How to analyze qualitative data
Dec 13, · Managing, Condensing, Displaying, and Interpreting Qualitative Data Analysis of qualitative data can be divided into four stages: data management, data condensation, data display, and drawing and verifying conclusions (9). Analyze Qualitative Data Qualitative data analysis involves the identification, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help answer the research questions at hand. Qualitative analysis is (NSF, ): Not guided by universal rules.
Qualitative data analysis involves the dafa, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help answer the eo questions at hand. How to do qualitative data analysis, this section will provide a loosely structured guide for the steps you should take when analyzing qualitative data.
It is important to note that qualitative data analysis is an ongoing, fluid, and cyclical process that happens throughout the data collection stage of your evaluation project and carries over to the data entry and analysis stages. Although the steps listed below are somewhat sequential they do not always and sometimes should not happen in isolation of each other. Figure lists examples of types of questions you should ask yourself throughout the analysis process.
While analyzing your qualitative data what is 3d video mapping is important that you continuously ask yourself the following types of questions:. As soon as data is collected it is critical that you immediately process the information and record detailed qualitativd. It is important to do this while the interaction is still fresh in your mind so that you can record your thoughts and reactions as accurately as possible.
Qualitative data analysis should begin as soon as you begin collecting the first piece of information. Qualitative anzlysis generally produce a wealth of data but not all of it is meaningful. After data has been collected, you will need to undergo a data reduction process in order to identify and focus in on what is meaningful. This is the process of reducing and transforming your raw data.
It is your job as the evaluator to comb sualitative the raw data to determine what is significant and transform the data into a simplified format that can be understood in the context of the research questions Krathwohl, ; Miles and Huberman, ; NSF, When trying to discern what is meaningful data you should always refer back to your research questions hlw use them as your framework. Additionally, you should rely on your own intuition as the evaluator and the expertise of other individuals with a thorough understanding how to do qualitative data analysis the program.
This step does anqlysis happen in isolation, it naturally occurs during the first two steps. You are already reducing data by not recording every single thing that occurs in your data collection interaction but only recording what you felt was most meaningful, usable, and relevant.
You are also reducing data by looking for themes from the beginning. This process helps you hone in on specific patterns and themes of interest while not focusing on other aspects of the qualjtative. The process of data reduction, however, must go beyond the data collection stage. Evaluators must take quzlitative to carefully review all of the analhsis you have collected as a whole.
This process is the core of qualitative data analysis. The type of analysis is highly dependent on the nature of the research questions and the type s of data you adta. Sometimes a study will use one type of analysis and other times, a study may use both types. This type of coding is done by going through all qualiitative the text and labeling words, phrases, and sections of text either using words or symbols that relate to your research questions of interest.
After the data is coded you can sort and examine the data by code to look for patterns. Thematic analysis — grouping the data into themes analysus will help answer the research question s. These themes may be Taylor-Powell and Renner, :. Once your themes have been identified how to mitigate ddos attack cisco is useful hw group the data into thematic groups so that you can analyze the meaning of the themes and connect them back to the research question s.
After identifying themes or content patterns, assemble, organize, and compress the data into a display that facilitates conclusion drawing. To verify these conclusions, you must revisit the data multiple times to confirm the conclusions that you have drawn. Print This Page. Analyeis Qualitative Data.
Qualitative analysis is NSF, : Not guided by universal rules Qualitativve a very fluid process that is highly dependent on the aualitative and the context of the study Likely to change and adapt as the study evolves and the data emerges. How do these patterns or lack thereof help to shed light on the broader study what does points mean in a mortgage loan s?
Are there qualiitative deviations from these patterns? If, yes, what factors could explain these atypical responses? What interesting stories emerge from the data? How can these stories help to shed light on the broader study question? Do any of they study questions need to be revised? Do the patterns that emerge support the findings of other corresponding qualitative analyses that have been conducted? Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Process and Record Data Immediately As soon as data is collected it is critical that you immediately process the information and record detailed notes.
It is helpful to make a reflection sheet template that you carry with you and complete after each interaction so that it is standardized across all analyeis collection points. Begin Analyzing as Data is Being Collected Qualitative data analysis should begin as soon as you begin collecting the first piece of information.
Daata moment the first pieces of data are collected you should begin reviewing the data and mentally processing it for themes or patterns that were exhibited. It is important to do this early so that you will be focused on these patterns and themes as they appear in subsequent data you collect. Data Reduction Qualitative studies generally produce a wealth of data but not all of it is meaningful. This process is generally conducted in two primary ways: Content analysis Thematic analysis The type of analysis is highly dependent on the nature of the research questions and the type s of data you collected.
Sometimes a study will use one type of analysis and other times, a study may use both types Content analysis is carried how to make a shoulder holster for a gun by: Coding the data for certain words or content Identifying their patterns Interpreting their meanings. These themes may be Taylor-Powell and Renner, : Directly evolved from the research questions and were pre-set before data collection even began, or Naturally emerged from the data as the study was conducted.
Data Display After identifying themes or content patterns, assemble, organize, and compress the data into a display that facilitates conclusion drawing. Through this process you should be able to identify patterns and relationships observed within groups and across groups.
For example, using our Summer Program study, you could examine patterns and themes both within a program city and across program cities. Conclusion Drawing and Verification Conclusion drawing and verification are the final step in qualitative data analysis.
To draw reasonable conclusions, you anlaysis need to Krathwohl, ; Miles tto Huberman, ; NSF, : Step back and interpret what all of your findings mean Determine how your findings help answer the research question anqlysis Draw hw from your findings To verify these conclusions, you must revisit the data multiple times to confirm the conclusions that you have drawn.
Analyze Qualitative Data
He has a background in emerging technologies, media and communications and international relations. Unpacking large amounts of qualitative data can be a daunting task but with a little preparation and some simple steps, drawing insights from you data can be made just that little bit easier.
The steps outlined below are especially useful if you have thoroughly planned your projects prior to engaging with your community. I highly recommend our recent webinar with Dan Popping for a good overview of planning for online engagement. The first step towards conducting qualitative analysis of your data is to gather all of the comments and feedback you want to analyse.
For this activity, you might consider a master spreadsheet as a place to collect all of your feedback or you might have other digital tools such as EngagementHQ to help you organise your content. As part of organising your content you want to setup your analysis template. If you are using a spreadsheet you might consider using variables as seen below to help get you started. Above: Example of how to setup a qualitative analysis spreadsheet.
In the first column, you can see a field for data source. This variable will allow you to filter through your responses to compare views collected via different means. Next, you can see a stakeholder type variable, which comes in handy for drilling down into different stakeholder groups which you might need to report on. The most important variable required for your dataset is the code field which you will use to code and organise you data in the next step.
Finally, you can also include an identifier for the question the data was collected for to further help you drill down into your insights. In your master data template you can also include multiple columns for collecting your coding and you might also be required to add any demographic fields you have captured.
You should allow a suitable amount of time for organising your data, especially if you are collecting it and entering it from a variety of sources. The next step in this process is about coding your comments and most importantly reading and making a decision about how each one should be organised.
The first way assumes that you are looking for a pre-defined set or list of issues or themes, whilst the other method is focused on unpacking themes without having any prior expectations about what they should be.
A good way to do this is to create a simple table outlining what each code is and what it covers. Above is an example of a coding table used for qualitative analysis. In this legend, you can outline your theme and description and if you want to take it a step further you might even add issues as a secondary tag within a theme.
If you decide to do the alternate method and unpack your qualitative data to try and derive themes for your code list, you are going to need to read a sample of your comments. Once you have completed this you should ask a colleague to read through the same sample and check to see if they agree with your coding. When this is complete, refer to the themes you have identified and complete a coding sheet as per above. Regardless of which option you choose, you will be required to read through your comments and make some decisions about them.
Intimate knowledge of the feedback is often missed with automated tag clouds and sentiment analysis and it can encourage lazy practice and unintentionaly lead you to jump to incorrect conclusions. Your reporting requirements will determine the extent and type of the queries you run during this step. Once you have run your queries and explored your data you should have a good foundation and enough insights to begin your reporting.
If you fail to do this step well, your community will absolutely lose faith in your process and you might even face potential community outrage. Use your insights to create a narrative about the issues and opportunities which your community have identified. When framing your insights you might consider using the following as a useful way of talking about and quantifying your findings;.
You should also include relative charts and visuals to help your community further explore your data. Generate these in a spreadsheet or other data visualisation software application such as Microsoft Power BI, Google Studio or Qualtrics to name a few.
At this stage, you can test whether you have framed their concerns and issues correctly and allow yourself to make and final changes before you submit a final report and make your decisions. As you can see, these four steps provide a simple process to follow for organising your data, determining your coding tables, running queries and reporting on your consultation. Make these steps a part of your project planning process and ensure you always have an end to end picture of how you are going to collect and report on your data before you begin your consultation.
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