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Sampling Theory and Methods
Published in M. Venkataswamy Reddy, Statistical Methods in Psychiatry Research and SPSS, 2019
The sampling bias is nothing but consistent errors that arise due to the sample selection. It may be due to wrong demarcation of the sample from the population, measuring the succeeding or proceeding units in the list, and the errors of estimation. Sampling bias means that the data collected may not represent one group.
Undertaking a research project
Published in Perry R. Hinton, Isabella McMurray, Presenting Your Data with SPSS Explained, 2017
Perry R. Hinton, Isabella McMurray
In some cases, researchers are not very worried about sampling bias – they just want to collect information from whoever they can get. For example, a researcher would like to collect some information about a store’s customers and testing the first 100 people who walk in the store will give them some information about them (when currently they have none). As a piece of exploratory research, this information might be useful to guide more detailed research later. In other cases, it is crucial to select the sample carefully (as the first 100 customers in the store might be a highly specific sample of early-morning commuters and not typical of the ‘general’ customer). For example, an experimenter looking at the effect of a new teaching method compared to the old method on children’s mathematics performance would not want a sample of high ability children following one method and a sample of the lower ability children following the other method, as any results could be unrelated to the teaching method and due to a biased selection of the samples. In this case, any differences and could be unrelated and due to the teaching method but simply due to the children’s intelligence. The researcher would want to make sure in advance that the children following the two methods did not differ in their intelligence (or age or another possibly biasing factor).
Development of palliative medicine in the United Kingdom and Ireland
Published in Eduardo Bruera, Irene Higginson, Charles F von Gunten, Tatsuya Morita, Textbook of Palliative Medicine and Supportive Care, 2015
Many studies in palliative care, whatever the design, can have problems with patient selection and recruitment. Selection (or sampling) bias occurs when the group of patients selected for or included in the study are different from the total population
“I still have issues with pronunciation of words”: A mixed methods investigation of the psychosocial and speech effects of Childhood Apraxia of Speech in adults
Published in International Journal of Speech-Language Pathology, 2023
Courtney Cassar, Patricia McCabe, Steven Cumming
There are numerous factors that limited the scope of this study. As with all survey research, there are some sources of bias that cannot be controlled. This includes sampling bias, selection bias and social desirability bias (McCabe, 2018). Further, participants were able to begin the survey and not complete it. The study lacked statistical power and the ability to make strong correlations and group comparisons due to the small sample size. This is most clearly seen in the BFNE-R results where 11/16 were clinically diagnosable but as a group there was no significant difference with the normative sample. We also suspect that the significant drop in participants who completed a speech sample may be attributable to the online recording procedures and future research should consider in-person data collection.
Validation of the World Health Organization Well-Being Index (WHO-5) among medical educators in Hong Kong: a confirmatory factor analysis
Published in Medical Education Online, 2022
Linda Chan, Rebecca K. W. Liu, Tai Pong Lam, Julie Y. Chen, George L. Tipoe, Fraide A. Ganotice
Our study has several limitations to be considered. First, our findings may not be generalisable to healthcare professionals, academics or researchers who are not involved in medical education. Second, convenience sampling may have resulted in self-selection and sampling bias. Third, our results may be less applicable in other non-Asian cultures or settings although participants had varied ethnic backgrounds. Fourth, given that our data collection took place during the fourth wave of the COVID-19 pandemic in HK, we do not have the longitudinal data to support the stability of medical educators’ well-being after this wave of the pandemic. Finally, our study participants were from one of the two government-funded universities in HK with a medical school. The sociodemographic backgrounds of their academic staff, as well as institutional financial support and availability of resources appear to be similar [43,44]. Additionally, our sample size was greater than the minimum calculated by power analysis [45]. However, to enhance the generalisability of our findings, we invite other researchers to replicate our study so that medical educators from both HK medical schools are represented.
Feedback to support examiners’ understanding of the standard-setting process and the performance of students: AMEE Guide No. 145
Published in Medical Teacher, 2022
Mohsen Tavakol, Brigitte E. Scammell, Angela P. Wetzel
Group bar charts are created to compare the standard setters’ ratings of item difficulty (e.g. Angoff ratings) and the actual mean item difficulty for borderline students. Figure 3 shows the mean Angoff ratings (A.R.) for each standard setter. The solid line represents the mean p-values for the borderline group, the dashed line represents the established pass mark, and the solid line represents the mean mark of borderline students. By looking at this chart, the standard setters (N = 10) observe a distinct difference between the pass mark and the actual mean item difficulty for borderline students. In addition, each standard-setter can compare their passing score with other standard setters (S.S.), with the established pass mark (Passmark) and with the mean difficulty of questions for borderline students (BPV). Further analysis of this group bar chart suggests the mean mark (Mean) of the borderline students is higher than the pass mark set by the standard setters, suggesting an underestimation of the performance of borderline students. Therefore, the borderline students do not accurately reflect the true borderline students, and thus, sampling bias occurs.