Posted: February 18th, 2022

Analyzing the significance of silence in qualitative

 

 

 

 

 

Week 2 Assignment

 

 

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Week 2 Assignment

Denham, M. A., & Onwuegbuzie, A. J. (2013). Beyond words: Using nonverbal communication 

data in research to enhance thick description and interpretation. International Journal of Qualitative Methods, 12(1), 670-696.

The authors, professors at Sam Houston State University, use ex facto data from articles published in The Qualitative Report journal to test their hypothesis that the collection of non-verbal cues and communication can result in a more prominent description and interpretation of the qualitative research finding. Further, the study tries to solve three issues, namely; how widely researchers make use of nonverbal communication is used in qualitative research, the extent to which researchers explore nonverbal communication during data collection, analysis and interpretation and lastly, the benefits derived from including nonverbal communication in the process of conducting qualitative research.

The authors delve into the subject matter by describing the typologies of nonverbal communication by expounding on the indicators of nonverbal cues, including kinesis, proxemics and chronemics. They explain the role of nonverbal communication during data collection by highlighting the concepts of talk, observation, imagery and documents and how each of these concepts can be enhanced by incorporating nonverbal communication data. Further, nonverbal communication data in the analysis and interpretation process is expounded through explanations in conversion and latent content analysis. In conversion analysis, the aim is to check for the actions of participants during a conversation. The participant behaviours become the main source of data. On the other hand, latent analysis focuses on identifying the underlying meaning of actions and behaviours portrayed by participants. 

The authors find their hypothesis supported in only a few articles within the qualitative report journal. In three-level classification criteria, namely mention, identify and justify, the authors observed that the nonverbal data were considered in only a few articles throughout the entire article. The first level, mentioned, indicated instances where the nonverbal communication data had been generally introduced. The second level identify, denoted instances where the non-verbal data were further named and classified in the article. The third level justifies cases where the nonverbal data was further justified and interpreted in the article. The authors noted that level three was underutilized since none of the articles ventured into this level. The concern brought forward by the authors is whether the qualitative research authors do not collect any nonverbal data at all, or they deliberately choose not to use the nonverbal data they have collected. By comparing the more than six hundred and eighty-five articles in the Qualitative report, the authors have satisfactorily addressed their hypothesis and concern. Their arguments that most qualitative researchers do not use nonverbal communication data have been well defended in the article.

By reading this article, I have appreciated the use of nonverbal communication data in developing my empirical study. Incorporating nonverbal data in the entire research process strengthens the research findings and increases inclusivity. I have learnt that it doesn’t only stop collecting the nonverbal communication data but proceeds to analyze and interpret the data to add value to the research findings. I have appreciated the need to understand the framework for collecting, analyzing and interpreting such nonverbal data.

In future, an acceptable standard framework can be developed to aid qualitative researchers in collecting and analyzing nonverbal communication data. Further, a general effort to accept the work of qualitative researchers who choose to use nonverbal communication data by journal editors is needed to propagate this practice.

 

St. Pierre, E. A., & Jackson, A. Y. (2014). Qualitative data analysis after coding. Qualitative 

Inquiry, 20(6), 715-719.

The authors, Pierre and Jackson, use empirical studies from different authors to try and explain the aspect of coding and its relationship to qualitative data analysis. The authors start by equating data analysis as the black hole of qualitative research. This is because, according to them, data analysis may be thought to be difficult to describe and explain, especially to those who seem to think otherwise. They raise a question about whether data can be coded if one is taught the coding process. The authors are concerned more about qualitative analysis in which words are treated as brute data that can be coded and labelled with other words. Such data can be fed into computer programs and even sometimes reduced to numbers. 

The authors discredit the vacuum approach of data collection. Researchers do a general sweep up of data in their studies and forget to check the quality of data being utilized. They argue that in this method, the researchers consider all the data as equal and worthy to be considered for analysis which, according to them, shouldn’t be the case. Instead, they propose that researchers use theory first to understand what qualifies as data and what is considered appropriate data. They argue that researchers can work with minimum but quality data in this case. They agree that researchers should be more focused on data analysis than conducting interviews.

A good argument arises within the article of what exactly should be regarded as data and how such data can be collected. The duo presents two interlocking issues regarding this case. First, they argue that using presence as a measure of quality assumes that the collected data is of high quality and worthy of being collected and analyzed. This makes researchers regard some words like data and disregard others as not data. Secondly, an assumption of empirical reality is made in which words are taken as brute data separate from the desired interpretation of the researcher. The authors thus think that this presents a non-sensical issue about coding. They argue that words can either be data that can be interpreted or codes that are already meaningful in coding. In other approaches, language is assumed to be entirely contaminated by meaning; as such, it can never be seen as a brute. The authors agree that data can not exist without theory. A theory that provides order and classification to everything in the world. The authors further highlight a tendency of people to identify patterns where none exist. In theory, if a researcher thinks in their mind that they have found a theme, they would be convinced that they have indeed found one. This, as put by the authors, can be an excuse for not reading theory. The authors denote that the data analysis has been wrongly equated to coding since analysis is not fixed but contains spaces of possibilities. It is neither fixed not is it homogenous.

By reading this article, I understood the different coding perspectives during qualitative analysis. The main idea has been reflected in the substitution of undertaking a deep understanding of the theory with coding. A comprehensive understanding of the theory is important to do a good qualitative analysis. Coding in itself can be biased and subjective without an elaborate conceptual foundation.

In future, an elaborate conceptual foundation and framework of coding can be developed to provide qualitative researchers with a guide on qualitative coding data. Further, emphasizing the focus on data and comprehension of the theory must be advocated for. This will discourage researchers from focusing more on coding than actually understanding the research data obtained.

 

Lynch, M., & Mah, C. (2018). Using internet data sources to achieve qualitative interviewing 

purposes: A research note. Qualitative research, 18(6), 741-752.

The authors, Meghan and Catherine, from the University of Toronto and Memorial University, respectively, use different empirical studies to scrutinize the advantages and challenges of social media and internet data sources in qualitative research. Their hypothesis alludes that social media analysis and email interviewing support the traditional approaches currently available. The authors begin by posing a challenge to interpret internet sources. For instance, should text messages and emails be classified as written or verbal forms of asynchronous text? The measure of trustworthiness and authenticity of data from internet sources is also highlighted as a concern. In the article, an examination on the collection and interpretation of face-to-face interviews, email interviews and social media discussions has been made.      

The authors delve into the subject matter by discussing two major internet methods, namely social media discussion analysis and email interviews. Social media analysis has been compared to the traditional face-to-face observation technique. This takes the form of social media communities with which the researcher familiarizes himself during data collection. This interaction can either be static or dynamic. It is static because there is no interaction between the researcher and the participants. Observations are made by the researcher on the written text discussions. Synchronous and asynchronous interactions between the participants and researcher result in the dynamic nature of social media discussions. Email interviews are considered face-to-face interviews that use email as a medium. The researcher and participant exchange mails as a way of interaction. Email interviews are strictly asynchronous. 

These internet data collection sources present several advantages. They both possess elements of ease of archiving, cost-effectiveness and time effectiveness. Potential interruptions are also minimized. Additionally, accessibility to socially isolated areas or where the population is highly dispersed is improved through internet sources. Email interviews, in particular, have been argued as the best method of collecting accurate data since there is a sense of privacy and protection. This makes the participants willing to disclose information since no one else will gain access to the information. Access to public perception is enabled through social media discussions. Further, a researcher can access both current and past data, increasing the research scope. 

On the other hand, data quality and trustworthiness have been identified as the main challenge of internet data sources. There is the exclusion of people who do not have an internet connection and communication gadgets when using these sources. Further, there is limited information regarding the participants since there is no physical interaction.

By reading this article, I have understood the types of internet sources, their usage, merits and demerits. I have gained knowledge of each internet source’s data collection process, its similarities and differences, and its application techniques. Although the sources threaten the quality and trustworthiness of data, their usage has proven to be efficient, especially in time and cost management.

In future, researchers should adopt a framework that brings together both the traditional methods of data collection and social media sources. This will aid in the collection of more quality data that transcends beyond the barriers of geographical distance, cost and time.

 

Bengtsson, T. T., & Fynbo, L. (2018). Analyzing the significance of silence in qualitative 

interviewing: Questioning and shifting power relations. Qualitative Research, 18(1), 19-35.

The authors, Bengtsson and Fynbo, use data obtained from interviewing thirty-six individuals to test their hypothesis that silence entails chances for the interviewees and interviewers to either maintain or lose control of the interview by handling the power of silence during a qualitative interview. The study analyses the importance of silence during a qualitative interview. The effect of the dynamic power of silence between the interviewee and the interviewer is described in length.

The authors state that speech is the primary source of data in interviews. Researchers, however, tend to neglect the aspect of silence during interviews. While some researchers argue that silence distorts the smooth flow of a conversation, the authors identify silence as a prescriptive power structure manifesting between the interviewee and the interviewer. This can take two forms; a disempowered interviewee and a powerful interviewer or powerful interviewee and a powerless interviewer. The latter occurs mostly when the interviewee refuses to be oppressed or intimidated. The authors argue that the power of silence can result in unpredictable relations that, when harnessed, can result in quality manifestations and revelations.

The authors argue that even though silence creates a general disruption in the flow of an interview, these disruptions can be constructive since they aid in identifying unexpected expressions that result in invaluable data. The power of silence is further dependent on the different agendas between the interviewer and the interviewee, the varying stories to disclose and the different social horizons. Silence creates an environment of experiencing diverse expressions. According to the authors, silence can imply several things, including; resistance and protest, strategy by the interviewer in deriving a hidden story from a stubborn interviewee and an emotional show, especially where the interviewee recounts unpleasant occurrences. 

By reading this article, I have understood that silence during interviews should not only be viewed as a breakdown or disruption of a conversation but as an important aspect of obtaining valuable information. Important power relations are manifested through the silence in qualitative data analysis. As a qualitative researcher, silence can provide meaningful information, and through silence, I can derive additional information, especially from un-willing interviewees. Silence has been associated with power by enabling qualitative interviews that are dynamic.

In future, qualitative researchers should create an open discussion on the strategies of harnessing the power of silence in obtaining data and information that can prove to be good n deriving the research findings. More emphasis can be put on this topic to help sensitize researchers on the need to stop ignoring silence and embrace its significance. Silence should no longer be avoided but be embraced during qualitative data collection.

 

Key Themes that Emerge from this week’s Resources

Nonverbal communication in data collection and their further interpretation and analysis is vital in qualitative research. A vital error of omission is created where qualitative researchers fail to appreciate and consider nonverbal communication in their qualitative research. 

In as much as coding is important in qualitative research, the main task of the researcher is to gain a deeper understanding of the theory first. Equating coding with qualitative data analysis brings problems that interfere with the quality of the research findings. This is brought about by identifying what to consider as data and what not to consider as data. 

Internet data sources, also known as social media sources, present as important data sources during data collection. The use of social media sources broadens the focus groups. Social media discussion analysis and email interviews both present a cost-effective and time-effective method of data collection. Caution should, however, be taken in considering the quality and trustworthiness of the data collected.

Silence during interviews presents a valuable source of additional information for the qualitative researchers. Silence should not be viewed as a disruption in communication or conversation, but the power thereof should be tapped to provide meaningful information useful in the research findings. 

 

Possible Challenges in the process of qualitative data analysis.

Limited sample size. Qualitative research has no generally accepted population sample size to be used. The analysis process becomes more costly, especially where the sample size is big. Since the quality of data is dependent entirely on the data obtained, qualitative research in most cases requires a considerate sample size.

Observer-expectancy effect

Nearly all the data collection techniques require the researcher’s presence in the field. The researcher’s presence unconsciously influences the behaviours of the participants in the field. This might affect the quality of data collected, which interferes with the whole process of data analysis, especially in developing codes and themes.

Coding Challenge

The coding process is challenging and requires resources in terms of time and know-how. Qualitative researchers have to develop codes that are then combined into themes that can be used to generate findings. However, the process of determining codes can end up being subjective and biased. 

 

References

Bengtsson, T. T., & Fynbo, L. (2018). Analyzing the significance of silence in qualitative 

interviewing: Questioning and shifting power relations. Qualitative Research, 18(1), 19-35.

Denham, M. A., & Onwuegbuzie, A. J. (2013). Beyond words: Using nonverbal communication 

data in research to enhance thick description and interpretation. International Journal of Qualitative Methods, 12(1), 670-696.

Lynch, M., & Mah, C. (2018). Using internet data sources to achieve qualitative interviewing 

purposes: A research note. Qualitative research, 18(6), 741-752.

St. Pierre, E. A., & Jackson, A. Y. (2014). Qualitative data analysis after coding. Qualitative 

Inquiry, 20(6), 715-719.

 

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