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Diet and Health Knothedjfi SUJ V'B‘I (DHK‘S) MC“?- 'i’i‘i‘)’ presented by Sitar; Obayashi has been accepted towards fulfillment of the requirements for ”'3‘ degree in Humam Nmriho'n k/m/éf/ Majo professor Date 1" W] ”3v" ”‘9 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE A (AC, .3' ,, ‘ Jr: #33“ ‘ "Tl-J Iii 1/98 CJCIRC/DaleDUGpESva 4 RELIABILITY AND VALIDITY OF SURVEY QUESTIONS ON FOOD LABEL USE, NUTRITION KNOWELDGE AND ATTITUDES OF US. ADULTS: DIET AND HEALTH KNOWLEDGE SURVEY (DI-IKS) 1994-1995 By Saori Obayashi A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Food Science and Human Nutrition 2000 Professor Won 0. Song ABSTRACT RELIABILITY AND VALIDITY OF SURVEY QUESTIONS ON FOOD LABEL USE, NUTRITION KNOWLEDGE AND ATTITUDES OF US. ADULTS: DIET AND HEALTH KNOWLEDGE SURVEY (DHKS) 1994-1995 By Saori Obayashi The assessment of reliability and validity of survey questions is essential to collect meaningful data with small measurement errors. The purposes of this study are: 1) to evaluate the reliability and validity of questions on nutrition knowledge, attitudes, and food label use of US. adults included in DHKS 1994-1995, and using the results of this analysis; 2) to assess changes in nutrition knowledge, attitudes, and use of the food label (the list of ingredients, nutrition facts, health claims and nutrient content descriptors) among the US. population between DHKS 1994 and 1995. Item total correlation, alpha if item deleted, and Cronbach’s alpha were calculated to test reliability. Validity was assessed by content validity, divergent validity, and discriminant validity. Twenty constructs were created from DHKS 1994-1995 in this study: five for nutrition knowledge, 14 for attitude, and one construct for frequency of food label use. Eight of 13 constructs tested had a good reliability with Cronbach‘s alpha r 2 0.70. Fifteen of 20 constructs were valid. Between 1994 and 1995, nutrition knowledge, attitudes, and food label use were improved. The reliable and valid national survey questions for selected constructs are important to establish theoretical models to understand the relationships among nutrition knowledge, attitudes, use of food label, and dietary intakes in the future. ACKNOWLEDGEMENTS I would like to acknowledge my major advisor, Dr. Won Song, for being patient with my academic achievement and for her support of my personal growth. I would like to acknowledge Dr. Neal Schmitt, Dr. Stephen Sapp from the Department of Sociology at Iowa State University, and Dr. James C. Schaper for their guidance in my research method development. A very special thank is also extended. to my committee members: Dr. Sharon Hoerr, Dr. Bruce Hass, and Dr. Lorraine Weatherspoon for their encouragement and advices for my thesis project. Thanks also go to my lab mates: Lydia Koemer, Yali Huang, Prodromou Prodromos, Yikyung Park, Debra Keast, Jean Kerver, Andrea Padgitt, and Sikhoya Wabuyele for their friendship and respect. In addition, I thank to Dr. Toshiro Innami and Dr. Frank D’itri for believing my dream to study at MSU to come true. At last but not least, my great appreciation goes to my dearest friends: Callie J Bair, Michel Viges and Jason Biel for their love, friendship and unconditional acceptance and to my parents for their unconditional love. iii List of Tables List of Figures Chapter One Chapter Two Chapter Three Chapter Four TABLE OF CONTENTS Introduction Summary of past studies 2.1. History of the food label 2.2. Nutrition knowledge, attitudes and behaviors depending on various food label formats and education programs before the 1990 Nutrition Labeling Education Act (NLEA) 2.3. Food label reading behaviors and their relation with individuals’ nutrition knowledge, attitudes, and dietary behaviors 2.4. Factors which might affect an individual’s dietary behaviors 2.5. Reliability and validity of questions related to food label use and dietary intakes in CSFII/DHKS 1994-1996 Methods 3.1. Description of the sample of DHKS 1994-1995 3.2. Reliability tests of DHKS 1994-1995 3 .3. Validity tests of DHKS 1994-1995 3 .4. Comparisons of nutrition knowledge, attitudes, and use of the food label by Americans between 1994 and 1995 Results 4.1. Description of the sample of DHKS 1994-1995 4.2. Reliability tests of DHKS 1994-1995 4.3. Validity tests of DHKS 1994-1995 4.4. Nutrition knowledge, attitudes, and food label use in the US. between 1994 and 1995 iv iv . viii 10 16 18 26 34 34 35 59 62 63 64 72 84 ..144 Chapter Five Chapter Six Appendices Bibliography Discussion and conclusion Recommendation for the fixture study Appendix A. Studies on nutrition knowledge, attitudes, and dietary behaviors in relation to various food label formats and education programs before the 1990 Nutrition Labeling Education Act (NLEA) Appendix B. Studies on the relationship among individuals’ sociodemographics, nutrition knowledge, attitudes, food label use and dietary habits Appendix C. Studies on factors which might affect on dietary habits Appendix D. ITC, alpha if item deleted, and Cronbach’s alpha for questions on diet- disease relationships in DHKS 1994 Appendix E. The results of Cronbach’s alpha with different question items Appendix F. Percentage of responses and mean score i SE. with the test of Significance for nutrition related questions between DHKS 1994-1995 .. 160 .. 173 .. 175 ..192 ..201 ..238 .. 240 .. 244 .. 253 Chapter One Table 1. Chapter Three Table 2. Table 3. Table 4. Table 5. Chapter Four Table 6. Table 7. Table 8. Table 9. Table 10. Table 1 1. LIST OF TABLES Comparison of the current (1994-1996) and previous CSFII/DHKS’S Definitions of selected variables in DHKS 1994-1995 Definitions of question items and scores created from DHKS 1994-1995 -Nutrition knowledge questions Definitions of question items and scores created from DHKS 1994-1995 -Attitude questions Definitions of question items and scores created from DHKS 1994-1995 -Food label use questions Distribution of subgroups in DHKS 1994-1995 Final constructs determined by ITC, alpha if item deleted (alpha deleted), and Cronbach’s alpha - Knowledge constructs in DHKS 1994-1995 Final constructs determined by ITC, alpha if item deleted (alpha deleted), and Cronbach’s alpha - Attitude constructs in DHKS 1994-1995 Final constructs determined by ITC, alpha if item deleted (alpha deleted), and Cronbach’s alpha - Frequency of food label use constructs in DHKS 1994-1995 Divergent validity for each construct by Pearson Correlation in DHKS 1994—1995 Weighted mean Scores of each construct/question item of DHKS 1994 vi 36 42 48 56 64 76 78 81 86 96 Table 12. Table 13. Table 14. Table 15. Weighted mean scores of each construct/question item of DHKS 1995 Percentage of responses and mean scores i SE. for questions created from DHKS 1995 and the test of significance between 1994 and 1995 - Nutrition knowledge questions Percentage of responses and mean scores i SE. for questions created from DHKS 1995 and the test of significance between 1994 and 1995 — Attitude questions Percentage of responses and mean scores 3: SE. for questions created from DHKS 1995 and the test of significance between 1994 and 1995 - Frequency of the food label questions vii ..115 ..148 .. 152 .. 158 LIST OF FIGURES Chapter Six Figure]. Path analysis on the relationship between nutrition knowledge, attitudes, food label use and dietary intakes 174 viii Chapter One INTRODUCTION The current food label legislation was enacted in 1994 in the US (Nutrition Labeling Education Act of 1990. Pub L No.101-535). The food label was developed through the cooperative efforts of the government, food industry, consumer groups and academia as a nationwide nutrition education tool. It was designed to enhance consumers’ awareness of nutrition issues, nutrition knowledge, and healthy eating habits (Kristal AR et al, 1998; Kessler DA, 1989; Zarkin GA et al, 1993). Factors associated with an individual’s nutrition knowledge, attitudes and use of food labels are important in achieving the intended purposes of the food label. In several studies, individuals’ nutrition knowledge, awareness, attitudes, perceptions, and dietary behaviors were affected by different label formats (Burton et a1, 1994; Burton and Biswas, 1993; Levy et al, 1992; Geiger et a1, 1991). However, these studies were carried out in limited locations or with small sample Sizes (most of study samples were less than n= 500). In addition, changes in subjects’ dietary behaviors were measured by food purchase at supermarkets, rather than by their dietary intake, quality and quantities. Characteristics of users of the former food label and the nutrient density of their diets have been reported by Guthrie et al (1995). Positively associated with food label use were: having a college education; being a female; living with others rather than living alone; knowledge about nutrition; beliefs in the importance of following the principles of the Dietary Guidelines for Americans; and concerns with nutrition and product safety, and less with taste when food Shopping. Guthrie and colleagues also reported that food label use was associated with higher vitamin C and lower cholesterol intakes than was non-use. Past studies have reported that those with low socioeconomic status are less knowledgeable about nutrition than those with high socioeconomic status (Morton and Guthrie, 1997; Jacoby et a1, 1977; Levy et al, 1992). Michel et al (1994) examined nutrition knowledge, attitudes and food label reading habits by clients in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) at two Northern Virginia clinics. Most respondents occasionally read food labels, but their understanding was generally poor. Current literature supports the theory that a variety of factors are associated with individuals’ nutrition knowledge, attitudes, perceptions, use of food labels, and dietary behaviors (Bender and Debby, 1992; Fullmer et a1, 1991; Guthrie et a1, 1995; Michel et a1, 1994). However, findings are not consistent across studies. For example, Morton et a] (1997) reported that low income respondents were Significantly less likely to follow low- fat and low-cholesterol eating practices than were high income respondents. Colavito et al (1996) and Frazao et al (1994), however, found no relation between income levels and fat or cholesterol consumption. Frazao et a1 (1994) reported that no gender differences were seen in awareness of fat, cholesterol, and saturated fat intakes and disease relationships. On the other hand, Variyam et a1 (1996) reported that females were more aware of diet-disease relationships than males. Inconsistent results across studies are due in part to lack of valid and reliable questions to inquire about respondents’ nutrition knowledge, attitudes, food label use and dietary behaviors (Sapp and Jensen, 1997; Axelson and Brinberg, 1992; Sims, 1981). Studies are limited, especially after the 1994 implementation of the Food label Act, on the relationship between food label use and dietary behaviors. Kristal et a1 (1998) examined the influence of the 1994 implementation by examination of Washington State residents’ nutrition knowledge, attitudes and use of the food label. Results Showed that use of and satisfaction with food labels increased after 1994. However, 70% of Washington State residents still wanted food labels to be easier to understand than the current food label. Thus, the relationship between current food label use and dietary behaviors. Multiple factors affect individuals’ dietary behaviors directly and indirectly as well as independently and interactively. A number of theoretical models consisting of factors such as sociodemographics, nutrition knowledge and attitudes were used to explain individuals’ dietary habits (Shepherd and Stockley, 1985; Shepherd and Stockley, 1987; Tuorila and Pangbom, 1988; Saunders and Rahilly, 1990). However, factors included in models were limited, and applicability of these models differed depending on subgroups. Thus, researchers suggested integrating models developed in the past so that individuals’ dietary behaviors could be understood from broader perspectives (Achterberg and Clark, 1992; Colavito and Guthrie, 1996). Continuing Survey of Food Intakes by Individuals/Diet and Health Knowledge Survey (CSFII/DHKS) between 1994 and1996 was developed, incorporating factors from various theoretical models and subgroups (Colavito and Guthrie, 1996). CSFII/DHKS had a large, complex, multistage clustered, nationally representative sample. The question items inquired about individuals’ nutrition knowledge, attitudes, food label use and dietary intake information. Other improvements of CSFII/DHKS 1994-1996 from previous CSFII/DHKS surveys are summarized in Table 1. Thus, CSFII/DHKS 1994- 1996 is the most appropriate data source for studying the association of individuals’ nutrition knowledge, attitudes, use of food labels and their dietary intake. Because inconsistency of the results from past studies was in part due to lack of valid and reliable questions, assessing reliability and validity of survey questionnaire is crucial to minimize measurement errors of the result. Otherwise, one couldn’t identify whether the result showed weak relationship because: a) the association was actually weak or b) because the ability of variables to measure the association was weak, and results were attenuated (Axelson and Brinberg, 1992). Thus, results could be misleading. Sapp and Jensen (1997) reported that the reliability of nutrition knowledge questions and nutrition awareness questions in DHKS 1989-1991 were modest (alpha < 0.7) and high (alpha 2 0.7), respectively. Because DHKS is the National Nutrition Monitoring and Related Research Program, the changes in nutrition knowledge and attitudes can be monitored if the question items are compatible, reliable and valid across different years. Thus, the reliability and validity of questions related to nutrition knowledge, attitudes, food label use in DHKS 1994-1995 Should be examined before assessing the relationship between those factors and dietary intake reported in CSFII 1994-1995. The results obtained from the reliability and validity tests can be also to improve the design of survey questionnaires in the future. The purposes of this study are: 1) to evaluate reliability and validity of questions on nutrition knowledge, attitudes, and food label use of US. adults included in DHKS 1994-1995, and using the results of this analysis; 2) to assess changes in nutrition .822. c. $.89 >866 E vm 62:63:88: N Eoouoa 0.0m .cosmanE. 9.3:. @255 9 23.8 053 wcmnEoE 29.839. 866.3 2:0 Eozmfiqoq wEooc.->>o. 9: .8802 6.6 BBEEEEES .6: new .598 c. :89 >566 E vm gnu ozSoomcoo m :5qu msm .coszcoE. 9.9:. 3305 2 nexwm 2o; mcmnEoE 22.839. ..< 888:. 26. cam 239 9.9.8.3 >3 w>mn mc.:_mEo._com.oa c. F 480 .m..moo._ >566 E vm .58 .8 .83 .5656 new 5an c8 :3 9:83:88: 0 .>.co £2 5 .6; 8-2 :22 98> Son .8 94» m4 c9220 :65 new 24» 8-3 :mEo>> 6:502. 26. Em 06mm. 9% $65 $6. @95me 3mm. 29229.. Co 2993 was: 9:39. c6 mEEEmEgo. anmm 9.0 $383 2983 02F 3.993 .8938 95... 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To establish the validity of nutrition knowledge, attitudes, and food label use scores created from the DHKS 1994 and 1995 by content (face) validity; divergent validity; and discriminant validity. 4. To compare changes in nutrition knowledge, attitudes and food label use in the US. between 1994 and 1995. Hypotheses of this study are: 1. Nutrition knowledge, attitudes and food label use scores created from DHKS 1994- 1995 are reliable. . 2. Nutrition knowledge, attitudes and food label use scores created from DHKS 1994- 1995 are valid. 3. The type of nutrients on food labels affect frequency of food label use. 4. Individuals’ nutrition knowledge and attitudes toward healthy dietary behaviors and use of food label had improved between 1994 and 1995. Chapter Two SUMMARY OF PAST STUDIES Food labels were developed to increase consumers’ knowledge so that consumers could ultimately improve their diet (Constance et al, 1991). Researchers tested different types of food labels and nutrition education programs to identify which one would facilitate more consumer use and understanding of the information (Burton et al, 1993; Levey et al, 1992; Geiger et al, 1991). Other researchers studied the mechanisms of how nutrition information was processed by consumers, and what factors were associated with these mechanisms (Moorman, 1990; Cole and Gaeth, 1990; Russo et al, 1986). Various studies examined the relationship between use of information on food labels or nutrition education programs and consumers’ demographic and socioeconomic characteristics, nutrition awareness, knowledge, attitudes, and dietary behaviors (Bender and Debby, 1992; Fullmer et al, 1991; Michel et al, 1994, Guthrie et al, 1995). Factors associated with dietary behaviors have also been extensively studied (Contento and Murphy, 1990; Beck and Lund, 1981; Maiman and Becker, 1974). Although results were not always consistent because of different definitions of variables, sample populations, and statistical analyses across studies, theories about consumers’ nutrition awareness, knowledge, attitudes and dietary behaviors including use of food labels were established. Thus, this section consists of five different parts: 1) History of the food label; 2) Nutrition knowledge, attitudes and behaviors depending on various food label formats and education programs before the 1990 Nutrition Labeling Education Act (NLEA); 3) Food label reading behaviors and their relation with individuals’ characteristics, nutrition knowledge, attitudes and dietary behaviors; 4) Factors which might affect an individual’s dietary behaviors; and 5) Reliability and validity of questions related to food label use and dietary intake in CSFII and DHKS. Tables for the summary of findings in the past are listed in the Appendices A through C. 2.1. History of the food label The goal of the food label is to improve consumers’ diets by increasing their understanding of nutrition (French et al, 1974). Survey results conducted by the Food and Drug Administration (FDA), food industry and consumer groups reported positive consumers’ opinions about nutrition labels in the early 19705 (Consumer Nutrition Knowledge Survey, 1973-1974; Consumer Nutrition Knowledge Survey, 1975; Opinion Research Corporation, 1990; Stokes, 1972). However, in the late 19705 and early 19805, Studies revealed that consumers were confused with the information on food labels, and wanted additional information and changes in formats (FDA 1978 Consumer Food Labeling Survey, 1979). Responding to this demand, an extensive number of experimental studies were conducted for nutrition information to be used most effectively through food labels and education programs (Geiger et al, 1991; Levy et a1, 1992; Jacoby et al, 1977; Cole et al, 1990; Moorman, 1990; Burton et al, 1993; Burton et al, 1994; Muller, 1985; Schucker et al, 1992; Levy et al, 1988; Mullis et al, 1987; Davis-Chervin et al, 1985; Ernst et al, 1986; Jeffery et a1, 1982; Pennington et al, 1988; Russo et al, 1986). In 1990, the Nutrition Labeling and Education Act was passed (Nutrition Labeling and Education Act of 1990. PL 101-535.]04 Stat 2353). It initiated new mandatory nutrition labeling of most foods under FDA jurisdiction, except meat and poultry (under USDA jurisdiction). It also encouraged nutrition education programs to teach consumers how to read labels. Changes on current food labels included: 1) expression of serving sizes; 2) selected nutrients; 3) the % Daily Value; 4) nutrient content descriptors; and 5) health claims. Up to this point, only Kristal et al (1998) had conducted a study on the current food label. The authors concluded that Washington State residents (n=1001 in 1993 and n=l450 in 1996) had positive attitudes toward the current food label, and their use had been increased since 1994. However, residents’ level of understanding food labels was still low, and some respondents wanted an easier to understand format than the current food label in the future (Kristal et al, 1998). In summary, changes and efforts have been made by cooperative efforts of the government, food industries, consumer groups and research institutes to make food labels more easily understandable and to be used for improving individuals’ diet. Although consumers generally Show positive attitudes toward food labels, some people still have difficulty in understanding the label. To date there are limited studies on the current food labels, and additional changes on the current food label might occur in the future depending on the findings generated from future studies. 2.2. Nutrition knowledge, attitudes and behaviors depending on various food label formats and education programs before the 1990 Nutrition Labeling Education Act (NLEA) Before the passing of the Nutrition Labeling Education Act (N LEA) in 1990, various nutrition education programs and formats were tested to develop a nutrition education tool to increase consumers’ nutrition knowledge and improve their diets. Information was provided on packaged food nutrition labels, supermarket tags and posters, and through other point-of-purchase nutrition information programs to meet the demands of consumers. Studies showed that participants’nutrition knowledge, attitudes and behaviors such as food purchases and food selections were influenced by not only user socioeconomic, demographic characteristics and lifestyle factors, but also by information formats and programs (Ernst et a1, 1986; Mullis et al, 1987; Jeffery et al, 1982; Schucker et al, 1992; Davis-Chervin et al, 1985; Mullis and Pirie, 1988; Jacoby et al, 1977; Cole and Gaeth, 1990; Russo et al, 1986; Viswanathan, 1994; Moorman, 1990). For example, Russo et al (1986) reported that making nutrition information more readily available increased respondents’ nutrition knowledge. Moorman (1990) found that the age was positively related to the ability to process the nutrition information. More detail is followed: 2.2.1. Nutrition education programs at point-of-purchase Davis-Chervin et al (1985) conducted a study to compare the effect of different nutrition education programs with intangible incentives between two cafeterias. The first cafeteria (dorml ), serving 175-200 first—year students, provided general nutrition information, recommendations for changing food selection, and information on the nutrient composition. The second cafeteria (dorm2), serving 450-500 undergraduate students from all four classes, provided only information of the amount of nutrients. The results showed that the first cafeteria influenced students’ likelihood of choosing low- cholesterol, - calorie and - fat foods more than the second cafeteria did. Ernst et al (1986) evaluated the effect of “Foods for Health”, a cardiovascular nutrition education program at the point of purchase in Washington DC, and Baltimore and other Maryland areas as control groups. The study was sponsored by the National Lung and Blood Institute and Giant Food Inc. The program was intended to change food selection and preparation behaviors to prevent cardiovascular diseases. It provided shoppers with a series of four page brochures on cardiovascular health, nutrition information, and practical suggestions from October 1978 to October 1979. Results were obtained from telephone surveys and food sales data. The results showed a significant effect of the program on increasing consumers’ awareness and knowledge of cardiovascular nutrition, but no apparent effect on the food sales. Schucker et al (1992) studied the effect of providing a brand—Specific nutrition shelf-tag and a supplementary explanatory booklet on consumers’ food purchases in the Baltimore area Giant Food chain, replicating a previous success of the program in Washington DC. (Levy et al, 1985). The results suggested that shoppers’ purchases were influenced by the number of nutrients listed on the tag as well as by the nature of the nutrient. For example, there was higher sales growth in products with larger number of nutrition Shelf-tags than in products with a smaller number of nutrition shelf-tags. In this study, the relationship between the subject demographic variables and the information use was also examined. The results showed that the shoppers’ concerns about nutrition and health status of family members correlated with the use of provided information use and shelf-tags product sales more than education, income and age. The previous study conducted by Levy et a1 (1985) hypothesized that self-labeling may be less effective in categories that already have reduced-nutrient product alternatives. In the study conducted by Schucker et al (1992), this hypothesis was accepted in the fluid milk category such as reduced-fat alternatives. From past studies of point-of-purchase nutrition information programs, it appears that programs with more nutrition information with targeted nutrients are more likely to be successful in influencing consumers’ nutrition awareness, knowledge, attitudes, and use of provided information than programs with less nutrition information (Schucker et al, 1992; Davis-Chervin et al, 1985). Factors such as concern about nutrition and health status of family members and demographic and socioeconomic characteristics were also found to be related to the use of information provided in the programs (Schucker et al, 1992). Furthermore, labeling was found to be less effective on the product which had already reduced nutrient product alternatives (Schucker et al, 1992; Levy et al, 1985) 2.2.2. Food label formats Burton et al (1993) tested potential impact of the nutrition labels proposed under the NLEA of 1990 on individuals’ attitudes and perceptions of product nutritional value and the resulting purchase likelihood. The author’s study concluded that the proposed label with additional information in the NLEA (i.e., cholesterol, amount of saturated fat, calories from fat, and fiber) tended to lower individuals’ nutrition perceptions and product purchase likelihood than the label used at the time of the study. Thus, the result suggested the potential negative effects from the food marketer’s perspective for products with negative nutritional values. Levy et a1 (1992) conducted a study on the relationship between different types of nutrition label formats and people’s preferences and understanding. They found that preference for certain types of labels was not necessarily correlated with the level of understanding. They also found that demographic characteristics such as age, education and race were related to understanding of labels. For example, older subjects took a longer time to process the nutrition information on food labels and had a lower accuracy of understanding this information than younger subjects. Geiger et al (1991) tested multiple levels and combinations of nutrition information formats, the amount of information, expression and order on consumers’ perceptions of label usefulness in purchase decisions. Geiger and colleagues found that consumers preferred the nutrition label that displayed all nutrient values by both absolute numbers and percentages of US. Recommended Daily Allowances (RDA’S) for adults. They also found that consumers preferred the nutrients that should be consumed in adequate amounts on the top (i.e., protein, total carbohydrate, complex carbohydrates, calcium, iron, vitamin A, thiamin, riboflavin, and niacin), calories in the middle, and nutrients that should be consumed in limited amounts on the bottom of the label (i.e., sodium, fat. cholesterol, and sugar). Rudd (1986) reported that a bar graph format was more effective than the traditiOnal label format to help consumers obtain better understanding of nutrition information. In summary, past studies indicated that consumers preferred labels that provided maximum information in the most easily understandable format, such as a graph (Geiger et al, 1991; Viswanathan, 1994). The preferences were not necessarily related to better understanding of nutrition information (Levy et al, 1992). Users’ degree of understanding of different formats appeared to be associated with their demographic and socioeconomic characteristics and life style factors (Levy et al, 1992). 2.2.3. Studies on nutrition information processing Marketing research groups have studied the mechanism of how individuals process nutrition information. Moorman (1990) emphasized that it was important to consider both consumer characteristics and stimulus characteristics (e. g. format and content of the information) in helping individuals to improve their nutrition information processing and decision quality. Cole and Gaeth (1990) conducted three experiments to indicate how age, cognitive style, and aid to stimulate perceptions affected consumers’ use of nutritional information. They found that age and aids to stimulate perceptions influenced consumers’ accuracy in choosing the most nutritious cereal from three choices. They also found that aids to stimulate perceptions interacted with cognitive style, influencing accuracy and decision time. Russo et a1 (1986) examined whether lists of information in supermarkets were effective in reducing the load of information processing in comparing alternative foods. They found that adding information, especially on nutrients that were perceived as negative (e. g., sugar) was an effective technique to increase information use. Thus, the study indicated the complexity of individuals’ nutrition information processing. Understanding this mechanism of how individuals process nutrition information and what factors were involved would be helpful for nutrition educators to develop effective nutrition education tools. In summary, various types of nutrition information and education programs were tested before the implementation of the current food labels in 1994 (Burton et a1, 1993; Levy et a1, 1992; Burton et al, 1994; Geiger et al, 1991; Muller et a1, 1985; Ernst et al, 1986). Nutrition information processing is a complex task. Results differed depending 15 on the selected subject’s ability to process the nutrition information, the type of information and programs, and other factors such as perception of nutrients. Thus, future studies need sophisticated models of individuals’ nutrition information processing to include all factors involved in the individuals’ use of information on food labels. 2.3. Food label reading behaviors and their relation with individuals’ characteristics, nutrition knowledge, attitudes and dietary behaviors. Bender and Debby (1992) identified the trends of ingredient lists and nutrition labels usage from 1982-1988 National Health and Diet Surveys. They reported a Significant increase of nutrition label usage from 1982 to 1986. In their study, users of both ingredient lists and nutrition labels were more likely to be young (25-34 yrs), white, female, highly educated, and following a self-initiated or doctor-prescribed low-sodium or low-cholesterol diet. Fullmer et a1 (1991) assessed consumers’ knowledge of fiber, understanding, and attitudes toward health claims on food labels of breakfast cereals from 241 grocery Shoppers in Utah. They found that education had positive effects on understanding of diet-disease relationships and positive attitudes toward health claims on food labels. Knowledge of fiber was significantly correlated with understanding and positive attitudes toward of health claims. However, consumers’ understanding of fiber was low regardless of their attitudes toward health messages. Michel et a1 (1994) examined food label reading habits of W1C clients in two Northern Virginia W1C clinics. They reported that clients understood very little food label information. Nutrients and products clients evaluated were vitamins, iron, calcium l6 and milk. Interestingly, these nutrients and food products correspond to the targeted nutrients and food supplements of the W1C program. Guthrie et al (1995) identified from the 1989 Continuing Survey of Food Intakes by Individuals and Diet and Health Knowledge Survey (CSFII/DHKS) the characteristics of individuals using nutrition labels and the effects of label use on diet quality. Positively associated with food label use were: being a female; having a college education; living with others rather than alone; knowledge about nutrition; beliefs in the importance of following the principles of the Dietary Guidelines for Americans; and concerns with nutrition and product safety, and less with taste when food shopping. They reported that label use was related to the consumption of diets high in vitamin C and lower in cholesterol. In summary, consumers’ awareness, attitudes and behaviors regarding food labels have been changing positively over a period of time. Many studies reported that females and those who had a college educational background and high income were likely to be food label users. Not yet adequately addressed were other possible factors determining food label use, such as having concerns about nutrition, or having a family member who has health problems. There are few studies that described the relationship of individuals’ nutrition knowledge, attitudes and use of food labels with their actual dietary intakes. Since the implementation of the current food label in 1994, only one Study of Kristal et a1 (1998) measured all of consumer knowledge, attitudes, use of food labels, and dietary intake (See history of food labels). Thus, the effectiveness of current food labels on increasing consumers’ nutrition knowledge and improving their attitude and dietary behaviors have not been evaluated. 2.4. Factors which might affect an individual’s dietary behaviors Nutrition educators in general classified various internal and external factors that might affect an individual’s dietary habits into four groups: 1) nutrition knowledge and attitude factors; 2) demographic and socioeconomic factors; 3) behavioral and lifestyle factors; and 4) sensory preferences and environmental factors. 2.4.1. Knowledge and attitude factors affecting dietary behaviors Various knowledge and attitude factors affecting individual’s dietary habits have been explained by a number of theories. Commonly accepted theories are: the Health Belief Model (Maiman and Becker, 1974); Theory of Reasoned Action (Fishbein and Ajzen, 1975); Social Cognitive Theory (Bandura, 1977); and the Theory of Diffusion of Innovation (Rogers, 1983). Brief descriptions of each theory along with some research findings are summarized below. Health Belief Model. According to this theory, preventive health behaviors were determined by four underlying beliefs: 1) perceived barriers to the behaviors; 2) perceived benefits of the behaviors; 3) perceived severity of the health risk associated with the behaviors; and 4) perceived susceptibility to risk (Maiman and Becker, 1974). Contento and Murphy (1990) used the Health Belief Model with other theories such as self-efficacy (Bandra, 1977) and behavioral intention model (Ajzen and Fishbein, 1980) to examine whether psychosocial factors differentiated those who reported making desirable changes in their diets from those who did not. They reported that perceived susceptibility and severity of the health risk certainly seemed important in dietary 18 changes. They also reported that those who made dietary changes felt not only susceptible, but also believed that given dietary behaviors would help reduce the threat of diet-related disease and bring about the health outcomes they desired. The importance of coexistence of feelings of susceptibility and self-efficacy to change behaviors was also emphasized (Beck and Lund, 1981). Theory of Reasoned Action. This theory was based on two factors: 1) perception of the outcome behavior and the evaluation of the outcome (e. g., whether the behavior is seen as good or bad); and 2) subjective norm (e.g whether the person believes that other people such as his/her family or friends think he or she should perform the behavior) (Fishbein & Ajzen’s, 1975). The reliability and validity of this theory have been reported by Stafleu et al (1994), Towler and Shepherd (1992) and Glanz et al (1993). Shepherd and Stockley (1985) examined the relationship between attitudes and subjective norms toward consumption of high fat foods in various demographic groups. They showed that a person’s own feelings toward consumption of a type of food were good predictors of fat consumption. They also found that behaviors were weakly correlated with the subjective norm, and strongly correlated with attitudes. They identified friends and family as subgroups who affected behaviors, followed by nutritionists, manufacturers and advertisers. Weight concern also appeared to be a Significant factor that predicted intention and consumption of high fat foods. However, those attitudes were found to vary depending on products. Tuorila and Pangbom (1988) reported that attitudinal and belief factors related to the consumption of fat-containing foods varied across products (e.g. milk, cheese, ice cream, chocolate and high fat foods). Social Cognitive Theory. In 1986, Bandura published a comprehensive framework of Social Cognitive Theory based on Social Learning Theory (Bandura, 1986). This theory explained human behaviors in the model in which behaviors, personal factors such as cognition, and environmental influences all interacted. The factors in this model were: environments, situations, behavioral capacities, expectations, reinforcements (responses to a person’s behavior that increase or decrease the likelihood of reoccurrence), self- efficacy, and reciprocal determination (interaction of the person, behavior, and the environment in which the behavior is performed). Lewis et a1 (1989) applied this theory to examine how specific nutrition/health knowledge, attitudes and behaviors were related to the frequency of consuming four beverages (whole milk, low fat/skim milk, regular soda, and diet soda) among a middle aged group, and a college student group. They concluded that factors associated with the consumption of the beverages differed between the two age groups and that variables of social reinforcement, behavior modeling, and nutrition knowledge might influence consumption indirectly through other factors, such as attitudes and behavior commitment. Model of Diffusion of Innovations. This model was often used for evaluating behavior changes over time as a result of adapting new behaviors (Rogers, 1983). It divided an individual’s innovation-decision process into five stages. The first stage was to gain knowledge. There were three types of knowledge: awareness, how-to knowledge, and knowledge of principles. Awareness motivated people to seek how-to and principles knowledge. How-to knowledge provided information on how to use the innovation. Principle knowledge gave the information of how the innovation worked. The second 20 stage was the persuasion stage, where people tested the innovation, and set their attitudes. The last three stages included decision making, implementation, and seeking either confirmation or reinforcement for the decision. Axelson and Brinberg (1992) and Sims (1981) suggested that broadening the conceptualization of nutrition knowledge to include cognitive processes necessary for integrating nutrition knowledge with dietary behaviors should be facilitated for the future program development. 2.4.2. Sociodemographic factors affecting dietary behaviors Although unmodifiable, sociodemographic factors are strongly related to dietary habits. Identification of subgroups who are at risk can help one develop various programs to improve the knowledge and dietary habits of the target population. Women have traditionally been and still are responsible for preparing meals and Show higher awareness and knowledge of nutrition related issues than other subgroups (F razao and Cleveland, 1994; Jensen et al, 1992; Glanz et al, 1993; Smallwood and Blaylock, 1994; Variyam et a1, 1996). Contento and Murphy (1990) reported that women were more likely to make desirable dietary changes than men. Women tended to have more negative attitudes toward consumption of high-fat foods and more positive attitudes and behaviors for healthy eating than men (Shepherd and Stockley, 1987; Towler and Shepherd, 1992; Shepherd and Stockley, 1985; Sheilham et a1, 1987). However, no gender differences were seen in the perception of the importance of reducing fat intake (Reid and Henddricks, 1994); and in the perception of risk factors on coronary heart disease (CHD), smoking, lowering blood pressure and blood cholesterol, and eating less fat (Hyman et a1, 1993). As far as dietary intake was concerned, in the study of Hyman et 21 a1 (1993), intakes of total and saturated fat among all races were positively influenced by male gender of the meal planner. On the other hand, Stafleu et al (1994) showed no gender differences for energy percentage of fat. Frazao and Cleveland (1994) found that both men and women tended to underestimate the amount of fat and saturated fat in their diets, but overestimated the amount of cholesterol intake. Woolcott et a1 (1983) concluded that men who participated in food preparation improved their diets more toward the direction of recommended dietary goals. Thus we are yet uncertain whether those differences are caused by gender or by other factors such as increased awareness and knowledge of food preparers. Education level showed positive effects on knowledge and awareness (Levy et al, 1992; Reid and Henddricks,1994; Variyam et al, 1996; Carlson and Gould, 1994; Frazao and Cleveland, 1994). Compared to people with a lower socioeconomic status, those with a higher socioeconomic status: have been more knowledgeable about nutrition (Frazao and Cleveland, 1994; Variyam et al, 1996; Carlson and Gould, 1994); had more positive attitudes to nutrition (Grotkowski and Sims, 1978); were more aware of nutritional issues (Nash and McIntyre, 1987); had more positive attitudes and behavior in relation to healthy eating (Sheiham et al, 1987); and had more negative attitudes toward consumption of high fat foods (Shepherd and Stockley, 1987). Thompson et al (1992) found that food sources of fat and fiber differed among various social strata. In contrast, Shepherd and Stockley (1985) found no differences among socioeconomic classes in nutrition knowledge and attitudes, and dietary intakes. Government programs have been developed to improve nutritional conditions of low-income segments. These include: Food Stamps, National School Lunch, National 22 School Breakfast, and nutrition education programs (Akin et al, 1985; Butler et al, 1985; Davis and Neenan, 1979; Devaney and Fraker, 1989; Long, 1991). Capps and Schmitz (1991) stated that most of these studies found participation in government food assistance programs had a positive influence on nutrition intake, if other conditions were the same. Age is another factor that showed various effects on knowledge, attitudes and behaviors across studies. Those 24-45 years of age tended to have more negative attitudes toward consumption of high fat foods, whereas older and youngest subjects had more positive attitudes to such foods (Shepherd and Stockley, 1987). Older subjects were reported to meet the recommendation of dietary intakes of fat, saturated fat and cholesterol more often than younger meal planners (Frazao and Cleveland, 1994; Carlson and Gould, 1994). Older subjects agreed with more healthful attitudes than younger subjects (Glanz et a1, 1993; Reid and Henddricks, 1994; Carlson and Gould, 1994). However, regardless of their positive attitudes and behaviors, older age was shown to have a negative effect on the level of knowledge (Levy et al, 1992; Variyam et al, 1996). On the other hand, Moorman (1990) reported that age had a positive influence on health maintenance behavior and a negative influence on health information acquisition. The inconsistency was due in part to different categorizations of age among studies. Older subjects were one of the commonly reported groups at risk for nutrition inadequacy, and educators need to understand the characteristics of each age group when they develop nutrition education programs. Others factors such as region, urbanization, household Size and occupation have been reported to be associated with dietary intakes in some studies. For example, Jensen et a1 (1992) reported that people from the South consumed less dairy products compared 23 to peOple from other regions. Many demographic and socioeconomic factors are correlated with each other such as income and education. Thus, controlling confounding effects is crucial in examining the associations of demographic and socioeconomic factors with dietary habits. 2.4.3. Behavioral and lifestyle factors affecting dietary behaviors Several behavioral and lifestyle factors were reported to affect dietary behaviors. Lewis et a1 (1989) examined milk consumption among subjects who were committed to select beverages low in fat and sugar. The results showed that subjects who were committed to select beverages low in fat and sugar tended to consume less whole milk and more low fat/Skim milk than those who were not committed. F razao and Cleveland (1994) reported that the presence of conditions in which a low fat/cholesterol diet may be recommended was positively correlated with adherence. Carlson and Gould (1994) reported a positive relationslu'p between the number of hours of watching TV and nutrition knowledge. The authors hypothesized that the number of hours of watching TV might be related to the physical activity level. However, the hours of watching TV may be positively related to the individuals’ diet because of increased nutrition knowledge, but may be also negatively related to unhealthiness because of decreased physical activity level. Other behavioral and lifestyle factors possibly affecting dietary behaviors were: frequency of exercise; vegetarianism; supplement use; and having small children (V ariyam et al, 1996; Jensen et al, 1992). Further studies on the association between those factors and dietary behaviors are needed. 24 2.4.4. Sensory preferences and other factors affecting dietary behaviors Sensory preferences are important factors that affect individuals’ food choices (Guthrie, 1994). Towler and Shepherd (1992) reported that individual belief-evaluation of the taste of products was closely related to attitudes toward high fat food consumption. Colavito et a1 (1996) reported from the 1989-91 CSFII/DHKS data that people who were concerned about utilities of food (e.g. easiness of preparation, taste, and price) were likely to have higher fat intake than people without these concerns. Lewis et a1 (1989) developed a model that incorporated factors for social environment, reinforcement, commitment, behavior modeling, knowledge, and attitudes relative to the frequency of consumption of four beverages (whole milk, low-fat/skim milk, regular soda, and diet soda) among 457 middle-aged adults. They found that enjoyment of the taste was related to frequency of beverage consumption by all respondents. Colavito et a1 (1996) examined the relationship between diet-health attitudes and nutrition knowledge of household meal planners and fat and fiber intakes of meal planners and preschoolers. They found that unpalatable taste was a significant barrier to healthy eating among younger adults. People who had lower fiber intakes had greater taste concerns than those who had higher fiber intake. Taste was an extremely important factor in determining food purchase for WIC participants (75% of 69 individuals, Michel et al, 1994). Thus, sensory preferences for selecting foods could lead most people to over-eat in this era of food abundance. Nutrition educators Should encourage people to better understand nutrition-related problems, including over-consumption, by increasing their 25 nutrition awareness and knowledge, and modifying attitudes to improve their health status. In summary, many theory-based research endeavors have attempted to understand individuals’ dietary behaviors. However, none of those theories could yield a satisfactory explanation which identified factors and the mechanisms for their effect on individuals’ dietary behaviors. They failed to explain individuals’ dietary behaviors, due to interaction of various involved factors. Different results of various studies were obtained because of the selection of different subjects, types of theories, and statistical analyses. To overcome these limitations, recently, many researchers suggested combining reported findings and various health-behavior models together to obtain reliable and valid information regarding factors affecting individual’s dietary behaviors (Achterberg and Clark, 1992; Colavito and Guthrie, 1996). Thus, a sophisticated study design to combine reported findings and theories is required for the future study to examine the effectiveness of food label usage on improving individuals’ diets. 2.5. Reliability and validity of questions related to food label use and dietary intake in CSFII/DHKS 2.5.1. CSFII/DHKS 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII) and Diet and Health Knowledge Survey (DHKS) was conducted in 1994-1996 by the Agricultural Research Service (ARS) of the US. Department of Agriculture (USDA). The CSFII 1994-1996 included two nonconsecutive days of dietary intake information by using the 24-hr 26 atary recall method for individuals of all ages. In the CSFII 1994, 5,589 individuals ovided at least one day of dietary intake data. Each year, a stratified, probability sample method was used to select samples. For 5 method, the population was stratified based on geographical location, degree of )anization, and sociodemographic considerations. Areas within these Strata were 'ided into smaller, relatively homogeneous sampling units or “clusters” (Colavito and thrie, 1996). Selected individuals within each household rather than from all Isehold members were obtained to avoid bias effects of the individual characteristics m the same household. The low-income population was over-sampled, and the sample resented the entire United States rather than only the 48 contiguous States and shington D.C. Data were collected from mid-January to mid-January of the next year 1g one day recalls on two non-consecutive days in the form of in-person interviews. s sampling method allowed users to obtain generalized population data, by using :ple weight and variance estimates within special Statistical software programs such as ;tvar and SUDAAN (Colavito and Guthrie, 1996). The DHKS 1994-1996, on the other hand, included questions on individuals’ dietary wledge and attitudes developed based on educational theories. Topics included: diet- ase relationships; food safety issues; information of the food label use; and other .tional issues such as importance of nutrition. Included in DHKS were participants in ISFII 1994-1996 that were 20 years of age and older. The sample size was 1,879 riduals in DHKS 1994. Other improvements made in CSFII/DHKS 1994-1996 from ious years are summarized in Table l. 27 In summary, CSFII/DHKS 1994-1996 is a unique database in which researchers n relate individuals’ actual food intakes with their dietary knowledge and attitudes in :U.S. It included a number of food label related questions, allowing the survey data to most appropriate for the studies on current food labels and dietary intakes among the 3. population at that time point. The complicated survey design, however, might have couraged many researchers from attempting to use the data of national surveys, such DHKS 1994-1996. 2. Reliability test for survey questions Many researchers have attempted to explain an individual’s dietary behaviors 1 socio-psychological theories such as the Health Belief Model, Social Cognitive ory, and Theory of Reasoned Action (Colavito and Guthrie, 1996). Various theories ained dietary behaviors only partially, because individuals’ dietary behaviors were :ted by multiple factors. The inability to explain the dietary behaviors substantially rose theories might have resulted from other reasons such as lack of reliable surements of dietary behaViors. Reliability is a measurement of the extent to which question items or scores of the rield the same result upon repeated administration, when all other factors are held the . When the same survey is conducted repeatedly, reliability is measured by lation of repeated measurements. However, when repeated measurements are not >le such as CSFII/DHKS, a group of question items (construct) is examined for ial consistency of the question items within a construct instead. Frequently internal stency of the question items is assessed by: correlation and covariance matrixes; 28 n total correlation (ITC); alpha if item deleted; and Cronbach’s alpha (Sapp and sen, 1997; Nunnally and Bernstein, 1994; SPSS professional statistics 6.1, 1994). The itation of this assessment is that it requires mode than 1 question item for the ability of the construct to be tested. 2.1. Correlation and covariance matrixes Correlation and covariance matrixes are to identify question items that have erent relations from the rest of items in the group (construct). When a question item vs a negative correlation with the rest of items and correlations among the rest of .s in the construct are positive, one concludes that the question item may represent :rent characteristics (content) from the rest of items, and Vice versa. If the content of [uestion item is apparently different from the rest of items, that question item is )ved from the construct. This quick and convenient method at the very beginning of est of reliability minimizes the number of the question items in the construct (SPSS :Ssional statistics 6.1, 1994). 2.2. Item total correlation (ITC) Item total correlation is a correlation of an item with the average of all items in instruct. While correlation and covariance matrixes examined all possible ation between two items within the same construct, ITC examined how the dual item associate with the construct. ITC ranges from 0 to 1.0 (Sapp and Jensen, Nunnally and Bernstein, 1994; SPSS professional statistics 6.1, 1994). 29 2.3. Alpha if item deleted Alpha if item deleted is examined to obtain the reliability of the construct after luding the question item (Sapp and Jensen, 1997; Nunnally and Bernstein, 1994; 1S professional statistics 6.1, 1994). Thus, this measurement examines the effect of a icular question item on the construct. While Cronbach’s alpha examines the average 11 possible correlations among items within the same construct, alpha if item deleted ws one to assess the average of all possible correlations without the item. Thus, if a if item deleted is higher than Cronbach’s alpha, there is a possibility that the item asents a different content from the rest of items in the construct. In this case, the ent of the item is examined. If the content of the question item is apparently different 1 the rest of items in the construct, the question is removed. Alpha if item deleted es from 0 to 1.0. .4. Cronbach’s alpha Conbach’s alpha is an average of all possible correlations within the same :ruct. Cronbach’s alpha measures the internal consistency of the question items in a .ruct. When a survey is not repeated but contains multiple question items in a ruct, we assume that the question items within a construct inquire about the same cteristic (internal consistency). Thus, a sum of responses from question items in the ruct would be equivalent to repeating surveys (Nunnally and Bernstein, 1994; SPSS ssional statistics 6.1, 1994). 30 To obtain a high Conbach’s alpha, question items in each construct should be ternally highly correlated. Cronbach’s alpha ranges from 0 to 1.0, representing the nge of no correlation to high correlation. It is calculated by the following equation: = kr—/ 1 +(k-1)r— when k is the number of iterfis, and r is the average correlation between items in .e construct. In nutrition, r 2 0.7 is considered desirable to measure nutrition knowledge 1d knowledge structure (Axleson and Brinberg, 1992). The limitation of Cronbach’s pha is that it increases when the number of items increase (SPSS professional statistics .1, 1994). .5.3. Validity test of survey questions A test of validity of the question items is to confirm that the question items reasure characteristics purported to be measured (Sapp and Jensen, 1997; Nunnally and lernstein, 1994; SPSS professional statistics 6.1, 1994). As long as this is confirmed, the est of validity can evaluate the construct with even one question item, while it is not iossible for the examination of the reliability. Validity is assessed for the content content validity) and the construct (construct validity). Content validity measures the extent to which the items or scores represent the lomain of a given characteristic (construct). Another word, content validity measures 10w much of the characteristic is represented by the responses to the question items. It is 1 judgement usually made through consensus agreement of scholars who determine if question items or scores cover the full domain of the characteristic without contamination 3y unrelated items. 31 Construct validity examines the structure of the construct. Construct validity is ien measured by discriminant validity or divergent validity. Discriminant validity tamines the ability of a question to separate respondents based on respondents’ known iaracteristics “criteria”. For example, females generally have higher nutrition iowledge than males (Jensen et al (1992); Frazao et a1 (1994); Smallwood et al (1994); ariyarn et a1 (1996)). Thus, if females correctly answered nutrition knowledge aestions more often than males, questions are determined to be valid. Divergent validity examines the ability of constructs to lead respondents into ifferent directions. Each construct is designed to measure different characteristics, and ivergent validity is measured by the correlation between constructs. Thus, correlation etween nutrition knowledge and nutrition perception, for example, is expected to be mall, although a certain degree of relationship between nutrition knowledge and utrition perception might exist. .5.4. Tests for reliability and validity of DHKS 1994-1996 Sapp and Jensen (1997) established the reliability and validity of nutrition nowledge and diet-health awareness questions included in the 1989-1991 DHKS. They ,sed KR-20 and Spearman-Brown rho calculated with a split halves procedure instead of Ironbach’s alpha for tests of reliability. KR-20 is often used for dichotomous data, while Ironbach’s alpha is used for continuous data (Sapp and Jensen, 1997). Split halves procedure is another popular method for the test of reliability. Question items in a construct are divided into two subconstructs in various parts, and :stimated by an average between pairs of these subconstructs. High correlation between 32 constructs is obtained when these two constructs share similar characteristics. The tation of this measurement is that it depends on how question items are divided into groups (SPSS professional statistics 6.1, 1994). Sapp and Jensen reported that: LR-20 reliability and split halves reliability of the 23-item nutrition knowledge test less than 0.7 for all three years of1989-91 CSFII (1989, 1990, and 1991); 2) KR-20 Ibility and split halves reliability of the 27-item diet-health awareness test was greater 0.7 for all three years of the survey; 3) both nutrition knowledge and diet-health reness validity tests received support for discriminant and convergent validity mines the ability of constructs to lead respondents into similar direction because of ' similarities); and 4) there was low the correspondence validity (which examines ther the construct correlates significantly or substantially with measures of behaviors) )th nutrition knowledge and diet-health awareness tests with three measures of ry quality (percentage of calories from fat; percentage of calories from saturated fat; Jercentage of the recommended daily allowance of calcium) was low. No studies to reported the reliability and validity of survey questions included in DHKS 1994- In summary, construction of question items that are reliable and valid is important tain accurate and meaningful information. Desirable questionnaires: include ple items that are internally highly correlated; represent characteristics of interest in; ave the ability to separate respondents based on the gold standard. No information late available for the reliability and validity of DHKS 1994-1996. Tests of ility and validity of the survey questions are necessary before the effect of current abels on improving an individual’s diet can be investigated. 33 Chapter Three METHODS Throughout the present study, sample weights provided in DHKS 1994-1995 was :d. All reliability tests and divergent validity test were conducted by SPSS 7.5. Mean tandard errors and tests of Significant differences in the discriminant validity test were culated using SUDAAN to account for the sample survey design effect of DHKS 14-1995 (Colavito and Guthrie, 1996). . Description of the sample of DHKS 1994-1995 (Objective 1) 1994-1995 CSFII/DHKS was provided from the US. Department of Agriculture on -ROM [US Department of Agriculture, Agricultural Research Service. 1994 and 1995 itinuing Survey of Food Intakes by Individuals and 1994 and 1995 Diet and Health iwledge Survey. Springfield, VA: National Technical Information Service, 1994 and 5. (Accession no. PB96-501010 for 1994 and PB97-500789 for 1995)]. The :ription of CSFII/DHKS 1994-1995 and how data was collected were collected are :ribed in chapter 2 (2.5.1. CSFII/DHKS 1994-1996). CSFII/DHKS 1994—1995 were gorized into different record types based on the information such as respondents’ intake information and nutrition knowledge and attitudes. Record type 50, which used in this study, included DHKS response data (n= 1879 and IF 1966 for 1994 and i, respectively). All subjects who participated in DHKS were 20 years of age and '. Question items that were not answered by subjects were excluded from statistical ’SCS. 34 The original DHKS had assigned numbers (e.g., 1,2,...) or strings (e.g., A, B,. . .) to iographic, socioeconomic, health and lifestyle variables based on their levels (e. g. , income) and subgroups (e.g. gender, race). The variables examined in the present ly were: demographic variables (gender, age, race, status of meal planner/preparer, on, degree of urbanization); socioeconomic variables (income, education, Status of 3 and food stamp participation); health indicators (pregnancy status; presence of gnosed diabetes, high blood pressure, heart disease, cancer, osteoprosis, high blood lesterol, and stroke; practicing special diet such as weight 1055/ low calorie, low :holesterol, low salt/sodium, high fiber, and diabetic diet); lifestyle factors (smoking .15, practice of physical exercise, hours of TV watching, use of Vitamin supplements). h of the variables was relabeled (i.e., age was relabeled as “reage”) for ease in the ;equent statistics, and listed on Table 2. Reliability tests of DHKS 1994-1995 (Objective 2) 1. Score definitions 1.1. 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Respondents were further asked to link dietary behaviors to health problems (KQ6a-g) if their answers to responses to the previous questions (KQSa-g) were correct. Correct answers to the knowledge questions were identified by graduate students and faculty members in the Department of Food Science and Human Nutrition at Michigan State University, referring to the Dietary Guidelines for Americans, the Food Guide Pyramid and other nutrition text books with over 80% of consensus. Responses to each of the question items were recorded (e.g. 1= correct response, O= incorrect response for KQla through c), and the sum of correct responses was obtained as a score (possible 40 construct). Later, reliability and validity of the possible construct was tested (refer to 3.2.2. through 3.3.2.2) and if its reliability and validity were confirmed, the score was accepted as a final knowledge construct (Table 3). 3.2.1.2. Attitude scores Questionnaires on individual’s nutrition and food label related perception and attitudes were available in 1994-1996 DHKS. For example, KQ23a asked, “ The nutrition information on food labels is useful to me? Respondents were asked the level of their agreement to each statement. For example, response to KQ23a was labeled as 1: Strongly disagree, 2= Somewhat disagree, 3= Somewhat agree, and 4= Strongly agree. Each of variables were recorded for ease in the subsequent statistics (i.e., KQ23a was relabeled as rekq23a), and the sum of responses was obtained as a score for that group of question items (possible construct). Question items asking similar attitude from two different groups (e.g., perceived adequacy of nutrients that were recommended to decrease: KQ3a; f; g; h; i; k; and KQ7) were merged together into one construct, if possible. Later, reliability and validity of the construct was tested (refer to 3.2.2. through 3.3.2.2). If reliability and validity were confirmed, the score was accepted as a final attitude construct (Table 4). 3.2.1.3. Scores for the food label use An extensive number of the food label related questions were included in 1994— 1996 DHKS, reflecting the enactment of the new food label regulation introduced in 41 E 6950 .on 6950:_ A0030: 0 0:0: _ :2, 5 £0: E20 00: 00005000 05E 00: 00005 0000.305. 50> :0 00000 E 6950 .20 6950:_ 500x00 0 5.004000 5 0:03 000 00: 00005000 05E 00: 00005 00000505. 50> :0 0000m A: 6950 .on 6950:_ A0030: «0:000:08 5 :ruflalm 00: 00005000 05E 00: 02:3 .0000_>>0:v_ 50> :0 0000m 07.0 ”00:0: 9000 2.5050 6:00:00 000: 0:0 50:5: 5 0000.265. 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The same methods for attitude scores were used to determine definitions of constructs, possible responses, and possible score ranges (Table 5). 3.2.2. Reliability tests The reliability of the question items in this study were measured by correlation and covariance matrixes, item total correlation (ITC), alpha if item deleted, and Cronbach’s alpha (Sapp and Jensen, 1997; Nunnally and Bernstein, 1994; SPSS professional statistics 6.1, 1994; Axelson and Brinberg, 1992) for the construct with more than 1 question item. 3.2.2.1. Correlation and covariance matrixes Correlation and covariance of question items were examined within each construct. The general rule during the reliability test was, if possible, not to change question items to avoid the loss of original information. However, when there was any question item that showed a different pattern from the rest of items (e. g., the question item shows a negative correlation with the rest of question items when correlations among the rest of items in the construct are positive), content of the question item was compared with other items. Only if the item apparently represented different characteristics from the rest of question items in the construct, the item was excluded. Thus, if there was any similarity between the items, it remained in the construct. 58 3.2.2.2. Item total correlation and alpha if item deleted For each construct, item total correlation of question items was examined. When an item had a lower item total correlation than other items, alpha if item deleted was examined. If alpha if item deleted showed an increase from the original Cronbach’s alpha, and the content of the question items were different from the rest of items, the item was removed. Otherwise, the item was kept (SPSS professional statistics 6.1, 1994). Some question items in different constructs seemed to measure the similar characteristic and used the similar responses. For example, both KQl and KQ22 inquired about individuals’ nutrition knowledge, and were labeled as 1= correct and O= incorrect. Thus, both questions were merged, and its item total correlation and alpha if item deleted were examined. If it showed an increase, the merged question was accepted as a construct. 3.2.2.3. Cronbach’s alpha When the content of questions from different constructs was similar, Cronbach’s alpha of merged question items of all constructs was examined along with item total correlation and alpha if item deleted. If measurements of reliability were feasible, the construct with the highest Cronbach’s alpha was accepted as a final construct. 3.3. Validity tests of DHKS 1994-1995 (Objective 3) 3.3.1. Content validity Content validity (i.e., examination of the extent to which question items cover the domain of characteristics such as nutrition knowledge and attitudes) was examined through discussion with faculty members and graduate students in our department, 59 referring to the Dietary Guidelines for Americans, the Food Guide Pyramid, and nutrition text books. 3.3.2. Construct validity Construct validity was assessed by divergent validity and discriminant validity. 3.3.2.1. Divergent validity After constructs were identified from the reliability test, correlations between different constructs were assessed to measure divergent validity. 3.3.2.2. Discriminant validity Mean 0 standard error (SE) of nutrition knowledge, attitudes and the food label use scores among various subgroups were calculated for both 1994 and 1995. Discriminant validity was assessed by testing significant differences of mean scores of nutrition knowledge, attitudes, and frequency of the food label use constructs between/among subgroups based on criteria by t-test and F -test. The present study used well-accepted findings and theories from past studies as criteria, which were frequently reported across studies, and whose study designs were carefully designed. They are: 1. Females are more knowledgeable about nutrition than males (Levy et al, 1992; Ernst et a1, 1986; Frazao et al, 1994; Smallwood et a1 1994; Variyam et a1, 1996; Smith et a1, 1997). 2. Females are more concerned about their diet than males (Shepherd et al, 1987; Towler et a1, 1992; Smith et a1, 1997; Shpherd et al, 1985). 60 10. 11. 12. Females are more motivated to improve their diet than males (Glanz et al, 1993; Contento et al, 1990). Females use food labels more frequently than males (Bender et al, 1992; Guthrie et al, 1995). Meal planners/preparers are more knowledgeable about nutrition than non-meal planners/preparers (Jacoby et al, 1977). Higher-income respondents are more knowledgeable about nutrition than lower- income respondents (Morton et al, 1997; Frazao et a1, 1994; Variyam et al, 1996; Reid et a1, 1994; Hyman et a1, 1993; Carlson et al, 1994; Grotkowski et al, 1978). Higher-income respondents are more concerned about their diet than lower-income respondents (V ariyam et al, 1996; Shepherd et al, 1987; Grotkowski et al, 1978). Lower-income respondents are more concerned about the price of food than higher- income respondents (Morton et al, 1997; Hyman et al, 1993). Higher-income respondents use food labels more frequently than lower-income respondents (Guthrie et al, 1995). There are differences in nutrition knowledge among the age group (Levy et al, 1992; Jacoby et al, 1977; Cole et al, 1990; Frazao et al, 1994; Smallwood et al, 1994; Variyam et a1, 1996; Carlson et al, 1994; Smith et al, 1997; Levy et a1, 1993). There are differences in motivation of improving own diet among the age group (Shpherd et a1, 1987; Reid et al, 1994; Towler et al, 1992). There are differences in frequency of food label use among the age group (Bender et al, 1992; Reid et al, 1994). 61 13. There are differences in understanding of nutrition knowledge depending on the education level (Levy et al, 1992; Ernst et a1, 1986; Jeffery et al, 1982', Fullmer et al, 1991; Frazao et al, 1994; Smallwood et al, 1994; Variyam et al, 1996; Smith et al, 1997; Levy et al, 1993). 14. There are differences in frequency of food label use depending on the education level (Bender et al, 1992; Guthrie et al, 1995). 15. Smokers are less knowledgeable about nutrition than non-smokers (Variyam et al, 1996) 16. Smokers are less concerned about their diet than non-smokers (Smith et al, 1997). 17. Smokers are less motivated to improve their diet than non-smokers (Glanz et al, 1993; Smith et al, 1997). 3.4. Comparisons of nutrition knowledge, attitudes and use of the food label by Americans between 1994 and 1995 (Objective 4) Mean i standard error (SE) of nutrition knowledge, attitudes and food label use scores for the DHKS 1994-1995 population was calculated. Significant differences in population scores between 1994 and 1995 were tested by t-test. 62 Chapter Four RESULTS 4.1. Description of the sample of DHKS 1994-1995 (Objective 1) Sample distributions categorized by each factor are summarized on Table 6. Both unweighted sample size and percentage of weighted sample are listed. The distribution of selected variables was similar between 1994 and 1995. The prevalence of females was slightly higher than males (52% vs. 48% in both years, respectively). All DHKS respondents were aged 20 years and older; about 32% of respondents were aged 20-34 years old (33% in 1994 and 32% in 1995); less than 40% of respondents were aged 35—54 years old (38% in 1994 and 39% in 1995); and less than 30% of respondents were aged 55—89 years old (29% in 1994 and 28% in 1995). There were few subjects aged ninety years and older (0.3% in both years). Subjects were predominantly white (81% in 1994 and 80% in 1995), followed by African-American (11% in both years), others (4% in both years), Asian and Pacific Islander (2% in 1994 and 3% in 1995), and American Indian (0.6% in 1994 and 0.7% in 1995). About 60% of respondents were meal planners/preparers (62% in 1994 and 59% in 1995). The income level of 16 percent of respondents was less than 130% poverty threshold in both years. More than 40% of respondents had more than a high school education (48% in 1994 and 51% in 1995). Over 40% of respondents (44% in 1994 and 43% in 1995) had a high school education (9-12 years). Less than 10 % (7% in 1994 and 6% in 1995) had less than a high school education. Less than 1% (0.4% in 1994 and 0.6% in 1995) of respondents participated in the Women, Infant, and Children 63 m. 3 SR woo woo oz woo om: oeo New; mm> .mamamatmccma Ems. :V mo 3 on 99.20 no 5 mo 5 966:. emotmE< Yo om vN No 59:22 oEomn. .5394 v. E mmm m. 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About 8% (9% in 1994 and 8% in 1995) of respondents received food stamps in the past 12 months from the surveillance period, and about 5% (5% in 1994 and 6% in 1995) of respondents were authorized to receive food stamps during the surveillance period. About 20% of respondents were diagnosed with high blood pressure (21% in 1994 and 20% in 1995). More than 10% of respondents were diagnosed with high blood cholesterol (14% in 1994 and 15% in 1995). About 5% of respondents were diagnosed with diabetes (5% in 1994 and 6% in 1995) and cancer (5% in 1994 and 6% in 1995). Less than 5% of respondents were diagnosed with osteoporosis (2% in 1994 and 3% in 1995) or stroke (2% in both years). More than 5% of respondents were on a weight 1053/ low calorie diet (7% in both years) or on a low fat/cholesterol diet (9% in both years). About 5% of respondents were on a low salt/sodium diet (4% in 1994 and 5% in 1995). Less than 5% of respondents were on a high fiber diet (2% in both years) or on a diabetic diet (2% in both years). We must remember that all of our respondents were aged 20 years and older. Smoking prevalence in our respondents slightly increased from 51% in 1994 to 55% in 1995. Forty-one percent of respondents in both years answered that they rarely or never exercised. About 25% (25% in 1994 and 24% in 1995) of respondents answered that they exercised 2-4 times a week, and 26% of respondents in both years answered that they exercised almost every day. Over 25% of respondents (29% in 1994 and 27% in 1995) watched TV more than 4 hours a day. More than 45% of respondents (49% in 1994 and 47% in 1995) used vitamin supplements. 71 It should be noted that the findings associated with respondents aged 90 years and older, Asian and Pacific Islander (1995 data only), American Indian, pregnant and lactating women, WIC participants were not reliable because of their small sample sizes. 4.2. Reliability tests of DHKS 1994-1995 (Objective 2) 4.2.1. Score definitions 4.2.1 .1 . Nutrition knowledge scores Five final knowledge constructs were established: knowledge of the Food Guide Pyramid construct (Knol; KQla-e); knowledge of nutrition and food construct (Kn081 1; KQ8b-d, KQ9a,c-f, KQlO and KQl 1); knowledge of the amount of nutrient content per servings construct (Kn022; KQ22a-e); awareness of diet-disease relationships construct (Awapr; KQSa-g); and awareness of the authority of the government to define phrases on the food label construct (Awauth; KQ21a-c) (Table 7). 4.2.1.2. Attitude scores Fourteen final attitude constructs were established: perceived adequacy of own nutrient intakes which are recommended to decrease construct (Adende; KQ3a-k); perceived adequacy of own nutrient intakes which are recommended to increase construct (Adenin); perceived adequacy of own weight construct (Adeqwt; KQ7); perceived barriers from using the food label construct (Barrier; KQ23c-d); perceived benefits of using the food label construct (Benef; KQ23 a, g-j); perceived easiness to understand the food label construct (Ease; Kq19a-g); perceived importance of practicing healthy dietary habits construct (Imdiet; KQ4a-k and KQl 5b); perceived importance of food safety 72 11‘ " ”if“ '5 construct (Imsafe; KQlSa); perceived importance of the price of food construct (Impric; KQl 5c); perceived importance of how well food keeps construct (Imlast; KQl 5d); perceived importance of easiness of the food to prepare construct (Imease; KQlSe); perceived importance of the taste of food construct (Imtast; KQl 5f); perceived reliability of descriptions on the food label construct (Conf; KQ20a-f); and willingness of learning more about the food label construct (Attitu; KQ23f) (Table 8). 4.2.1.3. Scores for the food label use One final construct was determined: frequency of the food label use (Fluse; KQ16a-e and KQ17a-h) (Table 9). 4.2.2. Reliability tests 4.2.2.1. Correlation and covariance matrixes Correlation and covariance matrixes within the same construct identified question items that showed different relations with the rest of items. For example, all of correlations among items in knowledge of the Food Group Pyramid construct (KQla to KQle) were small, but positively related to each other. On the other hand, correlations among items in knowledge of nutrition and fat construct (KQ9a to KQ9f) showed positive correlation with each other, except KQ9b that showed a negative correlation with other items in the same construct. These matrixes identified question items that showed different patterns from the rest of items in the construct. However, they didn’t provide any information on what extent each question item related to the construct. Because there were no apparent 73 explanations for the items to have different patterns from the rest of items, these items were kept in the construct for the following analyses. 4.2.2.2. Item total correlation and alpha if item deleted The average ITC of the nutrition knowledge constructs ranged from low (less than 0.4) to high (higher than 0.7). Nutrition knowledge constructs with low average ITC were on: the Food Group Pyramid (Knol) with 0.2 in 1994 and 1995; nutrition and food (Kn0811) with 0.3 in both years; and the amount of nutrient content per servings (Kn022) with 0.3 in both years. Nutrition knowledge constructs with moderate average ITC (0.4- 0.6) were on: the awareness of nutrition-disease relationships (Awapr) with 0.4 in both years. Nutrition knowledge construct with high average ITC was on the awareness of authority of the government to define phrases on the food label (Awauth) with 0.7 both years (Table 7). The average ITC of the attitude constructs also ranged from low to high. Attitude constructs with low ITC were on: the perceived adequacy of own nutrient intakes which were recommended to increase (Adenin) with 0.3 both years; and the perceived barriers from using the food label (Barrier) with 0.3 in 1995. Attitude constructs with moderate ITC were on: the perceived adequacy of own nutrient intakes which were recommended to decrease (Adende) with 0.5 in both years; the perceived benefits of using the food label (Benet) with 0.6 in both years; the perceived barriers from using the food label (Barrier) was 0.4 in 1994; the perceived easiness to understand the food label (Base) with 0.5 in both years; the perceived importance of practicing healthy dietary habits (Imdiet) with 0.6 both years. Attitude construct with high ITC was on perceived reliability of 74 descriptions on the food label with 0.7 in both years (Table 8). The average ITC of frequency of the food label use construct (Fluse) was moderate with 0.6 in both years (Table9). Alpha if item deleted identified question items whose exclusion would increase the final reliability (Cronbach’s alpha). Thus, this measurement was used to determine whether the item should be kept or not in the construct, depending on the amount of increase between the final reliability and alpha if item deleted. For example, if the question item inquiring about the appropriate number of servings from grains (KQld) in the Food Guide Pyramid construct was excluded, would be Cronbach’s alpha increase from 0.42 to 0.44 in 1994 in knowledge of the Food Guide Pyramid construct. The item was kept not to loose the original information for small increase with Cronbach’s alpha. The final average alpha if item deleted of the nutrition knowledge constructs ranged from moderate to high. Nutrition knowledge constructs with moderate average alpha if item deleted were on: the Food Guide Pyramid (Knol) with 0.4 in 1994 and 1995; nutrition and food (Kno811) with 0.6 in both years; and the amount of nutrient content per servings (Kno22) with 0.5 in both years. Nutrition knowledge constructs with high average alpha if item deleted were on: the awareness of nutrition-disease relationships (Awapr) with 0.7 in both years; and the awareness of the authority of the government to define phrases on the food label (Awauth) with 0.8 in both years (Table 7). The average Alpha if item deleted of the attitude constructs also ranged from moderate to high. The attitude construct with moderate alpha if item deleted was on the perceived adequacy of own nutrient intakes which were recommended to increase 75 «36% Owumhzwmm C_ >>O_ LO 76 mm. mm. om. 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Attitude constructs with high alpha if item deleted were on: the perceived adequacy of own nutrient intakes which were recommended to decrease (Adende) with 0.7 in both years; the perceived benefits of using the food label (Benet) with 0.8 in both years; the perceived easiness to understand the food label (Base) with 0.8 in both years; the perceived importance of practicing healthy dietary habits (Imdiet) with 0.9 in 1994 and 0.8 in 1995, respectively; the perceived reliability of descriptions on the food label (Cont) was 0.9 in both years (Table 8). The average Alpha if item deleted of frequency of the food label use construct (Fluse) was high at 0.9 in both years (Table 9). 4.2.2.3. Cronbach’s alpha Cronbach’s alpha of the knowledge of diet-disease relationships constructs (KQ6a-g) was either too small (r<0.3) or out of normal range (F 0.0-1.0). Thus, those constructs were excluded (Appendix F). Final Cronbach’s alpha of nutrition knowledge constructs ranged from moderate to high. Nutrition knowledge constructs with moderate Cronbach’s alpha were on; the knowledge of the Food Guide Pyramid (Knol) with 0.4 in 1994 and 1995; and the amount of nutrient contents per servings (Kno22) with 0.5 in both years; nutrition and food (Kn0811) was 0.6 in both years. Nutrition knowledge constructs with high Cronbach’s alpha were on: the awareness of the authority of the government to define phrases on the food label (Awauth) with 0.8 in both years; and the awareness of diet-disease relationships construct (Awapr) was 0.7 in both years (Table 7). Cronbach’s alpha of attitude constructs ranged from moderate to high. Attitude constructs with moderate Cronbach’s alpha were on; the perceived adequacy of own 82 nutrient intakes which were recommended to increase (Adenin) with 0.5 in both years; and the perceived barriers from using the food label (Barrier) with 0.5 in both years. Attitude constructs with high Cronbach’s alpha were on: the perceived adequacy of own nutrient intakes which were recommended to decrease (Adende) with 0.7 in both years; the perceived reliability of descriptions on the food label (Cont) with 0.9 in both years; the perceived benefits of using the food label (Benet) with 0.8 in both years; the perceived easiness to understand the food label (Base) with 0.7 in both years; and the perceived importance of practicing healthy dietary habits (Imdiet) with 0.9 in both years (Table 8). Cronbach’s alpha of frequency of the food label use construct was high with 0.9 in both years (Table 9). Final reliabilities as determined by Cronbcah’s alpha for the knowledge constructs on the Food Guide Pyramid, on nutrition and food, and on the amount of nutrient content per servings were moderate (I: 0.4, 0.6, and 0.5, respectively). Even the average item total correlations were low (r=0.2, 0.2, and 0.3, respectively). Among attitude constructs, similar results were seen in the perceived adequacy of own nutrient intakes that were recommended to increase construct (the average item total correlation was r= 0.3; the Cronbach’s alpha was 1: 0.5) and in the perceived barriers from using the food label construct (the average item total correlation was r=0.3 in 1995; the Cronbach’s alpha was r= 0.5). These results suggested that those constructs were consisted of question items that didn’t represent the characteristic of the construct well. Thus, the final reliability of these constructs increased possibly because of increased number of question items, not because of strong relationships among question items. 83 4.3. Validity tests of DHKS 1994-1995 (Objective 3) 4.3.1. Content validity Final constructs covered a wide range of nutritional issues associated with individual’s dietary habits and food label use. Nutrition knowledge questions and attitudes toward healthy dietary habits corresponded with what the American Dietary Guidelines, The Food Guide Pyramid, and other nutrition textbooks emphasized. Thus, the consensus of content validity of final constructs included in DHKS 1994-1995 was obtained after discussions with faculty members and graduate students in our department, reaching to over 80% of the agreement. 4.3.2. Construct validity 4.3.2.1. Divergent validity Most correlations among final constructs included in DHKS 1994-1995 were small (r= 0.0 — 0.3). Moderate correlations (0.4-0.6) in DHKS 1994 were seen between: perceived easiness to understand the food label construct (Base) and perceived barriers from using the food label construct (Barrier) with r= 0.4; willingness to learn more about the food label construct (Attitu) and perceived benefits of using the food label construct (Benet) with r= 0.4; frequency of the food label use construct (Fl use) and perceived benefits of using the food label construct (Benet) with F 0.5; and frequency of using the food label construct and importance of practicing the healthy diet construct with r=0.5. In DHKS 1995, moderate correlations were seen between: perceived easiness to understand the food label construct (Base) and perceived barriers from using the food label (Barrier) with r= 0.5; perceived benefits of using the food label construct (Benet) 84 and perceived importance of practicing healthy diet (Imdiet) with F 0.4; perceived benefits of using the food label construct (Benet) and willingness to learn more about the food label construct (Attitu) with r=0.4; perceived benefits of using the food label construct (Benet) and frequency of the food label use construct (Fl use) with r= 0.6; perceived importance of practicing healthy dietary habits (Imdiet) and perceived importance of food safety (Imsafe) with r=0.4; and perceived importance of practicing healthy dietary habits (Imdiet); perceived adequacy of own weight construct (Adeqwt) and knowledge of the Food Guide Pyramid construct (Knol) with r=0.5; and frequency of the food label use construct (Fl use) with r = 0.5. Thus, results from divergent validity test assured that final constructs were good enough to represent different characteristics without being contaminated by other constructs (Table 10). 4.3.2.2. Discriminant validity 4.3.2.2.1. Knowledge constructs (Knol, Kno811, Kn022, and Awapr) Females were significantly more knowledgeable about the Food Guide Pyramid (Knol) than males (10.6 vs. 10.1 in 1994; 10.8 vs. 10.1 in 1995). They were significantly more knowledgeable about nutrition and food (Kn081 1) than males (7.0 vs. 6.8 in 1994; 7.0 vs. 6.7 in 1995), and were significantly more aware of diet-disease relationships (Awapr) than males (6.1 vs. 5.6 in 1994; 6.0 vs. 5.7 in 1995). No significant mean score difference was seen in knowledge of the amount of nutrient content per servings construct (Kno22) in this group category. Thus, results obtained from knowledge of nutrition and food construct (Kn0811), knowledge of the Food Guide Pyramid construct (Knol) and awareness of diet-disease construct (Awapr) corresponded with criteria 1 85 8. 8. 8. 8. 8. 8.- 8. E. 8. 8. 8: r. 8. 8.- 8.- 8. 8. 8.- 8.- 8.- 8. 8.- 2.5. 8. 8. 8. 8. o 8. o 8. E. 8. £83. 8.- 8. S. 8. E. 8. 8.- 8. 8. 8. .822. 8. 8. 8.- o 8. 8. 8.- 8.- 8. 8 use. 8. 8. 8. 8. 8. 8. 8.- 8.- 8. 8. 885. 8. 8.- 8.- 8.- 8. 8.- 8. .1.- 8. 8.- 8.5. 8. 8.- 8.- 8. 8.- 8.- 8. 8.- 8. 8.- 8.5.5. 8. 8. 8. 8. 8. 8.- 8. 8.- 8. 8. £me. 8. 8. 8. 8. 8. 8.- 8. 8. 8. 8. 6.85. 8. 8. 8. 8.- 8.- 8. 8.- 8. 8 ES 8. 8. 8. 8. 8.- 8. 8. 8. 8. 88. 8. o... 8. 8. 8.- 8. 8. 8. E. 8.8 8. 8. om. 8. 8.- 8.- 8. 8. 8. 88mm 8.- o 8.- o R. 8.- 8. 8.- 8. .283. 8. 8. 8. 8. 8.- 8.- 8. 8.- 8. 883. 8.- :- 8.- 8.- 8. o 8 .- 8.- 8. 8.- 8.82 8.- 8. 8. 8. 8. :- 8. :. 8. :85. 8. 8. 8. 8. 8. 8. 8.- 8. 8. ~85. 8.- 8. 8. 8. 8. 8.- 8.- 8. 8. 85. :50 3mm 5.25 chmm E063. 88:63. 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However, results obtained from knowledge of the amount of nutrient content per servings construct (Kno22) didn’t correspond with this criteria. Significant mean score differences were seen in knowledge of the Food Guide Pyramid construct (Knol) and awareness of diet-disease relationships construct (Awapr) among age groups. People who were aged 55-89 years old were less knowledgeable about the Food Guide Pyramid (Knol) than those who were younger than this group (2.1, while others scored 2.4 and higher in 1994; 2.2, while others scored 2.6 in 1995). People who were aged 20-34 years old were less aware of diet-disease relationships (Awapr) than those who were older than this group (5.6, while others scored 5.8 and higher in 1994; 5.7, while others scored 5.9 and higher in 1995). In 1994, a significant mean score difference was seen in knowledge of the amount of nutrient content construct (Kn022) among age groups. People who were aged 55-89 years were less knowledgeable about the amount of nutrient content per servings than those who were younger than this group (1.0, while others scored 1.1 and higher). In 1995, a significant mean score difference was seen in knowledge of nutrition and food construct (Kn0811) among age groups. People who were aged 55-89 years were less knowledgeable about nutrition and food than those who were younger than this group (6.7, while others scored 6.8 and higher). Thus, results obtained from knowledge of the Food Guide Pyramid construct (Knol) and awareness of diet-disease construct closely corresponded with criteria 10 (i.e., there are differences in understanding nutrition knowledge among age groups). Although there were significant differences in knowledge of nutrition and food construct (Kn081 l) and 89 from knowledge of the amount of nutrient content per servings construct (Kn022) among the age group, results were not consistent between two years. Meal planners/preparers were significantly knowledgeable about the Food Guide Pyramid (Knol) than non-meal planners/preparers (2.4 vs. 2.2 in 1994; 2.6 vs. 2.3 in 1995). They were also significantly more aware of diet-disease relationships (Awapr) than non-meal planners/preparers (6.0 vs. 5.6 in 1994; 6.0 vs. 5.8 in 1995). In 1994 only, meal planners/preparers were significantly more knowledgeable about nutrition and food (Kno811) than non—meal planners/preparers (7.0 vs. 6.7). No significant mean score difference was seen in knowledge of the amount of nutrient content per servings (Kn022) in this group category. Thus, results obtained from the knowledge of the Food Guide Pyramid construct (Knol) and awareness of diet-disease relationships construct (Awapr) corresponded with criteria 5 (i.e., meal planners/preparers are more knowledgeable about nutrition than non-meal planners/preparers). Although there was a significant difference in knowledge of nutrition and food construct (Kn0811) between meal planners and non- planners, results were not consistent between two years. No significant mean score difference in knowledge of the amount of nutrient content per servings construct (Kn022) was seen between meal planners and non-planners. Higher income respondents were significantly more knowledgeable about nutrition and food (Kn0811) than lower income respondents (7.0 v3.6.1 in 1994; 7.1 v3.5.9 in 1995). They were also significantly more aware of diet-disease relationships (Awapr) than lower income respondents (5.9 vs. 5.4 in 1994; 6.0 vs. 5.1 in 1995). No significant mean score differences were seen in knowledge of the Food Guide Pyramid construct (Knol) nor in knowledge of the amount of nutrient content per servings 90 construct (Kn022) in this group category. Thus, results obtained from knowledge of nutrition and food construct (Kno811) and from awareness of diet-disease relationships construct (Awapr) corresponded with criteria 6 (i.e., higher-income respondents are more knowledgeable about nutrition than lower-income respondents). However, results btained fiom knowledge of the Food Guide Pyramid construct (Knol) nor from knowledge of the amount of nutrient content per servings construct (Kn022) corresponded with this criteria. People who had a higher educational background had significantly higher mean scores in four knowledge constructs: knowledge of the Food Guide Pyramid construct (Knol); knowledge of nutrition and food construct (Kn081 1); knowledge of the amount of nutrient content per servings construct (Kn022); and awareness of diet-disease construct (Awapr). For example, respondents who had more than a high school education were more aware of diet-disease relationships (Awapr) than those who had less than a high school education (6.2 vs. 5.1 in 1994; 6.2 vs. 4.9 in 1995). Respondents who had more than a high school education were more knowledgeable about nutrition and food (Kno811) than those who. had less than a high school education (7.3 vs. 5.4 in 1994; 7.4 vs. 4.9 in 1995). Thus, results obtained from four knowledge constructs mentioned above corresponded with criteria 13 (i.e., there are differences in understanding of nutrition knowledge depending on the education level). Respondents whose family member received food stamps within past 12 months and respondents who were authorized to receive food stamps had significantly lower ,mean scores in knowledge of nutrition and food construct (Kn0811) and awareness of diet-disease relationships construct (Awapr) than those whose family members didn’t 91 receive food stamps and those who were not authorized to receive food stamps. For example, respondents who were authorized to receive food stamps were significantly less knowledgeable about nutrition and food (Kn0811) than those who were not authorized to receive food stamps (5.9 vs. 7.0 in 1994 and 1995). No significant mean score differences were seen in knowledge of the Food Guide Pyramid construct (Knol) nor in knowledge of the amount of nutrient content per servings construct (Kn022) in this group category. Few significant mean score differences were seen in knowledge constructs between people who were diagnosed with diet-related diseases such as heart disease and high blood pressure and those who were not diagnosed with those diseases. Significant mean score differences were only seen in knowledge of nutrition and food construct (Kn0811) and awareness of diet-disease relationships construct (Awapr) between respondents who were diagnosed with high blood cholesterol and those who were not diagnosed with high blood cholesterol. People who were diagnosed with high blood cholesterol were significantly more knowledgeable about nutrition and food than those who were not diagnosed with high blood cholesterol (7.6 vs. 6.8 in 1994; 7.2 vs. 6.8 in 1995). They were significantly more aware of diet-disease relationships than those who were not diagnosed with high blood cholesterol (6.3 vs. 5.8 in 1994; 6.2 vs. 5.8 in 1995). People who were on a weight loss/low calorie diet were significantly more aware of diet-disease relationships (Awapr) than those who were not on this diet (6.1 vs. 5.8 in 1994; 6.4 vs. 5.8 in 1995). PCOple who were on a low fat/cholesterol diet were significantly more knowledgeable about the Food Guide Pyramid (Knol) and nutrition and food (Kno811), and were significantly more aware of diet-disease relationships than 92 those who were not on this diet. For example, they had a significantly lower mean score in knowledge of the Food Guide Pyramid construct (Knol) than those who were not on this diet (2.6 vs. 2.3 in 1994; 2.7 vs. 2.5 in 1995). Other significant differences in knowledge mean scores based on a presence of special diets were not consistent between the two years. Smokers were significantly less knowledgeable about nutrition and food (Kno811) and less aware of diet-disease relationships (Awapr) than non-smokers. Smokers had a significantly lower mean score in knowledge about nutrition and food construct (Kno811) than non-smokers (6.4 vs. 7.3 in 1994; 6.4 vs. 7.3 in 1995). They had a significantly less mean score in awareness of diet-disease relationships construct (Awapr) than non-smokers (5.5 vs. 6.0 in 1994; 5.6 vs. 6.2 in 1995). In 1994 only, smokers had a significantly lower mean score in knowledge of the Food Guide Pyramid construct (Knol) (2.1 vs. 2.4). No significant mean score differences were seen in knowledge of the amount of nutrient content per servings construct (Kn022) in this group category. Thus, results obtained from knowledge of nutrition and food construct (Kn081 1) and awareness of diet-disease relationships construct (Awapr) corresponded with criteria 15 (i.e., smokers are less knowledgeable about nutrition than non-smokers). However, results obtained from knowledge of the Food Guide Pyramid (Knol) was not consistent between the two years, and results obtained from knowledge of the amount of nutrient content per servings construct (Kn022) didn’t correspond with this criteria. Mean score in knowledge of nutrition and food construct (Kn0811) varied significantly depending on the frequency of exercises. People who rarely or never exercised and those who exercised almost everyday were less knowledgeable about 93 nutrition and food (Kn081 1) than those who exercised once or 2-4 times per week. For example, people who rarely or never exercised scored 6.7 in mean score of knowledge of nutrition and food construct (Kn0811), while others scored 7.2 and higher in 1995. Other significant mean score differences were not consistent between the two years. However, it seemed that people who rarely or never exercised and those who exercised almost every day were less knowledgeable about nutrition than people who exercised once or 2- 4 times per week. Few significant mean score differences were seen depending on the hour of TV watching. In 1994, a significant difference was seen in mean score of knowledge of nutrition and food construct (Kn081 1). People who watched TV more than 11 hours were least knowledgeable about nutrition and food compared to others (5.5, while others scored 6.6 and higher). In 1995, significant difference was seen in mean score of awareness of diet-disease relationships construct (Awapr). People who watched TV more than 11 hours were least aware of diet-disease relationships compared to others (5.1, while others scored 5.6 and higher). Mean scores in knowledge of the Food Guide Pyramid construct (Knol) and knowledge of nutrition and food construct (Kn081 1) varied depending on the frequency of vitamin supplement use. For example, people who took vitamin supplements every day or almost every day were more knowledgeable about the Food Guide Pyramid than those who didn’t take supplements at all (2.5 vs. 2.2 in 1994; 2.6 vs. 2.2 in 1995, respectively). Other significant mean score differences were not consistent between the two years (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 94 4.3.2.2.2. Attitude constructs. 4.3.2.221. Perceived benefits of using the food label construct (Benet) People who were on a weight loss/low calorie diet perceived benefits of using the food label significantly more than those who were not on this diet (16.1 vs. 15.3 in 1994; 17.2 vs. 15.3 in 1995). The same trends were seen between people who were on a low fat/cholesterol diet and those who were not on a low fat/cholesterol diet (16.7 vs. 15.2 in 1994; 17.2 vs. 15.2 in 1995) and between people who were on a high fiber diet and those who were not on a high fiber diet (17.7 vs. 15.4 in 1994; 17.1 vs. 15.4 in 1995). The mean score of perceived benefits of using the food label construct also differed significantly depending on the frequency of vitamin supplement use. People who used vitamin supplements every day or almost every day perceived benefits of using the food label more than those who didn’t use vitamin supplements at all (15.9 vs. 15.1 in 1994; 16.1 vs. 14.9 in 1995). In 1994, respondents whose family member received food stamps within the past 12 months perceived benefits of using the food label significantly less than those whose family member didn’t receive food stamps within past 12 months (14.6 vs. 15.5). In 1995, females had a significantly higher mean score of perceived benefits of using the food label construct than males (15.8 vs. 14.9). Meal planners/preparers perceived benefits of using the food label significantly more than non-meal planners/preparers (15.7 vs. 15.0). 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N 0.0.0.00 000.0 000“. 00. 050.000. 00000000 00003000.. .0 .0-0. 00.000 0008 .0 E00 n .05. . .. w .. 00.0v0 0000.00 0.000.0 .0000 .0000 .0000 0000.0 00000>0.0oe_0.000000>m 0.00000 0000.0 .0000 .0000 .0000 0000.0 000080.90. 0000.00 0.000.0 .00.... 0000.0 .0000 0.000.0 .0062 000.0330 500.5 .0080. 0.0.00. 20.00.: 0.002“. 3.00.0: 0.0000E_ N..00_E_ ..0_.0E_ 0.0.00.5 0“. 0.0000000 000.0.< 00_00_.0> 000000. 000. 0x10 .0 0.0.. 00000000000008 0000 .0 00.000 0000. 00.00.0>> .N. 030... 132 00.< _000000.m 0050000020.. 000 _000_ 000. 00. 0. 050.000. 00000000 .0 .000.. 00.000 0000. .0 E00 n 000.... 0. 000. .0 0.00. 00. 050.000. 00000000 000.0.0 .0 00.000 0000. .0 E00 n .095 ... .000_ 000. 00. 000.0.0000 0. 00050000 050.000. 00000000 .0 00.000 000E .0 E00 n 0085 0. 0.0.00. 0. 050. .0. 0.0580 133 4.3.2.222. Perceived barriers from using the food label construct (Barrier) Mean scores in perceived barriers from using the food label varied significantly depending on the level of education. People who had less than a high school education perceived barriers from using the food label more than those who had more than a high school education (3.9 vs. 5.0 in 1994; 4.3 vs. 5.2 in 1995). In 1994, mean scores in perceived barriers from using the food label varied significantly among age groups. Respondents who were 55-89 years old perceived barriers from using the food label more than younger respondents (4.5, while others scored 4.8 and higher). Non-meal planners/preparers perceived barriers from using the food label significantly more than meal planners/preparers (4.6 vs. 4.9). Respondents whose family member received food stamps within the past 12 months and those who were authorized to receive food stamps perceived barriers from using the food label significantly more than those whose family members didn’t receive food stamps within the past 12 months and those who were not authorized to receive food stamps (e.g., respondents who were authorized to receive food stamps scored 4.2, while those who were not authorized to receive food stamps scored 4.8). People who were diagnosed with high blood pressure, heart disease, osteoporosis, or stroke perceived barriers from using food labels significantly more than those who were not diagnosed with these diseases (e.g., respondents who were diagnosed with high blood pressure scored 14.2, while those who were not diagnosed with high blood pressure scored 14.9). The mean score in perceived barriers from using the food label varied significantly depending on the hour of TV watching. Respondents who watched TV 4-10 hours per day perceived barriers from using the food label more than those who watched TV an hour per day (4.6 vs. 5.0). 134 In 1995, lower-income respondents perceived barriers from using the food label significantly more than higher-income respondents (4.6 vs. 5.0). People who were on a low fat/cholesterol diet perceived barriers from using the food label significantly less than those who were not on this diet (5.3 vs. 4.9). People who were on a low salt/sodium diet perceived barriers from using the food label significantly less than those who were not on this diet (5.4 vs. 4.9). Also, people who were on a high fiber diet perceived barriers from using the food label significantly less than those who were not on this diet (5.6 vs. 5.0). The mean score in perceived barriers from using the food label varied significantly depending on the frequency of exercise. For example, people who rarely or never exercised perceived barriers from using the food label more than those who exercised once in a week (4.7 vs. 5.2). The mean score in perceived barrier from using the food label varied significantly depending on the frequency of vitamin supplement use. For example, people who didn’t use vitamin supplements perceived barriers from using the food label more than those who used vitamin supplements every day or almost every day (4.8 vs. 5.2) (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.3.2.223. Perceived easiness to understand the food label construct (Ease) People who were diagnosed with high blood pressure perceived easiness to understand the food label significantly less than those who were not diagnosed with high blood pressure (14.4 vs. 15.1 in 1994; 14.2 vs. 14.9 in 1995). In 1994, meal planners/preparers perceived easiness to understand the food label significantly more than non-meal planners/preparers (15.0 vs. 14.3). People who were diagnosed with heart disease, osteoporosis, or stroke perceived easiness to understand the food label 135 significantly less than those who were not diagnosed with these diseases. For example, people who were diagnosed with heart disease scored 13.6, while those who were not diagnosed with heart disease scored 14.9. People who were on a weight loss/low calorie diet perceived easiness to understand the food label significantly more than those who were not on this diet (15.8 vs. 14.7). In 1995, mean score of perceived easiness to understand the food label varied significantly among age groups. People who were 55-89 years perceived easiness to understand the food label less than those who were younger than this group (e.g., people who were 55-89 years scored 14.4, while those who were 20-34 years scored 15.0). Mean score of perceived easiness to understand the food label varied significantly depending on the level of education. People who had less than a high school education perceived easiness to understand food labels less than those who had more than a high school education (12.9 vs. 15.3). Mean score of perceived easiness to understand the food label varied significantly depending on the frequency of exercise. People who rarely or never exercised perceived easiness to understand the food label less than those who exercised 2-4 times in a week (14.3 vs. 15.0) (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.3.22.2.4. Perceived importance of practicing healthy dietary habits construct (Imdiet) Females perceived importance of practicing healthy dietary habits significantly more than males (42.1 vs. 39.5 in 1994; 41.7 vs. 39.3 in 1995). This result corresponded with criteria 2 (i.e., females are more concerned about their diet than males). The mean score of perceived importance of practicing healthy dietary habits varied significantly 136 among age groups. People who were aged 20-34 years perceived importance of practicing healthy dietary habits less than those who were older than this group (e. g., 39.3, while others scored 41.1 and higher in 1994; 39.0, while others scored 40.8 and higher in 1995). Meal planners/preparers perceived importance of practicing healthy dietary habits significantly more than non-meal planners/preparers (41.7 vs. 39.5 in 1994; 41.3 vs. 39.4 in 1995). People who were diagnosed with diabetes perceived importance of practicing healthy dietary habits significantly more than those who were not diagnosed with diabetes (42.2 vs. 40.8 in 1994; 42.3 vs. 40.4 in 1995). People who were diagnosed with high blood pressure perceived importance of practicing healthy dietary habits significantly more than those who were not diagnosed with high blood pressure (42.2 vs. 40.6 in 1994; 42.6 vs. 40.1 in 1995). People who were on weight loss/low calorie diet, on a low fat/cholesterol diet, on a low salt/sodium diet, or on a high fiber diet perceived importance of practicing healthy dietary habits significantly more than those who were not on these diets. For example, people who were diagnosed with high blood pressure scored 42.2 in 1994 and 42.6 in 1995, while those who were not diagnosed with high blood pressure scored 40.6 in 1994 and 40.1 in 1995. Smokers perceived importance of practicing healthy dietary habits significantly less than non-smokers (38.9 vs. 41.7 in 1994; 38.7 vs. 41.3 in 1995). This result corresponded with criteria 16 (i.e., smokers are less concemed about their diet than non- smokers). The mean score in perceived importance of practicing healthy dietary habits varied significantly depending on the frequency of vitamin supplement use. People who didn’t use vitamin supplements at all perceived importance of practicing healthy dietary 137 habits less than those who took vitamin supplements every day or almost every day (40.4 vs 41.8 in 1994; 41.8 vs. 38.7 in 1995). In 1994, the mean score in perceived importance of practicing healthy dietary habits varied significantly depending on the hour of TV watching. People who watched TV more than 11 hours perceived importance of practicing healthy dietary habits less than those who watched TV an hour per day (37.6 vs. 41.7). In 1995, the mean score in perceived importance of practicing healthy dietary habits varied significantly depending on the education level. People who had more than a high school education perceived importance of practicing healthy dietary habits more than those who had less than a high school education (41.0 vs. 40.5). People who were diagnosed with high blood pressure, heart disease, cancer, or osteoporosis perceived importance of practicing healthy dietary habits significantly more than those who were not diagnosed with these diseases. For example, people who were diagnosed with high blood pressure scored 42.0, while those who were not diagnosed with high blood pressure scored 40.1. People who were on a diabetic diet perceived importance of practicing healthy dietary habits significantly more than those who were not on this diet (42.8 vs. 40.5). There was no significant mean score difference in perceived importance of practicing healthy dietary habits depending on the income level. Thus, results didn’t correspond with criteria 7 (i.e., higher-income respondents are more concerned about their diet than lower-income respondents) (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 138 4.3.22.2.5. Perceived importance of food safety construct (Imsafe) Females perceived importance of food safety significantly more than males (3.9 vs. 3.7 in both years). Meal planners/preparers perceived importance of food safety significantly more than non-meal planners/preparers (3.8 vs. 3.7 in 1994; 2.6 vs. 2.3 in 1995). In 1994, people who were on a diabetic diet perceived importance of food safety significantly more than those who were not on this diet (4.0 vs. 3.8). In 1995, peOple who were on a weight loss/low calorie diet, on a low fat/cholesterol diet, on a low salt/sodium diet, or on a high fiber diet perceived importance of food safety significantly more than those who were not on these diets. For example, people who were on a weight loss/low calorie diet scored 3.9, while those who were not on this diet scored 3.8 (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.3.2.226. Perceived importance of the price of food construct (Impric) Females perceived importance of the price of food significantly more than males (3.3 vs. 3.2 in both years). Lower-income respondents perceived importance of the price of food significantly more than higher-income respondents (3.5 vs. 3.2 in 1994; 3.6 vs. 3.1 in 1995). This result corresponded with criteria 8 (i.e., lower-income respondents are more concerned about the price of food more than higher-income respondents). Respondents whose family member received food stamps within the past 12 months and those who were authorized to receive food stamp perceived importance of the price of food significantly more than respondents whose family member didn’t receive food stamps and those who were not authorized to receive food stamps. For example, respondents who were authorized to receive food stamps scored 3.6 in 1994 and 3.7 in 139 1995, while those who were not authorized to receive food stamps scored 3.2 in 1994 and 3.2 in 1995 (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.3.2.227. Perceived importance of how well food keeps construct (Imlast) Females perceived importance of how well food kept significantly more than males (3.3 vs. 3.2 in both years). Meal planners/preparers perceived importance of how well food kept significantly more than non-meal planners/preparers (3.5 vs. 3.4 in both years). Lower-income respondents perceived how well food kept significantly more than higher—income respondents (3.6 vs. 3.4 in 1994; 3.7 vs. 3.4 in 1995). Respondents whose family member received food stamps within the past 12 months and those who were authorized to receive food stamps perceived importance of how well food kept significantly more than respondents whose family member didn’t receive food stamps and those who were not authorized to receive food stamps. For example, respondents who were authorized to receive food stamps scored 3.7 in 1994 and 1995, while those who were not authorized to receive food stamps scored 3.4 in 1994 and 1995 (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.3.2.228. Perceived importance of easiness of the food to prepare construct (Imease) Females perceived importance of easiness of the food to prepare significantly more than males (3.1 vs. 3.0 in 1994; 3.2 vs. 3.0 in 1995) (Table 11 for DHKS 1994; Table 12 for DHKS 1995). I40 4.3.2.229. Perceived importance of taste of food construct (Imtast) Females perceived importance of the taste of food significantly more than males (3.9 vs. 3.8 in 1994; 3.9 vs. 3.7 in 1995) (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.32.22.10. Willingness to learn more about the food label construct (Attitu) Lower-income respondents were willing to learn more about the food label significantly more than higher-income respondents (3.4 vs. 3.3 in 1994; 3.5 vs. 3.2 in 1995). The mean score in willingness to learn more about the food label construct varied significantly depending on the education level. People who had less than a high school education were willing to learn about the food label more than those who had more than a high school education (3.6 vs. 3.2 in 1994; 3.3 vs. 3.2 in 1995). People who were on a weight loss/low calorie diet were willing to learn about the food label significantly more than those who were not on this diet (3.6 vs. 3.3 in 1994; 3.5 vs. 3.2 in 1995). The same trends were seen between people who were on a low fat/cholesterol diet and those who were not on this diet (3.5 vs. 3.3 in 1994; 3.5 vs. 3.2 in 1995). In 1995, females were willing to learn more about the food label significantly more than males (3.3 vs. 3.1). Thus, results correspond with criteria 3 (i.e., females were more motivated to improve their diet than males) in 1995, but not in 1994. Respondents whose family member received food stamps within the past 12 months and those who were authorized to receive food stamps were willing to learn more about the food label significantly more than respondents whose family member didn’t receive food stamps and those who were not authorized to receive food stamps. For example, respondents 141 who were authorized to receive food stamps scored 3.5, while those who were not authorized to receive food stamps scored 32. No mean score difference in willingness to learn more about the food label was seen among age groups. Thus, results didn’t correspond with criteria 11 (i.e., there are differences in motivation of improving own diet among age groups) at least by learning more about the food label. No mean score difference in willingness to learn more about the food label was seen between smokers and non-smokers. Thus, results didn’t correspond with criteria 17 (i.e., smokers are less motivated to improve their diet than non-smokers) at least by learning more about the food label (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.3.2.2211. Frequency of the food label use construct (Fluse) Females used food labels significantly more frequently than males (38.9 vs. 35.7 in 1994; 39.0 vs. 36.0 in 1995). Thus, results corresponded with criteria 4 (i.e., females used food labels significantly more frequently than males). The mean score in frequency of the food label use construct varied significantly among age groups. People who were aged 20-34 years used food labels less frequently than those who were aged 5 5-89 years (36.0 vs. 39.7 in 1994; 36.3 vs. 38.8 in 1995). Thus, results corresponded with criteria 12 (i.e., there are differences in frequency of food label use among age groups). Meal planners/preparers used food labels significantly more frequently than non-meal planners/preparers did (38.4 vs. 35.5 in 1994; 38.3 vs. 36.5 in 1995). People who were diagnosed with high blood cholesterol used food labels significantly more frequently than those who were not diagnosed with high blood cholesterol (40.3 vs. 37.0 in 1994; 40.7 vs. 142 37.2 in 1995). People who were on a weight loss/low calorie diet, on a low fat/cholesterol diet, on a low salt/sodium diet, or on a high fiber diet used food labels significantly more frequently than those who were not on these diets. For example, people who were on a weight loss/low calorie diet scored 40.1 in 1994 and 42.9 in 1995, while those who were not on this diet scored 37.2 in 1994 and 37.3 in 1995. Smokers used food labels significantly less frequently than non-smokers did (35.8 vs. 37.9 in 1994; 34.5 vs. 38.3 in 1995). The mean score in frequency of the food label use varied significantly depending on the frequency of vitamin supplement use. People who used vitamin supplements used food labels more frequently than those who didn’t use vitamin supplements at all (39.3 vs. 36.6 in 1994; 39.8 vs. 36.4 in 1995). In 1994, the mean score in frequency of the food label use varied significantly depending on the education level. People who had less than a high school education used food labels less frequently than those who had more than a high school education (35.2 vs. 38.1). Thus, results corresponded with criteria 14 (i.e., there are differences in frequency of the food label use depending on the education level) in 1994, but not in 1995. People who were authorized to receive food stamps used food labels significantly less frequently than those who were not authorized to receive food stamps (35.4 vs. 37.5). In 1995, lower-income respondents used food labels significantly less frequently than higher income respondents (35.9 vs. 38.0). Thus, results corresponded with criteria 9 (i.e., higher-income respondents used food labels more frequently than lower-income respondents) in 1995, but not in 1994. In summary, constructs created from DHKS 1994-1995 sorted respondents into subgroups, meeting 14 criteria out of 17 criteria set initially in either one of 1994 and 143 1995 or in both years. Also, those constructs sorted respondents into subgroups based on other criteria which were not completely established but reported in the past. Thus, constructs created from DHKS 1994-1995 appeared to be valid (Table 11 for DHKS 1994; Table 12 for DHKS 1995). 4.4. Nutrition knowledge, attitudes, and food label use in the US. between 1994 and 1995 (Objective 4). 4.4.1. Nutrition knowledge between 1994 and 1995 Only nutrition knowledge question which had significant difference between 1994 and 1995 was the one which inquired about the relationship between not eating enough calcium and cavities and tooth problems (11% correct response in 1994 vs. 15% in 1995). Majority of respondents (about 80% of respondents) recognized dietary behaviors, which would cause health problems such as eating too much fat (88% in both years); eating too much salt (87% in 1994 and 89% in 1995, respectively); not eating enough calcium (81% in both years); eating too much calorie (89% in 1994 and 90% in 1995, respectively); eating too much sugar (81% in 1994 and 79% in 1995, respectively); and being overweight (95% in 1994 and 94% in 1995, respectively). Over 60% of respondents recognized that not eating enough fiber would cause health problems (64% in 1994 and 66% in 1995, respectively). However, when respondents were asked to identify health problems caused by those dietary behaviors, correct response ratio varied. Questions which received about 75% of correct responses were: not eating enough calcium would cause bone problems/rickets and osteoporosis (74% in both years); eating too much cholesterol 144 would cause heart diseases and problems of arteries (74% in 1994 and 77% in 1995, respectively); and being overweight would cause heart disease and problems of arteries (75% in 1994 and 74% in 1995, respectively). On the other hand, less than 10% of respondents could answer correctly that: eating too much fat would cause cancer (8% in 1994 and 6% in 1995, respectively); not eating enough fiber would cause heart disease and problems of arteries (3% in both years); eating too much salt would cause kidney problems and renal disease (3% in 1994 and 2% in 1995, respectively). Fruit group in the food guide pyramid received the highest correct responses (72% in 1994 and 73% in 1995, respectively) to questions on the number of adequate servings per day among all food groups, while grain group received the lowest correct responses (6% in 1994 and 8% in 1995, respectively). Given two sets of food items, 49-93 % of respondents could correctly identify which food item had higher amount of saturated fat or fat than the other. For example, comparison between whole milk and skim milk for the amount of saturated fat had 93% of correct responses in both years, while comparisons between porterhouse steak and round steak for the amount of fat had 50% of correct responses in 1994 (49% in 1995). Only 27% of respondents could correctly identify the difference in characteristics of saturated fat and polyunsaturated fat (KQIO) in both years. Less than 25% of respondents had adequate knowledge of the amount of nutrient content per servings (KQ22a-e) except the amount of fiber (51% of respondents had correct responses in both years). About 30% of respondents knew the authority of the government to define the phrases on the food label (KQ21a-c) (Table 13). 145 4.4.2. Attitudes between 1994 and 1995 Significant mean score differences were seen in: perceived importance of maintaining a healthy weight; and perceived barriers from using the food label between 1994 and 1995. People perceived less importance of maintaining a healthy weight in 1995 than in 1994 (3.69 vs. 3.63). On the other hand, people perceived less barriers from using the food label in 1995 than in 1994 (2.33 vs. 2.22). Mean scores, which indicated respondents’ perceived importance of practicing various recommended dietary behaviors such as using salt in moderation and choosing a diet low in fat, showed that respondents perceived those dietary behaviors between somewhat important (Mean= 3.00) and very important (Mean= 4.00). For example, the mean score of “perceived importance of eating at least 2 servings of dairy products daily” was 3.00 in 1994 and 2.99 in 1995, respectively, while the one of perceived importance of maintaining a healthy weight was 3.69 in 1994 and 3.63 in 1995, respectively. Mean scores, which indicated respondents’ perceived easiness of understanding the food label, ranged from not too easy (Mean=] .00) to very easy (Mean=3.00). For example, the mean score of “perceived easiness of understanding the number of gram or milligram of nutrients like fat in serving” was 1.82 (close to somewhat easy) in 1994 (1.95 in 1995), while the mean score of “perceived easiness of understanding the number of calories in a serving” was 2.49 (somewhat easy) in 1994 (2.43 in 1995). Mean scores, which indicated respondents’ perceived benefits of using the food label, ranged from somewhat disagree (Mean=2.00) to somewhat agree (Mean=3.00). For example, the mean score of “the nutrition information on the food label was useful for me” was 3.21 in 1994 and 3.24 in 1995, respectively. 146 Finally, respondents were somewhat willing to learn how to use the food label (Mean=327 in 1994 and 3.23 in 1995, respectively) (Table 14). 4.4.3. Frequency of the food label use between 1994 and1995 The mean score of frequency of the information on the food label use ranged from rarely (Mean=2.00) to sometimes (Mean=3.00). 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Sample of DHKS 1994-1995 (Objectivel) This study established the reliability and validity of questions from DHKS 1994- 1995 using a large sample of US. representative population included in DHKS (n=1,879 and n=l ,966 for 1994 and 1995, respectively). Thus, results are generalizable for the nation and also for subgroup populations categorized by: demographic variables (eg. gender, age, race, and income); socioeconomic variables (eg. education and the status of the Food Stamps participation); health indicators (eg. presence of diagnosed diseases such as heart disease, high blood pressure, and diabetes, presence of special the diet such as weight loss/low calorie diet, low fat/cholesterol diet and low salt/sodium diet); life style factors (eg. smoking, frequency of the exercise, vitamin supplement use, and the hours of TV watching). Since findings regarding to various subgroups in the past tend to be limited with locations, the information characteristic of the subgroups obtained from the national survey, which is free from the restrictions of the locations, are very variable. The sample sizes of the WIC participants, pregnant and lactating women, respondents who were 90 years and older, Asian and Pacific Islanders (1995 only) and American Indians were found to be too small to make any conclusions. Since the WIC program teaches how to read food labels, and studies on WIC participants and food label reading behaviors are limited, the sample size describable of the WIC population would be desirable in the future DHKS. 160 2. Reliability of the final constructs (Objective 2) Thirteen final constructs were established in the reliability tests: 5 nutrition knowledge constructs with reliability ranging from moderate to high (r=0.4—O.8); 7 attitude constructs with reliability ranging from moderate to high (r=0.5-0.9); and 1 frequency of food label use construct with high reliability (r =O.9). Findings from the present study are comparable with the results reported by Sapp et al (1997) who used the DHKS 1989-1991. DHKS 1989-1991 included similar types of questions as DHKS 1994-1995. For example, 23 question items of the nutrition knowledge construct created by Sapp and colleagues were comparisons of sources for the cholesterol and fat between two food items and the characteristics of nutrients. Ten question items of knowledge of nutrition and food construct used in this study were comparisons of sources for fat and saturated fat between two food items and the characteristics of nutrients. Although nutrients inquired about in the two studies were different (cholesterol and fat vs. fat and saturated fat), these constructs did inquire about the same information (sources of nutrients and the characteristics of nutrients), and were defined as a nutrition knowledge construct. The reliability of these constructs was less than 1: 0.70 (r= 0.58-0.69 during three years in the study by Sapp and colleagues; r= 0.59-0.63 during two years in this study). Thus, knowledge of nutrition and food construct created in the present study was as reliable as the one created by Sapp and colleague with even fewer items. Another construct common between two studies was knowledge of diet-disease relationships construct. The reliability of this construct established by Sapp and colleagues was greater than F 0.70 from DHKS 1989 to 1991 (r= 0.76-0.81). However, the present study failed to establish the adequate reliability for 161 this construct. This could be due to the differences in the study design between two studies or inconsistency of the question items in the construct (Appendix E). Additionally, the present study created 3 knowledge constructs, 7 attitude constructs, and 1 frequency of food label use construct not used in the 1989-1991 DHKS: knowledge of the amount of nutrient content per servings construct (reliability r= 0.4); awareness of the authority of the government to define phrases on the food label construct (reliability r= 0.8); awareness of diet-disease relationships construct (reliability F 0.7); perceived adequacy of own nutrient intakes which were recommended to decrease construct (reliability 1: 0.7); perceived adequacy of own nutrient intakes which were recommended to increase construct (reliability r= 0.5); perceived benefits of using the food label construct (reliability r= 0.8); perceived barriers from using the food label (reliability 1: 0.5); perceived easiness to understand the food label construct (reliability r= 0.8); perceived reliability of descriptions on the food label construct (reliability r=0.9); perceived importance of practicing healthy dietary habits construct (reliability r=0.9); and frequency of food label use construct (reliability r= 0.9). Fullmer et al (1991) reported that consumers were more familiar with the role of fiber might play in the prevention or treatment of certain diseases or conditions than with sources, classifications, and recommended intakes. Viswanathan (1994) reported that verbal presentation of nutrition information was found to lead to a greater degree of usage of such information than numerical presentation. Thus, Consumers respond to the nutrition information differently depending on the type of questions. It is possible that nutrition knowledge questions selected by the nutrition experts in this study were not appropriate to measure consumers’ levels of knowledge. Or, because knowledge and 162 attitudes are difficult concepts to measure, researchers may obtain wide range of reliability of knowledge and attitude constructs depending on how they categorize question items into different constructs. For example, knowledge of the Food Guide Pyramid construct expected that respondents answered the number of servings from grains correctly as well as the number of servings from vegetables and fruits, because all question items equally measured the respondents’ understanding of the Food Guide Pyramid. On the other hand, for the knowledge of nutrition and food construct, all question items were expected to equally measure the respondents’ understanding of nutrition and food. Because question items inquiring about the number of servings from dairy products (KQlc) and meat, poultry, fish, dry beans and eggs (KQle) in knowledge of the Food Guide Pyramid construct and question items inquiring about fat (KQ9a, c-f) in knowledge of nutrition and food construct could equally measure respondents’ knowledge of fat (One inquires about the adequate amount of intakes of food high in fat, while the other inquired about food sources of fat), those items can be merged to create another construct. To avoid the loss of original information, this study maintained the categorization that the original data used as much as possible. However, it would be interesting to test reliability of constructs with question items that were not tested in this study in the future. Variyam et a1 (1996) developed statistical equations to compare pairs of knowledge questions such as orange juice/apple and white/whole wheat bread to decide question items that indicated fiber knowledge than other question items. Continuing efforts should be made to develop question items that represent the construct better than the other items and are inconsistent with other items. 163 3. Validity of final constructs (Objective 3) Twenty constructs were confirmed for their divergent validity: 5 nutrition knowledge constructs; l4 attitude constructs; and 1 frequency of food label use construct. Low correlation (r=0.3) in the divergent validity test between perceived importance of practicing healthy dietary habits construct and awareness of diet-disease relationships construct created from DHKS 1994-1995 correspond to findings reported by Contento et al (1990) and Shepard et a1 (1987); knowledge did not related to behaviors directly, but only indirectly through attitudes, intentions, or perceived threats to health. Moderate correlation (r=0.6) between perceived benefits of using the food label construct and frequency of the food label use construct corresponded to Fishbein & Ajzen’s (1975) theory of reasoned action; behaviors could be predicted by the individual’s intention to perform that behavior. Thus, the final constructs were not strongly related to each other, representing different characteristics, although some of constructs were related each other corresponding to the theories established in the past. Further studies are necessary to understand the detail of these relationships. Discriminant validity successfully confirmed the validity of 8 final constructs (4 nutrition knowledge constructs, 3 attitude constructs, and 1 frequency of food label use) for their ability to sort respondents into different subgroups corresponding to 14 out of 17 criteria established from the findings and theories from the past studies on food label use and dietary habits (See the method of discriminant validity). For example, the first criteria was that females were more knowledgeable about nutrition than males (Levy et a1, 1992; Ernst et al, 1986; Jensen, 1992). Knowledge of nutrition and food construct, knowledge of the Food Guide Pyramid construct, and awareness of diet-disease 164 relationships construct created from DHKS 1994 and 1995 corresponded to this criteria. The sixth criteria was that higher-income respondents were more knowledgeable about nutrition than lower-income respondents (Morton et a1, 1997; Frazao et al, 1994; Smallwood et a1, 1994). Knowledge of nutrition and food construct and awareness of diet-disease relationships construct created from DHKS 1994 and 1995 corresponded to this criteria. In addition, significant differences were seen in other constructs among various subgroups. Mean scores differed significantly in knowledge of nutrition and food construct in 1994 and in awareness of diet-disease relationships in 1995 depending on the hour of TV watching. This result is comparable with the study reported by Carlson and Gould (1994), who reported that the relationship between the hours of watching TV and nutrition knowledge was positive. Another example was that meal planners/preparers used food labels significantly more frequently than non-meal planners in the present study. This result is comparable with the study conducted by Woolcott et a1 (1983), who reported that men who participated in food preparation reported more changes in diet towards the recommended dietary goals. Because meal planners/preparers tend to be females, the effect of being a meal planner on the food label use and dietary habits should be examined. People who were on a weight loss/low calorie diet or low fat/cholesterol were willing to learn more about the food label significantly more than those who were not on either diet in the present study. This example is comparable with the study by Bender et al (1992) who reported that those who were on a low-sodium diet used the list of ingredient more frequently than those who were not on this diet. 165 Thus, validity of final constructs was confirmed based on the findings and theoretical models on food label use and dietary habits established in the past. However, past findings and theoretical models on food label use and dietary habits were limited in certain aspects of nutrition knowledge, attitudes, food label use, and dietary behaviors. There are few studies that examined nutrition knowledge, attitudes, food label use and dietary intakes simultaneously. Also, theories used in the past were limited to certain populations and underestimated the effects of individual characteristics and situational differences. For example, health belief model successfully established that health behaviors were determined by people’s belief of their susceptibility to a particular disease (Becker, 1974). This model, however, hasn’t been applied to the frequency of food label use, which can be possible by using the presence of diagnosed disease in DHKS 1994- 1995. Fishbein and Ajzen’s (1975) theory of reasoned action explained that behaviors could be predicted by an individual’s intention to perform that behavior. Saunders and Rahilly (1985) concluded that attitudes toward the behavior predicted the intention of reducing fat and sugar intakes of university health majors, but subjective norms predicted the intakes of non-health majors in this study. Thus, variables within a same theory predict the behaviors differently depending on subjects. Willingness to learn more about the food label construct in DHKS 1994-1995, for example, can be used to represent this theory along with other factors and subgroups not examined in the past. On the other hand, Belk (1985) suggested that habits and situational factors explained why intention wouldn’t determine behaviors. According to Belk, if the behavior is a habit, factors involved in the behavior differ from those involved in a behavior taken for the first time. The enactment of the food label in 1994 could be a situational factor affecting on 166 respondents’ knowledge, attitudes, and use of the food label. By monitoring the responses to the food label related constructs in the future DHKS, one might be able to contribute some information to this question. Another recent argument among several health behavior researchers was that there were similarities and overlaps in many of the variables defined by these theories (Commings et al, 1980; Janz and Becker, 1984; Rosenstock et al, 1988). As already described above, some of question items and subgroups used in theoretical models on the food label use and dietary habits established in the past were included in DHKS 1994- 1995. Thus, integrating different theories together was suggested (Colavito and Guthrie, 1996; Wallston and Wallston, 1984; and Rosenstock et al, 1988). Reliable and valid constructs created from DHKS 1994-1995 can contribute to understand the relationships between nutrition knowledge, attitudes, use of the current food label and individuals’ dietary habits relative to behavioral theories and models. During this process, the definition of variables is particularly important and has to be specific rather than to be general, so that nutrition educators can plan specific strategies for each targeted population. Food label related questions in DHKS 1994-1995, for example, will be specific examples of how people assessed new nutrition information, how much they understand them, and how frequently and what information on the food label they used to improve their diet. As a summary, 5 nutrition knowledge constructs were tested either for their reliability or validity or for both. Four out of 5 final nutrition knowledge constructs established in this study (knowledge on the Food Guide Pyramid, knowledge on nutrition and food, knowledge on the amount of nutrient content per servings, and awareness of 167 diet-disease relationships) were either moderately or highly reliable, and their divergent and discriminant validity were confirmed. The other nutrition knowledge construct (awareness of the authority of the government to define phrases on the food label) was highly reliable, and its divergent validity was confirmed. Since there was no criteria, discriminant validity of this construct was not obtained. Fourteen attitude constructs were tested either for their reliability or validity or for both. Perceived importance of practicing healthy dietary habits construct was highly reliable, and its divergent and discriminant validity was confirmed. Six attitude constructs (perceived adequacy of own nutrient intakes which are recommended to decrease, perceived adequacy of own nutrient intakes which are recommended to increase, perceived benefits of using the food label, perceived barriers from using the food label, perceived easiness to understand the food label, and perceived reliability of descriptions on the food label) were moderately or highly reliable, and their divergent validity was confirmed. Since there was no criteria, discriminant validity of those constructs was not obtained. Divergent and discriminant validity of perceived importance of the food construct was confirmed. However, since there was no more than one item in this construct, reliability of this construct was not obtained. Divergent validity of 6 attitude construct (perceived adequacy of own weight construct, perceived importance of food safety, perceived importance of how well food keeps, perceived importance of easiness of food to prepare, perceived importance of the taste of the food, willingness to learn more about the food label) was confirmed. However, since there was no criteria and was no more than one item in this construct, discriminant validity and reliability of this construct were not obtained. Frequency of food label use was highly 168 reliable, and its divergent and discriminant validity was confirmed. As far as applying those constructs to the future studies, all constructs are reasonable as long as the user recognizes the limitations of each construct. 4. Changes in nutrition knowledge, attitudes and use of the food label in the US. between 1994 and 1995 The result from the comparisons between DHKS 1994 and 1995 showed that: l) respondents in DHKS 1995 answered to the question of relationship between calcium and cavities/caries, and tooth problems significantly more correctly than respondents in DHKS 1994; 2) respondents in DHKS 1995 perceived barriers from using the food label significantly less than respondents in DHKS 1994; 3) respondents in DHKS 1995 used health claims on the food label significantly more frequently than respondents in DHKS 1994; 4) respondents in DHKS 1995 perceived the importance of maintaining health weight significantly more than respondents in DHKS 1994. Thus, some aspects of individuals’ nutrition knowledge and attitudes toward healthy dietary behaviors were improved between 1994 and 1995. One piece of information added to the food label enacted in 1994 was the health claim. It was interesting to see the increase of the use of health claims and the decrease of perceived barriers from using the food label between 1994 and 1995. This result was not surprising, because various shelf-labeling and point-of-purchase campaigns would have caught consumers’ attention, and consumers usually requested additional nutritional information (Schucker et a1, 1992; Burton et al, 1994). Jacoby (1977) reported, however, that most nutrition information would not be comprehended once it was received, because such information was not coupled with comprehensive programs of education for 169 consumer to help them apply them into their dietary behaviors. Even for the nutrition education program conducted by Schucker et a1 (1992) which was successful, Schucker and colleagues concluded that the lasting effect of the program was unknown. Thus, use of the information on the food label and dietary behaviors among consumers should be monitored. Examination of frequency of the information use on the food label revealed that people used the information of fat on the food label most frequently, followed by the information of calorie in both 1994 and 1995. This indicated that the effort of sending the messages to reduce fat intake of the US. population by the public community for several decades seemed to be effective. Because many factors involved in individuals’ nutrition knowledge, attitudes, and food label use, it is not appropriate to conclude that these improvement was due to the 1994 enactment of the new food label. Further studies are necessary to examine whether these changes were due to the 1994 enactment of the new food label or due to other factors such as increased individuals’ nutrition knowledge or increased individuals’ perceived benefits of using food labels. The majority of people were aware of the six out of seven dietary behaviors/physical conditions that could cause health problems. Most known physical condition by people was overweight, followed by eating too much cholesterol, too much salt and too much fat. On the other hand, the dietary behavior which least people were aware of was not eating enough fiber. Same questions were asked in DHKS 1989-90. Compared to the responses in DHKS 1989-90, more people recognized the potential risks of eating too much cholesterol (87% in 1989-90, 89% in 1994, and 90% in 1995, respectively), eating too much fat (76% in 1989-90, 88% in 1994-95, respectively), and not eating enough fiber (54% in 1989-90, 64% in 1994, and 66% in 1995, respectively) 170 (F razao and Cleveland (1994); and Smallwood and Blaylock (1994)). Among knowledge of the Food Guide Pyramid construct, less than 10% of respondents could answer the number of servings from grain group correctly. These results suggested that people had less knowledge on dietary fiber, and the nutrition campaigns such as 5 a day program should be encouraged to help people recognize the importance of increasing dietary fiber in their diet. More than 60% of respondents could link diet-disease relationships such as: eating too much fat and arteriosclerosis; not eating calcium and bone problems; and being overweight and arteriosclerosis. All of those responses were higher than responses from past DHKS (eg. 74% of respondents were aware of the link between not eating enough calcium and bone problems in 1995, while 51.] % of respondents were aware of this link in 1991) (Sapp and Jensen, 1997). However, few could link other diet-disease relationships such as eating too much fat and obesity/overweight and not eating enough fiber and arteriosclerosis. Similar results were reported by Sapp and Jensen (1997) in DHKS 1989-1991. More than 60% of people could answered most of knowledge questions that were comparisons of sources for fat and saturated fat between two food items. Similar results were obtained by Sapp and Jensen in DHKS 1989-91 with the questions regarding to the comparisons of food items high in fat. However, less than 30% of respondents knew the adequacy of the amount of nutrients inquired. Understanding what type of nutrition knowledge would help people to improve their diet is important as well as creating question items to characterize nutrition knowledge. Effort should be made to identify the type of knowledge questions which distinguish people who practice healthy diet from those who don’t practice healthy diet in the future study. 171 Mean score obtained from the DHKS 1994-1995 respondents indicated that they perceived practicing healthy dietary habits somewhat important. They also perceived some degree of benefits of using the food label. 172 Chapter Six RECOMMENDATIONS FOR THE FUTURE STUDY The next step is to examine the relation among nutrition knowledge, attitudes, use of food labels, and dietary intakes built on the findings of the current study. In the past studies, nutrient was used extensively to assess individual diet quality, the major measurements as the mean adequacy ratio and percentage of Recommended Dietary Allowance (RDA). Many of the studies, however, limited nutrients to total fat and/or saturated fat, cholesterol, and/or sodium (Kennedy et al, 1995). Because people consume various foods, it is not a realistic approach to consider that a limited number of nutrients would represent the individual diet quality. Thus, the first recommendation for the future research is to use a dietary index which measures both quality and quantity aspects of diet such as Healthy Eating Index (Kennedy et al, 1995). The recommendation is to use path analysis to examine the relation among nutrition knowledge, attitudes, use of food labels and dietary intakes. General regression analysis examines direct affects only on a dependent variable from each independent variable. Path analysis on the other hand, examines the affect of an independent variable to another independent variable. 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No.- 663m: 3666:. .m.mo_:o.6>_n 6.6.66 62666 60.69660 686.66 66063.60 8.- 6. 869.9 .86 __w. .8er 6.. mo. S696. xowew tmme65266 69. 62.26 696m 65 6 6.629. .0666 .6660 .6066 nmmmoB .6606662m..66606.owo.6t< ...666360 69.. 666 6.66 62 6962666. 6962665680 No. or. wor mvwmcxme . 6666a? .236me 606 6.... S.- No. «6696. 3.666... .m.mo_6.6>.n @6606 @2666 60.69660 6:6an 6606....00 6.- .8. 8839 .68.. 6. .8er .6.- oo. 696 emmeEmfioa .69. .666 mow vo. .666. we. 6 6.699. 6686 >666”. .6066 6306 .6606669me66.moem_omo6t< .35on :66an 6. 62: no. 6.6m 966 nm66n 6.6365 966 0.: .oz m6...e.6g 6.6.6660 vamp wxzo e. 3.66.6.2 06666.66 :0 66.625 6. .266 9:03:20 new .n066n ES. .. 266 .0... d x.n6aq< 238 9. mo. no. N666. 66660.... .2666 60.0 6.... ...0. mo. 6606. 696 606 6.: .6665 E. 6. 889.2 .86 6. .860 9. 8.- 8606. 66060660 666.6826... meom v.66 69:66.66 :6: .666 mo. no. 5606. we. 6 me.6n6e .6666 66060 .666 6306 .6.66.066e66.66.6066. 69.3. 36660 696366 6.6 n F .- vmr 6.- m :66. 6902666.. ..662663660 3. 3.- @0606. 696 606 6.; .6665 5.. mo.- 6606. 9:966 500. 6060.63.60 63$. 36660 696 6:8 00. mezww 2% 3.2% 6.62.6.0 2.06 0.: .02 66.20.60 .6396me 62 9.10 e. 6.600. 6.66.6.2 6666-66 6 6636:: 6.. 266 6.6.36.0 new .6676 ES. 0. 666 .0 .... .0 x.n6nn< 239 APPENDIX E THE RESULTS OF CRONBACH’S ALPHA WITH DIFFERENT QUESTION ITEMS 00.030000 .ocnoz mmmF-vmmv wXIO c. 00020:. 6.03.950 _mcE .. 02 8.- N. w 8 .8 .8 883$. 02 8. .- 8 w 8 863$. 02 8.- 8 w 583$. 02 3 .- B ... 883$. 02 $.- 3 a 8.5838”. 02 8. T 8 w 8 883$. 8.88.8.8. 02 .9..- 2 ... 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Km. 00.9.00. 0 0.00300. m .9. w 09. 00.00000 .0 00000008. 00200.00 ..00. .. .0. .00 0-00.0000. 00v. 00.0.0. 000.30 0.50 .0 0.0030000 00200.00 .000. ..00. ..00. 0.002300. 0.9. 000. 00. 00.000.0.0003 .0 0000.000 00200.00 .2. .E. v. .0 _ .0 .0 .. 83$. 09. 030.... 8.8.. 8 88.88 828.00 02 00. 0 w 0009.01 000v. 003 .000. 000. 00. 0.0.. 0.00.00 00200.00 ..00. .00. 0 w 00000.00. 000v. 003 .000. 000. 00. 0.0.. 0.00.00 00200.00 .8. .8. .. 0. 0 .0 083$. 89. 8: .08. 80. 0... .o 0.880 828.00 .000. 000. 00. ..00. .00. 00009.00. 89. 00 0000000000 .0 0.30:2 00200.00 02 on. 00300.00. 0 .Ov. 000. .0 00......3 .0 00000008. 00200.00 0.0.0 8.8.. oz 00. .000.0”. 09. 00.00000 .0 0000000E. 00200.00 00 00 050.. 00..0000 00-000. wxxo 0. 00.800.00.000 000.0 0.0000020 .02 0020030 mam 0-000.. wXID 0. 0.0:..0000 00.5.3.0 - 0.00.. 02.0000 .:0.0...0 0..? 000.0 0.0000020 .0 0.300. 00... .w x.00000< 242 00.030000 .00uoz 0.007000.- mXID 0. 0003.00. 0.02.0000 .00.“. .. ...0. ...0. 0-00.300 .0 0-00.300 Dev. .0 05v. 003 .000. 000. 00. .0 3000300.“. oz 00. 000.300 ...00 0.00.030 >0 003 .000. 000. 00. .0 3000300.... 02 B. 0-00.300 0.0V. 0.00.000 >0 003 .000. 000. 00. .0 500300.“. 00 00 00.0.. 00..0030 00-000. 00.10 0. 02.000.00.000 000.0 0.0000020 .02 02.0000 000 .3000. 00:0 0. 0.0.0.0000 003 .000. 000. .0 >000—.00.... - 0.00.. 02.0000 .:0.0...0 0..; 000.0 0.0000090 .0 0..—.00. 00 ... .m x.00000< 243 APPENDIX F PERCENTAGE OF RESPONSES AND MEAN SCORE i S.E. WITH THE TEST OF SIGNIFICANCE FOR NUTRITION RELATED QUESTIONS BETWEEN DHKS 1994-1995 02 N00 0 N00 0.- N0 N00 0 00.0 00 00 00.0300 02 .0.0 0 E0 .N E .0.0 0 .0.0 0. .0 .0300 02 N00 0 N..0 00 N. .0.0 0 0.0 .0 0. .000300 02 00.0 ... N00 00 N 00.0 0 N00 00 N 0000300 02 .0.0 0 R0 N R N00 0 E0 0N E .000300 02 .0.0 0 00.0 0. 00 .0.0 0. 00.0 .. 00 00300 N00 N00 0 0.0 00 0. .0.0 0 ...0 00 .. 300300 02 N00 0 E0 0N E N00 0 E0 0N E 0000300 02 .0.0 0 .0.0 0. .0 N00 0 .0.0 0. .0 00300 02 00.0 0 N00 00 N .0.0 0 00.0 .0 0 0.00300 02 N00 0 00.0 .0 00 00.0 0 ..0.0 00 00 N.00300 02 N00 ..- 0N.0 NE 0N .0.0 0 .N0 N. N .000300 02 .0.0 0 00.0 .. 00 .0.0 0. .0.0 0. E0 00300 02 N00 0 .0.0 00 .0 N00 0 000 N0 00 0000300 02 .0.0 0 E0 00 0. .0.0 0 0.0 00 0. 0000300 02 00.0 0 00.0 .0 0 00.0 0 00.0 .0 0 .000300 02 N00 0 00.0 00 00 N00 0 v0.0 00 00 00300 02 N00 0 0N0 E 0N N00 .0 0N0 0E 0N 0.00300 02 .0.0 0 0.0 00 0. .0.0 0 00.0 .0 0 N.00300 02 .0.0 0 00.0 .0 0 .0.0 0 00.0 00 0 0000300 02 .0.0 0 00.0 00 0 .0.0 0 000 N0 0 0000300 02 N00 0 .0.0 00 .0 N00 0 .0.0 00 .0 .000300 02 .0.0 0 00.0 N. 00 .0.0 0 .0.0 N. 00 00300 033020320. 00000_U-:O_._._.:: no ambflgocx 9m .m.w H :00—)— 0.0.5003 0.0.56. .m.w H .522 0.03003 0002?. ..00.-.0003 b09000 >000-.0003 .3950 30. 000000000 30. 000000000 000100000 .000.-00.00 0000000 0.....[22600 00-000. 0000 0003.00 002.0000 00.0.0. 0000.00 3. 000005090 .0 .00. 00. 0.05 .m.m 0 00.000 000.0 000 00000000. .0 000.000.00 .0 00000.5( 244 02 .0.0 H 00.0 n 00 No.0 0 00.0 . 00 00300 02 No.0 0 00.0 om 00 .0.0 0 .0.0 0. .0 00300 02 No.0 0.. 0.0 ...0. 0. 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