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Product customer survey data for 100 customers
Product customer survey data for 100 customers
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kaggle
2019-03-13 更新
2024-03-07 收录
顾客调查
因子分析
数据链接：
https://www.kaggle.com/datasets/ipravin/hair-customer-survey-data-for-100-customers
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资源简介：
Factor Analysis Practice Dataset
因子分析练习数据集
应用场景：
创建时间：
2019-03-13
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Results of factor analysis extracting four common factors explaining the correlations amongst responses to Likert-type statement questions.
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Note: Responses to the first five statements with a loading higher than 0.4 were grouped into one variable which we called ‘Avicentric’.
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EFA of the PBQ; comparing three different factor solutions.
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EFA of the PBQ; comparing three different factor solutions.
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Factor loading scores from PCA analysis of the sensitivity component of risk.
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Factor loading scores from PCA analysis of the sensitivity component of risk.
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Pattern matrix components of the five factors of the developing scale after factor analysis using Promax rotation with Kaiser normalization.
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Pattern matrix components of the five factors of the developing scale after factor analysis using Promax rotation with Kaiser normalization.
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Rotated component matrix from the factor analysis, plus Cronbach Alpha (reliability) for each construct.
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Rotated component matrix from the factor analysis, plus Cronbach Alpha (reliability) for each construct.
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