scholarly journals Minimum Sample Size Requirements for a Validation Study of the Birth Satisfaction Scale-Revised (BSS-R)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Martin Colin R ◽  
Martin Caroline J Hollins
2020 ◽  
Vol 10 (3) ◽  
pp. 140-150
Author(s):  
Colin R. Martin ◽  
Elaine Jefford ◽  
Caroline J. Hollins Martin

BACKGROUNDBehavioral and medical science is currently in the grip of a “replication crisis,” circumscribed by the failure to replicate a large proportion of key studies and a consequential impact on confidence in the veracity of the scientific method. Given the contemporary nature of the debate it is surprising that the psychometric properties of commonly used outcome measures have not been evaluated in this context, despite the obvious potential for the measurement characteristics of the measures themselves to be a source of error within a study.The current investigation sought to replicate the original validation study of the Australian version of the 10-item Birth Satisfaction Scale—Revised (A-BSS-R) with respect to key psychometric aspects and the issues of replicability.METHODSA replication study of all quantitative aspects of Jefford et al. (2018) with an increased sample size. Participants were a purposive sample of Australian postnatal women (n = 445).RESULTSMost key quantitative aspects of the original validation study were found to be replicable and consistent with Jefford et al. (2018), the A-BSS-R was found to have excellent psychometric properties fundamentally mirroring the measurement characteristics observed previously. However, a small number of instances of nonreplicability were found.CONCLUSIONSThe A-BSS-R is a valid and reliable measure of birth satisfaction. Replicability, at least in part, is influenced by participant group characteristics, statistical power and sample size. More focus is required on the influence of self-report measures themselves on the germane aspects of successful study replication.


2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  

Nephron ◽  
1983 ◽  
Vol 34 (3) ◽  
pp. 192-195 ◽  
Author(s):  
M. Oberholzer ◽  
J. Torhorst ◽  
E. Perret ◽  
M.J. Mihatsch

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.


Author(s):  
Reza Omani-Samani ◽  
Caroline J. Hollins Martin ◽  
Colin R. Martin ◽  
Saman Maroufizadeh ◽  
Azadeh Ghaheri ◽  
...  

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