Janet K. Holt

Growth of Finiteness in the Third Year of Life: Replication and Predictive Validity

PURPOSE The authors of this study investigated the validity of tense and agreement productivity (TAP) scoring in diverse sentence frames obtained during conversational language sampling as an alternative measure of finiteness for use with young children. METHOD Longitudinal language samples were used to model TAP growth from 21 to 30 months of age for 37 typically developing toddlers. Empirical Bayes (EB) linear and quadratic growth coefficients and child sex were then used to predict elicited grammar composite scores on the Test of Early Grammatical Impairment (TEGI; Rice & Wexler, 2001) at 36 months. RESULTS A random-effects quadratic model with no intercept best characterized TAP growth, replicating the findings of Rispoli, Hadley, and Holt (2009). The combined regression model was significant, with the 3 variables accounting for 55.5% of the variance in the TEGI composite scores. CONCLUSION These findings establish TAP growth as a valid metric of finiteness in the 3rd year of life. Developmental and theoretical implications are discussed.

Literacy practices among different ethnic groups: The role of socioeconomic and cultural factors

Abstract This study investigated socioeconomic and cultural factors that interplay with racial differences in adult literacy. African‐American adults were shown to have statistically significantly higher book and periodical reading practices than European‐Americans when income was controlled, demonstrating the important relationship between socioeconomic factors and African‐Americans’ literacy. African‐Americans and other racial minorities obtained more information from television, magazines, and books than European‐Americans, but these activities were not sufficient to overcome the gap in literacy skills between minority and majority groups. Results are discussed from Ogbus (1990) cultural identity perspective.

Input Subject Diversity Enhances Early Grammatical Growth: Evidence from a Parent-Implemented Intervention

ABSTRACT Purpose: The current study used an intervention design to test the hypothesis that parent input sentences with diverse lexical noun phrase (NP) subjects would accelerate growth in children’s sentence diversity. Method: Child growth in third person sentence diversity was modeled from 21–30 months (n = 38) in conversational language samples obtained at 21, 24, 27, and 30 months. Treatment parents (n = 19) received instruction on strategies designed to increase lexical NP subjects (e.g., The baby is sleeping.). Instruction consisted of one group education session and two individual coaching sessions which took place when children were approximately 22–23 months of age. Results: Treatment substantially increased parents’ lexical NP subject tokens and types (ηp2 ≥ .45) compared to controls. Children’s number of different words was a significant predictor of sentence diversity in the analyses of group treatment effects and individual input effects. Treatment condition was not a significant predictor of treatment effects on children’s sentence diversity, but parents’ lexical NP subject types was a significant predictor of children’s sentence diversity growth, even after controlling for children’s number of different words over time. Conclusions: These findings establish a link between subject diversity in parent input and children’s early grammatical growth, and the feasibility of using relatively simple strategies to alter this specific grammatical property of parent language input.

Input Subject Diversity Accelerates the Growth of Tense and Agreement: Indirect Benefits from a Parent-Implemented Intervention.

Purpose This follow-up study examined whether a parent intervention that increased the diversity of lexical noun phrase subjects in parent input and accelerated childrens sentence diversity (Hadley et al., 2017) had indirect benefits on tense/agreement (T/A) morphemes in parent input and childrens spontaneous speech. Method Differences in input variables related to T/A marking were compared for parents who received toy talk instruction and a quasi-control group: input informativeness and full is declaratives. Language growth on tense agreement productivity (TAP) was modeled for 38 children from language samples obtained at 21, 24, 27, and 30 months. Parent input properties following instruction and childrens growth in lexical diversity and sentence diversity were examined as predictors of TAP growth. Results Instruction increased parent use of full is declaratives (ηp2 ≥ .25) but not input informativeness. Childrens sentence diversity was also a significant time-varying predictor of TAP growth. Two input variables, lexical noun phrase subject diversity and full is declaratives, were also significant predictors, even after controlling for childrens sentence diversity. Conclusions These findings establish a link between childrens sentence diversity and the development of T/A morphemes and provide evidence about characteristics of input that facilitate growth in this grammatical system.

Comparison of Linear Weighting Schemes for Perfect Match and Mismatch Gene Expression Levels from Microarray Data

AbstractBackground: Data analytic approaches to Affymetrix® microarray data include: (a) a covariate model, in which the observed signal is some estimated linear function of perfect match (PM) and mismatch (MM) signals; (b) a difference model [PM-MM]; and (c) a PM-only model, in which MM data is not utilized. Methods: By decomposing the correlations among the variables in the statistical model and making certain assumptions, we theoretically derive the statistical model that reflects the actual gene expression level under a variety of conditions expected in microarray data. Results and conclusion: When modeling non-systematic variation, the covariate model provides maximum flexibility and often reflects the actual gene expression levels better than the difference model. However, the PM-only model demonstrates superior power in an overwhelming majority of realistic situations, which provides theoretical support for the current trend to employ PM-only models in microarray data analyzes.

Blended Learning Training for Mentors of STEM Team Competitions

This paper describes the findings of a research study of a blended-learning approach to train mentors of teams in the Botball® Educational Robotics Program. Botball is an international team-based robotics competition for secondary students designed to build skills in computer programming, robotics, teamwork, and problem solving. For this study, we recruited new teams comprising 8-10 middle school students per team and a mentor. Teams were randomly assigned to one of three treatment groups or a control group. Mentors of teams in the experimental groups received training in one of three types of mentor practices: best practices, mentoring for self-efficacy, or a combination of best practices and self-efficacy. The training format consisted of web-based self-paced tutorials, a face-to-face workshop, and webinars. Dependent variables were student post-test scores on three assessments: Efficacy for Science-Related Jobs, STEM Achievement-Related Choices, and STEM Self-Efficacy. A priori statistical analyses showed no difference between the groups; however, post hoc analyses showed that the use of self-efficacy techniques was positively related to the three dependent measures. Post-competition surveys of mentor practices indicated that students in the treatment groups did not appear to receive distinctly different treatments, revealing some of the potential challenges of the blended learning approach for professional development of teachermentors.