Obesity has long been a problem in the United States, with obesity rates increasing exponentially over the last twenty years. A factor contributing to the rising obesity rate can be attributed to a lack of consumer nutritional knowledge. Specifically, because if consumers are unaware of the adverse health effects of the foods they consume, they are more likely to consume an excess of unhealthy and fattening foods. This problem is especially prevalent among college students, as they are just entering adulthood and forming lifelong dietary habits. This study aims to examine the relationship between college students’ declarative nutritional knowledge and their practical knowledge of the dietary quality of food items in the university dining hall. The question this study aims to answer is whether there is an association between declarative nutritional knowledge and nutrition accuracy scores for dining hall foods consumed daily by students. Data was collected from a convenience sample from campus tabling, distribution of flyers, and in-class recruitment. This data was then analyzed using a Pearson correlation test and simple linear regression in R. Similar to some of the inconsistent results yielded in similar studies discussed throughout this paper, the test showed no statistically significant correlation between declarative nutritional knowledge and nutritional accuracy scores. However, it would be difficult to generalize the results of this study because of the limited sample (exclusively Binghamton University students). It could, however, demonstrate the positive effect of nutritional knowledge on college students’ awareness of the dietary quality of the foods they eat daily.
Keywords
obesity, nutrition, declarative knowledge, college student, college dining
1 Results
##Load
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 4.0.0 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(psych)
Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':
%+%, alpha
library(knitr)library(tibble)library(dplyr)library(tidyr)library(ggplot2)library(hexbin)library(scales) # for number formatting like comma()
Attaching package: 'scales'
The following objects are masked from 'package:psych':
alpha, rescale
The following object is masked from 'package:purrr':
discard
The following object is masked from 'package:readr':
col_factor
library(english) # to convert numbers to words
Attaching package: 'english'
The following object is masked from 'package:scales':
ordinal
library(stringr) # for text functions like str_c()library(NHANES)library(haven)library(readxl)# source: (Hei & McCarty, 2025) https://shanemccarty.github.io/FRIplaybook/import-once.html
library(readxl)
##IMPORT
library(readxl)library(dplyr)primary_data <-read_excel("/cloud/project/10.20.2025.perceptiondata.team5.clean.xlsx", col_names =TRUE)# source: (Hei & McCarty, 2025) https://shanemccarty.github.io/FRIplaybook/import-once.html # explanation: import perceptions survey data as data frame primary_data
StartDate EndDate Status
Min. :2025-09-26 13:34:04.00 Min. :2025-09-26 13:57:36.00 Min. :0
1st Qu.:2025-10-03 12:11:36.50 1st Qu.:2025-10-03 12:21:32.50 1st Qu.:0
Median :2025-10-08 13:24:48.00 Median :2025-10-08 13:27:30.00 Median :0
Mean :2025-10-08 13:34:56.26 Mean :2025-10-08 13:57:32.34 Mean :0
3rd Qu.:2025-10-13 13:05:30.50 3rd Qu.:2025-10-13 13:16:45.00 3rd Qu.:0
Max. :2025-10-19 00:06:38.00 Max. :2025-10-19 00:29:46.00 Max. :0
NA's :1 NA's :1 NA's :1
IPAddress Progress Duration (in seconds) Finished
Length:188 Min. : 3.00 Min. : 5.0 Min. :0.0000
Class :character 1st Qu.: 16.00 1st Qu.: 23.0 1st Qu.:0.0000
Mode :character Median :100.00 Median : 124.0 Median :1.0000
Mean : 70.88 Mean : 1355.5 Mean :0.6364
3rd Qu.:100.00 3rd Qu.: 778.5 3rd Qu.:1.0000
Max. :100.00 Max. :80341.0 Max. :1.0000
NA's :1 NA's :1 NA's :1
RecordedDate ResponseId RecipientLastName
Min. :2025-10-01 11:15:05.98 Length:188 Mode:logical
1st Qu.:2025-10-08 12:42:28.44 Class :character NA's:188
Median :2025-10-10 14:03:43.45 Mode :character
Mean :2025-10-11 03:03:06.03
3rd Qu.:2025-10-15 14:45:23.24
Max. :2025-10-19 11:36:44.72
NA's :1
RecipientFirstName RecipientEmail ExternalReference LocationLatitude
Mode:logical Mode:logical Mode:logical Min. :39.95
NA's:188 NA's:188 NA's:188 1st Qu.:40.77
Median :41.53
Mean :41.60
3rd Qu.:42.10
Max. :43.18
NA's :1
LocationLongitude DistributionChannel UserLanguage Q_RecaptchaScore
Min. :-76.22 Length:188 Length:188 Min. :0.4000
1st Qu.:-75.89 Class :character Class :character 1st Qu.:1.0000
Median :-74.05 Mode :character Mode :character Median :1.0000
Mean :-74.73 Mean :0.9775
3rd Qu.:-73.90 3rd Qu.:1.0000
Max. :-71.06 Max. :1.0000
NA's :1 NA's :1
CONSENT AFFILIATION MEAL_PLAN MEAL_PLAN_-50_TEXT
Min. :0.0000 Min. :1.000 Min. :-50.00 Mode:logical
1st Qu.:1.0000 1st Qu.:1.000 1st Qu.: 3.00 NA's:188
Median :1.0000 Median :1.000 Median : 3.00
Mean :0.9462 Mean :1.382 Mean : 1.36
3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.: 6.00
Max. :1.0000 Max. :5.000 Max. : 9.00
NA's :2 NA's :18 NA's :63
VEGGIE_PARTICIPATION PASSWORD UNHEALTHY_QUAL HEALTHY_QUAL
Min. :0.0000 Length:188 Length:188 Length:188
1st Qu.:1.0000 Class :character Class :character Class :character
Median :1.0000 Mode :character Mode :character Mode :character
Mean :0.9278
3rd Qu.:1.0000
Max. :1.0000
NA's :91
DECISION_QUAL CHOICES_QUAL DINING HEALTHY
Length:188 Length:188 Min. :1.000 Min. :1.000
Class :character Class :character 1st Qu.:2.000 1st Qu.:1.000
Mode :character Mode :character Median :2.000 Median :2.000
Mean :2.036 Mean :1.929
3rd Qu.:2.000 3rd Qu.:2.000
Max. :4.000 Max. :4.000
NA's :160 NA's :160
KNOW1 KNOW2 KNOW3 KNOW4
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
Median :0.0000 Median :1.0000 Median :1.0000 Median :1.0000
Mean :0.3671 Mean :0.7975 Mean :0.9747 Mean :0.9241
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
NA's :109 NA's :109 NA's :109 NA's :109
KNOW5 KNOW6 KNOW7 KNOW8
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
Mean :0.6835 Mean :0.8861 Mean :0.9241 Mean :0.8481
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
NA's :109 NA's :109 NA's :109 NA's :109
NOVA_ACCURACY_0_GROUP NOVA_ACCURACY_1_GROUP NOVA_ACCURACY_2_GROUP
Length:188 Length:188 Length:188
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
NOVA_ACCURACY_3_GROUP NOVA_ACCURACY_4_GROUP NOVA_ACCURACY_0_1_RANK
Length:188 Length:188 Min. :1.000
Class :character Class :character 1st Qu.:1.500
Mode :character Mode :character Median :2.000
Mean :1.667
3rd Qu.:2.000
Max. :2.000
NA's :185
NOVA_ACCURACY_0_4_RANK NOVA_ACCURACY_0_5_RANK NOVA_ACCURACY_0_6_RANK
Min. :1.000 Min. :1.0 Min. :1.000
1st Qu.:1.000 1st Qu.:1.5 1st Qu.:2.000
Median :2.000 Median :2.0 Median :3.000
Mean :2.286 Mean :2.0 Mean :3.097
3rd Qu.:3.000 3rd Qu.:2.5 3rd Qu.:4.000
Max. :5.000 Max. :3.0 Max. :6.000
NA's :167 NA's :186 NA's :157
NOVA_ACCURACY_0_7_RANK NOVA_ACCURACY_0_8_RANK NOVA_ACCURACY_0_9_RANK
Min. :1.00 Min. :1.00 Min. :1.000
1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.000
Median :2.00 Median :2.00 Median :3.000
Mean :2.30 Mean :2.22 Mean :3.222
3rd Qu.:2.75 3rd Qu.:3.00 3rd Qu.:5.000
Max. :8.00 Max. :7.00 Max. :5.000
NA's :158 NA's :138 NA's :179
NOVA_ACCURACY_0_10_RANK NOVA_ACCURACY_0_13_RANK NOVA_ACCURACY_1_1_RANK
Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:2.500 1st Qu.:1.500
Median :2.000 Median :3.000 Median :3.000
Mean :2.244 Mean :3.455 Mean :2.947
3rd Qu.:3.000 3rd Qu.:4.500 3rd Qu.:4.000
Max. :5.000 Max. :6.000 Max. :8.000
NA's :143 NA's :177 NA's :169
NOVA_ACCURACY_1_4_RANK NOVA_ACCURACY_1_5_RANK NOVA_ACCURACY_1_6_RANK
Min. :1.000 Min. :1.000 Min. :1.0
1st Qu.:1.000 1st Qu.:1.750 1st Qu.:2.0
Median :2.000 Median :2.000 Median :2.0
Mean :2.217 Mean :2.833 Mean :2.5
3rd Qu.:3.000 3rd Qu.:3.250 3rd Qu.:3.0
Max. :6.000 Max. :9.000 Max. :6.0
NA's :165 NA's :176 NA's :156
NOVA_ACCURACY_1_7_RANK NOVA_ACCURACY_1_8_RANK NOVA_ACCURACY_1_9_RANK
Min. :1.000 Min. :1.000 Min. :1.00
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00
Median :2.000 Median :1.500 Median :2.00
Mean :2.533 Mean :2.222 Mean :2.45
3rd Qu.:3.750 3rd Qu.:2.750 3rd Qu.:3.00
Max. :7.000 Max. :6.000 Max. :6.00
NA's :158 NA's :170 NA's :168
NOVA_ACCURACY_1_10_RANK NOVA_ACCURACY_1_13_RANK NOVA_ACCURACY_2_1_RANK
Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :3.000 Median :2.000 Median :2.000
Mean :2.619 Mean :2.316 Mean :1.852
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000
Max. :8.000 Max. :7.000 Max. :5.000
NA's :167 NA's :150 NA's :161
NOVA_ACCURACY_2_4_RANK NOVA_ACCURACY_2_5_RANK NOVA_ACCURACY_2_6_RANK
Min. :1.0 Min. :1.000 Min. :1
1st Qu.:1.0 1st Qu.:1.000 1st Qu.:1
Median :2.0 Median :2.000 Median :2
Mean :2.4 Mean :1.808 Mean :2
3rd Qu.:3.0 3rd Qu.:2.000 3rd Qu.:3
Max. :6.0 Max. :4.000 Max. :3
NA's :168 NA's :162 NA's :183
NOVA_ACCURACY_2_7_RANK NOVA_ACCURACY_2_8_RANK NOVA_ACCURACY_2_9_RANK
Min. :1.000 Min. :1.000 Min. :1
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1
Median :2.000 Median :1.000 Median :2
Mean :1.778 Mean :1.667 Mean :2
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2
Max. :3.000 Max. :3.000 Max. :5
NA's :179 NA's :185 NA's :155
NOVA_ACCURACY_2_10_RANK NOVA_ACCURACY_2_13_RANK NOVA_ACCURACY_3_1_RANK
Min. :1 Min. :1.000 Min. :1.000
1st Qu.:1 1st Qu.:1.500 1st Qu.:1.000
Median :1 Median :2.000 Median :1.000
Mean :1 Mean :2.316 Mean :1.273
3rd Qu.:1 3rd Qu.:3.000 3rd Qu.:1.750
Max. :1 Max. :4.000 Max. :2.000
NA's :184 NA's :169 NA's :166
NOVA_ACCURACY_3_4_RANK NOVA_ACCURACY_3_5_RANK NOVA_ACCURACY_3_6_RANK
Min. :1.000 Min. :1.000 Min. :3
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3
Median :1.500 Median :1.000 Median :3
Mean :1.833 Mean :1.452 Mean :3
3rd Qu.:2.750 3rd Qu.:2.000 3rd Qu.:3
Max. :3.000 Max. :3.000 Max. :3
NA's :182 NA's :157 NA's :187
NOVA_ACCURACY_3_7_RANK NOVA_ACCURACY_3_8_RANK NOVA_ACCURACY_3_9_RANK
Min. :2 Mode:logical Min. :1.000
1st Qu.:2 NA's:188 1st Qu.:1.000
Median :2 Median :2.000
Mean :2 Mean :1.556
3rd Qu.:2 3rd Qu.:2.000
Max. :2 Max. :2.000
NA's :187 NA's :179
NOVA_ACCURACY_3_10_RANK NOVA_ACCURACY_3_13_RANK NOVA_ACCURACY_4_1_RANK
Mode:logical Min. :1.0 Min. :1
NA's:188 1st Qu.:1.5 1st Qu.:1
Median :2.0 Median :1
Mean :2.0 Mean :1
3rd Qu.:2.5 3rd Qu.:1
Max. :3.0 Max. :1
NA's :185 NA's :187
NOVA_ACCURACY_4_4_RANK NOVA_ACCURACY_4_5_RANK NOVA_ACCURACY_4_6_RANK
Min. :3.0 Min. :1 Min. :1
1st Qu.:3.5 1st Qu.:1 1st Qu.:1
Median :4.0 Median :1 Median :1
Mean :4.0 Mean :1 Mean :1
3rd Qu.:4.5 3rd Qu.:1 3rd Qu.:1
Max. :5.0 Max. :1 Max. :1
NA's :186 NA's :187 NA's :185
NOVA_ACCURACY_4_7_RANK NOVA_ACCURACY_4_8_RANK NOVA_ACCURACY_4_9_RANK
Min. :2.00 Min. :6 Min. :7
1st Qu.:2.25 1st Qu.:6 1st Qu.:7
Median :2.50 Median :6 Median :7
Mean :2.50 Mean :6 Mean :7
3rd Qu.:2.75 3rd Qu.:6 3rd Qu.:7
Max. :3.00 Max. :6 Max. :7
NA's :186 NA's :187 NA's :187
NOVA_ACCURACY_4_10_RANK NOVA_ACCURACY_4_13_RANK CALORIES_1
Min. :2.0 Min. :2 Min. : 50.0
1st Qu.:2.5 1st Qu.:2 1st Qu.:102.0
Median :3.0 Median :2 Median :185.0
Mean :3.0 Mean :2 Mean :191.3
3rd Qu.:3.5 3rd Qu.:2 3rd Qu.:251.5
Max. :4.0 Max. :2 Max. :500.0
NA's :186 NA's :187 NA's :117
CALORIES_2 CALORIES_3 CALORIES_4 CALORIES_5
Min. : 23.0 Min. : 31.0 Min. : 20.0 Min. : 21.0
1st Qu.: 97.5 1st Qu.:170.5 1st Qu.:100.0 1st Qu.:150.0
Median :139.0 Median :207.0 Median :132.0 Median :197.0
Mean :142.9 Mean :233.0 Mean :142.8 Mean :200.1
3rd Qu.:180.5 3rd Qu.:286.0 3rd Qu.:182.0 3rd Qu.:246.0
Max. :342.0 Max. :500.0 Max. :334.0 Max. :497.0
NA's :117 NA's :117 NA's :117 NA's :117
CALORIES_6 CALORIES_7 CALORIES_8 CALORIES_9
Min. : 16.00 Min. :129.0 Min. : 19.0 Min. : 30.0
1st Qu.: 59.00 1st Qu.:247.5 1st Qu.: 72.0 1st Qu.:135.0
Median : 90.00 Median :286.0 Median :105.0 Median :181.0
Mean : 99.69 Mean :298.6 Mean :131.5 Mean :203.3
3rd Qu.:123.00 3rd Qu.:348.5 3rd Qu.:162.5 3rd Qu.:265.0
Max. :293.00 Max. :500.0 Max. :500.0 Max. :500.0
NA's :117 NA's :117 NA's :117 NA's :117
SATFAT_1 SATFAT_2 SATFAT_3 SATFAT_4
Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.000
1st Qu.: 8.25 1st Qu.: 3.0 1st Qu.: 6.00 1st Qu.: 2.000
Median :10.50 Median : 4.0 Median :11.00 Median : 4.000
Mean :11.38 Mean : 5.2 Mean :10.51 Mean : 3.883
3rd Qu.:15.00 3rd Qu.: 7.0 3rd Qu.:15.00 3rd Qu.: 5.000
Max. :20.00 Max. :17.0 Max. :20.00 Max. :15.000
NA's :122 NA's :123 NA's :123 NA's :128
SATFAT_5 SATFAT_6 SATFAT_7 SATFAT_8
Min. : 0.000 Min. : 0.0 Min. : 0.00 Min. : 0.000
1st Qu.: 3.000 1st Qu.: 1.0 1st Qu.: 8.00 1st Qu.: 1.750
Median : 4.000 Median : 2.0 Median :12.00 Median : 3.000
Mean : 5.422 Mean : 3.4 Mean :11.34 Mean : 3.783
3rd Qu.: 7.000 3rd Qu.: 4.0 3rd Qu.:15.00 3rd Qu.: 5.000
Max. :20.000 Max. :15.0 Max. :20.00 Max. :14.000
NA's :124 NA's :128 NA's :123 NA's :128
SATFAT_9 CARBS_1 CARBS_2 CARBS_3
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 2.00
1st Qu.: 3.000 1st Qu.: 2.000 1st Qu.: 4.000 1st Qu.:12.00
Median : 5.000 Median : 6.000 Median : 6.000 Median :17.00
Mean : 6.238 Mean : 7.933 Mean : 7.397 Mean :16.94
3rd Qu.: 9.000 3rd Qu.:11.250 3rd Qu.:10.000 3rd Qu.:21.00
Max. :16.000 Max. :30.000 Max. :25.000 Max. :30.00
NA's :125 NA's :128 NA's :130 NA's :123
CARBS_4 CARBS_5 CARBS_6 CARBS_7
Min. : 2.00 Min. : 0.000 Min. : 0.000 Min. : 2.00
1st Qu.: 8.00 1st Qu.: 4.000 1st Qu.: 3.000 1st Qu.: 9.00
Median :13.50 Median : 7.000 Median : 5.000 Median :14.00
Mean :14.86 Mean : 8.367 Mean : 6.576 Mean :13.73
3rd Qu.:21.00 3rd Qu.:10.250 3rd Qu.: 9.000 3rd Qu.:17.25
Max. :30.00 Max. :24.000 Max. :21.000 Max. :30.00
NA's :124 NA's :128 NA's :129 NA's :124
CARBS_8 CARBS_9 SUGAR_1 SUGAR_2
Min. : 0.000 Min. : 4.00 Min. : 0.000 Min. : 0.000
1st Qu.: 3.000 1st Qu.:10.50 1st Qu.: 2.000 1st Qu.: 2.000
Median : 5.000 Median :15.00 Median : 3.500 Median : 3.000
Mean : 6.456 Mean :15.95 Mean : 4.661 Mean : 3.368
3rd Qu.: 8.000 3rd Qu.:22.00 3rd Qu.: 6.250 3rd Qu.: 5.000
Max. :20.000 Max. :30.00 Max. :15.000 Max. :11.000
NA's :131 NA's :125 NA's :132 NA's :131
SUGAR_3 SUGAR_4 SUGAR_5 SUGAR_6
Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
1st Qu.: 3.000 1st Qu.: 2.000 1st Qu.: 3.00 1st Qu.: 2.250
Median : 4.500 Median : 3.000 Median : 5.00 Median : 4.000
Mean : 5.167 Mean : 4.379 Mean : 5.15 Mean : 4.707
3rd Qu.: 7.250 3rd Qu.: 6.750 3rd Qu.: 7.00 3rd Qu.: 6.000
Max. :15.000 Max. :13.000 Max. :12.00 Max. :13.000
NA's :128 NA's :130 NA's :128 NA's :130
SUGAR_7 SUGAR_8 SUGAR_9 PROTEIN_1
Min. : 2.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.: 4.000 1st Qu.: 2.000 1st Qu.: 3.000 1st Qu.: 7.00
Median : 6.000 Median : 3.000 Median : 4.000 Median :10.00
Mean : 6.677 Mean : 3.661 Mean : 4.772 Mean :11.34
3rd Qu.: 9.000 3rd Qu.: 5.000 3rd Qu.: 6.000 3rd Qu.:16.00
Max. :15.000 Max. :13.000 Max. :15.000 Max. :30.00
NA's :126 NA's :129 NA's :131 NA's :123
PROTEIN_2 PROTEIN_3 PROTEIN_4 PROTEIN_5
Min. : 3.00 Min. : 0.000 Min. : 0.00 Min. : 2.00
1st Qu.: 9.00 1st Qu.: 1.750 1st Qu.: 1.75 1st Qu.:13.00
Median :12.00 Median : 2.500 Median : 3.00 Median :17.00
Mean :13.17 Mean : 3.732 Mean : 4.75 Mean :17.95
3rd Qu.:17.00 3rd Qu.: 5.000 3rd Qu.: 6.00 3rd Qu.:23.00
Max. :30.00 Max. :14.000 Max. :19.00 Max. :30.00
NA's :123 NA's :132 NA's :136 NA's :123
PROTEIN_6 PROTEIN_7 PROTEIN_8 PROTEIN_9
Min. : 0.00 Min. : 5.00 Min. : 0.000 Min. : 0.000
1st Qu.: 2.00 1st Qu.:11.00 1st Qu.: 2.000 1st Qu.: 2.000
Median : 3.00 Median :15.00 Median : 3.000 Median : 3.000
Mean : 3.92 Mean :16.48 Mean : 4.868 Mean : 4.966
3rd Qu.: 5.00 3rd Qu.:22.00 3rd Qu.: 5.000 3rd Qu.: 7.750
Max. :17.00 Max. :30.00 Max. :30.000 Max. :30.000
NA's :138 NA's :124 NA's :135 NA's :130
ILLNESS_1 AGE HEALTHSTATUS HOPE
Min. : 1.000 Length:188 Min. :1.0 Min. :1.000
1st Qu.: 1.000 Class :character 1st Qu.:3.0 1st Qu.:3.000
Median : 1.000 Mode :character Median :3.0 Median :4.000
Mean : 2.394 Mean :3.2 Mean :3.625
3rd Qu.: 4.000 3rd Qu.:4.0 3rd Qu.:4.000
Max. :10.000 Max. :5.0 Max. :5.000
NA's :122 NA's :123 NA's :124
ANXIETY GENDER RACIALIZED RACIALIZED_8_TEXT
Min. :1.000 Min. :0.0000 Length:188 Length:188
1st Qu.:2.750 1st Qu.:0.0000 Class :character Class :character
Median :3.000 Median :0.0000 Mode :character Mode :character
Mean :3.359 Mean :0.2923
3rd Qu.:4.000 3rd Qu.:1.0000
Max. :5.000 Max. :1.0000
NA's :124 NA's :123
SOCIALSTATUS INCOME
Min. :3.0 Min. :1.000
1st Qu.:6.0 1st Qu.:3.000
Median :7.0 Median :3.000
Mean :6.6 Mean :3.185
3rd Qu.:7.0 3rd Qu.:4.000
Max. :9.0 Max. :5.000
NA's :123 NA's :123
# source: (Hei & McCarty, 2025) https://shanemccarty.github.io/FRIplaybook/import-once.html # explanation: Filtered out NA responses from primary_data and used names() and summary() functions to see the column names and summarize the column descriptive statistics from the data frame based on code from the FRI playbook (Hei & McCarty, 2025)
1.1 Transform
1.1.1 Defining True Values
## list of calories for each foodCAL_true <-c(BACON_CAL_true =250, # true value for food 1EGGS_CAL_true =180, # true value for food 2TOTS_CAL_true =320, # true value for food 3SESRICE_CAL_true =150, # true value for food 4 SESCHICK_CAL_true =140, # true value for food 5SPROUTS_CAL_true =150, #true value for food 6KORCHICK_CAL_true =300, #true value for food 7BOK_CAL_true =60, #true value for food 8VEGGIERICE_CAL_true =150#true value for food 9)## list of fat for each foodFAT_true <-c(BACON_FAT_true =1.5, # true value for food 1EGGS_FAT_true =4.5, # true value for food 2TOTS_FAT_true =3, # true value for food 3SESRICE_FAT_true =0, # true value for food 4SESCHICK_FAT_true =1, # true value for food 5SPROUTS_FAT_true =1, # true value for food 6KORCHICK_FAT_true =4.5, # true value for food 7BOK_FAT_true =0.5, # true value for food 8VEGGIERICE_FAT_true =0# true value for food 9)## list of carbohydrates for each food CARB_true <-c(BACON_CARB_true =0, # true value for food 1EGGS_CARB_true =1, # true value for food 2TOTS_CARB_true =20, # true value for food 3SESRICE_CARB_true =21, # true value for food 4SESCHICK_CARB_true =0, # true value for food 5 SPROUTS_CARB_true =12, # true value for food 6KORCHICK_CARB_true =7, # true value for food 7BOK_CARB_true =3, # true value for food 8VEGGIERICE_CARB_true =25# true value for food 9)## list of sugar for each food SUG_true <-c(BACON_SUG_true =0, # true value for food 1EGGS_SUG_true =0, # true value for food 2TOTS_SUG_true =1, # true value for food 3SESRICE_SUG_true =0, # true value for food 4SESCHICK_SUG_true =0, # true value for food 5SPROUTS_SUG_true =5, # true value for food 6KORCHICK_SUG_true =4, # true value for food 7BOK_SUG_true =1, # true value for food 8VEGGIERICE_SUG_true =2# true value for food 9)## list of protein for each food PRO_true <-c(BACON_PRO_true =5, # true value for food 1EGGS_PRO_true =13, # true value for food 2TOTS_PRO_true =2, # true value for food 3SESRICE_PRO_true =2, # true value for food 4SESCHICK_PRO_true =26, # true value for food 5SPROUTS_PRO_true =2, # true value for food 6KORCHICK_PRO_true =21, # true value for food 7BOK_PRO_true =2, # true value for food 8 VEGGIERICE_PRO_true =4# true value for food 9)# source: datacamp# explain: created true values for every column of the macro-nutrient accuracy assessment. these values will compared to the estimated values chosen by the participant.
1.1.2 Create Difference Scores
# Create absolute difference scores (absolute value of raw differences)## combined absolute difference scoresprimary_data <- primary_data %>%mutate(## calorie absolute difference scoresBACON_CAL_abs_diff =abs(CALORIES_1 - CAL_true["BACON_CAL_true"]),EGGS_CAL_abs_diff =abs(CALORIES_2 - CAL_true["EGGS_CAL_true"]),TOTS_CAL_abs_diff =abs(CALORIES_3 - CAL_true["TOTS_CAL_true"]),SESRICE_CAL_abs_diff =abs(CALORIES_4 - CAL_true["SESRICE_CAL_true"]),SESCHICK_CAL_abs_diff =abs(CALORIES_5 - CAL_true["SESCHICK_CAL_true"]),SPROUTS_CAL_abs_diff =abs(CALORIES_6 - CAL_true["SPROUTS_CAL_true"]),KORCHICK_CAL_abs_diff =abs(CALORIES_7 - CAL_true["KORCHICK_CAL_true"]),BOK_CAL_abs_diff =abs(CALORIES_8 - CAL_true["BOK_CAL_true"]),VEGGIERICE_CAL_abs_diff =abs(CALORIES_9 - CAL_true["VEGGIERICE_CAL_true"]),## fat absolute difference scores BACON_FAT_abs_diff =abs(SATFAT_1 - FAT_true["BACON_FAT_true"]),EGGS_FAT_abs_diff =abs(SATFAT_2 - FAT_true["EGGS_FAT_true"]),TOTS_FAT_abs_diff =abs(SATFAT_3 - FAT_true["TOTS_FAT_true"]),SESRICE_FAT_abs_diff =abs(SATFAT_4 - FAT_true["SESRICE_FAT_true"]),SESCHICK_FAT_abs_diff =abs(SATFAT_5 - FAT_true["SESCHICK_FAT_true"]),SPROUTS_FAT_abs_diff =abs(SATFAT_6 - FAT_true["SPROUTS_FAT_true"]),KORCHICK_FAT_abs_diff =abs(SATFAT_7 - FAT_true["KORCHICK_FAT_true"]),BOK_FAT_abs_diff =abs(SATFAT_8 - FAT_true["BOK_FAT_true"]),VEGGIERICE_FAT_abs_diff =abs(SATFAT_9 - FAT_true["VEGGIERICE_FAT_true"]),## carbohydrate absolute difference scoresBACON_CARB_abs_diff =abs(CARBS_1 - CARB_true["BACON_CARB_true"]),EGGS_CARB_abs_diff =abs(CARBS_2 - CARB_true["EGGS_CARB_true"]),TOTS_CARB_abs_diff =abs(CARBS_3 - CARB_true["TOTS_CARB_true"]),SESRICE_CARB_abs_diff =abs(CARBS_4 - CARB_true["SESRICE_CARB_true"]),SESCHICK_CARB_abs_diff =abs(CARBS_5 - CARB_true["SESCHICK_CARB_true"]),SPROUTS_CARB_abs_diff =abs(CARBS_6 - CARB_true["SPROUTS_CARB_true"]),KORCHICK_CARB_abs_diff =abs(CARBS_7 - CARB_true["KORCHICK_CARB_true"]),BOK_CARB_abs_diff =abs(CARBS_8 - CARB_true["BOK_CARB_true"]),VEGGIERICE_CARB_abs_diff =abs(CARBS_9 - CARB_true["VEGGIERICE_CARB_true"]),## sugar absolute difference scoresBACON_SUG_abs_diff =abs(SUGAR_1 - SUG_true["BACON_SUG_true"]),EGGS_SUG_abs_diff =abs(SUGAR_2 - SUG_true["EGGS_SUG_true"]),TOTS_SUG_abs_diff =abs(SUGAR_3 - SUG_true["TOTS_SUG_true"]),SESRICE_SUG_abs_diff =abs(SUGAR_4 - SUG_true["SESRICE_SUG_true"]),SESCHICK_SUG_abs_diff =abs(SUGAR_5 - SUG_true["SESCHICK_SUG_true"]),SPROUTS_SUG_abs_diff =abs(SUGAR_6 - SUG_true["SPROUTS_SUG_true"]),KORCHICK_SUG_abs_diff =abs(SUGAR_7 - SUG_true["KORCHICK_SUG_true"]),BOK_SUG_abs_diff =abs(SUGAR_8 - SUG_true["BOK_SUG_true"]),VEGGIERICE_SUG_abs_diff =abs(SUGAR_9 - SUG_true["VEGGIERICE_SUG_true"]),## protein absolute difference scores BACON_PRO_abs_diff =abs(PROTEIN_1 - PRO_true["BACON_PRO_true"]),EGGS_PRO_abs_diff =abs(PROTEIN_2 - PRO_true["EGGS_PRO_true"]),TOTS_PRO_abs_diff =abs(PROTEIN_3 - PRO_true["TOTS_PRO_true"]),SESRICE_PRO_abs_diff =abs(PROTEIN_4 - PRO_true["SESRICE_PRO_true"]),SESCHICK_PRO_abs_diff =abs(PROTEIN_5 - PRO_true["SESCHICK_PRO_true"]),SPROUTS_PRO_abs_diff =abs(PROTEIN_6 - PRO_true["SPROUTS_PRO_true"]),KORCHICK_PRO_abs_diff =abs(PROTEIN_7 - PRO_true["KORCHICK_PRO_true"]),BOK_PRO_abs_diff =abs(PROTEIN_8 - PRO_true["BOK_PRO_true"]),VEGGIERICE_PRO_abs_diff =abs(PROTEIN_9 - PRO_true["VEGGIERICE_PRO_true"]) )# source: (2024) https://www.projectpro.io/recipes/subtract-2-numbers-r # explanation: created difference scores for true values (as defined in the previous code chunk) and subtracted them from the estimates in the columns corresponding to each difference score in primary_data. these values had to be absolute so the function abs() was used in order to ensure there were no negative difference scores.
1.1.3 Create Summed Difference Scores
library(dplyr)# Create summed absolute difference scores primary_data <- primary_data %>%mutate(## sum calorie absolute difference scoresSUM_CAL_abs_diff = BACON_CAL_abs_diff + EGGS_CAL_abs_diff + TOTS_CAL_abs_diff + SESRICE_CAL_abs_diff + SESCHICK_CAL_abs_diff + SPROUTS_CAL_abs_diff + KORCHICK_CAL_abs_diff + BOK_CAL_abs_diff + VEGGIERICE_CAL_abs_diff,## sum fat absolute difference scoresSUM_FAT_abs_diff = BACON_FAT_abs_diff + EGGS_FAT_abs_diff + TOTS_FAT_abs_diff + SESRICE_FAT_abs_diff + SESCHICK_FAT_abs_diff + SPROUTS_FAT_abs_diff + KORCHICK_FAT_abs_diff + BOK_FAT_abs_diff + VEGGIERICE_FAT_abs_diff,## sum carbohydrate absolute difference scores SUM_CARB_abs_diff = BACON_CARB_abs_diff + EGGS_CARB_abs_diff + TOTS_CARB_abs_diff + SESRICE_CARB_abs_diff + SESCHICK_CARB_abs_diff + SPROUTS_CARB_abs_diff + KORCHICK_CARB_abs_diff + BOK_CARB_abs_diff + VEGGIERICE_CARB_abs_diff,## sum sugar absolute difference scoresSUM_SUG_abs_diff = BACON_SUG_abs_diff + EGGS_SUG_abs_diff + TOTS_SUG_abs_diff + SESRICE_SUG_abs_diff + SESCHICK_SUG_abs_diff + SPROUTS_SUG_abs_diff + KORCHICK_SUG_abs_diff + BOK_SUG_abs_diff + VEGGIERICE_SUG_abs_diff,## sum protein absolute differencesSUM_PRO_abs_diff = BACON_PRO_abs_diff + EGGS_PRO_abs_diff + TOTS_PRO_abs_diff + SESRICE_PRO_abs_diff + SESCHICK_PRO_abs_diff + SPROUTS_PRO_abs_diff + KORCHICK_PRO_abs_diff + BOK_PRO_abs_diff + VEGGIERICE_PRO_abs_diff ) # source: https://rstudio.github.io/cheatsheets/html/data-transformation.html# explanation: used mutate() function to create new columns that summed all the individual foods' difference scores for all the different macro-nutrients.
1.1.4 Create combined total difference scores
## create total absolute differences scores by summing all nutrient difference scoresprimary_data <- primary_data %>%mutate(TOTAL_abs_diff =rowSums(across(c( SUM_CAL_abs_diff, SUM_FAT_abs_diff, SUM_CARB_abs_diff, SUM_SUG_abs_diff, SUM_PRO_abs_diff ))) )# source: https://dplyr.tidyverse.org/articles/rowwise.html# explanation: took the sum of all the individual macro-nutrient difference score to obtain the final macro-nutrient accuracy score.
1.1.5 Create Knowledge Scores
library(dplyr)#| label: total knowledge score ## create 'KNOW_total' which is the sum of all KNOW columnsprimary_data <- primary_data %>%mutate(KNOW_total = KNOW1 + KNOW2 + KNOW3 + KNOW4 + KNOW5 + KNOW6 + KNOW7 + KNOW8 )# source: https://rstudio.github.io/cheatsheets/html/data-transformation.html# explanation: created total nutritional knowledge score by summing the 'know' columns, which corresponded to nutritional knowledge scores (0 or 1).
# source: https://rstudio.github.io/cheatsheets/html/data-transformation.html# explanation: mean and standard deviation calculations for variables KNOW_total and TOTAL_abs_diff.
The study sample consisted entirely of Binghamton University students (N = 208), and participants were between the ages of 18 and 22, with a mean age of 18.94 years. Female students accounted for about 70% of the sample, and about 80% of participants were White or Asian. According to the Binghamton University statistics, about 51% of students are female, and about 66% of students identify as either White or Asian (Binghamton University | Data USA, n.d.). Thus, this sample may be somewhat unrepresentative of Binghamton University’s demographics, as it includes significantly more female, White, and Asian students than the general Binghamton University population, which may make it difficult to generalize the study’s results to the larger population.
Displayed in Table 1 are the means and standard deviations of both variables, total nutritional knowledge (M = 6.405, SD = 1.428) and total macro nutrient accuracy scores (combined calories, saturated fat, carbohydrate, sugar, and protein) (M = 843.837, SD = 242.650). Total macronutrient accuracy score was calculated as the sum of the differences between individual macronutrient accuracy scores.
1.2.1 Create histogram
library(ggplot2)## create histograms to check for normalityggplot(primary_data, aes(x = TOTAL_abs_diff)) +geom_histogram(binwidth = .5)
Warning: Removed 145 rows containing non-finite outside the scale range
(`stat_bin()`).
Two histograms depicting the distribution of nutritional knowledge scores and macro-nurtient accuracy scores.
Warning: Removed 109 rows containing non-finite outside the scale range
(`stat_bin()`).
# source: https://rstudio.github.io/cheatsheets/data-visualization.pdf# explanation: made a histogram to check for normal distribution of variables.
The charts above (figures 1 and 2) represents the distribution of both variables, nutritional knowledge scores and macro-nutrient accuracy scores. Because neither of these histograms appear to be normally distributed, both are heavily right skewed, the next step is to check for normality of residuals in a linear model of both variables.
1.2.2 Create scatterplot
library(ggplot2)## create scatter plot to plot data ggplot(data = primary_data, aes(x = KNOW_total, y = TOTAL_abs_diff)) +geom_point() +geom_smooth(method ="lm")
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 145 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 145 rows containing missing values or values outside the scale range
(`geom_point()`).
A scatterplot depicting the relationship between nutritional knowledge scores and macro-nutrient accuracy scores.
Warning: Removed 145 rows containing non-finite outside the scale range
(`stat_smooth()`).
Removed 145 rows containing missing values or values outside the scale range
(`geom_point()`).
# source: https://rstudio.github.io/cheatsheets/html/data-visualization.html#two-variables---both-continuous # explanation: created scatter plot as a way to visualize the relationship between the two variables before fitting to a linear model.
As shown in Figure 1, there is no correlation between declarative nutritional knowledge and macronutrient accuracy scores. Thus, these findings fail to reject the null hypothesis that declarative nutritional knowledge and macronutrient accuracy scores are not related.
1.3 Model
1.3.1 Fit to linear model to check for normality of residuals
## check for normal distribution of residuals### fit to linear modellm_primary_data <-lm(TOTAL_abs_diff ~ KNOW_total , data = primary_data)### summarize linear modelsummary(lm_primary_data)
Call:
lm(formula = TOTAL_abs_diff ~ KNOW_total, data = primary_data)
Residuals:
Min 1Q Median 3Q Max
-371.01 -184.01 -57.92 168.94 646.99
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1130.38 252.27 4.481 5.85e-05 ***
KNOW_total -41.91 36.50 -1.148 0.258
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 241.7 on 41 degrees of freedom
(145 observations deleted due to missingness)
Multiple R-squared: 0.03115, Adjusted R-squared: 0.007521
F-statistic: 1.318 on 1 and 41 DF, p-value: 0.2576
### plot linear modelplot(lm_primary_data)
## because residuals are normally distributed, run Pearson correlation test# source: (2025) https://www.datacamp.com/tutorial/linear-regression-R # explanation: fit both variables to a linear model in order to check for normal distribution of residuals in order to run a pearson correlation.
In order to run a Pearson Correlation test both variables tested must be normally distributed and sample size should be at least 30. Due to the sample size for this study being greater than 30 and the appearance of a normal distribution of residuals for both variables (as shown by the QQ plot above) the researchers deemed it appropriate to conduct a Pearson Correlation test for association.
1.3.2 Run Pearson correaltion
## run pearson correlationcor.test(primary_data$TOTAL_abs_diff, primary_data$KNOW_total, method ="pearson", use ="complete.obs")
Pearson's product-moment correlation
data: primary_data$TOTAL_abs_diff and primary_data$KNOW_total
t = -1.1482, df = 41, p-value = 0.2576
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.4528362 0.1307794
sample estimates:
cor
-0.1764971
A Pearson correlation showed no statistically significant correlation between declarative nutritional knowledge and macro-nutrient accuracy scores (r = -0.18, p = .2576). This suggests that there is no relationship between declarative nutritional knowledge and practical macronutrient knowledge.
2 Disucssion
The data collected in this study suggest that there is no correlation between declarative nutritional knowledge, as measured by an eight-item validated nutritional knowledge measure, and practical nutritional accuracy scores of dining hall foods. This study aimed to determine whether there was a link between Binghamton students’ declarative (factual) nutritional knowledge and their ability to accurately assess the macronutrient content of dining hall foods they eat daily. The researchers expected to find that there was a positive relationship between declarative nutritional knowledge and macronutrient accuracy; however, after running a Pearson correlation, no statistically significant relationship was found between the two variables. This lack of significance may be attributable to the limited scope of the sample, which was a small sample of Binghamton University students who were recruited through on-campus tabling. Additionally, a general lack of existing research on the relationship between declarative nutritional knowledge and the practical assessment of macronutrient content in food underscores the need for further research on this topic before a conclusion on the relationship between declarative and practical nutritional knowledge can be reached. Even though this study did not find a significant relationship between declarative nutritional knowledge and macronutrient assessment accuracy, multiple studies have shown associations between various nutritional knowledge interventions and dietary quality, suggesting that declarative nutritional knowledge may be correlated with increased awareness of dietary quality (Shahril et al., 2013). However, these findings have been inconsistent, especially when researching college students (Yahia et al., 2016). Because of these inconsistent research results, future researchers may benefit from increasing research efforts on the effect of nutritional knowledge and food choices, as well as the link between declarative nutritional knowledge and its link to awareness of macro-nutrients in food consumed daily.
