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The Moderation Effects of Family Structure and Low Self-Control

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Abstract

Gottfredson and Hirschi’s (1990) theory of low self-control (LSC) asserts the etiology of LSC lies in the ability of parents to correctly control their children. These parental behaviors are crucial to understanding how LSC may or may not be transmitted from parents to children. Little research has explored the potential moderating effects of family structure regarding parenting, LSC, and delinquency. Multi-group structural equation modeling is used with data from the National Evaluation of the Teens, Crime, and the Community/Community Works (TCC/CW) program (n = 1,409) to assess these relationships. Results show LSC mediates the relationship between family structure and delinquency. Additionally, the relationships between parenting, LSC, and delinquency are invariant for children from intact or broken homes. It is suggested that future research explore the origins of parental efficacy and LSC beyond family structure while considering self-control as a multidimensional concept. Policy recommendations are also discussed.

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Notes

  1. The selection of the first wave of data was made because this would produce the largest sample size, due to attrition. Since Gottfredson and Hirschi contest that self-control is stable throughout the life-course after age 10, the use of cross-sectional data is preferred (for support of this “stability hypothesis”, see Arneklev, Cochran, & Gainey, 1998). Although some contest that self-control is variable over time (see Turner & Piquero, 2002) and others have found it to vary over time depending on the type of offender (Winfree et al., 2006) the current study is a test of Gottfredson and Hirschi’s theory as close to their conceptualization as possible.

  2. The decision was made to delete these so that the data would be consistent in regards to which wave the responses came from.

  3. Watkins and Melde (2007) note that those with low self-control are more likely to leave non-responses on survey items. A test was conducted to see if low self-control predicted the likelihood an individual would leave self-control responses blank. These 12 respondents were included in this analysis as a robustness check. A dichotomous variable was created with one group being all of those students that did not respond to as little as one self-control question (n = 226) and those that responded to all measures (n = 1,195). A binary logistic regression was conducted with this dichotomous variable as the dependent variable and a LSC scale used as the independent variable. For this scale, mean substitution was used for missing respondents, which allowed the analysis to run. Otherwise, these cases are listwise deleted and no analysis can be conducted. The LSC scale did not predict whether or not someone had a greater chance of being in the group of non-responders (p > .05). A second check was conducted including all variables used in the later analyses, to see if controlling for these variables would still show a non-significant relationship. There was still no statistically significant relationship (p > .05) implying that differential non-response was not contingent on self-control and the assumption was made that these items are missing at random. Thus, deleting these 12 cases appears justified.

  4. Although some researchers (including Gottfredson & Hirschi, 1990) prefer capturing low self-control through behavioral rather than attitudinal measures (Piquero, MacIntosh, & Hickman, 2000), a comprehensive meta-analysis of the theory showed similar effect size estimates from both (Pratt & Cullen, 2000). In support of behavioral over attitudinal measures was Watkins and Melde’s (2007) research which found item non-response on surveys attempting to measure self-control was related to self-control. That is, an individual’s self-control limits their ability to complete a survey accurately, impacting the validity in attitudinal measures. The previous footnote, however, found this to not be an issue with this data. See also Arneklev et al. (2006) supporting the use of attitudinal over behavioral measures.

  5. Since SEM is used in later analyses it might seem appropriate to use a confirmatory factor analysis (CFA). Unfortunately, with the amount of parameters this creates in the later analyses, the sample size is too small for an additional 16 parameters, plus 4 more for the higher-order factor. Jackson (2003) has shown that an adequate sample size for the amount of parameters in SEM is typically about 20:1.

  6. The summation of the four scales into one, essentially gets rid of the multidimensionality of low self-control (see Arneklev et al., 1999) that the EFA showed. Other researchers have created scales in this same way and have found them to be reliable measures, even over time (see Winfree et al., 2006, p. 261). This technique is preferred over simply scaling all 16 questions into one construct since the extra step of scaling different dimensions from the EFA is thought to be a more accurate measure of self-control, albeit unidimensional. Later analyses assessed each construct individually.

  7. The log transformation was computed using the formula: Ln(Delinquency Scale + 1), adding 1 since some respondents had committed 0 delinquent acts.

  8. The original racial categories were: “White/Anglo”, “not Hispanic”, “Black/African American”, “Hispanic/Latino”, “American Indian/Native American”, “Asian/Pacific Islander/Oriental”, “Other”, “Mixed/Bi-Racial.”

  9. The coding scheme used in this research is similar to past studies in this same area (e.g. Boutwell & Beaver, 2010; Winfree et al., 2006), and is on par with the original statements made by Gottfredson and Hirschi (1990) regarding their hypothesis that intact families should have the highest levels of parental monitoring compared to non-traditional family settings.

  10. This single indicator is admittedly not the best proxy for SES, but past research in this area has been limited in the use of single-indicators of SES (e.g. Unnever et al., 2006).

  11. It is possible that the amount of respondents not knowing their parents education level was so high it could confound the subsequent SEM analyses. To control for this, a dichotomous variable for the respondents that did not know their parents’ education level was created (0 = know at least one parent’s education, 1 = don’t know parent’s education,). This variable was used in the subsequent main effects SEM (i.e. Model 1) in order to see if those not knowing their parent’s education had significant relationships with either parental monitoring, self-control, or delinquency. Results showed that those not knowing parent’s education had lower levels of parental monitoring but higher levels of delinquency with no statistically significant relationship with self-control. This somewhat reduces the reliability of the SES measure so readers are cautioned on relationships between parent’s education and any of the other variables in the models. Slocum et al. (2010) included a similar dummy variable in their research, but since a variable of “not knowing parents education” makes little theoretical sense in the later SEMs, in which parsimony is critical, this variable was not used in any of the models.

  12. The decision was made to not trim the model when statistically significant relationships were found in one group but not the other. The following parameters were found to be not statistically significant for non-intact families but were significant for intact families (thus they remained in the model): parental education to parental monitoring, other race to parental monitoring, Hispanic to delinquency, other race to delinquency, male to delinquency . Only the parameter from age to self-control was found not statistically significant for intact homes but was statistically significant for non-intact homes (this parameter also remained in the model).

  13. To check if the different age cohorts varied regarding the main effects model, multi-group SEM was conducted to assess potential age group interactions with these key variables. Three major age groups were assessed since children aged 10 (n = 8), 14 (n = 110), and 15 (n = 6) were each too small for SEM. After deleting the age variable from the main effects model (and retaining the family structure variable) (n = 1,266) a main effects model was compared to the three-group age cohort analysis. Comparing this main effects model with the three-group model showed that the χ 2 difference was not significant (p < .05) for age cohorts. This implies that the main variables were invariant regarding age group. The later multi-group models regarding family structure could not further be divided up into groups based on age cohorts since three-way interactions are difficult to interpret in SEM.

  14. One way past researchers have done this is to constrain parameter estimates to be equal across groups and then allow for modification indices to suggest what structural paths might be freed to create significant relationships (see Higgins & Boyd, 2008). Unfortunately, modification indices cannot be used in AMOS while using the maximum-likelihood function. This was why modification indices were not inspected to determine statistically significant parameters and the critical ratios were used instead. It is the opinion of this researcher that the strengths of ML outweigh the need for modification indices in this circumstance.

  15. Problems arise, however, because prior to obtaining the critical ratio, these parameters were not statistically significant for non-intact homes (p > .05) but were statistically significant for intact homes (p < .05). Thus, readers are cautioned when looking at the critical ratio difference between these two parameters since they were not statistically significant in the first place for one of the groups.

  16. Despite sample size limitations, an attempt was made to run these models. AMOS was unable to fit them and could not produce a chi-square statistic, nor any model-fit indices.

  17. These researchers used intentions to offend rather than self-reports of offending.

  18. See Footnote 16.

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Correspondence to Jesse R. McKee.

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The author would like to thank Bradley Brick, Randy Gainey, and the anonymous reviewers for their constructive feedback on earlier versions of this manuscript. The author would also like to thank Finn-Aage Esbensen for granting access to the data used in this research.

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McKee, J.R. The Moderation Effects of Family Structure and Low Self-Control. Am J Crim Just 37, 356–377 (2012). https://doi.org/10.1007/s12103-011-9143-1

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