Sample size for qualitative research

2016 ◽  
Vol 19 (4) ◽  
pp. 426-432 ◽  
Author(s):  
Clive Roland Boddy
Keyword(s):  
Qualitative Research ◽  
Sample Size ◽  
Single Case ◽  
Short Paper ◽  
Sample Sizes ◽  
Homogeneous Population ◽  
Content Type ◽  
Data Saturation ◽  
Theoretical Saturation

Purpose Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research. Design/methodology/approach The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years. Findings In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population. Practical implications Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research. Originality/value Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

Causality in qualitative market and social research

2019 ◽  
Vol 22 (3) ◽  
pp. 405-413
Author(s):  
Clive Roland Boddy
Keyword(s):  
Qualitative Research ◽  
Design Methodology ◽  
Social Research ◽  
Pragmatic Approach ◽  
Social Phenomena ◽  
Content Type ◽  
The Past ◽  
The Moment ◽  
Practical Implications

Purpose Academic qualitative researchers have been criticized for rejecting the idea that their research can establish causality while market and social researchers, with their realist and pragmatic approach to research, take for granted that it can. This paper aims to explore the ability of qualitative research to determine cause and effect in terms of market and social phenomena. Design/methodology/approach The literature on causality in qualitative research is reviewed and discussed. The discussion is further informed by the author’s own experience of undertaking commercial and academic market and social qualitative research over the past 33 years. Findings In qualitative market and social research, the determination of causality is often needed but rarely discussed. This paper explores this occurrence and brings to the fore, via discussion and the use of example, the ways in which causality can be determined by qualitative research. Practical implications A determination of what events bring about predictable changes in social and market environments can be established via qualitative research particularly at a probabilistic level of causality. This implies that policymakers should give a greater emphasis to qualitative findings than then sometimes do at the moment. Originality/value Causality in market and social research is rarely discussed by practitioners but is nevertheless a premise of much of the qualitative research that is undertaken. This paper is therefore distinctive in that it examines whether this premise is justifiable.

Estimating sample size in the presence of competing risks – Cause-specific hazard or cumulative incidence approach?

2015 ◽  
Vol 27 (1) ◽  
pp. 114-125 ◽  
Author(s):  
BC Tai ◽  
ZJ Chen ◽  
D Machin
Keyword(s):  
Sample Size ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Exponential Model ◽  
Sample Sizes ◽  
Subdistribution Hazard ◽  
Competing Events ◽  
Event Times ◽  
The Impact

In designing randomised clinical trials involving competing risks endpoints, it is important to consider competing events to ensure appropriate determination of sample size. We conduct a simulation study to compare sample sizes obtained from the cause-specific hazard and cumulative incidence (CMI) approaches, by first assuming exponential event times. As the proportional subdistribution hazard assumption does not hold for the CMI exponential (CMIExponential) model, we further investigate the impact of violation of such an assumption by comparing the results obtained from the CMI exponential model with those of a CMI model assuming a Gompertz distribution (CMIGompertz) where the proportional assumption is tenable. The simulation suggests that the CMIExponential approach requires a considerably larger sample size when treatment reduces the hazards of both the main event, A, and the competing risk, B. When treatment has a beneficial effect on A but no effect on B, the sample sizes required by both methods are largely similar, especially for large reduction in the main risk. If treatment has a protective effect on A but adversely affects B, then the sample size required by CMIExponential is notably smaller than cause-specific hazard for small to moderate reduction in the main risk. Further, a smaller sample size is required for CMIGompertz as compared with CMIExponential. The choice between a cause-specific hazard or CMI model in competing risks outcomes has implications on the study design. This should be made on the basis of the clinical question of interest and the validity of the associated model assumption.

scholarly journals Theoretical saturation in qualitative research: an experience report in interview with schoolchildren

2018 ◽  
Vol 71 (1) ◽  
pp. 228-233 ◽  
Author(s):  
Luciana de Cassia Nunes Nascimento ◽  
Tania Vignuda de Souza ◽  
Isabel Cristina dos Santos Oliveira ◽  
Juliana Rezende Montenegro Medeiros de Moraes ◽  
Rosane Cordeiro Burla de Aguiar ◽  
...  
Keyword(s):  
Qualitative Research ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Theoretical Data ◽  
Physical Activities ◽  
Experience Report ◽  
Primary Sources ◽  
Cell Disease ◽  
Data Saturation ◽  
Theoretical Saturation

ABSTRACT Objective: report the experience of applying the theoretical data saturation technique in qualitative research with schoolchildren. Method: critical reading of primary sources and compilation of raw data, followed by thematic grouping through colorimetric codification and allocation of themes/types of statements in charts to find theoretical saturation for each grouping. Results: colorimetric codification occurred according to previously established themes: bodily hydration; physical activities and play; handling of sickle-cell disease; feeding and clothing. On the eleventh interview, it was possible to reach the theoretical saturation of themes, with four additional interviews being performed. Conclusion: this experience report enabled the description of the five sequential steps for identification of theoretical data saturation in qualitative research conducted with schoolchildren.

Developing an effective renovation plan: the influence of data collection tools

2018 ◽  
Vol 36 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Edoghogho Ogbeifun ◽  
Charles Mbohwa ◽  
Jan-Harm Christiaan Pretorius
Keyword(s):  
Qualitative Research ◽  
Data Collection ◽  
Single Case ◽  
Education Institution ◽  
Condition Index ◽  
Facilities Management ◽  
Content Type ◽  
History Of

Purpose All built facility begins to show signs of deterioration immediately after the facility is completed and put to use, thus necessitating routine maintenance. Increase in defects due to age, usage, and other factors, requires extensive maintenance activities known as renovation. The data used for a typical renovation plan can be collected using the condition assessment (CA) tool which depends on physical inspection of the defects or through a facility condition index which hinges on harnessing and analyzing the information in the operational history of the facility. The purpose of this paper is to examine the quality of a typical renovation plan using both tools. Design/methodology/approach The single case study of qualitative research was adopted. The data were collected through the principle of semi-structured questionnaire complemented with interviews and document analysis. The documents include periodic operational reports and a CA report used for planned renovation exercise of the Facilities Management (FM) Unit in a higher education institution in South Africa. Findings The findings revealed that although the FM Unit produces periodic reports, but there was no evidence of detailed analysis of the reports. Therefore, the programmed renovation exercises are based purely on the information from a CA. Research limitations/implications This research is a single site case study of qualitative research; the data collected are limited and not sufficient for generalization of the results. Furthermore, the lack of record of the analysis of the operational history in the periodic reports negatively affected the computation of facilities condition index (FCI). Thus it was not possible to demonstrate the strength of FCI over CA from empirical information. Originality/value The quality of a typical renovation plan is influenced by the tool used for data collection. Although the CA tool is commonly used, experience shows that the renovation exercise developed from such records is prone to many execution setbacks, such as frequent scope changes and the associated cost and time overruns. These setbacks can be minimized if the FCI is used as the tool for data collection.

Sampling in Qualitative Research

2019 ◽  
pp. 25-51 ◽  
Author(s):  
Musarrat Shaheen ◽  
Sudeepta Pradhan ◽  
Ranajee
Keyword(s):  
Qualitative Research ◽  
Qualitative Study ◽  
Sample Size ◽  
Sample Size Estimation ◽  
Sampling Techniques ◽  
Size Estimation ◽  
The Past ◽  
Purposeful Sampling ◽  
Data Saturation ◽  
The Right

The chapter discusses different types of sampling methods used in qualitative research to select information-rich cases. Two types of sampling techniques are discussed in the past qualitative studies—the theoretical and the purposeful sampling techniques. The chapter illustrates these two types of sampling techniques relevant examples. The sample size estimation and the point of data saturation and data sufficiency are also discussed in the chapter. The chapter will help the scholars and researchers in selecting the right technique for their qualitative study.

scholarly journals Continuing to build community in qualitative research

2015 ◽  
Vol 10 (4) ◽  
pp. 310-313 ◽  
Author(s):  
Ann L. Cunliffe ◽  
Karen Locke
Keyword(s):  
Qualitative Research ◽  
Design Methodology ◽  
Review Article ◽  
Short Paper ◽  
Content Type ◽  
The World ◽  
Supportive Community ◽  
The Relationship

Purpose – This short paper celebrates the tenth year Anniversary of QROM by highlighting the importance of continuing to build community and support for qualitative researchers across the world. It also elaborates the relationship between the journal and the biennial international Qualitative Research in Management conference. The paper aims to discuss these issues. Design/methodology/approach – Review article. Findings – The importance of a supportive community of qualitative scholars. Originality/value – The need for collaboration.

scholarly journals Adequacy of sample size in a qualitative case study and the dilemma of data saturation: A narrative review

2021 ◽  
Vol 10 (3) ◽  
pp. 180-187
Author(s):  
Felix Chukwuma Aguboshim
Keyword(s):  
Data Collection ◽  
Sample Size ◽  
Qualitative Case Study ◽  
Study Data ◽  
Narrative Review ◽  
Sample Sizes ◽  
Significant Information ◽  
Probability Sample ◽  
Data Saturation

The consensus of many researchers on data saturation is that data saturation is a key driver for determining the adequacy of sample size in a qualitative case study. Despite these global consensuses, some researchers described data saturation as complex because the decision to stop data collection is solely dictated by the judgment and experience of researchers. Other researchers claimed that guidelines for determining non-probability sample sizes, used as an indication of data saturation are virtually non-existent, problematic, or controversial. Others claimed that data saturation hitched to sample size is practically weak, because data are never truly saturated, as there are always new data to be discovered. This narrative study highlights the dilemma of data saturation and strategies to adequately determine sample size in a qualitative case study. A narrative review of prior research that focused on the vast works of literature that revealed significant information on data saturation and strategies to adequately determine sample size was adopted. Peer-reviewed articles within the last five years from electronic databases, using some keywords such as “qualitative case study”, “sample size in a qualitative case study”, “data saturation”, etc., were also extracted. Results show that data saturation is very helpful especially at the conceptual stage, but its concept and standard is elusive, because it lacks practical guidance for estimating sample size for a robust research prior to data collection. Findings from this study may encourage researcher on better guidelines for determining non-probability sample sizes.

Determination of level of correlation for products of pharmaceutical industry by using modified X-bar chart

2016 ◽  
Vol 33 (6) ◽  
pp. 724-746 ◽  
Author(s):  
D.R. Prajapati ◽  
Sukhraj Singh
Keyword(s):  
Pharmaceutical Industry ◽  
Sample Size ◽  
Control Chart ◽  
Service Industries ◽  
Shop Floor ◽  
Sample Sizes ◽  
Content Type ◽  
Floor Level ◽  
Warning Limits ◽  
Run Lengths

Purpose – It is found that the process outputs from most of the industries are correlated and the performance of X-bar chart deteriorates when the level of correlation increases. The purpose of this paper is to compute the level of correlation among the observations of the weights of tablets of a pharmaceutical industry by using modified X-bar chart. Design/methodology/approach – The design of the modified X-bar chart is based upon the sum of χ2s, using warning limits and the performance of the chart is measured in terms of average run lengths (ARLs). The ARLs at various sets of parameters of the modified X-bar chart are computed; using MATLAB software at the given mean and standard deviation. Findings – The performance of the modified X-bar chart is computed for sample sizes of four. ARLs of optimal schemes of X-bar chart for sample size of four are computed. Various optimal schemes of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.00, 0.25, 0.50, 0.75 and 1.00 are presented in this paper. Samples of weights of the tablets are taken from a pharmaceutical industry and computed the level of correlation among the observations of the weights of the tablets. It is found that the observations are closely resembled with the simulated observations for the level of correlation of 0.75 in this case study. The performance of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.50 and 0.75 is also compared with the conventional (Shewhart) X-bar chart and it is concluded that the modified X-bar chart performs better than Shewhart X-bar chart. Research limitations/implications – All the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for modified X-bar chart can also be used for other industries; where the manufacturing time of products is small. This scheme may also be used for any sample sizes suitable for the industries Practical implications – The optimal scheme of modified X-bar chart for sample size (n) of four is used according to the computed level of correlation in the observations. The simple design of modified X-bar chart makes it more useful at the shop floor level for many industries where correlation exists. The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation, the suggested control chart parameters can be used. Social implications – The design of modified X-bar chart uses very less numbers of parameters so it can be used at the shop floor level with ease. The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society as suggested by Taguchi (1985). Originality/value – Although; it is the extension of previous work but it can be applied to various manufacturing and service industries; where the data are correlated and normally distributed.

Human Factors Testing Sample Size Requirements: Is it Time to Reevaluate?

2021 ◽  
Vol 10 (1) ◽  
pp. 243-246
Author(s):  
Savannah Esteve ◽  
Denise Forkey ◽  
Shannon Clark
Keyword(s):  
Human Factors ◽  
Sample Size ◽  
Study Data ◽  
Optimal Number ◽  
Usability Study ◽  
Sample Sizes ◽  
Usability Problems ◽  
User Groups ◽  
Validation Testing

According to the 2016 FDA Human Factors Guidance, Human Factors Validation Testing should include a minimum of 15 test participants from each intended user group for simulated use Human Factors Validation Testing, but there is not specific guidance on sample sizes for studies focused on labeling comprehension. This sample size for labeling comprehension testing has been debated, as some experts suggest that fewer than 15 test participants may be acceptable to achieve similar results. The authors collected data from multiple labeling comprehension studies, to investigate the optimal number of test participants necessary to uncover all use errors specifically when evaluating comprehension and understandability of written instructions, i.e., labeling. While smaller sample sizes may be able to detect most common usability problems, UserWise recommends further investigation of usability study data considering device complexity, user groups, and prior experience level of users to aid the determination of the optimal sample size for adequacy of labeling assessments.

Qualitative Sampling Methods

2020 ◽  
Vol 36 (4) ◽  
pp. 579-581
Author(s):  
Sara L. Gill
Keyword(s):  
Qualitative Research ◽  
Sample Size ◽  
Sampling Methods ◽  
Sampling Techniques ◽  
Sampling Strategies ◽  
Quantitative Sampling ◽  
Data Saturation ◽  
Pros And Cons ◽  
Research Studies

Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of each. Sample size and data saturation are discussed.

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