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Testing the Invariance of the National Health and Nutrition Examination Survey’s Sexual Behavior Questionnaire Across Gender, Ethnicity/Race, and Generation

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Abstract

Federal and state policies are based on data from surveys that examine sexual-related cognitions and behaviors through self-reports of attitudes and actions. No study has yet examined their factorial invariance—specifically, whether the relationship between items assessing sexual behavior and their underlying construct differ depending on gender, ethnicity/race, or age. This study examined the factor structure of four items from the sexual behavior questionnaire part of the National Health and Nutrition Examination Survey (NHANES). As NHANES provided different versions of the survey per gender, invariance was tested across gender to determine whether subsequent tests across ethnicity/race and generation could be done across gender. Items were not invariant across gender groups so data files for women and men were not collapsed. Across ethnicity/race for both genders, and across generation for women, items were configurally invariant, and exhibited metric invariance across Latino/Latina and Black participants for both genders. Across generation for men, the configural invariance model could not be identified so the baseline models were examined. The four item one factor model fit well for the Millennial and GenerationX groups but was a poor fit for the baby boomer and silent generation groups, suggesting that gender moderated the invariance across generation. Thus, comparisons between ethnic/racial and generational groups should not be made between the genders or even within gender. Findings highlight the need for programs and interventions that promote a more inclusive definition of “having had sex.”

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Notes

  1. A lack of invariance can indicate that the relationship between scale items or measures and their latent variable(s) are not the same across groups.

  2. To examine configural invariance, baseline models for each group of interest are first established. A baseline model allows all parameters to be estimated freely, thus establishing the “best” fitting model, independently, for each specific group. The factor loadings of each latent variable are then examined for significance and magnitude. If the significance and magnitude of the factor loadings of each observed variable on its corresponding latent variable are the same across groups, the model can be considered configurally invariant.

  3. While reported for completeness, the Chi square statistic is strongly influenced by sample size. Model fit with large sample sizes (as is the case with NHANES data) are therefore better interpreted through descriptive fit indices.

  4. CFI, TLI, and RMSEA are some of the most widely used descriptive fit indices. Both the CFI and TLI are categorized as incremental fit indices, where the fit of the estimated model is compared to an independent (null) model (Brown, 2006). The CFI is normed to the 0–1 range, and higher CFI values reflect the relative improvement of the estimated model from the independence (null) model. The TLI is a non-normed index (values do not have to fall between 0 and 1) that penalizes for model complexity and is not strongly influenced by sample size. RMSEA compares the estimated model to a saturated (perfect) model, and values indicate the degree of “lack of fit.” That is, smaller RMSEA values indicate a closer fitting model (Hu & Bentler, 1999).

  5. A metric invariance model constrains factor loadings to equivalency across groups. The model simultaneously estimates the parameters of all groups under this constraint, and produces a null model to which the previously estimated baseline models can be compared.

  6. Models are considered nested if one model is a “more restrictive” version of another model. Here, as the metric invariance model is more restrictive (constraining factor loadings to equivalency), it is considered the “nested” model. If a chi-square difference test reveals no significant differences between models, then the more parsimonious model (the more restrictive model using fewer degrees of freedom) is considered a better fit to the data.

  7. The factor variance invariance model constrains the factor variances to equivalency across groups.

  8. The WLSMV estimator does not assume normally distributed variables and thus provides the best option for modeling categorical or ordered data (Brown, 2006).

  9. Unstandardized factor loadings are based on raw data, and are preferred when comparing across groups that may have different variances. Unstandardized factor loadings can be interpreted as regression coefficients (covariance between observed variable and latent variable).

  10. The lambda parameter specifications are the item’s factor loadings.

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Correspondence to Anne Q. Zhou.

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Zhou, A.Q., Hsueh, L., Roesch, S.C. et al. Testing the Invariance of the National Health and Nutrition Examination Survey’s Sexual Behavior Questionnaire Across Gender, Ethnicity/Race, and Generation. Arch Sex Behav 45, 271–280 (2016). https://doi.org/10.1007/s10508-015-0537-x

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  • DOI: https://doi.org/10.1007/s10508-015-0537-x

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