Statistical and psychometric methodology for psychological science

The overarching goal of this research program is to strengthen the inferential foundations of psychological science. The work develops, evaluates, and compares statistical models and psychometric methods, asking under what conditions a given method recovers the structures and effects it is meant to recover, and what happens to substantive conclusions when its assumptions fail. Findings are translated into concrete guidance on design, sample size, model selection, and reporting, together with software tools that make the methods usable in practice.

Multilevel and Latent Variable Modeling

A long-standing line of work concerns models with discrete latent variables for nested data, especially multilevel latent class and latent transition models. Contributions include sample size recommendations, approaches for incorporating covariates, simultaneous decisions on the number of latent clusters and classes, and analyses of the consequences of ignoring nesting structure. Related work addresses the specification of random effect structures in linear mixed effects models, with applications to psycholinguistic data.

Representative work: Park and Yu (2018, *Educational and Psychological Measurement*); Park and Yu (2018, *Structural Equation Modeling*); Yu and Park (2014, *Multivariate Behavioral Research*); Park, Cardwell, and Yu (2020, *Methodology*); Yu (2013, *Behavior Research Methods*).

Psychometric Network Modeling

This theme examines the relations between latent variable models and psychometric network models: when the two representations are distinguishable, how partial correlation networks behave under common factor data-generating processes, and how networks can be compared across groups, levels, and populations in a calibrated way. Several manuscripts currently under review develop a unified framework for differential network functioning and a factor–network mixture model.

Representative work: see [Working Papers]; funded by the NSTC project "An Alternative Approach of Latent Variable Modeling: Psychometric Network Modeling" (2020–2022).

Intensive Longitudinal Data and EMA

Current work, funded by an NSTC project running from 2025 to 2027, investigates statistical and methodological issues in the analysis of data from ecological momentary assessment (EMA) designs, including the characterization of within-person change trajectories and the comparison of continuous-time and discrete-time modeling frameworks for intensive longitudinal data.

Representative work: see [Working Papers].

Research Synthesis and Meta-Analysis

This theme addresses two persistent threats to the validity of meta-analytic conclusions: dependence among effect sizes and selection bias in the published literature. Ongoing work also develops power and sample size planning tools for moderator inference in sparse meta-regression settings.

Representative work: funded by the NSTC project "Investigating Statistical and Methodological Issues in Meta-Analysis: Selection Bias and Dependent Effect Sizes" (2023–2024); see [Working Papers].

Single-Case Experimental Designs

Work in this area quantifies and evaluates intervention effects in single-case research designs, examining nonoverlap effect size indices and the behavior of statistical methods under serial dependence, with the aim of balancing validity and precision in small-sample idiographic research.

Representative work: funded by the MOST project "Quantifying and Evaluating Intervention Effect in Single-Case Research Design" (2022–2023); see [Working Papers].

Measurement, Testing, and Data Quality

This theme spans psychological measurement in practice: the psychometric scaling of response anchors in Likert-type scales, scale development and validation, applications of aptitude testing to national examinations, and the detection of invalid and insufficient-effort responding in survey data, funded by the NSTC project "Invalid Responses in Survey Data: Impacts and Detection Methods" (2024–2025).

Representative work: Yu (2022, *Survey Research: Method and Application*); Yu, Tsai, and Yu (2015, *Psychological Testing*); Weng, Chen, and Yu (2019, *Chinese Journal of Psychology*).

Earlier Work: Judgment, Decision Making, and IRT

Earlier research examined the aggregation of probability judgments from multiple advisors and the correspondence between item response theory models and log-multiplicative association models.

Representative work: Anderson and Yu (2007, *Psychometrika*); Budescu and Yu (2006, 2007); Budescu, Rantilla, Yu, and Karelitz (2003, *Organizational Behavior and Human Decision Processes*).

Research Funding (Principal Investigator)

• | 2025–2027
  | Exploring Statistical and Methodological Issues in the Analysis of Data from Ecological Momentary Assessment Designs
  | NSTC 114-2410-H-004-174-MY2 |

• | 2024–2025
  | Invalid Responses in Survey Data: Impacts and Detection Methods
  | NSTC 113-2410-H-004-184 |

• | 2023–2024
  | Investigating Statistical and Methodological Issues in Meta-Analysis: Selection Bias and Dependent Effect Sizes
  | NSTC 112-2410-H-004-136 |

• | 2022–2023
  | Quantifying and Evaluating Intervention Effect in Single-Case Research Design
  | MOST 111-2410-H-004-168 |

• | 2020–2022
  | An Alternative Approach of Latent Variable Modeling: Psychometric Network Modeling
  | MOST 109-2410-H-004-075-MY2 |

• | 2019–2020
  | Methodological Issues in Analyzing Data with Multilevel Structure: Dependency Indices, Sample Sizes, Effect Sizes and Power
  | MOST 108-2410-H-004-100 |

• | 2018–2019
  | Covariate Effects in Multilevel Latent Class Models: Comparisons of Different Specifying and Estimating Approaches
  | MOST 107-2410-H-004-102 |

• | 2017–2018
  | Multilevel Latent Class Models: Determining the Required Sample Sizes and Developing the Distinguishability and Association Measures
  | MOST 106-2410-H-004-064 |

• | 2010–2015
  | Generalizations and Extensions of Multilevel Latent Transition Models
  | NSERC Discovery Grant (Canada) |

In addition, she has served as Co-Principal Investigator on funded projects on experience sampling studies of emotion dynamics and depression, the HiTOP model of psychopathology in Taiwan, AI for the public good, and suicide risk modeling for the Taiwan national suicide prevention hotline.