2020
DOI: 10.1007/s42519-020-00154-z
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Profile Analysis in High Dimensions

Abstract: The three tests in profile analysis: test of parallelism, test of level and test of flatness are modified so that high-dimensional data can be analysed. Using specific scores, dimension reduction is performed and the exact null distributions are derived for the three hypotheses.

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Cited by 2 publications
(1 citation statement)
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“…Data were analyzed using Repeated Measures Analysis of Variance in SPSS 28, following the guidelines for Profile Analysis as described by Cengiz et al (2021). In Profile Analysis, the three criteria to be met to determine trends in longitudinal data include: (1) Parallelism of trend lines, (2) group equality, and (3) profile flatness.…”
Section: Resultsmentioning
confidence: 99%
“…Data were analyzed using Repeated Measures Analysis of Variance in SPSS 28, following the guidelines for Profile Analysis as described by Cengiz et al (2021). In Profile Analysis, the three criteria to be met to determine trends in longitudinal data include: (1) Parallelism of trend lines, (2) group equality, and (3) profile flatness.…”
Section: Resultsmentioning
confidence: 99%