The Application of Latent Curve Analysis to Testing Developmental Theories in Intervention Research

Abstract

The effectiveness of a prevention or intervention program has traditionally been assessed using time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time-specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. It is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do the LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed-effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful but cost-efficient intervention programs for the future.

Impact of the Good Behavior Game, a universal classroom-based behavior intervention, on young adult service use for problems with emotions, behavior, or drugs or alcohol

“…That the GBG impact was for children at highest risk of poor outcomes, males with high levels of aggressive, disruptive behavior in the fall of first grade, is in keeping with previous reported work from our trials as well as other trials of universal preventive interventions in which intervention impact was found for the students who entered the intervention period at the highest risk (Brown and Liao, 1999;Curran and Muthèn, 1999;Muthèn and Curran, 1997;Muthèn et al, 2002;Stoolmiller et al, 2000). GBG aims at reducing aggressive, disruptive behavior in the first-grade classroom through socializing first graders to the role of student.…”

Overweight in Infancy: Which Pre- and Perinatal Factors Determine Overweight Persistence or Reduction A Birth Cohort Followed for 11 Years

“…For the longitudinal overweight patterns, a minimum of four observations is advised to model higher-order polynomials [17]. We were able to analyze overweight patterns in 3,550 children (90% of 3,963).…”

Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves

“…When data are missing either completely at random (MCAR) or at random (MAR) and have a multivariate normal distribution, full information maximum likelihood produces parameter estimates, standard errors and test statistics that are consistent and efficient (Arbuckle 1996) 1 . Further discussions and applications for this model may be found, for example, in Bollen and Curran (2006), Browne andDu Toit (1991), Curran andMuthén (1999), Duncan et al (2006), McArdle (1986McArdle ( , 1988McArdle ( , 1989McArdle ( , 1991), McArdle and Epstein (1987), Meredith and Tisak (1990), Muthén (1991Muthén ( , 1996, Muthén and Curran (1997), Reinecke (2005), Reinecke and Seddig (in press) and Willett and Sayer (1994). In the next section we turn to the description of our data to test the development of the different elements of GFE.…”