Conducting a Participatory Situation Analysis of.pdf - Global HIV ...

collection is just a small component of a larger quantitative
study and was conducted mainly to provide further
depth in predefined areas of interest. The steps in conducting
a basic analysis of qualitative data are:
1. Assemble all materials. Copies of topic or interview
guide, transcripts, tapes, demographic information
about participants, assistant moderators’ and observers’
field notes, written summaries of each group.
2. Read all summaries at one sitting. Refresh one’s memory
about the setting, participants, tone of the discussion,
general reactions to the discussion, potential
trends or patterns, and strongly held or frequently
held opinions.
3. Read each transcript completely. Mark sections
that relate to each question in the topic guide. Mark
comments that might be useful for future quotation.
Highlight words, quotes, or phrases that might represent
potential classification categories.
4. Examine one question at a time. After reading all
responses to a single question, prepare a brief summary
statement that describes the discussion. Identify
themes or patterns across groups as well as themes
relating to respondents with similar demographic
characteristics.
5. Create a global synthesis of the qualitative data, examining
the findings in light of the original situation
analysis objectives. Based on the findings, the analyst
should provide insight, recommendations, and
hypotheses about these objectives. In addition, the
analyst may pose new research questions or informational
needs suggested by the qualitative data findings.
Complex Qualitative Analysis
The extent of the situation analysis needs and the qualitative
data may require a more in-depth and inductive
approach to analysis. This would be the case, for example,
when there are multiple topic guides and multiple
audiences. It is then necessary to follow a sequence of
five interrelated steps for the data analysis: reading, coding,
displaying, reducing, and interpreting. The process
begins with immersion—reading and re-reading texts
and reviewing notes. As you read, you “listen” for
emerging themes and begin to attach labels or codes to
the chunks of text that represent those themes. Using
computer software makes coding easier. Once the texts
have been coded, explore each thematic area, first displaying
in detail the information relevant to each category
and then reducing this information to its essential
points. At each step search for the core meaning of the
thoughts, feelings, and behaviors described in the texts
or field notes; that is, interpret the data. Finally, provide
an overall interpretation of the study findings, showing
how thematic areas relate to one another, explaining
how the network of concepts responds to the original
situation analysis questions, and suggesting what these
findings mean beyond the specific context of the situation
analysis.
The five analysis steps (reading, coding, displaying data,
data reduction, and interpretation) relate to one another
in a way that is both structured and flexible. It is structured
in the sense that each of these five steps builds on
previous steps. In general, one must first carefully read
the field notes and transcripts and then begin to code the
data. Reading and coding should be initiated while the
data are still being collected in the field. The data display
and reduction processes are often conducted after all
the data have been collected. However, even during these
later steps in the qualitative analysis process, researchers
may loop back through earlier steps to refine codes, reread
texts, and revise aspects of the analysis. Resources
providing more detailed information on each of the following
five steps are provided in Appendix A—
Resources, “Qualitative Methods.”
Step 1. Reading: Developing an intimate relationship
with the data
Most qualitative researchers agree that qualitative analysis
begins with data immersion. This means reading and re-
Triangulating Quantitative and
Qualitative Data
Following the separate analyses of quantitative and
qualitative data, the integration of the data findings
can be mapped out.Triangulation of data is the
process of bringing different types of data together to
build a more dimensional description of an analysis
finding. For example, if the quantitative data show that
orphans attend school less often than non-orphans,
you can consult the qualitative data for some reasons
“why” this is happening.The ability to triangulate data,
however, requires that the topics and lines of inquiry
be represented across all the data sources being used.
One critical benefit of integrating various data
sources during analysis is that it produces findings
with wide appeal. Some audiences want to see the
numbers, others want a contextual understanding, and
some want both.
68
Guidelines and Tools