Connecting to the Good Life through outdoor adventure leadership experiences designed for Indigenous youth

Indigenous voices are largely silent in the outdoor education and adventure therapy literature. The purpose of this research collaboration was to understand how a 10-day outdoor adventure leadership experience (OALE) may promote resilience and well-being for Indigenous youth through their participation in the program. The process was examined through a community-based participatory research project that sought insight from the perspectives of one First Nations community in Canada. The OALE was implemented with six different groups for a total of 43 youth participants (ages 11.9–18.7 years) from Wikwemikong Unceded Indian Reserve in northeastern Ontario. Field data were collected from multiple sources including participant interviews, journals, focus groups, and talking circles. Using a critical ethnographic lens, we analyzed the data inductively to understand how the OALE promoted resilience and well-being. We listened to Indigenous voices, adhered to principles of Indigenous coding for thematic content and respected Indigenous ways of knowing for interpreting results. The process of connecting to the Good Life (Anishinaabe Bimaadziwin) or waking up (nsidwaaswok) to the Good Life emerged as the dominant theme. Connecting to the Good Life may offer a simple yet compelling way to understand the net impact of the OALE.     

Bridge over troubled waters? Connecting research and pedagogy in composition and business/technical communication

Although different writing courses exist because of the different forms and purposes of writing they teach, some continuity between composition and business/technical communication would allow students to move more readily from one course to the next, while clarifying for them that writing is primarily a system of options based on analyses of situations, readers, obstacles, and goals. In this article, I call for that kind of continuity not only in pedagogy but also in research. Explaining the value of connections in pedagogy and research between composition and professional communication studies, I conclude with various questions and avenues for further research.     

Confirming the Factor Structure of the Cognitive Test Anxiety Scale: Comparing the Utility of Three Solutions

This study validated the factor structure of a popular assessment of learner's cognitive test anxiety. Following recent findings in a study with Argentinean students' use of the Spanish version of the Cognitive Test Anxiety Scale (CTAS), this study tested the factor structure using data from 742 students who completed the original English version in the United States. Comparison of three confirmatory factor analyses confirmed the prior finding that reverse-coded items included in the original CTAS produced a second factor that did not add to the model fit for the CTAS. In addition, the results identified that a reduced length (17-item) version of the CTAS that removed all reverse-coded items was preferable to the two-factor solution for the full scale. Implications for assessment of test anxiety as well as the use of reverse-coded items in self-report survey research are discussed.     

COMMUNITY COLLEGE RETENTION: DIFFERENTIATING AMONG STOPOUTS,DROPOUTS, AND OPTOUTS

One fact often overlooked in discussing the retention issue is that not everyone who fails to return to school is a dropout. Some persons return after an absence of a semester or more; they can be called stopouts. Some intended to take only a few classes and stop attending when they accomplish their educational goals; they can be called optouts. The only persons who should be labeled dropouts are those who failed to accomplish their educational goal and have no definite plans to accomplish it later.

During the 1990‐91 school year, Del Mar College (DMC) and the Texas A&M Adult and Extension Education program developed and executed a plan to identify, count, and interview nonreturnees at DMC. The purposes of the study were toprovide DMC with baseline data on its nonreturnees and to test the feasibility of gathering interview information by telephone.

Stopouts far outnumbered dropouts and optouts among respondents, with 303 of 399 nonreturnees identifying themselves as such.

In this article, we define relevant terms, outline the data‐gathering process and describe the findings of the present study, and present recommendations regarding data gathering and the focus of retention efforts.     

Estimation of MIMIC Model Parameters with Multilevel Data

The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible and effective tool for relating observed and latent variables. The data were simulated in a hierarchical framework (e.g., individuals nested in schools) so that a multilevel modeling approach would be appropriate. Analyses were conducted accounting for and not accounting for the nested data to determine the impact of ignoring such multilevel data structures in full structural equation models. Results highlight the differences in modeling results when the analytic strategy is congruent with the data structure and what occurs when this congruency is absent. Type I error rates and power for the standard and multilevel methods were similar for within-cluster variables and for the multilevel model with between-cluster variables. However, Type I error rates were inflated for the standard approach when modeling between-cluster variables.     

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.     

Decision Science and Applied Neuroscience: Emerging Possibilities

Recent neuroscientific research describes how the brain and extended nervous system make decisions. This article depicts current theory as it relates to decision‐making strengths, shortcuts, simplifications, biases, and serious data restricting habits of the brain. This information demonstrates that the brain, both individually and collectively, can be a deceptive guide for decision making in emergent situations when information is based on preexisting, subconscious frames of reference. Training is proposed to strengthen conscious information‐processing capacity.     

Stability of switching linear systems with switching signals driven by stochastic processes

This paper presents conditions to assure the exponential stability in probability for autonomous switching linear systems. The switching signal acting on the autonomous system produces intervals that follow independent, identically distributed stochastic processes—the stability then follows by verifying simple-to-check linear matrix inequalities.     

The current state of human performance technology: A citation network analysis of Performance Improvement Quarterly, 1988–2010

This study conducted a citation network analysis (CNA) of human performance technology (HPT) to examine its current state of the field. Previous reviews of the field have used traditional research methods, such as content analysis, survey, Delphi, and citation analysis. The distinctive features of CNA come from using a social network analysis approach to see relational and network patterns in the citation data. This CNA found that the Performance Improvement Quarterly network showed more widely but loosely connected and less centralized structural attributes than the human resource development network; the 10 most influential articles in the field were identified; frequently co‐cited articles were recognized as having a strong relationship by subsequent researchers; and five cohesive subgroups showed key topics in the field (performance, instructional design, performance support, organization/workplace, and transfer of training). Implications from the results of the CNA were elaborated on for future research in HPT.