【作者】 朱晓峰；

【导师】 魏登云；

【作者基本信息】 安徽师范大学 ， 体育人文社会学， 2006， 硕士

【摘要】 在前人对主成分分析与因子分析应用研究的基础上,本文概括了主成分分析与因子分析在体育科研中应用的一般方法,全面系统地分析了主成分分析与因子分析在体育科研应用中值得注意的问题、存在的问题及修正方法,总结了主成分分析与因子分析在体育科研应用中的常用步骤。相关系数矩阵是主成分分析与因子分析的前提条件,影响相关系数的因素也必将影响主成分分析与因子分析的应用效果,本文首次全面系统地分析了主成分分析与因子分析常用的相关系数——积差相关系数的影响因素,进而提高了主成分分析与因子分析在体育科研中的应用效果;首次界定认知数据的性质,揭示了认知数据中不等权指标的主成分分析与因子分析在体育科研中应用存在的问题,并给予修正;首次提出基于肯德尔相关系数下的主成分分析与因子分析,解决了认知数据的非正态分布对主成分分析和因子分析在体育科研中应用产生的影响。更多还原

【Abstract】 Based on the former research of main principal analysis and factor analysis for application, this thesis generalize the common method of main principal analysis and factor analysis applied in sports research, comprehensively and systematically analyze several amendment method and problem about the application of main principal analysis and factor analysis, which is worth paying more attention, and conclude main step for application of main principal analysis and factor analysis in sports research.The correlation matrix is the premise for main principal analysis and factor analysis. The factor affecting the correlation is sure to influence the applicative effect of main principal analysis and factor analysis. Initiatively, comprehensively and systematically analyze the affecting factor of Pearson correlation, often used in main principal analysis and factor analysis, enhance the effect of main principal analysis and factor analysis in sports research. Originally define recognition data, discover and amend the problem existing in the main principal analysis and factor analysis. Initiatively offer the main principal analysis and factor analysis under the Kendell’s correlation, dissolve the influence of non-normal in recognition data on the main principal analysis and factor analysis applied in sports research.更多还原