|Title:||Identifying Risk Factors and Predictors of Literacy Skills for Adults Performing at the Lowest Levels of PIAAC in the United States|
|Principal Investigator:||Tighe, Elizabeth||Awardee:||Georgia State University|
|Program:||Postsecondary and Adult Education [Program Details]|
|Award Period:||2 years (09/01/2018 - 08/31/2020)||Award Amount:||$571,864|
Co-Principal Investigator: Petscher, Yaacov
Purpose: The purpose of this project is to explore a nationally representative sample of U.S. adults with low reading skills to identify factors that may predict reading performance. Roughly 36 million (17 percent) of U.S. adults read at or below basic levels. This group has inherent heterogeneity, but there may also be commonalities that could be leveraged to help improve instruction or other interventions. These researchers will explore data from adults who score low in literacy to understand their literacy skills (e.g., vocabulary, comprehension) and possible correlations with other malleable factors (e.g., reading behaviors, program participation), numeracy skills, and background factors (e.g., demographics, education).
Project Activities: Using the U.S. data from an international survey of adult skills, the Program for the International Assessment of Adult Competencies (PIAAC), the researchers will create profiles of adult struggling readers' abilities and possible risk factors to identify malleable factors for future interventions.
Products: Researchers will produce a refined theory of adult literacy for U.S. adult struggling readers, evidence of the validation of the PIAAC for adult struggling readers, and peer-reviewed publications.
Setting: This study is using the nationally representative U.S. sample from the PIAAC dataset.
Sample: The U.S. PIAAC data include nationally representative cohorts (collected in 2012 and 2014) of adults aged 16 to 74 (total N = 8,760). The research team will focus on a subset of adults who score at or below Levels 1 and 2 (the lowest levels) on the PIAAC literacy domain, all low-skilled adults who completed a paper-based reading components subsection, and adults who scored low in the PIAAC numeracy domain (N = 5,660).
Malleable Factors: In this study, the researchers are exploring whether the component reading skills measured in the PIAAC (i.e., vocabulary knowledge, sentence processing, and passage comprehension subsections) are predictive of one another and overall literacy scores. They will also explore relations among adults' performance with reading skills and literacy scores and numeracy scores, demographic factors (e.g., gender, age, race/ethnicity, English language proficiency, disability status, and educational background), and other more malleable factors (e.g., motivation, reading behaviors).
Research Design and Methods: Using secondary data analysis and various psychometric approaches, the researchers will conduct three lines of study. Combined, these studies will verify that both the literacy and component skills portions of the PIAAC are valid for low-skilled adults and will provide information about possible subgroups of adult struggling readers and factors that may predict performance. In Study 1, they will assess the psychometrics of the item-level data from the component skills portion and the overall literacy assessment to see how they function for adult struggling readers. In Study 2, they will use the PIAAC data to create profiles and identify risk factors of adult struggling readers. In Study 3, they will look for predictors of reading component skills and possible mediators and moderators of these skills. A final, and more exploratory subcomponent of the third study, is to explore the relations among literacy, component reading skills, and numeracy skills for low-skilled adults.
Control Condition: Due to the exploratory nature of the research design, there is no control condition.
Key Measures: The measures will include literacy, reading component skill, and numeracy scores from the 2012 and 2014 U.S. cohorts of PIAAC.
Data Analytic Strategy: For Study 1, the researchers will use item response theory and bi-factor models. For Study 2, they will use latent class analyses and structural equation modeling. For Study 3, they will use multiple group structural equation modeling.
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