Comparing longitudinal profile patterns of Mathematics and Reading in early child longitudinal study, kindergarten: The Profile Analysis via Multidimensional Scaling (PAMS) approach

Kim, Se‐Kang

doi:10.1007/s12564-010-9074-4

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…However, different from ordinary multidimensional scaling (MDS), PAMS interprets an array of scale values in a dimension as their profile pattern and considers it as a core profile for person response profiles. Interpreting dimensions as core profiles has been supported and validated by numerous studies (e.g., Davison et al, ; Davison & Kuang, ; Frisby & Kim, ; Kim, , , ; Kim et al, ; Kim, Davison, & Frisby, ; McKay et al, ; Olatunji et al, ).…”

Section: Methodsmentioning

confidence: 91%

Profile analysis of treatment effect changes in eating disorder indicators

Kim

Annunziato

Olatunji

2017

Int J Methods Psych Res

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We investigated differential treatment effects on specific eating disorder (ED) indicators to enhance conclusions about treatment efficacy. Profile Analysis via Multidimensional Scaling, which identifies core profiles in a population and interprets person profiles with core profile information, was utilized to identify core profiles from a sample of 5,177 patients who were repeatedly measured with the ED inventory-2 at admission and at discharge. To assess differential treatment effects for individual ED indicators, we compared the core profiles at admission with those at discharge. Three core profiles were identified and labeled as High Body Dissatisfaction with Low Bulimia (Core Profile 1), High Interoceptive Awareness with Low Body Dissatisfaction (Core Profile 2), and High Ineffectiveness with Low Bulimia (Core Profile 3). Treatment had the greatest effects on Core Profile 2. The patients whose profile patterns were similar to that of Core Profiles 1 and 2 were positively related with weight gain. However, treatment was least on Core Profile 3, and the patients whose profile patterns were like that of Core Profile 3 were negatively related with weight gain. In conclusion, those patients who fit Core Profile 3 may benefit from different treatment modalities than those that are standard in inpatient settings.

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Section: Methodsmentioning

confidence: 91%

Profile analysis of treatment effect changes in eating disorder indicators

Kim

Annunziato

Olatunji

2017

Int J Methods Psych Res

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“…These pattern profile vectors of boldv2, boldv3, …, and boldvK (identified from raw data) correspond to boldv1 boldv2, …, and boldvK−1 profile vectors of ipsatized data. The phenomenon of dominant level effect usually typically occurs in educational achievement, cognitive ability, or psychological data (e.g., Davison et al, 2009; Kim, 2010b; Kim et al, 2007; McKay et al, 2018); therefore, to identify latent profiles carrying central response patterns, the dominant level effect should be controlled through ipsatizing (or row-centering) data; otherwise, the important central pattern information would be overlooked due to their perceived trivial contributions. If there is neither a general factor nor a level effect in the data, then the profile analysis of row-centered or uncentered data would be identical.…”

Section: Discussionmentioning

confidence: 99%

Factorization of person response profiles to identify summative profiles carrying central response patterns.

Kim¹

2024

Psychological Methods

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A data matrix, where rows represent persons and columns represent measured subtests, can be viewed as a stack of person profiles, as rows are actually person profiles of observed responses on column subtests. Profile analysis seeks to identify a small number of latent profiles from a large number of person response profiles to identify central response patterns, which are useful for assessing the strengths and weaknesses of individuals across multiple dimensions in domains of interest. Moreover, the latent profiles are mathematically proven to be summative profiles that linearly combine all person response profiles. Since person response profiles are confounded with profile level and response pattern, the level effect must be controlled when they are factorized to identify a latent (or summative) profile that carries the response pattern effect. However, when the level effect is dominant but uncontrolled, only a summative profile carrying the level effect would be considered statistically meaningful according to a traditional metric (e.g., eigenvalue ≥1) or parallel analysis results. Nevertheless, the response pattern effect among individuals can provide assessment-relevant insights that are overlooked by conventional analysis; to achieve this, the level effect must be controlled. Consequently, the purpose of this study is to demonstrate how to correctly identify summative profiles containing central response patterns regardless of the centering techniques used on data sets.

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“…Some researchers might be interested in long-term effects of principal leadership on school improvement (e.g., Heck & Hallinger, 2010), or the patterns of growth in students' academic achievement over time might be of interest (e.g., S. Kim, 2010). In fact, the search term longitudinal returned 24,774 results in the ERIC (EBSCO) database and 63,985 hits in PsycINFO (EBSCO), which indicates the significance of longitudinal factors in educational and psychological research.…”

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confidence: 99%