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Extensive research has considered the risk factors that predict child maltreatment outcomes, but little, if any, research has examined risk using methods other than those related to the summation of those risk factors. The primary objective of this study is to evaluate common predictors of child maltreatment from a mixture modeling perspective. This quantitative study uses eight risk factors for child maltreatment and associates them with two outcomes: parent perpetration of child maltreatment and parental attitudes toward sensitivity. The study sample consists of 604 biological mothers from four sites of the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN). Risk factor data was used to create latent classes that represent a risk typology. Individual sites from Baltimore (n = 163), Chicago (n = 176), North Carolina (n = 132), and Seattle (n = 133) were compared to see if a similar risk typology was found for the individual sites when compared to the entire sample. A three latent class risk typology emerged from the entire sample and three of the four LONGSCAN sites. The latent class with the most risk emerged as having the highest percentage of child maltreatment outcomes. With these types of outcomes, multiple risk factors coming together should be the strongest hallmark in the assessment of child maltreatment. Maternal history of victimization was also determined to be an important factor in child maltreatment outcomes, therefore, highlighting the importance of the individual nature of risk as it relates to child maltreatment. In addition, mothers who are younger in age and have low income have lower sensitivity scores. These scores are predictive of less than ideal parenting attitudes. The research presented in this study has been dedicated to taking the popular approach of summing risk factors to a new level of understanding through the use of latent class analysis. These latent classes challenge current thinking on potential risk for children and families. Specifically, mothers with multiple risk factors demonstrate the strongest predictor of child maltreatment outcomes. Also, multiple risk factors need not be present to result in rates of child maltreatment that are higher than what might be expected.