{"title":"使用具有协变量的潜在马尔可夫模型评估自我报告的物质幸福感","authors":"Ewa Genge","doi":"10.1332/175795921X16719290621875","DOIUrl":null,"url":null,"abstract":"<p><p>A household's financial satisfaction is one of the most significant factors driving subjective well-being. However, Poland ranks close to the lowest position, 22nd out of the 28 EU members, in self-reported financial status. The paper investigates the problem of determining patterns of Polish households' behaviour and shows the evolution of the subjective assessment of financial situation based on the eight waves of the Polish Household panel data. The analysis is carried out on the basis of latent Markov (LM) models, which allow for socio-economic features affecting the parameters of the latent process. We compare different types of LM models considering: (1) different numbers of latent structures; (2) different types of the latent process constraints; (3) socio-economic background characteristics; and (4) survey weights (being excluded in most of the empirical analyses). The final model identifies three latent states, specifies common initial and transition probabilities over a 15-year period and, as a result, enables us to better characterise the families likely to change their position, especially families reporting worsening in their financial situation. To show the main direction of self-reporting financial condition, we present the predicted path for respondents characterised by the selected socio-economic features, relying on algorithm maximising posterior probabilities of the selected LM model.</p>","PeriodicalId":45988,"journal":{"name":"Longitudinal and Life Course Studies","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An evaluation of self-reported material well-being using latent Markov models with covariates.\",\"authors\":\"Ewa Genge\",\"doi\":\"10.1332/175795921X16719290621875\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A household's financial satisfaction is one of the most significant factors driving subjective well-being. However, Poland ranks close to the lowest position, 22nd out of the 28 EU members, in self-reported financial status. The paper investigates the problem of determining patterns of Polish households' behaviour and shows the evolution of the subjective assessment of financial situation based on the eight waves of the Polish Household panel data. The analysis is carried out on the basis of latent Markov (LM) models, which allow for socio-economic features affecting the parameters of the latent process. We compare different types of LM models considering: (1) different numbers of latent structures; (2) different types of the latent process constraints; (3) socio-economic background characteristics; and (4) survey weights (being excluded in most of the empirical analyses). The final model identifies three latent states, specifies common initial and transition probabilities over a 15-year period and, as a result, enables us to better characterise the families likely to change their position, especially families reporting worsening in their financial situation. To show the main direction of self-reporting financial condition, we present the predicted path for respondents characterised by the selected socio-economic features, relying on algorithm maximising posterior probabilities of the selected LM model.</p>\",\"PeriodicalId\":45988,\"journal\":{\"name\":\"Longitudinal and Life Course Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Longitudinal and Life Course Studies\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1332/175795921X16719290621875\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Longitudinal and Life Course Studies","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1332/175795921X16719290621875","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
An evaluation of self-reported material well-being using latent Markov models with covariates.
A household's financial satisfaction is one of the most significant factors driving subjective well-being. However, Poland ranks close to the lowest position, 22nd out of the 28 EU members, in self-reported financial status. The paper investigates the problem of determining patterns of Polish households' behaviour and shows the evolution of the subjective assessment of financial situation based on the eight waves of the Polish Household panel data. The analysis is carried out on the basis of latent Markov (LM) models, which allow for socio-economic features affecting the parameters of the latent process. We compare different types of LM models considering: (1) different numbers of latent structures; (2) different types of the latent process constraints; (3) socio-economic background characteristics; and (4) survey weights (being excluded in most of the empirical analyses). The final model identifies three latent states, specifies common initial and transition probabilities over a 15-year period and, as a result, enables us to better characterise the families likely to change their position, especially families reporting worsening in their financial situation. To show the main direction of self-reporting financial condition, we present the predicted path for respondents characterised by the selected socio-economic features, relying on algorithm maximising posterior probabilities of the selected LM model.