波兰工作满意度分析的LC-IRT协变量模型

IF 0.6 4区 经济学 Q4 ECONOMICS
E. Genge
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引用次数: 1

摘要

就业是欧盟(EU)政策的核心,因为它是创造财富的基础。了解欧盟居民对其职业的满意度是非常重要的,因为失去工作可能会破坏一个人的生活满意度及其整体意义(European Commission 2015)。根据欧盟统计局的最新数据(欧盟委员会2017年),波兰的平均工作满意度远高于欧盟平均水平,排名第8(排在丹麦、冰岛、奥地利、芬兰、挪威、瑞士和瑞典之后)。因此,对波兰工人的工作满意度进行分析是很有趣的,波兰是一个移民国家,在欧洲拥有最高的临时合同比例(欧盟委员会2016年)。我们研究的主要目的是了解不同的社会经济特征如何影响波兰具有相似工作满意度的工人群体。大多数波兰工作满意度研究都集中在波兰选定地区的选定专业群体。本文提出了另一种,潜在变量模型的方法,以异构数据集的不同子组的工人在波兰的所有地区。两种潜在变量模型的结合,可以找到具有相似潜在能力水平的同类个体,同时也可以进行项目特征分析(通常称为区分指数和难度参数)。与传统的项目反应理论(IRT)模型相比,潜在类项目反应理论(LC-IRT)模型更为灵活,通常基于限制性假设,如潜在特质的正态性(明确引入)。此外,作者还在包含个人社会人口特征(如年龄、性别、教育程度、婚姻状况或当前财务状况)的潜在特征的离散假设下应用了扩展潜在变量模型。本文使用R软件分析了作为2015年国际社会调查计划一部分收集的数据。研究结果可能有助于政策制定者制定就业政策,并为波兰社会的特殊社会经济群体创造和提供服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LC-IRT models with covariates in Polish job satisfaction analysis
Employment is at the heart of European Union (EU) policies as it is the basis for wealth creation. Knowing how satisfied EU residents are with their occupation is very important, since losing one’s job may undermine one’s life satisfaction and its overall meaning (European Commission 2015). According to the most recent Eurostat data (European Commission 2017), Poland reported an average job satisfaction well above the EU mean, ranked 8th (behind Denmark, Iceland, Austria, Finland, Norway, Switzerland and Sweden). Thus, it is interesting to present an analysis focused on the job satisfaction of workers in Poland – a country of emigration, with the highest percentage of temporary contracts in Europe (European Commission 2016). The main aim of our study is understanding how the different socio-economic features affect the groups of workers with similar job satisfaction levels in Poland. Most of the Polish job satisfaction studies are focused on selected professional groups, in selected regions of Poland. This article presents another, the latent variable models approach to the heterogeneous data set for different subgroups of workers in all the regions of Poland. The combination of the two latent variable models enables to find homogeneous classes of individuals characterized by the similar latent ability levels, and at the same time, the item characteristics analysis (usually identified as discrimination indices and difficulty parameters) as well. Latent Class Item Response Theory (LC-IRT) models are more flexible in comparison with traditional formulations of Item Response Theory (IRT) models, often based on restrictive assumptions, such as normality of latent trait (explicitly introduced). Moreover, the authors also apply the extended latent variable models under the discrete assumption of the latent trait including individual socio-demographic features, such as age, sex, education, marital status or current financial situation. The article analyzes data collected as part of the International Social Survey Programme 2015 using R software. The results may help policymakers tailor their employment policies as well as to create and deliver services focused on special socio-economic groups of the Polish society.
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CiteScore
1.10
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0.00%
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2
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