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An analysis of the four food groups using multidimensional scaling
An Analysis of the Four Food Groups Using Multidimensional Scaling MARTA
L.
AXELSON,1 NATALIE KURINIJ,2 AND DAVID BRINBERC 3
1Department of Food, Nutrition and Institution Administration, College of Human Ecology, University of Maryland, College Park, Maryland 20740; 2Epidemiology and Biometry Research Program, National Institute of Child Health and Human Development. National Institutes of Health, Bethesda, Maryland 20205; and 3Department of Marketing, State University of New York, Albany, New York 12222
ABSTRACT To date, researchers have not tested whether the food groupings represented by various food guides reflect consumers' food classification systems. We undertook this study to explore whether multidimensional scaling (MDS) was a useful technique for examining respondents' perceived similarity among foods representing the Four Food Groups guide. This technique allows us to address the following questions: If individuals are given foods representative of the Four Food Groups, how would they group the foods? And, given the individuals' food groupings, what are the underlying dimensions or criteria on which they grouped the foods? Fifty-one college students rated all possible pairs of 23 foods, representing the Four Food Groups, on similar/dissimilar scales. Using an ALSCAL MDS analysis, we found that respondents grouped foods on 3 dimensions. Although the respondents used dimensions (or criteria) other than the nutrient composition of foods, they grouped the foods in a manner similar to but more complex than the Four Food Groups. The respondents seemed to perceive similarities among foods based on convenience in preparation, health-related properties, and source (animal and plant). Our results indicate that the MDS technique seems to be an appropriate method by which to examine whether the food groupings represented by a food guide reflects consumers' food classification systems. (JNE 18:265-273, 1986)
Professionals in a variety of disciplines have developed food classification systems. The bases for the systems vary and have included food uses in cultures (1, 2), culturally perceived attributes or meanings of foods (2, 3), and food preferences (4). Nutritionists have developed classification systems (Le., food guides) that translate Recommended Dietary Allowances into familiar food patterns to guide individuals' selection of nutritionally adequate diets (5). Schutz and co-workers (2) have criticized food classification systems developed by professionals on the grounds that the systems tend to reflect the professionals' perceptions of food groupings rather than the food users' groupings. If Schutz's contention is accurate, food guides developed by nutritionists may be ineffective tools for nutrition educators because those guides are inconsistent with people's perceptions of foods. VOLUME 18
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To date, researchers have not tested whether the food groupings represented by various food guides reflect consumers' food classification systems. One possible technique to examine their classification systems is multidimensional scaling (MDS), an analytical procedure that can provide a visual display of the respondents' groupings of objects, even when the bases (or dimensions) on which similarity judgments made by respondents are unknown to the investigator (6). Objects (e.g., foods) that are perceived as similar will be closer together, and those that are perceived as dissimilar will be further apart. Although this technique has been used in many fields of study, relatively few researchers (e.g., 7-9) have applied this technique to questions about food-related behavior. Therefore, we undertook this study (Note 1) to explore whether MDS was a useful technique for examining respondents' perceived similarity among foods representing a food guide. This technique allowed us to address two questions: 1) If we asked individuals to group given foods that are representative of the Four Food Groups, how would they group the foods? And, 2) given the individuals' food classification system, what are the underlying dimensions on which the individuals grouped the foods? We chose to study the Four Food Groups (10) because a) it is the food guide thought to be best known by consumers and the most widely used in nutrition education (5), and b) other food guides developed more recently (11-14) are modifications of the Four Food Groups guide. METHODS Respondents. A total of 51 students, enrolled in an introductory-level human ecology course at a large mid-Atlantic university, volunteered for this study. We excluded from participation students majoring in food and nutrition because of their knowledge about food guides. The respondents were primarily female (79%) with a median age of 20 years (range 16-40). The students' academic standings were either freshJOURNAL OF NUTRITION EDUCATION
265
men (39%). sophomore (33%). or junior (27%). Most (75%) had never taken a nutrition course; 25% had completed one course. Data collection. The respondents completed the questionnaire in a consumer behavior laboratory-a well-lighted, quiet room with tables and chairs. Completion times ranged from 45 to 75 minutes. Before respondents started the task, an investigator gave them written examples illustrating the scales to be used and told them that they were participating in a study about people's food perceptions. Respondents were not told that this was an investigation of the Four Food Groups. After a respondent completed the task, the investigator examined the questionnaire for missing information and asked the respondent to complete any missed items. At this time, participants also received a written explanation of the study. Instrument and analysis. First, we chose foods to represent the spectrum of foods within each group of the Four Food Groups guide (10). Foods were used only if they were considered familiar to and liked by college students (15) and if they were not mixtures (such as spaghetti and sauce) that could be placed in more than one of the food groups. Twenty-three foods represented the Four Food Groups: cake doughnuts, brown rice, white bread, and whole-wheat bread represented the bread-cereals group; broccoli, carrots, whole kernel corn, frenchfried potatoes, oranges, and tomatoes represented the fruit-vegetable group; bacon, chicken (baked). flounder, hamburger, hot dogs, and roast beef, as well as meat substitutes such as peanut butter, eggs, and navy beans (in soup) represented the meat group; and cheddar cheese, ice cream, whole milk, and yogurt represented the milk group. Although bacon is now placed in a fats, sweets, and alcohol group in a five food group system (14), it is in the meat group in the Four Food Groups (10). We made the assumption that cake doughnuts and white bread were enriched because, in the geographical area of this study, these products are enriched for the most part. We then constructed a four-part questionnaire to assess the manner in which respondents categorized foods and to collect demographic information. The order of presentation of the four parts remained constant for all respondents. We randomly ordered the questions and counter balanced the pages within each part of the questionnaire to reduce possible bias due to respondent fatigue and order of presentation. Part one. Part one contained the questions that were used in the MDS analysis. We presented the respondents with all possible pairs of the 23 foods, resulting in 253 paired combinations. The respondents judged the perceived similarity of each food pair on an 266
JOURNAL OF NUTRITION EDUCATION
ll-point scale ranging from 10 (similar) to 0 (dissimilar). We had the respondents complete this part first because we wanted them to make their similarity judgments based on their own implicit cognitive schema and not on any attributes that we might suggest. MDS is an effective technique for identifying respondents' implicit groupings of foods (objects) because the researcher does not need to a) impose certain categories or criteria on the respondents or b) have a priori knowledge about the criteria respondents will use to make similarity judgments. There are, however, two main disadvantages ofMDS. First, once the MDS solution is obtained, the burden of interpretation of the dimensions on which the foods are placed falls on the investigators. Second, the MDS solution could change if other foods had been included because it is based on the specific foods judged by the respondents and their perceptions of the foods. Only a limited number of foods can be included because all possible pairs of foods must be judged, and the number of judgments required can become unreasonable; for example, 25 foods would require 300 judgments, 30 foods 435 judgments, and 50 foods 1,225 judgments (6). We used the computer program PROC ALSCAL to perform the MDS analysis and specified the INDSCAL model-an individual differences MDS model in which an alternating least squares monotonic transformation is used (16). Missing data (0.3% of the data) were ignored in the analysis. By judging the similarity of each food pair, respondents produced measures of proximities (perceived distances) between foods. These proximities provide the values which are used to create the classification system. We used nonmetric scaling to fit the rank order of proximities to the distances in the classification system because we assumed the measurement level of the data to be ordinal. We treated the data as continuous and specified the untie option of PROC ALSCAL because the respondents rated a large number of food pairs on only an 11point scale, which results in a good number of ties (6). We included a reliability check by repeating 10 food pairs selected randomly from the first part of the questionnaire. We converted the correlation coefficients between repeated measures to Fisher-z scores before calculating the mean (17) and found that the mean correlation coefficient was 0.71 (range 0.50-0.79). Part two. Because we had no a priori knowledge of the bases on which the respondents would make their similarity judgments, we had the respondents rate each of the 23 foods on a series of 13 Il-point foodattribute scales to facilitate interpretation. We either took the 13 food-attribute scales directly from Fewster et al. (3) and Schutz et al. (2), or we based the scales on ideas presented by them. These Il-point food-attribute scales were anchored with bipolar stateVOLUME 18
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1986
ments, adjectives, or phrases in which the first statement, adjective, or phrase anchored the high end of the scale (e.g., I like this food/! dislike this food, familiar/unfamiliar, I frequently use this food/I infrequently use this food, eaten when healthy/eaten when sick, good for your healthlbad for your health, beneficial/harmful, low in calories/high in calories, light/ heavy, slimminglfattening, feminine/masculine, filling/not filling, for adultslfor children, and expensive/inexpensive. ) We regressed the 23 foods' stimulus coordinates from each of the dimensions of the map onto the 23 foods' mean scores for each food-attribute scale by using the computer program Statistical Package for the Social Sciences (18). Missing data were replaced with the appropriate variable means. We included a reliability check by repeating one food with its 13 food-attribute scales. The mean correlation coefficient between repeated measures was 0.73 (range 0.50-0.92). Part three. The respondents rated each of the 23 foods on eight nutrient scales. Each food was rated as a poor, fair, good, or excellent source (coded 1, 2, 3, and 4, respectively) of vitamin A, vitamin C, protein, fat, carbohydrate, calcium, iron, and dietary fiber. We also regressed the 23 foods' stimulus coordinates from each of the dimensions of the classification system onto the 23 foods' mean scores for each nutrient scale in a manner similar to the food-attribute scales. We repeated one food with its nutrient scales as a reliability check. The mean correlation coefficient of the repeated measures was 0.79 (range 0.63-0.88). To assess nutrient composition knowledge of the respondents, we first rated each of the 23 foods on the 8 nutrients as either a poor to fair or good to excellent source of the nutrient. Although we had the respondents rate the nutrient content of the foods on a 4point scale to allow use of regression analysis, we did not want to assess their nutrient composition knowledge on a scale with multiple categories because we did not expect a high level of food and nutrition knowledge from nonmajors. A food was considered a poor to fair source of a nutrient if a serving (Note 2) contained less than 10% of the U.S. Recommended Daily Allowance (U.S. RDA) (19) for the nutrient (20, 21). If a serving of food had more than 10% of the U. S. RDA, it was considered a good to excellent source of that nutrient. We made an exception in two cases: 6 g of protein instead of 6.5 g was considered good to excellent for bacon and eggs because they are considered high-quality protein sources. We considered a food a poor to fair source of carbohydrate if it contained less than 11 g/serving; a poor to fair source of fat if less than 4 g/serving; and a poor to fair source of dietary fiber if less than 1 g/serving. We then calculated a nutrient composition test score for each respondent by assigning a score of 1 for VOLUME 18
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each correct nutrient rating and then summing: a perfect score would be 184 (Le., 8 nutrients for 23 foods). Missing data were marked incorrect. Additionally, we calculated respondents' nutrient composition knowledge of each food by summing the correct answers for a food across all nutrients (perfect score equals 8); their knowledge of each nutrient was calculated by summing the correct answers for a nutrient across all foods (perfect score equals 23). Using a t statistic (17), we tested the deviation of the mean scores from the scores that would be obtained if the respondents were guessing (92 for nutrient composition test scores, 4 for individual food scores, and 11.5 for individual nutrient scores). Part four. Respondents provided information about their academic standing, age, gender, and number of nutrition courses taken. RESULTS AND DISCUSSION MDS solution. Figure 1 and Table 1 illustrate the 3dimensional, MDS solution of the respondents' similarity judgments of the 23 foods-the closer two foods are, the more those foods are perceived as similar by the respondents. The positions of the foods on the structure are provided by stimulus coordinates on each dimension (Table I). The foods' stimulus coordinates for each dimension are presented in Table 1 because of the difficulty of representing a 3-dimensional figure on a 2-dimensional surface. The actual numbers for each stimulus coordinate are not representative of the II-point scale on which the respondents rated their similarity judgments. The stimulus coordinates are normalized for each dimension, which means that their sum is equal to zero and that their sum of squares equals the number of stimuli. Thus, the coordinates indicate relative distances among the foods. Dimension 1 runs from left (negative end) to right (positive end) of the figure. Dimension 2 is represented by the plane that comes out towards the reader; the end of the plane that appears closest to the reader is the positive end, and the end furthest away is the negative end. Dimension 3 runs from top (positive end) to bottom (negative end) of the figure. We named the dimensions to indicate the positive end first and the negative end second. We chose a 3-dimensional MDS solution (Figure 1 and Table 1), after examining 2-, 3-, 4-, 5-, and 6-dimensional MDS solutions, because it was the most interpretable and represented the point at which the Kruskal's stress values showed little decrease and the squared correlation coefficients showed little increase. The Kruskal's stress value for this MDS solution was 0.21, and the proportion of variance (RZ) explained by the solution was 0.60. MDS solutions with JOURNAL OF NUTRITION EDlJCATION
267
Flounder Chicken I Roast Beef
Cheese
Hamburger
Milk Ice Cream
Yogurt
Hot Dogs Bacon
I~
Peanut. Butter'
..tt.V
/
/'
/
L
/
/
L
ill
I
\
~
Oranges
Carr~ts
\
Bro~oli
Tomatoes '-
I Inconvenient-convenient II Bad-good Animal- plant
m Corn Doughnuts White Bread
Whole7Wheat Bread
)Navy Beans (in soup) ~
Foods from the Milk Group
l!. Foods from the Bread -cereal s Group
7Rice
French-fried Potatoes
o Foods from the Meat Group • Foods from the Fruit-v 4!9etable Group
Figure 1.
Plot of the three dimensions for the multidimensional scaling analysis of the 23-food matrix .
Table 1.
Stimulus coordinates for the 23 foods on three dimensions of multidimensional scaling (MDS) solution.
Dimensions Inconven ient -Con venien t I
Food
Coordi nate
Food
Bacon Chicken Roast beef Hot dogs Flounder Hamburger Navy bean soup Broccoli French fries Tomatoes Carrots Corn Rice Eggs Oranges Peanut butter Bread (WW)·' Bread (wh ite) Cheddar cheese Milk Doughnuts Yogurt Ice cream
1.48 1.18 1.18 1.05 1.02 1.00 0.98 0.63 0.50 0.47 0.42 0.16 0.06 -0.30 -0.49 -0.67 -0.78 -0.80 -0.83 -1.29 -1.45 -1.50 - 2.02
Doughnuts Bread (white) French fries Hot dogs Peanut butter Hamburger Bread (ww)a Bacon Roast beef Rice Ice cream Navy bean soup Chicken Eggs Milk Cheddar cheese Flounder Corn Yogurt Broccoli Tomatoes Carrots Oranges
"ww
268
=
Animal-Plant
Bad-Good If
Ifl
Coordinate 1.30 1.29 1.26 1.22 1.11 0.86 0.75 0.74 0.44 0.40 0.19 0.00 -0.01 -0.21 -0 .2 2 -0.43 -0.43 -0.70 -0.82 -1.56 - 1.59 -1 .63 -1.96
Food
Coordina te
Eggs Cheddar cheese Flounder Chicken Roast beef Hamburger Milk Hot dogs Bacon Peanut butter Yogurt Ice cream Oranges Doughnuts Tomatoes Carrots Bread (white) Broccoli Frenc h fries Bread (ww),' Navy bean soup Corn Rice
1.43 1.24 1.22 1.16 1.06 0.94 0.78 0.64 0.62 0.61 0.58 0.56 -0.36 -0.62 -0.78 -0.80 -0.81 -0 .83 -1.14 -1.14 -1.24 -1.52 -1.61
whole wheat.
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more dimensions increased the complexity of the structure without accounting for a greater amount of variance in the data. Kruskal's stress value is a measure of the goodness-of-fit of the actual and predicted distance scores. The squared correlation coefficient indicates the proportion of variance of the transformed data that is accounted for by the structure (6). Question one. If individuals were given foods representative of the Four Food Groups, how would they group the foods? To answer this question, we in spected the MDS solution to examine how the foods were placed on the respondents' classification system (Figure 1 and Table 1). Ice cream, yogurt, milk, cheese, eggs, and peanut butter seem to form a group that contains foods mostly from the milk group. Flounder, chicken, roast beef, hamburger, hot dogs, and bacon cluster and are all from the meat group. Cake doughnuts and bread (white and whole wheat) cluster and are from the bread-cereals group. Carrots, tomatoes, and broccoli, with corn and oranges on the periphery, seem to form the fruit-vegetable group. In addition, rice, french-fried potatoes, and navy beans (in soup) seem to cluster as a high-starch group. The respondents seemed to group the foods in a manner related to but more complex than the Four Food Groups (10). Some foods like eggs, peanut butter, and navy beans (in soup), which are the meat substitutes, are not placed with the meat group. Furthermore, there may be additional groups in their classification system, such as a high-starch group. We did not perform a statistical analysis of the relationship between the respondents' classification system and the Four Food Groups guide because of the limited number of foods that we used to represent each of the groups. Question two. Given the individuals' food classification system, what are the underlying dimensions on which the individuals grouped the foods? The respondents in this study seemed to have used three dominant criteria (dimensions) to make their similarity judgments. We interpreted the three dimensions by examining the positions of foods along each dimension (Table 1). The greater a food's stimulus coordinate (plus or minus) is, the more important the food is in defining that dimension. Consideration also was given to the respondents' ratings of each food on each food-attribute and nutrient scale (Table 2). Two of the scales, feminine/masculine and calci um, were inversely related to dimension 1 (Table 2). Foods with a high positive stimulus coordinate on dimension 1 (i.e., bacon, chicken, roast beef, hot dogs, flounder, hamburger, and navy beans) were perceived to be masculine and to contain little calcium. In contrast, foods on the negative end of the continuum (i.e., VOLUME 18
NUMBER 6
1986
ice cream, yogurt, doughnuts, and milk) were perceived to be feminine and to be high in calcium. The feminine/masculine scale, however, also is related to dimension 2, which indicates that it probably is not the dominant criterion the respondents used for making their similarity judgments. Although these food-attribute scales were related to dimension 1, we feel that an alternative interpretation may account for the arrangement of the foods along the dimension. The meats, which are on the positive end, generally require some preparation; whereas the doughnuts and milk products, which are on the negative end, can be eaten without further preparation by the consumer. The degree of convenience in preparation from a consumer's perspective has been described by Bayton (22), and for these respondents who are college students, it probably is important. Therefore, we labeled dimension 1 the inconvenient-convenient dimension. Dimension 2 was inversely related to the following scales: good for your healthlbad for your health, beneficial/harmful, low in calories/high in calories, light/ heavy, slimmingifattening, feminine/masculine, vitamin A, vitamin C, and dietary fiber (Table 2). Foods at the positive end of the dimension (Le., doughnuts, breads, potatoes, hot dogs, peanut butter, hamburger, bacon) were perceived to be bad for your health, harmful, high in calories, heavy, fattening, masculine, and low in vitamin A, vitamin C, and dietary fiber. On the other end, the good for your health, beneficial, low in calories, light, slimming, feminine foods, which are high in vitamin A, vitamin C, and dietary fiber were oranges, carrots, tomatoes, broccoli, yogurt, and corn. Other investigators (2-4) have found a health-related category or dimension that people use to categorize foods. We, therefore, labeled dimension 2 the badgood dimension. The food-attribute scale feminine/masculine seemed incongruent with the health-related attributes. Schutz et al. (2) found a food-use factor called utilitarian; it was defined by foods like peanut butter, roast beef, and bread, which were considered appropriate for men as well as for teenagers and children. A physiologic group, discussed by Jelliffe (1), includes foods reserved for people of certain ages or physiology (such as men). Perhaps the respondents (mostly young women) considered foods higher in energy more masculine because they felt that men are able to consume more high-energy foods without gaining weight. In any case, foods reflective of gender stereotypes suggest an interesting question for future research. With the exception of peanut butter, all of the foods on the positive end of dimension 3 are animal products, and all foods on the negative end are plant foods (or their major ingredients are of plant origin). The respondents correctly perceived that the foods on the JOURNAL OF NUTRITION EDUCATION
269
Table 2.
Multiple regression of 23 foods' stimulus coordinates on food-attribute and nutrient scales. (3 Values Dimen sions
Rating Scale"
I like this food/I dislike this food. Familiar/unfam iliar I frequently use this food/I infrequently use this food . Eaten when healthy/eaten when sick Good for your health/bad for your health Beneficial/harmfu I Low in calories/high in calories Light/heavy Slimming/fattening Feminine/masculine Filling/not filling For ad u Itslfor chi Id ren Expensive/inexpensive Vitamin A Vitamin C Protein Fat Carbohydrate Cal cium Iron Dietary fiber
InconvenientConvenient I
-0.22 (1.00)" -0.22 (1.06) -0.13 (0.34) -0.05 (0.05) -0.09 (0.47) -0.15 (1.65) 0.20 (2.56) 0.00 (0.00) 0.12 (1.16) -0.55" (15.50) 0.19 (0.94) 0.25 (1.70) 0.28 (2.20) -0.10 (0.47) -0.07 (0.40) 0.32 (2 .59) 0.06 (0.10) -0.21 (1.10) -0.54* (9.66) 0.26 (1.80) 0.04 (0.07)
Bad-Good /I
-0.22 (0.98) -0.12 (0.33) -0 .32 (2 .20) 0.02 (0.01) -0.81" (37.11) -0.85" (51.47) -0.80" (41.38) - 0.82* * (42 .51) -0 .85** (52 .87) -0.60 * • (18 .63) 0.51 (6.98) -0.46 (5.64) 0.19 (1.05) - 0.80" (32 .92) -0 .86*' (57.83) -0.23 (1.40) 0.45 (5.65) 0.39 (3.89) -0.25 (2.12) -0.46 (5.49) -0.51" (10.71)
Animal-Plant /II
0.02 (0.01) 0.29 (1.78) 0.12 (0 .30) 0.11 (0.25) -0.03 (0.04) 0.01 (0.01) -0.07 (0.32) -0.11 (0.70) -0.02 (0.04) -0.07 (0 .27) 0.12 (0.42) -0.10 (0.27) 0.48 (6.42) 0.04 (0.08) -0.10 (0.72) 0.39 (3.85) 0.30 (2.45) -0.31 (2.46) 0.30 (2.98) 0.09 (0.22) -0.48" (9.70)
R2
R~dl
F'U'I'
0.09
0.00
0.64
0.15
0.01
1.08
0.12
0.00
0 .89
0.02
0.00
0.11
0.67
0.61
12.64' *
0.74
0.69
17.60'*
0.71
0.67
15.56* •
0.70
0.66
14.96* *
0.75
0.71
18.60**
0.64
0.58
11.10**
0.31
0.20
2.82
0.31
0.20
2.79
0.33
0.23
3.18
0.64
0.58
11 .07* *
0 .76
0.72
20.16' ,
0.27
0.16
2.36
0.32
0.21
3.00
0.26
0.15
2.26
0.44
0.35
4.99'
0.28
0.17
2.51
0.60
0.48
7.72**
"First statement, adjective, or phrase listed anchored the high end of the ll-point scale; for th e nutrients, scale ranged from 4 (excellent) to 1 (poor). bNumbers in parentheses are the F-value for each {3 value. *p < .05 **p < .01
negative end of the dimension are higher in dietary fiber (Table 2), but the dietary fiber scale also was related significantly to dimension 2, which indicates it was probably not the dominant criterion used for this dimension. We, therefore, labeled dimension 3 the animal-plant dimension. By examining the respondents' classification sys270
JOURNAL OF NUTRITION EDUCATION
tern (Figure 1 and Table 1) and using the interpretations of the three dimensions, the foods can be put in eight possible cells. Roast beef, hamburger, hot dogs, and bacon would represent the inconvenient-bad-animal foods; ice cream and peanut butter, the convenient-bad-animal foods; flounder, the inconvenientgood-animal food; milk, yogurt, cheese, and eggs, the VOLUME 18
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convenient-goad-animal foods; french-fried potatoes and rice, inconvenient-bad-plant foods; doughnuts and bread, convenient-bad-plant foods; carrots, tomatoes, broccoli, and corn, the inconvenient-goad-plant foods; and oranges, convenient-goad-plant food. Two foods represent special cases because they were considered neither good nor bad: navy beans (in soup) were perceived as an inconvenient-plant food and chicken as an inconvenient-animal food.
Table 3.
Nutrient composition knowledge. Respondents' average total score for the nutrient composition test was 124.2 (S .E. = 2 .85) or 67 .5%. This mean score was significantly different (t = 11.28, dJ. = 50, P :5 .01) from the mean score of 92 .0, which would have been obtained if the respondents were guessing. The percentage of respondents that provided the correct answer to each food-nutrient combination is presented in Table 3.
Percent correct responses to nutrient composition of 23 foods.
Nutrient Food Bacon Broccoli Bread (white) Bread (whole-wheat) Carrots Cheddar cheese Chi cken (baked) Corn (whole kernel) Doughnuts (cake) Eggs Flounder French fries Hamburger Hot dogs Ice cream Milk Navy bean soup Oranges Peanut butter Rice (brown) Roast beef Tomatoes Yogurt
Vitamin A
Vitamin C
Protein
Fat
Carbohydrate
Ca lciu m
Iron
74 .5 (poor)" 82.4 (good) 60.8 (poor) 41.2 (poor) 90.2 (good) 39.2 (poor) 52.9 (poor) 35.3 (poor) 96 .1 (poor) 45 .1 (poor) 33.3 (poor) 86. 3 (poor) 74.5 (poor) 84.3 (poor) 64.7 (poor) 17.6 (poor) 54.9 (poor) 25.5 (poor) 47.1 (poor) 60.8 (poor) 56.9 (poor) 74 .5 (good) 41.2 (poor)
86.3 (poor) 64.7 (good) 78.4 (poor) 64.7 (poor) 29.4 (poor) 54.9 (poor) 80.4 (poor) 60.8 (poor) 100.0 (poor) 66 .7 (poor) 66.7 (poor) 86.3 (poor) 82.4 (poor) 96.1 (poor) 84 .3 (poor) 49.0 (poor) 74.5 (poor) 100.0 (good) 64.7 (poor) 80.4 (poor) 76 .5 (poor) 64.7 (good) 52.9 (poor)
64.7 (good) 41.2 (poor) 58.8 (poor) 39.2 (poor) 43.1 (poor) 88.2 (good) 88.2 (good) 54.9 (poor) 90 .2 (poor) 96 .1 (good) 88 .2 (good) 70.6 (poor) 84.3 (good) 66.7 (good) 51.0 (poor) 88 .2 (good) 66.7 (good) 70.6 (poor) 94.1 (good) 43.1 (poor) 82.4 (good) 64 .7 (poor) 80.4 (good)
88.2 (good) 90.2 (poor) 64.7 (poor) 58 .8 (poor) 92.2 (poor) 66.7 (good) 47.1 (poor) 76.5 (poor) 70.6 (good) 51.0 (good) 82.4 (poo r) 74.5 (good) 90.2 (good) 74 .5 (good) 80.4 (good) 80.4 (good) 68.6 (poo r) 90.2 (poor) 74.5 (good) 60 .8 (poor) 82.4 (good) 92.2 (poor) 47.1 (good)
51.0 (poor) 78.4 (poor) 82.4 (good) 72.5 (good) 78.4 (poor) 43.1 (poor) 66.7 (poor) 51.0 (good) 82.4 (good) 47 .1 (poor) 80.4 (poor) 76.5 (good) 51.0 (poor) 41.2 (poo r) 66.7 (good) 60.8 (good) 62.7 (good) 25.5 (good) 37.2 (poor) 74.5 (good) 56 .9 (poor) 84 .3 (poor) 47.1 (good)
96.1 (poor) 68.6 (poor) 72.5 (poor) 45 .1 (poor) 62.7 (poor) 78.4 (good) 68 .6 (poor) 78.4 (poor) 84.3 (poor) 49.0 (poor) 62.7 (poor) 82.4 (poor) 76.5 (poor) 82.4 (poor) 82.4 (good) 96.1 (good) 76.5 (poor) 74.5 (poor) 60.8 (poor) 74.5 (poor) 70 .6 (poor) 80.4 (poor) 78.4 (good)
74.5 (poor) 33.3 (poor) 68.6 (poor) 47.1 (poor) 33.3 (poor) 60.8 (poor) 56.9 (poor) 60.8 (poor) 98.0 (poor) 43.1 (poor) 25.5 (poor) 88.2 (poor) 39.2 (good) 25.5 (poor) 80.4 (poor) 23.5 (poor) 52.9 (good ) 70.6 (poor) 45.1 (poor) 70.6 (poor) 56.9 (good) 62.7 (poor) 56.9 (poor)
Dietary Fiber 92.2 (poor) 66.7 (good) 68.6 (poor) 80.4 (good) 76.5 (good) 82.4 (poor) 80.4 (poor) 43.1 (good) 94.1 (poor) 80.4 (poor) 54.9 (poor) 86. 3 (poor) 90.2 (poor) 94.1 (poor) 100.0 (poor) 74.5 (poor) 31.4 (good) 54.9 (good) 76.5 (poor) 43 .1 (poor) 80.4 (poor) 51.0 (good) 66 .7 (poor)
"Ratings of foods' nutrient compositions are in parentheses : poor = poor to fair; good = good to exce llent.
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The respondents' mean scores for individual foods across nutrients differed significantly (p ::5 .05) from the mean of 4.0. The mean scores (S.E.) ranged from 7.2 (0 .12) for cake doughnuts to 4.5 (0.24) for wholewheat bread. All individual nutrient mean scores (S.E.) differed significantly (p ::5 .05) from the mean of 11 .5, which would have been obtained if the respondents were guessing; these scores ranged from 17.0 (0 .37) for fat to 13.2 (0.59) for iron. These results indicate that the respondents had some knowledge of the nutrient composition offoods; however, only 4 of the 8 nutrient scales were related significantly to the 3 dimensions of the MDS solution, indicating that the nutrient composition of foods is important, but not a dominant criterion used by the respondents to group foods.
developing a food guide, or modifying the Four Food Groups , professionals should consider consumers' food classification systems as well as the criteria on which they group foods. With further studies similar to this one, professionals could develop groupings that are understandable and useful to consumers in addition to being compatible with the goal of food and nutrition educators-to guide individuals in selecting health-promoting diets. 0
ACKNOWLEDGMENTS The authors thank Ms. Carmen Samuel for her assistance in data collection and processing and Mr. Daniel W. Denman for statistical assistance. The computer time for this project was supported in part through the facilities of the Computer Science Center of the University of Maryland.
IMPLICATIONS Even though the results of this study are based on only a limited number of foods examined by a sample of college students, we feel that there are implications worth considering. The results of this study suggest that the Four Food Groups guide is not entirely unlike the respondents' food groupings, but it probably needs to be expanded to include more groups (e.g., a legume group) as well as modified to consider consumers' perceptions of foods' attributes. A food guide recently developed by Pennington (13) contains four main food groups (vegetable and fruit ; grain products; vegetable, dairy, and meat sources of protein ; and luxury foods) with 12 subgroups . These subgroups were represented graphically by an inverted pyramid, which had four suggested levels of consumption (liberal to sparse). Thus, Pennington's food guide included more food groups (e.g., legumes and eggs) and an implied bad-good dimension (levels of suggested consumption). Lachance (11) suggested that the Four Food Groups should be represented visually to emphasize consumption of foods in the fruit-vegetable group and bread-cereals group. USDA's Daily Food Guide (14) , in addition to the Four Food Groups , contains a fats, sweets, alcohol group, but with no suggested number of servings. In their modified Basic Four, however, King et al. (12) include a fat and oil group with a suggested number of servings. They also subdivide their protein group into animal protein and legumes and/or nuts (with suggested servings) as well as make more specific suggestions for consumption patterns from the fruits and vegetable group . The MDS technique seems to be an appropriate method by which to examine whether the food groupings represented by a food guide reflect consumers ' food classification systems . This question is a very important one for food and nutrition educators. When 272
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NOTES 1 Permission to conduct this study was obtained from the
University of Maryland Committee on the Use of Human Subjects in Research. 2 Standard serving sizes were considered to be one-half cup of broccoli, carrots , corn , navy beans (cooked). rice (cooked), and ice cream; 3 oz of chicken, flounder , hamburger, hot dog, and roast beef; 3 slices of bacon; 1 medium-size orange and tomato ; 1 large egg; 1 cup of milk ; 8 oz of yogurt; 1 oz of cheese ; 2 T of peanut butter; 1 oz of bread and doughnut; and 50 g of french-fried potatoes.
LITERATURE CITED Jelliffe, D. B. Parallel food classifications in developing and industrialized countries . American Journal ofClinical Nutrition 20:279-83, 1967 . 2 Schutz, H. G., M. H. Rucker, and G. F. Russell. Food and food-use classification systems. Food Technology 29(3) : 1
50-64, 1975.
3 Fewster, W. J., L. R. Bostian, and R. D. Powers . Measuring the connotative meanings of foods. Hom e Economics Research Journal 2(1) :44-53 , 1973 . 4 Pilgrim, F. J. , and J. M. Kamen . Patterns of food preference through factor analysis. Journal of Marketing 24:68-72,1959.
5 Light, L., and F. J. Cronin . Food guidance revisited . Journal of Nutrition Education 13:57-62, 1981. 6 Schiffman, S., M. L. Reynolds, and F. W. Young . Introduction to multidimensional scaling: Theory, methods, and applications, New York: Academic Press, 1981, pp . 3-28, 55-86. 7
Birch, L. L. Dimensions of preschool children's food preferences . Journal of Nutrition Education 11 : 77-80, 1979 .
8 Birch, L. L. Preschool children 's food preferences and consumption patterns . Journal of Nutrition Education 11 :189-92,1979 . 9 Rozin, P., A. Fallon, and M. L. Augustoni-Ziskind. The
child's conception of food: The development of categoVOLUME 18
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ries of acceptable and rejected substances. Journal of Nutrition Education 18:75-81, 1986. IOU. S. Department of Agriculture. Agricultural Research Service. Essentials of an adequate diet-Facts for nutrition programs, Home Economics Research Report No.3. Washington, DC: Department of Agriculture, 1957, 21 pp. 11 Lachance, P. A. A suggestion on food guides and dietary guidelines. Journal of Nutrition Education 13:56, 1981. 12 King, J. C., S. H. Cohenour, C. G. Corruccini, and P. Schneeman. Evaluation and modification of the Basic Four food guide. Journal of Nutrition Education 10:2729, 1978. 13 Pennington, J. A. T. Considerations for a new food guide. Journal of Nutrition Education 13:53-55, 1981. 14 U.S. Department of Agriculture. Science and Education Administration. Food, Home and Garden Bull. No. 228. Washington, DC: Government Printing Office, 1979, 64 pp. 15 Einstein, M. A., and I. Hornstein. Food preferences of college students and nutritional implications. Journal of Food Science 35:429-36, 1970.
16 Reinhardt, P. S., ed. SAS supplemental library user's guide, Cary, NC: SAS Institute, Inc., 1980, pp. 5-18. 17 Sokal, R. R., and F. J. Rohlf. Introduction to biostatistics, San Francisco: W. H. Freeman and Co., 1973, pp. 128, 34l. 18 Hull, C. H., and N. H. Nie. SPSS update-79, New York: McGraw-Hill, 1981, pp. 94-12l. 19 Whitney, E. N., and E. M. N. Hamilton. Understanding nutrition, 3d ed. New York: West Publishing Co., 1984, pp. 135, 48l. 20 Gebhardt, S. E., and R. H. Matthews. Nutritive value of foods, Home and Garden Bull. No. 72. Washington, DC: U.S. Department of Agriculture, 1985, 72 pp. 21 Lanza, E., and R. Butrum. A critical review of food fiber analysis and data. Journal of the American Dietetic Association 86:732-43, 1986. 22 U.S. Department of Agriculture. Problems of communication of nutrition information, by J. A. Bayton. National Nutrition Conferences Proceedings, Misc. Publ. No. 1075. Washington, DC: Department of Agriculture, 1966, pp. 16-19.
ANEMIA AND SCHOOL PERFORMANCE The detrimental effects of iron deficiency and anemia on the physiological and cognitive development of young children have been well documented. Recently, investigators have become interested in examining the effects of iron deficiency on the behavior and academic achievement of school-age children. The effect of iron supplementation on measures of school performance among 78 irondeficient anemic and 41 nonanemic school-age children in rural Indonesia has been investigated (American Journal of Clinical Nutrition 42:1221-28,1985). After random assignment, 43 of the anemic and 16 of the nonanemic children received an oral treatment of ferrous sulfate (10 mg/kg/day) for three months; the remaining study participants received a placebo tablet containing only saccharin and tapioca. After the three-month period, those iron-deficient anemic children who had received the iron treatment showed substantive increases in mean hemoglobin, hematocrit, and transferrin saturation values. In contrast, there was no significant increase in the mean values of these same measures among the nonanemic children treated with iron. These changes in the iron status of the iron-deficient anemic children were associated with positive changes in their performance on the school achievement and concentration tests. The once-anemic children did not, however, score as high as the nonanemic children. The authors point out that this finding is expected in light of the fact that although iron repletion probably enhances the learning process, it cannot compensate for the deficits in learning accrued over a three-month period. Poor school achievement is explained, in part, by a deficit in the child's reception of information, or attentional processes. It is not known, however, which of the attentional processes-selective attention, attention span, or vigilance-are specifically involved. The attentional deficits which are apparently present in iron deficiency and anemia may be related to a disturbance in the physiological arousal of the attentional processes present in the autonomic nervous system. Regardless of the exact physiological mechanism involved, the focus of future research must be on the magnitude of the effects that nutritional deficiencies have on the learning process.
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L.
AXELSON,1 NATALIE KURINIJ,2 AND DAVID BRINBERC 3
1Department of Food, Nutrition and Institution Administration, College of Human Ecology, University of Maryland, College Park, Maryland 20740; 2Epidemiology and Biometry Research Program, National Institute of Child Health and Human Development. National Institutes of Health, Bethesda, Maryland 20205; and 3Department of Marketing, State University of New York, Albany, New York 12222
ABSTRACT To date, researchers have not tested whether the food groupings represented by various food guides reflect consumers' food classification systems. We undertook this study to explore whether multidimensional scaling (MDS) was a useful technique for examining respondents' perceived similarity among foods representing the Four Food Groups guide. This technique allows us to address the following questions: If individuals are given foods representative of the Four Food Groups, how would they group the foods? And, given the individuals' food groupings, what are the underlying dimensions or criteria on which they grouped the foods? Fifty-one college students rated all possible pairs of 23 foods, representing the Four Food Groups, on similar/dissimilar scales. Using an ALSCAL MDS analysis, we found that respondents grouped foods on 3 dimensions. Although the respondents used dimensions (or criteria) other than the nutrient composition of foods, they grouped the foods in a manner similar to but more complex than the Four Food Groups. The respondents seemed to perceive similarities among foods based on convenience in preparation, health-related properties, and source (animal and plant). Our results indicate that the MDS technique seems to be an appropriate method by which to examine whether the food groupings represented by a food guide reflects consumers' food classification systems. (JNE 18:265-273, 1986)
Professionals in a variety of disciplines have developed food classification systems. The bases for the systems vary and have included food uses in cultures (1, 2), culturally perceived attributes or meanings of foods (2, 3), and food preferences (4). Nutritionists have developed classification systems (Le., food guides) that translate Recommended Dietary Allowances into familiar food patterns to guide individuals' selection of nutritionally adequate diets (5). Schutz and co-workers (2) have criticized food classification systems developed by professionals on the grounds that the systems tend to reflect the professionals' perceptions of food groupings rather than the food users' groupings. If Schutz's contention is accurate, food guides developed by nutritionists may be ineffective tools for nutrition educators because those guides are inconsistent with people's perceptions of foods. VOLUME 18
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To date, researchers have not tested whether the food groupings represented by various food guides reflect consumers' food classification systems. One possible technique to examine their classification systems is multidimensional scaling (MDS), an analytical procedure that can provide a visual display of the respondents' groupings of objects, even when the bases (or dimensions) on which similarity judgments made by respondents are unknown to the investigator (6). Objects (e.g., foods) that are perceived as similar will be closer together, and those that are perceived as dissimilar will be further apart. Although this technique has been used in many fields of study, relatively few researchers (e.g., 7-9) have applied this technique to questions about food-related behavior. Therefore, we undertook this study (Note 1) to explore whether MDS was a useful technique for examining respondents' perceived similarity among foods representing a food guide. This technique allowed us to address two questions: 1) If we asked individuals to group given foods that are representative of the Four Food Groups, how would they group the foods? And, 2) given the individuals' food classification system, what are the underlying dimensions on which the individuals grouped the foods? We chose to study the Four Food Groups (10) because a) it is the food guide thought to be best known by consumers and the most widely used in nutrition education (5), and b) other food guides developed more recently (11-14) are modifications of the Four Food Groups guide. METHODS Respondents. A total of 51 students, enrolled in an introductory-level human ecology course at a large mid-Atlantic university, volunteered for this study. We excluded from participation students majoring in food and nutrition because of their knowledge about food guides. The respondents were primarily female (79%) with a median age of 20 years (range 16-40). The students' academic standings were either freshJOURNAL OF NUTRITION EDUCATION
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men (39%). sophomore (33%). or junior (27%). Most (75%) had never taken a nutrition course; 25% had completed one course. Data collection. The respondents completed the questionnaire in a consumer behavior laboratory-a well-lighted, quiet room with tables and chairs. Completion times ranged from 45 to 75 minutes. Before respondents started the task, an investigator gave them written examples illustrating the scales to be used and told them that they were participating in a study about people's food perceptions. Respondents were not told that this was an investigation of the Four Food Groups. After a respondent completed the task, the investigator examined the questionnaire for missing information and asked the respondent to complete any missed items. At this time, participants also received a written explanation of the study. Instrument and analysis. First, we chose foods to represent the spectrum of foods within each group of the Four Food Groups guide (10). Foods were used only if they were considered familiar to and liked by college students (15) and if they were not mixtures (such as spaghetti and sauce) that could be placed in more than one of the food groups. Twenty-three foods represented the Four Food Groups: cake doughnuts, brown rice, white bread, and whole-wheat bread represented the bread-cereals group; broccoli, carrots, whole kernel corn, frenchfried potatoes, oranges, and tomatoes represented the fruit-vegetable group; bacon, chicken (baked). flounder, hamburger, hot dogs, and roast beef, as well as meat substitutes such as peanut butter, eggs, and navy beans (in soup) represented the meat group; and cheddar cheese, ice cream, whole milk, and yogurt represented the milk group. Although bacon is now placed in a fats, sweets, and alcohol group in a five food group system (14), it is in the meat group in the Four Food Groups (10). We made the assumption that cake doughnuts and white bread were enriched because, in the geographical area of this study, these products are enriched for the most part. We then constructed a four-part questionnaire to assess the manner in which respondents categorized foods and to collect demographic information. The order of presentation of the four parts remained constant for all respondents. We randomly ordered the questions and counter balanced the pages within each part of the questionnaire to reduce possible bias due to respondent fatigue and order of presentation. Part one. Part one contained the questions that were used in the MDS analysis. We presented the respondents with all possible pairs of the 23 foods, resulting in 253 paired combinations. The respondents judged the perceived similarity of each food pair on an 266
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ll-point scale ranging from 10 (similar) to 0 (dissimilar). We had the respondents complete this part first because we wanted them to make their similarity judgments based on their own implicit cognitive schema and not on any attributes that we might suggest. MDS is an effective technique for identifying respondents' implicit groupings of foods (objects) because the researcher does not need to a) impose certain categories or criteria on the respondents or b) have a priori knowledge about the criteria respondents will use to make similarity judgments. There are, however, two main disadvantages ofMDS. First, once the MDS solution is obtained, the burden of interpretation of the dimensions on which the foods are placed falls on the investigators. Second, the MDS solution could change if other foods had been included because it is based on the specific foods judged by the respondents and their perceptions of the foods. Only a limited number of foods can be included because all possible pairs of foods must be judged, and the number of judgments required can become unreasonable; for example, 25 foods would require 300 judgments, 30 foods 435 judgments, and 50 foods 1,225 judgments (6). We used the computer program PROC ALSCAL to perform the MDS analysis and specified the INDSCAL model-an individual differences MDS model in which an alternating least squares monotonic transformation is used (16). Missing data (0.3% of the data) were ignored in the analysis. By judging the similarity of each food pair, respondents produced measures of proximities (perceived distances) between foods. These proximities provide the values which are used to create the classification system. We used nonmetric scaling to fit the rank order of proximities to the distances in the classification system because we assumed the measurement level of the data to be ordinal. We treated the data as continuous and specified the untie option of PROC ALSCAL because the respondents rated a large number of food pairs on only an 11point scale, which results in a good number of ties (6). We included a reliability check by repeating 10 food pairs selected randomly from the first part of the questionnaire. We converted the correlation coefficients between repeated measures to Fisher-z scores before calculating the mean (17) and found that the mean correlation coefficient was 0.71 (range 0.50-0.79). Part two. Because we had no a priori knowledge of the bases on which the respondents would make their similarity judgments, we had the respondents rate each of the 23 foods on a series of 13 Il-point foodattribute scales to facilitate interpretation. We either took the 13 food-attribute scales directly from Fewster et al. (3) and Schutz et al. (2), or we based the scales on ideas presented by them. These Il-point food-attribute scales were anchored with bipolar stateVOLUME 18
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ments, adjectives, or phrases in which the first statement, adjective, or phrase anchored the high end of the scale (e.g., I like this food/! dislike this food, familiar/unfamiliar, I frequently use this food/I infrequently use this food, eaten when healthy/eaten when sick, good for your healthlbad for your health, beneficial/harmful, low in calories/high in calories, light/ heavy, slimminglfattening, feminine/masculine, filling/not filling, for adultslfor children, and expensive/inexpensive. ) We regressed the 23 foods' stimulus coordinates from each of the dimensions of the map onto the 23 foods' mean scores for each food-attribute scale by using the computer program Statistical Package for the Social Sciences (18). Missing data were replaced with the appropriate variable means. We included a reliability check by repeating one food with its 13 food-attribute scales. The mean correlation coefficient between repeated measures was 0.73 (range 0.50-0.92). Part three. The respondents rated each of the 23 foods on eight nutrient scales. Each food was rated as a poor, fair, good, or excellent source (coded 1, 2, 3, and 4, respectively) of vitamin A, vitamin C, protein, fat, carbohydrate, calcium, iron, and dietary fiber. We also regressed the 23 foods' stimulus coordinates from each of the dimensions of the classification system onto the 23 foods' mean scores for each nutrient scale in a manner similar to the food-attribute scales. We repeated one food with its nutrient scales as a reliability check. The mean correlation coefficient of the repeated measures was 0.79 (range 0.63-0.88). To assess nutrient composition knowledge of the respondents, we first rated each of the 23 foods on the 8 nutrients as either a poor to fair or good to excellent source of the nutrient. Although we had the respondents rate the nutrient content of the foods on a 4point scale to allow use of regression analysis, we did not want to assess their nutrient composition knowledge on a scale with multiple categories because we did not expect a high level of food and nutrition knowledge from nonmajors. A food was considered a poor to fair source of a nutrient if a serving (Note 2) contained less than 10% of the U.S. Recommended Daily Allowance (U.S. RDA) (19) for the nutrient (20, 21). If a serving of food had more than 10% of the U. S. RDA, it was considered a good to excellent source of that nutrient. We made an exception in two cases: 6 g of protein instead of 6.5 g was considered good to excellent for bacon and eggs because they are considered high-quality protein sources. We considered a food a poor to fair source of carbohydrate if it contained less than 11 g/serving; a poor to fair source of fat if less than 4 g/serving; and a poor to fair source of dietary fiber if less than 1 g/serving. We then calculated a nutrient composition test score for each respondent by assigning a score of 1 for VOLUME 18
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each correct nutrient rating and then summing: a perfect score would be 184 (Le., 8 nutrients for 23 foods). Missing data were marked incorrect. Additionally, we calculated respondents' nutrient composition knowledge of each food by summing the correct answers for a food across all nutrients (perfect score equals 8); their knowledge of each nutrient was calculated by summing the correct answers for a nutrient across all foods (perfect score equals 23). Using a t statistic (17), we tested the deviation of the mean scores from the scores that would be obtained if the respondents were guessing (92 for nutrient composition test scores, 4 for individual food scores, and 11.5 for individual nutrient scores). Part four. Respondents provided information about their academic standing, age, gender, and number of nutrition courses taken. RESULTS AND DISCUSSION MDS solution. Figure 1 and Table 1 illustrate the 3dimensional, MDS solution of the respondents' similarity judgments of the 23 foods-the closer two foods are, the more those foods are perceived as similar by the respondents. The positions of the foods on the structure are provided by stimulus coordinates on each dimension (Table I). The foods' stimulus coordinates for each dimension are presented in Table 1 because of the difficulty of representing a 3-dimensional figure on a 2-dimensional surface. The actual numbers for each stimulus coordinate are not representative of the II-point scale on which the respondents rated their similarity judgments. The stimulus coordinates are normalized for each dimension, which means that their sum is equal to zero and that their sum of squares equals the number of stimuli. Thus, the coordinates indicate relative distances among the foods. Dimension 1 runs from left (negative end) to right (positive end) of the figure. Dimension 2 is represented by the plane that comes out towards the reader; the end of the plane that appears closest to the reader is the positive end, and the end furthest away is the negative end. Dimension 3 runs from top (positive end) to bottom (negative end) of the figure. We named the dimensions to indicate the positive end first and the negative end second. We chose a 3-dimensional MDS solution (Figure 1 and Table 1), after examining 2-, 3-, 4-, 5-, and 6-dimensional MDS solutions, because it was the most interpretable and represented the point at which the Kruskal's stress values showed little decrease and the squared correlation coefficients showed little increase. The Kruskal's stress value for this MDS solution was 0.21, and the proportion of variance (RZ) explained by the solution was 0.60. MDS solutions with JOURNAL OF NUTRITION EDlJCATION
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Flounder Chicken I Roast Beef
Cheese
Hamburger
Milk Ice Cream
Yogurt
Hot Dogs Bacon
I~
Peanut. Butter'
..tt.V
/
/'
/
L
/
/
L
ill
I
\
~
Oranges
Carr~ts
\
Bro~oli
Tomatoes '-
I Inconvenient-convenient II Bad-good Animal- plant
m Corn Doughnuts White Bread
Whole7Wheat Bread
)Navy Beans (in soup) ~
Foods from the Milk Group
l!. Foods from the Bread -cereal s Group
7Rice
French-fried Potatoes
o Foods from the Meat Group • Foods from the Fruit-v 4!9etable Group
Figure 1.
Plot of the three dimensions for the multidimensional scaling analysis of the 23-food matrix .
Table 1.
Stimulus coordinates for the 23 foods on three dimensions of multidimensional scaling (MDS) solution.
Dimensions Inconven ient -Con venien t I
Food
Coordi nate
Food
Bacon Chicken Roast beef Hot dogs Flounder Hamburger Navy bean soup Broccoli French fries Tomatoes Carrots Corn Rice Eggs Oranges Peanut butter Bread (WW)·' Bread (wh ite) Cheddar cheese Milk Doughnuts Yogurt Ice cream
1.48 1.18 1.18 1.05 1.02 1.00 0.98 0.63 0.50 0.47 0.42 0.16 0.06 -0.30 -0.49 -0.67 -0.78 -0.80 -0.83 -1.29 -1.45 -1.50 - 2.02
Doughnuts Bread (white) French fries Hot dogs Peanut butter Hamburger Bread (ww)a Bacon Roast beef Rice Ice cream Navy bean soup Chicken Eggs Milk Cheddar cheese Flounder Corn Yogurt Broccoli Tomatoes Carrots Oranges
"ww
268
=
Animal-Plant
Bad-Good If
Ifl
Coordinate 1.30 1.29 1.26 1.22 1.11 0.86 0.75 0.74 0.44 0.40 0.19 0.00 -0.01 -0.21 -0 .2 2 -0.43 -0.43 -0.70 -0.82 -1.56 - 1.59 -1 .63 -1.96
Food
Coordina te
Eggs Cheddar cheese Flounder Chicken Roast beef Hamburger Milk Hot dogs Bacon Peanut butter Yogurt Ice cream Oranges Doughnuts Tomatoes Carrots Bread (white) Broccoli Frenc h fries Bread (ww),' Navy bean soup Corn Rice
1.43 1.24 1.22 1.16 1.06 0.94 0.78 0.64 0.62 0.61 0.58 0.56 -0.36 -0.62 -0.78 -0.80 -0.81 -0 .83 -1.14 -1.14 -1.24 -1.52 -1.61
whole wheat.
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more dimensions increased the complexity of the structure without accounting for a greater amount of variance in the data. Kruskal's stress value is a measure of the goodness-of-fit of the actual and predicted distance scores. The squared correlation coefficient indicates the proportion of variance of the transformed data that is accounted for by the structure (6). Question one. If individuals were given foods representative of the Four Food Groups, how would they group the foods? To answer this question, we in spected the MDS solution to examine how the foods were placed on the respondents' classification system (Figure 1 and Table 1). Ice cream, yogurt, milk, cheese, eggs, and peanut butter seem to form a group that contains foods mostly from the milk group. Flounder, chicken, roast beef, hamburger, hot dogs, and bacon cluster and are all from the meat group. Cake doughnuts and bread (white and whole wheat) cluster and are from the bread-cereals group. Carrots, tomatoes, and broccoli, with corn and oranges on the periphery, seem to form the fruit-vegetable group. In addition, rice, french-fried potatoes, and navy beans (in soup) seem to cluster as a high-starch group. The respondents seemed to group the foods in a manner related to but more complex than the Four Food Groups (10). Some foods like eggs, peanut butter, and navy beans (in soup), which are the meat substitutes, are not placed with the meat group. Furthermore, there may be additional groups in their classification system, such as a high-starch group. We did not perform a statistical analysis of the relationship between the respondents' classification system and the Four Food Groups guide because of the limited number of foods that we used to represent each of the groups. Question two. Given the individuals' food classification system, what are the underlying dimensions on which the individuals grouped the foods? The respondents in this study seemed to have used three dominant criteria (dimensions) to make their similarity judgments. We interpreted the three dimensions by examining the positions of foods along each dimension (Table 1). The greater a food's stimulus coordinate (plus or minus) is, the more important the food is in defining that dimension. Consideration also was given to the respondents' ratings of each food on each food-attribute and nutrient scale (Table 2). Two of the scales, feminine/masculine and calci um, were inversely related to dimension 1 (Table 2). Foods with a high positive stimulus coordinate on dimension 1 (i.e., bacon, chicken, roast beef, hot dogs, flounder, hamburger, and navy beans) were perceived to be masculine and to contain little calcium. In contrast, foods on the negative end of the continuum (i.e., VOLUME 18
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ice cream, yogurt, doughnuts, and milk) were perceived to be feminine and to be high in calcium. The feminine/masculine scale, however, also is related to dimension 2, which indicates that it probably is not the dominant criterion the respondents used for making their similarity judgments. Although these food-attribute scales were related to dimension 1, we feel that an alternative interpretation may account for the arrangement of the foods along the dimension. The meats, which are on the positive end, generally require some preparation; whereas the doughnuts and milk products, which are on the negative end, can be eaten without further preparation by the consumer. The degree of convenience in preparation from a consumer's perspective has been described by Bayton (22), and for these respondents who are college students, it probably is important. Therefore, we labeled dimension 1 the inconvenient-convenient dimension. Dimension 2 was inversely related to the following scales: good for your healthlbad for your health, beneficial/harmful, low in calories/high in calories, light/ heavy, slimmingifattening, feminine/masculine, vitamin A, vitamin C, and dietary fiber (Table 2). Foods at the positive end of the dimension (Le., doughnuts, breads, potatoes, hot dogs, peanut butter, hamburger, bacon) were perceived to be bad for your health, harmful, high in calories, heavy, fattening, masculine, and low in vitamin A, vitamin C, and dietary fiber. On the other end, the good for your health, beneficial, low in calories, light, slimming, feminine foods, which are high in vitamin A, vitamin C, and dietary fiber were oranges, carrots, tomatoes, broccoli, yogurt, and corn. Other investigators (2-4) have found a health-related category or dimension that people use to categorize foods. We, therefore, labeled dimension 2 the badgood dimension. The food-attribute scale feminine/masculine seemed incongruent with the health-related attributes. Schutz et al. (2) found a food-use factor called utilitarian; it was defined by foods like peanut butter, roast beef, and bread, which were considered appropriate for men as well as for teenagers and children. A physiologic group, discussed by Jelliffe (1), includes foods reserved for people of certain ages or physiology (such as men). Perhaps the respondents (mostly young women) considered foods higher in energy more masculine because they felt that men are able to consume more high-energy foods without gaining weight. In any case, foods reflective of gender stereotypes suggest an interesting question for future research. With the exception of peanut butter, all of the foods on the positive end of dimension 3 are animal products, and all foods on the negative end are plant foods (or their major ingredients are of plant origin). The respondents correctly perceived that the foods on the JOURNAL OF NUTRITION EDUCATION
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Table 2.
Multiple regression of 23 foods' stimulus coordinates on food-attribute and nutrient scales. (3 Values Dimen sions
Rating Scale"
I like this food/I dislike this food. Familiar/unfam iliar I frequently use this food/I infrequently use this food . Eaten when healthy/eaten when sick Good for your health/bad for your health Beneficial/harmfu I Low in calories/high in calories Light/heavy Slimming/fattening Feminine/masculine Filling/not filling For ad u Itslfor chi Id ren Expensive/inexpensive Vitamin A Vitamin C Protein Fat Carbohydrate Cal cium Iron Dietary fiber
InconvenientConvenient I
-0.22 (1.00)" -0.22 (1.06) -0.13 (0.34) -0.05 (0.05) -0.09 (0.47) -0.15 (1.65) 0.20 (2.56) 0.00 (0.00) 0.12 (1.16) -0.55" (15.50) 0.19 (0.94) 0.25 (1.70) 0.28 (2.20) -0.10 (0.47) -0.07 (0.40) 0.32 (2 .59) 0.06 (0.10) -0.21 (1.10) -0.54* (9.66) 0.26 (1.80) 0.04 (0.07)
Bad-Good /I
-0.22 (0.98) -0.12 (0.33) -0 .32 (2 .20) 0.02 (0.01) -0.81" (37.11) -0.85" (51.47) -0.80" (41.38) - 0.82* * (42 .51) -0 .85** (52 .87) -0.60 * • (18 .63) 0.51 (6.98) -0.46 (5.64) 0.19 (1.05) - 0.80" (32 .92) -0 .86*' (57.83) -0.23 (1.40) 0.45 (5.65) 0.39 (3.89) -0.25 (2.12) -0.46 (5.49) -0.51" (10.71)
Animal-Plant /II
0.02 (0.01) 0.29 (1.78) 0.12 (0 .30) 0.11 (0.25) -0.03 (0.04) 0.01 (0.01) -0.07 (0.32) -0.11 (0.70) -0.02 (0.04) -0.07 (0 .27) 0.12 (0.42) -0.10 (0.27) 0.48 (6.42) 0.04 (0.08) -0.10 (0.72) 0.39 (3.85) 0.30 (2.45) -0.31 (2.46) 0.30 (2.98) 0.09 (0.22) -0.48" (9.70)
R2
R~dl
F'U'I'
0.09
0.00
0.64
0.15
0.01
1.08
0.12
0.00
0 .89
0.02
0.00
0.11
0.67
0.61
12.64' *
0.74
0.69
17.60'*
0.71
0.67
15.56* •
0.70
0.66
14.96* *
0.75
0.71
18.60**
0.64
0.58
11.10**
0.31
0.20
2.82
0.31
0.20
2.79
0.33
0.23
3.18
0.64
0.58
11 .07* *
0 .76
0.72
20.16' ,
0.27
0.16
2.36
0.32
0.21
3.00
0.26
0.15
2.26
0.44
0.35
4.99'
0.28
0.17
2.51
0.60
0.48
7.72**
"First statement, adjective, or phrase listed anchored the high end of the ll-point scale; for th e nutrients, scale ranged from 4 (excellent) to 1 (poor). bNumbers in parentheses are the F-value for each {3 value. *p < .05 **p < .01
negative end of the dimension are higher in dietary fiber (Table 2), but the dietary fiber scale also was related significantly to dimension 2, which indicates it was probably not the dominant criterion used for this dimension. We, therefore, labeled dimension 3 the animal-plant dimension. By examining the respondents' classification sys270
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tern (Figure 1 and Table 1) and using the interpretations of the three dimensions, the foods can be put in eight possible cells. Roast beef, hamburger, hot dogs, and bacon would represent the inconvenient-bad-animal foods; ice cream and peanut butter, the convenient-bad-animal foods; flounder, the inconvenientgood-animal food; milk, yogurt, cheese, and eggs, the VOLUME 18
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convenient-goad-animal foods; french-fried potatoes and rice, inconvenient-bad-plant foods; doughnuts and bread, convenient-bad-plant foods; carrots, tomatoes, broccoli, and corn, the inconvenient-goad-plant foods; and oranges, convenient-goad-plant food. Two foods represent special cases because they were considered neither good nor bad: navy beans (in soup) were perceived as an inconvenient-plant food and chicken as an inconvenient-animal food.
Table 3.
Nutrient composition knowledge. Respondents' average total score for the nutrient composition test was 124.2 (S .E. = 2 .85) or 67 .5%. This mean score was significantly different (t = 11.28, dJ. = 50, P :5 .01) from the mean score of 92 .0, which would have been obtained if the respondents were guessing. The percentage of respondents that provided the correct answer to each food-nutrient combination is presented in Table 3.
Percent correct responses to nutrient composition of 23 foods.
Nutrient Food Bacon Broccoli Bread (white) Bread (whole-wheat) Carrots Cheddar cheese Chi cken (baked) Corn (whole kernel) Doughnuts (cake) Eggs Flounder French fries Hamburger Hot dogs Ice cream Milk Navy bean soup Oranges Peanut butter Rice (brown) Roast beef Tomatoes Yogurt
Vitamin A
Vitamin C
Protein
Fat
Carbohydrate
Ca lciu m
Iron
74 .5 (poor)" 82.4 (good) 60.8 (poor) 41.2 (poor) 90.2 (good) 39.2 (poor) 52.9 (poor) 35.3 (poor) 96 .1 (poor) 45 .1 (poor) 33.3 (poor) 86. 3 (poor) 74.5 (poor) 84.3 (poor) 64.7 (poor) 17.6 (poor) 54.9 (poor) 25.5 (poor) 47.1 (poor) 60.8 (poor) 56.9 (poor) 74 .5 (good) 41.2 (poor)
86.3 (poor) 64.7 (good) 78.4 (poor) 64.7 (poor) 29.4 (poor) 54.9 (poor) 80.4 (poor) 60.8 (poor) 100.0 (poor) 66 .7 (poor) 66.7 (poor) 86.3 (poor) 82.4 (poor) 96.1 (poor) 84 .3 (poor) 49.0 (poor) 74.5 (poor) 100.0 (good) 64.7 (poor) 80.4 (poor) 76 .5 (poor) 64.7 (good) 52.9 (poor)
64.7 (good) 41.2 (poor) 58.8 (poor) 39.2 (poor) 43.1 (poor) 88.2 (good) 88.2 (good) 54.9 (poor) 90 .2 (poor) 96 .1 (good) 88 .2 (good) 70.6 (poor) 84.3 (good) 66.7 (good) 51.0 (poor) 88 .2 (good) 66.7 (good) 70.6 (poor) 94.1 (good) 43.1 (poor) 82.4 (good) 64 .7 (poor) 80.4 (good)
88.2 (good) 90.2 (poor) 64.7 (poor) 58 .8 (poor) 92.2 (poor) 66.7 (good) 47.1 (poor) 76.5 (poor) 70.6 (good) 51.0 (good) 82.4 (poo r) 74.5 (good) 90.2 (good) 74 .5 (good) 80.4 (good) 80.4 (good) 68.6 (poo r) 90.2 (poor) 74.5 (good) 60 .8 (poor) 82.4 (good) 92.2 (poor) 47.1 (good)
51.0 (poor) 78.4 (poor) 82.4 (good) 72.5 (good) 78.4 (poor) 43.1 (poor) 66.7 (poor) 51.0 (good) 82.4 (good) 47 .1 (poor) 80.4 (poor) 76.5 (good) 51.0 (poor) 41.2 (poo r) 66.7 (good) 60.8 (good) 62.7 (good) 25.5 (good) 37.2 (poor) 74.5 (good) 56 .9 (poor) 84 .3 (poor) 47.1 (good)
96.1 (poor) 68.6 (poor) 72.5 (poor) 45 .1 (poor) 62.7 (poor) 78.4 (good) 68 .6 (poor) 78.4 (poor) 84.3 (poor) 49.0 (poor) 62.7 (poor) 82.4 (poor) 76.5 (poor) 82.4 (poor) 82.4 (good) 96.1 (good) 76.5 (poor) 74.5 (poor) 60.8 (poor) 74.5 (poor) 70 .6 (poor) 80.4 (poor) 78.4 (good)
74.5 (poor) 33.3 (poor) 68.6 (poor) 47.1 (poor) 33.3 (poor) 60.8 (poor) 56.9 (poor) 60.8 (poor) 98.0 (poor) 43.1 (poor) 25.5 (poor) 88.2 (poor) 39.2 (good) 25.5 (poor) 80.4 (poor) 23.5 (poor) 52.9 (good ) 70.6 (poor) 45.1 (poor) 70.6 (poor) 56.9 (good) 62.7 (poor) 56.9 (poor)
Dietary Fiber 92.2 (poor) 66.7 (good) 68.6 (poor) 80.4 (good) 76.5 (good) 82.4 (poor) 80.4 (poor) 43.1 (good) 94.1 (poor) 80.4 (poor) 54.9 (poor) 86. 3 (poor) 90.2 (poor) 94.1 (poor) 100.0 (poor) 74.5 (poor) 31.4 (good) 54.9 (good) 76.5 (poor) 43 .1 (poor) 80.4 (poor) 51.0 (good) 66 .7 (poor)
"Ratings of foods' nutrient compositions are in parentheses : poor = poor to fair; good = good to exce llent.
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The respondents' mean scores for individual foods across nutrients differed significantly (p ::5 .05) from the mean of 4.0. The mean scores (S.E.) ranged from 7.2 (0 .12) for cake doughnuts to 4.5 (0.24) for wholewheat bread. All individual nutrient mean scores (S.E.) differed significantly (p ::5 .05) from the mean of 11 .5, which would have been obtained if the respondents were guessing; these scores ranged from 17.0 (0 .37) for fat to 13.2 (0.59) for iron. These results indicate that the respondents had some knowledge of the nutrient composition offoods; however, only 4 of the 8 nutrient scales were related significantly to the 3 dimensions of the MDS solution, indicating that the nutrient composition of foods is important, but not a dominant criterion used by the respondents to group foods.
developing a food guide, or modifying the Four Food Groups , professionals should consider consumers' food classification systems as well as the criteria on which they group foods. With further studies similar to this one, professionals could develop groupings that are understandable and useful to consumers in addition to being compatible with the goal of food and nutrition educators-to guide individuals in selecting health-promoting diets. 0
ACKNOWLEDGMENTS The authors thank Ms. Carmen Samuel for her assistance in data collection and processing and Mr. Daniel W. Denman for statistical assistance. The computer time for this project was supported in part through the facilities of the Computer Science Center of the University of Maryland.
IMPLICATIONS Even though the results of this study are based on only a limited number of foods examined by a sample of college students, we feel that there are implications worth considering. The results of this study suggest that the Four Food Groups guide is not entirely unlike the respondents' food groupings, but it probably needs to be expanded to include more groups (e.g., a legume group) as well as modified to consider consumers' perceptions of foods' attributes. A food guide recently developed by Pennington (13) contains four main food groups (vegetable and fruit ; grain products; vegetable, dairy, and meat sources of protein ; and luxury foods) with 12 subgroups . These subgroups were represented graphically by an inverted pyramid, which had four suggested levels of consumption (liberal to sparse). Thus, Pennington's food guide included more food groups (e.g., legumes and eggs) and an implied bad-good dimension (levels of suggested consumption). Lachance (11) suggested that the Four Food Groups should be represented visually to emphasize consumption of foods in the fruit-vegetable group and bread-cereals group. USDA's Daily Food Guide (14) , in addition to the Four Food Groups , contains a fats, sweets, alcohol group, but with no suggested number of servings. In their modified Basic Four, however, King et al. (12) include a fat and oil group with a suggested number of servings. They also subdivide their protein group into animal protein and legumes and/or nuts (with suggested servings) as well as make more specific suggestions for consumption patterns from the fruits and vegetable group . The MDS technique seems to be an appropriate method by which to examine whether the food groupings represented by a food guide reflect consumers ' food classification systems . This question is a very important one for food and nutrition educators. When 272
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NOTES 1 Permission to conduct this study was obtained from the
University of Maryland Committee on the Use of Human Subjects in Research. 2 Standard serving sizes were considered to be one-half cup of broccoli, carrots , corn , navy beans (cooked). rice (cooked), and ice cream; 3 oz of chicken, flounder , hamburger, hot dog, and roast beef; 3 slices of bacon; 1 medium-size orange and tomato ; 1 large egg; 1 cup of milk ; 8 oz of yogurt; 1 oz of cheese ; 2 T of peanut butter; 1 oz of bread and doughnut; and 50 g of french-fried potatoes.
LITERATURE CITED Jelliffe, D. B. Parallel food classifications in developing and industrialized countries . American Journal ofClinical Nutrition 20:279-83, 1967 . 2 Schutz, H. G., M. H. Rucker, and G. F. Russell. Food and food-use classification systems. Food Technology 29(3) : 1
50-64, 1975.
3 Fewster, W. J., L. R. Bostian, and R. D. Powers . Measuring the connotative meanings of foods. Hom e Economics Research Journal 2(1) :44-53 , 1973 . 4 Pilgrim, F. J. , and J. M. Kamen . Patterns of food preference through factor analysis. Journal of Marketing 24:68-72,1959.
5 Light, L., and F. J. Cronin . Food guidance revisited . Journal of Nutrition Education 13:57-62, 1981. 6 Schiffman, S., M. L. Reynolds, and F. W. Young . Introduction to multidimensional scaling: Theory, methods, and applications, New York: Academic Press, 1981, pp . 3-28, 55-86. 7
Birch, L. L. Dimensions of preschool children's food preferences . Journal of Nutrition Education 11 : 77-80, 1979 .
8 Birch, L. L. Preschool children 's food preferences and consumption patterns . Journal of Nutrition Education 11 :189-92,1979 . 9 Rozin, P., A. Fallon, and M. L. Augustoni-Ziskind. The
child's conception of food: The development of categoVOLUME 18
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ries of acceptable and rejected substances. Journal of Nutrition Education 18:75-81, 1986. IOU. S. Department of Agriculture. Agricultural Research Service. Essentials of an adequate diet-Facts for nutrition programs, Home Economics Research Report No.3. Washington, DC: Department of Agriculture, 1957, 21 pp. 11 Lachance, P. A. A suggestion on food guides and dietary guidelines. Journal of Nutrition Education 13:56, 1981. 12 King, J. C., S. H. Cohenour, C. G. Corruccini, and P. Schneeman. Evaluation and modification of the Basic Four food guide. Journal of Nutrition Education 10:2729, 1978. 13 Pennington, J. A. T. Considerations for a new food guide. Journal of Nutrition Education 13:53-55, 1981. 14 U.S. Department of Agriculture. Science and Education Administration. Food, Home and Garden Bull. No. 228. Washington, DC: Government Printing Office, 1979, 64 pp. 15 Einstein, M. A., and I. Hornstein. Food preferences of college students and nutritional implications. Journal of Food Science 35:429-36, 1970.
16 Reinhardt, P. S., ed. SAS supplemental library user's guide, Cary, NC: SAS Institute, Inc., 1980, pp. 5-18. 17 Sokal, R. R., and F. J. Rohlf. Introduction to biostatistics, San Francisco: W. H. Freeman and Co., 1973, pp. 128, 34l. 18 Hull, C. H., and N. H. Nie. SPSS update-79, New York: McGraw-Hill, 1981, pp. 94-12l. 19 Whitney, E. N., and E. M. N. Hamilton. Understanding nutrition, 3d ed. New York: West Publishing Co., 1984, pp. 135, 48l. 20 Gebhardt, S. E., and R. H. Matthews. Nutritive value of foods, Home and Garden Bull. No. 72. Washington, DC: U.S. Department of Agriculture, 1985, 72 pp. 21 Lanza, E., and R. Butrum. A critical review of food fiber analysis and data. Journal of the American Dietetic Association 86:732-43, 1986. 22 U.S. Department of Agriculture. Problems of communication of nutrition information, by J. A. Bayton. National Nutrition Conferences Proceedings, Misc. Publ. No. 1075. Washington, DC: Department of Agriculture, 1966, pp. 16-19.
ANEMIA AND SCHOOL PERFORMANCE The detrimental effects of iron deficiency and anemia on the physiological and cognitive development of young children have been well documented. Recently, investigators have become interested in examining the effects of iron deficiency on the behavior and academic achievement of school-age children. The effect of iron supplementation on measures of school performance among 78 irondeficient anemic and 41 nonanemic school-age children in rural Indonesia has been investigated (American Journal of Clinical Nutrition 42:1221-28,1985). After random assignment, 43 of the anemic and 16 of the nonanemic children received an oral treatment of ferrous sulfate (10 mg/kg/day) for three months; the remaining study participants received a placebo tablet containing only saccharin and tapioca. After the three-month period, those iron-deficient anemic children who had received the iron treatment showed substantive increases in mean hemoglobin, hematocrit, and transferrin saturation values. In contrast, there was no significant increase in the mean values of these same measures among the nonanemic children treated with iron. These changes in the iron status of the iron-deficient anemic children were associated with positive changes in their performance on the school achievement and concentration tests. The once-anemic children did not, however, score as high as the nonanemic children. The authors point out that this finding is expected in light of the fact that although iron repletion probably enhances the learning process, it cannot compensate for the deficits in learning accrued over a three-month period. Poor school achievement is explained, in part, by a deficit in the child's reception of information, or attentional processes. It is not known, however, which of the attentional processes-selective attention, attention span, or vigilance-are specifically involved. The attentional deficits which are apparently present in iron deficiency and anemia may be related to a disturbance in the physiological arousal of the attentional processes present in the autonomic nervous system. Regardless of the exact physiological mechanism involved, the focus of future research must be on the magnitude of the effects that nutritional deficiencies have on the learning process.
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