Featured Articles | The Methodology Center

Featured Articles

November 10, 2014

Donna CoffmanPrevious literature has established that gang membership is associated with higher rates of drug use. In the forthcoming article, “Gang membership and substance use: Guilt as a gendered causal pathway,” in The Journal of Experimental Criminology, Methodology Center Investigator Donna Coffman and two other researchers examine whether anticipated guilt for substance use explains this association. The authors also expand the available set of methods for causal inference when assessing mediation in the presence of moderation and time-varying confounding.  

September 25, 2014
classroom with kids and teacherHead Start is the largest federally funded preschool program for disadvantaged children. Some people have questioned its worth because research has shown relatively small impacts on cognitive and social skills. In a forthcoming article in Child Development, Methodology Center Affiliate Brittany Rhoades Cooper and Methodology Center Scientific Director Stephanie Lanza perform latent class analysis (LCA) on the Head Start Impact Study dataset to identify subgroups of children who have similar home environments and caregivers.

 

The authors then examine cognitive, behavioral, and relationship skills measured two years later to determine whether the effects of Head Start vary across these subgroups. Their approach could have broad application by researchers interested in using latent class analysis to understand differential effects of an intervention by simultaneously considering multiple potential moderators. 

 

Donna Coffman

April 3, 2014

When parents know more about their teenage children’s activities, those children are less likely to engage in risky behavior (e.g., delinquency, substance use initiation). Though this connection is widely acknowledged, it is not clear whether the knowledge is the determining factor in the reduced risk, because many other factors are involved in the parent-child relationship. In a recent article in Prevention Science, the authors use propensity scores to examine the causal nature of this relationship. The research team includes former Penn State graduate student Melissa Lippold, Methodology Center Principal Investigator Donna Coffman, and Edna Peterson Bennett Endowed Chair in Prevention Research Mark Greenberg. 

Constantino LagoaMarch 5, 2014

In control engineering, devices continually monitor the performance of a system and then operate to control that system. Common examples include automobile thermostats and autopilot systems on commercial airliners. These same principles can be used to design behavioral interventions that adapt over time to help patients alter behaviors that affect their health. Potential applications include maintaining an exercise regimen, maintaining a healthy diet, or abstaining from tobacco or illicit drug use.

 

In a forthcoming article in the Journal of Consulting and Clinical Psychology, Methodology Center Investigators Constantino Lagoa, Stephanie Lanza, Susan Murphy, and their colleague Korkut Bekiroglu describe this new approach to building adaptive, intensive interventions. The authors show how data, in this case simulated smoking-cessation data, can be used to inform the design of an adaptive, intensive intervention by applying control-engineering techniques. This intervention, which is designed to provide treatment only when needed, is shown to improve effectiveness while decreasing patient burden. This approach holds great promise for informing clinical decisions and for informing the development of smartphone-based adaptive interventions. 

Linda CollinsDecember 10, 2013

The multiphase optimization strategy (MOST) provides a framework for building effective and efficacious behavioral interventions by following principles from the field of engineering. MOST emphasizes efficiency and resource management to move intervention science forward, and one of the cornerstones of MOST is selecting the most efficient and appropriate design for each experiment. In many instances this means conducting a factorial experiment. (Read more about factorial experimental designs.) In a new article in Translational Behavioral Medicine, Linda Collins and her coauthors explore how to use data from a factorial screening experiment to decide what should be included in the experimental intervention treatment package. This article is aimed at scientists who are considering conducting a factorial screening experiment.

Will quitting smokers lapse?November 15, 2013

In an article in a forthcoming special issue of Nicotine and Tobacco Research, a team of Methodology Center scientists examines the changing relationships between factors that predict relapse for smokers who are quitting. The researchers, led by Research Associate Sara Vasilenko, used time-varying effect models to analyze ecological momentary assessment data and found that predictors of relapse changed over the two weeks post quit. Cigarette cravings significantly predicted relapse throughout the two weeks. However, baseline dependence was significant early in the quit process but was unimportant after one week. Conversely, negative mood became a stronger predictor of relapse as time progressed.

How to Use Propensity Scores for Causal InferenceOctober 21, 2013

Researchers often would like to draw causal inferences from observational data; however, confounders, variables related to both the predictors and the outcome, typically undermine the validity of these inferences. In a recent article in American Journal of Community Psychology, Methodology Center Scientific Director Stephanie Lanza and her collaborators demonstrate propensity scores as a straightforward method for drawing causal inferences from observational data. As an empirical demonstration, the authors estimate the causal effect of Head Start versus parental care on later reading development.

Bethany BrayStephanie LanzaSeptember 24, 2013

Latent class analysis (LCA) has become a popular tool for identifying subgroups within populations. Despite the fact that these are latent subgroups and, therefore, membership in each class is unknown, research questions sometimes make it necessary to assign individuals to classes and then treat class membership as known in later analyses. Current standard practice is to retain from LCA each individual’s posterior probabilities of class membership, and then either assign them to the most likely class or to repeatedly assign them using pseudo-class draws. These practices, however, are known to attenuate estimates in subsequent analyses.

 

In a new article in Structural Equation Modeling, "Eliminating bias in classify-analyze approaches for latent class analysis," by Center Investigators Bethany Bray and Stephanie Lanza and Xianming Tan of McGill University, the authors propose a straightforward solution that includes all variables used in the subsequent analyses as covariates in the LCA. Then, class assignment is carried out using either the most likely class or pseudo-class draws. With adequate measurement quality and sample size, this approach substantially reduces the bias that results from common approaches to class assignment.

Nicole ButeraStephanie LanzaDonna Coffman

August 1, 2013

Donna Coffman and Stephanie Lanza lead the Methodology Center’s research projects on causal inference and latent class analysis (LCA), respectively. Recently, they began integrating modern causal inference techniques with LCA to reveal the impact that nonrandomized risks have on later, complex, behavioral outcomes. In a new article in Prevention Science, “A Framework for Estimating Causal Effects in Latent Class Analysis: Is There a Causal Link Between Early Sex and Subsequent Profiles of Delinquency?” Center researchers Nicole Butera, Stephanie, and Donna examine data on 1,890 adolescents from the National Longitudinal Study of Adolescent Health to determine whether early sexual initiation impacts later profiles of delinquent behavior.

Featured Article: Can We Prevent Children’s Adjustment Problems by Teaching Parenting Skills? March 7, 2013

Emotional problems between ages one and three are a risk factor for developing more serious problems later in life, and researchers sometimes seek to prevent these problems through parenting-skills education. In the article “The Effects of the Family Foundations Prevention Program on Coparenting and Child Adjustment,” to appear in Prevention Science, a team of Penn State researchers including Methodology Center Principal Investigator Donna Coffman examine the pathways through which one such prevention program works.

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