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Sample size estimation and sampling techniques for selecting a representative sample
Abstract
Introduction: The purpose of this article is to provide a general understanding of the concepts of sampling as applied to healthrelated
research.
Sample Size Estimation: It is important to select a representative sample in quantitative research in order to be able to generalize
the results to the target population. The sample should be of the required sample size and must be selected using an appropriate
probability sampling technique. There are many hidden biases which can adversely affect the outcome of the study. Important factors
to consider for estimating the sample size include the size of the study population, confidence level, expected proportion of the
outcome variable (for categorical variables)/standard deviation of the outcome variable (for numerical variables), and the required
precision (margin of accuracy) from the study. The more the precision required, the greater is the required sample size.
Sampling Techniques: The probability sampling techniques applied for health related research include simple random sampling,
systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. These are more recommended
than the nonprobability sampling techniques, because the results of the study can be generalized to the target population.
Keywords: Sample, sample size, sampling techniques
... In-depth interviews, according to the literature, require a sample size of 10-15 patients to achieve data saturation assuming the population integrity in 17 recruiting study participants . In the current study, data saturation was reached at participant number ten. ...
... Purposive sampling was employed to select the participants. This sampling method was used because it is based on the researcher's judgment in relation to the issue, that is, with knowledge of the topic, the researcher can decide that this is the best 17 candidate for the research .The hospitals were also selected by purposive sampling. The private hospitals with the most sophisticated radiology equipment were selected. ...
Introduction: The advent of artificially intelligent systems in the field of medical imaging has attracted a lot of attention and sparked a lot of discussion regarding the future roles of radiographers. It is widely believed that Artificial Intelligence (AI) will revolutionize the entire medical imaging field in the near future and alter the current practice of radiographers. Aim: The aim of the study was to explore Zimbabwean radiographers’ perspectives on the impact of AI use on their future roles. Methods: A qualitative explorative design employing in-depth interviews to explore the perceptions of radiographers towards AI use in medical imaging. The study recruited 10 participants and the study was conducted at 5 hospitals in Harare, 2 government and 3 privatehospitals. The interview data was then analyzed using thematic analysis according to Braun and Clarke. Results: Four themes emerged from the interview data. The themes include; Reduce roles of radiographers, Elimination of human errors, Expansion of knowledge and AI will promote radiography. Conclusion: Radiographers must be trained and have underpinning knowledge of AI.This study recommends that AI use should be included in the curriculum of radiography students.
... In-depth interviews, according to the literature, require a sample size of 10-15 patients to achieve data saturation assuming the population integrity in 17 recruiting study participants . In the current study, data saturation was reached at participant number ten. ...
... Purposive sampling was employed to select the participants. This sampling method was used because it is based on the researcher's judgment in relation to the issue, that is, with knowledge of the topic, the researcher can decide that this is the best 17 candidate for the research .The hospitals were also selected by purposive sampling. The private hospitals with the most sophisticated radiology equipment were selected. ...
Introduction: The advent of artificially intelligent systems in the field of medical imaging has attracted a lot of attention and sparked a lot of discussion regarding the future roles of radiographers. It is widely believed that Artificial Intelligence (AI) will revolutionize the entire medical imaging field in the near future and alter the current practice of radiographers.
... It was followed because it aids in developing a thorough understanding of the research problem. This method also permits a more comprehensive study with a bigger sample size, which improves the generalizability of the findings and increases the study's objectivity and clarity (Babbie, 2005;Gay & Airasian, 2005;Omair, 2014). ...
... By dividing a population into smaller sections, or 'strata,' a stratified random sampling procedure can be carried out. Stratified sampling provides reasonably accurate estimates for all subgroups relevant to your research question (Omair, 2014;Taherdoost, 2016). Before enrolling in the English programme, the participants had over eight years of classroom English instruction. ...
Writing apprehension is an overwhelming feeling of stress and anxiety when writing in a foreign language. In the past ten years, it has been a hot topic of debate. Due to its influence on writing quality, this study aimed to investigate this topic among a group of university EFL students, measuring the level of this apprehension and identifying its main factors. To accomplish this objective, 137 Saudi EFL students were given a questionnaire containing 26 statements spanning four essential writing apprehension factors on a five-point Likert scale. The results indicated that Saudi EFL students generally received moderate writing apprehension. Negativity towards writing was the furthermost common factor of writing apprehension. Self-efficacy in writing was regarded as the least apprehensive factor compared to other writing apprehension factors. The potential causes of this apprehension in each factor were addressed. Statistically significant gender and university level differences were found favouring males and beginner university levels for negativity towards writing. Based on these findings, limitations and future study recommendations are suggested.
... Systematic random sampling is widely used in many scientific fields as its use guarantees the statistical principle of equal probability (Morillas et al., 2011). This sampling technique is applicable when the study population is relatively large (100 or more) and a list is available (Omair, 2014). ...
The primary purpose of this study is to investigate the effect of human resource management practices and leadership styles on customer satisfaction in the Palestinian insurance sector through the mediating role of employee performance. Empirical evidence to support the importance of customer satisfaction and what constitutes high levels of customer satisfaction is still lacking in the Palestinian context. This research framework, based on the resource-based view theory of the firm, aimed at finding out whether factors such as human resource management (HRM) practices, leadership styles, and employee performance have significant influences on customer satisfaction. Nine hypotheses, based on past research and underlying theory, were developed to test the relationships. The study sample was taken from employees of the insurance sector in Palestine, who have direct dealing with customers. The sample consisted of 358 participants selected through a systematic random process. Of the questionnaires posted, 269 were returned of which 258 were found usable, constituting a 72% response rate. The data was analyzed using the Partial Least Square (PLS). The findings indicated that HRM practices had an indirect relationship with customer satisfaction. Similarly, transactional leadership style and transformational leadership style showed indirect relationships with customer satisfaction. The findings also revealed that HRM practices and transactional leadership style and transformational leadership style had significant and positive relationships with employee performance. Finally, the results indicated that employee performance had a significant and positive relationship with customer satisfaction while also mediating the relationship between HRM practices and leadership styles respectively and customer satisfaction. The study concludes with discussions on theoretical and practical contributions, the study limitations, and suggestions for future research
... The sample consists of households selected using cluster sampling (Omair, 2014). This method was chosen to ensure the sample's representativeness because a formal list of households in the study area was unavailable. ...
Human-wildlife interaction is a complex issue that has positive as well as negative implications for both humans and wildlife that share the same habitat. In this paper, we used the Wildlife Tolerance Model (WTM) as the theoretical framework to determine the factors that affect tolerance towards the African savannah elephant (Loxodonta africana), chacma baboon (Papio ursinus) and spotted hyena (Crocuta crocuta) among the Tonga indigenous people of Zimbabwe. We used structural equation modeling for the identification of causal pathways to see which variables – namely, exposure, positive and negative interactions, costs and benefits – affect tolerance. Our study finds that intangible benefits are the most significant determinants of tolerance across all three species. Contradictory to the expectations, tangible cost had no effect on the tolerance for any of the three species. We find that reducing exposure would also have a strong mediating effect on tangible and intangible costs from the three species. We discuss the roles that socio-economic and cultural factors play to help explain the differences in communities’ attitudes towards the three species. We conclude that more emphasis should be given to increasing the awareness of the intangible benefits, such as the ecosystem services provided by the species. Finally, we recommend using the WTM to help establish a mitigation strategy for the targeted communities and then conducting a Before-After-Control-Impact (BACI) study to evaluate the true impact of those mitigation strategies on the communities’ wildlife tolerance.
... In addition, the sampling technique makes the method quicker and easier, creates a sample more likely to match the studied population, and allows for easier comparison between subgroups. It can also lead to bias if certain groups are over-or under-represented (Omair, 2014). ...
Global meat consumption will continue to increase in response to the growth in population and demand. Numerous studies have emphasized the nutritional, economic, and environmental benefits of using insect meal as an alternative protein source for animal feed. This article analyses Spanish consumers' willingness to consume pork, chicken, eggs, and fish, and explores the effect of sociodemographic and psychometric characteristics on their willingness to consume when the animals are fed with insect protein as a sustainable feed alternative. The study used an online Spanish consumers' panel belonging to Qualtrics© (market company services) and distributed it to 1260 individuals stratified by age, gender, and region. The data were analysed using the non-parametric Kruskal–Wallis and Mann–Whitney U tests. Overall, the results demonstrated that most Spanish consumers expressed high uncertainty regarding their willingness to consume animal-fed insect meals. They also exhibited moderate levels of food neophobia and showed a highly ecocentric attitude. The non-parametric comparison analysis confirmed significant differences in willingness to consume sustainable animal products across sociodemographic characteristics: financial situation and gender were significant variables, with females more willing to consume sustainable animal products than males. For the psychometric traits, significant differences in willingness to consume sustainable animal products were confirmed across food neophobia, food neophilia, opinions on using insects in animal feed, and ecocentric attitude. The results also confirmed a significant difference in the effects of sociodemographic variables on psychometric attributes. Financial situation affects food neophilia, anthropocentric attitudes, and opinions on the use of insects in animal feed. Age influences food neophobia, ecocentric attitudes, and opinions on the use of insects in animal feed. Education also influences food neophilia, ecocentric attitudes, and opinions on the use of insects in animal feed. For gender, it is observed that females are more neophobic than males. This study has provided comprehensive initial insights into consumer willingness to consume insect-fed animal products in Spain, and the findings from this study will serve as a valuable resource for producers, policymakers, and governments in making informed decisions that promote more sustainable production practices.
... In this cross-sectional study, a multistage cluster random sampling method was used [21]. We defined six primary schools in the Wangsa Maju Township, and two primary schools were randomly selected: Sekolah Kebangsaan Wangsa Maju Zon R10 and Sekolah Kebangsaan Wangsa Melawati. ...
Background: Uncorrected refractive error is the most common cause of vision impairment and the second leading cause of blindness worldwide. Its prevalence differs among and within countries. This study was aimed at exploring the pattern and prevalence of refractive error among school children in the Wangsa Maju Township, Kuala Lumpur, Malaysia. Methods: A stratified, multistage, cluster random sampling in a geographically defined area was employed, and 245 school children, aged 8-12 years, from two primary schools in Wangsa Maju Township, Kuala Lumpur, were recruited. The cross-sectional study employed interviewing, measuring uncorrected distance visual acuity (UCDVA) using the Snellen chart, cycloplegic refraction under a streak retinoscope refined subjectively, and a detailed slit-lamp examination to assess the anterior and posterior segments. Myopia, hyperopia, and astigmatism were defined as spherical equivalent (SE) ≥-0.50, SE ≥ + 2.00, and cylindrical ≥ 0.75 D, respectively. Reduced UCDVA was defined as an unaided visual acuity < 6/9. Results: The mean (standard deviation) age of the participants was 10.42 (1.22) years. The overall prevalence of refractive error was 47.8%. Of 245 screened school children, including 42 (35.9%) boys and 75 (64.1%) girls, 117 had refractive error, with a prevalence of refractive error of 17.1% and 30.6% in boys and girls, respectively. Myopia was the most common type (30.2%), followed by astigmatism (16.3%) and hyperopia (1.2%). The prevalence of reduced UCDVA was 36.3% among the screened school children, attributable to refractive error with a significantly high positive correlation (r = +.721; P < 0.01). Among those with refractive errors, sex differences in the magnitude of refractive errors were not statistically significant in the three types of refractive errors (all P > 0.05). Conclusions: The prevalence of refractive error among primary school children in Wangsa Maju Township, Kuala Lumpur, Malaysia was 47.8%; girls outnumbered boys, but the magnitude of refractive errors showed no sex differences. The prevalence of reduced UCDVA was 36.3%, attributable to refractive error. Irrespective of sex, myopia had the highest prevalence compared to other refractive errors, and its prevalence increased with age. Future population-based studies are required to address the limitations concerning environmental risk factors for refractive error and the impact of ethnic or familial backgrounds on their prevalence in a similar but larger population using the same protocol.
... All patients who have been diagnosed for diabetes and confirmed as a type-2 diabetic patient and normal after standard diagnostic activities from 2012 GC up to April 22/2020 GC were used as a sample. The whole dataset used as a sample because data mining needs considerable amount of data for effective prediction and classification by reducing the probability of error to be committed 9 . Based on this, the study has been conducted on a total of 2239 population. ...
Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned J 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis.
One of the most common requests that statisticians get from investigators are sample size calculations or sample size justifications. The sample size is the number of patients or other experimental units included in a study, and determining the sample size required to answer the research question is one of the first steps in designing a study.
Although most statistical textbooks describe techniques for sample size calculation, it is often difficult for investigators to decide which method to use. There are many formulas available which can be applied for different types of data and study designs. However, all of these formulas should be used with caution since they are sensitive to errors, and small differences in selected parameters can lead to large differences in the sample size. In this paper, we discuss the basic principles of sample size calculations, the most common pitfalls and the reporting of these calculations.
It is well documented that nonresponse and noncoverage rates have increased steadily in random digit dial (RDD) surveys. As these rates continue to rise, there are increasing challenges to reducing bias and increasing operational efficiency. This paper discusses several methods to consider when conducting RDD surveys. We also discuss the benefits and shortcomings of the approaches – shortcomings if it is decided not to implement a particular procedure. For example, there is a growing concern with nonresponse bias; what is the impact of by-passing the household nonresponse adjustment? We also consider the impact of cell phone only households, purging nonresidential numbers, mailings incentives, subsampling of cases prior to refusal conversion, level of effort, and other uses of auxiliary data.
