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

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

35.0K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
35.0K
What is a Mode?01:07

What is a Mode?

27.3K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
27.3K
Ranks01:02

Ranks

523
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
523
Nominal Level of Measurement00:56

Nominal Level of Measurement

40.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
40.1K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.9K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.9K
Relative Frequency Distribution00:55

Relative Frequency Distribution

13.9K
A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
13.9K

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

Population-scale network embeddings expose educational divides in network structure related to right-wing populist voting.

Scientific reports·2026
Same author

Polarization in increasingly connected societies.

Physical review. E·2026
Same author

A theory-construction methodology for network theories in psychology.

Psychological methods·2026
Same author

Non-random patterns in the co-occurrence and accumulation of adverse life events in two national panel datasets.

Communications psychology·2026
Same author

The Role of Feedback Loops in Dynamical Symptom Networks.

Scientific reports·2026
Same author

The statistical physics of psychological networks: Zero matters.

Psychological review·2026

関連する実験動画

Updated: Feb 21, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

ReMoDe - 順序データ分布における再帰的モダリティ検出

Madlen Hoffstadt1, Lourens Waldorp1, Javier Garcia-Bernardo2

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

The British journal of mathematical and statistical psychology
|February 19, 2026
PubMed
まとめ

ReMoDeは,オーダーナルデータのための新しいリキュルティブモダリティ検出方法である. ReMoDeは,ディストリビューションのモードを正確に識別し,シミュレーションにおける既存の方法を上回ります.

キーワード:
バイモダリティー・ビモダリティーモダリティー 検出 検出マルチモダリティー,マルチモダリティーオーダーナルデータ オーダーナルデータピーク検出ピーク検出

さらに関連する動画

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

8.2K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

18.0K

関連する実験動画

Last Updated: Feb 21, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

8.2K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

18.0K

科学分野:

  • 統計局 統計局 統計局 統計局 統計局
  • データ分析 データ分析

背景:

  • オーダーナルデータのモードを検出することは,心理学や医学のような学問に不可欠です.
  • モダリティ検出の既存の方法は,しばしば記述的であり,並列データには適さない.

研究 の 目的:

  • 単変数順位分布のための新しい再帰的モダリティ検出方法 (ReMoDe) を提案する.
  • オーダーナルスケールに適用される現在の方法の限界に対処するために.

主な方法:

  • モード検出のための再帰的意味性テストアプローチを開発した.
  • 異なるサイズの172のシミュレートされた順序データセットを使用してベンチマーク研究を実施しました.
  • 検出されたモードの安定性試験と計算されたp値とベイズ因子.

主要な成果:

  • ReMoDeは,シミュレーションで確立されたモダリティ検出方法と比較して優れたパフォーマンスを示しました.
  • この方法は,検出されたモードの統計的測定値 (p値,ベイズ因数) を提供します.
  • オープンソースのRとPythonパッケージは,簡単に実装できるように提供されています.

結論:

  • ReMoDeは,オーダーナルデータにおけるモダリティ検出のための堅牢で正確なソリューションを提供します.
  • この方法は,分布の分析を強化し,研究者が極化や発生群などのパターンを特定するのを助けます.