Saturation in qualitative research: An evolutionary concept analysis

“There are several reasons for pseudo-saturation, such as prematurely ending data collection, not exploring different perspectives or contexts, or overlooking important sources of information (Aldiabat & Le Navenec, 2018; Guest et al., 2006; Morse, 2015a).”

“…still susceptible to false saturation due to the lack of discovery of relationships between codes and obtained themes, and still requires more data for the researcher to fully understand the depth, richness, and complexity of the phenomenon (Hennink et al., 2017; Low, 2019; Saunders et al., 2018).”

“…still susceptible to false saturation due to the lack of discovery of relationships between codes and obtained themes, and still requires more data for the researcher to fully understand the depth, richness, and complexity of the phenomenon (Hennink et al., 2017; Low, 2019; Saunders et al., 2018).”

“Information redundancy helps conceptualize saturation (Alam, 2021; Braun & Clarke, 2021; Bryman, 2006).”

“An important aspect of theoretical saturation is that in an iterative process, the researcher simultaneously engages in sampling, data collection, and data analysis (Sandelowski, 1995).”

“Information redundancy helps conceptualize saturation (Alam, 2021; Braun & Clarke, 2021; Bryman, 2006).”

“S. Rahimi and M. khatooni 2017; Saunders et al., 2018), thematic saturation (Bowen, 2008; Faulkner & Trotter, 2017; Guest et al., 2006; Guest et al., 2020; Hennink & Kaiser, 2022; Hennink et al., 2017; Kerr et al., 2010; Low, 2019; Saunders et al., 2018) and, meaning saturation as a surrogate term,…”

“There are several reasons for pseudo-saturation, such as prematurely ending data collection, not exploring different perspectives or contexts, or overlooking important sources of information (Aldiabat & Le Navenec, 2018; Guest et al., 2006; Morse, 2015a).”

“The researcher continues data collection until the emerging theory becomes strong and accounts for all the evidence (Bryant & Charmaz, 2007).”

“On the other hand, lengthy interviews can lead to participant fatigue or decreased efficiency due to repetitive data collection (Mason, 2010).”

“In contrast, the positivist paradigm prioritizes objectivity and generalizability and sees saturation as a point that can be reached through precise data analysis (Bowen, 2008; Creswell & Poth, 2016), so it may be possible to achieve saturation with a smaller sample size (Creswell & Poth, 2016).”

“Reaching partial saturation is vital in qualitative research as it helps ensure the strength and validity of data collection (Bowen, 2008; Fusch & Ness, 2015; O ’ reilly & Parker, 2013).”

“The sample size is always a topic of discussion among qualitative researchers (Guest et al., 2020; Hennink & Kaiser, 2022; Hennink et al., 2017; Hennink et al., 2019; Mwita, 2022).”

“This means that simply stating that codes or themes have been repeated and no new code or theme has been found is not enough (Kerr et al., 2010), and the explanatory and interpretive dimension of each code or theme must start repeating and no further explanation is found to claim that saturation…”

“Information redundancy helps conceptualize saturation (Alam, 2021; Braun & Clarke, 2021; Bryman, 2006).”

“Code or thematic saturation refers to a stage in the data analysis process where repetitive codes or themes are identified, and no new information or relationships between them emerge (Bowen, 2008; Guest et al., 2020; Hennink et al., 2017; Low, 2019; Saunders et al., 2018; Yang et al., 2022).”

“…a homogeneous population, a diverse sample in the study population requires the collection of more data to encompass all perspectives and experiences, thus increasing the likelihood of reaching saturation (Guest et al., 2006; Hennink et al., 2017; Hennink et al., 2019; Morse, 2015b; Mwita, 2022).”

“Generally, data, code, or thematic saturation focuses on the breadth of collected data, while meaning saturation and theoretical saturation focus on the depth of research data (Conlon et al., 2020; Saunders et al., 2018).”

“…size estimation shifted, centering on what “ saturation ” means and how to study it (Aldiabat & Le Navenec, 2018; Daher, 2023; Daniel, 2019; Hennink et al., 2017; Leese et al., 2021; Marshall et al., 2013; Mason, 2010; Naeem et al., 2024; Rahimi, 2024; Saunders et al., 2018; Sebele-Mpofu, 2020).”

“…discussions often suffice for code coverage under homogeneity/narrow aims while diversity/ multi-site or meaning/theory goals require larger samples. rahimi and khatooni (2024) • engages in a concept analysis that classifies four models of saturation (code/thematic, data, meaning, theoretical)…”