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Can Item-Level Error Correlations Correct for Projection Bias in Perceived Peer Deviance Measures? A Research Note

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Abstract

Objectives

Research indicates respondents overestimate the similarity between their own deviance and that of their peers. Extending Rebellon and Modecki’s (J Quant Criminol 30:163–186, 2014) study, we examine if item-level error correlations in structural models reduce bias for non-peer-based, theoretically derived covariates such as self-control. Our specific interest lies in investigating the theoretical implications and practical value of using the correlated error technique in ‘everyday’ structural equation modeling.

Methods

Using dyadic data and multiple constructs of deviance, we present three sets of structural equation analyses. The first assesses the relationship between peer behavior and deviance via perceptual measures. The second uses identical constructs, but estimates item-level error correlations between perceptual and deviance items. The third replaces perceptions of peer deviance with items measuring peers’ self-reported behavior.

Results

Self-control and demographic variables have equivalent effects in perceptually-based correlated error models and models controlling peer self-reported deviance. However, latent variable adjustments to perceptions of peer behavior fail to bring perceived peer deviance coefficients into line with corresponding coefficients from models using peer self-reports, indicating that perceptions and peer self-reports are distinct constructs.

Conclusion

Researchers cannot use item-level error-correlations to model peer effects without collecting data from peers. They may, however, use these correlations to control for peer effects even when peer self-reports are not available. Because we find strong effects of self-control while maintaining social learning theory’s emphasis on perceptions, we argue that the technique is a form of theoretical reconciliation and recommend criminologists adopt the use of correlated errors in all social influence-based structural models.

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Notes

  1. This means the first set of models is nested within the second set of models.

  2. The violence construct has only three items. Since a CFA with three items necessarily fits the data perfectly, the factor structure of the self-reported and perceptual violence measures was determined by principle component analyses (PCAs). Dimensionality was diagnosed based on proportional eigen decomposition and the elbow rule on scree plots. Consistently, the PCAs demonstrated that the three self-reported and perceptual violence items were in fact measuring one distinct construct, respectively.

  3. We do not include age as a covariate due to its limited variability in our sample of undergraduate college students (μ = 19.3 years old, SD = 1.4 years).

  4. Minor amounts of missing data (average item missingness was less than 1 %) were imputed with the full-information maximum likelihood technique.

  5. Because of disagreement in how self-control should be measured (e.g., Evans et al. 1997), we re-estimated all analyses with a behavioral measure of self-control called the ‘Retrospective Behavioral Self-Control Scale-reduced’ (RBS-r; see Ward et al. 2010). The results with the behavioral measure were identical to those reported with the attitudinal Grasmick et al. (1993) scale.

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Authors and Affiliations

  1. Department of Criminal Justice, University of Wyoming, 1000 E. University Ave., 223 A&S Building, Laramie, WY, 82071, USA

    John H. Boman IV

  2. University of New Hampshire, Durham, NH, USA

    Cesar J. Rebellon

  3. Florida International University, Miami, FL, USA

    Ryan C. Meldrum

Authors
  1. John H. Boman IV
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  2. Cesar J. Rebellon
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  3. Ryan C. Meldrum
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Correspondence to John H. Boman IV.

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Boman, J.H., Rebellon, C.J. & Meldrum, R.C. Can Item-Level Error Correlations Correct for Projection Bias in Perceived Peer Deviance Measures? A Research Note. J Quant Criminol 32, 89–102 (2016). https://doi.org/10.1007/s10940-015-9255-8

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  • DOI: https://doi.org/10.1007/s10940-015-9255-8

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