World Food Day - PPTMON_3_3.World Food Day - PPTMON_3_3.pptx

Nutrition BMINDS Food
Insecurity Survey and stress
(Responses)Project

01. Dataset summary
03. Data Preprocessing
02. Descriptive Analysis
04. Methodology
Contents

Dataset summary
 Variables:
○ Q[C-K]: Demographic characteristics of respondents (N=476)
○ Q[M-U]: Assess food insecurity.
o Q[ V-AA] : Resilience Section
o Q[ AB-BB]: Academic Motivation Section
o Q[ BD-BU]: Stress Mindset and Perceived Stress Section
o Q[ PV-CP]: Mental Distress and Food Consumption Section
3

4
Table 1. Descriptive statistics of covariates
Covariate Frequency Percent
Gender
Female 801 72.88
Male 298 27.12
Age:
Age<30 867 78.89
Age_30_50 232 21.11
Currently Student:
No 306 27.84
Yes 793 72.16
SNAP:
Not sure 213 19.38
No 760 69.15
Yes 126 11.46
Education Level:
High School or Less 591 53.78
Bach 420 38.22
Master & Phd 88 8.01

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Table 1. Descriptive statistics of covariates
Covariate Frequency Percent
Physical activity:
4-8 times 58 54.21
Less than 4 43 40.19
None 6 5.61
GPA
NA 8 0.73
<2 953 86.72
>2 138 12.56
Activity
0-2 193 17.56
3-5 528 48.04
6-7 378 34.39
Food Thinking
<3 346 31.48
4-8 506 46.04
More than 8 247 22.47

Data Preprocessing
 Converting string data into a categorical variable:
6

Methodology
 Factor analysis is used mostly for data reduction purposes to get a small set of variables (preferably
uncorrelated) from a large set of variables (most of which are correlated to each other).
 Factor analysis is a statistical method that identifies a latent factor or factors that underlie observed
variables.
 Specifically, factor analysis addresses the following questions:
 How many latent factors underlie observed variables?
 How are these latent factors related to observed variables?
 What do these factors mean?
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Factor analysis
8
Factors Correlation
Factor 1
Factor 2
Factor Loading Error Variance

Confirmatory Factor Analysis Exploratory Factor Analysis
01 02
Methodology
• EFA examines (1) how many
factors a measure estimates
and (2) what these factors
are.
• CFA examines whether the
number of latent factors, factor
loadings, factor correlations, and
factor means are the same for
different populations or for the
same people at different time
points
• CFA is used when the factorial
structure of the measures has
been established

Internal reliability results
• We estimated internal consistency reliability using Cronbach’s ɑ, McDonald’s ώ across two subsamples.
Results show a high for all factors, suggesting a satisfactory internal consistency reliability of all scales
used in the survey.
Factor Cronbach’s alpha McDonald's omega/
Insecurity Factor 0.8608 0.8621
Resilience 0.8690 0.8695
Academic 0.9324 0.9339
Stress 0.8661 0.8570
Mental 0.6905 0.6660

CD 0.997 Coefficient of determination
SRMR 0.108 Standardized root mean squared residual
Size of residuals
TLI 0.727 Tucker–Lewis index
CFI 0.739 Comparative fit index
Baseline comparison
BIC 12560.310 Bayesian information criterion
AIC 12303.718 Akaike's information criterion
Information criteria
pclose 0.070 Probability RMSEA <= 0.05
upper bound 0.067
90% CI, lower bound 0.049
RMSEA 0.058 Root mean squared error of approximation
Population error
p > chi2 0.000
chi2_bs(775) 1801.269 baseline vs. saturated
p > chi2 0.000
chi2_ms(741) 1008.433 model vs. saturated
Likelihood ratio
Fit statistic Value Description
12
Model Performance

ANOVA
 To analyze the difference in participant perceptions towards five factors (Insecurity Factor Resilience
Academic Mental Stress) across demographic and other characteristics, we applied the
ANOVA test.
 Insecurity Factor is significantly affected by Food Thinking, and SNAP.
 Resilience is significantly affected by all covariates
 Academic factor is significantly affected by Age, student, education level, GPA, and physical activity.
 Mental factor is significantly affected by all covariates except education level.
 Stress factor is significantly affected by all covariates.
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Regression Results
 Age groups of 30-50, doing the activity 6-7 times for 20 minutes per week, will significantly decrease food
insecurity.
 Being female or having SNAP or being food overthinking will significantly increase resilience.
 Resilience decreases between the 30-50 age group, and people who play activities more than 5 times a week.
 Academic motivation significantly increases as your degree increases, females more than males, with people
who play activity more than 5 times a week.
 Mental factor significantly increases among women, people who have SNAP, and food-overthinking
individuals.
 People who play activities more than 5 a week, age group 30-50 will significantly decrease the Mental factor.
 Stress mindset factor significantly increases among females, people having SNAP, or food overthinking.
 Stress mindset significantly decreases with individuals who exercise more than 5 times, and among age
group 30-50.
15

Regression-Males and Females
 Females in the age groups of 30-50 will significantly decrease the food insecurity factor.
 Females in the age group 30-50 who play activities more than 6 times a week, will decrease the resilience
factor.
 Female students who have a Bach degree or higher will increase the academic motivation factor.
 Females in the age group 30-50, who play activities more than 6 times a week, will significantly reduce the
Mental factor.
 Females in the age group 30-50, who play activities more than 6 times a week, will significantly reduce the
Stress factor.
 Males in the age group 30-50, who are currently students will significantly increase the academic motivation
factor.
 Males with GPA>2 will significantly increase Mental and Stress factors.
16

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World Food Day - PPTMON_3_3.World Food Day - PPTMON_3_3.pptx

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