Reporting Quality of Single-Case Research Published in Learning Disabilities Journals

Single-case research designs are useful for learning disability researchers interested in refining novel interventions (e.g., new mathematics intervention) before scaling, small n populations (e.g., students with comorbid learning and emotional and behavioral disabilities), and testing unique conditions (e.g., remote locales). Much attention has been devoted to developing research quality standards, which is essential for replication and implementation fidelity; however, little is known about the reporting quality of single-case design studies. To gain insight into the quality of learning disability single-case research design study reporting, we analyzed 72 articles published between 2010 and 2021 in six learning disability-focused journals using the Single-Case Reporting Guideline In BEhavioral interventions (SCRIBE) standards. Findings suggest that reporting quality has remained stagnant, and none of the studies reviewed met all of the reporting standards. Alignment with standards ranged from 33% to 77%. Implications for researchers and practitioners are discussed.     

On recursive averaging processes and Hilbert space extensions of the contraction mapping principle

If T maps a convex domain D_T into itself, and if {ω_n} is a real sequence with range in (0, 1] then the recursive averaging process,

$\text{X}{}_{\mathrm{n+1}}\text{=(1?omega;}{}_{\mathrm{n}}\text{)}\text{X}{}_{\mathrm{n}}\text{+ω}{}_{\mathrm{n}}\text{+ω}{}_{\mathrm{n}}\text{T}\text{x}{}_{\mathrm{n}}\text{,}\text{x}{}_0\text{=ξ?D}{}_{\mathrm{T}}$

generates a sequence {x?_n}; with range in D_T. Under suitable conditions on D_T, T and {ω_n} the sequence {x?_n} will converge in some sense to a fixed point of T. We prove that if D_T is a closed convex subset of a complex Hilbert space H, if Tω = (1 ? ω) I + ωT is a strict contraction for some ω ? (0, 1], and if {ω_n} satisfies the conditions,

$\text{ω}{}_{\mathrm{n}}\text{→ 0}$

and

$\text{∑}\text{n=0}\text{∞}\text{ω}{}_{\mathrm{n}}\text{=∞}$

then, for arbitrary ξ ? D_T, {x?_n} converges strongly to (the unique) fixed point of T. We also prove that if D_T and {ω_n} satisfy the foregoing conditions, if T has at least one fixed point, and if Tω is non-expansive for some ω ? (0, 1], then for all ξ ? D_T, {x?_n} converges at least weakly to some fixed point of T. Finally, we apply these results to linear equations involving bounded normal operators and obtain an extension of the classical Neumann operator series.     

Case Study: Improving Customer Satisfaction,Quality, and Performance Using Performance‐Centered Solutions at ADDS Communications

A team of performance consultants looks closely at the operations of a cable services provider to identify existing performance gaps, barriers to performance, declining customer satisfaction ratings, and increased call volumes. The team used performance‐centered design (PCD) solutions in order to overcome technical support challenges through increased first‐call resolution. This article summarizes the root cause analysis, process, and the application of PCD solutions to close performance gaps, along with lessons learned from this case study.     

A Comparative Demographic Study of Student Teachers from the North and South of Ireland

90.4% of the 1977 entrants to concurrent general Teacher Education courses in Ireland North and South responded to a demographic questionnaire. The results are analysed and the two regions contrasted with respect to personal, professional and geographical variables. These indicate that some interesting differences exist but that there are many overall similarities.