Who likes to learn new things: measuring adult motivation to learn with PIAAC data from 21 countries

Gorges, Julia; Maehler, Débora B.; Koch, Tobias; Offerhaus, Judith

doi:10.1186/s40536-016-0024-4

Cited by 42 publications

(43 citation statements)

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“…Hence, adults scoring high on the motivation-to-learn scale are assumed to readily engage in and gain as much as possible from learning activities. In particular, we find that motivation to learn is significantly related to participation in further education in most PIAAC countries even after controlling for level of education (Gorges et al 2016).…”

Section: Motivation To Learn In Piaacmentioning

confidence: 75%

“…Previous research identifies an invariant measure of adult motivation to learn using items from the PIAAC background questionnaire (Gorges et al 2016). Motivation generally pertains to "the process whereby goal-directed activities are instigated and sustained" (Schunk et al 2014, p. 5, italics in original).…”

Section: Motivation To Learn In Piaacmentioning

confidence: 99%

“…The four items used by Gorges et al (2016) primarily tap enjoyment of learning and goals of knowledge expansion. These aspects are commonly referred to as intrinsic forms of motivation (Ryan and Deci 2000) and mastery goal orientation (Maehr and Zusho 2009).…”

Section: Motivation To Learn In Piaacmentioning

confidence: 99%

“…In order to account for motivation when analyzing PIAAC data, four items from the PIAAC background questionnaire have recently been compiled into a motivation-to-learn scale (Gorges et al 2016). The scale so far has been found to measure motivation to learn in an equivalent way across 21 countries and thus allows comparative analyses.…”

mentioning

confidence: 99%

“…To enable in-depth analyses of different groups concerning their motivation to learn, this paper addresses the applicability of the scale for group comparisons within each of the 21 countries included in Gorges et al (2016). In particular, measurement invariance-an important prerequisite for valid comparisons of estimates across groups-has been investigated with respect to four socio-demographic variables.…”

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confidence: 99%

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

Gorges

Koch

Maehler

et al. 2017

Large-scale Assess Educ

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BackgroundThe release of the (first round of ) PIAAC data in 2013 has drawn educational researchers' attention to a thus far neglected target group in education: adult learners. With respect to literacy, numeracy, and problem solving in technology-rich environments (ICT) skills, PIAAC data reveals differences across the participating countries (OECD 2013a). Going beyond cross-national comparisons, the general OECD report (OECD 2013a) and country-specific publications (e.g., Maehler et al. 2013; Statistics Canada 2013) provide in-depth analyses of specific population subgroup competencies that are relevant for researchers, educators, and policy-makers alike. These analyses show systematic skill differences across gender, age groups, level of education, and migration 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. Open Access© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Motivation To Learn In Piaacmentioning

confidence: 75%

Section: Motivation To Learn In Piaacmentioning

confidence: 99%

Section: Motivation To Learn In Piaacmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Gorges

Koch

Maehler

et al. 2017

Large-scale Assess Educ

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Analysing PIAAC Data with Structural Equation Modelling in Mplus

Scherer

2020

Methodology of Educational Measurement and Assessment

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Structural equation modelling (SEM) has become one of the most prominent approaches to testing substantive theories about the relations among observed and/or unobserved variables. Applying this multivariate procedure, researchers are faced with several methodological decisions, including the treatment of indicator variables (e.g. categorical vs. continuous treatment), the handling of missing data, and the selection of an appropriate level of analysis. The PIAAC data pose additional issues, such as the clustering of individual-level data, the large number of participating countries, the representation of performance scores by a set of plausible values, and the differences in the selection probabilities. Therefore, a flexible software package is required to handle them. This chapter introduces readers to analysing PIAAC data with SEM in the software Mplus by (a) presenting the key concepts behind SEM, (b) discussing the complexities of the PIAAC data and their possible handling, (c) illustrating the specification and evaluation of measurement and structural models, and (d) pointing to current developments in the areas of measurement invariance testing and multilevel SEM. Sample input and output files are provided. Structural equation modelling (SEM) represents a broad range of multivariate approaches that allow researchers to test hypotheses related to the means, variances, and covariances of manifest and latent variables (Kaplan 2009). It includes approaches such as path analysis, confirmatory factor analysis, and structural models that are based on researchers' hypotheses and theories about the relations among variables. In his seminal book, Kline (2016) emphasised that SEM requires

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Learning Agility and Learning Organizations in Disruptive Times

Tan

2023

International Handbook on Education Development in Asia-Pacific

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Who likes to learn new things: measuring adult motivation to learn with PIAAC data from 21 countries

Cited by 42 publications

References 45 publications

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

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

Analysing PIAAC Data with Structural Equation Modelling in Mplus

Learning Agility and Learning Organizations in Disruptive Times

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