September November ; 5 4: This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3. This article has been cited by other articles in PMC. Interviewing This is the most common format of data collection in qualitative research. Observation Observation is a type of qualitative research method which not only included participant's observation, but also covered ethnography and research work in the field.
Conclusion Research can be visualized and perceived as painstaking methodical efforts to examine, investigate as well as restructure the realities, theories and applications. Buckley JW, Chiang H. Natl Assoc of Accat; Research Methodology and Business Decisions.
The Foundations of Social Research: Meaning and Perspective in the Research Process. A strategic research methodology. Am J Appl Sci. Towards a definition of mixed method research. J Mix Methods Res. The potential contributions of quantitative research to symbolic interactionism.
Corbin J, Strauss A. Use terms that participants can understand, given their knowledge, language skills, cultural background, age, gender, etc. Be mindful of the social or cultural contexts of your questions. Keep the questions as short and specific as possible. Avoid questions with a strong positive or negative association. Avoid phrasing questions as negatives e. Prepare and have copies ready to be signed.
Determine how information will be recorded. Ask warm-up or demographic questions first; then, using the interview guide, move on to more focused questions. Allow flexibility for dialogue. Here are some other tips to keep in mind during the interview. Use probing questions to gather as much information as possible.
Try not to interrupt participants; make a note and come back to the idea later. If a participant gives an answer relating to a question you have not yet asked, record the answer and avoid repeating the question later. Table 1 shows that the overall range of the numbers of participants used was from 95 using a case study approach to 1 also using a case study and a life history approach. Of the studies analysed the median and mean were 28, and 31 respectively, suggesting perhaps that the measures of central dispersion were generally consistent.
However, the distribution is bi-modal 20 and 30 and the standard deviation was Below this Figure 1 provides an illustration of the distribution of the sample, i. Number of studies by each individual sample size [ 31 ]. Figure 1 shows a bi-modal distribution with a skewness 13 of 0. The most important result from the chart however is the distribution of the sample.
Figure 1 shows the prevalence of the studies that included 10, 20, 30 and 40 participants as their sample size. This pattern continues with the prevalence of studies using 50 and 60 as their sample size comparative to the numbers around them i. In total, the sample sizes ending in a zero account i. A test for the randomness of fluctuations 15 indicated that there was very strong evidence against the randomness of fluctuations: The pattern of non-fluctuation is more clearly illustrated below in Figure 2.
Number of studies with a sample ending in each integer [ 34 ]. A Chi-squared "goodness-of-fit" test 16 was then used to test the null hypothesis that samples used in qualitative studies are equally likely to end on any integer. Table 1 also shows the descriptive results of the analysis of the 26 approaches identified by TESCH, in an attempt to discover whether the methodological approach affects the number of interviews undertaken.
The analysis returned an uneven distribution of approaches among the studies used in the sample. Of the 26 approaches identified by TESCH, seven did not return any studies that fitted the search criteria, and a further one did not return any studies into the sample once the inclusion criteria were applied. As a result, detailed statistical analysis was not possible. However, it was clear that there were approaches that utilised interviews in their method more frequently than others did.
Of the 26 qualitative approaches, nine returned more than 10 studies eight after the inclusion criteria were applied. The most popular approaches used in PhD studies for this analysis were: The approach utilising interviews most frequently were case study projects 1, This is followed by grounded theory studies However, these means are achieved from comparatively few studies.
The more studies returned into the sample for this analysis, the lower the mean tended to become. Perhaps more worthy of note is the fact that of the major approaches i. Finally, the data were compared to the guidelines given by various authors for achieving saturation in qualitative interviews see page 3.
The number of studies used in this analysis is shown below as a proportion of the whole for that approach:. No ethnoscience studies were found that fitted the inclusion criteria. A wide range of sample sizes was observed in the PhD studies used for this analysis. The smallest sample used was a single participant used in a life history study, which might be expected due to the in-depth, detailed nature of the approach, while the largest sample used was 95 which was a study utilising a case study approach.
The median, and mean were 28 and 31 respectively, which suggests a generally clustered distribution. However, the standard deviation at The most common sample sizes were 20 and 30 followed by 40, 10 and The significantly high proportion of studies utilising multiples of ten as their sample is the most important finding from this analysis. There is no logical or theory driven reason why samples ending in any one integer would be any more prevalent than any other in qualitative PhD studies using interviews.
If saturation is the guiding principle of qualitative studies it is likely to be achieved at any point, and is certainly no more likely to be achieved with a sample ending in a zero, as any other number. However, the analysis carried out here suggests that this is the case. Of the samples achieved in this study there does not seem to be any real pattern as to how far PhD researchers are adhering to the guidelines for saturation, established by previous researchers.
However, there were a proportion of studies that used more than 50 as their sample—these larger qualitative studies are perhaps the hardest to explain. While none of the guidelines presented here are intended to be faultless reference tools for selecting qualitative samples sizes, all authors agree that saturation is achieved at a comparatively low level e.
Without more detail of the studies it is not possible to conclude whether these larger samples were truly inappropriate. They constructed a sample of 60 participants: Their study was longitudinal and utilised over participants in eight different organisations.
There is no way of knowing that the samples analysed in this study were similarly arranged. LEECH suggests that it is a mistake to presume that all qualitative research must inevitably use small samples. She feels that this ignores what she calls a growing body of research studies that utilise text-mining 18 e. Text-mining was not identified by TESCH as a separate methodological approach and as a result was not used in this analysis.
Further analysis might examine samples from these studies in more detail. This highlights a potential weakness of this study—the interpretation of methodological approach. While it is believed that PhD researchers own descriptions of their work are likely to be accurate, it may place studies into certain categories when they might be better suited to others.
Further research might also seek to quantify the other issues that affect sample size and undertake regression analysis to see what percentage of variance in the sample size can be explained by these factors.
This would require a larger sample than that achieved in this paper as the unit of analysis would be the methodological approach or the existence of supplementary methods for example.
Finally, this paper has sought to examine the use of personal interviewing in PhD studies for the reasons already given. Further research could feasibly examine whether these patterns exist in published research. Ultimately, qualitative samples are drawn to reflect the purpose and aims of the study. A study schedule is then designed, the study is carried out and analysed by researchers with varying levels of skill and experience.
This is as a result of an interaction between the interviewer and the participant. There could be an argument, for example, which suggests that ten interviews, conducted by an experienced interviewer will elicit richer data than 50 interviews by an inexperienced or novice interviewer.
Any of these factors along the qualitative journey can affect how and when saturation is reached and when researchers feel they have enough data. However, while it is clear that these issues can affect saturation, they should not dictate it. Results from this analysis suggest that researchers are not working with saturation in mind, but instead a quota that will allow them to call their research "finished".
Further research to shed light on this might explore whether the number of methods used might affect saturation. MORSE also supports this by saying that "the number of participants required in a study is one area in which it is clear that too many factors are involved and conditions of each study vary too greatly to produce tight recommendation's" p.
However, to have too detailed a discussion of the flexible nature of saturation and whether the sample sizes in this analysis are appropriate or not is to lose sight of the most important findings of this study. The over-representation of certain sample sizes in qualitative PhD studies suggests a potential deficiency in the teaching and supervision of qualitative methods.
As external criteria often impinge on sample sizes such as ethics committee requirements to state numbers at the outset, where guestimates are likely to be round numbers , it is obviously easier for a student to defend the approach they cited, rather than stop data collection apparently early, when compared to their proposal. On closer examination however, perhaps this should not be surprising. Nearly all of the examples of sample guidelines presented here by previous researcher are in multiples of five.
This is all the more curious when empirical examples presenting guidelines for saturation e. So to conclude then, there is a range of potential conclusions that might be drawn as a result of this analysis:.
This ensures that their sample sizes, and therefore their data, are defensible. Alternatively PhD researchers do understand the concept of saturation but they find it easier to submit theses based on larger samples than are needed "just to be on the safe side" and therefore feel more confident when it comes to their examination. Irrespective of their understanding of saturation, PhD researchers are using samples in line with their proposal to suit an independent quality assurance process i. Whatever the appropriate reason there are clear implications for research students using qualitative interviews.
SIBLEY suggests that in her experience the teaching of qualitative research is often not as rigorous as other forms of social research, and many assumptions about its teaching are taken for granted. It is therefore more important than ever to make qualitative methods as robust and defensible as possible. So what does this all mean?
The common sample sizes and the preference for a certain "type" of approach suggest something preconceived about the nature of the PhD studies analysed here. Are students completing their samples based on what they feel they can defend, and what their supervisors and institutions require, rather than when they feel their work is actually complete? Interviewing 38 or 57 people potentially risks triggering awkward questions from readers less familiar with the concept of saturation.
However, we might expect PhD students to be predominantly orientated towards their discipline, and less concerned by challenge. Sample sizes of round numbers suggest, perhaps, an insufficient grounding in the concept of saturation. The point of saturation is, as noted here, a rather difficult point to identify and of course a rather elastic notion. New data especially if theoretically sampled will always add something new, but there are diminishing returns, and the cut off between adding to emerging findings and not adding, might be considered inevitably arbitrary.
Information gathered during semi-structured interviews can move the innovation process from general topics (domains) to more specific insights (factors and variables). It can be used to develop a preliminary hypothesis, explain relationships and create a foundation for further research.
Semi-structured interviewing in practice The research interests framing the face-to-face interview reviewed here are perceptual relationships between course choice, character and ideas of success.
Semi-structured interviews are often preceded by observation, informal and unstructured interviewing in order to allow the researchers to develop a keen understanding of the topic of interest necessary for developing relevant and meaningful semi-structured questions. The semi-structured interview is a qualitative data collection strategy in which the researcher asks informants a series of predetermined but open-ended questions.
Interviewing. This is the most common format of data collection in qualitative research. According to Oakley, qualitative interview is a type of framework in which the practices and standards be not only recorded, but also achieved, challenged and as well as reinforced. As no research interview lacks structure most of the qualitative research interviews are either semi-structured, lightly. Semi-structured interviewing is most effective when practiced by a well trained and experienced interviewer. Interviewers with less experience may have difficulty extracting all the necessary information to assess whether a candidate meets the full qualifications for the job without a set list of questions. Research the company; knowing.