by : Aashiya Mittal
September 25th 2023

Market research is a crucial component of advancing knowledge in various fields. It encompasses a range of methodologies, including qualitative and quantitative research. There has always been confusion about which method to adopt for data analysis.

The actual difference lies in the type of data they collect and analyze. 

Qualitative research explores subjective experiences, meanings, and interpretations through interviews and observations. On the other hand, quantitative research relies on numerical data and statistical analyses to uncover patterns and relationships. 

Understanding these research approaches provides researchers with a comprehensive toolkit to analyze and interpret data, ensuring rigorous and insightful conclusions.

For those who are still struggling through their applications and usage, we will be highlighting in and out of both methodologies to help you understand and implement better.

What is Data Analysis in Market Research?

Research data analysis refers to examining, organizing, interpreting, and deriving meaningful insights from collected data. It involves applying various statistical and analytical techniques to conclude, identify patterns, test hypotheses, and make informed decisions based on the data. 

Data analysis helps researchers make sense of the information gathered and provides a systematic approach to uncovering relationships, trends, and underlying meanings within the data. It is crucial in validating research findings, informing research questions, and understanding the research topic.

Need to Analyze Data in Market Research

Data analysis is a critical component of research for several reasons.

  • Drawing Conclusions- Analyzing data allows researchers to draw valid and reliable conclusions based on the collected information. It helps summarize the findings, identify patterns, and understand the relationships between variables.
  • Validating Research Questions and Hypotheses- Data analysis helps researchers evaluate the validity of their research questions and hypotheses. By examining the data, researchers can determine whether the evidence supports or refutes their initial assumptions and expectations.
  • Making Informed Decisions- Data analysis provides the necessary information for making informed decisions. Whether in academia, business, or policymaking, analyzing data allows researchers to identify trends, patterns, and insights that tell decision-making processes.
  • Enhancing Research Validity- Through data analysis, researchers can improve the validity of their research. By employing appropriate statistical techniques and methods, they can ensure that the findings are reliable, replicable, and representative of the studied population.
  • Identifying Research Gaps and Future Directions- Analyzing data helps researchers identify gaps in the existing knowledge and potential avenues for future research. By examining the results, researchers can gain insights into areas that require further investigation or can be expanded upon in subsequent studies.

Type of Data in Research: Qualitative Vs. Quantitative Market Research

Type of Data in Research

Understanding Qualitative Market Research

Qualitative research is an approach that aims to explore and understand the complexities of human experiences, behaviors, and social phenomena. It seeks to uncover meanings, patterns, and underlying motivations by delving into the subjective interpretations and context of the participants. 

Characteristics of Qualitative Market Research

  • Subjectivity– Qualitative research recognizes the subjective nature of human experiences and acknowledges the role of the researcher’s perspective in shaping the understanding of the phenomena under study.
  • Contextual understanding- Qualitative research emphasizes the importance of studying phenomena within their natural settings and social contexts to gain a holistic experience.
  • In-depth data collection– Researchers employ techniques like open-ended interviews, participant observations, and detailed document analysis to collect rich and complex data.
  • Emergent design- Qualitative research often involves a flexible and iterative method that allows for exploring new themes, ideas, and research questions as data collection and analysis progress.
  • Interpretive analysis– Qualitative research involves analyzing data by identifying patterns, themes, and categories and constructing narratives or explanations to make sense of the findings.
  • Small sample sizes- Qualitative research often focuses on in-depth exploration with a relatively small number of participants, aiming for detailed insights rather than statistical representation.

Qualitative Data Analysis

Qualitative data analysis systematically explores and interprets non-numerical data, such as text, images, and interviews. It involves coding, categorizing, and identifying patterns to uncover rich insights and understand the underlying meanings. Through carefully examining the data, qualitative data analysis helps researchers gain a deeper understanding of the research topic and generate meaningful narratives that capture the complexities of human experiences and perspectives.

There are two different approaches to Qualitative data analysis.

 two different approaches to Qualitative data analysis.

Below are the steps to do qualitative research.

Below are the steps to do qualitative research.

There are different methods to conduct qualitative research.

There are different methods to conduct qualitative research.

Why do we need Qualitative Data Analysis in Market Research?

Below are the reasons we need qualitative data analysis.

  • In-depth and contextual understanding– Qualitative research allows for a comprehensive exploration of complex phenomena, providing detailed insights into the depth and richness of human experiences, behaviors, and social interactions.
  • Rich data collection– Qualitative research methods, such as interviews and observations, enable researchers to gather rich and descriptive data, capturing participants’ nuances, context, and subjective perspectives.
  • Flexibility and adaptability– Qualitative research offers flexibility in data collection, allowing researchers to adapt their approach and research questions during the study to explore emerging themes or unexpected findings.
  • Participant perspectives– Qualitative research focuses on understanding the attitudes and meanings attributed by participants, giving voice to their experiences and allowing for a more inclusive and diverse understanding of the phenomenon.
  • Theory development– Qualitative research contributes to theory development by generating new concepts, hypotheses, or theories based on qualitative data analysis, allowing for exploring novel ideas and perspectives.
  • Validity and authenticity– Qualitative research prioritizes establishing trustworthiness and credibility through methods such as prolonged engagement, triangulation of data, member checking, and reflexivity, enhancing the validity and authenticity of the findings.
  • Exploratory and hypothesis-generating– Qualitative research is experimental, making it valuable in the early stages of research when little is known about a phenomenon, helping to generate hypotheses for further investigation.
  • Real-world applications– Qualitative research findings inform real-world applications in healthcare, education, social work, marketing, and policy-making, providing practical insights and guiding evidence-based decision-making.

Limitations of Qualitative Market Research

It is important to note that these limitations do not diminish the value and significance of qualitative research but rather highlight the considerations and trade-offs associated with this research approach.

  • Limited generalizability– Qualitative research often involves small sample sizes and focuses on specific contexts, making it difficult to generalize findings to larger populations or different settings.
  • Potential for researcher bias– The subjective nature of qualitative research introduces the possibility of researcher bias in data collection, analysis, and interpretation, impacting the objectivity of the findings.
  • Time-consuming data collection and analysis– Qualitative research requires substantial time and resources for data collection through interviews, observations, and document analysis. The analysis process of qualitative data can be time-consuming due to the need for detailed coding and interpretation.
  • Lack of quantifiable data– Qualitative research does not rely on numerical data and statistical analysis, which can limit the ability to measure and quantify phenomena, making it challenging to establish statistical relationships.
  • Limited statistical analysis– Qualitative research does not typically employ statistical tests and measurements, which may limit the ability to draw statistical conclusions or analyze quantitative relationships.

Understanding Quantitative Market Research

Quantitative research is a systematic and empirical approach that collects and analyzes numerical data to understand and explain phenomena. It involves using statistical methods to draw objective conclusions and generalize findings to larger populations.

As per Statista, below are the traditional quantitative methods used in the market research industry worldwide in 2022.

quantitative methods used in the market research industry worldwide in 2022

Image Credits: Statista

Characteristics of Quantitative Market Research

  • Objective measurement– Quantitative research relies on standardized and measurable variables, allowing for precise and accurate data collection.
  • Numerical data collection– Researchers gather numerical data through structured surveys, experiments, or existing datasets to quantify and analyze relationships, patterns, and trends.
  • Statistical analysis– Quantitative research utilizes statistical techniques to analyze data, including measures of central tendency, correlation, regression, and inferential statistics to make statistical inferences.
  • Generalizability– Quantitative research aims to generalize findings from the sample to the larger population, seeking to draw broader conclusions and make predictions.
  • Control over variables– Researchers often strive to control extraneous variables and manipulate independent variables to establish cause-and-effect relationships.
  • Large sample sizes– Quantitative research typically involves larger sample sizes to ensure statistical power and enhance the representativeness of findings.
  • Replicability– Quantitative research emphasizes the ability to replicate studies to test the robustness and reliability of findings, contributing to scientific validity.
  • Deductive reasoning– Quantitative research often follows a reasoned approach, starting with a hypothesis or theory tested and refined through data analysis.

Quantitative research is widely used in psychology, economics, sociology, and natural sciences, where numerical data and statistical analysis are instrumental in understanding patterns and relationships and making evidence-based decisions.

Quantitative Data Analysis

Quantitative data analysis is like uncovering the secrets of numbers. It’s a process of crunching data, exploring statistical relationships, and unlocking patterns and trends. Rigorous analysis allows researchers to make sense of large datasets and draw precise conclusions. With the power of numbers, quantitative data analysis empowers decision-making, reveals insights, and provides a quantitative lens to understand the world around us.

Different methods for quantitative data analysis are-

Different methods for quantitative data analysis are-

Why do we need Quantitative analysis in Market Research?

There are several ways in which Quantitative research is helpful for Research purposes.

Why need Quantitative analysis

  • Objectivity and reliability– Quantitative research focuses on measurable data, promoting objectivity and increasing the reliability of findings as multiple researchers can replicate the study and obtain similar results.
  • Generalizability– With larger sample sizes, quantitative research allows for the generalization of findings to broader populations, enhancing the external validity of the research.
  • Statistical analysis– Quantitative research employs statistical methods that enable precise analysis, facilitating the identification of patterns, correlations, and causal relationships among variables.
  • Efficiency in data analysis– Quantitative data analysis can be automated and conducted using software, enhancing the efficiency of analyzing large datasets and reducing the potential for human errors.
  • Precise measurement– Quantitative research provides numerical data, allowing for accurate measurement of variables, which is particularly useful in fields where precise quantification is necessary, such as physics or economics.
  • Trend identification– Quantitative research can detect trends and changes over time, facilitating the identification of long-term patterns or shifts in behaviors, attitudes, or phenomena.
  • Evidence-based decision-making– Quantitative research provides empirical evidence that supports evidence-based decision-making in various domains, such as healthcare, policy-making, and business strategy development.

Limitations of Quantitative Market Research

It is essential to recognize that these limitations do not undermine the value of quantitative research but rather highlight the considerations and trade-offs associated with its application in specific research contexts.

  • Lack of depth and context– Quantitative research often focuses on numerical data, which may limit understanding of complex phenomena and fail to capture the depth and context of human experiences or social interactions.
  • Limited exploration of new concepts– Quantitative research may not be suitable for exploring new or emerging ideas, as it relies on predefined variables and measures, potentially overlooking essential aspects of the phenomenon.
  • Restrictive data collection methods– Quantitative research relies heavily on structured surveys or experiments, which may limit the types of data collected compared to more flexible qualitative methods.
  • Potential for researcher bias- Despite the objective nature of quantitative research, researcher bias can still influence study design, data collection, and interpretation, potentially compromising the validity of the findings.
  • Difficulty capturing context-specific factors– Quantitative research may need help to account for unique contextual factors that can significantly influence outcomes, as it often emphasizes generalizability over context specificity.
  • Inability to explore complex social phenomena– Some social phenomena, such as power dynamics, cultural meanings, and subjective experiences, may be challenging to quantify accurately, limiting the insights provided by quantitative research.
  • Potential for measurement errors– Quantitative research relies on the accurate measurement of variables, and errors in data collection or measurement instruments can affect the reliability and validity of the findings.

Tabular Difference: Quantitative vs Qualitative Market Research

Quantitative vs Qualitative Research

The above table provides a general overview of qualitative and quantitative research’s main characteristics and differences. The actual practice and implementation may vary depending on the research design and field of study.

Real-world example: Combining the power of Qualitative and Quantitative Market Research

Understanding Customer Satisfaction in an E-commerce Company

For example, a large e-commerce company aimed to enhance customer satisfaction and improve its services. They employed qualitative and quantitative data analysis methods to gain comprehensive insights.

Qualitative Research

The company conducted in-depth interviews with customers to understand their experiences, preferences, and pain points. The interviews were transcribed and analyzed using thematic analysis. Themes such as delivery speed, product quality, and customer support emerged, providing a deeper understanding of customer perceptions.

Quantitative Research

A survey was designed and distributed to a more extensive customer base to collect quantitative data. The survey included Likert-scale questions to measure satisfaction levels and demographic questions for segmentation purposes. Statistical analysis techniques, such as descriptive statistics and correlation analysis, were used to examine the relationships between customer satisfaction and various factors.

Outcomes and Insights

The combined analysis of qualitative and quantitative data provided valuable outcomes and insights. The qualitative analysis revealed areas where the company needed improvement, such as enhancing customer support and streamlining the delivery process. The quantitative analysis showed overall satisfaction levels, identified customer segments with different preferences, and highlighted factors significantly influencing satisfaction.

The company implemented targeted strategies to address the identified issues based on the findings. They improved their customer support system, optimized the delivery process, and introduced personalized recommendations based on customer segments. As a result, customer satisfaction increased, leading to higher customer loyalty, positive word-of-mouth, and improved business performance.

Improve Market Research with OnGraph

The debate between qualitative and quantitative market research methods regarding data analysis is not a matter of one being inherently superior to the other. Both approaches have unique strengths and limitations, and their appropriateness largely depends on the research objectives, the nature of the phenomenon under investigation, and the available resources.

Ultimately, the choice between qualitative and quantitative research should be driven by the research question and the study’s specific needs, with the recognition that a combination of both methods can often yield the most comprehensive and robust understanding of a phenomenon.

We have the tools to help you make the best from qualitative and quantitative methodologies. So drop us a query today and see the results yourself.


Aashiya Mittal

A computer science engineer with great ability and understanding of programming languages. Have been in the writing world for more than 4 years and creating valuable content for all tech stacks.

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