基本信息
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Career Trajectory
Bio
Research topics include generalized latent variable models, generalized linear and nonlinear mixed-effects models, generalized additive mixed models, mixed-effects machine learning, parameter estimation, model assessment and selection, and model diagnostics, with a focus on item response, multilevel, and longitudinal/time-series modeling.
Data complexity Dr. Cho has dealt with consists of (1) multiple manifest person categories such as a control group versus a treatment group in an experimental design, (2) multiple latent person categories (or mixtures or latent classes) such as phenogroups, (3) multiple item groups that may lead to multidimensionality such as number operation, measurement, and representation item groups in a math test, (4) multiple groups such as hospitals where patients are nested in a multilevel (or hierarchical) data structure, (5) repeated measures such as pretest and posttest in intervention studies, (6) intensive (many time points) binary, ordinal, nominal, and count time series (e.g., from ambulatory physiological recording, wearable devices, eye-tracking, emotional responses, experience sampling methods, ecological momentary assessment, dynamic treatment regimes, and N-of-1 or single case trials), (7) response processes (e.g., multinomial processing), (8) spatial dependence, (9) multiple sequences or multivariate time series from multi-sourced big process data, (10) nonlinear interactions, (11) multiway categorical data, and (12) functional response time effects (e.g., in signal detection theory and item response theory).
Data complexity Dr. Cho has dealt with consists of (1) multiple manifest person categories such as a control group versus a treatment group in an experimental design, (2) multiple latent person categories (or mixtures or latent classes) such as phenogroups, (3) multiple item groups that may lead to multidimensionality such as number operation, measurement, and representation item groups in a math test, (4) multiple groups such as hospitals where patients are nested in a multilevel (or hierarchical) data structure, (5) repeated measures such as pretest and posttest in intervention studies, (6) intensive (many time points) binary, ordinal, nominal, and count time series (e.g., from ambulatory physiological recording, wearable devices, eye-tracking, emotional responses, experience sampling methods, ecological momentary assessment, dynamic treatment regimes, and N-of-1 or single case trials), (7) response processes (e.g., multinomial processing), (8) spatial dependence, (9) multiple sequences or multivariate time series from multi-sourced big process data, (10) nonlinear interactions, (11) multiway categorical data, and (12) functional response time effects (e.g., in signal detection theory and item response theory).
Research Interests
Papers共 84 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
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期刊级别
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合作机构
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICSno. 1 (2024): 123-142
Journal of Psychopathology and Behavioral Assessmentpp.1-10, (2024)
Psychometrikapp.1-32, (2024)
Behavior research methodsno. 3 (2023): 2094-2113
APPLIED PSYCHOLOGICAL MEASUREMENTno. 7-8 (2023): 478-495
Ear and hearingno. 5 (2023): 1251-1261
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Author Statistics
#Papers: 84
#Citation: 1754
H-Index: 21
G-Index: 40
Sociability: 5
Diversity: 0
Activity: 2
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