A Meta-analysis Approach for Estimating Salary Mean and Its Confidenca Interval

• Tipo: Livro de Atas de Conferência Internacional
• Data: 2022-07-23

Autores

Fernanda Campos de Sousa (Autor) FEUP; Flora Ferreira (Autor) Outra; José Soares (Autor) Outra; Magalhães, F. (Autor) FEUP; Isabel Martins Ribeiro (Autor) FEUP; Pedro Pacheco (Autor) FEUP; Dânia Pinto (Autor) Outra

Editora

Springer Nature

Cham

Conferência Internacional

International Conference on Computational Science and its Applications - ICCSA 2022

Malaga - Spain, July 2022

Outras Informações

Assunto: ICCSA 2022: Computational Science and Its Applications – ICCSA 2022 Workshops pp 627–639

Abstract (EN): A meta-analysis is the statistical pooling of the summary statistics from several selected studies to estimate the outcome of interest. A job’s salary estimate is important information for both job applicants and companies, that is reported on different websites. By combining data from different sources a mean estimate more representative of the target population can be obtained, especially when the data has high variability and dependence on different factors, as in the salary case. However, data are not reported in each source by the same statistics values. Sometimes, the data are summarized by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles. Additionally, the sample size is not always reported. In this paper, we aim to provide a step-by-step process to estimate the salary mean and its confidence interval by combining the data from different sources. We illustrate the process with an example dataset of seven job titles. The performance of two different alternatives to estimate the sample mean is evaluated, and the variation in the outcomes between websites is discussed. A high range of other quantitative data could benefit from the proposed process to obtain an estimate representative of the target population.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Notas: This article is a result of the project BEIS (Bridge Engineering Information System), supported by Operational Programme for Competitiveness and Internationalisation (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).

Nº de páginas: 13

ISBN Eletrónico: 978-3-031-10536-4

Coleção: Lecture Notes in Computer Science book series (LNCS,)

Vol.: 13377