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- Home
- FCRR
- Florida State University's PIRT
Program
- PIRT Faculty
- Ongoing Research of Faculty
-Christopher J. Lonigan, Ph.D.
-Christopher Schatschneider, Ph.D.
-Joseph K. Torgesen, Ph.D.
-Richard K. Wagner, Ph.D.
-Laura B. Hassler, Ph.D.
-Alysia D. Roehrig, Ph.D.
-Stephanie Al Otaiba, Ph.D.
-Richard L. Tate, Ph.D.
-Akihito Kamata, Ph.D.
-Carol M. Connor, Ph.D.
-Roxanne F. Hudson, Ph.D.
- PIRT Program of Studies
- PIRT Funding and Benefits
- How to Apply
- Resources
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A Florida State University Center
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Ongoing Research, Archival Data, and Their Uses in Training.
Akihito Kamata, Ph.D.
Differential Item Functioning Analyses for Students with Test Accommodations on NAEP test items.
(PI: Kamata; funded by Institute of Education Sciences; 2003-2005). This project investigates differential item functioning (DIF) between test
accommodated sample and non-accommodated sample on National Assessment of Educational Progress mathematics and science assessment data. This
proposed project attempts to identify DIF items and estimates the variation of DIF between schools. Furthermore, this study attempts to identify
school characteristic variables that explain such variation of DIF between schools. This "random-effect DIF" analysis is conducted in the framework
of the hierarchical Rasch model (Kamata, 2001). Two purposes of this proposed project are to stimulate new thinking about the sources of variation
in DIF and to produce suggestive results that may be useful for school administrators and policy makers.
Relationships between Multilevel, Structural Equation, and Item Response Models. (Fellowship to Kamata; Statistical and Applied Mathematical
Sciences Institute, Research Triangle Park, North Carolina; 2004-2005). This project is conducted by a working group of the Latent Variable in
Social Science program at SAMSI. The project investigates relationships between three seemingly different latent variable models (multilevel model,
SEM, and IRT). Links between SEM and multilevel model (e.g., Bauer, 2003) and between multilevel model and IRT model (e.g., Kamata, 2001) have
been explored. However, links between all three models has not yet been clarified. This project will show ways to model and estimate parameters in
psychometric models by simultaneously taking account of the strengths of all three models.
Carol M. Connor, Ph.D.
Child-Instruction Interactions in Early Reading: Examining Causal Effects of Individualized Instruction
(PI: Connor, funded by US Department of Education, Institute for Education Sciences; 2004-2007). Using a
cluster-randomized design, this study is examining the causal effects of individualizing first grade reading
instruction based on students entering vocabulary and reading skills. Algorithms for recommended amounts and
types of instruction are based on research revealing child-by-instruction interactions using HLM. The proposed
implementation of individualized instruction is supported in two ways, first through intensive teacher training
and, second, through computer-based networked Assessment-to-Instruction and Simulated Classroom Interactive Interface
(SCII) technology and teacher education software, Knowledge Networks on the Web (KNOW, http://know.soe.umich.edu),
which are currently in development. Classroom implementation will begin Fall, 2005.
NICHD Study of Early Child Care and Youth Development (PI: Early Child Care Research Network, ECCRN).
This is a longitudinal data base, which follows over 1000 typically developing children from birth through grade 5.
Data include child language and academic outcomes, mother-child interaction, home and classroom observations, and
information derived from parent and teacher questionnaires, which capture social and developmental variables.
Roxanne F. Hudson, Ph.D.
Wordwork to Optimize Reading Development: Decoding Instruction Designed to Increase Reading Fluency.
(PI: Hudson; funded by Office of Special Education Programs, U.S. Department of Education; 2004 to 2007).
This project is designed to increase knowledge and understanding of the basic component processes of reading
fluency and how best to teach reading fluency to second-grade children with reading disabilities. This will
be accomplished through diagnostic, RCT intervention, and follow-up studies that examine the component processes
of decoding fluency and the most effective ways to teach them.
Understanding Teacher Knowledge of Reading Fluency and its Relationship to Student Reading Achievement
(PI: Hudson; Pending funding from FSU Council on Research and Creativity). This study assesses the knowledge of
reading fluency among first- through third-grade teachers in Reading First schools using an online survey.
These data are then compared to the oral reading fluency of their students to determine what relationship,
if any, exists between teacher knowledge and student performance.
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