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Investigating the Number of Non-linear and Multi-modal Relationships Between Observed Variables Measuring Growth-oriented Atmosphere

机译:研究观测到的以增长为导向的大气之间的非线性和多峰关系的数量

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This study investigates the number of non-linear and multi-modal relationships between observed variables measuring the Growth-oriented Atmosphere. The sample (N = 726) represents employees of three vocational high schools in Finland. The first stage of analysis showed that only 22% of all dependencies between variables were purely linear. In the second stage two sub samples of the data were identified as linear and non-linear. Both bivariate correlations and confirmatory factor analysis (CFA) parameter estimates were found to be higher in the linear sub sample. Results showed that some of the highest bivariate correlations in both sub samples were explained via third variable in the non-linear Bayesian dependence modeling (BDM). Finally, the results of CFA and BDM led in different substantive interpretations in two out of four research questions concerning organizational growth.
机译:这项研究调查了观测变量之间的非线性和多峰关系,这些变量测量了以生长为导向的大气。样本(N = 726)代表芬兰三所职业中学的雇员。分析的第一阶段表明,变量之间的所有依赖关系中只有22%是纯线性的。在第二阶段,将数据的两个子样本识别为线性和非线性。发现线性子样本中的双变量相关性和确认性因子分析(CFA)参数估计值均较高。结果表明,通过非线性贝叶斯依赖模型(BDM)中的第三个变量,可以解释两个子样本中一些最高的双变量相关性。最后,CFA和BDM的结果在涉及组织成长的四个研究问题中有两个导致了不同的实质性解释。

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