Modeling Educational Effectiveness in Classrooms: The Use of Multi-Level Structural Equations to Model Students’ Progress

Rowe, Kenneth K.J.; Hill, Peter W.

doi:10.1076/edre.4.4.307.6953

Cited by 52 publications

(27 citation statements)

References 86 publications

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“…Lindenberger & Ghisletta, 2009;Rowe & Hill, 1998). The analyses on the 37× 37 covariance matrix that excluded the two retest random effects produced virtually identical results with respect to the Intercept and Slope components.…”

Section: Exploratory Factor Analysissupporting

confidence: 48%

Two thirds of the age-based changes in fluid and crystallized intelligence, perceptual speed, and memory in adulthood are shared

et al. 2012

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Many aspects of cognition decline from middle to late adulthood, but the dimensionality and generality of this decline have rarely been examined. We analyzed 20-year longitudinal data of 6203 middle-aged to very old adults from Greater Manchester and Newcastle-upon-Tyne, UK. Participants were assessed up to eight times on 20 tasks of fluid intelligence, perceptual speed, memory, and vocabulary. We controlled for potential effects due to retest, city, sex, and socio-economic class. Average performance in all tasks declined with age, and individual differences in decline were present for all but one memory and two vocabulary tasks. Half of the variance in level of performance was shared across tasks. This proportion increased to 66% for individual differences in change. General level of performance and change therein correlated positively. We conclude that cognitive decline is heterogeneous across individuals and rather general at the within-individual level.

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Section: Exploratory Factor Analysissupporting

confidence: 48%

Two thirds of the age-based changes in fluid and crystallized intelligence, perceptual speed, and memory in adulthood are shared

et al. 2012

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“…Findings from fitting explanatory multilevel models to relevant data (at the For relevant examples, see: Embretson and Hershberger (1999);Goldstein (1997Goldstein ( , 2003; Goldstein and Spiegelhalter (1996);Hill and Rowe (1996, 1998);Masters (2004b); Masters and Keeves (1999); Millmann (1997); Raudenbush and Bryk (1988); Raudenbush and Willms (1991, 1995); Rowe (2000Rowe ( , 2004bRowe ( , 2006bRowe ( , 2007b; Rowe and Hill (1998).…”

mentioning

confidence: 99%

Conceptualising and Evaluating Teacher Quality: Substantive and Methodological Issues

Ingvarson

Rowe

2008

Australian Journal of Education

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Whereas findings from recent research highlight the importance of teacher quality in improving students' academic performances and experiences of schooling, substantive and methodological issues surrounding the conceptualisation and evaluation of teacher quality are not well-understood. Such deficiencies are particularly evident in claims for 'findings' derived from econometric research -especially from those studies that merely employ conceptualisations and proxy 'measures' of quality in terms of teachers' qualifications, experience, and students' academic outcomes. Moreover, the econometric models fitted to the available, mostly aggregated data, typically fail to conceptualise and 'measure' teacher quality in terms of what teachers should know (subject-matter knowledge) and be able to do (pedagogical skill). Nor do such models account for the measurement, distributional and structural properties of the data for response and explanatory variables -failings that all too frequently yield misleading interpretations of findings for both policy and practice.Following brief introductory comments related to current contexts, the paper focuses on two approaches towards the resolution current deficiencies -both of which have important implications for conceptualising and evaluating teacher quality, namely: (a) capacity building in teacher professionalism grounded in evidence-based pre-service teacher education content and subsequent in-service professional development, and (b) the specification and evaluation of teaching standards. The paper concludes by arguing that since the most valuable resources available to any school are its teachers, there is a crucial need for both a substantive and methodological refocus of the prevailing economic teacher-quality/student-performance/merit-pay research and policy agenda to one that focuses on the need for capacity building in teacher professionalism (and its evaluation) in terms of teaching standards related to what teachers should know and be able to do. Introductory commentsConsistent with the adoption of corporate management models in educational governance and the prevailing climate of outcomes-driven economic rationalism in which such models operate, policy activity related to issues of: accountability, assessment, standards monitoring and benchmarking, performance indicators, quality assurance, teacher quality, school and teacher effectiveness, are widespread.2 However, political, economic and industrial issues surrounding educational effectiveness are sensitive, despite the level of non-partisan political consensus (at least in Australia) regarding the macro and micro economic importance of teacher quality and quality teaching for equipping students adequately to meet the constantly changing demands the modern workplace (e.g., Bishop, 2007;Macklin, 2006;Nelson, 2002Nelson, , 2004.The global economic, technological and social changes under way, requiring responses from an increasingly skilled workforce, make high quality educational provision an imperative - (2...

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“…Concern over precision in effect size estimation in US curriculum research is not unique to SCALE-uP. The field of education research in general is beginning to acknowledge that too often researchers have ignored the fact that student outcomes are affected by complex, multidimensional, interrelated factors that interact in organisational settings Á the most basic of which is classrooms within schools (Rowe & Hill, 1998;Rowe et al, 1995). For example, researchers implementing interventions at the school or classroom level commonly measure and analyse the effect of treatments at the student level, overlooking the nested structure of the data.…”

Section: Need For Multi-level Analysismentioning

confidence: 99%

Using Multilevel Analyses to Study the Effectiveness of Science Curriculum Materials

Rethinam

Pyke

Lynch

2008

Evaluation & Research in Education

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This paper explores the use of HLM analysis in determining the implementation effects of two science curriculum units on student learning. It also compares HLM results with prior results from ANCOVA analyses. HLM analyses were considered as an alternative to ANCOVA because student data were nested within classrooms. The data for this study are from the Scaling-up Curriculum for Achievement, Learning and Equity Project (SCALE-uP). The sample consists of Grade 6 and Grade 7 students from five matched pairs of middle schools in a large, diverse, metropolitan school district in the US. The HLM null model indicates that approximately 15% of the total variance in student gain scores was found between classrooms. Curriculum treated as a classroom factor was significant for both units tested, as was also the case when treated at the individual level in ANCOVA. However, there were large differences in the effect sizes reported by the two analysis techniques. A major implication is that when the students are nested in classrooms and schools, researchers should consider multi-level analysis and account for classroom/school contexts.

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Modeling Educational Effectiveness in Classrooms: The Use of Multi-Level Structural Equations to Model Students’ Progress

Cited by 52 publications

References 86 publications

Two thirds of the age-based changes in fluid and crystallized intelligence, perceptual speed, and memory in adulthood are shared

Two thirds of the age-based changes in fluid and crystallized intelligence, perceptual speed, and memory in adulthood are shared

Conceptualising and Evaluating Teacher Quality: Substantive and Methodological Issues

Using Multilevel Analyses to Study the Effectiveness of Science Curriculum Materials

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