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PUR3500 Chapter 5
Terms in this set (38)
(1) broad philosophy and approach to research
(2) a research methodology
(3) a specific set of research techniques
Paradigm: an accepted set of theories, procedures, and assumptions about how researchers look at the world
Positivist paradigm: oldest and most widely used (involves concepts as quantification, hypotheses, and objective measures)
Interpretive paradigm: to understand how people in everyday natural settings create meaning and interpret events of their world. (Max Weber and Wilhelm Dilthey.)
Critical paradigm: draws on analysis models used in humanities. Concepts such as distribution of power in society and political ideology.
5 major research areas
(1) role of researcher: P= strives for objectivity & is separated from the data. I= integral part of data; without the active participation of researcher, no data exist.
(2) design: P= design of study is determined before it begins. I= design evolves during research and may be adjusted throughout
(3) setting: P= tries to limit contaminating variables by conducting investigations in controlled settings. I= conducts studies in field, natural surroundings, trying to capture normal flow of events without effecting it.
(4) measurement instruments: P= measurement instruments exist apart from the researcher. I= researcher is instrument & no one else can substitute
(5) theory building: P= uses research to test, support or reject theory. I= develops theories as part of research process; theory is data driven and emerges as part of process
Advantages to mixed methods research
Can produce stronger evidence for a conclusion through a convergence of findings
Researcher can answer broader range of questions b/c research is not confined to a single method
Can provide info that might be missed if only a single method were used
disadvantages to mixed methods research
Requires more time & effort
Requires researcher to be skilled in both methods
Data analysis may be more difficult, particularly if methods yield conflicting results
phase I of data analysis
The mass of qualitative data - interview transcripts, field notes, observations, etc. - is reduced and organized.
-For example: categories, coding, writing summaries, discarding irrelevant data, etc.
Try and discard all irrelevant information, have access to it later if required, unexpected findings may need you to re-examine some data previously considered unnecessary.
tips for phase I of data analysis
Arrange data in chronological order
Code to identify the source
Consider prior research or theory when making categories
Come up with a way to categorize (comments in the margins, index cards, etc.)
Be aware of yourself as the interviewer
phase II of data analysis
To draw conclusions from the mass of data, Miles and Huberman suggest that a good display of data, in the form of tables, charts, networks, and other graphical formats is essential.
A continual process, rather than just one to be carried out at the end of the data collection.
Phase III of data analysis
Analysis should allow you to begin to develop conclusions regarding your study.
Coding data and following the stages of coding.
steps in constant comparison method
Comparative assignment of incidents to categories
Elaboration and refinement of categories
Searching for relationships and themes among categories
Simplifying and integrating data into a coherent theoretical structure
constant comparison method
each piece of information is coded and compared to other pieces for similarity and differences.
First Coding stage
The data is carefully read, all statements relating to the research question are identified, and each is assigned a code, or category.
These codes are then noted, and each relevant statement is organized under its appropriate code. This is referred to as open coding.
Second coding stage
Using the codes developed in stage one, the researcher rereads the qualitative data, and searches for statements that may fit into any of the categories.
Further codes may also be developed in this stage. This is also referred to as axial coding.
Basically: elaborating and refining
third coding stage
Look for patterns and explanation in the codes
Questions should be asked such as:
Can I relate certain codes together under a more general code?
Can I organize codes sequentially (for example does code A happen before code B)?
Can I identify any causal relationships (does code A cause code B)?
fourth coding stage
Selective coding involves reading through the raw data for cases that illustrate the analysis, or explain the concepts.
You are looking for an understanding.
Look for data that is contradictory, as well as confirmatory.
Avoid confirmation bias, or the tendency to seek out and report data that supports your own ideas about the key findings of the study.
What should you look for once you code the data?
Look for patterns or regularities that occur.
Within each code, look for data units that illustrate or describe the situation you are interested in.
Identify key words or phrases and try to make sense of the data.
Look for statements that not only support your theories, but also refute them.
Refers to a situation in data analysis where participants' descriptions become repetitive and confirm previously collected data.
An indication that data analysis is complete.
When data analysis is complete, data collection is terminated.
Phase IV of data analysis
Verification of Data
The initial conclusions can then be verified, that is their validity examined through reference to your existing field notes or further data collection.
Holloway and Wheeler (2009) summarise the means by which you can try to ensure the trustworthiness of your data.
Data must be complete
Qualitative researchers cannot simply dismiss data that do not fit a favored interpretation of the data. They must analyze these cases and offer explanations as to why the data don't seem to fit.
Qualitative research often raises the question of reactivity: when the act of observing some situation changes the situation itself
In-Person interviews v. online
Data are "richer" in that observers can see physical responses and surroundings of their respondents (body language & facial expressions add to understanding)
Respondents do not need special computer/keyboarding skills
Projective tests and product demonstrations are possible
Group dynamics can offer clues to analysis & interpretation
Research is integral part of data collection
online interviews v. in-person
Online qualitative research:
Coverage of wide geographic areas is possible. Neither respondent nor researcher have to be in same spot
Online behavior of large groups (ex. FB groups) can be observed
Responses may be more thoughtful and contain more info b/c responses can be done at convenience
No bias for or against vocal/outgoing respondents
Expenses are often lower than other approaches
Useful for collecting data & generating hypotheses and theories
Like all qualitative, concerned more with description and explanation than with measurement
Classified among two major dimensions
(1) degree to which researcher participates in behavior under observation
(2) degree to which observation is concealed
Advantages of Field Analysis
Helps researcher define basic background info necessary to frame a hypothesis and isolate independent and dependent variables
Make excellent pilot studies b/c they identify important variables and provide useful preliminary info
b/c info is gathered firsthand, observation is not dependent on subjects' ability or willingness to report their behavior
***study takes place in the natural environment (can provide data rich in detail)
Disadvantages of Field Analysis
Poor choice if concerned with external validity
Experimenter bias may favor specific preconceptions of results b/c field ob. Relies heavily on researcher's perceptions
Cross-validation: having multiple observers in a field study to eliminate potential bias from one observer
Suffer from problem of reactivity (the very process of being observed may influence the behavior under study)
Six stages of field study
(1) choosing the research site
(2) gaining access
(4) collecting data
(5) analyzing data
Group interviewing (6-12 people) with a moderator
Usually provide qualitative data
Allow for collection of preliminary info
Can be conducted quickly
Flexibility in questioning design & follow-up
Extended focus group: respondents are required to complete a written questionnaire before session begins. Forces respondent to commit to a particular answer or position before entering the group. Eliminates the problem of a person not speaking their opinion b/c they may be the minority
Focus groups can be
(1) self-contained: only means of data collection
(2) supplementary: follow-up data for quantitative study for a quantitative study
(3) multi-method: focus groups are only one of a number of qualitative techniques used
disadvantages of focus groups
There is sometimes a self-appointed group leader who will hog the questions/talk a lot. Others gain resentment toward these respondents and may affect the group
Small group samples may not represent population from which they were drawn
Equipment or other characteristics of location may inhibit respondents
Generally use smaller samples
Provide detailed background about specific answers
Allow for lengthy observation
Can be customized
Can be influenced by interview climate
Wealth of detail
More accurate responses on sensitive issues
Generalizability (people will always answer in a slightly different way)
Especially sensitive to interview bias
Problems in data analysis
Case Studies: 4 essential characteristics
(1) particularistic- study focuses on particular situation, event, program, making it a good method for studying practical, real-life problems
(2) descriptive- final product of a case study is detailed description
(3) Heuristic- helps people to understand what's being studied
(4) Inductive- studies depend on inductive reasoning
Advantages/Disadvantages of case studies
Provide tremendous detail
Can suggest why something occurred
Allows researcher to deal with a wide spectrum of evidence
It is easy to do a sloppy case study; rigorous studies require time and detail
Not amendable to generalization
Often time consuming and may occasionally produce massive quantities of data that are hard to summarize
Ethnographic research sometimes used as a synonym for qualitative research
Ethnography: the process in which researchers spent long periods of time living with an observing other cultures in a natural setting
Macro-ethn. (study of entire cultures) & micro-ethn. (study of smaller units)
Two categories: descriptive and critical (critical studies power and hegemony and attempts to uncover hidden agendas and unquestioned assumptions. Goal is often political)
Online research blog: personal diary kept by sample of respondents who have something in common
Online research community: targeted group of people who are recruited to join a private online website to participate in research over period of time
Netnography: qualitative research method that uses ethnographic research to study communities that are linked together via computer-mediated communication
Based on published messages rather than direct observation
Relies on archives
Thanks to social media, it examines a form of private interaction that takes place in public space
styles of qualitative reports
(1) realist: a dispassionate third-person pov
(2) confessional: first-person pov that reveals much about author
(3) impressionist: metaphors and vivid imagery to get point across
*much longer than quantitative reports
Error of segregation: occurs when data are separated so far from each other in the analysis that readers cannot make the connection
Writing the Qualitative Research Report
Very straightforward, but complicated
Difficult to condense qualitative data into numerical tables and charts b/c it comes in the form of sentences, quotes, descriptions, pictures, etc.
There is less standardization of methods
Researchers may try to give readers a subjective "feel" for research setting
Reports can use more free-form & literary styles than quantitative
Credibility refers to accuracy.
Description must be plausible and recognized by participants.
Prolonged time in the field repeatedly observing and interacting with participants.
Using different data sources, methods, data type.
Conducting member checks.
Involving other investigators in the study. Read over your notes.
Dependability refers to the stability and trackability of the changes in data over time and conditions.
Want to determine the extent to which another researcher with similar training and rapport with participants would make the same observations.
This is determined by an audit trail
Involves auditing research process, documenting all the raw data generated, and assessing method of data analysis.
Transferability refers to the generalizability of the study findings to other settings, populations, and contexts.
Report must provide sufficient detail so that readers can assess this.
Lack of transferability is viewed as a weakness of qualitative methods.
Confirmability refers to the objectivity of the data.
Would another researcher agree about the meanings emerging from the data.
An audit trail is used in which the researcher explicates how personal biases may have come into play.
no qualifications or restrictions related to who gets into a sample
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