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Related Concept Videos

Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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Two-Way ANOVA01:17

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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA01:18

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One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
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The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
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Anthropometric data reduction using confirmatory factor analysis.

, Jafri Mohd Rohani1, Akanbi Gabriel Olusegun2

  • 1Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia.

Work (Reading, Mass.)
|January 18, 2013
PubMed
Summary

This study validates anthropometric data for Nigerian school design using factor analysis. The findings confirm the data

Keywords:
Exploratory factor analysismeasurement modelschool ergonomics

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Area of Science:

  • Ergonomics
  • Anthropometry
  • Educational Facility Design

Background:

  • Lack of localized anthropometric data hinders ergonomic school design in developing nations.
  • Existing data from developed countries is often unsuitable for diverse populations.
  • This study addresses the need for relevant anthropometric databases for Nigerian tertiary institutions.

Purpose of the Study:

  • To apply factor analysis to anthropometric data collected from Nigerian students.
  • To determine the suitability of this data as a reliable database for school facility design.
  • To investigate the variability within anthropometric measurements for improved design considerations.

Main Methods:

  • Collected anthropometric data from 288 male Nigerian students (ages 18-25).
  • Measured nine vertical height-related dimensions using traditional equipment.
  • Employed exploratory and confirmatory factor analysis to develop and validate a two-factor model.

Main Results:

  • A two-factor model, explaining 81% of data variation, was developed with adequate validity and reliability.
  • Factor analysis successfully categorized anthropometric variables.
  • Stature height and eye height sitting emerged as key variables for standing and sitting designs.

Conclusions:

  • Factor analysis is a relevant statistical tool for analyzing anthropometric data in diverse populations.
  • The collected anthropometric data is suitable for designing school facilities for Nigerian students.
  • This research provides a foundation for evidence-based ergonomic design in educational settings.