Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.     

Automated Integrative Complexity: Current Challenges and Future Directions

Automating integrative complexity is fraught with many challenges. To address these challenges, we discuss the tension between a specificity approach and a more flexible multiple‐pass approach, the multifaceted nature of the complexity construct, the gold standard for complexity measurement, the difficulty of human scoring and its consequences for automation, and some ways forward for creating the best complexity measurements. In so doing, we present new data demonstrating (1) initial evidence for the validity of a new automated system for measuring two different forms of complexity (elaborative and dialectical), (2) the danger of constructing measurements in a purely ad hoc fashion that ignores prospective testing, (3) human‐to‐computer correspondence is in part a function of human‐to‐human correspondence, (4) human‐to‐computer correspondence increases systematically as one uses tests with larger units of analysis, and (5) the lack of correspondence of different systems (both human and automated) may occur in part because they were designed for different units of analysis.     

Weak ventral striatal responses to monetary outcomes predict an unwillingness to resist cigarette smoking

As a group, cigarette smokers exhibit blunted subjective, behavioral, and neurobiological responses to nondrug incentives and rewards, relative to nonsmokers. Findings from recent studies suggest, however, that there are large individual differences in the devaluation of nondrug rewards among smokers. Moreover, this variability appears to have significant clinical implications, since reduced sensitivity to nondrug rewards is associated with poorer smoking cessation outcomes. Currently, little is known about the neurobiological mechanisms that underlie these individual differences in the responsiveness to nondrug rewards. Here, we tested the hypothesis that individual variability in reward devaluation among smokers is linked to the functioning of the striatum. Specifically, functional magnetic resonance imaging was used to examine variability in the neural response to monetary outcomes in nicotine-deprived smokers anticipating an opportunity to smoke—circumstances found to heighten the devaluation of nondrug rewards by smokers in prior work. We also investigated whether individual differences in reward-related brain activity in those expecting to have access to cigarettes were associated with the degree to which the same individuals subsequently were willing to resist smoking in order to earn additional money. Our key finding was that deprived smokers who exhibited the weakest response to rewards (i.e., monetary gains) in the ventral striatum were least willing to refrain from smoking for monetary reinforcement. These results provide evidence that outcome-related signals in the ventral striatum serve as a marker for clinically meaningful individual differences in reward-motivated behavior among nicotine-deprived smokers.     

An Initial Investigation of the Factor-Analytic Structure of the Impact of Event Scale-Revised With a Volunteer Firefighter Sample

We provide an initial evaluation of the factor structure of the Impact of Event Scale-Revised (IES-R) when used with a volunteer firefighter and a similar community participant sample. A volunteer firefighter sample (n = 65) and a sample of similar community respondents (n = 103) completed a questionnaire study, including responses to the IES-R. The IES-R data from both groups were entered into a three-factor principal components analysis with direct oblimin rotation. We found further support for the validity of the IES-R when used with a community sample. However, our data suggested that when using the IES-R with a community sample, the choice between a two- and a three-factor model may depend on the composition of the participants. For volunteer firefighters, the factor-analytic structure of the IES-R appeared to be similar to that of the community sample, with more scatter in terms of item loadings. To our knowledge, there is no previous research considering the use of the IES-R with a strictly volunteer firefighter sample. In addition, despite adequate research on the factor-analytic structure of the original IES, little research has considered the factor-analytic structure of the more recent IES-R, even with community samples.     

Meta-Analysis of the Effects of Computerized Cognitive Training on Executive Functions: a Cross-Disciplinary Taxonomy for Classifying Outcome Cognitive Factors

The growing prevalence of neurodegenerative disorders associated with aging and cognitive decline has generated increasing cross-disciplinary interest in non-pharmacological interventions, such as computerized cognitive training (CCT), which may prevent or slow cognitive decline. However, inconsistent findings across meta-analytic reviews in the field suggest a lack of cross-disciplinary consensus and on-going debate regarding the benefits of CCT. We posit that a contributing factor is the lack of a theoretically-based taxonomy of constructs and representative tasks typically used. An integration of the Cattell-Horn-Carroll (CHC) taxonomy of broad and narrow cognitive factors and the Miyake unity-diversity theory of executive functions (EF) is proposed (CHC-M) as an attempt to clarify this issue through representing and integrating the disciplines contributing to CCT research. The present study assessed the utility of this taxonomy by reanalyzing the Lampit et al. (2014) meta-analysis of CCT in healthy older adults using the CHC-M framework. Results suggest that: 1) substantively different statistical effects are observed when CHC-M is applied to the Lampit et al. meta-analytic review, leading to importantly different interpretations of the data; 2) typically-used classification practices conflate Executive Function (EF) tasks with fluid reasoning (Gf) and retrieval fluency (Gr), and Attention with sensory perception; and 3) there is theoretical and practical advantage in differentiating attention and working-memory tasks into the narrow shifting, inhibition, and updating EF domains. Implications for clinical practice, particularly for our understanding of EF are discussed.     

Finding a Balance: Reconciling the Needs of the Institution,Patient, and Genetic Counselor for Optimal Resource Utilization

The current practice of cancer genetic counseling is undergoing widespread change and scrutiny. While there are clinical resources for genetic counselors (GCs) regarding the delivery of cancer genetic services, there is limited literature regarding effective management of a genetic counseling clinical program. We have developed administrative tools to manage a large team of GCs at a single academic medical center over a period of increasing demand for genetics services, with the initial aim of decreasing wait time for urgent genetic counseling visits. Here, we describe the three main elements of the clinical operations: Balancing patient volume between GCs, scheduling tracks for both routine and urgent appointments, and a team of triaging GCs to ensure appropriate patient referrals. For each of these elements, we describe how they have been modified over time and present data to support the utility of these strategies. The preliminary evidence offered here suggests that these tools allow for an equitable distribution of patient volume between team members, as well as the timely and accurate scheduling of urgent patients. As a result of the experiences presented here, other genetic counseling programs grappling with similar issues should be aware that it is possible to shift clinical operations to serve certain patient populations in a more timely fashion while keeping both providers and GC staff satisfied.     

Ethics and Confidentiality for Psychologists in Academic Health Centers

Psychologists in academic health centers (AHC) face important ethical issues including confidentiality when working with a multidisciplinary team, sharing of information through the electronic health record, obtaining informed consent in a fast-paced healthcare environment, cultural competency in the medical setting, and issues related to supervision and training. The goal of this paper is to describe ethical issues for psychologists in AHCs in the context of case examples, and to consider ethical decision-making tools to enhance clinical care. Considerations for best practices in integrated care settings will be discussed, and the APA Ethical Standards will be referenced throughout.