Search

Efforts to improve the public welfare and child welfare system sparked an unprecedented amount of federal legislation in the 1990s, including the Adoption and Safe Families Act of 1997 (ASFA), the Multiethnic Placement Act of 1994 and Interethnic Adoption Provisions of 1996 (MEPA-IEP), and welfare reform. Such reforms allow an unprecedented degree of flexibility, but little is known about their implementation. Researchers administered the Local Agency Survey to the first national probability sample of public child welfare agencies from 1999 to 2000. Findings indicate that ASFA has had the most effect on child welfare service delivery. Welfare reform has had less effect, and MEPA-IEP seems to have had little effect at all.

Scale score measures are ubiquitous in the psychological literature and can be used as both dependent and independent variables in data analysis. Poor reliability of scale score measures leads to inflated standard errors and/or biased estimates, particularly in multivariate analysis. Reliability estimation is usually an integral step to assess data quality in the analysis of scale score data. Cronbach's [alpha] is a widely used indicator of reliability but, due to its rather strong assumptions, can be a poor estimator (L. J. Cronbach, 1951). For longitudinal data, an alternative approach is the simplex method; however, it too requires assumptions that may not hold in practice. One effective approach is an alternative estimator of reliability that relaxes the assumptions of both Cronbach's [alpha] and the simplex estimator and thus generalizes both estimators. Using data from a large-scale panel survey, the benefits of the statistical properties of this estimator are investigated, and its use is illustrated and compared with the more traditional estimators of reliability. (Contains 4 footnotes, 4 tables and 4 figures.)

School-based relationship education programs offer an opportunity to identify youth who are experiencing teen dating violence (TDV), support their safety, and connect them with individualized services or referrals. However, no research has tested the feasibility or accuracy of approaches to create opportunities for TDV disclosure in the context of school-based programs. The current study presents the results of a field test comparing three tools used to provide opportunities for TDV disclosure (two questionnaire-style tools and one universal education discussion guide). High school students from two federally funded healthy marriage and relationship education (HMRE) program sites (N = 648) were offered the three tools in random order over the course of the HMRE program, which lasted between 3 weeks and 3 months and took place during the school day. Onsite qualitative interviews with HMRE program staff and their local domestic violence program partners assessed how service providers saw the tools and the process of implementing them. Latent class models examined the accuracy of the tools in identifying TDV. Sensitivities of the tools were low and specificities were high; the questionnaire-style tools tended to have higher sensitivities and fewer classification errors than the universal education tool. Several three-item combinations from across the tools performed better than any intact tool, suggesting that shorter assessments may be effective, provided they include items on sexual coercion and physical violence. Qualitative findings suggested that implementation of TDV assessment and universal education in school settings is a viable strategy, provided programs are able to gain support from school staff, adapt to tight time constraints, and plan procedures for protecting student privacy and confidentiality. [ABSTRACT FROM AUTHOR]

Over the last two decades, in‐person interviewing costs continued to increase while the data quality advantages traditionally identified with this data collection mode have faded. Consequently, some longitudinal surveys have begun transitioning from in‐person to web data collection despite risks to data quality and longitudinal comparability. This paper addresses the major issues involved in the transition process and proposes a multi‐sample, multi‐phase responsive design that attempts to minimize the data quality risks while preserving the considerable cost savings promised by the transition. The paper describes the design as it was applied to the National Longitudinal Study of Adolescent to Adult Health (Add Health)—a nationally representative panel survey of around 20,000 adolescents selected from grades 7 to 12 (typically 13 to 18 years of age) in the 1994–95 school year. Also described are key results from several experiments embedded within the design and the analysis of mode effects. Also presented are some lessons learned and recommendations for other in‐person panel surveys that may be contemplating a similar transition to web or mixed‐mode data collection. [ABSTRACT FROM AUTHOR]

The final two articles fit more closely within the second category identified above - exploring the use of big data analysis techniques, including spatial analysis and machine learning approaches, to enhance the analysis of survey-collected data. Big data and surveys have great potential to complement one another ([1]; [2]) to allow scientists to better understand people and the world in which we live - for example, by combining the low cost per data point of big data (offsetting the rising costs of survey-based data collection) with the ability to collect very specific information addressing research questions using survey data. This includes research describing how nonsurvey data sets created through technology (e.g., physical sensor measurements, smartphone app data) could be combined with survey data to provide additional context and broader insights than either source of data individually. [Extracted from the article]