Analysis of Qualitative Data
What is it?
Qualitative data are forms of information gathered in a nonnumeric form. Qualitative Data Analysis is the range of processes and procedures whereby we move from the qualitative data that have been collected into a form of explanation, understanding, or interpretation of the people and situations we are investigating. [1]
How is it used?
Analysis of Qualitative Data is accomplished by:
· Documenting the data and the process of data collection
· Organizing/Categorizing the data into concepts
· Connecting the data to show how one concept might influence another
· Corroboration, by evaluating alternative explanations, disconfirming evidence, and searching for negative cases
· Reporting the findings. [2]
Why is it used?
· Process studies (which are aimed at understanding the internal dynamics of program operations). A process focus implies emphasizing how a product or outcome is produced (rather than the product or outcome itself).
· Assessing individualized outcomes (how well a program or product meets individual needs).
· Qualitative methods permit the evaluator to study selected issues, cases, or events in depth and detail. Data collection is not constrained by predetermined categories of analysis, allowing for a level of depth and detail that quantitative strategies can't provide. [3]
Advantages:
· Greater awareness of the perspectives of program participants (or product users)
· Capability for understanding dynamic developments in a program (process) as it evolves
· Awareness of time and history
· Sensitivity to the influence of context
· Ability to “enter the program scene” without contrived preconceptions … a more fluid approach to finding out “what’s happening”
· Alertness to unanticipated and unplanned events. [3]
Disadvantages
· Analysis produced might not generalize to other people or settings.
· Does not conveniently allow for collection of statistical data.
· It is more difficult to test hypotheses and theories with large participant pools.
· It generally takes an immense amount of time and effort to collect and analyze data.
· The results are easily influenced by the analyst’s personal biases and idiosyncrasies.
Resources and Bibliography
· Lewins, A., Taylor, C., & Gibbs, G. R. (2010, December 1). What is Qualitative
Data Analysis. Online QDA. Retrieved March 23, 2014, from: http://onlineqda.hud.ac.uk/Intro_QDA/what_is_qda.php.
· Schutt, R. K. (2012). Qualitative Data Analysis. Investigating the social world: the
process and practice of research (7th ed., pp. 320 - 329). Thousand Oaks, Calif.: Sage Publications.
· Patton, M. Q. (2002). Qualitative Research and Evaluation Methods
(3rd ed.). Thousand Oaks, CA: SAGE Publications.
· Johnson, B., & Christensen, L. B. (2008). Educational Research: Quantitative,
Qualitative, and Mixed Approaches (3rd ed.). Los Angeles: Sage Publications.
Guide for Analyzing Qualitative Data
· Three data gathering strategies typically characterize qualitative methodology: in-depth, open-ended interviews; direct observation; and written documents (including program records, personal diaries, logs, etc.).
· Data from interviews, observations and document reviews are organized into major themes, categories, and case examples. The most common strategy for analyzing qualitative data is constant-comparison, but there are many other techniques from which to choose.
· There are a variety of ways to report the results of qualitative research/evaluation; common among them is the sense of story – which includes: attention to detail, descriptive vocabulary, direct quotes from those observed or interviewed, and thematic organization.
· A mix of qualitative and quantitative data gathering enriches evaluation; the open-ended comments provide a way to elaborate and contextualize statistical "facts.
· Qualitative designs are naturalistic to the extent that the evaluator does not attempt to manipulate the program or its participants for purposes of the evaluation.
· Fieldwork is the central activity of qualitative data gathering. To be in the field means to have direct, personal contact with people in their own environments. It is the researcher's desire to contextualize program or product implementation that allows him/her to capture important "results" (effects, outcomes) that standardized measures cannot.
· Most qualitative evaluators strive to understand programs and situations as a whole; there is an ongoing search for totality -- "the unifying nature of particular settings." A holistic stance assumes that understanding of a program or product depends on awareness of its political and social contexts. A holistic outlook allows the evaluator to be attentive to nuance, setting, and idiosyncrasies. [3]
Qualitative data are forms of information gathered in a nonnumeric form. Qualitative Data Analysis is the range of processes and procedures whereby we move from the qualitative data that have been collected into a form of explanation, understanding, or interpretation of the people and situations we are investigating. [1]
How is it used?
Analysis of Qualitative Data is accomplished by:
· Documenting the data and the process of data collection
· Organizing/Categorizing the data into concepts
· Connecting the data to show how one concept might influence another
· Corroboration, by evaluating alternative explanations, disconfirming evidence, and searching for negative cases
· Reporting the findings. [2]
Why is it used?
· Process studies (which are aimed at understanding the internal dynamics of program operations). A process focus implies emphasizing how a product or outcome is produced (rather than the product or outcome itself).
· Assessing individualized outcomes (how well a program or product meets individual needs).
· Qualitative methods permit the evaluator to study selected issues, cases, or events in depth and detail. Data collection is not constrained by predetermined categories of analysis, allowing for a level of depth and detail that quantitative strategies can't provide. [3]
Advantages:
· Greater awareness of the perspectives of program participants (or product users)
· Capability for understanding dynamic developments in a program (process) as it evolves
· Awareness of time and history
· Sensitivity to the influence of context
· Ability to “enter the program scene” without contrived preconceptions … a more fluid approach to finding out “what’s happening”
· Alertness to unanticipated and unplanned events. [3]
Disadvantages
· Analysis produced might not generalize to other people or settings.
· Does not conveniently allow for collection of statistical data.
· It is more difficult to test hypotheses and theories with large participant pools.
· It generally takes an immense amount of time and effort to collect and analyze data.
· The results are easily influenced by the analyst’s personal biases and idiosyncrasies.
Resources and Bibliography
· Lewins, A., Taylor, C., & Gibbs, G. R. (2010, December 1). What is Qualitative
Data Analysis. Online QDA. Retrieved March 23, 2014, from: http://onlineqda.hud.ac.uk/Intro_QDA/what_is_qda.php.
· Schutt, R. K. (2012). Qualitative Data Analysis. Investigating the social world: the
process and practice of research (7th ed., pp. 320 - 329). Thousand Oaks, Calif.: Sage Publications.
· Patton, M. Q. (2002). Qualitative Research and Evaluation Methods
(3rd ed.). Thousand Oaks, CA: SAGE Publications.
· Johnson, B., & Christensen, L. B. (2008). Educational Research: Quantitative,
Qualitative, and Mixed Approaches (3rd ed.). Los Angeles: Sage Publications.
Guide for Analyzing Qualitative Data
· Three data gathering strategies typically characterize qualitative methodology: in-depth, open-ended interviews; direct observation; and written documents (including program records, personal diaries, logs, etc.).
· Data from interviews, observations and document reviews are organized into major themes, categories, and case examples. The most common strategy for analyzing qualitative data is constant-comparison, but there are many other techniques from which to choose.
· There are a variety of ways to report the results of qualitative research/evaluation; common among them is the sense of story – which includes: attention to detail, descriptive vocabulary, direct quotes from those observed or interviewed, and thematic organization.
· A mix of qualitative and quantitative data gathering enriches evaluation; the open-ended comments provide a way to elaborate and contextualize statistical "facts.
· Qualitative designs are naturalistic to the extent that the evaluator does not attempt to manipulate the program or its participants for purposes of the evaluation.
· Fieldwork is the central activity of qualitative data gathering. To be in the field means to have direct, personal contact with people in their own environments. It is the researcher's desire to contextualize program or product implementation that allows him/her to capture important "results" (effects, outcomes) that standardized measures cannot.
· Most qualitative evaluators strive to understand programs and situations as a whole; there is an ongoing search for totality -- "the unifying nature of particular settings." A holistic stance assumes that understanding of a program or product depends on awareness of its political and social contexts. A holistic outlook allows the evaluator to be attentive to nuance, setting, and idiosyncrasies. [3]