CONVENIENCE SAMPLING: A REVIEW AND GUIDELINES FOR QUANTITATIVE RESEARCH

Public attitudes toward stuttering in Turkey: Probability versus convenience sampling

The popular and professional press is filled with discussions of major changes on the organizational landscape, including organizational design experiments at entrepreneurial firms as well as at major corporations, the slashing of corporate staffs, the downsizing, delayering and revitalization of firms, the emerging electronic organization, mergers and acquisitions, failures of high reliability organizations, and time-based competition. Each of these issues has been associated with the redesign of organizations, yet these redesigns seem far removed from academic research, and they do not typically utilize the academic body of knowledge. Although the field has progressed enormously in new methods and insights during a century of research, it seems to us that organization studies have been a source of recurrent disappointment for practitioners and academics alike (Bedian 1989; Cummings 1983; Luthans 1986; Slocum 1984). For example, Miner (1984) analyzed 32 established organizational science theories and concluded that with the exception of theories of motivation there is no relationship between usefulness and validity. Is the field of organization studies irrelevant? Organizations have become the dominant institution on the social landscape. Yet the body of knowledge published in academic journals has practically no audience in business or government. Unlike a field such as economics, research on organizations has not typically focused on problems relevant to business and government organizations, and the real world of organizations has not drawn on the work undertaken by organizational scientists. From colleagues within our field and in allied disciplines, we hear complaints that manuscripts espousing radical ideas, or topics outside the mainstream, are difficult to publish. Reviewers for established journals seem to value papers whose theses are anchored in established theories or that use "legitimate" methods, thus implicitly creating a publication barrier for research that falls outside mainstream topics or methods. Moreover, we observe that scholars with interests in organizations span many disciplines and fields of inquiry such as anthropology, economics, history, information science, communication theory, artificial intelligence, systems theory, psychology, sociology, political science, policy sciences, as well as organization behavior, strategic management and organization theory. We sense that a new discipline of organization science is evolving and we envision that a new journal can become a forum for a discipline defined more broadly. The purpose of this essay is to discuss these issues and the need for reorienting research away from incremental, footnote-on-footnote research as the norm for the field. Although current research approches have made solid contributions, they do not

Online convenience samples are a quick and low-cost way to study health behavior, but the comparability to findings from probability samples is not yet well understood. We sought to compare convenience and probability samples' findings for experiments, correlates, and prevalence in the context of tobacco control research. Participants were a probability sample of 5,014 U.S. adults recruited by phone from September 2014 through May 2015 (cost ~U.S.$620,000) and an online convenience sample of 4,137 U.S. adults recruited through Amazon Mechanical Turk (MTurk) in December 2014 (cost ~U.S.$17,000). Participants completed a survey with experiments, measures of tobacco product use and demographic characteristics. MTurk convenience and probability samples showed the same pattern of statistical significance and direction in almost all experiments (21 of 24 analyses did not differ) and observational studies (19 of 25 associations did not differ). Demographic characteristics of the samples differed substantially (1 of 17 estimates did not differ), with the convenience sample being younger, having more years of education, and including more Whites and Asians. Tobacco product use also differed substantially (1 of 22 prevalence estimates did not differ), with the convenience sample reporting more cigarette and e-cigarette use (median error 19%). Using MTurk convenience samples can yield generalizable findings for experiments and observational studies. Prevalence estimates from MTurk convenience samples are likely to be over- or underestimates.

Quantitative researchers need a probabilistic sample to generalise their findings, but research constraints often compel them to use non-probabilistic samples. The use of non-probability sampling methods in quantitative studies has therefore become a norm. Interestingly, even studies published in top-quality journals compromise best practices that the use of non-probabilistic samples requires. Based on a thorough review of relevant studies, we developed a typology of non-probability sampling methods used in quantitative health studies. An attempt was made to discuss the limit of inference under each type of non-probability sampling method. Non-probability sampling in quantitative research was also delineated as a way to maximise response rate. This study is expected to guide students and early career epidemiologists to understand how to apply non-probabilistic sampling methods in quantitative approaches and plausibly document or report their chosen methods.

Sampling refers to the process used to identify and select cases for analysis (i.e., a sample) with the goal of drawing meaningful research conclusions. Sampling is integral to the overall research process as it has substantial implications on the quality of research findings. Inappropriate sampling techniques can lead to problems of interpretation, such as drawing invalid conclusions about a population. Whereas sampling in quantitative research focuses on maximizing the statistical representativeness of a population by a chosen sample, sampling in qualitative research generally focuses on the complete representation of a phenomenon of interest. Because of this core difference in purpose, many sampling considerations differ between qualitative and quantitative approaches despite a shared general purpose: careful selection of cases to maximize the validity of conclusions. Achieving generalizability, the extent to which observed effects from one study can be used to predict the same and similar effects in different contexts, drives most quantitative research. Obtaining a representative sample with characteristics that reflect a targeted population is critical to making accurate statistical inferences, which is core to such research. Such samples can be best acquired through probability sampling, a procedure in which all members of the target population have a known and random chance of being selected. However, probability sampling techniques are uncommon in modern quantitative research because of practical constraints; non-probability sampling, such as by convenience, is now normative. When sampling this way, special attention should be given to statistical implications of issues such as range restriction and omitted variable bias. In either case, careful planning is required to estimate an appropriate sample size before the start of data collection. In contrast to generalizability, transferability, the degree to which study findings can be applied to other contexts, is the goal of most qualitative research. This approach is more concerned with providing information to readers and less concerned with making generalizable broad claims for readers. Similar to quantitative research, choosing a population and sample are critical for qualitative research, to help readers determine likelihood of transfer, yet representativeness is not as crucial. Sample size determination in qualitative research is drastically different from that of quantitative research, because sample size determination should occur during data collection, in an ongoing process in search of saturation, which focuses on achieving theoretical completeness instead of maximizing the quality of statistical inference. Theoretically speaking, although quantitative and qualitative research have distinct statistical underpinnings that should drive different sampling requirements, in practice they both heavily rely on non-probability samples, and the implications of non-probability sampling is often not well understood. Although non-probability samples do not automatically generate poor-quality data, incomplete consideration of case selection strategy can harm the validity of research conclusions. The nature and number of cases collected must be determined cautiously to respect research goals and the underlying scientific paradigm employed. Understanding the commonalities and differences in sampling between quantitative and qualitative research can help researchers better identify high-quality research designs across paradigms.

Authors: 28 founding members of the Community for Responsible Research in Business and Management. The list of authors is integrated in the position paper.This position paper presents a vision of a future in which business schools and scholars worldwide have successfully transformed their research toward responsible science,3 producing useful and credible knowledge that addresses problems important to business and society. This vision is based on the belief that business can be a means for a better world if it is informed by responsible research. The paper begins with a set of principles to support responsible research and proposes actions by different stakeholders to help realize this vision. It explains the impetus for the proposal by describing the current business research ecosystem, which encourages research oriented toward scholarly impact much more than societal relevance. Changing the incentives and culture around publications are essential to promoting responsible research. Research is the foundation of business education and practice, yet business research has failed to live up to its promise in promoting better policies and best practices. If nothing is done, business research will lose its legitimacy at best; at worst, it will waste money, talent, and opportunity. This paper ends with a call to action for directing research toward achieving humanity’s highest aspirations. The paper invites discussion and debate on the prospect of creating a responsible research ecosystem to realize this future vision when business and management research has become a force for change toward a better world.The Position Paper starts us on a journey toward a substantive rethinking of business research and, more broadly, about its scientific and social role in society. Our audience goes beyond the higher education community, and we invite broad participation in this discussion.

Sustainability has been the subject of an intense debate in the academic literature. The concern on sustainability is associated with the way managers run companies, as well as the behavior of worldwide societies towards sustainable practices or actions. It involves an actual relationship among government, markets, corporations and sustainability, in which government exerts a major role, by the regulations and provisions of environmental standards. This study aims to identify the current academic literature on sustainability, as a topic in management and business research, using bibliometric analysis, in order to gather insight into such a body of knowledge, as well as to investigate which topics are associated with this subject and considered as the most relevant ones. Data analysis involved both qualitative and quantitative approaches. In terms of qualitative research, subjects were dealt with aspects regarding their content, keywords and subject relevance. Concerning quantitative data, the following variables were adopted: total number of publications, authors, subject areas, document sources, titles, publishing years, institutions, languages and countries. The methodology in this paper involves extracting data from the ISI web of science, description of predominant journals and their impact factor are also mentioned, by checking those at Journal Citation Report (JCR). It is possible to verify the most prolific authors and their impact, as well as the frequency the researched articles have been cited. Finally, deriving from such analyses, conclusions and research opportunities for further studies are displayed. Key words: Sustainability, management, business, bibliometric analysis.

Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape‐based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience‐sample approach. Using chronic wasting disease in free‐ranging deer ( Odocoileus virginianus ) as a simple illustration, we show that using probability sample designs with appropriate estimators provides unbiased surveillance parameter estimates but that the selection bias and coverage errors associated with convenience samples can lead to biased and misleading results. We also suggest practical alternatives to convenience samples that mix probability and convenience sampling. For example, a sample of land areas can be selected using a probability design that oversamples areas with larger animal populations, followed by harvesting of individual animals within sampled areas using a convenience sampling method.

This research was aimed to find out the effect of Islamic business ethic’s perception against the behavior of Batak Moslem’s merchants in Jakarta. This research used questionmaires in primary data collection and quantitative approaches. Experiments were done using simple linear regression, where the dependent variable (Y) is behavior of Batak Moslem’s merchant and the independent variable (X) is Islamic business ethics. The research sample was taken by probability sampling with simple random sampling method. The research sample was 65 Batak Moslem’s merchants who were members of KKMM in Jakarta. Based on the findings, it obtained simple linear regression equation: Y = 22,376 + 0,279X. The results of this research showed that the influence of Islamic business ethics has positive effect and signifficant against the behavior of Batak Moslem’s merchants who were members of KKMM in Jakarta. The R2 on this research showed the number 0.423, which mean that 42.3% of behavior of Batak Moslem’ merchants can be explained by the variables of Islamic business ethics. These results also explain that there are other variables were greater (57.7%) were able to explain the behavior of Batak Moslem’s merchants.

This article evaluates the impact of academic research in management and business on a sample of the leaders of Australia's largest companies and public sector organizations. The data from a questionnaire survey conducted in 2005 indicate that the impact of the collective research outputs of business/management academics on senior private and public sector leaders is almost non-existent. The implications of these findings for the conduct of research in business and management in the future are evaluated, as well as the emerging challenges we face from new non-university research and business/management education providers. The broader consequences of a possible paradigm shift from a largely academic research/teaching orientation, towards a more explicitly professional and business/industry orientation are discussed towards the end of the article.

This article evaluates the impact of academic research in management and business on a sample of the leaders of Australia's largest companies and public sector organizations. The data from a questionnaire survey conducted in 2005 indicate that the impact of the collective research outputs of business/management academics on senior private and public sector leaders is almost non-existent. The implications of these findings for the conduct of research in business and management in the future are evaluated, as well as the emerging challenges we face from new non-university research and business/management education providers. The broader consequences of a possible paradigm shift from a largely academic research/teaching orientation, towards a more explicitly professional and business/industry orientation are discussed towards the end of the article.

BackgroundChronic fatigue syndrome (CFS) is a debilitating illness with particular difficulties for healthcare providers because there are no diagnostic signs or laboratory tests and because management aims to merely improve symptoms. Further complicating management, healthcare providers' awareness concerning CFS has not been rigorously assessed. The present study aimed to ascertain United States (U.S.) healthcare providers' awareness of CFS and to assess their knowledge, attitudes, and beliefs (KAB) related to diagnosis and management of the illness. This information forms the foundation for developing CFS educational strategies.MethodsWe combined convenience and probability samples to measure CFS KAB among healthcare providers. In the convenience sample, 1,255 healthcare providers (81% response rate) from 13 professional conferences completed a 12-item form. Descriptive statistics were reported for 9 KAB item responses and chi-square tests were performed for examining their association with giving a diagnosis of CFS. We used principal component analysis to construct multidimensional subscales and perform a general linear model to examine factors associated with subscales. The probability sample involved data on 15 CFS-specific questions from 2006 and 2007 DocStyles web-based panel surveys collected from 2,750 physicians (average response rate 55%). We calculated descriptive and chi-square statistics. The significance was set at two-tailed with the alpha level of 0.05.ResultsHealthcare providers in both samples were aware of CFS and exhibited a high level of knowledge. Overall, 96% of respondents in the DocStyles (probability) sample had heard about CFS. Healthcare providers in the conference (convenience) sample demonstrated good KAB scores; physicians' scores were highest on KAB scales and lowest in perception. Nurses' scores were lowest in knowledge. More than 40% of physicians reported ever giving a CFS diagnosis and in the DocStyles (probability) sample more than 80% of physicians correctly identified CFS symptoms. Physicians reported professional journals, the Internet, and continuing education programs as the top 3 sources from which they obtain CFS information.ConclusionsFindings from these combined samples fill a gap in the evidence-base of U.S. healthcare providers' and knowledge, attitudes, and beliefs concerning CFS. Importantly, respondents in both samples expressed similar knowledge, attitudes, beliefs and perceptions. Awareness was high and negative attitudes were low. The primary areas for future education should address diagnosis and management of CFS and should be delivered through those venues providers indicated they primarily use. Data from this study provide a benchmark for evaluation the success of these future efforts.

This article compares the recruitment costs and participant characteristics associated with the use of probability and nonprobability sampling strategies in a longitudinal study of older hemodialysis patients and their spouses. Contrasts were made of people who accrued to the study based on probability and nonprobability sampling strategies. Probability-based sampling was more time-efficient and cost-effective than nonprobability sampling. There were no significant differences between the respondents identified through probability and nonprobability sampling on age, gender, years married, education, work status, and professional job status. Respondents from the probability sample were more likely to be Protestant and less likely to be Catholic than those from the nonprobability sample. Respondents from the probability sample were more likely to be Black, whereas those from the nonprobability sample were more likely to be White. There are strengths and shortcomings associated with both nonprobability and probability sampling. Researchers need to consider representativeness and external validity issues when designing sampling and related recruitment plans for health-related research.

Purposive sampling has been extensively utilized across research disciplines, particularly in business and management studies. While existing literature has primarily focused on its application in qualitative research, the rationale for using it in quantitative contexts remains underexplored, leaving researchers with limited practical guidance. Moreover, much of the available literature is found within academic texts, specifically books, which may not always provide sufficiently actionable guidance on successfully implementing and reporting purposive sampling in quantitative research. This editorial addresses these gaps by presenting structured guidelines specifically tailored for applying purposive sampling in quantitative research. It provides practical recommendations for systematically implementing and transparently reporting purposive sampling. The editorial discusses different types of purposive sampling, including criterion sampling, maximum variation sampling, and theoretical sampling, and illustrates their applications in quantitative contexts. It also discusses how purposive sampling can be combined with other techniques, such as snowball sampling and quota sampling, to enhance data quality and relevance. Detailed step-by-step guidelines are presented for employing purposive sampling and reporting its use in quantitative studies in a robust manner. Additionally, we put forward a salient checklist for authors and reviewers to ensure rigor, transparency, and consistency in reporting. This work contributes to the growing discourse about sampling by presenting one of the first comprehensive frameworks for employing purposive sampling in quantitative research. By guiding researchers to adopt purposive sampling with greater rigor and transparency, we hope it will be useful to establish the representativeness of data and provide more credible estimations to explain or predict the phenomena under investigations.

While probability samples are generally the preferred approach in survey research, nonprobability samples continue to be of interest and are used for multiple purposes. We discuss the use of a convenience sample in one study and social media recruitment in another when probability-based samples fell short of reaching target sample sizes for low-incidence populations. Both studies collected rich survey data, particularly regarding household finances, enabling comparisons between respondents in the two types of samples. Analysis shows that while demographic characteristics were not consistent across the two types of samples, the source of the data—the probability sample or the nonprobability sample—was not significant in predicting the primary research variables of interest. We conclude that combining cases from the two types of samples may be appropriate for analyses in these studies. We suggest that nonprobability samples may be particularly appropriate for low-incidence populations; we also suggest that similar techniques may be useful for other researchers as they investigate the utility of nonprobability samples.