Teachers’ Perceptions Structured through Facet Theory: Smallest Space Analysis versus Factor Analysis

Maslovaty, Nava; Marshall, Anne E.; Alkin, Marvin C.

doi:10.1177/00131640121971077

Cited by 27 publications

(17 citation statements)

References 14 publications

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“…Guttman and Shoham, 1982;Levin et al, 1983;Masolavty et al, 2001). As Guttman (1982) indicates, however, knowledge of the relationship between these approaches seems to have been slow to diffuse even in that field.…”

Section: Introductionmentioning

confidence: 99%

Factor analysis versus multi-dimensional scaling: binary choice roll-call voting and the US Supreme Court

Brazill

Grofman

2002

Social Networks

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We evaluate factor analysis and multi-dimensional scaling (MDS) as tools for the analysis of voter decisions over a series of dichotomous choices. We simulate binary voting data with a known form and illustrate that standard factor analyses of these types of data yield additional artifactual dimensions. This effect may be exacerbated by the choice of inter-voter measures of similarity used as input. These results call in question the conclusions of others based on standard factor analyses of empirical voting data from the US Supreme Court. We demonstrate that MDS methods produce more parsimonious descriptions of these data.

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Section: Introductionmentioning

confidence: 99%

Factor analysis versus multi-dimensional scaling: binary choice roll-call voting and the US Supreme Court

Brazill

Grofman

2002

Social Networks

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“…This technique is the equivalent of traditional principal component or factor analysis, but the outputs are visual (Maslovaty et al, 2001). It is based on the principle that the greater the similarity between two items (e.g., correlations), the smaller the distance between their positioning on the map.…”

Section: Discussionmentioning

confidence: 99%

A Facet metatheoretical approach to advance consumer behavior knowledge

Hornik

Cohen

Amar

2007

Psychology and Marketing

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This paper provides an overview of Facet Theory, a systematic approach to facilitate theory construction, research design, and data analysis for complex studies. Facet Theory is appropriate to marketing theories and particularly consumer behavior. In Facet Theory, the definition of the behavioral domain facilitates the rationale for hypothesizing structural relationships among variables employed in a research study. Examples are shown from various areas to illustrate the methods and results of structural analysis using Facet Theory data analysis techniques such as Smallest Space Analysis (SSA), Discrimination Coefficient (DISCO), and Partial Order Scalogram Analysis (POSA). The use of Facet Theory and conceptual tools like the "mapping sentence" and "universe of content," is shown to provide new insights into existing consumer data. It allows for the diagnosis and discrimination of consumers' behavioral traits and makes the generalization and replication of findings easier.

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“…Moreover, it is acknowledged that statistical methods regularly used in studies with large samples may not necessarily be adequate for studies with fewer participants. Rather, statistical methods aiming for similar outcomes but specifically attuned to the smaller sample size are welcomed, for example, the smallest space analysis as an alternative to exploratory factor analysis (Dinsmore & Zoellner, 2018;Maslovaty, Marshall, & Alkin, 2001) or partial least squares path modelling (PLS) as an alternative for structural equation modelling (Willaby, Costa, Burns, MacCann, & Roberts, 2015).…”

Section: Continuedmentioning

confidence: 99%

The relation between cognitive and metacognitive processing: Building bridges between the SRL, MDL, and SAL domains

Coertjens

2018

Brit J of Edu Psychol

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Aim. The main aim of this commentary was to connect the insights from the contributions of the special issue on the intersection between depth and the regulation of strategy use. The seven contributions in this special issue stem from three perspectives: self-regulated learning (SRL), model of domain learning (MDL), or the student approaches to learning (SAL).Procedure. Prior to combining insights from different studies, the definition and operationalization of cognitive and metacognitive processing in the seven contributions is described. Subsequently, the grain size and statistical methods used in these contributions are discussed. This information allows us to -albeit cautiously -combine the results from the different studies regarding the relation between cognitive and metacognitive processing. Conclusion.Deep processing and self-regulation/monitoring showed a strong correlation, regardless of the theoretical framework or data collection method chosen. The strength of the correlation between surface processing and metacognitive processing differed, however, between the studies. Pathways for future research on students' cognitive and metacognitive processing are suggested, at the methodological level as well as regarding the conceptualization of unregulated learning and surface processing.

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Teachers’ Perceptions Structured through Facet Theory: Smallest Space Analysis versus Factor Analysis

Cited by 27 publications

References 14 publications

Factor analysis versus multi-dimensional scaling: binary choice roll-call voting and the US Supreme Court

Factor analysis versus multi-dimensional scaling: binary choice roll-call voting and the US Supreme Court

A Facet metatheoretical approach to advance consumer behavior knowledge

The relation between cognitive and metacognitive processing: Building bridges between the SRL, MDL, and SAL domains

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