A Matter of Principle? On the Relationship Between Racial Resentment and Ideology

Abstract

Though most scholars of race and politics agree that old-fashioned racism largely gave way to a new symbolic form of racism over the course of the last half century, there is still disagreement about how to best empirically capture this new form of racism. Racial resentment, perhaps the most popular operationalization of symbolic racism, has been criticized for its overlap with liberal-conservative ideology. Critics argue that racially prejudiced responses to the items that compose the racial resentment scale are observationally equivalent to the responses that conservatives would provide. In this manuscript, I examine the racial resentment scale for differential item functioning (DIF) by level of adherence to ideological principles using the 1992, 2004, and 2016 American National Election Studies. I find that responses to some of the racial resentment items are, indeed, affected by ideology. However, the problem is largely confined to 2016 and more egregious with respect to ideological self-identifications than adherence to ideological principles. Moreover, even after correcting for DIF, the racial resentment scale serves as a strong predictor of attitudes about racial issues.

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Notes

  1. 1.

    I suppose that one could, alternatively, consider whether responses to questions about ideological self-identifications and policy preferences exhibit differential item functioning by level of racial resentment. However, the survey items assessing those predispositions and attitudes have been more successfully (i.e., less controversially) validated over the course of time, and the question at hand regards the viability of racial resentment as a measure of racial prejudice rather than ideological beliefs, more specifically.

  2. 2.

    Data and replication files can be found on the Political Behavior Harvard Dataverse page here.

  3. 3.

    The particular policy areas queried are not consistent across ANES surveys. After removing references to non-domestic policies (e.g., “aid to former Soviet Union countries” and “foreign aid,” more generally) and blacks, specifically (e.g., “assist blacks,” in 1992), there are 8 items in the 2004 and 2016 scales, and 14 in the 1992 scale. Since the spending items focused on welfare and aid to the poor have a racial dimension to them (e.g., Gilens 2001), I replicated all analyses below with a version of the spending scale omitting these items. These analyses revealed even less differential item functioning in the racial resentment items than did the analyses using the complete scale. In other words, the principled conservatism thesis finds even less support when I measure spending preferences in this alternative way.

  4. 4.

    For recent examples of the effect of differential item functioning on political science constructs, see Pietryka and MacIntosh Pietryka and MacIntosh (2013), Hare et al. (2015), or Pérez and Hetherington (2014).

  5. 5.

    The structural equation modeling approach that employs multiple group CFA usually refers to DIF as “measurement invariance.” When one conducts a factor analysis, for example, one implicitly assumes that the coefficients relating the individual indicator variables to the latent factors are statistically identical across groups. If this is not the case, the data/model combination violate the principle of measurement invariance, potentially biasing inferences made using analytical results obtained with the model (e.g., predicted factor scores). This is simply a different way—one born of scholars working in the factor analysis and structural equation modeling traditions, specifically—of talking about the same problem.

  6. 6.

    Note that although the exact procedure for carrying out a DIF analysis in the MIMIC approach is still being investigated, at its core the MIMIC approach is identical to the IRT-logistic regression approach I employ. A one-factor categorical CFA—which is at the heart of the MIMIC approach when ordinal variables are involved—is identical to an ordinal IRT model, assuming a constraint on the variance of the latent variable to identify the model Takane and de Leeuw (1987). In the IRT-logistic regression approach, the latent variable from the IRT model is estimated and then used in the three equations presented below to detect uniform and non-uniform DIF. In the MIMIC approach, the exact same equations are estimated, but they are estimated simultaneously with the measurement model in the fashion of structural equation models with latent variables.

  7. 7.

    This model takes the following form: \(Pr(Y_{ij} \le k|\theta _i) = \frac{\text {exp}\{\alpha _{j}(\theta _i - b_{jk})\}}{1 + \text {exp}\{\alpha _{j}(\theta _i - b_{jk})\}} \quad \theta _i \sim N(0, 1)\). Estimates from this uncorrected (i.e., DIF-afflicted) racial resentment IRT models for each year are presented in the Supplemental Appendix. The correlations between the estimated latent trait scores from the IRT model and the additive index operationalization of racial resentment range from 0.95 (1992) to 0.99 (2016) across years. This reflects the fact that the discrimination parameters from IRT model—those coefficients which relate the individual items to the latent trait—are nearly identical across items. Regardless of the similarity of the DIF-afflicted latent trait scores at this point in the analysis, the IRT framework will make for easier and more robust corrections to DIF in the next section of the manuscript.

  8. 8.

    Correcting the single racial resentment item for self-identification-based DIF in 1992 and 2004 does not result in substantive differences in the respective racial resentment scales.

  9. 9.

    Using multiple category distinctions between “levels” of adherence to ideological principles or altering the (already generous) threshold between principled and unprincipled for the non-symbolic measures of ideology does not alter substantive inferences.

  10. 10.

    This procedure, like most DIF-correction procedures, assumes that at least one item does not exhibit significant DIF. This is necessary so that the DIF-afflicted items can be “anchored” to the appropriately-functioning item. With each measure of ideology, there is at least one item that does not exhibit significant uniform or non-uniform DIF, providing the opportunity to safely make corrections.

  11. 11.

    Question wording and variable coding can be found in the Supplemental Appendix.

  12. 12.

    This finding is at odds with recent work by Tesler (2012) about the spillover racialization of healthcare attitudes. However, I provide rather extensive controls for ideological principles in the models. If I remove the controls for spending preferences and beliefs about the size and scope of government, effects of both the DIF-corrected and -uncorrected racial resentment scale become statistically significant. Again, though, I wish to emphasize that the test of my theory rests on an examination of differences in the estimates associated with the corrected and uncorrected scale, not the statistical significance of any given estimate.

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Acknowledgements

I would like to thank Miles Armaly, Jamil Scott, Paul Sniderman, and three anonymous reviewers for their helpful comments, critiques, and suggestions. All errors are my own.

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Enders, A.M. A Matter of Principle? On the Relationship Between Racial Resentment and Ideology. Polit Behav (2019). https://doi.org/10.1007/s11109-019-09561-w

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Keywords

  • Race
  • Racial resentment
  • Symbolic racism
  • Ideology
  • Conflicted conservatism