Change and Continuity in Attitudes Toward Homosexuality Across the Lifespan

ABSTRACT

This study examines differential stability in attitudes toward homosexuality using panel data representative of the American adult population. While attitudes toward homosexuality have shifted considerably on the aggregate-level over the past few decades, this study shows that such attitudes are remarkably stable on the individual-level. Employing conditional change models, this study also provides a test of the aging-stability hypothesis with regard to attitudes toward homosexuality. That hypothesis is confirmed, as attitude stability is found to gradually increase with age. However, no other socio-demographic variables are found to have a consistent relationship with stability. The finding of an age-graded increase in stability suggests that attitudes toward homosexuality are formed predominantly early in life and that susceptibility to attitude change declines across the adult lifespan. This finding also supports a generational replacement explanation of recent changes in American public opinion on homosexuality as aging-stability translates into cohort effects on the aggregate-level.

KEYWORDS:

• Homosexuality
• attitudes
• stability
• change
• socialization
• lifespan development
• age effects

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Outside of the American context, Hooghe and Meeusen (2012) found an indicator of homophobia to be highly stable among young adults (aged 18–21) in a Belgian three-year panel.

2. The later-life decline model has also been referred to as the midlife-stability model (Alwin, 1994) or as the life-stages model (Visser & Krosnick, 1998).

3. A gradual increase in stability across the lifespan is also compatible with a Bayesian perspective on learning, in which the marginal impact of each new experience declines as a function of the accumulation of experiences (see Achen, 1992; Bartels & Jackman, 2014).

4. Age-period-cohort analysis is subject to an identification problem since age, period, and cohort are confounded with one another in repeated cross-sectional data (period—cohort = age). This renders estimation of effects associated with the three variables impossible unless a constraint on at least one of them is imposed, but it is the constraint that determines the best-fitting solution out of the infinite set of possible solutions of linear age, period, cohort effects that exist due to the fact that the three variables are not mathematically independent of one another. Because of this, APC models are highly sensitive to model specification and different constraints can produce different results (Bell & Jones, 2013; Ekstam, 2021; Glenn, 2005).

5. Consult the General Social Surveys 2006–2014 Panel Codebook (Smith & Schapiro, 2017) for information about sampling procedure and interviewing technique used in the GSS panel studies.

6. If surveys are analyzed separately, observations are less than 10 for several age-year units across the range of age.

7. In the raw data, age has a range from 18 to 89. Restricting the age range to 18–80 results in a 2.8% (110 observations) decrease in sample size for the pooled T1–T3 data.

8. Outside of the American context, the British Household Panel Studies, which includes an item on homosexuality biannually between 2000 and 2008, represent an interesting data source for future research.

9. The GSS includes additionally three items that concerns homosexuality. These are all binary (agree or disagree), asking the respondent whether or not a male homosexual: (1) should be allowed to make public speeches in the respondent’s local community, (2) should be allowed to teach at colleges or universities, and (3) should have the pro-gay book he has written removed from the respondent’s public library. However, these items have an overwhelming preponderance of nondiscriminatory responses in the data, skewness that can artificially suppress test-retest reliability (Dunlap, Chen, & Greer, 1994). For this reason, these items are not used.

10. The correlation between the same-sex marriages item and the acceptance of homosexuality item is .69 in the pooled T1 sample, .70 in the pooled T2 sample, and .70 in the pooled T3 sample.

11. The choice between specifying a covariate as Xt or Xt-1 in a conditional change model depends on whether the causal lag of the given covariate to influence the dependent variable is assumed be shorter than the time elapsed between waves of measurement. However, this choice is of little importance with respect to variables that are highly stable over time (Finkel, 1995, pp. 12–13).

12. Models are also fitted to the T1–T1 data and results from these models are continually reported in the text.

13. Weighting is done using the wtpannr12 weight for the pooled T1–T2 sample and the wtpannr123 weight for the pooled T1–T3 sample.

14. While education, sex, religious affiliation, and political ideology are approximately time-invariant on the individual-level, they nevertheless covary with age (cohort) in the data. For example, age has a statistically significant hump-shaped relationship with education in the pooled T1 sample, and individuals with high levels of education tend to have more stable attitudes than individuals with low levels of education (Visser & Krosnick, 1998; Westholm & Niemi, 1992). Similarly, having a religious affiliation is more common among older panel participants and it is conceivable that some religious beliefs exert a stabilizing influence on one’s views on homosexuality.

15. Measuring education, sex, religious affiliation, and political ideology at Tt-1 does not substantially change the results.

16. The party identification item is preferred over the liberal-conservative self-placement item as a measure of political ideology because it exhibits a considerably higher temporal stability in the data.

17. A negative relationship between stability and panel length is also reported by Sears and Funk (1999) and by Roberts and DelVecchio (2000).

18. The extraordinary level of stability of party identification reported here is in line with the results of previous studies (e.g., Alwin et al., 1991; Jennings & Markus, 1984; Stoker & Jennings, 2008).

19. The test-retest reliability coefficients for the items on racial issues change only slightly if samples are restricted to respondents registered as white.

20. Ceteris paribus, test-retest reliability is likely to be higher for an item with a dichotomous (agree or disagree) response scale than for an item with a Likert response scale, as movement across the former scale entails greater change.

21. The aggregate-level shift in the pooled panel sample corresponds fairly well to the shift seen across cross-sectional samples during the time period at issue (Daniels, 2019; Pampel, 2016).

22. For example, the estimated test-retest relationship for a respondent aged 18 at T1 is .63 (.576 + .003 $\times$ 18) for the acceptance of homosexuality item, and .61 (.550 + .003 $\times$ 18) for the same-sex marriages item.

23. If panels are analyzed separately, a statistically significant positive interaction effect between age and the lagged dependent is estimated for both ATH items in the 2008–2010–2012 panel and in the 2010–2012–2014 but for neither item in the 2006–2008–2010 panel.

24. In the pooled T1–T2 data, a statistically significant positive interaction effect between age and the lagged dependent variable is estimated for the acceptance of homosexuality item (b = .003, p < .05) but the interaction is not statistically significant for the same-sex marriages item. See Table A2 in the Appendix for the complete regression results of models using the pooled T1–T2 data.

25. Females have more stable attitudes than men in the pooled T1–T2 data when the acceptance of homosexuality item constitutes the dependent variable (see Table A2 in the Appendix).

26. If the ideology variable is “folded” (0 = independent; 1 = independent near democrat/republican; 2 = not strong democrat/republican; 3 = strong democrat/republican), the interaction term between it and the lagged dependent variable is positive for both ATH items (suggesting that stability is positively associated with partisan strength) but these interaction effects are not statistically significant.

27. If the age range is uncapped (18–89), the test-retest relationship has more of a hump-shaped trajectory across the range of age. However, the interaction effect between age-squared and the lagged dependent variable remains outside of conventional levels of statistical significance even in this case.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.