Respond using one or more of the following approaches:

Ask a probing question, substantiated with additional background information, and evidence.

Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.

Statistical Methods in Qualitative Research 

  

Statistical Method

What is measured by this method

Circumstances for Use

Examples of use in Research Studies

 

Qualitative Content Analysis

Analyzes narrative data, and in-depth interviews. Can evaluate large volumes of data with   intent to identify recurring themes and patterns. Attempts to break down   elements of data into clusters. May be concurrent or sequential (Polit &Beck,   2017).

Good method for evaluating personal histories, perspectives,   experiences. Best method for studying personal, sensitive situations (Sauro,   2015).

Examples of this methodology include evaluation of the experience of a   rape victim, what it feels like to have an abortion, how it feels to have   lived through a disaster.

 

Ethnographic analysis

Evaluates cultural phenomena, patterns, perspectives. Requires   “participant observer” technique. No preconceived hypothesis. May take months or years to complete. Maps and flowcharts are tools to help   illustrate findings (Polit & Beck, 2017).

Method to “acquire a deep understanding of the culture being studied”   (Polit & Beck, 2017 p. 538).

An example of ethnographic analysis could include a research study   with ethnographers integrating with Native Americans living on a reservation   while observing everyday life seeking to extrapolate overlying cultural   issues.

 

Phenomenologic Analysis

Attempts to understand the   essence of experiencing a particular phenomenon by observation, interviews,   and outside research. Descriptive analysis 

Method for understanding individual perspectives of experiencing a   certain phenomenon. Seeks to extrapolate commonalities and themes among   subjects (Sauro, 2015). 

Conducting interviews with persons who have experienced   hallucinations, with the intent to understand their perspective and   experience of the phenomenon, is an example of this method of research.

 

Grounded Theory Analysis

Aim is to provide theories and explanations for phenomena based on   previously coded information Uses interviews and previous accepted research. Unlike Qualitative content analysis, which   seeks to break down information, Grounded theory strives to put information   back together (Polit & Beck, 2017).

Method for development of theories, Could be used meta-analyses or   systematic reviews.

An example of a grounded theory analysis is” Beck’s (2002) model of   mothering twins” as cited in Polit & Beck (2017).

 

Focus Group Analysis 

Analyzes group data in relation to a specific topic. Group interviews, recordings, and field   notes .are instruments for conducting this type of research.

May be used for evaluation of a potential survey tool, consensus on a   new product. Researchers seek to   extrapolate recurring themes.

An example of a focus group analysis might be to evaluate perceptions   of a new product being marketed to test for general consensus of its   desirability. 

Quasi-statistics: a tabulation of the frequency with which certain themes or insights are supported by the data

Qualitative content analysis: analysis of the content of narrative data to identify prominent themes and patterns among the themes

Domain analysis: 1st of 4 levels of data analysis, domains are units of cultural knowledge, are broad categories that encompass smaller ones. Ethnographers identify rational patterns among terms in the domains are used by members of the culture. Ethnographer focuses on the cultural meaning of terms and symbols used in a culture

Taxonomic analysis: second level of data analysis, ethnographers decides how many domains the analysis will encompass. Taxonomy is then developed to illustrate the internal organization of a domain and the relationship among the subcategories of the domain

Taxonomy: a system of classifying and organizing terms

Componential analysis: relationships among terms in the domains are examined; ethnographer analyzes data for similarities and differences among cultural terms in a domain.

Theme analysis: cultural themes are uncovered; domains are connected in cultural themes, which help to provide a holistic view of the culture being studied. The discovery of cultural meaning is the outcome.

Holistic approach: researchers view the text as a whole and try to capture is meanings 

Selective approach: researchers highlight or pull out statements or phrases that seem essential to the experience under study 

Detailed approach: researchers analyze every sentence 

Hermeneutic circle: signifies a methodological process in which to reach understanding, there is continual movement between the parts and the whole of the text being analyzed 

Exemplars: illuminate aspects of a paradigm case or theme

Substantive codes: substance of the topic under study is conceptualized through substantive codes. Substantive codes are either open or selective

Open coding: used in the first stage of the constant comparative analysis, 

captures what is going on in the data. May be actual words stated by participant. In open coding, 

data are broken down into incidents and their similarities and differences are examined. Raw  

data interpreted 

Three Levels of Open Coding: Levels I, II, III

Level I codes: in vivo codes, derived directly from the language of the 

substantive area and have vivid imagery 

Level II codes: Researchers constantly compare new level one codes to 

previously identified ones and then condense them into broader level II 

codes

Level III codes: theoretical constructs, most abstract, add scope beyond local 

meanings

Core category: pattern of behavior that is relevant and/or problematic for participants

Selective coding: can have 3 levels of abstraction, researchers code only those data that are related to the core variable

Basic social process (BSP): evolves over time in two or more phases, all BSP’s are core variables, but not all core variables have to be BSPs

Emergent fit: prevents individual substantive theories from being “respected little islands of knowledge”

Axial coding: analyst codes for context

Paradigm: used as an analytical strategy to help integrate structure and process

Central category: core category, which is the main theme of the research 

Initial coding: pieces of data (words, lines, segments, incidents) are studied so the researcher begins to learn what the participants view as problematic 

Focused coding: the analysis is directed toward using the most significant codes from the initial coding

Congruent methodological approach: analyzes interaction data in the same manner as a group or individual data 

Sociograms: can be used to understand the flow of conversation as it goes around the members of the focus group

Incubation: process of living the data, a process in which researchers must try to understand their meanings, find their essential patterns, and draw legitimate, insightful conclusions

Conceptual files: physical files in which coded excerpts of data relevant to specific categories are placed

Themes: involves the discovery nor only of commonalities across participants but also of natural variation and patterns in the data

Metaphors: figurative comparisons used to evoke a visual or symbolic analogy

Quasi-statistics: involves a tabulation of the frequency with which certain themes or relations are supported by the data

Qualitative content analysis: can vary in terms of an emphasis on manifest content or latent content and in the role of induction

Managing Qualitative Data

Computer-assisted qualitative data analysis software (CAQDAS): a program that can take uploaded data files, code the narratives, retrieve information, and display text for analysis

Within a qualitative data analysis there is not statistical tests, because qualitative research is based on thoughts, open ended questions, interpretations and interviews not numerical values. Data within qualitative research is understood and analyzed during the entirety of the process. “Researchers interpret the data as they read and reread them, categorize and code them, inductively develop a thematic analysis, and integrate the themes into a unified whole,” (Polit & Beck, 2017, p.549). There is not a step by step understanding of how the process occurs of interpreting the data, researchers “live” within the data by understanding the meanings, looking for patterns, draw valid, discerning conclusions. An additional importance of understanding of the facts is having the inventiveness to find the “aha” meaning of the information and discovery of the meanings of the facts gained (Polit & Beck, 2017). 

The importance of the interpretation is just as important as the validity of the data. Thorough and sensible researchers have a high standard of their data interpretation by dissecting themselves, peers and outside reviewers. It is vital that the qualitative researchers consider possible different explanations or meanings other than their own (Polit & Beck, 2017).

It is important nurses to understand statistical data because this is a large part of the work nurses base the practice on is evidence based, which means understanding the research behind the reason of the practice is important to understand. According to Hayat, it is important to understand the difference between statistical significance and clinical importance, researchers tend to use statistics to claim proof and scientific breakthrough. Significance testing can be used to decide which data may be considered evidence to support a practice change (2010). “Judgment and subjectivity are necessary and part of the decision-making process. Statistical significance is not a measure of importance; it is a subjective and qualitative construct. Researchers conducting quantitative analyses should quantify the magnitude of an effect. The value of the data collected should be assessed by examining study design, bias, and confounding variables, as well as meaningfulness of the results to the topic under study,” (Hayat, 2010, p.222). Nurses must consider this and have an understanding when utilizing statistical methods to base their practice changes. 

References

Hayat, M. J. (2010). Understanding Statistical Significance. Nursing Research, 59(3), 219–223

Polit, D.E. & Beck, C.T. (2017). Nursing Research: Generating and Assessing Evidence for Nursing 

Practice 10th ed. Philadelphia, PA: Wolters Kluwer

Sauro, J., (2015. October 13). Five types of qualitative methods, Retrieved from https://flic.kr/p/4PXXCYp.

By: Casey Hoffman, Tami Frazier, Sarah Pudenz, and Elizabeth Wilson