The Society for Social Work and Research

2014 Annual Conference

January 15-19, 2014 I Grand Hyatt San Antonio I San Antonio, TX

The Influence of Caregiver and Child Age On Protective Factors Within a Child Welfare Setting

Saturday, January 18, 2014
HBG Convention Center, Bridge Hall Street Level (San Antonio, TX)
* noted as presenting author
Aislinn R. Conrad-Hiebner, MSW, Graduate Research Assistant, University of Kansas, Lawrence, KS
Stephanie Wallio, PhD, Dissertation Chair, North Central University, Prescott Valley, AZ
Alexander Schoemann, PhD, Research Associate, University of Kansas, Lawrence, KS
Purpose.  Child maltreatment is a devastating social problem with far-reaching negative consequences.  The child abuse and neglect prevention field has expanded to measuring protective factors against child abuse to demonstrate outcomes and better engage families.  The Protective Factors Survey (PFS) was developed in response to this transition and measures five family-level protective factors: Family Functioning/Resiliency (FFR); Concrete Supports (CS); Social Supports (SS); Nurturing and Attachment (NA) (all measured as subscales); and Knowledge of Parenting/Child Development (KPCD) (individual items).

As a new measure, little is known about the impact of families’ demographics on their levels of protective factors, unlike the more established relationships between maltreatment rates and demographics such as age and income.  The present study investigates whether the age of the caregiver and child targeted for intervention influence reported levels of protective factors.


Data Collection.  Caregivers receiving family support services through Community-based Child Abuse Prevention (CBCAP) programs were asked to complete the PFS at the beginning and end of services (pre-post design).  All CBCAP programs in Kansas administered the PFS to caregivers who received services for a minimum of three months (n=1993).

Measure.  The PFS is a valid and reliable pre-post program evaluation tool with 20 Likert items to measure agreement and frequency.  In addition to reporting levels of protective factors, caregivers also reported the age of the child believed to benefit most from the intervention services.

Sample Characteristics.  The caregivers’ ages ranged from 14 to 77 years (M=32.59, SD=9.99), while the target child’s ages ranged from 0 to 22 years (M=6.39, SD=4.72).  Predominately, caregivers were female (75.9%), 36 years old and younger (69.7%), White (82.8%), and partnered (50.4%); earned less than $50,000 (85.1%); and received a GED or less education (42.9%).

Statistical Approach.  We conducted multiple regression analyses to determine the relationship between caregiver and target child age, their interaction, and reported levels of protective factors. 

Results. For pretest scores, results indicated a negative relationship between caregiver age and CS (β=-.065, p=.032), NA (β=-.064, p=.032), and 2 KPCD items. Results also indicated a negative relationship between target child age and NA (β=-.202, p<.001) and all 5 KPCD items.

For change scores (posttest controlling for pretest scores), results indicated a negative relationship between caregiver age and SS (β=-.080, p=.046) and NA (β=-.104, p=011). Results also indicated a negative relationship between target child age and NA (β=-.097, p=.020) and 4 of the KPCD items. 

There were no interactions for caregiver age and target child age.  

Implications.  Results demonstrate that for older caregivers and target children pretest scores were lower and change scores were smaller, primarily for the NA subscale and KPCD items. These items of the PFS appear to be most sensitive to age. Older caregivers may appraise themselves more honestly or may face more stressors than younger caregivers, especially as grandparents or other nontraditional caregivers.  In addition, older children may pose unique challenges compared to younger children.  Family support programs should attend to these differences for planning service delivery and the impact on outcome evaluation results.