Food insecurity and unhealthy eating habits continue to challenge college students, and a person’s sense of self-efficacy plays a major role in shaping their cooking confidence and nutrition choices. This study explored whether self-efficacy differed between genders, asking the research question: Are there differences in different groups of self-efficacy based on gender? Understanding these patterns was key to identifying where gaps in confidence, safety awareness, and nutrition knowledge might exist—factors that can influence long-term health and eating behaviors. Using a cross-sectional quantitative design, pre-intervention survey data were analyzed from 65 undergraduate students at Binghamton University who participated in the Cooking for Change program. After removing incomplete or post-intervention responses, composite self-efficacy scores were calculated for three areas: SELFCONF (cooking confidence), SELFSAFE (cooking safety), and SELFNUTRI (nutrition knowledge). Gender was treated as a categorical variable, and analyses were conducted in R. Female participants reported slightly higher mean self-efficacy scores in all three domains, with the largest difference in cooking confidence. These findings suggested that gender shaped how students perceived their cooking abilities. Overall, the results supported previous studies showing gender-based differences in food-related self-efficacy and highlighted the need for inclusive, skill-building public health programs that help all students feel capable and confident in the kitchen.
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✖ psych::%+%() masks ggplot2::%+%()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(naniar)library(ggpol)#source: Introduction to Data Import (Silhavy & McCarty, 2025)#explanation: packages for code
1.2 Import
## note: the df was cleaned manually to remove after and no intervention responses# load data indata <- readxl::read_excel("10.20.2025.pretestdata.team4.clean.xlsx")#source: Import Data Once (Silhavy & McCarty, 2025)#explanation: reading in the data once
2 Transform
2.0.1 Exclude -99 and -50 as NA values
# replace with NAdata <- data %>%replace_with_na_all(condition =~.x %in%c(-99, -50))#source: Transforming Your Data (Silhavy & McCarty, 2025)#explanation: Making -99 ("Prefer Not to Say") and -50 ("I don't know") NA
2.0.2 Creating Keys & Composite Scores
# define keys (no reverse items assumed example)keys <-list(SELFCONF =c("EFFCONF1", "EFFCONF2", "EFFCONF3", "EFFCONF4"),SELFSAFE =c("EFFSAFE1", "EFFSAFE2", "EFFSAFE3", "EFFSAFE4"),SELFNUTRI =c("EFFNUTRI1", "EFFNUTRI2", "EFFNUTRI3"))## note: if you have reverse-coded items, you would prefix them with a “-” in the keys list - #source: Creating Composite Scores from Multi-Item Measures (Silhavy & McCarty, 2025)#explanation: this creates a list called keys which tells R which survey questions belong to which composite score
## another way is rowMeans(select(data, one_of(keys$SELFCONF)), na.rm = TRUE) etc — but you lose the reliability check.# compute composites and reliability# the scoreItems() function handles missing values and gives you alphasscores <-scoreItems(keys, data, impute ="mean")# view reliability# scores$alpha # take the '#' away to run this line# add the composite scores to the data framedata <- data %>%mutate(SELFCONF = scores$scores[ , "SELFCONF"],SELFSAFE = scores$scores[ , "SELFSAFE"],SELFNUTRI = scores$scores[ , "SELFNUTRI"] )## note the data is currently in wide format#source: Creating Composite Scores from Multi-Item Measures (Silhavy & McCarty, 2025)#explanation1: this creates the actual composite scores#explanation2: can view view reliability using scores$alpha
2.0.3 Factor Gender
# 2 = male, 1 = femaledata <- data %>%mutate(GENDER01 =factor(GENDER, levels =c(2, 1),labels =c("Male", "Female")))#source: R Code, Example 1 (Silhavy & McCarty, 2025)#explanation: factor() used for gender to be categorical variable
2.0.4 Make Data Long Format
# make data to long format for visualizinglongdata <- data %>%select(GENDER01, SELFCONF, SELFSAFE, SELFNUTRI) %>%pivot_longer(cols =c(SELFCONF, SELFSAFE, SELFNUTRI),names_to ="SelfEfficacy",values_to ="Score" )## note: check the structure using the line of code below# head(longdata) # take '#' and run this line#source: Long Form (Silhavy & McCarty, 2025)#explanation1: reading in the data once#explanation2: when errors occurred, I ran the lines below to check#names(data) # check if I am addressing the name of the columns correctly#str(scores) # I saw that there were NAs so that means ScoreItems() was not running properly
3 Visualize
ggplot(longdata,aes(x = SelfEfficacy, y = Score, fill = GENDER01)) +geom_violin(trim =TRUE, alpha =0.6, position =position_dodge(width =0.8)) +geom_boxplot(width =0.1, outlier.shape =NA, position =position_dodge(width =0.8)) +geom_jitter(position =position_jitterdodge(jitter.width =0.1, dodge.width =0.8), alpha =0.4) +scale_fill_manual(values =c("Male"="#69b3a2","Female"="cornflowerblue")) +scale_color_manual(values=c('SELFCONF'='white','SELFSAFE'='black','SELFNUTRI'="darkorchid4")) +ggtitle("Difference in Self-Efficacy Due to Gender") +theme_minimal(base_size =14)
Warning: No shared levels found between `names(values)` of the manual scale and the
data's colour values.
Figure 1. A violin and boxplot depicting the relationship between gender and three types of self-efficacy (Cooking Self-Efficacy, Cooking Safety Self-Efficacy, and Nutrition Knowledge).
# save the visual as png#ggsave("plots/plot1_gender_selfeff.png", # plot = plot_gender_selfeff,# width = 10, height = 8, dpi = 300)# note: this gave me an error so I commented it out for now#source: Example: Violin Plot (Silhavy & McCarty, 2025)#explanation: this shows the violin and boxplot of each type of self-efficacy for female and male gender
4 Model
4.0.1 Run t-test For Each Type of Efficacy
# note: wide format data needed for statistical test# the data was originally in wide format# run t-testst_conf <-t.test(SELFCONF ~ GENDER, data = data)t_safe <-t.test(SELFSAFE ~ GENDER, data = data)t_nutri <-t.test(SELFNUTRI ~ GENDER, data = data)#source: Example: Gender and Zero-Sum Beliefs (Hei, 2025)#explanation: running t-tests
4.0.2 Print the Tests
cat("Self-Confidence t-test:")
Self-Confidence t-test:
print(t_conf)
Welch Two Sample t-test
data: SELFCONF by GENDER
t = 2.7342, df = 15.82, p-value = 0.01482
alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
95 percent confidence interval:
0.2126187 1.6861894
sample estimates:
mean in group 1 mean in group 2
4.663623 3.714219
#source: Example: Gender and Zero-Sum Beliefs (Hei, 2025)#explanation: t-test for confidence
cat("Self-Safety t-test:")
Self-Safety t-test:
print(t_safe)
Welch Two Sample t-test
data: SELFSAFE by GENDER
t = 1.8457, df = 15.433, p-value = 0.08421
alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
95 percent confidence interval:
-0.1006339 1.4246709
sample estimates:
mean in group 1 mean in group 2
4.884357 4.222339
#source: Example: Gender and Zero-Sum Beliefs (Hei, 2025)#explanation: t-test for safety
cat("Self-Nutrition t-test:")
Self-Nutrition t-test:
print(t_nutri)
Welch Two Sample t-test
data: SELFNUTRI by GENDER
t = 1.8179, df = 16.218, p-value = 0.0876
alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
95 percent confidence interval:
-0.08817192 1.15788134
sample estimates:
mean in group 1 mean in group 2
4.748477 4.213623
#source: Example: Gender and Zero-Sum Beliefs (Hei, 2025)#explanation1: t-test for nutrition#explanation2: for errors run these lines of code below#str(data_gendered$GENDER) # check the name#levels(data_gendered$GENDER) # check levels#table(data_gendered$GENDER, useNA = "ifany") # check values in each
4.1 Gender Differences in Self-Efficacy
Independent-samples t-tests were conducted to examine whether self-efficacy scores differed significantly between male and female participants across three domains: self-confidence, self-safety, and self-nutrition.
4.1.1 Confidence Self-Efficacy
A Welch Two-Sample t-test revealed a statistically significant difference in self-confidence scores between genders, t(15.82) = 2.73, p = .015. Female participants (M = 4.66) reported higher levels of cooking self-confidence compared to male participants (M = 3.71). The 95% confidence interval for the mean difference ranged from 0.21 to 1.69, indicating that this effect is likely meaningful in the broader population. These findings suggest that female students may feel more confident in their cooking abilities than their male peers.
4.1.2 Cooking Safety Self-Efficacy
Results from the Welch Two-Sample t-test for self-safety scores indicated no statistically significant difference between genders, t(15.43) = 1.85, p = .084. Females (M = 4.88) scored slightly higher than males (M = 4.22), but this difference did not reach conventional significance levels. The confidence interval (–0.10, 1.42) includes zero, suggesting that perceived safety efficacy while cooking does not differ reliably by gender in this sample.
4.1.3 Nutrition Knowledge Self-Efficacy
For self-nutrition efficacy, the Welch Two-Sample t-test also showed no statistically significant difference between genders, t(16.22) = 1.82, p = .088. Female participants (M = 4.75) reported higher average nutrition self-efficacy than males (M = 4.21), but the confidence interval (–0.09, 1.16) again crossed zero. Thus, while females tended to score higher, the difference was not statistically significant.
4.1.4 Summary
Overall, the analyses partially support the hypothesis that self-efficacy levels vary by gender. A significant difference was observed for self-confidence, indicating that gender may influence confidence in cooking tasks. However, differences in safety and nutrition efficacy were not statistically significant. These results suggest that while female students may feel more confident in their cooking abilities, gender does not appear to substantially affect feelings of safety or nutritional knowledge during food preparation.
5 Discussion
This study examined how gender influences three areas of cooking-related self-efficacy—cooking confidence, cooking safety, and nutrition knowledge—among undergraduate students at Binghamton University. The results showed that female students reported significantly higher cooking confidence than male students, while there were no significant gender differences in cooking safety or nutrition knowledge. Overall, both groups demonstrated moderately high levels of self-efficacy across all domains, suggesting that most students viewed themselves as fairly capable in the kitchen. Understanding gender-based differences in cooking self-efficacy is important because college students are in a period of transition, developing independence and learning to make their own food choices. Prior research shows that cooking confidence and self-efficacy strongly influence diet quality and food security (Knol et al., 2018; Morgan et al., 2021). The present findings add to this literature by showing that gender may shape confidence more than knowledge or safety skills, indicating an area where targeted interventions could make a difference. The original hypothesis—that gender differences would exist across all three domains—was partially supported. As expected, female students expressed greater cooking confidence, aligning with past studies suggesting that women are more likely to have early exposure to food preparation and hands-on experience (Boek et al., 2012). However, the lack of gender differences in safety and nutrition knowledge suggests that these topics may now be more equally emphasized across genders, possibly due to improved access to nutrition education and broader cultural shifts in household roles.
These findings fit well within Social Cognitive Theory, which explains that self-efficacy develops through experience and social learning (Bandura, 1997, as cited in Garcia et al., 2016). Female students may have gained more vicarious experience or encouragement in cooking settings, which strengthened their confidence. The results also align with the Social-Ecological Model (Sogari et al., 2018), emphasizing that behavior is shaped by individual, social, and environmental factors—such as family expectations, campus culture, and availability of cooking spaces. Evidence from this study supports the idea that gender influences certain aspects of self-efficacy, especially confidence. This pattern reflects prior work showing that higher cooking self-efficacy is linked to greater cooking frequency, food literacy, and improved dietary outcomes (Knol et al., 2018; Morgan et al., 2021). By reinforcing the connection between self-confidence in cooking and healthier eating habits, the findings highlight a potential pathway for improving student health and food security through skill-building programs. While these results expand on earlier research, the study had some limitations. The sample size was relatively small and predominantly female, which limits how broadly the results can be generalized. Self-reported measures may have introduced bias, and the cross-sectional design prevented conclusions about cause and effect. Additionally, since the data were collected before an intervention, the results only reflect baseline differences rather than the impact of skill development over time. Even with these limitations, the findings build on the introduction’s discussion of how self-efficacy connects to health behaviors. Similar to (Knol et al. 2018) and (Morgan et al. 2021), this study reaffirmed that cooking confidence plays a meaningful role in students’ food choices, but it adds a new dimension by identifying gender as a key influence.
From a public health perspective, these findings suggest the need for campus programs that are both inclusive and gender-responsive. Increasing cooking confidence among male students, for instance, could help close existing gaps and promote more balanced participation in food preparation. Programs like “Cooking with a Chef” (Warmin et al., 2012) or “College CHEF” (McMullen & Ickes, 2017) demonstrate how hands-on, skill-based approaches can empower students to make healthier choices. Future research should build on this study by using a larger and more gender-balanced sample to validate these results. The next FRI cohort could include post-intervention data to test whether gender differences persist after cooking skill training. A less obvious but important next step would be to explore how gender interacts with food security and self-efficacy to affect diet quality. Ideally, a longitudinal study could follow students throughout college to track how growing independence and experience shape their confidence, safety practices, and nutrition habits over time.
