Gender-based food intake stereotype scale (GBFISS) for adolescents: development and psychometric evaluation

Figures & data

Table 1. Description of invariance levels tested.

Invariance level	Constraints involved	Interpretation
Configural level	No constraints between subgroups	The same set of items reflects the same latent constructs across subgroups.
Metric level (First-order measurement weights)	First-order factor loadings are constrained to be equal across groups.	The strength of the relationship between each item and its underlying construct is the same for both groups.
Scalar level (Intercepts of measured variables)	First-order factor loadings and intercepts are constrained to be equal across groups.	The same set of items reflects the same first-order latent constructs, and their meanings are the same across subgroups.
Structural weights level (Second-order factor loadings)	First-order factor loadings and intercepts, as well as second-order factor loadings, are constrained to be equal across groups	The strength of the relationship between each first-order construct and its underlying second-order construct is the same for both groups.
Structural covariances level (Second-order covariance)	First-order factor loadings and intercepts, as well as second-order factor loadings and covariance(s), are constrained to be equal across groups	The same set of items reflects the same first-order latent constructs, the same set of first-order constructs reflects the same second-order latent construct(s), and their meanings are the same across subgroups.
Structural residuals level (Disturbances of first-order factors)	First-order factor loadings and intercepts, as well as second-order factor loadings and covariance(s), are constrained to be equal across groups.	The same set of items reflects the same first-order latent constructs, the same set of first-order constructs reflect the same second-order latent construct(s), and their meanings are the same across subgroups. Additionally, there is no appreciable difference in the disturbances.

Table 2. Exploratory Factor Analysis: item-to-factor loading.

Items	Factor 1 Non-normative subordinate masculinity	Factor 2 Normative subordinate femininity	Factor 3 Normative hegemonic masculinity
A man who only eats salads is definitely gay	.68
Men who bring fruits to school are usually effeminate	.67
Men who watch what they eat to avoid gaining weight are gay	.76
A man who eats little is gay	.82
Men who eat healthy food to stay in shape are effeminate	.73
Men who eat slowly are effeminate	.73
Queer men mind their manners when eating	.55
Men who eat little are gay	.78
Men prefer women who watch what they eat		.41
Women who eat quickly appear less feminine		.44
Beautiful women generally eat little		.56
Women who don’t watch what they eat are not appealing to men		.67
The more feminine a woman is, the more fruits she eats		.64
If a woman wants to be successful with men, she must watch what she eats		.63
A woman who eats a lot looks manly		.59
Thin women are more feminine		.55
An average man eats a lot			.52
Real men eat very quickly			.40
Men don’t care if the food they eat is greasy			.74
Men eat whatever they want without remorse			.74
Men do not care about what they eat			.58

Note: In this table, items are freely translated from Spanish into English. The original items in Spanish are provided in Appendix 1. KMO = .868, Bartlett test = 1853.05 (p < .001). Item numbers are reported based on the order they had in the study questionnaire.

Figure 1. Note. Fit model: χ2 (186) = 457.27, p < .001, χ2 /df = 2.46, CFI = .91, RMSEA = .060, 90% CI [.053; .067]. Coefficients are standardized. No item-factor loading was below the recommended level of β = .30 (Kline, 2016). Loadings were all significant (p < .001).

Figure 2. Note: χ2 (186) = 618.65, p < .001, χ2 /df = 3.32, CFI = .92, RMSEA = .053, 90% CI [.049; .058]. Coefficients are standardized. No item-factor loading was below the recommended level of β = .30 (Kline, 2016). Loadings were all significant (p < .001).

Table 3. Fit of gender, age, and place of residence subgroups in Study 2.

Fit by group categories	χ^2	χ^2/df	CFI	RMSEA [90% CI]	χ^2	χ^2/df	CFI	RMSEA [90% CI]
Gender	Boys				Girls
	407.19	2.19	.90	.063 [.055, .072]	476.94	2.56	.91	.055 [.049, .61]
Age	Younger				Older
	450.67	2.42	.92	.055 [.048, .061]	440.75	2.37	.90	.064 [.056, .71]
Residence area	Urban				Rural
	588.41	3.16	.88	.073 [.066, .079]	400.17	2.15	.91	.053 [.046, .61]

Note: Degrees of freedom were 186 for all the analyses in these groups. There were 297 boys and 516 girls, 475 younger (< 15 years) and 338 older (> 15 years) participants, and 409 urban and 404 rural inhabitants.

Table 4. Invariance results by gender and age subgroups in Study 2.

	Gender groups					Age groups
Invariance level	χ^2	df	χ^2/df	CFI	RMSEA [90% CI]	χ^2	df	χ^2/df	CFI	RMSEA [90% CI]
Configural	884.26	372	2.37	.91	.041 [.038, .045]	891.48	372	2.39	.91	.041 [.038, .45]
Metric (Measurement weights)	943.35	390	2.42	.90	.042 [.038, .045]	929.19	390	2.38	.91	.041 [.038, .45]
Scalar (Measurement intercepts)	970.87	411	2.36	.90	.041 [.038, .044]	1004.55	411	2.44	.90	.042 [.039, .46]
Second-order loadings (Structural weights)	974.21	413	2.36	.90	.041 [.038, .044]	1008.64	413	2.44	.90	.042 [.039, .45]
Second-order covariance (structural covariance)	975.31	414	2.35	.90	.041 [.038, .044]	1011.70	414	2.44	.90	.042 [.039, .45]
Second-order residuals (structural residuals)	994.45	417	2.38	.90	.041 [.038, .045]	1013.98	417	2.43	.90	.042 [.039, .45]

Note: ***p < .001, **p < .01.

Kline, R. B. (2016). Principles and practice of structural equation modeling. New York, NY: The Guilford Press.

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