Explaining validity and reliability of qualitative data in Nvivo

By Divya Dhuria & Priya Chetty on July 20, 2017

It is important to establish the reliability and validity of the data before going further into the procedure of using NVivo for qualitative data processing, manipulation, analysis and representation. Scholars have studied and acknowledged the significance of meeting the condition of reliability and validity in data for generating reliable results. Qualitative analysis lacks details about methods used to generate results. In majority of the cases, qualitative data is manually handled. One can analyze data on the basis of individual understanding and interpretation of the responses, attitudes and behavior of the participants. Therefore, reliability and validity can be achieved by maintaining quality, rigor and trustworthiness through processes such as triangulation.

Validity and reliability in Nvivo

However, in the case of Nvivo, validity and reliability of the results or data representation are not sufficiently explained. Earlier studies have shown that qualitative researches are biased as they are based on the researcher’s own interpretation. Therefore, depicting reliability and validity of the present case (Nvivo) is important (Golafshani 2003).

In order to explain the application of Nvivo, interview transcripts are used to analyse the case of assessing secondary education. However, to show the reliability and variability of this case, this article aims to assess the accuracy of results of Nvivo and thus, these methods.

Nvivo minimises error through search facility

Nvivo helps in finding answers to research questions in qualitative data easily.

For example, if researchers intend to search a project component in terms of attributes. In such case it is important to identify the sample of secondary school teachers from South Delhi and Nvivo can assist in finding an accurate answer for this.

In case where qualitative analysis is performed manually using a big sample, the chances of getting an accurate answer quickly are limited. Setting aside time, getting insightful answers using manual analysis are still difficult. Furthermore, carrying out a search electronically will yield more reliable results because human error is ruled out.

Nvivo replaces thematic connections with electronically produced coding

Nvivo, after collecting and importing data, classifies the same in different nodes and applies coding, bringing them together under common themes. Either manually or automatically, coding in Nvivo helps in arranging similar responses at one place (Node).

For example, all the responses about the student’s performance is coded together in one node. Similarly one can code all the responses about student’s participation in another node.

In case of manual analysis & interpretation (e.g. matrix method), building connections between each of the responses are difficult. First, identifying similar responses can be confusing. It is easy to mistake responses of participation. Even after identification, it is difficult and  time consuming to manually arrange similar responses at one place. In contrast, the electronic coding process and creation of matrix are quick (compared to cutting and pasting pieces of text manually).

Hence, more coding takes place in a study that uses software over the one that uses only manual methods. Thus, electronically- produced coding provides more reliable results (minimum error).  It also validates the research as searching for the same connection again and again will bring similar results.

Nvivo allows for both manual and automatic procedures

Often, researchers worry about using automatic routes to perform analysis; more so if the sample size is small. In such cases, Nvivo also allow researchers to conduct the research manually and act as a data management tool. Thus, Nvivo provides the dual option of coding manually and automatically. In order to achieve the best results, it is important that researchers do not rely only on electronic or manual methods. Instead one should combine the best features of both.

For example, if a researcher wants to conduct manual coding, one can simply code the transcripts containing all the responses by selecting each and every response manually.

Coding uses software and hence Nvivo helps minimize the chances of human error as well as possible automatic error. Automatic error can also occur in Nvivo as it may code two words which have close meaning but used in different responses as has been explained in Auto-coding article. Manual Coding in that case minimizes that error as well and provides an accurate response. Nvivo saves coded content for reuse, if any. This establishes the validity of the results.

Nvivo allows for building themes based on linked information

The expertise of the analyst can determine the extent the software is used beyond this basic utility.

For example, writing memos in the software rather than manually (writing in a notebook for example) and linking different pieces of data together through electronic memos can be useful when building themes in data.

In order to make sense of these memos though, it is useful to return to “manual” methods. This involves going through coded text as well as memos and making notes on how all of these link together. Nvivo also provides different tools such as Project Maps, Mind Maps and Concept Maps (hyperlink of each article needed) to keep linking the Nodes and Cases and save it in the electronic memo. Through such tools, Nvivo maintains the reliability of the results derived from different themes.

Therefore, to conclude,  NVivo application benefits gigantic data to obtain systematic and valid results. This despite the fact is not completely reliable and valid medium for data processing. Manual effort helps to confirm these results. The next article outlines the case research to understand each step of data processing in Nvivo, its utility and interpretation.


  • Golafshani N. 2003. The Qualitative Report Understanding Reliability and Validity in Qualitative Research Understanding Reliability and Validity in Qualitative Research.

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

  • Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
  • Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
  • Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).