199 research outputs found

    Adult Cognitive and Non-cognitive Skills: An Overview of Existing PIAAC Data

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    As of summer 2019, more than 60 PIAAC datasets from participating countries worldwide were available for research purposes. These datasets can be differentiated, for example, in terms of their accessibility, the extent of the information provided, the population group in focus, and the design of the underlying study. PIAAC Public Use Files, for instance, are freely available and are therefore highly anonymised, whereas PIAAC Scientific Use Files are available only for scientific research purposes and provide access to more detailed variables. The majority of the PIAAC data are available as public use files, but some participating countries (e.g. Germany and the United States) have also made several scientific use files or other extended file versions available to the research community. Some of the available PIAAC datasets focus on specific population groups - for example, the incarcerated adult population in the United States. Regarding the design of the underlying studies, most available datasets are cross-sectional, but some longitudinal data already exist (e.g. PIAAC-L in Germany). The present chapter provides an overview of the structure, accessibility, and use of the PIAAC datasets available worldwide

    Design and Key Features of the PIAAC Survey of Adults

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    This chapter gives an overview of the most important features of the Programme for the International Assessment of Adult Competencies (PIAAC) survey as it pertains to two main goals. First, only a well-designed survey will lead to accurate and comparable test scores across different countries and languages both within and across assessment cycles. Second, only an understanding of its complex survey design will lead to proper use of the PIAAC data in secondary analyses and meaningful interpretation of results by psychometricians, data analysts, scientists, and policymakers. The chapter begins with a brief introduction to the PIAAC survey followed by an overview of the background questionnaire and the cognitive measures. The cognitive measures are then compared to what was assessed in previous international adult surveys. Key features of the assessment design are discussed followed by a section describing what could be done to improve future PIAAC cycles

    Same but different? Measurement invariance of the PIAAC motivation-to-learn scale across key socio-demographic groups

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    Abstract Background Data from the Programme for the International Assessment of Adult Competencies (PIAAC) revealed that countries systematically differ in their respondents’ literacy, numeracy, and problem solving in technology-rich environments skills; skill levels also vary by gender, age, level of education or migration background. Similarly, systematic differences have been documented with respect to adults’ participation in education, which can be considered as a means to develop and maintain skills. From a psychological perspective, motivation to learn is considered a key factor associated with both skill development and participation in (further) education. In order to account for motivation when analyzing PIAAC data, four items from the PIAAC background questionnaire were recently compiled into a motivation-to-learn scale. This scale has been found to be invariant (i.e., showing full weak and partial strong measurement invariance) across 21 countries. Methods This paper presents further analyses using multiple-group graded response models to scrutinize the validity of the motivation-to-learn scale for group comparisons. Results Results indicate at least partial strong measurement invariance across gender, age groups, level of education, and migration background in most countries under study (all CFI > .95, all RMSEA < .08). Thus, the scale is suitable for comparing both means and associations across these groups. Conclusions Results are discussed in light of country characteristics, challenges of measurement invariance testing, and potential future research using PIAAC data
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