Ulitzsch相关文献与解析，生知库 | 链接生命科学的每一步

日期影响因子

Investigating dynamics in attentive and inattentive responding together with their contextual correlates using a novel mixture IRT model for intensive longitudinal data

利用一种新型混合IRT模型，基于密集型纵向数据，研究专注和非专注反应的动态变化及其情境相关性。

doi:10.1111/bmsp.12373

Vogelsmeier, Leonie V D E; Uglanova, Irina; Rein, Manuel T; Ulitzsch, Esther

发表日期：2026

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Assessing the Speed-Accuracy Tradeoff in Psychological Testing Using Experimental Manipulations

利用实验操作评估心理测试中的速度-准确性权衡

doi:10.1177/00131644241271309

Alfers, Tobias; Gittler, Georg; Ulitzsch, Esther; Pohl, Steffi

发表日期：2025

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A Beta Mixture Model for Careless Respondent Detection in Visual Analogue Scale Data

用于视觉模拟量表数据中粗心受访者检测的Beta混合模型

doi:10.1017/psy.2025.10041

Zhang, Lijin; Domingue, Benjamin W; Vogelsmeier, Leonie V D E; Ulitzsch, Esther

发表日期：2025

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Differences in response-scale usage are ubiquitous in cross-country comparisons and a potential driver of elusive relationships

跨国比较中，反应量表使用上的差异普遍存在，这可能是导致结果难以捉摸的原因之一。

期刊:Scientific Reports

影响因子:3.9

doi:10.1038/s41598-024-60465-0

Ulitzsch, Esther; Henninger, Mirka; Meiser, Thorsten

发表日期：2024

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Accounting for careless and insufficient effort responding in large-scale survey data-development, evaluation, and application of a screen-time-based weighting procedure

针对大规模调查数据开发中因粗心大意和回答不足而导致的问题，提出基于屏幕时间的加权程序，并对其进行评估和应用。

期刊:Behavior Research Methods

影响因子:3.9

doi:10.3758/s13428-022-02053-6

Ulitzsch, Esther; Shin, Hyo Jeong; Lüdtke, Oliver

发表日期：2024

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A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks

一种基于机器学习的程序，利用点击流数据来研究交互式任务失败的早期可预测性。

期刊:Behavior Research Methods

影响因子:3.9

doi:10.3758/s13428-022-01844-1

Ulitzsch, Esther; Ulitzsch, Vincent; He, Qiwei; Lüdtke, Oliver

发表日期：2023

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A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data

基于反应时间的潜在反应混合模型，用于识别和建模调查数据中的粗心和不充分的作答行为

doi:10.1007/s11336-021-09817-7

Ulitzsch, Esther; Pohl, Steffi; Khorramdel, Lale; Kroehne, Ulf; von Davier, Matthias

发表日期：2022

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Book Review Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment

书评：《计算心理测量学：面向新一代数字学习与评估的新方法》

doi:10.1007/s00048-021-00320-9

Mayer, Alexander; Ulitzsch, Esther

发表日期：2022

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Analyzing Stability and Change in Dyadic Attachment: The Multi-Rater Latent State-Trait Model With Autoregressive Effects

分析二元依恋的稳定性和变化：具有自回归效应的多评分者潜在状态-特质模型

期刊:Frontiers in Psychology

影响因子:2.9

doi:10.3389/fpsyg.2021.604526

Bohn, Johannes; Holtmann, Jana; Ulitzsch, Esther; Koch, Tobias; Luhmann, Maike; Eid, Michael

发表日期：2021

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A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models With Small Sample Sizes

惩罚最大似然估计法与马尔可夫链蒙特卡罗方法在小样本量验证性因子分析模型估计中的比较

期刊:Frontiers in Psychology

影响因子:2.9

doi:10.3389/fpsyg.2021.615162

Lüdtke, Oliver; Ulitzsch, Esther; Robitzsch, Alexander

发表日期：2021

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