Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study.

By M. Vaismoradi, H. Turunen, T. Bondas. Nursing and Health Sciences, 2013

7,956 citations recorded by the source corpus.

Subfields: Education, Issues, ethics and legal aspects

View Paper: https://www.semanticscholar.org/paper/5e5fa1de11a715d65008074a24060368b221f376

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Citations in the corpus

Characteristics of Qualitative Descriptive Studies: A Systematic Review (2016, methodology): “In general, the overall characteristics were consistent with design features of QD studies described in the literature (Neergaard et al., 2009; Sandelowski, 2000, 2010; Vaismoradi et al., 2013).”
Theme development in qualitative content analysis and thematic analysis (2016, background): “However, some researchers believe that the application of qualitative content analysis and thematic analysis is suitable for those who want to employ a lower level of inference interpretation, rather than a more abstract interpretation.[2, 8] In other words, they focus on the explicit description of”
Pets in danger: exploring the link between domestic violence and animal abuse (2017, background): “The limitations of attempting to quantify qualitative data have been discussed elsewhere (see Basit, 2003; Loffe & Yardley, 2004; Vaismoradi et al., 2013).”
Exploring the Tourist Experience (2017, background): “…at different times—as in the case of our study—allowing for the identification of repeated patterns (themes) “in informant [sic] accounts of experience” (McLeod 2011, 145–47) within and across data (Boyatzis 1998; Creswell 2009; Miles and Huberman 1994; Vaismoradi, Turunen, and Bondas 2013).”
Spatializing energy justice (2017, methodology): “These publications were analysed with the aid of a thematic analysis approach (Clarke and Braun, 2014; Vaismoradi et al., 2013), using interpretive coding that resulted in the definition of four headings: ‘landscapes of material deprivation’, ‘geographic underpinnings of energy affordability’, ‘vici”
Artificial Intelligence in Education: A Review (2020, background): “Based on the above discussion, AI has great potential in automating and expediting administrative tasks for both institutions and instructors [7]–[10].”
Qualitative Content Analysis (2014, background): “However, high intercoder reliability (ICR) is required when more than one coder is involved in deductive data analysis (Vaismoradi et al., 2013).”
Directed qualitative content analysis: the description and elaboration of its underpinning methods and data analysis process (2018, methodology): “This also can increase inter-coder reliability (Vaismoradi et al., 2013) and the trustworthiness of the study.”
Academics’ perceptions of the benefits and challenges of self and peer assessment in higher education (2018, methodology): “All three researchers (CA, JT and PD) then met to discuss the codes with attached data, and refined the themes into the resultant seven values and five challenges reported in this paper, drawing on a thematic and content analysis framework (Vaismoradi, Turunen, and Bondas 2013).”
One size fits all? What counts as quality practice in (reflexive) thematic analysis? (2020, neutral): “…is assumed to offer a low level of interpretation compared to approaches such as grounded theory or narrative analysis (e.g. Aguinaldo 2012; Vaismoradi, Turunen, and Bondas 2013), and this impover-ished conceptualisation of TA is often used to justify combining TA with other approaches…”
Applying Thematic Analysis to Education: A Hybrid Approach to Interpreting Data in Practitioner Research (2020, background): “It seems confusing for researchers to distinguish and choose between TA and content analysis, considering many similarities between the approaches and the least thoughtful discussion in the literature (Vaismoradi et al., 2013).”
Racialized Sexism/Sexualized Racism: A Multimethod Study of Intersectional Experiences of Discrimination for Asian American Women (2018, methodology): “TCA (or thematic analysis) is a foundational method for qualitative research (Braun & Clarke, 2006; Vaismoradi et al., 2013) and involves analyzing themes within data (Braun & Clarke, 2006, p. 70).”