Non-random Study Attrition: Assessing Correction Techniques and the Magnitude of Bias in a Longitudinal Study of Reentry from Prison
Objectives Longitudinal data offer many advantages to criminological research yet suffer from attrition, namely in the form of sample selection bias. Attrition may undermine reaching valid inferences by introducing systematic differences between the retained and attrited samples. We explored (1) if...
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| Contributors: | ; ; |
| Format: | Electronic Article |
| Language: | English |
| Published: |
2022
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| In: |
Journal of quantitative criminology
Year: 2022, Volume: 38, Issue: 3, Pages: 755-790 |
| Online Access: |
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| Summary: | Objectives Longitudinal data offer many advantages to criminological research yet suffer from attrition, namely in the form of sample selection bias. Attrition may undermine reaching valid inferences by introducing systematic differences between the retained and attrited samples. We explored (1) if attrition biases correlates of recidivism, (2) the magnitude of bias, and (3) how well methods of correction account for such bias. Methods Using data from the LoneStar Project, a representative longitudinal sample of reentering men in Texas, we examined correlates of recidivism using official measures of recidivism under four sample conditions: full sample, listwise deleted sample, multiply imputed sample, and two-stage corrected sample. We compare and contrast the results regressing rearrest on a range of covariates derived from a pre-release baseline interview across the four sample conditions. Results Attrition bias was present in 44% of variables and null hypothesis significance tests differed for the correlates of recidivism in the full and retained samples. The bias was substantial, altering effect sizes for recidivism by a factor as large as 1.6. Neither the Heckman correction nor multiple imputation adequately corrected for bias. Instead, results from listwise deletion most closely mirrored the results of the full sample with 89% concordance. Conclusions It is vital that researchers examine attrition-based selection bias and recognize the implications it has on their data when generating evidence of theoretical, policy, or practical significance. We outline best practices for examining the magnitude of attrition and analyzing longitudinal data affected by sample selection. |
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| ISSN: | 1573-7799 |
| DOI: | 10.1007/s10940-021-09516-7 |