NURS 6052/NURS 5052 Week 9: Statistical Methods in Qualitative Research

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: 1

st

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.NURS 6052 Week (1-11) – Essentials of Evidence-Based Practice Essay.

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.