2020-12-01, 17:25–17:30, Short video submissions (view anytime)
Leximancer is gaining traction in the social sciences as an alternative, automated, approach to analysing text. Leximancer is an automated content analysis tool which uses Bayesian theory to identify the frequency of concepts and their relationships (Cheng & Edwards 2017; Johns, 2019; Smith & Humphreys 2006). The automated nature of the analysis allows for larger volumes of texts to be analysed quickly, and is particularly useful for disparate bodies of text, allowing for comparative analysis (Cheng & Edwards 2017; Young & Munksgaard 2017). This also ensures that the analysis emerges from the data and is not affected by a researcher’s preconceptions (Cheng & Edwards 2017; Smith & Humphreys 2006), with subtle and unusual relationships more likely to emerge, strengthening the reliability and reproducibility of the results (Angus, Rintel & Wiles 2013; Rooney 2005).
The key output of Leximancer is the concept map which indicates important concepts and their relationships, with semantically linked concepts clustered in colour-coded themes (Leximancer 2018; Rooney 2005; Smith & Humphreys 2006). Lines link words and phrases within and across themes, indicating relationships This visualisation is a powerful tool that aids in the interpretation and presentation of the data (Young & Munksgaard 2017).
This research uses Leximancer to gain a deeper understanding of the experience of age discrimination in employment in Australia by analysing the submissions to the “Willing to Work Inquiry” (Australian Human Rights Commission, 2016) (n=160). In Australia, older persons (over 50 years of age) represent more than half of the total number of Australians not in the workforce, despite one in five of those indicating they would like to participate. To mediate the economic and social challenges of ageing populations we need to engage this population of older Australians who wish to work but are not. To do so we need to understand the barriers they face including age discrimination.
The analysis employed a comparative approach to explore the age discrimination experiences of older Australians and the barriers to their employment. The research also considered the intersectionality of older Australians regarding gender and disability together with submissions from other stakeholder perspectives.
Leximancer analysis indicated that age discrimination occurred throughout the employment process, affecting both older Australians who were seeking employment and those already employed. Differences in experience due to gender and disability status were identified. Both individual and organisation submissions acknowledged the difficulty older Australians faced in substantiating discrimination under current anti-discrimination laws and highlighted the role training, for both employers and older Australians, could play in increasing the employability of older Australians and decreasing the discrimination they experience. The findings major focus was on the lived experience of older Australians but also identified the importance of recruitment agencies in the employment of older Australians, with contributions by unions and industry associations but the silence of employers in contributing to the inquiry.
This research highlights the value of using Leximancer to analyse secondary data to gain new insights, without researcher biases due to their background or theoretical leanings, and across disparate bodies of text.