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14.1 Description and Integration
By now, you should have responses to your survey and be ready to start reviewing the results.
Survey results are often used by researchers as part of the data published in scholarly articles.
Because they are often working with large, developed data sets often over long periods of time,
they can interpret their findings in context and use them to make and support complex claims,
none of which is our goal. Our goal is to practice describing the results and to integrate the
findings into our Analytic Exploration.
We discussed how to begin gathering data, and future courses or employers may build on this
foundational skill with the exact specifications your field requires for data collection. The next
step in the process is doing something with the data, or performing data analysis. Many
professional and academic fields prefer workers to have a general knowledge of data analysis,
and specific positions exist for experts in a dedicated type of data analysis. A basic
understanding of data analysis is also valuable for readers and audiences of research and any
source-informed content. Much of the content you encounter will include interpretations of
analyzed data, so having a basic understanding of data analysis will prepare to read and write
within and beyond USF.
Data sets can be analyzed in many ways and with many tools. With small data sets, you might
determine that your critical reading skills are all you need to locate patterns and connections
within the data and describe them within context. Larger data sets, like the ones you may work
with later in your career, often require analytic technologies to parse the data. These
technologies include: AntConc, QDA Miner, Nvivo, R, SPSS, Python, Power BI, Tableau, and
others. Some tools are utilized more in academic research, and some are preferred by industry,
but the overlap is expanding. Some have a dashboard that helps viewers dive into the specifics
of the data set. USF uses Tableau to make a significant amount of data public (USF system
facts, strategic performance data, rankings, survey research, post-graduation outcomes).
These technologies are capable of interpreting multiple variables and entries, so they can make
working with very large data sets easier. While we won’t work with these tools in this course,
expertise with R, SPSS, and Python is often requested and rewarded by employers. Learning
coding languages/technologies can also provide you with an edge over other candidates on the
Your current data set is probably very small, so you should be able to read it closely and
critically without aid from analytic tools. Use Google Forms to look at your responses. When you
click on Responses at the top of your Form, you will see some answers displayed as graphics. If
you included questions that allow for short answers or longer text, the responses are also
included in the Responses, but to manipulate at these results, export the responses to sheets
by clicking the green icon. Sheets and Excel share similar functionality. Once your data is in
sheets, you can move it around to create different views that allow you to see different patterns.
Some basic Forms functions are freezing the first row with the column titles and sorting a sheet
by A to Z. You can also move columns around so that you can see the results of two answers
together. Simply moving columns and arranging them alphabetically can reveal useful
information. If your survey asked whether or not people were familiar with your topic and asked
what their major was, you might not notice much by reading down the list of yes and no
answers. But if you move those two columns together, you might start to see a connection. And
if you ordered the yes/no column alphabetically, you could see all the yes answers together.
When you alphabetize a column, the responses in all the rows should move, too, so that the
answers stay aligned. By looking at the yes answers next to the list of majors, you might notice
that many of most of the respondents who are familiar with the topic are in the same discipline,
which would be a pattern you could mention in your findings.
If your survey asked people their opinion on their topic and asked where they got their
information about the topic, you could line up those two columns to see if there is any
connection between the personal opinion and the sources. Describing that information is useful
for our Analytic Exploration. Interpretation is an option, too, but be careful not to overstate or
assume. For instance, if you see a pattern between a specific opinion on your topic and a
source that survey takers with that opinion say they use to get their information, can you
suggest that the source is the reason for their opinion? What if confirmation bias is at play and
the people who already have an opinion on the topic seek out sources that confirm their
opinions? And what if respondents forgot other sources they read? You can describe the
connection, but you don’t have enough information to make causal claims.
The Analytic Exploration will allow us to practice describing and connecting our findings. The
first step is simply describing the data, which is less simple than it might seem. As you read
through and organize the data set in search of connections and patterns, think about what you
are learning about the people who completed the survey. Consider what the data tell you about
what people know and think about the subject. Your data will allow you to make suggestions
about what people think or know, so be careful to use suggests, supports, or similar verbs to
phrase your claims. Data never prove anything definitively. When you are working with a small
sample, in particular, your data can only support certain claims about the subject.
For 14.1, you will write one paragraph describing the data. As you are describing the data,
include details about the survey, your delivery, and your sample. Explaining to your readers that
your results came from a survey distributed to a specific number of users via a specific website
helps establish your credibility. Writers who don’t state where their data came from appear to
have something to hide, and many readers will discount your claims if you do not provide this
information. After providing context, describe what you think are important elements of the
findings and note why they are of value. Making and stating that connection can move you
beyond summary and description, which is ok in this case, but be sure not to go too far.
Next, you will write one sentence summarizing the results. If you wrote a piece about the
survey results, this would be the thesis. Remember to avoid overstating or extrapolating the
findings. Because you know that your goal will be to connect this work to the larger assignment,
your summary should’ve already started to do that work by highlighting a takeaway that you
know is relevant to what you looked for and at in your other research and readings. For
instance, if your topic were electric vehicles, and your academic and popular articles provided
specific information on electrical vehicle usage, hopefully your created your survey questions
with that in mind. If one of the articles you read argued that people who bought electric vehicles
were most likely to do so for environmental reasons, your survey might have asked readers if
they have an electric vehicle and if so, why they bought it. Perhaps you had five respondents
who did have an electric vehicle and perhaps all five said they bought it for the options. Your
one-sentence summary might state that 100% of the electric vehicle owners you surveyed
purchased their electric vehicle for the option.
More than one takeaway can be noted in your summary, but if you know what you plan to do
with the findings, you only need to note what is relevant. You could say that your results related
to the reasons for purchase did not align with the argument from that article. Perhaps it did align
with the findings of other articles, and if so, both of those pieces of information would be
valuable in your one-sentence summary. In fact, a summary like that would prepare you for
Finally, you will write one sentence describing how the finding connects to the other
sources. You can also write how the finding differs from other sources if your results suggest
something the other research does not support, which is a common finding with smaller,
focused samples like the one you cultivated. Remember that many research articles use large,
random samples to collect data, so their findings make claims about a broad group of people,
while your findings make claims about a small, focused group. The difference doesn’t invalidate
either their findings or yours and can add critical nuance to your research topic. As you think
about nuance, or different perspectives and meanings, consider how your primary data
compares to your secondary and even tertiary sources. The goal of research is to generate
knowledge, so work with all your sources to create knowledge about a subject that did not exist
before. The goal of your research is to discuss all of your sources in relation to each other.
Your survey will serve as one of the six sources you consider as part of your Analytic
Exploration, so when you start to think through the connections across sources, consider both of
the popular sources and all three of the academic articles. To revisit our electric vehicle
example, you could say that your results related to the reasons for purchase did not align with
the results from one article. Perhaps they did align with the findings of other articles, and if so,
both of those pieces of information would be valuable in your one-sentence summary. You
might even find that your finding aligns with the three academic sources but none of the popular
sources, which would be worth exploring if this were a different or longer assignment. For now,
allow your work in 14.1 to prepare you for synthesis.
1. Write a one-paragraph description of the results of your survey.
2. Write one sentence summarizing any key finding(s).
3. Write one sentence connecting the finding(s) to the other sources.