qualitative data analysis · qualitative data analysis is the process of examining qualitative data...

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Learn about key tips and steps to analyze qualitative data effectively.

Qualitative Data Analysis

Conducting Qualitative Data Analysis

What is Qualitative Data?

Qualitative data is non-numerical data, usually in the form of words, which captures concepts and opinions. Qualitative data analysis is different from “quantitative” data analysis, where data is in the form of numbers and scales. Some examples of qualitative data include:

• Transcripts from interviews and focus group discussions

• Notes from observations

• Audio and video recordings

• Surveys with open-ended questions

• Case studies and stories

• Newspaper articles

• Social media posts

• Photographs

• Symbols

• Drawings

• Behavior

• Objects


What is Qualitative Data Analysis?Qualitative data analysis is the process of examining qualitative data to provide some level of explanation, interpretation, and understanding of a specific issue, question, or research objective by revealing patterns and themes in your data. Quantitative analysis focuses on the size or scale of a concept or opinion while qualitative analysis focuses on depth, breadth, and the nature of the concept or opinion. Qualitative data analysis helps answer questions such as:

• How?

• Why?

• What does it mean?

Qualitative Analysis MethodsQualitative analysis can be used at any stage of a program or project cycle. It can be used as a single data analysis method or alongside quantitative methods. Qualitative analysis methods need to be planned and implemented carefully in order to generate findings that are useful, credible, and reliable, especially for the people or organizations who will need to act on these findings. Common qualitative analysis methods include:


The Purpose of Qualitative Data Analysis Qualitative analysis can be carried out to explore and define the problem, develop an approach to the problem, or look deeper into specific issues. These are different purposes of qualitative data analysis:

Uncover unexpected changes.

Shed light on processes that led to change and the reasons behind why something happened or how it happened.

Capture change or lessons learned from different points of view.

Equip staff to use their experience, knowledge, and expertise to develop insights, and create deeper understanding of the change processes.

Generate case studies or stories of change that can communicate in-depth information on meaningful changes in peoples’ lives.

Promote in-depth understanding of underlying reasons and motivations of a change or specific behavior.

Provide insight when very little is known about a situation or problem.

Generate ideas and hypotheses for later research, and help shape the questions that need to be answered.


Types of Qualitative Analysis

There is no single method for carrying out qualitative analysis. Two common types of qualitative analysis are thematic analysis and narrative analysis. Many qualitative analysis processes combine both of these types.

Thematic Analysis

Data is sorted and analyzed around key themes or groupings of information. Sometimes the themes:

• Are decided before the analysis takes place (based on program/project objectives, indicators, questions, dimensions of change, or key lines of inquiry)

• Emerge out of the analysis

Narrative Analysis

Focuses on particular cases and results that produce quotes, anecdotes, testimonials, case studies, or stories of change. Narrative analysis may be used on its own or to supplement thematic analysis.


Analyzing Qualitative Data

5 Steps to Analyzing Qualitative Data

Qualitative data analysis is more than just listening and making notes. Each step in the process requires careful planning, significant time and using specific skills and resources. Qualitative analysis can start at the same time as data collection. Follow these five steps to analyze qualitative data effectively.


Step 1: Familiarize Yourself with the Data

Review the Data

• Transcribe any audio material into text.

• Re-read your notes and transcripts several times before analyzing them. This will help you familiarize yourself with the content and form first impressions.

• List key ideas and recurrent themes.

Store the Data

Store the raw data before analyzing it following these steps:

1. Create a data bank containing all the data collected (transcripts, survey answers, extracts from secondary literature, observation notes, media cuttings, photographs, recordings, films, etc.).

2. Log the data along with supplementary information such as the demographics of people interviewed (age, ethnicity, gender, etc.), consent for the data to be used in different ways, and the dates the data was generated.


Step 2: Identify Themes

Identify Themes and Issues

• Identify the key issues, concepts, and themes to use in the analysis.

• Some themes may exist before the data is analyzed (evaluation questions, indicators, objectives, etc.).

• Examine the following as needed:

9 Context in which an issue arises

9 Apparent meanings that an issue has in people’s lives

9 Steps in a process (accessing and using a service)

9 Common emotions across stories

9 Language associated with an issue and its connotations

Ask Questions About the DataAsk the following questions about the data you are analyzing:

• What is happening?

• What are people doing?

• What are people saying?

• What is ‘implicit’? What do these actions and statements take for granted?


Step 3: Code the Data

Identify Elements to CodeIdentity elements that can be coded and analyzed such as:

• Behaviors and specific acts

• Events (lifechanging events or things people have done that can be told as a story)

• Activities (of a longer duration or involving other people within a particular setting)

• Strategies, practice, or tactics

• States (general conditions experienced by people or found in organizations)

• Relationships or interaction

• Participation (adaptation to a new setting or involvement)

• Conditions or constraints

• Consequences

• Settings (the entire context of the events under study)

• Reflexive (researcher’s role in the process and how intervention generated the data)

• Meanings and interpretations which are important sources of participants’ actions. Questions to determine meanings include:

9 What concepts do participants use to understand their world?

9 What norms, values, and rules guide their actions?

9 What meaning or significance do concepts have for participants?

9 How do participants construe events? How do they feel?

9 What symbols do people use to understand their situation?

9 What names do they use for objects, events, persons, roles, setting, and equipment?


Code the Text

• Divide the text into the smallest possible meaningful unit, such as a sentence or action.

• Apply the thematic framework or index to all the data by systematically annotating the transcripts with codes from the list you have created.

• For single passages of text that contain a large number of different themes, record each theme by adding multiple codes to a single piece of text.

• Code the text using one of the following methods:

9 Hand-write the codes beside the marked text using a highlighter pen.

9 Create a table using a word processing program such as Microsoft Word. Convert all the text into a single-column table, breaking at paragraphs or after each line. Then, add a column where you can place the codes.

9 Create a spreadsheet using Microsoft Excel. First, convert the text into a table and then export it as a spreadsheet.

9 Use qualitative data analysis software (e.g. NVivo, AtlasTI, Dedoose).

• Create a “code book” to define each code clearly and update the file as codes are redefined. This will enable other people to take part in coding and make it easier to analyze future data.


Step 4: Interpret the Data

Summarize the DataOnce you have coded the text, you are now ready to analyze it in detail following these steps:

• Sort the text using the labels to group similar items together.

• Summarize the clusters of information using the participants’ own words as much as possible. Do not ignore contradictions – capture them.

• Create a summary for each key subject area or theme as needed. This process often involves a considerable amount of abstraction and synthesis.

Validate and Verify the DataThere are many ways to validate data such as:

• Triangulate information to validate individual pieces of information. This means collecting information from different sources or in different ways to ensure that the information is consistent.

• Verify interpretations, conclusions, or recommendations that arise out of qualitative analysis by asking participants who were interviewed or took part in discussions if they agree or disagree with your interpretations.

• Examine cases that do not seem to fit the general conclusions and investigate further to see why they do not fit.

Interpret the Data Qualitative analysis often relies heavily on interpretation (sometimes known as second level analysis) to look across different themes and narratives. There are many ways and approaches to interpreting data. Essentially, this step is about working out what it all means. It may involve:

• Looking for patterns or trends including: similarities or differences in responses; relationships between themes or groupings; differences in how strongly people feel about issues; typical or unusual responses

• Working with teams of people to interpret data who have a good knowledge of the context

• Using preliminary findings to shape further data collection and analysis


Develop Case Studies from the DataNarrative analysis, whether carried out on its own or alongside thematic analysis, may involve developing detailed case studies or stories of change. This may include:

• Going back and collecting further data on particular cases in order to make them more meaningful or fill in gaps in the evidence

• Checking or triangulating evidence

Step 5: Report the Findings

Determine How to Use the DataThe purpose of any analysis is to generate information that can be used. This can mean:

• Drawing conclusions about what has changed and the contribution of a project or program

• Involving the generation of lessons, conclusions, and recommendations for further action

• Using information for fundraising, publicity, marketing, or other purposes

Select the Appropriate Reporting FormatSelect the most suitable presentation and reporting options for the qualitative data and for your target audience such as:

• Formal report

• Slide presentation

• Factsheet

• Poster or infographic

• Brochure

• Website

• Video

• Meeting or workshop


Using Qualitative Data Analysis Software

Qualitative data analysis software can help manage, store, code, and process large amounts of data. The actual coding and interpretation must be done by people. There is no standard qualitative data analysis software that will suit every situation, however, different software can support qualitative data analysis by making it easier to:

• Organize and code data

• Search for keywords or phrases (and automatically code them if relevant)

• Explore frequencies, sequences, and locations of words and phrases

• Visualize data to show patterns and trends

• Create graphical mapping and diagrams to illustrate findings or theories


Tips for Effective Qualitative Data Analysis

There are many challenges associated with qualitative analysis that may not be evident when carrying out the analysis process. Qualitative data collection methods can result in vast amounts of information, partly because the information is easy to collect. However, analyzing this information can become difficult and time-consuming. Follow these tips to design and conduct a meaningful and effective qualitative data analysis.

Tips for Effective Qualitative Data

Analysis

Be aware of too much information that can lead to “data drowning”. Set up criteria to avoid collecting more data than you actually need.

Identify up front how you will want to use the data. This will help avoid the common problem of “trawling” – collecting large amounts of qualitative data without knowing how it should be used.

Always lead with the data. Sometimes there is so much information you can conclude almost anything based on the data. This can cause you to “cherry pick” - look for evidence to support pre-defined beliefs and opinions, rather than being led by the data. Try to avoid this!

Distinguish when different kinds of evidence are needed for different purposes. For example, information used for decision making may need to be weighted or quantified, and therefore further investigation may be required to test initial, tentative findings.


Summarize and condense qualitative data carefully when conducting a thematic analysis. Be aware of how you summarize data, so you do not over-simplify the analysis and lose detail and nuance from the original data.

Watch out for potential areas of bias. Bias can occur at any phase: during documenting, summarizing, sampling, interpreting, or reporting qualitative data. Biases can also arise from how interviews or observations are conducted (asking leading questions or not fully recording answers) – you may detect this during analysis.

Know when to consider a more technical analysis. If you have large quantities of very short comments (from large-scale surveys), you may need to consider conducting a more technical analysis (linguistic analysis or semantic analysis), rather than thematic qualitative analysis.

Do not underestimate the effort involved. Allocate sufficient time, resources, training, and support to the qualitative data analysis process.

Use technology appropriately to save time. Find ways to automate some of the elements of the analysis process so you spend more time thinking rather than organizing data.

qualitative data analysis · qualitative data analysis is the process of examining qualitative data...

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