Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Response Surface Methodology01:16

Response Surface Methodology

263
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
263
Modeling in Therapy01:26

Modeling in Therapy

145
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
145
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
Steps in the Modeling Process01:14

Steps in the Modeling Process

306
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
306
Transient and Steady-state Response01:24

Transient and Steady-state Response

273
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
273

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

SELF-Tree: An Interpretable Model for Multivariate Causal Direction Heterogeneity Analysis.

Psychometrika·2025
Same author

Unlocking employee creativity: How learning orientation and transformational leadership spark innovation through creative self-efficacy.

PloS one·2025
Same author

Ideal Point or Dominance Process? Unfolding Tree Approaches to Likert Scale Data with Multi-Process Models.

Multivariate behavioral research·2025
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 10, 2025

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.6K

連続した制限された応答の応答スタイルをモデル化:モデル開発と検証

Youxiang Jiang1, Biao Zeng1, Siwei Peng2

  • 1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.

Behavior research methods
|August 27, 2025
PubMed
まとめ
この要約は機械生成です。

この研究では,絶え間ないデータのための新しい項目応答モデルを導入し,極端とミッドポイント応答スタイルを効果的に測定します. このモデルは堅実な有効性を示し,連続測定における応答スタイル効果を軽減します.

キーワード:
連続した制限応答アイテムレスポンスモデルレスポンススタイル視覚的なアナログスケール

さらに関連する動画

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.9K

関連する実験動画

Last Updated: Sep 10, 2025

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.6K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.9K

科学分野:

  • サイコメトリクス
  • 統計モデリング
  • 調査方法

背景:

  • アイテムレスポンス理論モデルは,ライカートスケールデータのために確立されていますが,視覚アナログスケール (VAS) やスライダーバーなどの連続形式ではあまり開発されていません.
  • エクストリームレスポンススタイル (ERS) やミッドポイントレスポンススタイル (MRS) などのレスポンススタイルは,連続測定形式で結果に偏りがあります.
  • 既存のモデルでは,これらの応答スタイルを継続的なデータで適切に取り扱ったり,孤立させたりできません.

研究 の 目的:

  • 連続した制限された応答形式を分析するための新しい項目応答モデルフレームワークを提案する.
  • 内容の特徴,極端な応答スタイル (ERS),およびミッドポイント応答スタイル (MRS) を統一モデルに柔軟に組み込む.
  • ERSとMRSを正確に推定し,観察された反応への影響を軽減するモデルの能力を検証する.

主な方法:

  • 偽応答を利用した階層的な項目応答モデルフレームワークの開発.
  • ERSとMRSの見積もりを評価するために,連続した境界応答データを用いて実証検証.
  • マルコフ・チェーン・モンテ・カルロ (MCMC) 方法を用いてパラメータ回復を評価するシミュレーション研究.

主要な成果:

  • 提案されたモデルは,既存のアプローチと比較して,継続的な制限された応答データに優れた適合性を示した.
  • このモデルは,極端な応答スタイル (ERS) とミッドポイント応答スタイル (MRS) を効果的に推定した.
  • マルコフ連鎖モンテカルロ (MCMC) 方法は,さまざまなシミュレーション条件でモデルパラメータを正確に復元しました.

結論:

  • 新しいアイテムレスポンスモデルフレームワークは,継続的な制限レスポンスデータを分析するための堅牢で有効なアプローチを提供します.
  • このモデルは,反応スタイル (ERS,MRS) を成功裏に分離し,測定し,観察された反応に対する有害な影響を軽減します.
  • このフレームワークは,継続的な測定器のサイコメトリックモデリングを進めて,データの正確性と解釈性を向上させます.