Lexical morphology and lexical access.

Research on morphology in word recognition has been plagued by conflicting results (McQueen & Cutler, 1998, give a recent review). Some findings suggest that words are accessed as full forms, while others suggest that words are accessed in terms of their component morphemes. The answer may lie in the properties of the affixes themselves: Kiparsky's (1982) Lexical Phonology and Morphology assigns affixes in English to different "levels" of attachment, based on their productivity, order of attachment, and phonological interaction with roots. We present data suggesting that productive, phonologically neutral, semantically transparent "Level 2" suffixes are "decomposed" for analysis in some cases, but that words with idiosyncratic, structure-changing, semantically opaque "Level 1" suffixes are not.     

Computer aided self‐monitoring to increase academic production and reduce self‐injurious behavior in a child with autism

Self‐monitoring to increase the on‐task behavior of students with learning disabilities has been the focus of numerous studies in the literature. This study examined the effectiveness of computer aided self‐monitoring of academic task completion to reduce self‐injurious behavior in a 13‐year‐old male student with autism. Using an ABAB design, data were collected over 22 sessions in a resource‐reading classroom. Visual and statistical analyses indicated that when self‐monitoring of activity completion was implemented, rates of completion increased and maladaptive behaviors such as self‐injurious behavior and tantruming decreased. Discussion follows for implications for self‐monitoring with students with autism. Copyright © 2009 John Wiley & Sons, Ltd.     

A Systematic Review of Student-Mediated Math Interventions for Students with Emotional or Behavior Disorders

Journal of Behavioral Education - Students identified with emotional or behavioral disorders (EBD) display deficits across academic content areas, most notably in mathematics. We reviewed research...     

Bottom-Up Analysis of Single-Case Research Designs

This paper defines and promotes the qualities of a ??bottom-up?? approach to single-case research (SCR) data analysis. Although ??top-down?? models, for example, multi-level or hierarchical linear models, are gaining momentum and have much to offer, interventionists should be cautious about analyses that are not easily understood, are not governed by a ??wide lens?? visual analysis, do not yield intuitive results, and remove the analysis process from the interventionist, who alone has intimate understanding of the design logic and resulting data patterns. ??Bottom-up?? analysis possesses benefits which fit well with SCR, including applicability to designs with few data points and few phases, customization of analyses based on design and data idiosyncrasies, conformation with visual analysis, and directly meaningful effect sizes. Examples are provided to illustrate these benefits of bottom-up analyses.     

Combining nonoverlap and trend for single-case research: Tau-U

A new index for analysis of single-case research data was proposed, Tau-U, which combines nonoverlap between phases with trend from within the intervention phase. In addition, it provides the option of controlling undesirable Phase A trend. The derivation of Tau-U from Kendall's Rank Correlation and the Mann-Whitney U test between groups is demonstrated. The equivalence of trend and nonoverlap is also shown, with supportive citations from field leaders. Tau-U calculations are demonstrated for simple AB and ABA designs. Tau-U is then field tested on a sample of 382 published data series. Controlling undesirable Phase A trend caused only a modest change from nonoverlap. The inclusion of Phase B trend yielded more modest results than simple nonoverlap. The Tau-U score distribution did not show the artificial ceiling shown by all other nonoverlap techniques. It performed reasonably well with autocorrelated data. Tau-U shows promise for single-case applications, but further study is desirable.     

Counterintuitive cross-linguistic differences: More morphological computation in English than in Finnish

This study investigates the role of derivational morphology in lexical processing in two typologically quite different languages: Finnish and English. While Finnish is a language with an extremely rich morphology, English morphology is relatively poor. Consequently, the role of morphology in storing and processing words would be expected to be greater in Finnish than in English. With a series of visual lexical decision experiments in both languages, we find that the opposite is the case for derivational morphology: for English, parsing of morphological constituents is often required, whereas for Finnish, full-form storage and access seems to be the rule. We try to explain this counterintuitive finding by making an appeal to the lexical-statistical properties of both languages.     

An Improved Effect Size for Single-Case Research: Nonoverlap of All Pairs

Nonoverlap of All Pairs (NAP), an index of data overlap between phases in single-case research, is demonstrated and field tested with 200 published AB contrasts. NAP is a novel application of an established effect size known in various forms as Area Under the Curve (AUC), the Common Language Effect Size (CL), the Probability of Superiority (PS), the Dominance Statistic (DS), Mann-Whitney’s U, and Sommers D, among others. NAP was compared with 3 other non-overlap-based indices: PND (percent of nonoverlapping data), PEM (percent of data points exceeding the median), and PAND (percent of all nonoverlapping data), as well as Pearson’s R². Five questions were addressed about NAP: (a) typical NAP values, (b) its ability to discriminate among typical single-case research results, (c) its power and precision (confidence interval width), (d) its correlation with the established effect size index, R², and (e) its relationship with visual judgments. Results were positive, the new index equaling or outperforming the other overlap indices on most criteria.