https://scholars.lib.ntu.edu.tw/handle/123456789/41084
標題: | 平行分析於李克式量表之評估(1/2) | 作者: | 翁儷禎 | 關鍵字: | 因素數目;平行分析;李克式量尺;Number of factors;parallel analysis;Likert-type scales | 公開日期: | 2005 | 出版社: | 臺北市:國立臺灣大學心理學系暨研究所 | 摘要: | 本模擬研究探討平行分析決定李克式資料因素數目之正確性,模擬資料為正交多因素模型,操弄變項包括因素模型複雜度,即非主要因素之負荷量(0 或0.1)、因素數目(3 或6)、各因素主要變項數(4 或8)、各因素主要因素負荷量(0.4、0.6、0.8)、樣本人數(100、200、500)、李克式量尺點數(3、5、7)與李克式量尺變項之分配(偏態為0、1、2)。平行分析進行時分別以1000 筆隨機資料相關矩陣特徵值之平均數、第95 百分位數、第99 百分位數為比較基準決定因素數目。結果發現,因素負荷量高低為影響平行分析在多因素模型李克式點數資料表現的主要因子。平行分析多數時候得以正確決定因素數目, 尤其是在主要因素負荷量高時。但當模型複雜度提高,主要因素負荷量低時,平行分析之正確率極可能下降。 Parallel analysis has been shown to be superior to other methods for determining the number of factors in exploratory factor analysis. The present study was designed to examine the performance of parallel analysis on orthogonal-factor models with indicators measured on Likert-type rating scales. The simulation study manipulated model complexity as indicated by the size of trivial loadings (0 or 0.1), number of factors (3 or 6), number of indicators per factor (4 or 8), size of major factor loadings (0.4, 0.6, 0.8), sample size (100, 200, 500), number of response categories on the Likert scale (3, 5, 7), and distribution of the Likert-type variables (skewness of 0, 1, or 2). Parallel analysis was performed using the mean, the 95th percentile, and the 99th percentile of the eigenvalues from 1000 random data correlation matrices. The size of major factor loading played the most significant role in the behavior of parallel analysis. In general, parallel analysis tended to suggest the correct number of factors on Likert-type data, especially with high major factor loadings. However, when the complexity of the factor model increased and the size of major factor loading decreased, parallel analysis might have difficulty identifying the correct number of factors. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/9747 | 其他識別: | 932413H002022 | Rights: | 國立臺灣大學心理學系暨研究所 |
顯示於: | 心理學系 |
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932413H002022.pdf | 56.25 kB | Adobe PDF | 檢視/開啟 |
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