard occurs when the garment leaves the possession of an individual who anticipates no further wear. There are numerous methods of disposal available to the consumer. To address environmental and health issues related to solid waste management, several of them are preferable. For example, handing down, donating to charity, selling or using for rags are means of recycling by finding new uses or owners for these items. In previous decades the purchase of used clothing in substantial amounts was limited to the poor (Winakor, 1969) . The expansion of the resale apparel industry that targets such market segments as children's and formal apparel provides an outlet by making the recycled items accessible and a viable 43 alternative (Hannon, 1991). The success of these businesses indicates that by making appropriate choices consumers can collectively make a positive contribution to the environment. In the 1990s, awareness of the environment is of increasing concern. This applies to the textile and apparel industries in view of the negative environmental impact of manufacture and disposal of these products. Finding an appropriate continued use for discarded apparel begins with an evaluation of their condition. This may extend the useful life of the item in some form and, in turn, avert their disposal in landfills. A great variety of factors influence the rate of wear of clothing and how long it will be used before reaching the discard phase. These include the age and activity of the wearers, their goals and values, the quantity and quality of the clothing in their wardrobe over which to distribute daily wear, and the equipment available for care of clothing (Winakor, 1969). Past studies acknowledge the difficulty in designing a method to simulate actual wear by laboratory tests. This research precludes the use of the normalized degradation curve since the previous history of recycled garments is not known or documented and no measures are available at the initial stage of 100% of total lifetime. Therefore, the goal of this research is to develop a model that may be used to indicate residual wear in discarded clothes that have unknown histories of use. 44 Research Question The literature shows that there is a general agreement that discard of clothing occurs before physical utility is depleted. However, the extent of the physical utility retained by discarded clothing has not been measured. Therefore, this research was designed to learn if the nondestructive assessments of discarded garments made by untrained judges provide information which relate to results of physical performance tests on the same items. Based on the establishment of such a relationship, the research design then proposes a link between the destructive and nondestructive results and the residual wear life retained in discarded clothing. The research question formulated for study is: Can a model be generated based on the use of simple assessments of judges to indicate continued performance in recycled clothing? The four objectives of this research designed to address this research question are the following: 1. To obtain a greater understanding of the condition of discarded clothing by employing a nondestructive assessment by judges with little training of selected sample garments. 2. To perform an evaluation of interrater consistency to determine the effect of any psychological component in their visual evaluation. Interrater consistency has implications on the ultimate application of this research by non-trained individuals. 3. To compare the results of assessments of judges with laboratory performance testing in an effort to uncover a link between laboratory performance of the garments and their visual appearance. 4. To make inferences concerning about residual wear life based on results of nondestructive and destructive tests. CHAPTER III THEORETICAL FRAMEWORK

A model for the process of clothing consumption has been developed that includes three main parts: acquisition, inventory, and discard (Winakor, 1969) . The interrelation and flow between the component parts are shown in Figure 4. The model was formulated as a framework for teaching the clothing consumption process. In this sense, consumption is not limited to the traditional concept of purchasing clothes in the store, but includes the entire range of activities which individuals undertake in relation to procurement and use of clothing. The model represents the clothing consumption process with one individual as the consuming unit. The diagram shows how clothing is added to inventory by acquisition and removed by discard. These stages occur intermittently. Garments are removed from active storage to be used and then returned to storage with or without undergoing care or maintenance. This demonstrates how wear occurs gradually in increments. Acquisition of a new or previously used garment is the initial stage. The average individual probably purchases most clothing new as ready-to-wear, although clothing may be

46 47

A. Acquisition

G. sources H. acquisition for temporary use a

r. active storage (continue to use S. u3r* for original (remodel purpose) or self)

inactiv storage

J . temporary disposals

temporary \ 3.Inventory use by j K . permanent others J disposals (loan,rooty / /

•recycle for other poop10/ purposes •abandon, destroy, throw away •give away, hand- down •trade,exchange •sell

FIGURE 4. Clothing Consumption Model With Discard Options obtained from several other sources, including through home construction, making-over, gift (including handing-down) , exchange or as payment, through rental and borrowing of apparel as a temporary source of clothing, and purchases from a clothing resale store. Inventory is the label given to the stock of garments available for regular use, including those in temporary storage. Over time, periods exist in which the clothing is used and cared for and then placed in active storage. A separate distinction is made for garments that the owner does not intend to wear within a one-year period; these garments would then be classified as in inactive storage. Discard occurs when the item leaves the possession of the individual, who does not expect to use it any longer. This is differentiated from temporary- disposal, in which the item is loaned or rented to another and returned to the original owner. This model recognizes multiple methods of permanent disposal, including giving to another person, abandoning, or selling. Assumptions of the model are: 1. inventory has an equilibrium level with feedback mechanisms interconnecting acquisition, inventory and discard. (Fqr example, market demand for new clothing may partly be explained by the amount of clothing the individual already owns.); and 2. the ratio of inventory to acquisition for a particular category of garment can be used as an indicator of the amount of time it is worn.

48 49 Jacoby (1977) developed another conceptual taxonomy to describe the major disposition behaviors practiced by individual consumers for the purpose of conducting exploratory research on what consumers do with products once the products have outlived their usefulness (Figure 5) . It complements Winakor's model by isolating the process of discard and presenting an exhaustive list of the options for the consumer relating to this phase in the form of a decision tree. Basically, Jacoby's model recognizes three possible outcomes following the consideration of the disposition of a product: 1) keep it 2) permanently dispose of it 3) temporarily dispose of it. Furthermore, there are further delineations within each category. If the product is kept, this includes: a) continue to use it for its original purpose b) convert it to serve another purpose c) store it, perhaps for later use. If the product is permanently discarded, this includes: a) throw it away or abandon it b) give it away c) sell it d) trade it Finally, if the product is disposed of temporarily, this includes: a) lend it b) rent it to someone else. Jackson (1993) proposes that by integrating the concepts of Winakor's model with Jacoby's taxonomy (Stephens, 1985) and PROOUCT

KEEP IT GET WO OF IT GET WO OF IT PERMANENTLY TEMPORARY

Store it Rent It Loan it

Throw it Trad* it away

To Middlaman

FIGURE 5. Disposition Decision Taxonomy extending the consumption process through the entire life cycle of the product, a new conceptualization of the process of clothing consumption results. A diagram which displays this model of the Life Cycle of a Textile Product is shown in Figure 6. This model is constructed from the perspective of the garment, which remains in the feedback system unless it is destroyed or thrown away. In this way, the history of use. and wear of each garment is tracked throughout its lifetime. This is illustrated by modifying the section of Winakor's model on permanent and temporary disposals, explicitly spelling out the options and their implications and extending the process with the feedback loop. Jackson views the process within a system. The system refers to the interrelationship between the processes. The concept of a system is demonstrated by the enclosure of the activities within two rectangles. The inner core consists of the pathways of potential activity (or inactivity) that may be experienced by the product throughout its life cycle. The next layer is the Internal Environment, which corresponds to the microenvironment at the level of the individual. Activities within the internal environment are expected to influence the consumption process. Examples of associated activities in the internal environment are those choices made by an individual regarding the methods and products used for refurbishing, storage, etc. For a recycled item, this includes those functions such as maintenance, transport, and 52

USE / \ CARE STORE

CONTINUED I TEMPORARY RECYCLE PERMANENT USE DISPOSAL DISPOSAL ---1----- it----- 1------

INTERNAL ENVIRONMENT EXTERNAL ENVIRONMENT

REMOVED FROM SYSTEM

FIGURE 6. Model for Life Cycle Analysis of a Textile Product allocation of space for storage or display. This isdifferentiated from those processes at the inner core since it addresses the precise environmental and physical exposures that the item encounters. The outer layer represents the External Environment or macroenvironment. This includes socio-psychological and economic variables that influence the consumption process. The socio-psychological factors encompass such areas as the reference group' s attitudes toward fashion and the individuals changing needs. These are part of the macroenvironment because they are a reflection of society. Examples of economic variables are current market forces such as the availability of alternative products. It should be noted that there is interaction between the different layers and it is this interplay that determines the amount of wear that the item endures. There are two distinct phases within the Model for the Life Cycle Analysis of a Textile Product. The rectangle on top represents the active period of the life cycle of the garment. There are three components of this phase: use, care and storage. They are displayed in a cyclical fashion to illustrate that there is repetition of these processes. The entry of a garment into the lifespan represented by the model begins with the process of acquisition. Due to the differences in product-related variables of a new item such as fiber, fabric, construction, seam structure, the initial condition and subsequent wear varies. After this point, 54 multiple options exist for the flow of the garment through the active stages of the model. For example, the garment may go into storage or may be used immediately, then the garment may be refurbished and returned to storage. Directly after any of the three options in the first phase, the garment may enter the second, or discard decision phase. At this point the garment has four potential routes to follow: continued use, tenporary disposal, recycle, or permanent disposal. Continued use is equivalent to the "keep it" option in Jacoby's model. In this case, the garment remains in the hands of the original owner, who may decide to use the item for its original purpose, assign a new purpose, or place it in storage. Either of these choices return the garment to the active phase, which is indicated by the arrows. When continued use is the route that the item takes, the owner's evaluation of the garment's functional and aesthetic characteristics will determine if it is suitable for continued use in the function originally intended or if it can be adapted to some new purpose. If deemed unusable, the item may be placed in storage and determination of the fate of the garment is postponed. If the owner keeps the item for its original purpose, this is advantageous in terms of the environment since it could prevent the need for replacement. If the function of the item is changed, thus allowing continued use, the environment benefits as well. Since the item with the original function may require replacement, the 55 environmental benefit of this choice comes from preventing the need for another product that serves the new function to which it is assigned. The advantages of storage include keeping the item out of the solid waste stream.until a decision on the disposition of the garment is made. Preferable to storage, if the original owner does not foresee a use for the item, it may be best to donate it to a charitable organization or textile recycling company in which the garment can be redistributed. Temporary disposal consists of a temporary change in ownership by lending or renting the garment. Once again, the garment returns to the active phase, indicated by the arrows. Options within this category include loaning or renting the garment to another person to fill their temporary needs. This has desirable financial and environmental implications for both the owner and the borrower. For example, the person who acquires the garment on a temporary basis does not need to purchase an item for which they do not have a long-term need. This saves the temporary owner money when compared to the purchase of a comparable item. The owner can benefit financially or otherwise by requesting an exchange of goods or services that the borrower may be able to provide. In terms of the environment, resources are conserved in the construction of a new garment for which one individual may have a limited use. Examples of these resources are-manpower or labor, machinery, materials, and processing. The third route for the discard decision phase is recycling, when the garment is given to a new owner in the same form or reprocessed into rags or fibers. The research reported in this manuscript focuses on this route for discarded garments. Specific options included under this heading are giving away, handing down, trading, exchanging, selling, or reprocessing. All of these choices allow the garment to return to the active phase. Examples of how this is done include passing the item on to a family member or friend or placing the item in a resale or consignment shop in exchange for money. Reprocessing may be described as a physical change in the textile by cutting into rags or by breaking down the material into the fibrous stage for incorporation into a new product. Reprocessing differs from continued use when the function of the textile is changed because reprocessing involves a change in ownership of the item, as well as change in purpose. Reprocessing also entails investment of energy in restructuring the item. Any of the choices under recycling are desirable forms of continued garment use since the garment is kept from placement in landfills. Furthermore, the garment fulfills another individual' s clothing needs at a reduced cost compared to the purchase of a new item. The implications of recycling are similar to those of temporary acquisition, except that with the latter after the garment is used for a specific purpose by the temporary owner, it is returned to the original owner. 57 Therefore, recycling provides another method of keeping the garment within the system. The fourth route is permanent disposal, also referred to as the ultimate discard. This includes throwing away or destroying the garment. The difference with this option is that the garment is now, permanently removed from the system, indicating the end of the product' s life. Methods within this route are the least desirable for garment disposal since the textiles are permanently removed from the feedback system as indicated by the arrow in this section of Figure 6. Permanent discard differs from storage under the first option (continued use) in that it is a permanent method of disposal. Furthermore, the items following this route generally would require replacement. Permanent disposal necessarily obviates the goals of extending the life of textile products and conservation of resources. All other choices of discard methods are ones which utilize a feedback loop in the model and which reintroduce the garment for further use. Such reintroduction into the system eliminates production of new garments, and, therefore, conserves energy, resources, and time, thus providing benefits to the environment. This is desirable as it helps to maintain a balance in the ecosystem by limiting the utilization of resources and the resulting pollution. VeVerka (1974) pointed out that discards which are subsequently acquired by others may represent a waste to the 58 former owner but a savings to the new owner. This model provides the theoretical framework for research on determining the useful residual wear life in recycled clothing. The evaluation of the useful residual wear life is based on changes that occur in the product or requirements of the owner during the stages of the life cycle. The condition of the castoff good dictates the appropriate disposition option(s) . In addition, recognition and awareness of the alternatives available for reuse assist in the determination of the most appropriate continued use for the item. The perspective used in this model is compatible with an environmentally conscious attitude by preventing the product1 s removal from the system until it is no longer suited for use at any level. Extending the use of a product delays or prevents the items addition to the solid waste stream, and utilizes resources to their fullest extent. The focus of this research involves certain specific aspects of the discard decision phase of the model. For example, the concern is not with ultimate discard nor with internal environment factors as they influence the product directly, but with recycling and the return of the product to the system. CHAPTER IV

METHODOLOGY

In order to address the research question "Can a model be generated based on the use of simple assessments of judges to indicate continued performance in recycled clothing?", four objectives were stated. Addressing the first objective "to obtain a greater understanding of the condition of discarded clothing by employing a assessment of judges of selected sample garments" is necessary in order to identify variables which can be visually assessed and which are indicators of residual wear life in discarded clothing. Due to the exploratory nature of this research and the lack of an a priori model, multiple statistical analyses were performed to use as preliminary guides to sort out the data. The statistical analyses provide a qualitative idea of the assessments of judges performed in Part I and the physical measurements performed in Part II. The research was conducted in two main parts. First, the assessment of judges of a sample of 65 woven cotton or cotton blend dress shirts was conducted by a panel of trained judges. The characteristics chosen for evaluation were based on ASTM

59 test method D3181 which outlines the standard practice for conducting wear tests on textiles (ASTM, 1992 p90). The second part involved choosing a random sample of a proportion of garments in each group to undergo destructive performance tests. Sampling was deemed necessary due to the amount of work required for each specimen. The extra time, work and cost that would be required by testing all specimens would not provide any substantial additional information. The quality of the data using the random sample should be comparable to that using all of the 65 shirts. The results of the two forms of evaluations were compared to establish the nature of any relationship between the assessments of judges and the performance tests. The next step was to determine which variables are indicative of residual wear life in the sample of recycled clothing. To describe the condition of the discard, information was recorded on the inferred reason of discard based on visible or functional evidence as previously defined. In this chapter the sampling techniques, selection and training of judges, worksheets used, data collection, and analysis employed are discussed for this research. Selection and Training of Judges Three judges were employed in this research in concordance with AA.TCC Test Method 124. The responses of the three judges were averaged together to determine the ratings used for the calculation of statistical tests. Because the evaluation technique will be used in the future by individuals

60 61 untrained in textiles and clothing, judges were chosen for this research in part because of their lack of formal instruction in textiles and clothing. There were two adult female judges and one adult male judge to represent both sexes. In preparing the training course, a pre-test of the judging process was conducted using an adult female. She went through a "mock" training period and was encouraged to provide feedback on the process. She also served to measure content validity. Based on her suggestions, minor changes were made in the language as well as clarification of points made in the script that was used for the individual training script for judges to insure consistency (Appendix B) . For example, it was decided that it was more efficient and less confusing to completely evaluate one shirt at a time rather than one characteristic such as abrasion resistance on each of the ten shirts. The three judges each attended a separate training session which averaged one hour in duration in which their role was explained and the judging process was practiced. Specific areas on the shirt were designated for the evaluation by the judges (ASTM 1992, p837) . They are the following:

Collar - right and left Cuff - right and left Elbow - right and left Underarm - right and left Pocket (if applicable) - right and left Front - right and left Back - right and left Placket 62

Observation of multiple locations is necessary since some parts of the garment may be almost undamaged, while others may show a few broken fibers, which affect appearance but have a negligible weakening effect; and others may show severe damage resulting in complete failure in the form of a hole, break, or tear (Hearle, 1989) . The judges then individually rated the shirts for each criteria in the presence of the researcher who recorded the information on the worksheet. The following are the characteristics which were examined: amount of wrinkling, wrinkle recovery, stains, pilling resistance, snagging resistance, abrasion resistance, fabric hand, color change, seam smoothness, presence of holes, and distortion of garment shape. During and immediately following the training, the judges were informally asked to rate some shirts so that the researcher could determine if they had any difficulty applying the information just presented. Approximately one week after the training session, a pilot test was conducted with each judge independently using 10 shirt samples as a trial of test procedures, instruments, and the effectiveness of their training. These shirts were specifically chosen by the researcher since they covered the range of conditions that could potentially be encountered in the sample garments. This process was used to uncover and correct any problems. 63 Following the pilot test, one of the judges suggested that the replicas be permanently affixed to the viewing board so that they were not distracted by the researcher constantly switching the replicas. This change also allowed the rating process to proceed smoothly and at a slightly quicker rate. Selection of Characteristics for Assessment The procedure for conducting wear testing on textile garments described in ASTM test method D3181 (ASTM, 1992 p89) provided a basis for the selection of evaluative criteria. This list was enhanced by a review of literature and intuitive knowledge of factors related to durability, performance, and quality. A discussion of each characteristic follows in this section. The strength properties of apparel have traditionally been considered the most obvious indicator of the serviceability of apparel. A limited survey was conducted by Knoll and Shiloh of the Israel Fiber Institute (April 1976) to determine the relative importance of laboratory tested properties in apparel and other textile items. The respondents were asked to rate, in terms of percent, the importance of strength and wear, comfort and aesthetics, dimensional stability and colorfastness. For all types of garments, the properties of strength and wear were rated highest indicating that consumers consider them the most important. The strength of the fabric or garment indicates its ability to resist mechanical damage due to the stress of 64 normal use and wear. Tearing strength of a fabric refers to its resistance to a tearing or shearing force. This is important in apparel fabrics such as those used for shirting, blouses, interlining, etc. Breaking and tearing strength were measured in Part II of this research. Wrinkles are undesirable residual bending deformations in a garment that render it aesthetically undesirable (Mehta, 1992) . Wrinkles are produced by compressing, at ambient conditions, a fold of cloth against the body where the temperature and moisture regain of the cloth will eventually rise. Wrinkle recovery is that property of a fabric which enables it to recover from being folded and from forming undesirable wrinkles. This is primarily dependent upon the fibers ability to absorb energy or work without permanent deformation or resiliency. Wrinkle resistance and recovery of a cellulosic or cellulosic blend fabric can be improved by application of resin finish. Other factors that affect the resiliency of fabrics include the following (Pizzuto, 1974): 1. Highly twisted yams, such as those found in crepe fabrics reduce the tendency of fabrics to wrinkle. 2. A tightly woven fabric will show more wrinkles than loosely woven fabric. 3. Thick fabrics do not wrinkle as much as thinner ones. 4. A plain weave with many y a m interlacings will wrinkle more than a basketweave with fewer y a m interlacings. The term used to describe a fabric or a garment which will retain its original shape through wear and laundering is 65 durable press (Mehta, 1992). Therefore, it will resist wrinkling and the seams will be flat and free from puckering, giving the fabric a smooth surface appearance. Two variables were measured that related to fabric wrinkling. Fabric Smoothness was evaluated by comparing the shirts to the AA.TCC Durable Press Replicas. Wear wrinkling was evaluated by crushing the fabric by hand, keeping it compressed for 1 minute and allowing the fabric to recover for the following minute after release. The AATCC Durable Press’ Replicas were also used in evaluating wrinkle recovery in this hand crush test. The ability of a fabric to resist or release stains is another important factor to assess. Staining can be unacceptable and render the garment useless to the owner. Characteristics of durable press finishes and synthetic fibers make the removal of certain types of stains difficult. For example, oily stains, often found on shirt collars are more difficult to remove from the durable press and 100% synthetic fabrics than from untreated cotton. To help solve this problem, soil release finishes have been developed for use on these fabrics. In this research, retention of stains was measured by comparison to the AA.TCC Stain Removal Photographic Replicas. Distortion of garment shape was measured as either 'present' or 'absent' by the raters using their conception of a dimensionally stable garment. This is based on such factors 66 as proportion between length and width, sleeve length and twisted seams. In addition, the judges were shown examples of garments chosen by the researcher to represent both ratings for the variable "distortion of garment shape". Pilling is a fabric surface defect characterized by fiber balls clinging to the cloth surface and giving the garment an unsightly appearance (Mehta, 1992) . Pills are formed during use and wear by the entanglement of loose fibers that protrude from the fabric surface. They are anchored to the fabric by some unbroken fibers. They appear on the areas of the garments where abrasion takes place such as the collar and cuff of shirts. Excessive pilling can render a garment aesthetically undesirable. Pills vary in appearance, depending on the presence of lint and degree of color contrast. Pills on a garment may be accompanied by other surface phenomena such as a loss of cover and color change. Factors affecting pilling propensity include fiber length and denier, fiber mechanical properties, y a m twist level and fabric construction and finishing treatments. In this research, the ASTM photographic replica for pilling resistance was used for this evaluation. Snagging is the result of yams that are pulled or plucked from the fabric surface. The appearance of snags in a fabric is dependent on the individual wearer and conditions of use. Replicas of standards for the ratings 1, 2, 3,4, and 5 (with 1 being the worst) were created for this study to evaluate snagging resistance. Abrasion is fabric attrition due to rubbing against another surface. Adequate abrasion resistance of textile materials is essential for consumer acceptance and satisfaction. Abrasive wear can result from any one or more of the following: 1. friction between cloth and cloth, such as the rubbing of a jacket or cloth lining on a shirt; pants pockets against pants fabric, etc. 2. friction between the cloth and external objects, such as that on the seat of trousers 3. friction between the fibers and dust, or grit in the fabric which results in the cutting of the fibers. This is an extremely slow process and may take years before it becomes noticeable. The measurement of the resistance to abrasion of textile materials is complex since it is dependent on multiple factors. These are similar to those causing pilling and include the inherent mechanical properties of the fibers, dimensions of the fibers, structure of the yams, construction of the fabric, and any finishes applied. Replicas were created to evaluate abrasion resistance using the Taber Abraser. Stiffness of textile fabrics is defined as resistance to bending. Hamburger (1985) defined drapability as "that property which permits a material to orient itself into graceful folds or configurations when acted upon by the force of gravity". This also affects the hand of fabrics. According to Hamburger (1985) , after color, the hand and drape of fabrics are the most important aesthetic properties of interest to fabric and apparel buyers. Fabric hand was measured on a scale of 1 (limp) through 5 (stiff) . The judges were given examples of shirts with the whole number ratings of one through five. The sample selected to represent the rating of ' five1 was a 100% cotton flannel shirt that was previously worn and washed approximately ten times. The sample selected to represent the rating of 'one' was a new 100% cotton oxford shirt with a durable press finish. Shirts were also selected to represent the intermediate ratings of 2, 3, and 4 by showing a gradual difference in hand as previously defined. All of the five shirts used as examples of fabric hand ratings were 100% cotton. In this research, the attribute of color change is an evaluation of whiteness retention or graying or yellowing as evaluated by the Gray Scale for color change. Since all of the 65 sample shirts were white, an evaluation of fabric whiteness was performed in Part II for comparison among the shirts. The evaluation reflects graying or yellowing of the original color or loss of brightness, or it may reflect a certain amount of wear or ineffective refurbishing. Since it was not possible to know the original color of the discarded shirts in the sample, two methods were used to infer this information. First, unexposed areas such as the inside of a pocket or near the seams were viewed in comparison to exposed areas of the garment. Second, a ten inch square of bleached 69 cotton test fabric was viewed as a reference. Whiteness is attributable in part to luminosity (green reflectance) and in part to freedom from yellowness. Therefore, this value was calculated by the Hunter LabScan spectrocolorimeter which measures blue and green reflectances and uses an empirical relationship to calculate whiteness. The formula for whiteness (W) is : W = 4B - 3G, where B stands for the blue reflectance level and G stands for the green reflectance (AATCC, 1990 - Test Method 110-1979). The warmth and bulk of fabrics are dependent upon fabric thickness. Thicker fabrics generally entrap more air within the fabric structure creating a thicker shield between the skin and the environment, thus providing more warmth. Fabric thickness measurements are also used in evaluating abrasion resistance of fabrics. Fabric thickness was measured in Part II.

Part I: Assessment of Judges A total of 65 discarded cellulosic or cellulosic blend dress shirts were used for this study. The sample size (n) was derived from the following formula (DeVore and Peck, 1986) : n = 1.96 s 2 B 70 where s is the standard deviation used to estimate sigma (o) and B is the specified error of estimation. Since sigma is unknown and not retrievable, it may be estimated by (range/4) , where the range is the high minus the low values. In this case the variables are measured on a scale of 1 through 5, so this becomes 4/4 = 1. The 1.96 multiplier is used for a 95% confidence interval based on the fact that for approximately 95% of all random samples, the sanple mean will be within 1.96 sigma/n of the population mean, (/x) . We take B = .25 for the specified error of estimation of /x. Therefore, the formula becomes: n = 1.96(1) 2 _ 61.46 .25

This was rounded to 65 shirts. The sanple garments were acquired from Grossman Industries, a local textile recycling company. To assure that the garments exhibited a wide range of conditions and were representative of the acquisitions of the supplier, complete truckloads of clothing from different states were sorted by clothing category beginning at the designated initial collection date (12/9/92) . From that time on, each truckload from a new location (Ohio and surrounding midwestem states) was included in the sample. This procedure was repeated until the sample size or closest amount above that was reached. In this case, 107 shirts were received. The collection process was completed on 12/19/92. This 71 encompassed the following four different collection sites with the number of shirts indicated in parentheses: 1. Newark, Ohio (24) 2. Gurnee, Illinois (25) 3. Pittsburgh, Pennsylvania (25) 4. Wheeling, West Virginia (33) The source of the truckload was recorded on the worksheet so that in the future information on donor sites and characteristics of the selected garments may be compared. It was decided that 16 shirts would come from the first three sites and 17 from the last since it constitutes the group with the largest proportion of the total amount of shirts. To choose the shirts that would constitute the study sample, each shirt from a separate source was given a number from 1 through 'n' . A table of random numbers was used, paying attention to the last two digits (Scheaffer, 1990) . To choose the random starting point on the number table, a pencil point was dropped by the researcher with her eyes closed. Subsequent numbers were selected by proceeding down the page, using those numbers that corresponded to shirts and skipping those that exceeded 'n' . The shirts corresponding to the first 16 (or 17) shirt numbers from the table of random numbers were included from the sample. Data Collection Instrument The worksheets used for this study were adapted from ASTM D3181, the procedure for conducting wear testing on textile garments (ASTM, 1992 p89- Appendix C) . Minor revisions or 72 substitutions were necessary due to the fact that the standard was developed for wear testing of garments in which textile garments are subjected to specified wear service conditions and evaluated for performance. Another sheet was prepared to record information such as fiber content, brand and size, and to diagram the location and extent of damage (Appendix D). Method The evaluation was performed in the Textile Laboratory in Campbell Hall at the Ohio State University, which is equipped with a conditioning room, a standard viewing board and lighting (AATCC Test Method 124) , and a MacBeth Spectralight chamber. After random selection of the sample garments, each was assigned an identification number from 1 through 'n' which was written inconspicuously in the collar or label with black indelible ink marking pen so that it would not be removed after laundering. The label number did not exceed one inch in size and was identified to the raters so that they would not include it in their evaluation. If there was no label, the number was written on a piece of cotton test fabric and hand stitched to the inner collar with sewing thread. The shirts were washed and dried in a Whirlpool Mark II Series model LA9800XP washer and Whirlpool Mark II Series dryer, model number LG9806XP in accordance with AATCC test method 143- 1984, specifications for a normal load (AATCC, 1989, p 253). This was done to remove any dirt accumulated from use, transport and storage and to allow the wrinkling accumulated in shipping to be eliminated by prompt removal from the dryer. In accordance with the test method, shirts plus dummy load or ballast were washed in a single load to reach a fabric weight of 4 + 0.25 pounds (1.8 + 0.1 kg) . (The average shirt weighed 184.46 grams, so that the average load contained 10 shirts.) The washer was set on a 12-minute washing cycle on the normal setting (hot temperature wash and rinse of 140+5 degrees Fahrenheit) . The temperature of the wash water was measured for each load and found to be 136.4 + 3 degrees Fahrenheit (about 58 degrees Celsius) . The washing machine was set to deliver the full water volume of 18+1 gallon, agitator speed 68±2 rpm, wash time of 12 minutes, spin speed of 510+15 rpm and final spin time of 6 minutes. Ninety grams of AATCC Standard Detergent #124 (without brighteners) was added. Next, the load was placed into the washer. After each final spin cycle was completed, the test specimens were removed immediately, any tangled pieces separated, and then all the shirts and dummy load were placed in the dryer. The dryer was set at a high temperature of 66+5 degrees C (150+10 degrees F) and clothing allowed to dry for 20 minutes. The clothes were immediately removed from the dryer and hung on a rack in the conditioning room with standard atmosphere of 70+2 degrees F and 65+2 percent relative humidity for at least 4 hours as designated in ASIM D1776-90 (ASTM, 1992 p 474) . 74

To facilitate the proper conditioning of all sides of the material by permitting free access of the air to all surfaces of the fabric, the garments were hung on a clothing rack using identical wire hangers to assure that each garment hung similarly. After conditioning, the information for each shirt was recorded on the form in .Appendix D which illustrates the location and type of obvious damage. The judges assessments were completed on the garments before any specimens were extracted for detailed examination and/or subsequent destructive tests. A photograph of representative samples was taken using a good quality camera with a close-up lens to show any damage. The shirts were then placed back on the wire hangers and stored on a clothing rack in the conditioning room until their evaluation. The conditioning room was monitored regularly to assure that standard conditions were maintained. During the assessment, the shirts were then individually placed on the viewing board. Each judge viewed the garments under standard lighting standing directly in front of the replicas and each shirt. After the completion of the judging process, each rater participated in a post-assessment interview. This provided feedback to the researcher on the level of difficulty of the rating technique. In addition, it was included to obtain 75 their opinion on the ability of the average untrained consumer to apply the rating system to garments. Replicas Since photographic replicas of snagging standards were not readily accessible, these were created using the Bean Bag Snag tester and cotton oxford cloth fabric. Each of the ratings, class one through class five were consistent with those used in other standardized test methods. The number of revolutions of the Bean Bag Tester to which the specimen were exposed to artificially generate the replicas appear in the following table.

Table 2 . Key for Snagging Resistance Replicas

Rating # of Revolutions 5 0 4 30 3 45 2 65 1 90

The number of revolutions of the Bean Bag Tester that corresponds to each rating was obtained by stopping the machine after every five revolutions and checking for a visual difference in the amount of snags that would correspond to a 76 point system used to evaluate the laboratory test for snagging resistance. As described later in this chapter, this point system was subsequently transformed to a rating scale of one through five. Abrasion resistance replicas were prepared using cotton oxford cloth top weight fabric and the Taber Abraser since such replicas are not commercially available. The visible differences were achieved by increasing the number of cycles of exposure. The number of cycles used to generate the replicas appear in the following table.

Table 3. Key for Abrasion Resistance Replicas

Rating # of Cvcles 5 0 4 22 3 30 2 44 1 52

In this case, the number of cycles that corresponds to each rating was determined by the researcher after making the replicas for the extreme ratings of one and five. Replicas for the subsequent ratings were produced by stopping the 77 machine every two cycles and comparing each replica for a stepwise visual difference in the samples. Data Analysis As part of the pilot test using 10 shirts, the assessment ratings of the three judges were initially compared for interrater consistency with Pearson's correlation coefficients generated as correlation matrices for each variable by Minitab Version 7.2. There were three separate comparisons for each variable to account for the different combinations of pairs of raters (1 and 2, 1 and 3, 2 and 3) with each datapoint corresponding to the rating of the two judges of one shirt for a particular variable such as abrasion resistance. A cutoff point of r=.70 was established to assure that it is reasonable to average the three ratings on each variable. Since this was not easily achieved, the data were reviewed for recording error and other possible explanations. The pilot test uncovered several points of consideration. First, it was determined that no more than one consecutive hour of rating shirts was optimal to prevent fatigue or diminished concentration by the judge. It was found that the established cutoff point of p=.70 for interrater consistency was not met for all variables. When the actual ratings were reviewed, differences between the raters were investigated by asking the judge to explain why they chose a particular ranking. When the same shirt was repeatedly evaluated by the same person, an identical rating was produced. This 78 introduces the possibility that a psychological component as well as a physiological component are present in the opinion of the rater. This may be due to a difference in levels of observation, perception, interpretation, or tolerance for the characteristic under evaluation. Histograms of the pilot test variables were reviewed for each of the three judges (n=10) and appeared to have a non­ normal distribution. This may be explained by the small sample size. Therefore, a more conservative method of comparison of the interrater consistency based on the ranking of the ratings, the Wilcoxon Signed Rank test, was performed (Table 4) . Test results indicated that there was no significant difference in the ratings between pairings of all judges at p =.010. Furthermore, when ratings of all three judges were simultaneously compared for each variable using a Friedman test for randomized blocks, there was again no difference in the judges' ratings at p =.010 (Table 5) . Therefore, it was felt that the training was effective and that it is acceptable to average the judges responses in the statistical analysis. To determine if any of the variables in the assessment of judges are related, factor analysis and cluster analysis were performed using SAS (Statistical Analysis System) . These data Table 4. Wilcoxon Test for Interrater Consistency - Pilot Study

VARIABLE JUDGES 1&2 JUDGES 2&3 JUDGES 1&3 (P value) (P value) (P value) Abrasion 0.042 0.022 0.418 Resistance Color 0.477 0.185 0.074 Change Distortion N/A* , N/A N/A of Shape Fabric 1.0 0.726 0.834 Smoothness Holes N/A N/AN/A Pilling 0.917 1.0 0.953 Seam 0.066 0.314 0.097 Smoothness Snagging 0.402 0.036 0.273 Resistance Staining 0.499 0.151 0.423 Fabric Hand 0.499 0.151 0.423 Wear 0.541 0.441 0.139 Wrinkling Total Score 0.086 0.059 1.0

*N/A = not applicable due to measurement of variable on a dichotomous scale of presence or absence of this criteria. reduction techniques are useful to show if any variables may be collapsed into a single representative category. Cluster analysis is a generic term for a set of techniques which produce classifications from initially unclassified data (Everitt, 1980) . The goal is simplification with minimal loss of information. The basic data for, cluster analysis is a set of N entities on which p measurements have been recorded. This initial choice of the particular set of measurements used to describe each entity constitutes a frame of reference within which to establish clusters, and the choice presumably reflects the investigator's judgement of relevance for the purpose of classification. The first assumption about the variables is whether the list of variables are complete and correct in the sense that they are relevant to the type of classification being sought. It is important to bear in mriind that the initial choice of variables is itself a categorization of the data which has no mathematical or statistical guidelines, and which reflects the investigators judgement of relevance for the purpose of classification. This statistical procedure was performed to help address the first objective and to obtain a greater understanding of the condition of the discarded clothing and how the sample of 65 shirts grouped based on the assessment of judges ratings. The method of factor analysis used was Principal Component Analysis (PCA) , which is a multivariate technique in which a nurmber of related variables are transformed to what is hoped 81

Table 5. Friedman Test for Interrater Consistency - Pilot Study

VARIABLE P VALUE* JUDGE PAIRS WITH (P value) SIGNIFICANT DIFFERENCES** Abrasion Resistance 0.008 (2,3) Color Change 0.215 Distortion of Shape N/A*** Fabric Smoothness 0.913 Holes N/A N/A Pilling 0.971 Seam Smoothness 0.015 (1,2) Snagging Resistance 0.062 Staining 0.792 Fabric Hand 0.327 Wear Wrinkling 0.349 Total Score 0.329 ■

*Adjusted for ties

**Using a small sample all-treatment comparison for Friedman test (r=13, p=.01)

***N/A = not applicable due to measurement of variable on a dichotomous scale of presence or absence of this criteria. to be a smaller set of uncorrelated variables (Jackson, 1991) . Hie method of principal components is primarily a data analytic technique that obtains linear transformations of a group of correlated variables such that certain optimal conditions are achieved. The most important of these conditions is that the transformed variables are uncorrelated. The purpose of the application of PCA in this work was to learn how the assessment of judges categories are related and if they could be collapsed into factors that relate to residual wear life. This knowledge itself is of interest as well as useful for the subsequent statistical analysis including model fitting. The difference between Cluster and Factor Analysis is that Cluster Analysis determines how the shirts grouped based on the ratings of all eleven assessment of judges categories, while Factor Analysis determines relationships between the assessment of judges variables based on the ratings. Upon completion of the Factor Analysis, the 65 shirts evaluated in this study were ranked according to a total of rankings in each of the factors. Classification of possible cause for discard was then made. Part II - Destructive Testing Using the sample command of Minitab Version 7.2, a simple random sample of 32 shirts were selected from the original sample size of 65. The 32 shirts included in the random selection were used in Part II to undergo destructive performance testing. When taking specimens for examination, they were removed from a range of different locations to cover the full spectrum of damage from none to slight to moderate to severe. There are two reasons for this procedure (Hearle, 1989). First, it is necessary to be sure which features are a result of the damage and which are representative of the original material. The less damaged regions are much more instructive in showing up the sequence of damage and giving clues to its cause. It may be difficult to find undamaged material because, even after only a few wear/wash cycles, the fabric suffers some damage which gets progressively worse as the shirt is worn. Undamaged or relatively undamaged fabric which can be found under the collar, in pockets (if applicable) and in front facings was examined. Slight damage caused mainly by the physical effects of laundering may be found in the center back regions and more severe wear may be found down the fronts and in elbow regions; these areas were viewed for contrast to the relatively undamaged areas. Finally, the most severely worn parts of the shirt which may occur along the collar fold, at collar points, and along the edges of the cuffs, were compared to any unexposed areas. Rips and tears can occur in various places and are usually accidental or stem from failure of weak places in the shirt. The way garments wear out depends on the working/wearing conditions and also on the individual wearer (Hearle, 1989). However, comparing the areas of expected least to most wear should provide a consistent procedure for 84 evaluation. Therefore, care was taken to avoid bias when taking specimens particularly in the case of making comparisons between several worn garments. Experience has shown that there are variations in wear patterns between users, so that taking specimens at the same places in each garment may solve the problem of personal bias in specimen selection but may not cover all the main areas of damage. Therefore, a cutting diagram was prepared that randomly placed the specimens for each test on each shirt. Finally, the nondestructive test results were compared to the results of the performance tests to determine if any relationships exist. If the outcome of physical tests may be used to indicate residual wear life, those visual cues which are found to be related to physical performance tests, may, in turn, be used to indicate wear life (assuming that performance testing does indicate real-life wear in some manner) . The destructive tests perfomed were (Table 6) : Breaking Strength, Tearing Strength - Warp and Fill, Abrasion Resistance, Pilling Resistance, Snagging Resistance, Flexural Rigidity, Fabric Thickness, Covering Power and Whiteness. The first eight were performed according to the corresponding ASTM tests listed in Table 6. Covering power was measured using the Hunter LabScan to determine the number of layers of fabric required to cause the color value to level due to maximum reflectance of light. For example, a transparent fabric would 85 have a higher value since it requires more layers to prevent any more change in the color measurement due to reflectance of light. The whiteness value was computed by the Hunter LabScan Spectrocolorimeter, and measured using 11 layers of fabric which corresponded to the largest value recorded for covering power. The results of all performance tests were compared to the ASTM standard specifications for a boys/mens woven dress shirt to see how closely they are met. Method The following tests were performed (Table 6): Tensile Strength, Tear Resistance, Abrasion Resistance, Snagging, Flexural Rigidity, Covering Power, and Fabric Thickness and Wear Patterns. Fabric thickness was measured using the Randall and Stickney Compressometer (Waltham, Massachusetts). Three measures were collected on identical areas of each shirt in the left and right front and back. The fabric was placed under the instrument in a single, flat layer for consistency and measured to the nearest .0001 inch. The average of the three values was recorded for each shirt. The Bean Bag Snag Test was performed to measure snagging resistance. A fabric pocket was sewn around a bean bag and placed in the chamber for 100 revolutions. After it was removed, the fabric was viewed. Table 7 shows the basis for the ratings. 86

Table 1 . List of Evaluative Tests for Recycled Shirts

METHOD EQUIPMENT EVALUATION Visual Inspection AATCC Standard Viewing (Non-destructive) Area Gray Scale Color Changes AATCC Soil Release Stains on Fabric Replicas AATCC Photographic Seam Smoothness Replicas AATCC 88B-1984 AATCC Photographic Pilling Resistance Replicas Visual Examples of Ratings Abrasion Resistance 1,2,3,4 & 5 ASIM 3884-80 Rating of Yes/No to Holes in Fabric indicate presence or absence of holes Visual Examples of Ratings Snagging Resistance 1,2,3,4 & 5 Judges Conception of Distortion of Garment Dimensionally Stable Shape Garment used as Standard. Also, Visual Examples of Ratings 1,2,3,4, Sc 5 Tactile Examples of Fabric Hand Ratings 1,2,3,4 & 5 AATCC DP Replicas Fabric Smoothness Crush with Hand Wear Wrinkling Performance Tests Instron Tensile Strength (Destructive) ASIM D1682-64 Elmendorf Aparatus Tear Resistance ASTM D1484-83 Taber Abraser Abrasion Resistance AATCC 3884-80 Random Tumble Pilling Pilling Resistance Tester ASIM 3512-82 Bean Bag Tester Fabric Snagging Shirley Stiffness Tester Flexural Rigidity ASIM D1388-64 Fabric Whiteness Hunter LabScan Covering Power Reflectance Level Fabric Thickness Compressometer 87

Table 7. Numerical Rating Based on the Points Accumulated

Numerical Rating Points Interpretation 5 (none) 0 - 2 Highly Acceptable 4 (slight) 3 -10 3 (moderate) 11-19 2 (severe) 20-39 1 (very severe) 40 or more Highly Unacceptable

Small snags of .15 inch or less without surface distortion were assigned one point each. Long snags greater than .15 inch without surface distortion were assigned two points each. Surface distortion of any length was assigned three points each. (Surface distortion is defined as noticeable streak in fabric created by lack of y a m or loops.) For fabric stiffness, the Cantilever test, option A was performed. One inch by 6 inch specimens (4 with warp and 4 with fill in the long direction) were preconditioned. A strip of fabric in the long dimension is slid in a direction parallel to its long dimension, so that its end projects from the edge of a horizontal surface. The length of the overhang is measured when the tip of the test specimen is depressed under its own weight to the point where the line joining the 88 tip to the edge makes an angle of 41.5 degrees with the horizontal. One half of this length is the bending length of the specimen. The cube of this quantity multiplied by the weight per unit area of the fabric is flexural rigidity. Bending length (C) in cm = 0/2, where 0 is length of overhang in cm. Flexural rigidity (G) , in mg/cm2 = W x (0/2)3 = W x C3, , where W is the weight per unit area, in mg/cm2. This is obtained by multiplying fabric weight in ounces/square yard by 3.39. Data Analysis After completing the Part II performance evaluations, statistical analyses were conducted on the data in order to empirically establish a relationship between the judges assessments and the physical measurements. Due to the exploratory nature of this research, a number of statistical manipulations were employed to see which yielded significant and reasonable results. Furthermore, the approach employed here is one which simulates the process intended when this research is applied in a practical manner in the future. That is, visual assessment which is nondestructive is used to infer properties of items of clothing without further testing. A model might have been designed in which the ratings of physical characteristics of a given shirt are measured by the destructive tests as fixed and the nondestructive test ratings 89 by various raters are viewed as random. Presumably the physical performance characteristics are correlated with the nondestructive test ratings and so multiple regression models with the destructive test results as predictors and the nondestructive test ratings for a "random" rater as the responses seem reasonable. This study, however, employed the opposite perspective. The nondestructive ratings were used to gain information about the destructive test values which, in turn, provide information about residual useful life of a garment. To this end, the collection of (mean) nondestructive measurements are viewed as varying from garment to garment. From a random sample of garments, one can explore the correlation between these measurements by means of canonical correlation analysis (discussed later) and by means of the conditional distribution of the (mean) nondestructive measurements given the destructive measurements. The latter is accomplished by fitting multiple regression models with the (mean) destructive ratings for a garment as predictors and the destructive test measurements as responses. Although specific quantitative results are obtained ,the results of the multiple regressions and canonical correlations are viewed primarily as descriptive indicators of the relationship explored. For the exploratory purposes of this research, the quantitative results are considered merely descriptive. 90 The following diagnostics were performed: 1. boxplot of each independent variable (to identify symmetry of distribution, outliers) 2. boxplot of residuals (to identify skewness, outliers) 3. normal probability plot of residuals (a linear plot is desirable since it suggests that error is normally distributed) 4. plot of residuals against independent variable 5. plot of residuals against fitted values (for appropriateness of the regression function, the constancy of the variance of error terms, and to identify residual outliers) Mallows Cq and adjusted R-squared were evaluated to assure that the variables placed in the model contribute measurably to the R-squared values. Cook's Distance was evaluated for each datapoint to identify influential cases. (If any were found, the fitted regression model would have been made with and without the case(s) of concern. If the estimates are not essentially changed, no remedial action for the case(s) is required. Alternately, serious changes in the estimates drawn from the fitted model when a case is omitted would have required consideration of remedial measures.) Variance inflation factors for each independent variable were examined for evidence of multicollinearity. In addition, canonical correlation analysis (CCA) was performed on SAS with the destructive tests as the multiple dependent variables and the nondestructive ratings as the multiple independent variables to be simultaneously compared. CCA. describes a multivariate statistical technique that investigates the relationship between two sets of variables. Inmost applications, however, the two sets of variables are not treated symmetrically. Rather, one set is the predictor set and the other is the set of criterion measures. In CCA the idea is much the same as multiple regression, except there are now two linear combinations produced, one for the predictor set and one for the criterion set, such that their ordinary product-moment correlation is as large as possible. This method is particularly appropriate when the criterion variables are themselves correlated. Ultimately, the relationships revealed between destructive performance tests were used to indicate continued wear life in the recycled shirts by defining significant variables and their interrelationship. By totaling the ratings for the variables in the model, one quantitative measure for the garment is achieved and the potential continued wear life is inferred by noting that the higher number (in a defined range) corresponds to a longer remaining wear life. CHAPTER V

PRESENTATION OF FINDINGS AND DISCUSSION FOR PART 1

The purpose of this study was to generate a model that indicates residual wear life in recycled clothing. The study encompassed two parts: 1) the assessment of judges of selected characteristics of a sample of discarded shirts and 2) the evaluation of physical properties of the shirt fabrics. This chapter includes a description of the pilot test and analysis of the research data related to the assessment of judges of selected characteristics of the sample of 65 discarded shirts.

Part 1: Analysis of Discarded Shirts Pilot Study Prior to collecting data on the sample shirts, a pilot test was performed to uncover any potential problems that might occur; to allow the judges to practice rating shirts; and to generate data on a small sample of ten shirts which could be tested for interrater consistency among the three judges. Initially, Pearson's correlation coefficients were used to compare each pair of judges on each variable to

92 measure interrater consistency. A cutoff point was set at p = 0.7 to indicate a good to moderate relationship (Colton, 1974) , thus providing evidence that the ratings between the judges did not substantially diverge and making it acceptable to average the three values for each variable. The Pearson correlations ranged from .09 to .892. Histograms of the variables revealed that the variables appeared not to be normally distributed in most cases, thus nonparametric tests are more appropriate measures to employ in this analysis. These distribution-free tests make less assumptions about the distribution of the data and use' the ranking of the ordered values. Wilcoxon signed rank tests and Friedman tests were chosen as most suitable for the data since the data should be treated as paired. The null hypothesis for the Wilcoxon test states that the differences between the ratings for each judge pair is zero. Tables 4 and 5 show the results of these tests. Using a p value of 0.01 as the level of significance, none of the Wilcoxon sign ranked tests were statistically significant. For the Friedman test, the treatment levels were the three judges and the blocks were the ten shirts. Significant differences found at p = .005 are shown on the following table. 94 Table 8. Summary of Significant Friedman Tests

Variable Judge Pairs Abrasion resistance (2,3)

Seam Smoothness (1 ,2)

In an ideal case there should be no significant findings for any of these tests. For most of the variables, no significant differences were observed. In the two cases where significant differences were found, different judges differed. No pattern seems to be present. It was concluded, therefore, that the training sessions provided adequate preparation for the raters. Part 1 - Assessment of Judges This part of the study involved the visual rating of 65 shirts randomly selected from a pool of 107 shirts. Each of the judges evaluated the sample of shirts at numerous sessions lasting approximately one hour each. The researcher recorded all of the ratings, and at no time were the judges provided information about the ratings of their peers. The Wilcoxon Signed Rank tests and Friedman tests were performed on these data to evaluate interrater consistency. Tables 9 and 10 show the results. For the Wilcoxon Signed rank test on the differences between all combinations of judge pairs with the null hypothesis stating that the differences are 0, the variables listed in the following table are statistically significant.

Table 9. Summary of Significant Wilcoxon Signed Rank Tests

Variable JudgePairs Color change (1,2) (1,3) Snagging Resistance (1,2) Staining (2,3) (1,3) Fabric hand (1,2) Wear wrinkling (1,2)

The Friedman test, with treatments of the judges blocked by shirts, revealed the significant differences at the p value of .01 shown on the following table. 96 Table 10. Summary of Significant Friedman Tests

Variable Judge Pairs Color Change (1,2) (1,3) (2,3) Snagging Resistance (1,2) (1,3) Pilling Resistance (1,3) Staining (1,3) (2,3) Wear wrinkling (1,2) (2,3) Total score (2,3)

Since the number of significant tests total 50% of the tests performed, it would not be reasonable to explain the number of significant tests by chance occurrence. Therefore, alternative explanations were explored. It was noted that the median scores for each of the raters on most of the significant tests only differ by a half score (i.e., 3 and 3.5). These are two consecutive ratings, and are not considerably divergent. After reviewing the significant variables and judge pairs involved, no pattern became evident. Assessments made by the judges will vary to some degree, therefore, it is necessary to use at least three individual judges and average their ratings. The small differences among ratings could be explained by differences in perception of the same characteristic by each individual. Brown (1992) mentions that individual standards affect the perception of quality. Consumers with high standards are dissatisfied when a garment does not meet their expectations; others with lower expectations might be satisfied with the performance of the same gament. Brown also states that many consumers lack the ability to objectively evaluate quality. It is noteworthy to recognize that consumers do not necessarily base their judgment on the inherent quality of a garment, but instead upon their perception of its quality. Therefore, consumers purchase garments with specific physical features that they believe will fulfill the performance which they expect. Stanton (1981) defines perception as "the process of organizing, interpreting, and deriving meaning from stimuli through the senses". Using this definition, O'Neal (1993) explains that the perception of apparel quality results from the links between expectations, salient product attributes, and performance evaluation. Furthermore, perception of quality involves the subjective response of people to objects and, therefore, is relative and differs between perceivers. Rejection of the garment occurs when it no longer meets the owner's needs for any reason, whether aesthetic or functional. Classification of Shirts Based on Evidence of Discard As assessed by the researcher, only eight (12%) of the shirts displayed no visual evidence of the cause for discard while 57 (88%) showed visible evidence of the cause for discard. Visual evidence for the cause of discard included 98 multiple pinholes, larger holes, stains, and evidence of distortion of shape. Other visual reasons for discard were dingy color, "ring around the collar" from perspiration and skin oils, pilling, slight staining in non-visible area such as the shirttail, and wrinkling, this indicates that from the perspective of function and durability, a potential residual wear life remained in the majority of garments that were discarded. Cluster Analysis A cluster analysis of the 65 sample shirts was performed on the 11 variables used for the assessment of judges in order to determine if the shirts would form into groups which differ on variables related to the residual wear life remaining in the garment. If this could be reasonably established, these groups would support the use of a stratified random sample to choose the 32 shirts to undergo destructive testing. Cluster analysis finds hierarchical clusters of the observations in a data set. The methods are based on the agglomerative hierarchical clustering procedure. Each observation begins in a cluster by itself. The two closest clusters are merged to form a new cluster that replaces the two old clusters. The distance between the two clusters is the maximum distance between an observation in one cluster and an observation in the other cluster. This analysis produced four clusters (Table 11) . The number of shirts in each group were 8, 32, 13, and 12 99 respectively. Examination of the separate arrays show that they primarily differ in the following respects: Cluster 1: - Lower ratings for wear wrinkling, i.e. - these were more wrinkled Cluster 2: - Higher ratings for distortion of shape and fabric smoothness, i.e. - these were smoother and less distorted Cluster 3: - Lower ratings for abrasion resistance and all except one have at least one hole Cluster 4: - Lower ratings for staining, i.e. - these were more stained These unique characteristics of the clusters do not logically facilitate the groupings of these shirts by representative variables related to either aesthetic or functional performance, which make up the definition of residual wear life used in this study. Therefore, this analysis did not lead to any useful findings, and it would not be optimal to use this categorization for the selection of a stratified random sample. Rather, a simple random sample of size 32 was chosen from the 65 shirts by using the sample command from Minitab version 7.2. Factor Analysis A factor analysis on the assessment of judges ratings was performed to determine if any of the variables were related and could be collapsed. A frequent source of confusion in the field of factor analysis is the term factor. It sometimes refers to a hypothetical, unobservable variable, as in the phrase common 100

TaKI<* 1 1 . Cluster Analysis

CLUSTER*! STAIN HAND wrink SHIRT ABR COL DIST SMOOTH PILL SEAM SNAG 3.00 4.33 2.67 4.33 3.83 2.83 1.67 51 3.83 4.33 1 2.67 1.67 61 4.00 4.50 1 3.00 4.67 2.33 4.33 4.00 3.83 1 3.17 4.67 2.50 4.50 3.50 2.17 2.33 65 4.33 2.83 2.00 14 4.00 3.67 1 2.33 4.00 3.50 3.83 3.00 4.33 1 3.33 4.00 3.00 4.00 2.83 3.00 2.50 40 4.00 3.50 2.00 33 3.33 4.00 1 2.83 4.67 3.00 4.17 3.50 4.33 1 3.50 4.67 3.83 3.83 4.33 3.00 2.17 3 3.67 2.50 1.33 29 4.67 4.50 1 1.33 4.33 2.33 4.33 3.67

- STAIN HMD WRINK SHIRT ABR COL OIST SMOOTH PILL s e a m SNAG 3.17 3.33 3.50 54 4.33 4.00 2 4.00 4.00 3.33 4.50 2 3.83 4.17 3.33 4.50 2.83 3.17 3.00 56 4.00 4.17 3.50 3.83 3.33 8 4.17 3.67 1 4.00 4.17 4.00 4.17 3.83 4.33 4.33 4.33 3.17 3.50 3.00 55 4.33 4.00 1 4.00 4.00 4.33 4.00 1 4.67 4.33 3.50 3.83 3.67 21 4.50 4.17 4.33 4.00 3.83 3.83 52 4.33 4.00 1 4.50 2.33 4.00 1 3.33 4.33 3.67 4.33 3.17 3.67 49 4.33 4.50 2.83 3.83 2.83 62 4.00 3.50 1 3.00 4.50 4.17 3.67 4.33 3.33 4.00 3.50 3.00 3.17 12 4.67 3.83 2 3.50 3.00 3.67 2.83 1 3.67 2.50 3.17 3.67 2.33 2 3.50 3.50 3.67 2.67 3.50 3.17 23 4.00 3.00 1 3.83 3.17 2 3.67 4.33 4.33 4.50 3.83 3.83 58 4.33 4.00 4.33 3.00 3.67 3.50 64 3.33 3.00 1 3.67 2.83 3.17 4.00 2.67 3.33 4.00 3.17 4.00 3.33 11 3.67 3.83 3.67 3.50 4.00 3.17 13 4.00 3.83 1 3.83 3.00 4.00 3.00 2.00 3.00 4.33 3.67 3.00 2.83 57 3.67 3.83 1 3.17 3.67 2.33 3.33 3.33 1 3.50 2.33 3.67 4.33 63 4.00 3.00 4.00 2.83 4.17 3.67 24 4.00 4.00 I 4.83 3.17 3.67 1 4.67 3.17 3.83 4.67 2.82 4.00 34 4.00 4.33 2.82 3.17 2.67 36 4.67 4.00 1 4.00 4.67 4.00 4.00 4.17 2.83 4.50 4.00 3.67 3.50 48 4.33 4.16 1 2.50 3.33 2.67 4 4.33 2.50 2 3.83 4.67 3.17 4.33 3.67 4.33 3.00 4.67 3.17 2.67 3.00 41 4.33 3.17 1 3.00 3.33 3.17 7 3.33 3.17 2 3.83 3.83 2.50 3.83 4.33 4.33 3.50 4.16 2.83 3.67 3.67 19 3.33 3.00 1 4.00 3.17 2.50 30 3.67 4.33 3.50 2.33 3.67 4.00 4.83 2.33 3.50 3.00 3.50 3.83 3.50 37 3.50 3.83 2 3.67 4.33 3.50 3.50 4.33 1 4.67 4.50 4.33 4.67 42 3.67 4.00 3.67 3.17 3.50 2.83 28 4.16 3.00 3.00 4.33 3.67 3.33 22 3.67 4.17 1 4.00 3.17 2.17 3.50 3.33 4.33 2.83 3.67 3.50 3.83 3.33 44 4.33 3.83 2.50 2.00 2.67 39 3.50 3.83 1 3.50 3.50 3.83 4.00 t 101

Table 11. (Continued)

CLUSTER3 3

SHIRT ABR COL DIST SMOOTH HOLE PILL SEAM SNAS STAINHAND WRINK 3.67 46 1.00 3.33 1 4.67 0 4.50 3.67 4.00 3.17 4.00 47 1.00 3.00 L 4.50 0 4.33 3.00 4.33 2.50 3.67 3.83 3.17 2 4.00 0 4.33 3.83 3.83 3.17 3.17 3.17 15 1.00 4.33 3.33 3.33 2.67 45 1.00 3.50 1 3.33 0 4.00 3.67 0 3.50 3.00 3.33 4.00 3.83 3.33 17 1.33 3.67 1 3.83 3.50 3.00 1 3.83 0 3.00 3.50 3.33 2.83 3.83 26 1.00 2.17 3.83 2.67 1.50 1.50 9 1.00 3.33 0 2.33 0 4.00 0 4.00 2.67 4.33 2.83 3.00 1.50 38 1.00 3.50 0 2.50 2.83 4.00 1 3.00 1 3.67 3.67 4.00 3.33 3.67 32 1.83 3.17 4.33 3.33 3.50 2.00 50 1.33 4.33 1 2.50 0 3.00 3.00 0 4.00 1.83 4.50 3.33 2.33 1.50 59 1.00 4.33 1 2.17 3.00 1.00 3.50 2 3.83 0 4.33 2.50 3.83 3.17 S 2.50 4.17 1.33 2.67 1.50 10 1.00 2.67 1 2.50 0 3.83

CLUSTER34 SMS STAIM m m WSXKK SHIRT a s r COL DI8S SMOOTH HOLE PILL SEAM 4.33 I 3.67 2.33 4.00 3.17 3.00 3 67 43 4.00 3.83 2.00 2.83 3 50 4.33 3.67 4.33 1 4.00 2.83 4.33 60 4.00 1 4.33 3.67 3.16 1.67 3.50 3 33 16 4.17 3.67 1.67 3.67 3 17 4.33 4.17 3.67 1 3.83 3.50 4.33 31 3.83 1 3.SO 3.83 3.83 1.33 3.17 3 00 18 3.67 3.50 4.33 1.67 3.00 3 33 53 3. SO 2.50 3.83 I 3.67 4.00 4.67 I 4.00 3.50 4.16 1.33 3.50 3 67 20 3.00 3.67 1.83 4.33 3 83 3.50 3.00 4.67 I 4.00 1.83 4.33 25 2.82 1 3.67 2.67 4.33 1.00 3.67 3 00 27 2.67 3.83 1.00 3.00 1 83 3.67 2.50 2.33 1 3.SO 3.SO 3.17 35 0 3.00 1 2.00 3.67 3.83 3.00 3.83 2 83 1 2.83 2.33 3.83 2.00 2.17 3 83 6 2.67 3.33 0 4.S0 1 1.67 3.33 102 factor. A common factor is an unobservable, hypothetical variable that contributes to the variance of at least two of the observed variables (Statistical Analysis System/Stat User's Guide, 1989). The assumptions of common factor analysis imply that the common factors are, in general, not linear combinations of the observed variables. After the factors have been estimated, it is necessary to interpret them. Interpretation usually means assigning to each common factor a name that reflects the importance of the factor in predicting each of the observed variables. Factor interpretation is a subjective process. The method of factor analysis used in this research was Principal Component Analysis on SAS (Table 12) . There were four large eigenvalues, which together account for 68.04 percent of the standardized variance. Thus, the first four principal components provide an adequate summary of the data for most purposes. This procedure retains four components on the basis of the eigenvalues-greater-than-one rule since the fifth eigenvalue is only .9504. The four components are summarized in Table 12. The first component has large positive loadings for these variables: abrasion resistance (.59289), fabric smoothness (.75472), seam smoothness (.56598), presence of holes (.61645), fabric hand (.69327) and wrinkle recovery (.79912). The second component includes abrasion resistance (.59517), color (.73739), stains (.50145) and snagging resistance (.50484). The third component is a 103

Factor Analysis

PRINCIPAL COMPONENT ANALYSIS Means and Standard Deviations from 65 observations ABR COL DIST SMOOTH HOLS < PILL Mean 3.32 3.64046154 1.12307692 3.63507692 0.81538462 3.79215385 Std Dev 1.19933445 0.54216704 0.51562136 0.72838246 0.39100462 0.74936534 SIAM SNAG STAIN HAND WRINK Mean 3.27938462 4.07815385 2.97138462 3.33092308 2.91276923 Std Dev 0.62239928 0.38306695 0.79929242 0.59275238 0.69664712 Correlations ABR COL DIST SMOOTH HOLE PILL ABR 1.00000 0.26470 0.18369 0.13652 0.90562 0.12960 COL 0.26470 1.00000 0.07748 -0.04758 0.17362 0.23391 DIST 0.18369 0.07748 1.00000 0.21132 0.11446 0.08705 SMOOTH 0.13652 -0.04758 0.21132 1.00000 0.15367 -0.01817 HOLE 0.90562 0.17362 0.11446 0.15367 1.00000 -0.07008 PILL 0.12960 0.23391 0.08705 -0.01817 -0.07008 1.00000 SEAM 0.23220 -0.06532 0.18233 0.25920 0.24672 -0.04560 SHAG 0.14195 0.24395 -0.02335 -0.04175 0.08323 0.33203 STAIN 0.11164 0.52252 0.09739 -0.01139 -0.00017 0.10158 HAND 0.15935 -0.02924 0.10340 0.47371 0.20030 -0.07388 WRINK 0.16888 -0.15781 0.16172 0.86339 0.21701 -0.11658 SEAM SNAG STAIN HAND WRINK ABR 0.23220 0.14195 0.11164 0.15935 0.16888 COL -0.06532 0.24395 0.52252 -0.02924 -0.15781 DIST 0.18233 -0.0233S 0.09739 0.10340 0.16172 SMOOTH 0.25920 -0.04175 -0.01139 0.47371 0.86339 HOLE 0.24672 0.08323 -0.00017 0.20030 0.21701 PILL -0.04560 0.33203 0.10158 -0.07388 -0.11658 SEAM 1.00000 -0.03090 0.01585 0.39740 0.29286 SNAG -0.03090 1.00000 0.08161 -0.04932 -0.12154 STAIN 0.01585 0.08161 1.00000 0.10126 -0.06628 HAND 0.39740 -0.04932 0.10126 1.00000 0.56342 WRINK 0.29286 -0.12154 -0.06628 0.56342 1.00000 Table 12. (Continued)

PRINCIPAL COMPONENT ANALYSIS Initial Factor Method: Principal Cooponenta Cooounality Estiaates t ONE :orrelation Matrix: Total a 11 Average - 1 2 3 4 Eigenvalue 2.8726 2.1171 1.3825 1.1119 Difference 0.75S4 0.7346 0.2706 0.1615 'proportion 0.2611 0.1925 0.1257 0.1011 cumulative 0.2611 0.4S36 0.5793 0.6804 5 6 7 8 Eigenvalue 0.9504 0.8443 0.6403 0.4876 Difference 0.1061 0.2040 0.1527 0.0757 Proportion 0.0864 0.0768 0.0582 0.0443 Cuaulativo 0.7668 0.8435 0.9017 0.9461 9 10 11 Eigenvalue 0.4119 0.1149 0.0665 Difference 0.2970 0.0484 Proportion 0.0374 0.0104 0.0060 Cumulative 0.9835 0.9940 1.0000 be retained by the MINEIGEN criterion. Factor Pattern FACT0R1 FACTOR2 FACTOR3 FACTOR* ABR 0.59289 0.59517 -0.47856 0.02487 COL 0.04678 0.73739 0.33085 -0.28730 DIST 0.34666 0.12399 0.18055 -0.03808 SMOOTH 0.7S472 -0.29883 0.34030 0.15470 HOLE 0.61645 0.45380 -0.60165 -0.02963 PILL -0.05968 0.46073 0.36665 0.57987 SEAN 0.56598 -0.06361 -0.04463 -0.03970 SKAfi -0.02965 0.50484 0.18637 0.58307 STAIN 0.07125 0.50145 0.50986 -0.55436 HAND 0.69327 -0.19273 0.23870 -0.08076 WRINK 0.79912 -0.38568 0.21345 0.10419 Variance explained by each factor FACTOR1 FACTOR2 FACTORS FACTOR* 2.872562 2.117120 1.382518 1.111900

Final CoMmality Estimates! Total ■ 7.484099 AIR COL DIST SMOOTH HOLE PILL 0.935388 0.737934 0.169592 0.798646 0.948799 0.686515 wm mML« STAIN HAMB MtB 0.327948 0.630445 0.823805 0.581273 0.843757 105 contrast of presence of holes (-.60165) and stains (.50986) . The fourth component includes high positive loadings on snagging resistance (.58307) and pilling resistance (.57987) and contrasted with stains (-.55436) . The final communality estimates show that all but two of the variables (distortion of shape = .16959 and seam smoothness = .327948) are well accounted for by four components, with final communality estimates of the remaining nine variables ranging from .58127 for fabric hand to .94879 for the presence of holes. The only variable that did not have high positive or negative loadings on any of the four factors was distortion of shape. Therefore, the results indicate that distortion of shape is not an important variable to measure in the future. A summary and interpretation of the findings of the factor analysis ensues. 106

Table 13. Factor Analysis of the Assessment of Judges Variables.

F A C T O R 1 FACTOR 2 FACTOR 3 FACTOR 4 Overall Overall Localized Localized Damage Damage Damage Damage Typ_e_l__ _Type_II_ — Typ e I___ Typje II__ Fabric Abrasion Presence of Snagging Smoothness Resistance Holes Resistance (negative)9 Seam Color Stains Pilling Smoothness Change Resistance Hand Stains Abrasion Stains Resistance (negative)3 Wear Snagging Wrinkling Resistance Abrasion Resistance Holes

"negative" indicates contrast

Discussion Defining the variables by reason of discard affecting either or both aesthetic appeal and physical performance of the garment, the four factors can be shown to have distinguishing characteristics. When the owner's tolerance level of acceptability for the garment's appearance is no longer met, the garment will most likely be discarded or placed in storage. The principal components are "scores" (computed from the measurements taken) that account for the majority of the variation in the data. Thus, the first principal component (factor) summarizes the primary source of variation in the data. In particular, Factor 1 (overall damage - type I) is the single number which accounts for the differences in the judges assessment measurements on the shirts in the sample. Factor 1 includes variables associated with wrinkling, smoothness of fabric structure, and abrasion resistance. The shirts, then, differ most in a set of overall inherent fabric characteristics which are influenced by fabric construction, y a m structure, fiber content, and finish. Factor 2 (overall damage - type II) is the single factor (score) that accounts for the variation in shirts not explained by Factor 1. The second factor contains four variables, abrasion resistance, color, stains, and snagging resistance, which affect the aesthetic appeal of the garment due to adverse changes from the initial appearance. Similar to the Factor 1 variables, the variables included in Factor 2 produce visible changes which affect the overall appearance of the garment but the source of these changes differs from those grouped in Factor 1. Factor 3 (localized damage - type I) is the single factor (score) that accounts for any variation in shirts that is not explained by factors 1 and 2 (i.e. for all shirts that are the same with respect to factors 1 and 2, factor 3 is the single factor explaining differences between them) . The third factor includes a distinction between the presence of hole(s) or stain(s) and abrasion resistance. The third factor, then, 108 includes types of localized catastrophic changes that can result in discard. These defects are in the form of a contrast, with the presence of either holes or one of the other defects. This means that the presence of only one defect is sufficient for the owner to discard the item. The fourth factor (snagging resistance, pilling resistance, and stains) includes variables which depend on the specific y a m type and fabric construction utilized. Thus, loosely twisted yams or loose fabric const me t ions are more susceptible to snagging or pilling. The contrast with the presence of stains, which is already included in the third factor, reinforces the fact that the presence of stains on a garment is a unique defect, which is sufficient for garment discard. Therefore, each of the four groups of variables is distinguishable from the others and each encompasses factors which influence the decision of discard. It should be noted that these factors do not directly explain why shirts were discarded, rather they explain differences in discarded shirts which might reflect causes for discard. Thus, for example, factor 3 indicates that discarded shirts differ in whether there are holes present versus whether there is local surface damage such as stains or abrasion. One way to interpret these factors is as follows. A low value for the total score of Factor 1 variables (which may have a minimum value of 5 and a maximum value of 26) suggests 109 that the shirt is in "bad shape" with respect to fabric characteristics and one might infer this as the reason for discard. A high score would indicate the shirt was in "good shape" with respect to fabric characteristics and one might infer that there must be some other reason for discard. In the latter case, values of factor 2, 3, or 4 might give clues as to why the shirt was discarded. A high score for all factors would indicate a shirt which is in good shape with no visible evidence of discard. Such a shirt may have been discarded for some other reason such as a lack of fit. This would correspond to the rating of garments by the textile recycling company as "Number Ones". The ratings of a particular shirt based on the above factors may be useful in deducing the past history of use and exposures that are unknown about a recycled garment. ’Also, it might be used to infer product quality based on ability to withstand use, wear, and maintenance. This is because certain evidence of use and wear as well as the severity of use and wear may be observed. For instance, color fading and soft hand may result from refurbishing. Pilling can result from the rubbing of fabric against another surface during wear or laundering. Harsh use and handling may be evident from tears, especially those that occur in regions other than stress points such as seams. Localized stains or holes that indicate a catastrophic event in the garment history can be seen in garments that have otherwise not deteriorated. On the other 110 hand, change in overall fabric characteristics such as smoothness and hand are indicative of accumulation of wear over long periods which affect the garment's overall condition. This information can be applied to provide the determination of an appropriate use for a textile item (e.g., reuse as original purpose versus cutting into rags). The only variable not included in any of the factors is distortion of shape. Since this is a somewhat difficult variable to accurately measure without knowing the original shape or dimensions of the garment, one may conclude that it is not necessary to measure in future studies^ because it does not significantly contribute to the amount of information obtained. Finally, it must be recognized that this factor analysis may be limited by the exclusion of an unexplored additional factor containing variables that were not measured but which could explain further reasons for discard. This factor could consist of the following determinations by the owner about the garment: 1. it lacks fashionability 2. it is inconpatible with his/her wardrobe 3. few opportunities for wear 4. change in tastes 5. it requires excessive maintenance 6. lack of storage space, and 7. it does not fit. Ill These variables were not possible to measure since it would require contact with the unknown donor of the garment. t

CHAPTER VI PRESENTATION OF FINDINGS AND DISCUSSION OF PART II

The purpose of the second part of this study was to assess the performance of a sample of 32 discarded shirts in selected laboratory tests. The chapter includes the analysis of the data on the laboratory testing of the sample of 32 discarded shirts. These results were compared to the assessments of judges performed under Part I to disclose any relationships which exist. Part II: Destructive Physical Testing Description There were ten fabric properties measured on a random sample of 32 cellulosic or cellulosic blend woven dress shirts selected from the 65 shirts evaluated in Part I. The abbreviations as they appear on the statistical analysis reports appear in parentheses. They were: fabric thickness (thick), cover factor (cover), whiteness (white), abrasion resistance (abrd), tearing force - fill direction (tearf), tearing force - warp direction (tearw), snagging resistance (snagd) , flexural rigidity (flex), pilling resistance (pilld) , and breaking strength (break) . Some of these variables had

112 113 exact corollaries which were visually assessed in Part I. Specifically, these were abrasion resistance (abr), snagging resistance (snag), pilling resistance (pill). Other physical variables which are related to the variables assessed by the judges due to the similarity in properties being evaluated are whiteness and color change, and flexural rigidity and fabric hand. Comparison of Physical Testing to ASTM Specifications The ASTM Standard Performance Specification for Men1 s and Boys' Woven Dress Shirt Fabrics (ASTM Test Method D 3477-84) outlines requirements for woven fabrics intended for this end use. Of the properties listed, only the following were tested in this research: Characteristic Requirements Breaking Strength 25 pounds minimum Tear Strength 1.5 pounds minimum Fabric Appearance 3.5 Durable Press minimum

The first two properties listed were measured in Part II of this research, while the last one was part of the judges assessments performed in Part I. Conformance to the minimum requirements for breaking strength was achieved on all 32 (100%) shirts. The minimal acceptable tearing strength was achieved on all 32 (100%) of the shirts. Forty-two (65%) of the 65 shirts evaluated by the judges had an average durable press rating of 3.5 or greater for fabric smoothness. This provides additional evidence that the sample of discarded 114 shirts possessed a potential residual wear life in terms of function and durability. Pearson's Correlations As part of this exploratory study, a correlation matrix was produced between all of the combinations of 21 variables observed on the 32 shirts tested in Part II. Ihe results are summarized in Table 14. Pearson's Correlations were used as an. initial method of assessment for all of the variables. This provides additional information to the Cluster and Factor Analyses previously described, because this technique also compared the assessment of judges ratings from Part I with the ratings of performance tests from Part II. Those combinations of pairs of variables found to be significant using a p value of 0.05 or less were the following: 115

Table 14. Summary of Significant Pearson's Correlations

Variable 1 Variable 2 Pearson's rho P value Thickness* Cover* .725 .0001 Thickness* Flexural .734 .0001 Rigidity* Thickness* Breaking Force* .757 .0001 Thickness* Seam .411 .0193 Smoothness Thickness* Hand .393 .0264 Cover* Flexural .450 .0098 Rigidity* Cover* Breaking Force* .491 .0044 Whiteness* Color Change .675 .0001 Abrasion* Snagging* .421 .0165 Abrasion* Fabric .414 .0185 Smoothness Abrasion* Hand .370 .0370 Abrasion* Wrinkling .493 .0041 Tear-fill* Tear-warp* .893 .0001 Tear-fill* Fabric .421 .0164 Smoothness Tear-fill* Pilling .365 .0398 Tear-fill* Wrinkling .493 .0041 Tear-warp* Fabric .382 .0307 Smoothness Tear-warp* Pilling .436 .0125 Tear-warp* Wrinkling .461 .0079 Snagging* Flexural . 410 .0199 Rigidity* Snagging* Fabric .518 .0024 Smoothness Snagging* Wrinkling .554 .0010 Flexural Breaking .474 .0061 Rigidity* Force* Flexural Seam .483 .0051 Rigidity* Smoothness Flexural Hand .604 .0003 Rigidity* Abrasion Hole .893 .0001 Color Change Snagging .476 .0059 Color Change Staining .547 .0012 Fabric Seam .425 .0153 Smoothness Smoothness Fabric Hand .477 .0058 Smoothness Fabric Wrinkling .882 .0001 Smoothness Seam Hand .612 .0002 Smoothness Seam Wrinkling .508 .0030 Smoothness Snagging Staining .518 .0024 Hand Wrinkling .518 .0024

* Indicates that these were measured in laboratory tests fromPart II 116

Certain correlations from Table 14 are particularly relevant to this investigation of residual wear life in recycled clothing. Presumably the important pairs are those that contain both Part I and Part II variables. Furthermore, for those that are related, perhaps one rating may be used as a rough estimate of the other measure taking into account the direction and strength of the association. Fabric thickness was positively correlated with flexural rigidity and breaking force while negatively associated with cover factor, and fabric hand. Therefore, thicker fabrics were also stiffer and had a higher breaking strength while thinner fabrics were softer and weaker. Fabric cover was negatively correlated with flexural rigidity. Covering power refers to the ability of fibers or yams to occupy an area when woven or knitted in the fabric. Covering power is determined by the type, length, size, shape, cross section and surface contour of fibers. Thick or bulked fibers and crimped or curled yams will provide better covering qualities than straight or smooth fibers and yams of small diameters. The thinner or finer and the more transparent the fiber or yam, the less covering power it has (Gioello, 1982) . These results show that a more sheer fabric was also softer and one with better covering power had a harsher hand. Recall that fabric cover was measured using the Hunter LabScan by recording the number of layers of fabric required to maximize 117 the reflectance of light after exposure to a light source. Thus, the relation between cover and flexural rigidity can be explained by the fact that the more transparent fabric (with a higher value for cover factor) contains fewer y a m s and/or more space between yams. This can produce better fabric drape as well. Whiteness had a high positive correlation to color change, where whiteness was measured by the brightness and purity of color using the Hunter LabScan in accordance with AATCC Test Method 110-1979 titled "Reflectance, Blue, and Whiteness of Bleached Fabric". This color measurement is empirically defined as: W = 4B - 3G, where W = whiteness, B = blue reflectance, and G = green reflectance. A higher value for whiteness means that the shirt being evaluated retained most of its nominal whiteness, and therefore had the least color change. Since color change was measured on a scale of one through five, with five being no change in color and one being the most amount of color change this relationship makes sense. Abrasion resistance was measured using the Taber abraser with the endpoint value defined as the first tear in both warp and fill yams that cross each other. A low endpoint value means that the fabric had low abrasion resistance. Other variables that were positively correlated with abrasion resistance were the laboratory test for snagging resistance and the visual ratings for fabric smoothness, fabric hand, and 118 wear wrinkling. Therefore, the fabric with high abrasion resistance also had high snagging resistance and was smoother, softer and more resilient. Conversely, the fabric with low abrasion resistance also had low snagging resistance and was more wrinkled, harsher and less resilient. Since the property of abrasion resistance is related to factors such as fiber content, y a m and fabric construction, and finishes, a direct association to any of these factors may be used to readily explain the associations. For instance, higher abrasion resistance can be found in a fabric with a tighter weave and/or one with less y a m floats than in a loosely constructed fabric or one with many y a m floats. In turn, a fabric with shorter y a m floats would also have high snagging resistance because less surface area of the y a m s is exposed (Gioello, 1982) . However, a fabric with a tighter weave may be stiffer and more susceptible to wrinkling since the yams have less potential for shifting away from the source of stress. The Elmendorf tear tests in the warp and fill direction were positively correlated. Examination of the raw values indicates that the warp tearing strength was higher for the majority of shirts. This may be expected since the essential characteristics of suitable warp and filling yams differ. Warp ya m s undergo greater stress and abrasion during weaving than do filling yams. Therefore, warp yams must be strong enough to withstand these pressures. Warp yams must also be clean, free from knots, and uniform in size. Furthermore, 12 of the 32 shirts (37.5%) were 2 x 1 oxford weaves and 1 shirt (3.1%) was a dobby weave with a vertical design that used more y a m s in the warp direction. Therefore, the greater number of warp yams coirpared to filling yams provided more resistance to tearing force. In both the warp and fill directions, tearing force was positively correlated with fabric smoothness and wear wrinkling and negatively associated with resistance to pilling. Therefore, those fabrics that had a higher tearing force also were less wrinkled, more resilient, and more susceptible to pilling. Since tearing force is influenced by many fabric features such as fiber content, and fabric and y a m construction, it might be expected to find that it has a relationship with numerous properties. The direction of the associations indicate that a fabric with a higher tearing force will also appear to be less wrinkled and show less wear wrinkling. The negative relationship between tearing force and pilling resistance might be explained by fiber content, since a stronger fabric may show less resistance to pilling because these surface defects are anchored tightly and cannot be rubbed off readily (Joseph, 1987). The laboratory test for snagging resistance was positively correlated to fabric smoothness and wear wrinkling and negatively correlated to flexural rigidity. This suggests that some of the variables related to fabric characteristics, as defined in Factor 1 of the Factor Analysis performed in 120 Part 1 are related to this performance test. It may be explained by the fact that a fabric that is perceived as having fewer wrinkles will have more resiliency due to a looser construction or texture which prevents or camouflages these features. The negative association with flexural rigidity means that those shirts with more snagging resistance were found to have a harsher hand. Therefore, it may also be stated that the softer fabrics are more susceptible to snagging. Softer hand in fabrics may be a reflection of a greater amount of wear and refurbishing which the fabrics have undergone, since these processes can be abrasive and result in fabric softness. In turn, those garments that undergo more use and treatment are also exposed to more potential opportunities for snagging to occur. In addition, yam surfaces become abraded and opened making them more susceptible to snagging. Flexural rigidity was used as a laboratory corollary for the tactile assessment by the judges of fabric hand. As expected, these two variables were found to be negatively associated (i.e.: high flexural rigidity was linked to a harsher hand) . The rating scales for fabric hand rank the roughest fabric as class one and the softest as rank five. Thus, there is an inverse relationship between tactile rank and flexural rigidity of fabric. For instance, a fabric made from coarse staple yams would have a harsher hand (high flexural rigidity, low rank for hand) than a fabric made from 121 smooth filament yams (low flexural rigidity, high rank for fabric hand). The assessments of judges for abrasion resistance and presence of a hole were positively correlated. The presence or absence of one or more holes was recorded on a dichotomous scale with ' 0' referring to the presence of holes and ' 11 for the lack of holes. Since abrasion refers to fabric attrition resulting from the fabric nibbing against another surface, the worst consequence of abrasion can be thought of as a hole in the fabric. Also, since the property of abrasion resistance is related to fabric strength, a weaker fabric would be more susceptible to developing a hole (or tear). Color change was found to be correlated to the assessments of judges for snagging resistance and staining. This may be related to another unmeasured variable such as amount of use and refurbishing, since these exposures provide opportunity for these changes to occur. The amount of garment use and refurbishing was only indirectly measured when evaluating the characteristics in Part I. Several variables are positively related to the judges assessment of fabric smoothness. They are seam smoothness, hand, and wear wrinkling. These findings include many of the variables in Factor 1 of the factor analysis which encompass general fabric characteristics. Fabric characteristics included in Factor 1 are those which are determined by fiber content, y a m structure, fabric construction and finishes. 122 Wear wrinkling is a subjective measure of fabric smoothness and, of course, is positively related. Fabric smoothness is logically related to seam smoothness if the seams are constructed correctly. The judges assessment of seam smoothness was positively related to the subjective evaluation of fabric hand and wear wrinkling. The shirts with a harsher hand were more susceptible to wear wrinkling. The most apparent finding in the correlations was the association of wear wrinkling with many other variables including those measured by the judges and through laboratory tests. They are: the laboratory tests for abrasion resistance, tearing force (warp and fill), snagging resistance, and the assessments of judges for fabric smoothness, seam smoothness, and fabric hand. Wear wrinkling can be connected to these variables by consideration of certain fiber, yam, and fabric characteristics. The interrelatedness of abrasion resistance, tearing force, and tensile strength with fabric structure has been discussed by Spivak (cited in Lyle, 1977) . This is demonstrated in Table 15 which follows: 123

Table 15. Interrelationship of Fabric Structure to Tensile and Tear Strength and Abrasion Resistance3

Key Fabric Tensile Tear Abrasion Structure Strength Strength Resistance 1 = Low Loose 1 3 1

2 = Medium Moderate 2 2 3

3 = High Tight 3 1 2

a Lyle, D. (1977) : Performance of textiles. John Wiley and Sons, pl44-5.

This table demonstrates that by holding factors such as fiber type and fabric finish constant, a tighter fabric construction generally contributes to high tensile strength but also lower tear strength and vice versa. The best abrasion resistance would be shown by a fabric with a moderate structure. Tighter fabric structures have more y a m interlacings and y a m crimp, thus contributing to greater tear resistance and strength. However, the mechanism of fabric tearing is very different from that of tensile strength, and relates to the ability of individual y am s to slide and pack together into a bundle, thereby increasing the tearing force required to fracture the yams. Thus, in the case of tearing, a loose fabric structure contributes to more y a m sliding and a higher tear strength. For abrasion resistance, very loose yams or 124 fabric structures are prone to snagging, which can readily rupture y a m s . Conversely, very tight structures inhibit fiber movement during abrasion, causing fibers to be stressed and fatigued beyond their yield point, resulting in fiber breakage. Thus, fabric structure is linked to abrasion resistance, tearing force, and tensile strength. Similarly, the correlation between wear wrinkling and abrasion resistance, snagging resistance, and tearing force implies that the wear wrinkling characteristic is one which reflects fabric structure and by inference reflects abrasion resistance, snagging resistance, and tearing force. The correlation of wear wrinkling with fabric smoothness, seam smoothness, and fabric hand may be explained by the resiliency of fibers or the looseness or tightness of the fabric structure or the finishes placed on the fabric. The attribute of wear wrinkling, therefore, represents some composite of factors which are not measured directly but exert influence on the variables which are measured. Gioello (1982, p 202) provides a list of factors that contribute to performance expectations of fabrics that may include those "hidden" variables: "1. fiber properties such as: origin and quality, length and diameter, density, crimp, surface characteristics, luster, toughness, elongation, elasticity, resiliency, moisture regain, conductivity, dimensional stability, strength, 125 resistance to heat, fire, sun; climate conditions, micro-organisms, insects, acids, alkalies, and other solvents 2. y a m properties such as: manufacturing influences, compactness of fiber within yam; shape, dimension and diameter; type and structure, ply and twist, crimp, texture and hand, covering power 3. fabric structure such as: manner and type, texture and surface interest, appearance, durability 4. finishing processes that aid, alter or change the fiber, y a m , or finished fabric 5. type and method of color and/or design application". Since each of these factors contribute to the performance of a fabric, evaluation of performance reflects the combination of the contributions of each factor. Equally important to note are some corollary visual evaluations and laboratory tests that are not statistically significant at p =.05. These are: abrasion resistance, snagging resistance, and pilling resistance. Although these factors do not show a statistically significant correlation between the two methods of testing these properties, in many cases there are other unexpected variables with which they were correlated. For instance, although the assessments of judges and laboratory test for abrasion resistance were not correlated, there were correlations seen between the laboratory test for abrasion resistance and fabric smoothness, fabric hand and wrinkling resistance. Thus, it is likely that some variables measured represent hidden factors which are not measured. Further exploration of these variables is required 126 in order to determine the root cause of the link between these seemingly unrelated variables. Results of the correlations provide an overview of the relationship between each pair of the variables. In order to study relationships between each of the destructive physical tests and the assessments of judges, stepwise regressions were performed with SAS (Tables 16-22) . A discussion of the technique and the findings follows. lac,1,S 15 • Multiple Regression - Fabric Thickness

Stepvlse Procedure for Dependent Variable THICK Step l Variable SEAM Entered R-square ■ 0.16920894 C(p) ■ 0.08277237 07 Sum of Squares Kean Square F Prob>F Regression 1 30.61150175 30.61150175 6.11 0.0193 Error 30 150.29798575 5.00993286 Total 31 180.90948750 Paraaeter Standard Type II Variable Estlaate Error Sun of Squares P Prob>P INTERCEP 16.96245526 2.35931506 258.96284470 51.69 0.0001 SEAM -1.73009750 0.69991325 30.61150175 6.11 0.0193 Bounds on condition nuabar: 1. 1

Step 2 Variable PILL Entered R-squar® * 0.21894095 C(p) a 0.40170865 07 Sum of Squares Kean Square F Prob>P Regression 2 39.60849419 19.80424710 4.06 0.0279 Error 29 141.30099331 4.87244805 Total 31 180.90948750 Paraaeter Standard Type II Variable Estlaate Error StsB of Squares P Prob?P INTERCEP 19.47240320 2.97075398 209.34035129 42.96 0.0001 PILL -0.64185297 0.47234626 8.99699244 1.85 0.1047 SEAM -1.74093246 0.69028883 30.99198355 6.36 0.0174 Bounds on condition amber: 1.000133. 4.000534

Step 3 Variable PILL Resoved R-square * 0.16920894 C(p) a 0.08277237 07 Sum of Squares Kean Square P Prob>F Regression 1 30.61150175 30.61150175 6.11 0.0193 Error 30 150.29798575 5.00993286 Total 31 180.90948750 Paraaeter Standard Type II Variable Estimate Error Sun of Squares P Prob>P B R H e 9 16.96245526 2.35931506 258.96284470 51.69 0.0001 SEAM -1.73009750 0.69991325 30.61150175 6.11 0.0193 Bounds on condition amber: 1, 1

All variables left In the model are significant at the 0.0500 level. Ho other variable met the O.SOOO significance level for entry Into the model.

Swasary of Stepeis® Procedure for Dependent Variable T U C K Variable Etaber Partial Model Step S&tsred geasved m B«*2 g**2 C(p) P ?rob>F 1 SEAM i 0.1692 0.1692 0.0828 6.1102 0.0193 2 PILL 2 0.0497 0.2189 0.4017 1.8465 0.1847 3 PILL 1 0.0497 0.1692 0.0828 1.8465 0.1847 Table IS. (Continued 128

Stepvise Procedure for Dependent Variable THICK Step 1 Variable S8AM Entered R-square ■ 0.16920894 C(p) ■ 2 .00000000 07 Sua of squares Mean Square F Prob>F Regression 1 30.61150X75 30.61150175 6.11 0.0193 Error 30 150.29798575 5.00993286 Total 31 110.90948750 Paraaeter Standard Type II Variable Estlaate Error Sum of Squares Prob>F INTBBCSP 16.96245526 2.35931506 258.96284470 51.69 0.0001 38AM -1.73009750 0.69991325 30.61150175 6.11 0.0193 Bounds on condition numbers 1.

All variables left In the model are significant at the 0.1500 level. No other variable net the 0.1500 significance level for entry into the nodel. iry of Stepvise Procedure for Dependent Variable THICK Variable Partial Model Step Entered Removed In R**2 R**2 C(p) F Prob>F 1 SEAM 1 0.1692 0.1692 2.0000 6.1102 0.0193 Table 17.. Multiple Regression - Fabric Whiteness

Stepvlse Procedure for Dependent Variable UHITI Step 1 Variable COL Entered R-square * 0.45566763 C(p) * 2.56457791 OF Sum of Squares Man Square F Prob>? Regression 1 2511.16142915 2511.16142915 25.11 0.0001 Error 30 2999.78832085 99.99294403 Total 31 5510.94975000 Parameter Standard Type 11 Variable Estimate Error Sum of Squares F ?rob>F IHTERCEP 24.99912272 12.42841273 404.56430098 4.05 0.0533 COL 16.42074308 3.27672630 2511.16142915 25.11 0.0001 Bounds on condition mnbens 1. 1

Stop 2 Variable STAIM Entered R-aquar® * 0.50305620 C(p) * 1.90368236 DP Stai of Squares Mean Square F Prob>F Regression 2 2772.31744493 1386.15872247 14.68 0.0001 Error 29 2738.63230507 94.43559673 Total 31 5510.94975000 Parameter Standard Type 11 Variable Estimate Error Sum of Squares F Prob>F IMTEKCEP 23.89622286 12.09630148 368.54374457 3.90 0.0578 COL 19.87881742 3.80320788 2579.98208288 27.32 0.0001 STAIH -4.00252955 2.40686867 261.15601578 2.77 0.1071 Bounds on condition number: 1.42644, S.705759

Step 3 Variable STAIM Removed R-squar© a 0.45566763 C(p) » 2.56457791 tsw Sts of squares M a n Square P Frob>F Regression 1 2511.16142915 2511.16142915 25.11 0.0001 Error 30 2999.78832085 99.99294403 Total 31 5510.94975000 Parameter Standard Type 11 Variable Estimate Error Sum of Squares r Prob>F nmSGSB 24.99912272 12.42841273 404.56430098 4.05 0.0533 COL 16.42074308 3.27672630 2511.16142915 25.11 0.0001 Bounds on condition number: 1. 1

All variables left la tfee model are significant at tb® O.OSOO level. So otber variable net tb® 0.5000 significance level for entry into tb® model.

Sumary Stepwise Procedure for Dependent Variable ifflxss Variable wr Partial M d e l Step watered fi®asv©d in S*«2 B**2 C(p) Prob»F 1 COL 1 0.4557 0.4557 2.5646 25.1134 0^0001 2 SCAB! 2 0.0474 0.5031 1.9037 2.7654 0.1071 3 SCAB! 1 0.0474 0.4557 2.5646 2.7654 0.1071 I 130

Table 17. (Continued)

Stapviaa Procedure for Dependant Variable WHITE

Step 1 Variable COL Entered R>square « 0.45566783 C(p) a 2. 00000000 07 Sub at Squares Mean Square F Prob>F Regression 1 2511.16142915 2511.16142915 25.11 0.0001 Error 30 2999.70032085 99.99294403 Total 31 5510.94975000 Paraaeter Standard „ fyp® ii Variable Estlaate Error Swa of Squares Prob>F 24.99912372 12.42041273 404.56430090 4.05 0.0533 COL 16.42074300 3.27672630 2511.16142915 25.11 0.0001 Bounds on condition ntabert 1.

All variables left in tbe aodel are significant at the 0.1500 level. Mo other variable net the 0.1500 significance level for entry into the aodel. SuBaary of Stepwise Procedure for Dependent Variable WHITE Variable Muaber Partial Model Step Entered Reaoved In R**2 R**2 C(p) F Prob»F COL 0.4557 0.4557 2.0000 25.1134 0.0001 131

— «-3.• Multiple Regression - Abrasion Resistance

Stepwise Procedure for Dependant Variable ABSD 3tap I Variable W I M K Entered R-square » 0.24323174 C(p) a 3.34764390 DP Su b of Squares if®an Square F Prob>F Regression 1 10S65S.66619407 105655.66619407 9.64 0.0041 Error 30 328727.05255593 10957.56841853 total 31 434382.71875000 Paraaeter Standard Type II Variable Estimate Error Sun of Squares P Prob>P xwrmcsB 35.53800196 74.64609444 2483.62501389 0.23 0.6375 mam 80.43187752 25.90233439 105655.66619407 9.64 0.0041 Bounds on condition mnbert 1, 1

Step 2 Variable STAIH Entered R-square * 0.28389131 C(p) * 4.13653706 DP Sra of Squares Steam Square P ?rot»P Regression 2 123317^47853173 61658.73926587 5.75 0.0079 Error 29 311065.24021827 10726.38759373 Total 31 434382.71875000 Paraaeter Standard Type II Variable Estimate Error Sun of Squares P Prob»F UfTERCEP 136.38672175 107.84811980 171S4.28064891 1.60 0.2161 STAIN -28.05956766 21.86704432 17661.81233766 1.65 0.2096 mam 74.14061607 26.09240289 86603.95291652 8.07 0.0081 Bounds on condition nunberi 1.0366. 4.146398

Step 3 Variable STAIN Reaoved R-square * 0.24323174 C(p) a 3.84764390 Of Sia of Squares M a n Square P Prob>F Regression 1 105655.66619407 105655.66619407 9.64 0.0041 Error 30 328727.05255593 10957.56841853 Total 31 434382.71875000 Paraaeter Standard Type II Variable Estimate Error Su b of Squares ? Prnb>F XNTSSCS? 35.53800196 74.64609444 2483.62501389 0.23 0.6375 WSIlSt 80.43187752 25.90233439 105655.66619407 9.64 0.0041 Bounds on condition mmtori 1, 1

All variables left in the model are significant at the 0.0300 level. Ho other variable rat the 0.3000 significance level for entry into the model.

Sssraary of stepwise Procedure for Dependent Variable AB!0 Variable ^aber Partial ftedel step Entered Renewed In R**2 R*»*2 C(p) P ?rob>F l m z m l 0.2432 0.2432 3.8476 9.6423 0.0041 2 STAIH 2 0.0407 0.2839 4.1365 1.6466 0.2096 3 s t &x h i 0.0407 0.2432 3.8476 1.6466 0.2096 132

Table 18. (Continued)

Stepwise Procedure for Dependent Variable *«en Step 1 Variable tfRXSK {Entered R-square ■ 0.24323174 C(p) > 2 . 00000000 07 Su b of Squares Mean Square E Prob>7 Regression 1 105655.66619407 105655.66619407 9.64 0.0041 Error 30 328727.0S25SS93 10957.56841853 Total 31 434382.71875000 Paraaeter Standard Type II Variable Estlaate Error Stn of Squares Prob>P 35.53800196 74.64609444 2483.62501389 0.23 0.637S 30.43187752 25.90233439 105655.66619407 9.64 0.0041 Bounds on condition nv »r» 1.

All variables left In the eodel are significant at the 0.1500 level. No other variable net the 0.1500 significance level for entry Into the sodel. ry of Stepvlse Procedure for Dependent Variable ABSD Variable Partial ftodel Step Entered In R*»2 R**2 C(p) 7 Prob»P 1 1 0.2432 0.2432 2.0000 9.6423 0.0041 133

Table 19. Multiple Regression - Tearing Strength, Fill Direction

Stepwise Procedure for Dependent Variable TEARP Stop X Variable WHINK Rotated R-square *> 0.2S071587 CF i o t e r c s p 1129.S3180817 280.96715657 2508978.5127985 16.16 0.0004 mim. 308.99838973 97.49612886 1558359.5557334 10.04 0.0035 Bounds on condition nrnfeor* 1,

Stop 2 Variable Pitt Entered R-squar® * 0.37940631 C(p) 3.00000000 DP Su b of Squares Mean Square P 9tol»F Regression 2 2359265.4034295 1179132.7017148 8.86 0.0010 Error 29 3857374.471S705 133012.91281277 Total 31 6215639.9750000 Paraaeter Standard Type II Variable Estlaate Error Sum of Squares P Prob»F IOTERCSP 1975.31567338 400.15686515 2921339.4705185 21.96 0.0001 WRZHR 305.99611490 90.25391584 1528950.8382679 11.49 0.0020 PILL -191.38798804 78.04447134 799905.84769612 6.01 0.0205 Bounds on condition mmfomzt 1.000172, 4.000688

All variables left in the aodel are significant at the 0.1500 level. Do other variable net the 0.1500 significance level for entry into the aodel. Sumary of 3tepsrt.se Prossdure for Dependent Variable IBIS? Variable Aaber Partial Model Step Entered Renoved In R**2 R**2 C(p) P Prob>P 1 NRZ9K 1 0.2507 0.2507 7.0137 10.0392 0.0035 2 PILL 2 0.1287 0.3794 3.0000 6.0137 0.0205 134 lable 2Q- Multiple Regression - Tearing Strength Warp Direction

Stepvlse Procedure for Dependent Variable TEARt# Stop 1 Variable WRZHR Entered R-square ■ 0.21270977 C(p) * 9.92607445 OS' Sun of Squares Kean Square F Prob>F Reqression 1 1519930.7613101 1519930.7613101 3.11 0.0079 Error 30 S62S630.7386699 167521.02462300 Total 31 7145561.5000000 Paraaeter Standard Type II Variable Estlaate Error 3m of Squares Prob>F INTERCSP 1521.41904794 308.79835494 4551952.3765301 24.27 0.0001 mim 305.06593313 107.15360676 1519930.7613101 8.11 0.0079 Bounds on condition nwberi 1.

Step 2 Variable PILL Entered R-square ■ 0.39800212 C(p) » 3.00000000 Of 3ia of Squares Mean Square P Prob>? Reqression 2 2843946.5918856 1421974.2959428 9.59 0.0006 Error 29 4301612.9081144 148331.47959015 Total 31 7145561.5000000 Paraaeter Standard Type II Variable Satinet® Error Sum of Squares F Prob>F XHTERC2P 2480.90907349 422.57133597 5112754.6780238 34.47 0.0001 mim 301.33200435 95.30941766 1482696.3465299 10.00 0.0037 PILL -246.23067634 82.41607078 1324017.@305755 8.93 0.0057 Bounds on condition numbers 1.000172, 4.000688

All variables left in tbe aodel are significant at the 0.1500 level. Ho otber variable m t tbe 0.1500 significance level for entry into tbe nodal. Swtary of Stepwise Procedure for Dependent Variable T E A W Variable r Partial Step Entered Removed In R**2 R**2 C(p) Prob>P 1 HUB 1 0.2127 0.2127 9.9261 8.1054 0.0079 2 PILL 2 0.1853 0.3980 3.0000 8.9261 0.0057 Table 21. Multiple Regression - Snagging Resistance

Stepviae Procedure for Dependent Variable «magn 3tep X Variable VRIMK Sneered R-square • 0.30706869 C(p) 2. 00000000 DP Sus of Squares Bean Square F ?rob>F Regression 1 6.14137374 6.14137374 13.29 0.0010 Srror 30 13.85862626 0.46195421 Total . 31 20.00000000 Paraaeter Standard Type II variable Bstlaate Srror Su b of Squares Prob>r ItfTEKCSP 1.03797326 0.48467370 2.11871403 4.59 0.0405 mim 0.6X321755 0.16818268 6.14137374 13.29 0.0010 Bounds on condition mashers 1,

All variables left in the nedel are significant at the 0.1500 level. Mo otber variable net the 0.1500 significance level for entry into tbe nodal. Susnary of Stepvlse Procedure for Dependent Variable Sana) Variable Partial Bedel Step Entered Resoved In R**2 R**2 C(p) P Prob>f 1 m i m 1 0.3071 0.3071 2.0000 13.2943 0.0010 136

Table 22. Multiple Regression - Flexural Rigidity

Stepvlse Procedure for Dependent Variable FL2X

Step 1 Variable HAND Entered R-square ■ 0.36462986 C(p) 3 5.31650619 DF Sua of Squares Mean Square F Prob>F Regression 1 1871.62723173 1871.62723175 17.22 0.0003 Error 30 3261.3238S946 108.71079532 Total 31 3132.93109122 Paraaeter Standard „ ?yp® XI Variable Estlaate Error Sun of squares Prob»F 87.S06S8261 10.98488550 6898.63315417 63.46 0.0001 -13.84424045 3.33653775 1871.62723175 17.22 0.0003 Bounds on condition estori 1,

Step 2 Variable COL Entered R-square * 0.44694879 C(p) > 3.00000000 OF Susa of squares M a n Square F Frot»F Reqression 2 2294.16627462 1147.08313731 11.72 0.0002 Error 29 2838.78481660 97.88913161 Total 31 5132.95109122 Parameter Standard Type II Variable Estimate Error Sun of Squares F Prob»F unsscsp 61.91948557 16.13472262 1441.6709S09S 14.73 0.0006 HAND -13.75305987 3.16642110 1846.69767120 18.87 0.0002 COL 6.73644298 3.24238405 422.S3904287 4.32 0.0467 Bounds on condition nunber: 1.000192, 4.000769

All variables left in tbe aodel are significant at the 0.1500 level. Ho other variable net the 0.1500 significance level for entry into the model. Siwsary of Stepvia® Procedure for Dependent Variable S W Variable r Partial Model Step Entered Removed In R**2 R**2 C(p) Prob>F 1 HAM) 1 0.3646 0.3646 5.3165 17.2166 0.0003 2 COL 2 0.0823 0.4469 3.0000 4.3165 0.0467 137 Multiple Regressions The regression analysis was performed to indicate the presence of and to summarize the nature of correlations between Part I and Part II variables. Since the analysis is not formally inferential, the numbers should not be strictly interpreted, but the procedures provide evidence concerning the relation between performance tests and visual assessment ratings. The stepwise method is similar to forward selection since it starts with no variables in the model and subsequently adds variables with a significant F statistic at the p =.50 level and allows them to stay in the model if the F statistic is significant at p =.15. The stepwise method is a modification of the forward selection technique and differs in that variables already in the model do not necessarily stay there. As in the forward selection method, variables are added one by one to the model. The stepwise process ends when none of the variables outside the model has an F statistic significant at the .15 level. Once a significant model was derived, the stepwise procedure was repeated to arrive at a final equation that only included those significant variables. Each variable measured in Part II was used as a separate dependent variable with all of the eleven assessments of judges entered into the equation as potential independent variables. Except for the three variables measured in Part II (breaking strength, fabric cover and pilling resistance) , all of the remaining variables were found to have at least one 138 predictor variable but at most two at the minimum p value of .05. Diagnostics tests were performed to make sure that the models meet the assumptions of multiple linear regression. The diagnostic tests performed were the following: residual plot of standardized residuals with the independent variables to check for a linear, relationship, residual plot of the residuals with the predicted variables for constancy of variance and the presence of outliers, variance inflation factor for multicollinearity, and Cook's Distance for influential points. All of the resultant models discussed are acceptable for these diagnostics. The fitted regression equations that are significant at p = .02 are (Tables 16-22):

Fabric Thickness (mils) = 16.96 - 1.73 Seam Smoothness (1) Fabric Whiteness (4B-3G) = 24.99 + 16.42 Color Change’ (2) Abrasion Resistance (cycles) = 35.53 + 80.43 Wear Wrinkling (3) Tear Force-Filling (gms) = 1875.32 - 191.39 Pilling Resistance (4) + 305.99 Wear Wrinkling Tear Force-Warp (gms) = 2480.91 - 246.23 Pilling Resistance (5) + 301.33 Wear Wrinkling Snagging Resistance (Rank 1-5) = 1.038 + 0.6132 Wear Wrinkling (6)

Flexural Rigidity (mg/cm2) = 61.92 + 6.74 Color Change - 13.75 Fabric Hand (7) 139 Description Based on the statistical results which include the regression diagnostics, the significant r-squared and interpretable coefficients, it can be concluded that the modeling approach is effective in indicating relationships between seven laboratory tests of physical characteristics and assessment of judges variables. In many cases, the regression equations actually showed a one-on-one relationship. The exception to this were the two variables related to tearing force, which each had two Part I variables included in the model. The three variables that were not able to fit a linear regression model were the laboratory tests for pilling resistance, breaking strength, and cover factor. This means that based on the assessment of judges variables measured in this study, the sample of shirts tested did not show any assessments of judges that could provide information on pilling resistance, breaking strength, and fabric cover. Equation 1 indicates that seam smoothness may be used to provide information on fabric thickness. These are both fabric characteristics. This indicates that smoother seams are associated with thinner fabrics. The r-square value indicates that 16.92% of the variance is explained by seam smoothness. Equation 2 indicates that the subjective measure of color change may be used to provide information on whiteness. Since all of the shirts in the sample were white, it was possible to 140 measure whiteness. However, in shirts other than white, this may not be an appropriate measure. Both of these variables relate to overall damage to the shirts, rather than localized damage or color change. The r-square value indicates that 45.57% of the variance in this model is explained by color change. Equation 3 indicates that wear wrinkling may be used to provide information on abrasion resistance. This may be explained by the fact that both of these variables are determined by multiple factors including fiber content, y a m size, y a m structure, fabric structure, and finishes. The r- square value indicates that 24.32% of the variance in this model is explained by wear wrinkling. Equations 4 and 5 indicate that pilling resistance and wear wrinkling may be used to provide information on the tearing strength (warp and fill directions) . The negative beta coefficient for pilling resistance means that a lower value for this variable will result in a higher value for tearing force. This may be due to the relationship of these variables to fabric characteristics such as y a m and fabric construction. A fabric with high pilling resistance, for example, will be one which is conposed of a tightly twisted yams made into a smooth-surfaced fabric. The nature of the structure is one which in some manner must also be linked to high tearing force required to tear in either warp or filling direction. The r-square value indicates that 37.94% of the 141 variance in this model using the equation for tearing force in the fill direction and 39.80% using the equation for tearing force in the warp direction is accounted for with pilling resistance and wear wrinkling. Equation 6 indicates that wear wrinkling may be used to provide information on snagging resistance. This equation shows that a more resilient fabric would also be more resistant to snagging, perhaps due to the fabric and y a m construction. For example, a fabric with a open or loose construction would snag more readily and have less of a tendency to retain wrinkles. The r-square value indicates that 30.71% of the variance in this model is explained by wear wrinkling. Equation 7 indicates that fabric hand and color change may be used to provide information on flexural rigidity. The relationship between fabric hand and flexural rigidity is readily explained by the fact that both of these tests were corollaries for fabric stiffness. The negative beta coefficient means that a higher value for hand (based on a scale of one through five with five being the softest) results in a lower value for flexural rigidity corresponding to a more limp fabric. The positive beta coefficient for color change (measured on a scale of 1 through 5 with one indicating the most color change and five indicating no color change) corresponds to a lower value for flexural rigidity (a limp fabric) in those materials that demonstrate a lower 142 degree of color change. Color change and flexural rigidity may be indicators of the amount of use and care the garment has experienced since abrasion in wear and in laundering results in a softer hand and more drapeable fabric and is also linked to overall loss of color. The overall benefit of these stepwise regression analyses is to be found in applying the results to determine the qualitative information about the dependent variables based on the knowledge of assessment of judges ratings that are easily obtained by visual examination of the garment rather than relying on a d h o c estimates of these variables. Although the regression could provide quantitative estimates of the relevant parameters needed to forecast different components of residual wear life, the purpose of the regression analyses conducted in this research is to gain qualitative information on the nature of correlations present. In many of the correlations disclosed by the stepwise regression analysis, the nature of the relationship seems to reflect root causes of the relationship which are unmeasured in this work. Factors such as fiber content, y a m structure and fabric structure are highly influential in the determination of the fabric properties evaluated. Canonical Correlation Analysis Canonical correlation analysis (CCA) is a technique used for exploring the relationships among multiple criterion (dependent) and predictor (independent) variables (Hair, J.F., 143 et.al, 1987) . The technique is primarily descriptive, although it may be used for predictive purposes. In this research, CCA. was performed to disclose any links between assessment of judges ratings and physical measurements. Therefore, for the purposes of this study, the two sets of variables should not be viewed as dependent or predictor variables. Results obtained from a canonical analysis suggest answers to questions concerning the number of ways in which the two sets of multiple variables are related, and the strengths and nature of those relationships. This technique is particularly useful for identifying overall relationships between multiple independent and dependent variables, particularly when there is little a priori knowledge about relationships among the sets of variables. The four most important types of output information derived through CCA are the canonical variates, the canonical correlations between the variates, the significance of the canonical correlations, and the redundancy measure of shared variance for the canonical functions (Hair, J.F., et al., 1987) . The canonical variates are interpreted on the basis of a set of correlation coefficients, usually referred to as canonical loadings. Similar to factor analysis, these coefficients reflect the importance of the original variables in deriving the canonical variates. Thus, the larger the coefficient, the more important it is in deriving the canonical variate. 144 CCA was performed on the set of eleven assessment of judges variables and the ten physical measurement variables using the CANCORR procedure in the SAS statistical package (Statistical Analysis System, 1989, p367. However, the application of CCA to the variables in this study should not Joe thought of as a prediction equation, but rather as a technique to summarize the nature of correlations present between the two groups of variables. As previously described in the Data Analysis section of the Methodology chapter, physical measurements were selected as the "dependent" variables because conceptually we would like to use the judges assessment variables (designated as the "independent" variables) to gain information about the physical measurements. Therefore, this is used as a descriptive, not quantitative tool. CANCORR first finds linear combinations of the two sets of variables and creates two canonical variables so that the correlation between these variables is maximized. The correlation between them is referred to as the canonical correlation. The procedure then finds another set of linear functions that produces a second set of canonical variables. The second set of variables has the second highest canonical correlation coefficient. The procedure continues until the number of pairs of canonical functions equals the number of variables in the smaller group, which is ten for this data. 145 According to Hair, et al. (1987), the most common practice for the interpretation of CCA is to analyze those functions whose canonical correlation coefficients are statistically significant beyond p =.05. The authors feel that the use of a single criterion such as the level of significance is too superficial. They recommend that three criteria be used in conjunction with each other to decide which canonical functions should be interpreted. The three criteria are the level of statistical significance of the function, the magnitude of the canonical correlation, and the redundancy measure for the percentage of variance accounted for from the two data sets. No accepted guidelines have been established regarding minimum sizes for canonical correlations. Rather, the decision is usually made based on the consideration of the findings to better understand the research problem being studied. The redundancy index is the equivalent of computing the squared multiple correlation coefficient between the total predictor set and each variable in the criterion set, and then averaging these squared coefficients to arrive at an average R-square. This helps to overcome the inherent bias and uncertainty in using canonical roots (squared canonical correlation) as a measure of shared variance. It provides a summary measure of the ability of a set of predictor variables (taken as a set) to explain variation in the criterion variables (taken one at a time) . Therefore, the redundancy 146 measure is entirely analogous to the multiple regression R- square statistic. If the canonical relationship is statistically significant and the magnitude of the canonical root (squared canonical correlation) and the redundancy index is acceptable, the analysis may proceed. This involves examining the canonical functions to determine the relative importance of each of the original variables in determining the canonical relationships. The traditional approach involves examining the significance and magnitude of the canonical weight assigned to each variable in computing the canonical functions. Variables with relatively larger weights contribute more to the function and vice versa. Similarly, variables whose weights have opposite signs exhibit an inverse relationship with each other and those with the same sign exhibit a direct relationship. Essentially the procedure is to apply CCA to a set of variables, select those independent and dependent variables that appear to be significantly related and run subsequent canonical correlation with the more significant variables remaining. The CCA was run with all 21 variables (10 dependent and 11' independent) and resulted in a statistically significant canonical relationship for the first canonical correlation of p = .0425 (Table 23) . When further examined, the canonical functions of the original variables in determining canonical 147

Sable 23- Canonical Correlation Analysis

Adjusted Approx Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation 1 0.917601 0.835701 0.028379 0.841992 2 0.897819 0.034829 0.806079 3 0.866020 0.816478 0.044903 0.749990 4 0.798536 0.727189 0.065078 0.637660 5 0.653122 0.484149 0.102991 0.426568 6 0.540474 0.336320 0.127140 0.292112 7 0.445260 0.212936 0.143997 0.198256 8 0.392323 , 0.151961 0.153918 9 0.165472 . 0.174683 0.027381 10 0.103429 . 0.177684 0.010697 Eigenvalues of INV(E)*H a canRsq/ F 1 0.00073543 1.4111 110 95.6663 0.0425 2 0.00465442 1.2294 90 91.6466 0.1632 3 0.02400157 1.0185 72 86.65525 0.4650 4 0.09600236 0.7857 56 80.7033 0.8299 5 0.26495138 0.5752 42 73.80832 0.9731 6 0.46204527 0.4684 30 66 0.9879 7 0.65270979 0.3935 20 57.3325 0.9881 8 0.81411280 0.3228 12 47.91503 0.9815 9 0.96221445 0.1232 6 38 0.9929 10 0.98930252 0.1081 2 20 0.8980

Multivariate Statistics and F Approximations 3*10 M*0 11*4.5 Statistic Value F Run OF Den OF Pr > F Wilks' 0.00073S43 1.4111 110 95.6663 0.0425 Pillai's Trace 4.1446S396 1.2870 110 200 0.0626 Hotclling-Lavley Trace IS.86991290 1.3273 110 92 0.0808 Roy's greatest Root 5.32879816 9.6887 11 20 0.0001 NOTEj F Statistic for Roy's Greatest Root is an upper bound. 148 relationships resulted in the selection of seven physical measurement variables and six assessment of judges variables to include in the model. The physical measurement variables were fabric thickness, fabric whiteness, abrasion resistance, tearing strength in both directions and breaking strength. The assessment of judges variables were abrasion resistance, color change, fabric smoothness, presence of holes, staining, and wear wrinkling. Next, the standardized canonical variables for the redundancy analysis were examined since the variables were measured by different scales. The canonical r-squared for the first function was 0.8420. Subsequent functions do not do an adequate job in explaining much of the variance . as the canonical r-squared drops to 0.8061. Since the canonical relationship is statistically significant and the magnitude of the squared canonical correlations as a redundancy measure of shared variance is acceptable, the next step in the analysis involves examining the canonical functions to determine the relative importance of each of the original variables in deriving the canonical relationships. These values are reported in Table 24 which follows. 149

Table 24. Standardized Canonical Coefficients of Variables

Function 1 Criterion Set Fabric Thickness 0.6608 Whiteness 0.7873 Abrasion Resistance 0.7688 Tear Resistance-Fill 0.5787 Tear Resistance-Warp -0.7210 Snag Resistance -0.6831 Breaking Strength 1.0711 Predictor Set Abrasion Resistance -0.6867 Color Change 1.0285 Fabric Smoothness -0.6910 Holes 0.5036 Stains -0.5579 Wear Wrinkling 0.5697

The sign of the coefficients indicate if these variables are similar or in contrast with the others that are included. As obtained from Table 24, the model becomes the following:

.661(FT)+.787(W)+.769(ARL)+.579(TRF)-.721(TRW)-.683(SR)+1.07(BS) = -.687 (ARV)+1.03(CC)-.691(FS) +.504(H)-.558(S) +.570(WW) (8) 150

Where: FT = Fabric Thickness (mils) Physical Measurement W = Whiteness (4B-3G) ARL = Abrasion Resistance (cycles) " TRF = Tearing Force, Fill (gms) " TRW = Tearing Force, Warp (gms) " SR = Snagging Resistance (1-5) " BS = Breaking Strength (pounds) " ARV = Abrasion Resistance (1-5) Assessment of judges CC = Color Change (Gray Scale 1-5)" FS = Fabric Smoothness (1-5) " H = Holes (Yes/No) " S = Stains (Gray Scale 1-5) " WW = Wear Wrinkling (1-5) "

The model indicates the group of assessment of judges variables that correlate with the group of physical measurements for the random sample of 32 shirts. The assessment of judges variables are of particular interest since they are the ones that can be readily measured and that include the variables previously identified: abrasion resistance, color change, fabric smoothness, holes, stains, and wear wrinkling. Those not included are: distortion of shape, snagging resistance, pilling resistance, fabric hand, and seam smoothness. However, the results of correlations and stepwise regressions indicate that these variables may be indirectly measured due to their relationship with the variables included in the model and for which the variance is already accounted. The seven physical measurement variables found to contribute the most to the model may be compared to those found to be significant in the stepwise regressions. Furthermore, this reduces the number of variables that need to be evaluated from eleven to six (45%) which makes the process 151 easier and less time consuming. With the exception of breaking strength, all of the variables are found to be associated with at least one assessment of judges variable in the Regression Analysis. This equation enables the identification of the judges assessment variables that may be used to provide information about the physical measurements. In turn, the results of these performance tests can be used as indicators of residual wear life; thereby the higher sum obtained when totaling the judges assessment ratings corresponds to a greater potential wear life remaining in the garment. Therefore, an individual with minimal training in the nondestructive assessments of textiles could evaluate six visual characteristics that may be used to provide information about the seven performance characteristics in the equation. Such an assessment may be worthwhile in resale of clothing or in reprocessing of garments for other uses. The equation derived from CCA displays complex relationships between the two groups of variables that are included on each side of the equation in the model. When comparing the two sets of variables from Part I and II of the study, corollary measures appear between each side of the equation. They are: color change with fabric whiteness, abrasion resistance and holes with abrasion resistance, wear wrinkling and fabric smoothness with fabric thickness (as measures of fabric characteristics in Factor 1 of the Factor Analysis) , stains with tearing force and breaking strength (as measures of types of localized damage as in Factor 3 of the Factor Analysis) , and stains with snagging resistance (as a measure of y a m and fabric construction as in Factor 4 of the Factor Analysis) . Interpretation of the canonical variates suggests that the judges assessment side of the equation correlates with some overall measurement of garment condition as does the performance variables side of the equation. This garment condition is .somewhere on the continuum from brand new to entirely worn out. Thus, each of the two sides provide some indication of residual wear life remaining or relative "wom-outness" of the garment being evaluated. Many relationships between single variables and groups of variables were disclosed in the exploratory data analysis phase of the research work. The following chapter discusses the implications and use of the model in qualitatively judging residual wear life in discarded clothing. CHAPTER VII

INTERPRETATION OF FINDINGS AND CONCLUSIONS

To assist in determining the appropriate route for redistribution of discarded garments, a method of evaluation of the residual wear life of the garments is necessary. This research was designed to identify a method of estimation of the condition of discarded clothing. Four objectives were delineated as steps in achieving the research goal. Discussion of the four research objectives follows along with discussion of the application of the derived model. Discussion of Objective #1 The first objective of this research work was to obtain a greater understanding of the condition of discarded clothing by employing an assessment by judges of a selected sample of cotton or cotton blend dress shirts. The target population for this study is individuals in the United States. The accessible population was the individuals in Ohio and in the surrounding midwestem states from which the textile recycling company receives donations. A random sample of four sites was used to obtain the shirts for this study. Thus two extrapolations were made in

153 154 generalizing the findings: from the sample to the accessible population, and from the accessible population to the target population. Assuming the sample was a random representation of the population, the first extrapolation is based upon the laws of probability. This normally is not of concern as long as proper procedures are followed. The second extrapolation is more risky. This is because more assumptions must be made about how the sample is representative of the target population. Due to the various sources of the sample garments, the results should be representative of discards from donors to charitable organizations in Ohio and other midwestem states for the purpose of reuse. However, little else may be said about the donors. The cause for discard of the 50 shirts (77%) which demonstrated visual evidence for discard may be categorized in terms of the four factors from the Factor Analysis. This was done by totaling the average ratings of the three judges for the variables included in each factor. This total score for each factor was performed on each shirt. Three intervals of equal length were created for each factor based on the potential minimum and maximum scores. Table 25 summarizes these intervals. 155 Table 25. Intervals for Determining Total Scores for Factors in Factor Analysis.

Factor Minimum Maximum Low Medium High 1 5 26 5 - 12 12 -19 19 - 26 2 4 20 4 - 9.3 9.3-14.7 14.8-20 3 2 11 2 - 5 5 - 8 8 - 1 1 4 3 15 3 - 7 7 -11 11 - 15

The possible reason for discard of a shirt was attributed to the factor with a score in the "low" interval. If a shirt did not have a low interval for any of the factors or combination of factors, the cause for discard may be attributed to other factors not measured such as poor fit, style or aesthetic considerations, excessive maintenance requirements, etc. Based on this classification there were 4 shirts (6.2%) that displayed overall damage described by Factor 1 alone such as the presence or absence of holes or severe wrinkling. Three (4.6%) of the shirts showed the type of overall damage described by Factor 2 alone such as the presence of abrasion or color change. The majority of shirts (21 or 32.3%) of the shirts had local damage of the catastrophic type such as stains or holes as described by Factor 3. Twelve shirts (32.3%) were classified as having the type of localized damage described by Factor 4. Therefore, the damage such as pilling, snagging, or stains that was present on these shirts may have resulted in their discard. For those shirts which had low scores for more than one factor, the following subsets of 156 factors were found: Factors 1 and 3 (4 shirts, 6.2%), Factors 1, 2, and 3 (3 shirts, 4.6%), Factors 3 and 4 (2 shirts or 3.1%), and Factors 2, 3, and 4 (1 shirt or 1.5%) . Finally, 15 (23.1%) of the shirts did not fall into the low category for any of the four factors and their discard is not explained by a severe level of damage in any of the physical or aesthetic characteristics measured.

Table 26. Distribution of Sample Shirts by the Four Factors

Factor(s) Number Percent 1 4 6.2% 2 3 4.6' 3 21 32.3 4 12 18.5 1/3 4 6.2 1,2,3 3 4.6 3,4 2 3.1 2,3,4 1 1.5 None 15 23.1

TOTALS 65 100% 157

Discussion of Objective #2 The second objective was to perform an evaluation of interrater consistency. This would have implications on the ultimate application by non-trained individuals of the evaluation method developed in this research. The assessments performed by trained judges on the sample of shirts support the importance of an individual' s perception of quality in determining the moment of discard. This was verified by the nonparametric statistical tests described in Chapter VI that revealed significant differences in interrater consistency for certain variables. When the data are treated as paired using the Wilcoxon Signed Rank Test, the variables that showed significant differences were the following: color change, snagging resistance, staining, fabric hand, and wear wrinkling. Using the Friedman test where all of the raters are simultaneously compared for each shirt, the findings were similar. Those variables found to have statistically significant differences were: color change, snagging resistance, pilling resistance, staining, and wear wrinkling. This implies a variation in the level of sensitivity and/or critical observation skills of the individual evaluators. It should be noted that this was further investigated and the differences between raters rarely exceeded one whole number on a scale of 1 through 5 with half steps included. Therefore, there is agreement about the general condition of the 158 garments, but some disagreement about degree. The small differences among ratings could be explained by differences in perceptions of the same garment characteristic by each individual. Consumers do not necessarily base their judgement on the inherent quality of a garment, but instead upon their discernment of its quality. Their discernment is influenced by past experience which may filter certain visual cues. Awareness of this fact can facilitate the process of redistribution. -For example, clothing serves different purposes to different people. Clothes may be used as a form of creative expression or simply may serve a function such as a form of protection. One textile consumer may be more interested in functional performance and cost rather than aesthetics. In addition, research has shown that individuals with a strong sense of self tend to exhibit less personal clothing interest (Aiken 1963; Perry et.al., 1983). However, they continue to be aware of and interested in the clothing of others. Therefore, clothing requirements can vary from person to person and garments which will not fulfill the needs of one person may well fill those of another. A separate post-study discussion with each of the judges confirmed that' it is possible for non-trained individuals to perform the garment ratings with minimal instruction. 159 Discussion of Objective #3 The third objective of this research was to compare the results of assessments of judges with the results of the performance testing in an effort to uncover any correlation existing between continued performance life and visual appearance. The outcome of the statistics are summarized as follows: Cluster Analysis - (only included variables in Part I) - no apparent useful information derived Factor Analysis - (only included variables in Part I) provides evidence that discarded shirts may vary in a number of ways which implies that shirts are discarded for various reasons. The reasons for discard group themselves into four categories. The principal factor might be interpreted as an overall measure of how "worn out" the garment is. Multiple Regressions - reinforce the hypothesis that the judges assessment measurements are correlated with specific performance measurements, in fact, in many cases there are 1-to-l correspondences. Canonical Correlation - identified the judges assessment variables which are correlated with performance variables. A correlation matrix for all combinations of the 21 independent and dependent variables was constructed as an initial investigation to uncover any relationships between them. Correlations were reported as significant at a p value of .05 or less. The most obvious finding was the correlation of wear wrinkling with seven of the other 20 variables. These are: seam smoothness, fabric smoothness, fabric hand, snagging resistance, tearing force in the warp and fill directions, and the laboratory test for abrasion resistance. The relatively high number of associations for wear wrinkling is fortunate, since this rating is easily achieved through a simple hand crush test. The fabric is simply crushed in the hand and held compressed for one minute. Upon release of crushing, the fabric is evaluated on a scale of one to five for the ability of the fabric to recover from the crushing and appear smooth. The correlation between wear wrinkling and so many other characteristics is especially fortuitous since four of the seven correlations were with laboratory tests. By employing a simple nondestructive assessment of textile behavior, one can indicate textile performance in a laboratory test. Pearson's correlations provide an initial step to quickly view the amount and types of relationships that may be expected in more advanced statistical methods. The stepwise regressions were performed using all of the ten laboratory tests from Part II as separate dependent variables with all of the 11 assessment of judges variables from Part I as the independent variables. For the purposes of this research, the quantitative values that are generated are not meaningful. Rather, they are used as indicators of the relationship between the variables. Using a p value of .02, seven of the physical measurement variables were found to have at least one assessment of judges variablet with which it is correlated. They are: fabric thickness, whiteness, abrasion resistance, tearing force - warp and fill directions, snagging resistance, and flexural rigidity. Of these seven variables, four of the equations included the variable wear wrinkling as predictors. The final equations are the following:

Fabric Thickness (mils) = 16.96 - 1.73 Seam Smoothness (1) Fabric Whiteness (4B-3G)= 24.99 + 16.42 Color Change (2) Abrasion Resistance (1-5) = 35.53 + 80.43 Wear Wrinkling (3) Tear Force-Fill (gms) = 1875.32 - 191.39 Pilling Resistance (4) + 305.99 Wear Wrinkling Tear Force-Warp (gms) = 2480.91 - 246.23 Pilling Resistance (5) + 301.33 Wear Wrinkling Snagging Resistance (1-5) = 1.038 + 0.6132 Wear Wrinkling (6) Flexural Rigidity (mg/cm2) = 61.92 + 6.74 Color Change - 13.75 Fabric Hand (7)

These results are in agreement with the Pearson's correlations which indicated that wear wrinkling is related to many other variables and is relatively important in predicting performance in laboratory tests. The associations reflected in the equation may be due to a "hidden." variables, which are 162 not directly measured in this work. Thus, factors such as fiber content or y a m structure which are influential in determining many performance properties may be the root causes for correlations between some of the properties found to be related in the stepwise regression. Although this provided useful information, the stepwise regressions are limited in that they only look at one physical assessment variable at a time. Ideally, the final model needs to look at all of the physical measurement variables simultaneously to get a complete picture. This entailed further statistical analysis. The canonical correlation analysis was the statistical method used in this research to achieve the goal of deriving a model that would show a relationship between the assessment of judges variables and laboratory performance tests. From these relationships another step must be taken to extrapolate laboratory and visual findings to real life wear of garments. The majority of the variance in the physical measurement variables was explained by: fabric thickness, whiteness, abrasion resistance, tearing strength - warp and fill directions, snagging resistance, and breaking strength. The final equation is the following:

.661(FT) +.787(W) +.769(ARL)+.579(TRF)-.721(TRW)-.683(SR)+1.07 (BS) = -.687 (ARV)+1.03(CC)-.691(FS) +.504(H)-.558(S) +.570(WW) (8) 163 Where: FT = Fabric Thickness (mils), Physical Measurement W = Whiteness (4B-3G) " ARL = Abrasion Resistance (cycles) " TRF = Tearing Force, Fill (gms) " TRW = Tearing Force, Warp (gms) " SR = Snagging Resistance (1-5) " BS = Breaking Strength (pounds) " ARV = Abrasion Resistance (cycles) Assessment of Judges CC = Color Change (Gray Scale 1-5) " FS = Fabric Smoothness (1-5) " H = Holes (Yes/No) " S = Stains (Gray Scale 1-5) " WW = Wear Wrinkling (DP Rating 1-5) "

This equation reflects a complex relationship between the variables but cannot be used to quantitatively predict performance. The variables that appear in the equation serve to indicate the inherent performance of the fabric or the effect of use and wear in decreasing the residual performance. The results of the analysis led to the ability to eliminate three of the variables for measuring residual wear life. They were: pilling resistance, flexural rigidity, and fabric cover. Pearson's correlations indicate that except for pilling resistance, there are other variables that appear in the model with which each of these are highly correlated. Difficulties arose with the use of the Random Tumble Pilling Tester as a measure of pilling resistance. The cover to the chambers would fall off, necessitating the repeated stopping and starting of the equipment during the 30 minute cycle. As a result, the reliability and validity of these test data are questionable. This might explain the result that no association was found between pilling resistance and 164 any other variable. Minimally, one would expect to find the laboratory test for pilling resistance to be related to the corollary visual appraisal of pilling resistance. Fabric cover was correlated to fabric thickness, which already appeared in the equation. Flexural rigidity was correlated to other dependent variables that appeared in the model: fabric thickness, snagging resistance, and breaking strength. Apparently, they are redundant variables to measure and allow the researcher to reduce the number of variables to be evaluated in future studies. The independent variables included in the model were: abrasion resistance, color change, fabric smoothness, presence of holes, staining, and wear wrinkling. Those variables that were not included were: distortion of shape, pilling resistance, seam smoothness, fabric hand, and snagging resistance. The factor analysis revealed that distortion of shape was not important in the evaluation of links between visual nondestructive test ratings and laboratory performance ratings because it did not appear in any of the four factors. This might be due to the difficulty in measuring this characteristic, since the original dimensions and shape of the garment are unknown to the rater. Each rater uses a conceptual ideal of garment shape with which to compare the garments examined. 165 Pearson's correlations did not show other independent variables that were related to pilling resistance. This may be explained by the equipment difficulties that produced less confidence in the results of this,evaluation. Seam smoothness and fabric hand had relatively high Pearson's correlation with fabric smoothness, which may be used as an independent variable that is representative of those not placed in the model. Finally, snagging resistance was correlated to two other independent variables that appeared in the model, color change and staining. Although the relationship is not readily apparent, they may be related to another factor such as y a m or fabric construction. Discussion of Objective #4 The fourth objective of this research was the extrapolation of the linked nondestructive and destructive test results to inferences of residual wear life. Using canonical correlation analysis, a statistical model was developed which uses assessments of judges to provide information about laboratory performance of garments. Furthermore, the statistical analysis reduced the number of variables which need to be measured in order to predict laboratory performance. The assumption is made, however, that performance tests actually predict performance of garments in real life. Such an assumption is required in inferring residual wear life from the nondestructive assessments. While 166 quantitative relationships between physical performance tests and actual performance of garments in use have proven elusive (e.g., Schiefer & Wemtz, 1952; Weiner, 1963; Gaspar & Hargreaves, 1978), qualitative relationships are assumed and form the foundation of research in textile science. Even in research such as Slater's (1986) in which the deterioration of a textile with respect to a single performance property is assessed and in which the initial condition of the textile is known, difficulty was encountered in extrapolation of the research to actual performance in real life conditions due to the complexity of factors contributing to textile performance and its deterioration. Such research, however, assumes that examination of performance of a single isolated characteristic in highly controlled laboratory testing can be used to infer performance of that textile in some respect in actual use. Thus, the model derived herein can be employed in qualitatively estimating or indicating actual residual wear life by summing the rankings for the variables derived from the CCA.. Specifically, the results of nondestructive assessments may be used to indicate the physical measurement variables which, in turn, are approximations of residual wear life. Application of the model may best be achieved by determining the ratings for the assessment of judges variables in the model and adding them together to determine a total score. Because a statistical link has been determined to 167 exist between the judges assessments and the physical performance results, a higher total score of judges assessments corresponds to a higher score in physical and aesthetic performance ratings and thereby corresponds to a higher potential residual wear life remaining in the garment. Conversely, a lower (score in physical and aesthetic performance ratings corresponds to a higher potential residual wear life remaining in the garment. Residual wear life was previously defined as "the performance of a garment in both functional and aesthetic aspects over a period of time after discard by the owner". Therefore, it takes into account both aesthetic and physical performance. One way to describe the six variables evaluated in Part I that were included in the model is that they'all contribute to the idea of "wom-outness" e.g., some portion of the properties of the garment are used up. The signs of the coefficients are immaterial and are just a way to combine them to get maximum correlation with the variables evaluated in Part II. Therefore, a new variable (X) is created that includes these six significant variables from the judges assessments from Part I . This new variable is more meaningful and easy to understand when qualitatively estimating residual wear life since both sign and weight are ignored. It may be expressed as follows: 168 X = L x, i=l

where each xA is a judges assessment variable found to contribute to the correlation between the two sets of variables. The highest possible total score is 30, and the lowest possible total score is 6. The score for each shirt is determined by summing the average of the ratings of the three judges for the six variables included in the CCA. Therefore, the lowest score is derived by totaling the following ratings: abrasion resistance (1) , color change (1), fabric smoothness (1), presence of stains (1), presence of holes (1), and wear wrinkling (1) for a total of 6. The highest possible score comes from the following total ratings: abrasion resistance (5) , color change (5), fabric smoothness (5), presence of stains (5) , presence of holes (5) , and wear wrinkling (5) for a total of 30. To make all of the variables contribute equally, the dichotomous scale for the variable "presence of holes" was transformed to a scale of 1 or 5, with a value of 1 indicating that one or more holes were observed on the garment and 5 indicating that no holes were seen on the garment. These total scores may be divided into the following three intervals of equal size: 6.0 through 14.0 (Bad) 14.1 through 22.0 (Intermediate) 22.1 through 30.0 (Good) 169

Categorization of the total scores of nondestructive ratings of a garment places it in a rank of bad, intermediate, or good wear life remaining in a discarded garment. A low score ranking indicates that multiple defects are apparent in the garment and its future usefulness is more limited than a high score ranking in which the total number and severity of defects is smaller. The qualitative category of residual wear life assigned to a garment according to this scale not only reflects the effect of use and wear on the item, but also the original quality of the product. Thus, the principal source « of correlation between the visual and performance measurements is how "worn-out" the shirt is. The reciprocal of the concept of extent of "wom-outness" is potential residual wear life remaining. With respect to the 65 sample shirts studied in this research, 3 (4.6%) fell into the low category for residual wear life, 30 (46.2%) fell into the medium category for residual wear life, and 32 (49.2%) fell into the high category for residual wear life. The minimum calculated total score is 11.2 and the maximum is 25.7. Those shirts that are in the low residual wear life category would be not be suitable for continued use as a garment. However, perhaps continued use in another form such as wiping rags or transformation through reprocessing would be appropriate. It is not surprising that of the sample shirts only three fell into the lowest residual wear life category. Since these shirts were donated by their original owners to charitable agencies for the purpose of garment reuse, it is not likely that garments which exhibit extreme deterioration would be represented in large numbers in the sample. Table 27 lists the rating values for each shirt for each of the variables included in the CCA. model as well as the total score of those variables. 171

Table 27: Average of Judges Ratings of 65 Shirts for Variables Determined by CCA and Ratings Total

Abrasion Color Fabric Presence Presence Wear Total Resistance Change Smoothnessi of Holes of Stains Wrinklina Score 2.83 2.50 3 .00 5.00 2.83 2.67 18.83 3.50 2.50 3.83 5.00 2.33 3.17 20.33 3.50 4.17 3.50 5.00 4.33 2.83 23.33 3.33 2.50 ' 3.17 1.00 2.83 2.00 14.83 1.83 3.83 3.50 1.00 3.50 2.83 16.50 2.83 3.00 4.50 1.00 1.50 3.83 16.67 3.17 3.33 3.83 5.00 3.83 3.33 22.50 4.17 4.00 4.00 5.00 4.00 3.33 24.50 1.67 3.00 2.33 1.00 2.67 1.67 12.33 1.33 2.67 2.33 1.00 2.17 1.67 11.17 1.33 3.67 3.83 1.00 4.00 3.33 17.17 3.67 3.50 3.83 5.00 1.33 3.00 20.33 3.33 3.00 4.33 5.00 2.83 3.67 22.17 3.00 3.67 4.67 5.00 1.67 3.67 21.67 4.33 4.00 4.67 5.00 3.67 4.00 25.67 3.67 4.17 4.00 5.00 4.33 3.33 24.50 4.00 3.00 3.83 5.00 2.67 3.17 21.67 4.00 4.00 4.83 5.00 2.83 3.67 24.33 3.50 3.00 4.67 5.00 1.83 3.83 21.83 1.00 3.00 3.83 1.00 2.83 3.50 15.17 3.33 4.00 2.83 5.00 3.83 2.00 21.00 4.00 3.67 4.67 5.00 2.83 3.17 23.33 3.67 2.50 2.33 5.00 1.00 1.83 16.33 4.67 4.00 4.00 5.00 2.83 2.67 23.17 3.50 3.83 4.83 5.00 3.50 3.50 24.17 1.00 3.50 2.50 1.00 2.83 1.50 12.33 3.50 3.83 3.50 5.00 2.83 2.67 21.33 4.00 4.33 3.33 5.00 3.83 2.50 23.00 4.33 3.17 3.67 5.00 3.17 3.33 22.67 3.50 4.33 4.67 5.00 3.67 3.50 24.67 4.00 3.83 4.33 5.00 3.17 3.67 24.00 4.33 3.83 3.33 5.00 3.50 3.33 23.33 1.00 3.50 3.33 1.00 3.33 2.67 14.83 1.00 3.33 4.67 1.00 3.17 3.67 16.83 1.00 3.67 4.50 1.00 2.50 3.83 16.50 4.33 4.17 4.00 5.00 4.00 3.50 25.00 4.33 4.00 3.33 5.00 3.17 2.33 22.17 1.33 4.33 2.50 1.00 3.33 2.00 14.50 3.83 4.33 3.00 5.00 3.83 1.67 21.67 4.33 4.00 4.50 5.00 4.00 3.83 25.67 3.50 2.50 3.83 5.00 1.67 3.33 19.83 4.33 4.00 4.00 5.00 3.17 3.50 24.00 4.33 4.00 3.83 5.00 3.17 3.33 23.67 4.00 4.17 3.83 5.00 2.83 3.00 22.83 3.67 3.67 3.00 5.00 3.67 2.83 21.83 4.33 4.00 3.67 5.00 3.83 3.17 24.00 1.00 4.33 3.00 1.00 3.33 1.50 14.17 4.33 3.67 4.33 5.00 2.00 3.50 22.83 4.50 4.50 3.00 5.00 4.00 1.67 18.67 4.00 3.50 3.00 5.00 2.50 2.83 20.83 3.33 3.33 3.50 5.00 3.17 2.33 20.67 3.33 3.00 3.67 5.00 3.00 3.50 21.50 4.33 3.83 3.17 5.00 3.50 2.33 22.17 3.67 3.83 4.00 5.00 3.17 3.33 23.00 4.67 3.83 3.67 5.00 3.50 3.17 23.83 4.00 3.83 3.83 5.00 3.50 3.17 23.33 4.00 3.67 2.33 5.00 3.00 2.00 20.00 1.00 3.17 4.00 1.00 3.17 3.17 15.50 172

Table 27 (Continued) . Abrasion Color Fabric Presence Presence_____ Wear______Total Resistance Change Smoothness of Holes of Stains Wrinkling Score

4.17 3.67 4.00 5.00 2.00 3.33 22.17 2.67 3.83 2.83 5.00 1.33 3.00 18.67 4.17 3.00 3.00 5.00 3.17 2.83 21.17 4.67 4.50 1.33 5.00 3.67 1.33 20.50 3.67 4.33 3.50 5.00 4.00 2.50 23.00 4.33 4.17 3.67 5.00 2.00 3.17 22.33 3.17 4.00 3.00 5.00 3.33 2.83 21.33

The scores calculated by totaling the ratings for the variables determined by canonical correlation analysis to be influential in determining performance appear in the last column of the table. These scores indicate that the sample shirts possess some defects but also possess some residual wear life. An appropriate continued use could be identified based on rankings of individual variables, e.g., presence of a single hole may preclude use of the garment for its original purpose, but absence of evidence of abrasion would indicate the fabric retains much of its original serviceability.

Conclusion The findings of this research provide evidence that discarded garments have potential wear life remaining after disposal and that the level of that potential can be categorized. The canonical correlation equation derived 173 reflects the link between the results of assessment of judges to laboratory performance tests. Assuming that performance tests actually indicate wear life, this study found that all of the discarded shirts in the sample possessed some useful residual lifetime. When judges evaluations results were totalled, the majority of the 65 shirts (62 or 95.4%) fell into the middle or high category for remaining wear life. In addition, all 32 shirts that were included in the laboratory testing conducted in Part 2 met the minimal ASTM standards for a men's woven dress shirt for breaking strength and tearing strength, i.e., for these performance evaluations, these shirts meet the requirements for performance of brand new shirts. This supports the need for the evaluation of residual wear life as described in this research to attempt to classify the state of the garmerit and match this with the most appropriate continued use. The model provides a means of apparel evaluation for the original owner as well as the potential new owner. The original owner can determine the most efficient route of disposal for the garment that would result in optimal use of any residual wear life which it possesses. With minimal effort, the potential new owner may examine the characteristics determined by the statistical model in order to determine if the product would be a suitable purchase. For example, an item that ranked low may be appropriate for rags while items that ranked high would be almost new, minimally 174 objectionable and potentially useful in their present form without restructuring. The final model derived in this work included only six of the original eleven assessment of judges characteristics. Since the application of this research requires measurement of the visual characteristics only as an estimate of residual performance, the results of this research minimize the amount of effort required by garment raters by including only those factors which contribute highly in explaining the variance among garments and which require simple nondestructive assessments. While the application of this model is relatively simple, a single factor such as wear wrinkling could also be used as a gross indicator of continued performance. Since wear wrinkling is linked to so many variables, the assessment of wear wrinkling reflects performance of the textile in many respects. Thus, a simple single nondestructive evaluation may provide some support for selection of discarded garments by purchase of recycled clothing. CHAPTER VIII

SUMMARY AND RECOMMENDATIONS

The goal of this study was to develop a model that can be used to indicate residual wear life in discarded clothing that have unknown histories of use. The research question was phrased "Can a model be generated based on simple assessments of judges to indicate continued performance in recycled clothing?". The definition of residual wear life developed for this study is "the performance of a garment in both functional and aesthetic aspects over a period of time after discard by the owner". A random sample of discarded cellulosic and cellulosic blend woven dress shirts was employed in the visual and laboratory assessments. These shirts were collected by a local textile recycling company from charitable organizations in four separate locations; Newark (Ohio), Gurnee (Illinois), Pittsburgh (Pennsylvania), and Wheeling (West Virginia) . Because the sample includes discards from various locations, the results of the analysis of sample garments are representative of the range of discard conditions in garments donated for the purposes of recycling or reuse by donors from different communities. Part I of the

175 176 study consisted of nondestructive assessments of 65 sample shirts by trained judges. The eleven variables evaluated were: abrasion resistance, pilling resistance, snagging resistance, color change, distortion of shape, presence of holes, seam smoothness, fabric smoothness, wear wrinkling, presence of stains, and fabric hand. This chapter summarizes the research conducted and the recommendations for further research. Statistical analyses were conducted on the interrater consistency among the judges in a pilot test. Nonparametric tests indicated that only two out of the eleven assessment of judges variables (18%) showed statistically significant differences between two of the raters. The variables were abrasion resistance and seam smoothness. Due to the relatively large number of tests that were performed (10) , it is reasonable to expect some significant differences due to chance. Therefore, it was concluded that the pilot test provided adequate preparation for the raters. The statistical analysis for Part I included nonparametric tests for interrater consistency, cluster analysis, and factor analysis. The results of tests for interrater consistency showed differences in the rank order of scores between at least one pair of judges on 50% of the variables. Since this is more than expected by chance, an alternative explanation is provided. Of all the concepts related to serviceability, 177 aesthetic appearance is the most subjective, because the judgement of a garment's attractiveness is dependent primarily on individual perspective or preferences rather than on absolute lab measurements. Accordingly, there is a lack of precision when evaluating aesthetic aspects of a textile item due to the amount of subjectivity. This pertains to the visual ratings since this includes a personal component based on one' s sensitivity to visual stimuli as well as those specific characteristics. Cluster analysis was performed to learn if the sample of 65 shirts would form into groups which differ on variables related to residual wear life and to provide general information on the diversity of conditions of the sample of shirts. The analysis produced four clusters with the following distinctions: lower rating for wear wrinkling, higher ratings for distortion of shape and fabric smoothness, lower ratings for abrasion resistance and all except one have at least one hole, and lower ratings for staining. However, the classifications do not relate to either aesthetic or functional performance alone, which make up the definition of residual wear life used in this study. Therefore, the clusters were not used in further statistical analysis or to group the shirts for the selection of a stratified random sample to include in the Part II testing. Factor analysis was performed to study relationships between the eleven variables examined in Part I. There were 178 four factors generated. Hie first factor consisted of overall damage due to fabric characteristics, specifically abrasion resistance, fabric smoothness, seam smoothness, presence of holes, fabric hand, and wear wrinkling. The second factor related to a different category of overall garment damage and consisted of abrasion resistance, snagging resistance, and color change. Factor 3 may be described as type of localized garment damage, consisting of a contrast between the presence of holes with stains and abrasion resistance. The common feature of the last factor is the relation to y a m and fabric construction. The variables included were snagging resistance, pilling resistance, contrasted with stains. Each of these four factors can be used to explain the motivation behind an individual's choice to discard a textile product. Part II of the study consisted of the laboratory assessment of ten garment characteristics performed on a subsample of 32 shirts. They were: fabric thickness, fabric cover, whiteness, abrasion resistance, tearing strength in warp and fill directions, snagging resistance, flexural rigidity, pilling resistance and breaking strength. After these were completed, the results of laboratory tests were compared to the assessments of judges using Pearson's correlations, stepwise regressions, and canonical correlation analysis. 179 Pearson's correlations were used as an initial statistical method to uncover relationships between all of the 21 variables assessed in Parts I and II. The most apparent finding in the correlations was the association of wear wrinkling with many other variables. They are: the laboratory tests for abrasion resistance, tearing force (warp and fill) , and snagging resistance. Equally irrportant to note are some corollary visual evaluations and laboratory tests that were not statistically significant at p = .05. These were: abrasion resistance, snagging resistance, and pilling resistance. Stepwise regressions were performed to study relationships between each of the destructive physical tests (dependent variables) and the assessments of judges (independent variables) . This analysis produced equations for seven of the ten dependent variables. These were: fabric thickness, fabric whiteness, abrasion resistance, tearing force (warp and fill), snagging resistance, and flexural rigidity. This gives information as to which assessment of judges variables can be used to provide information on the physical measurement variables. Canonical correlation analysis was the final statistical technique employed to address the research question. It provided a means of accumulating all of the component parts of this research to achieve the ultimate goal. A formula was derived from the CCA which provided indicators of residual 180 garment wear life. The assumption is made that performance in laboratory tests estimates or indicates actual wear life. The results of destructive physical tests were used as the dependent variables and the assessment of judges results as the independent variables. A final model was produced that was statistically significant at p = . 0425 which contained the following variables:

Physical Measurement Variables fabric thickness whiteness abrasion resistance tear resistance - warp tear resistance - fill snagging resistance breaking strength Assessment of Judges Variables abrasion resistance color change fabric smoothness holes stains wear wrinkling The final equation derived from CCA is:

.661(FT) +.787(W) +.769 CARL) +.579(TRF)-.721(TRW)-.683(SR)+1.07(BS) = -.687 (ARV)+1.03(CC)-.691(FS) +.504(H)-.558(S) +.570(WW) (8)

Where: FT = Fabric Thickness (mils),Physical Measurement W = Whiteness (4B-3G) " ARL = Abrasion Resistance (cycles) " TRF = Tearing Force, Fill (gms) " TRW = Tearing Force, Warp (gms) " SR = Snagging Resistance (1-5) " BS = Breaking Strength (pounds) " ARV = Abrasion Resistance (cycles), Assessment of judges CC = Color Change (Gray Scale 1-5) " FS = Fabric Smoothness (1-5) " H = Holes (Yes/No) " S = Stains (Gray Scale 1-5) " WW = Wear Wrinkling (BP Rating 1-5) 11 181 It should be noted that although quantitative values are obtained with stepwise regression and canonical correlation analysis, the numbers are not considered absolute, but rather as general indicators of trends or relationships between the two sets of variables. A complex relationship exists between the performance ratings on the left hand side of the equation and the judges assessment variables on the right hand side of the equation. Since the relationships are qualitative rather than quantitative ones, no implication of mechanism of textile performance can be inferred. The canonical correlation analysis was successful in identifying a subset of the assessment of judges variables that correlate with the physical measurement variables. These reduced sets of variables may be used to indicate those characteristics that are important in the evaluation of continued garment performance. Extrapolating the results of these nondestructive and destructive tests to actual performance of garments in real life, ranks of total scores were developed which reflect low, medium, and high categories of residual wear life retained by discarded items of clothing. The vast majority of the 65 shirts assessed in this research ranked in either the medium or high residual wear life category indicating that they retained some performance ability even though they were discarded. Totaling the ratings for the variables determined to be significant has practical implications for the evaluation of garments and determination of the life cycle of that product. The Life Cycle Analysis of a Textile Product was discussed in Chapter 3. The perspective of this theoretical framework differs from the other preceding models in that the garment, rather than the owner, is the focal point. The research conducted deals with specific aspects within the product discard phase of the life cycle analysis model with the available options for redistribution and the implications of each option. In terms of conservation of resources and environmental benefits, the most desirable choices are those which allow the garment to remain in use until the residual wear life is entirely depleted. Since the definition of residual wear life employed in this research includes both a subjective and objective assessment of the minimally acceptable qualities of a garment, the redistribution process is dependent on matching the performance in these qualities with the needs of another individual for the same type of product. Alternately, the item can be used for another function, such as cutting into wiping rags. When the garment fails to fulfill the required level of performance in the entire range of subjective and objective qualities, the redistribution process must be able to conform the garment to a new end use with another set of performance requirements. Thus, for example, a stained shirt may no 183 longer meet the aesthetic standards of people who wear shirts but the fabric may still possess the performance abilities to allow the shirt to be cut and used for other purposes. Thus, identification of the amount of potential residual wear life as qualitative measures can assist in the proper determination of the route for garment redistribution. This, in turn, can save natural resources by keeping the garment in the system and preventing the need for a new product to fill this new role. Implications and Recommendations In this research an exploratory approach was taken in which many modes of statistical analysis were employed in order to gain knowledge concerning the condition of discarded garments and their potential usefulness. The results of the research provide methods which can be used to describe the condition of discarded garments, ascribe some of the possible causes for discard, and provide a guide to the relationships between nondestructive assessments of judges and performance tests of the component textiles. Thus, the outcome of this research points toward many future avenues of research. This study may be of interest to standards organizations, such as the American Society for Testing and Materials (ASTM) and the American Association of Textile Chemists and Colorists (AATCC) , that are interested in formulating a method of comparing accelerated laboratory tests with actual wear life. The canonical correlation analysis showed that ratings on six 184 of the judges assessment variables were related to scores on seven laboratory performance tests. Such links between nondestructive and destructive test results could prove useful in many areas of textile science research. Furthermore, when ratings are assigned to a garment for the six variables, a total score may be computed which may be classified as high, medium, or low residual wear life categories for a particular garment, thus aiding in the determination of route for its redistribution for discarded clothing. Another potential use for the results of this work is to assist consumers and vendors of used clothing in determining serviceability expectations for the merchandise. This can enable the quality ranking of goods with the guarantee of certain performance levels. It can also be used to describe the condition of discards thereby assisting in the determination of appropriate uses for these items. This research is only the beginning in an investigation of residual performance in recycled clothing. Although one garment type was used for this study, the technique, without much modification, is applicable to other garment categories. This study helped to identify characteristics that are important to assess in determination of the potential residual wear life in discarded clothing. While correlation between judges assessment variables and performance was established, further study is needed to quantitatively determine the correlation between accelerated laboratory testing and 185 conditions of actual wear. This relationship has remained difficult to define due to the artificial and accelerated methods of simulating conditions of wear in the laboratory. In this study, the AATCC Random Tumble Pilling Tester did not provide reliable results. The individual chamber covers would periodically fall off due to the pressure from the compressed air. The air was used to keep the specimens from lodging in the comer which would prevent them from maintaining contact with the abradant. To replace the plastic covers, the equipment had to be stopped during the half hour cycle and restarted. In the canonical correlation analysis, pilling resistance was not one of the significant variables included in the final model. Therefore, special attention should be paid to this variable in the future. For clothing, function and aesthetics must be considered together to obtain the serviceability desired from the garment. One aspect of functional performance is comfort. Several properties are involved in the evaluation of comfort. They include absorbency, wicking, electrical conductivity, allergenic potential, thermal conductivity, and density. These characteristics were not measured in this study, but may be considered for future research. Another potential area for future research is the description of the characteristics of a person who donates clothing to be recycled and determination of factors which motivate donor behavior. For example, one benefit to the 186 original owner of participating in recycling is financial incentives. These are in the form of obtaining receipts for the donations which can be deducted on income taxes as charitable donations. Another monetary incentive may come from the private textile recycling company which may pay a fixed rate by weight for donations. The profile of characteristics and incentives of recyclers may be helpful in the development of recycling programs as well as consumer education. Human Ecology may be viewed as a problem-solving discipline with a mission to improve the quality of human life. The goal of this study was to develop a model that uses simple assessments of judges to evaluate continued performance in recycled garments. The application of this model contributes to the mission of Human Ecology by providing alternatives to discarding textiles items that are no longer useful to the original owner. By keeping these items in the system through a feedback loop, their deposit in already overburdened landfills is averted. APPENDIX A DEFINITIONS

187 188

Definitions: Source (Annual book of ASTM standards, 1986) Abrasion is "the wearing away of any part of a material by rubbing against another surface" (p 13) . Accuracy of a test method is "the degree of agreement between the true value of the property being tested and the average of many observations made according to the test method, preferably by many observers" (p 14). Breaking length is "a method of expressing the breaking stress of a y a m or fiber; the calculated length of a specimen the weight of which is equal to its breaking load" (p 17) . Breaking load is "the maximum force applied to a specimen in a tensile test carried to rupture" (p 17) . Breaking strength is "the maximum internal cohesive forces of a material which resist rupture during a tensile test" (p 17). Breaking_ tenacity is "the maximum resistance per unit size of material to deformation in a tensile test carried to rupture; that is the breaking load, or force, per unit linear density of the unstrained specimen" (p 17) . Count (in woven textiles) is "the number of warp yams (ends) and filling yams (picks) per unit distance as counted while the fabric is under zero tension, and is free of folds and wrinkles" (p 24). Durable press is "having the ability to retain substantially the initial shape, flat seams, pressed-in creases, and unwrinkled appearance during use and after laundering or drycleaning" (p 29) . Elexural rigidity is "resistance to bending" (p 35) . Gage length is "the length of a specimen measured between the points of attachment to clamps while under uniform tension" (p 36) . Pills are "bunches or balls of tangled fibers which are held to the surface of a fabric by one or more fibers" (p 49) . Precision is "the degree of agreement within a set of observations or test results obtained as directed in a method" _(p 50) . Rating is "a quantitative or qualitative scale for evaluation of a specific performance property" (p 51). Refurbish is "to brighten or freshen up and restore to wearability or use by cleaning such as drycleaning, laundering, or steam cleaning" (p 52). 189 Resilience is "that property of a material by virtue of which it is able to do work against restraining forces during return from a deformed state" (p 53). Resistance to slippage in seam testing is "the force required to separate the parts of a standard seam by a specified amount" (p 53). Sample is "a group of specimens used, or observations made, which provide information that can be used for making statistical inferences about the population(s) from which they were drawn" (p 54). Snagging is "a textile defect caused by (or due to) the pulling or plucking of yam(s) or filaments from a fabric surface" (p 57). Standard atmosphere for testing textiles is "air maintained at a relative humidity of 65+2% and at a temperature of 21+1 degree Celsius (70+2 degrees Fahrenheit) " (p 59) . .St.i£.fn.e..s.s. (see "flexibility") is "resistance to bending" (p 60). Stress-stain curve is "a graphical representation, showing the relationship between the change in dimension (in the direction of the applied stress) of the specimen from the application of an external stress, and the magnitude of that stress" (p 61). Standard condition is "the state or condition of being in moisture equilibrium with the standard atmosphere for testing textiles" (p 59). (Resistance to) Wear-wrinkling is "a term applied to textile fabrics which satisfactorily maintain their appearance by recovery from sharp folds imposed during wear" (p71) . «

APPENDIX B SCRIPT FOR TRAINING JUDGES

190 191

DOCUMENTATION OF PROCEDURE FOR TRAINING OF THREE JUDGES

General Please interrupt at any time for clarification or any other reason. Purpose of the Research Project To generate a statistical model that indicates residual performance in recycled shirts. This may of use to merchandisers and consumers of recycled clothing and to organizations that are involved in performance testing. This consists of two parts; your involvement is in Part 1. You are one of three individuals with no formal training in textiles and clothing who will provide ratings based on visual observations of the condition of a sample of shirts. After this is completed, I will perform destructive tests (meaning that these tests require cutting or destroying the garment) which may be used to estimate any relationship with your visual ratings. This information may be combined to determine an approximate amount of wear remaining in each of the shirts. Each of the three judges will go through an identical individual training session which will be followed by a pre-test of 10 shirts to demonstrate the effectiveness of your training. (The pre-test will also be done at individual sessions for each of the judges.) Statistical tests will be performed to compare the responses of the judges as a measure of interrater consistency. You will be standing in front on the viewing board on which each shirt and the replicas will be mounted. Feel free to touch the garment, making sure to inspect all of the areas that will be requested. Script ofPresentation of General Facts to Judges (Reading this documentation will ensure that each presentation is consistent in content.) 1. Each variable that is evaluated (such as abrasion resistance) was selected as a measure of potential wear in the garments. 192

2. You should look for damage in each of these areas of potential wear of a shirt: collar, right and left cuff, right and left elbow, right and left underarm, right and left pocket (if applicable), right and left front, right and left back placket. A 3 x 5 index card listing these areas will be posted on the viewing board so that it does not have to be committed to memory and to assure that none of these areas are omitted in their rating. 3. The ratings used are on a scale of 1 - 5, with 1 showing the most change in the shirt and 5 indicating no change. Therefore, a higher the score corresponds to a better the rating of the condition of the garment. [The only exception is the variable "holes" and "distortion of shape" which uses a dichotomous scale of 0 (presence) or 1 (absence) of any holes. These were selected to allow the more desirable conditions for the garment to correlate with the higher rating since it would contribute to the total score. ] Even though most of the visual aids do not show ratings other than whole numbers, you may choose one with a .5 value (1.5, 2.5, 3.5 or 4.5) if you believe that it is between two whole numbers. Note that each rating is given based on the most worn area seen on the areas that are viewed, NOT an average of the overall shirt. This is consistent with the "weak link" theory, which applied to this research states that the durability of a shirt is dependent on the area that is the weakest, and it is at this point that the ultimate failure will occur. 4. Visual aids in the form of photographs, fabric replicas or shirts will- be provided as an example of at least the ratings 1, 2, 3, 4, and 5 for comparison to the garment. The assigned value may be interpolated between whole numbers from these visuals as long as it is a number from 1 through 5 represented by one of the nine index cards. Examples of shirts will be provided to demonstrate all of the criteria. 193 The criteria to be evaluated are defined as the following:

Abrasion, defined as wearing from rubbing against another surface will be evaluated on the rating scale of 1 - 5. The visible effect of abrasion is in areas of fabric attrition (thinning), color loss, pilling and ultimately holes. Examples of the whole number 1-5 ratings will be provided using cotton oxford shirting fabric that were, artificially created using the Taber Abrader. Color changes will be evaluated using the Gray Scale. This does not include stains, which will be rated as a separate category. It has an overall effect on the shirt or on areas subjected to wear. Examples of changes are fading, yellowing, and frosting which will be defined as followed: fading - fabric color diminishes in brightness (intensity). yellowing - an undesirable change in fabric color that appears as yellowish hue frosting - an undesirable a change in fabric color caused by localized abrasive wear (AATCC) To detect these changes the areas that are inspected will be compared to unexposed areas within seam allowance or inside pocket (s) . Distortion of the garment shape will be measured on a dichotomous scale of 1 (absence) and 0 (presence) . This is regardless of when it could have occurred (during manufacture if the fabric was cut or sewn incorrectly) or after wear or care. This is done by holding the shirt straight on the hanger and looking for differences in length or width between areas or inconsistency between length of garment and sleeves, for example. Fabric smoothness will be measured using AATCC Durable press replicas. Holes will be measured on a dichotomous scale of 0 (indicating presence) and 1 (absence). Pilling will be measured on a scale of 1 to 5 using ASTM photographic replicas. Seam smoothness (puckering) will be measured on a scale of 1 to 5 using the appropriate AATCC photographic replicas based on the use of single or double needle stitching. 194 Snagging will be measured on a scale of 1 to 5 using replicas for snagging. The replicas were artificially generated by the Bean Bag Snag tester and the rating is based on the number of snags as well as their length and subsequent fabric distortion (puckering).

Stains will be measured using the AATCC photographic replicas for soil release.

Hand will be measured on a scale of 1 to 5 using tactile examples of the two extreme values and bipolar adjectives of stiff to limp at each end of the scale. Propensity to wear wrinkling will be measured by having the raters crush the fabric in their hands, holding it for one minute. Before evaluating, we ' will wait one minute to allow the fabric to recover to the original smoothness. Durable press replicas will be used to assign a rating. In the 'Comments' section of the checklist, additional remarks will be requested to provide a means of describing characteristics not previously measured that contribute to the wear of the shirt. Photographic examples from the textbook entitled "Ready to Wear Analysis" (Brown, 1992) will be provided for seam slippage, seam grinning and seam pucker. pl33, 154.

Garment examples will be shown for practice in rating with the individual providing a verbal justification for the assigned values. When the judges feel comfortable with the evaluation process, the pilot test using 10 shirts will be initiated. APPENDIX C WORKSHEETS FOR VISUAL ASSESSMENTS

195 196

Adapted from ASTM D3183-89 Directions: Place garment on hanger and evaluate under viewing area Wear Test Identification Number Source Fiber and Fabric Identification Fabric Construction

Judges 1 2 3 AVG

Evaluation N/A date Abrasion Color Change Distortion of - Shape (yes=0, no=l) Fabric Smoothness Hole(s) (yes=0, no=l) Pilling ’ Seam Puckering Snagging Stains Washdown (Hand) Wear Wrinkling Total (Maximum of 47)

Comments: Visual or functional evidence of discard? (Missing buttons, reparable damage, seam cracking, seam slippage, seam grinning, dye defects such as streaks, barring, etc.) APPENDIX D DIAGRAM FOR LOCATION OF WEAR IN SHIRT

197 198

SHULTJt,

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/,/ to aTe 5 :

/ O Hales /?// Surface Wesr

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