Multidimensional scaling modelling approach to latent profile analysis in psychological research

Ding, Cody

doi:10.1080/00207590500412219

Cited by 41 publications

(36 citation statements)

References 29 publications

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“…The data obtained were analyzed to identify the latent profile of the behavioral characteristics of academically talented students. Ding (2001) suggested a model that can be used to analyze data in order to see its latent profile. The model suggested is known as Profile Analysis via Multidimensional Scaling (PAMS).…”

Section: Discussionmentioning

confidence: 99%

Behavioral Characteristics Prototypes of Academically Talented Students: Implications for Educational Interventions

Majid¹,

Jelas²,

Ishak³

2012

ASS

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Gifted and talented students must be identified for teachers to be able to help students realize their potential. The Malay version of Renzulli's Scales for Rating the Behavioral Characteristics of Superior Students (SRBCSS) has been used by teachers to rate behavioral characteristics of 33 academically talented students. The data obtained were analyzed by multi-dimensional scaling analysis to yield two prototypes of behavioral characteristics of those students. The first prototype demonstrated high ratings in Dramatic, Artistic, and Musical characteristics. The second prototype showed high ratings in Expressive Communication, Precision Communication, Leadership, Motivation, Learning, Creativity, and Planning characteristics. Academically talented students need appropriate teaching and learning strategies to improve the behavioral characteristics for which they received low ratings.

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Section: Discussionmentioning

confidence: 99%

Behavioral Characteristics Prototypes of Academically Talented Students: Implications for Educational Interventions

Majid¹,

Jelas²,

Ishak³

2012

ASS

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“…Table 6 and Table 7 show the distance between codes and items. The low value of distance provides the high similarity and the high value provides the high dissimilarity (Ding, 2006). Fig.…”

Section: Student 15mentioning

confidence: 99%

Students’ Learning Styles and Preferences in Learning Selected Statistical Topics Based on Interview and Multidimensional Scaling Technique

Mahmud

Ibrahim²,

Sapri

et al. 2016

IJAEDU

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Students very often perceive statistics as a difficult subject to learn and have generally shown very little interest in it much to the dismay of educationists and instructors alike. This study investigates the problems students face when learning certain statistical topics by firstly relating these learning difficulties with the effort put in when understanding the topics. This is followed by categorizing students" learning styles and preferences based on Kolb"s Learning Model. Phase 1 explores these levels of learning difficulty, while Phase 2, the case study of 22 students, further investigates the students" learning styles. The analysis is based on a content matrix of students" learning of statistical topics using Kolb"s Learning Model, Qualitative Data Analysis (QDA) and Multidimensional Scaling. The study results indicate that learning difficulties can be overcome by employing different learning styles. The majority of the students fall into the "Converging" category as they prefer to do practice exercises and study on their own. A small number of students fall into the "Accommodating" and "Diverging" group, as they prefer to study in small groups and consult lecturers when the need arises. Finally, an even smaller number comes under the "Assimilating" group as they prefer to read textbooks and look up for notes from other sources from the internet. Other factors such as prior knowledge in statistics, ability to work with statistics and personal effort are also found to support students" interest in learning statistics.

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“…The outcome of MDS is a "map" that conveys, spatially, the relationships among items, wherein similar items are located proximal to one another, and dissimilar items are located proportionately further apart. From this map, one may infer the underlying dimensions of a data set (or confirm prior hypotheses) by subjectively examining the organization of the space ( 22 ). For example, if one were to apply MDS to pairwise similarity ratings among a set of color patches (e.g., "On a scale from 1-9, how similar are these two colors?…”

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confidence: 99%

Multidimensional Scaling

Ding¹

2013

Oxford Handbooks Online

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The concept of similarity, or a sense of "sameness" among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis.

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Multidimensional scaling modelling approach to latent profile analysis in psychological research

Cited by 41 publications

References 29 publications

Behavioral Characteristics Prototypes of Academically Talented Students: Implications for Educational Interventions

Behavioral Characteristics Prototypes of Academically Talented Students: Implications for Educational Interventions

Students’ Learning Styles and Preferences in Learning Selected Statistical Topics Based on Interview and Multidimensional Scaling Technique

Multidimensional Scaling

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