Aim-To determine whether NTrainer patterned orocutaneous therapy affects preterm infants' non-nutritive suck and/or oral feeding success.Subjects-Thirty-one preterm infants (mean gestational age 29.3 weeks) who demonstrated minimal non-nutritive suck output and delayed transition to oral feeds at 34 weeks post-menstrual age.Intervention-NTrainer treatment was provided to 21 infants. The NTrainer promotes nonnutritive suck output by providing patterned orocutaneous stimulation through a silicone pacifier that mimics the temporal organization of suck.Method-Infants' non-nutritive suck pressure signals were digitized in the NICU before and after NTrainer therapy and compared to matched controls. Non-nutritive suck motor pattern stability was calculated based on infants' time-and amplitude-normalized digital suck pressure signals, producing a single value termed the Non-Nutritive Suck Spatiotemporal Index. Percent oral feeding was the other outcome of interest, and revealed the NTrainer's ability to advance the infant from gavage to oral feeding.Results-Multilevel regression analyses revealed that treated infants manifest a disproportionate increase in suck pattern stability and percent oral feeding, beyond that attributed to maturational effects alone. Conclusion-TheNTrainer patterned orocutaneous therapy effectively accelerates non-nutritive suck development and oral feeding success in preterm infants who are at risk for oromotor dysfunction.

Mediation is a causal process that evolves over time. Thus, a study of mediation requires data collected throughout the process. However, most applications of mediation analysis use cross-sectional rather than longitudinal data. Another implicit assumption commonly made in longitudinal designs for mediation analysis is that the same mediation process universally applies to all members of the population under investigation. This assumption ignores the important issue of ergodicity before aggregating the data across subjects. We first argue that there exists a discrepancy between the concept of mediation and the research designs that are typically used to investigate it. Second, based on the concept of ergodicity, we argue that a given mediation process probably is not equally valid for all individuals in a population. Therefore, the purpose of this article is to propose a two-faceted solution. The first facet of the solution is that we advocate a single-subject time-series design that aligns data collection with researchers’ conceptual understanding of mediation. The second facet is to introduce a flexible statistical method—the state space model—as an ideal technique to analyze single-subject time series data in mediation studies. We provide an overview of the state space method and illustrative applications using both simulated and real time series data. Finally, we discuss additional issues related to research design and modeling.

Coefficient alpha (α) has been described as a lower bound for test reliability. However, previous research indicates that when certain assumptions are violated, α can either overestimate or underestimate reliability. Raykov (1997a) has shown how structural equation modeling (SEM) can be used to estimate reliability. This study has introduced method factors into the model in Raykov (1997a) to avoid a potential limitation of the SEM approach. Monte Carlo simulation shows that when certain assumptions are violated, either method (α or SEM) can show a substantial bias, though in the most extreme circumstances the bias of α estimates are larger than the bias of SEM-based reliability estimates. Circumstances that favor one method or the other are described and explored.

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Large-scale surveys are common in social and behavioral science research. Missing data often occur at item levels due to nonresponses or planned missing data designs. In practice, the item scores are typically aggregated into scale scores (i.e., sum or mean scores) for further analyses. Although several strategies to handle item-level missing data have been proposed, most of them are not easy to implement, especially for applied researchers. Using Monte Carlo simulations, we examined a practical hybrid approach to deal with item-level missing data in Likert scale items with a varying number of categories (i.e., four, five, and seven) and missing data mechanisms. Specifically, the examined approach first uses proration to calculate the scale scores for a participant if a certain proportion of item scores is available (a cutoff criterion of proration) and then use full information maximum likelihood to deal with missing data at the scale level when scale scores cannot be computed due to the selected proration cutoff criterion. Our simulation results showed that the hybrid approach was generally acceptable when the missing data were randomly spread over the items, even when they had different thresholds/means and loadings, with caution to be taken when the missingness is determined by one of the scale items. Based on the results, we recommend using the cutoff of 30% or 40% for proration when the sample size is small and the cutoff of 40% or 50% when the sample size is moderate or large.

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Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.