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  • What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

  • In descriptive research , you simply seek an overall summary of your study variables.
  • In correlational research , you investigate relationships between your study variables.
  • In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

  • Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

  • Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

  • Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

  • Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

  • Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

  • Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Example of Quantitative Analysis in Finance

Drawbacks and limitations of quantitative analaysis, using quantitative finance outside of finance, the bottom line.

  • Quantitative Analysis

Quantitative Analysis (QA): What It Is and How It's Used in Finance

meaning of quantitative analysis data

Ariel Courage is an experienced editor, researcher, and former fact-checker. She has performed editing and fact-checking work for several leading finance publications, including The Motley Fool and Passport to Wall Street.

meaning of quantitative analysis data

Investopedia / Hilary Allison

Quantitative analysis (QA) refers to methods used to understand the behavior of financial markets and make more informed investment or trading decisions. It involves the use of mathematical and statistical techniques to analyze financial data. For instance, by examining past stock prices, earnings reports, and other information, quantitative analysts, often called “ quants ,” aim to forecast where the market is headed.

Unlike fundamental analysis that might focus on a company's management team or industry conditions, quantitative analysis relies chiefly on crunching numbers and complex computations to derive actionable insights.

Quantitative analysis can be a powerful tool, especially in modern markets where data is abundant and computational tools are advanced, enabling a more precise examination of the financial landscape. However, many also believe that the raw numbers produced by quantitative analysis should be combined with the more in-depth understanding and nuance afforded by qualitative analysis .

Key Takeaways

  • Quantitative analysis (QA) is a set of techniques that use mathematical and statistical modeling, measurement, and research to understand behavior.
  • Quantitative analysis presents financial information in terms of a numerical value.
  • It's used for the evaluation of financial instruments and for predicting real-world events such as changes in GDP.
  • While powerful, quantitative analysis has some drawbacks that can be supplemented with qualitative analysis.

Understanding Quantitative Analysis

Quantitative analysis (QA) in finance refers to the use of mathematical and statistical techniques to analyze financial & economic data and make trading, investing, and risk management decisions.

QA starts with data collection, where quants gather a vast amount of financial data that might affect the market. This data can include anything from stock prices and company earnings to economic indicators like inflation or unemployment rates. They then use various mathematical models and statistical techniques to analyze this data, looking for trends, patterns, and potential investment opportunities. The outcome of this analysis can help investors decide where to allocate their resources to maximize returns or minimize risks.

Some key aspects of quantitative analysis in finance include:

  • Statistical analysis - this aspect of quantitative analysis involves examining data to identify trends and relationships, build predictive models, and make forecasts. Techniques used can include regression analysis , which helps in understanding relationships between variables; time series analysis , which looks at data points collected or recorded at a specific time; and Monte Carlo simulations , a mathematical technique that allows you to account for uncertainty in your analyses and forecasts. Through statistical analysis, quants can uncover insights that may not be immediately apparent, helping investors and financial analysts make more informed decisions.
  • Algorithmic trading - this entails using computer algorithms to automate the trading process. Algorithms can be programmed to carry out trades based on a variety of factors such as timing, price movements, liquidity changes, and other market signals. High-frequency trading (HFT), a type of algorithmic trading, involves making a large number of trades within fractions of a second to capitalize on small price movements. This automated approach to trading can lead to more efficient and often profitable trading strategies.
  • Risk modeling - risk is an inherent part of financial markets. Risk modeling involves creating mathematical models to measure and quantify various risk exposures within a portfolio. Methods used in risk modeling include Value-at-Risk (VaR) models, scenario analysis , and stress testing . These tools help in understanding the potential downside and uncertainties associated with different investment scenarios, aiding in better risk management and mitigation strategies.
  • Derivatives pricing - derivatives are financial contracts whose value is derived from other underlying assets like stocks or bonds. Derivatives pricing involves creating mathematical models to evaluate these contracts and determine their fair prices and risk profiles. A well-known model used in this domain is the Black-Scholes model , which helps in pricing options contracts . Accurate derivatives pricing is crucial for investors and traders to make sound financial decisions regarding buying, selling, or hedging with derivatives.
  • Portfolio optimization - This is about constructing a portfolio in such a way that it yields the highest possible expected return for a given level of risk. Techniques like Modern Portfolio Theory (MPT) are employed to find the optimal allocation of assets within a portfolio. By analyzing various asset classes and their expected returns, risks, and correlations, quants can suggest the best mix of investments to achieve specific financial goals while minimizing risk.

The overall goal is to use data, math, statistics, and software to make more informed financial decisions, automate processes, and ultimately generate greater risk-adjusted returns.

Quantitative analysis is widely used in central banking, algorithmic trading, hedge fund management, and investment banking activities. Quantitative analysts, employ advanced skills in programming, statistics, calculus, linear algebra etc. to execute quantitative analysis.

Quantitative Analysis vs. Qualitative Analysis

Quantitative analysis relies heavily on numerical data and mathematical models to make decisions regarding investments and financial strategies. It focuses on the measurable, objective data that can be gathered about a company or a financial instrument.

But analysts also evaluate information that is not easily quantifiable or reduced to numeric values to get a better picture of a company's performance. This important qualitative data can include reputation, regulatory insights, or employee morale. Qualitative analysis thus focuses more on understanding the underlying qualities of a company or a financial instrument, which may not be immediately quantifiable.

Quantitative isn't the opposite of qualitative analysis. They're different and often complementary philosophies. They each provide useful information for informed decisions. When used together. better decisions can be made than using either one in isolation.

Some common uses of qualitative analysis include:

  • Management Evaluation: Qualitative analysis is often better at evaluating a company's management team, their experience, and their ability to lead the company toward growth. While quantifiable metrics are useful, they often cannot capture the full picture of management's ability and potential. For example, the leadership skills, vision, and corporate culture instilled by management are intangible factors that can significantly impact a company's success, yet are difficult to measure with numbers alone.
  • Industry Analysis: It also includes an analysis of the industry in which the company operates, the competition, and market conditions. For instance, it can explore how changes in technology or societal behaviors could impact the industry. Qualitative approaches can also better identify barriers to entry or exit, which can affect the level of competition and profitability within the industry.
  • Brand Value and Company Reputation: The reputation of a company, its brand value, and customer loyalty are also significant factors considered in qualitative analysis. Understanding how consumers perceive the brand, their level of trust, and satisfaction can provide insights into customer loyalty and the potential for sustained revenue. This can be done through focus groups, surveys, or interviews.
  • Regulatory Environment: The regulatory environment, potential legal issues, and other external factors that could impact a company are also analyzed qualitatively. Evaluating a company's compliance with relevant laws, regulations, and industry standards to ascertain its legal standing and the potential risk of legal issues. In addition, understanding a company's ethical practices and social responsibility initiatives, that can influence its relationship with stakeholders and the community at large.

Suppose you are interested in investing in a particular company, XYZ Inc. One way to evaluate its potential as an investment is by analyzing its past financial performance using quantitative analysis. Let's say, over the past five years, XYZ Inc. has been growing its revenue at an average rate of 8% per year. You decide to use regression analysis to forecast its future revenue growth. Regression analysis is a statistical method used to examine the relationship between variables.

After collecting the necessary data, you run a simple linear regression with the year as the independent variable and the revenue as the dependent variable. The output gives you a regression equation, let's say, R e v e n u e = 100 + 8 ( Y e a r ) Revenue=100+8(Year) R e v e n u e = 100 + 8 ( Y e a r ) . This equation suggests that for every year, the revenue of XYZ Inc. increases by $8 million, starting from a base of $100 million. This quantitative insight could be instrumental in helping you decide whether XYZ Inc. represents a good investment opportunity based on its historical revenue growth trend.

However, while you can quantify revenue growth for the firm and make predictions, the reasons for why may not be apparent from quantitative number crunching.

Augmenting with Qualitative Analysis

Qualitative analysis can provide a more nuanced understanding of XYZ Inc.'s potential. You decide to delve into the company's management and industry reputation. Through interviews, reviews, and industry reports, you find that the management team at XYZ Inc. is highly regarded with a track record of successful ventures. Moreover, the company has a strong brand value and a loyal customer base.

Additionally, you assess the industry in which XYZ Inc. operates and find it to be stable with a steady demand for the products that XYZ Inc. offers. The regulatory environment is also favorable, and the company has a good relationship with the local communities in which it operates.

By analyzing these qualitative factors, you obtain a more comprehensive understanding of the company's operational environment, the competence of its management team, and its reputation in the market. This qualitative insight complements the quantitative analysis, providing you with a well-rounded view of XYZ Inc.'s investment potential.

Combining both quantitative and qualitative analyses could therefore lead to a more informed investment decision regarding XYZ Inc.

Quantitative analysis, while powerful, comes with certain limitations:

  • Data Dependency: Quantitative analysis is heavily dependent on the quality and availability of numerical data. If the data is inaccurate, outdated, or incomplete, the analysis and the subsequent conclusions drawn will be flawed. As they say, 'garbage-in, garbage-out'.
  • Complexity: The methods and models used in quantitative analysis can be very complex, requiring a high level of expertise to develop, interpret, and act upon. This complexity can also make it difficult to communicate findings to individuals who lack a quantitative background.
  • Lack of Subjectivity: Quantitative analysis often overlooks qualitative factors like management quality, brand reputation, and other subjective factors that can significantly affect a company's performance or a financial instrument's value. In other words, you may have the 'what' without the 'why' or 'how.' Qualitative analysis can augment this blind spot.
  • Assumption-based Modeling: Many quantitative models are built on assumptions that may not hold true in real-world situations. For example, assumptions about normal distribution of returns or constant volatility may not reflect actual market conditions.
  • Over-reliance on Historical Data: Quantitative analysis often relies heavily on historical data to make predictions about the future. However, past performance is not always indicative of future results, especially in rapidly changing markets or unforeseen situations like economic crises.
  • Inability to Capture Human Emotion and Behavior: Markets are often influenced by human emotions and behaviors which can be erratic and hard to predict. Quantitative analysis, being number-driven, struggles to properly account for these human factors.
  • Cost and Time Intensive: Developing accurate and reliable quantitative models can be time-consuming and expensive. It requires skilled personnel, sophisticated software tools, and often, extensive computational resources.
  • Overfitting: There's a risk of overfitting , where a model might perform exceedingly well on past data but fails to predict future outcomes accurately because it's too tailored to past events.
  • Lack of Flexibility: Quantitative models may lack the flexibility to adapt to new information or changing market conditions quickly, which can lead to outdated or incorrect analysis.
  • Model Risk: There's inherent model risk involved where the model itself may have flaws or errors that can lead to incorrect analysis and potentially significant financial losses.

Understanding these drawbacks is crucial for analysts and decision-makers to interpret quantitative analysis results accurately and to balance them with qualitative insights for more holistic decision-making.

Quantitative analysis is a versatile tool that extends beyond the realm of finance into a variety of fields. In the domain of social sciences, for instance, it's used to analyze behavioral patterns, social trends, and the impact of policies on different demographics. Researchers employ statistical models to examine large datasets, enabling them to identify correlations, causations, and trends that can provide a deeper understanding of human behaviors and societal dynamics. Similarly, in the field of public policy, quantitative analysis plays a crucial role in evaluating the effectiveness of different policies, analyzing economic indicators, and forecasting the potential impacts of policy changes. By providing a method to measure and analyze data, it aids policymakers in making informed decisions based on empirical evidence.

In the arena of healthcare, quantitative analysis is employed for clinical trials, genetic research, and epidemiological studies to name a few areas. It assists in analyzing patient data, evaluating treatment outcomes, and understanding disease spread and its determinants. Meanwhile, in engineering and manufacturing, it's used to optimize processes, improve quality control, and enhance operational efficiency. By analyzing data related to production processes, material properties, and operational performance, engineers can identify bottlenecks, optimize workflows, and ensure the reliability and quality of products. Additionally, in the field of marketing, quantitative analysis is fundamental for market segmentation, advertising effectiveness, and consumer satisfaction studies. It helps marketers understand consumer preferences, the impact of advertising campaigns, and the market potential for new products. Through these diverse applications, quantitative analysis serves as a bedrock for data-driven decision-making, enabling professionals across different fields to derive actionable insights from complex data.

What Is Quantitative Analysis Used for in Finance?

Quantitative analysis is used by governments, investors, and businesses (in areas such as finance, project management, production planning, and marketing) to study a certain situation or event, measure it, predict outcomes, and thus help in decision-making. In finance, it's widely used for assessing investment opportunities and risks. For instance, before venturing into investments, analysts rely on quantitative analysis to understand the performance metrics of different financial instruments such as stocks, bonds, and derivatives. By delving into historical data and employing mathematical and statistical models, they can forecast potential future performance and evaluate the underlying risks. This practice isn't just confined to individual assets; it's also essential for portfolio management. By examining the relationships between different assets and assessing their risk and return profiles, investors can construct portfolios that are optimized for the highest possible returns for a given level of risk.

What Kind of Education Do You Need to Be a Quant?

Individuals pursuing a career in quantitative analysis usually have a strong educational background in quantitative fields like mathematics, statistics, computer science, finance, economics, or engineering. Advanced degrees (Master’s or Ph.D.) in quantitative disciplines are often preferred, and additional coursework or certifications in finance and programming can also be beneficial.

What Is the Difference Between Quantitative Analysis and Fundamental Analysis?

While both rely on the use of math and numbers, fundamental analysis takes a broader approach by examining the intrinsic value of a security. It dives into a company's financial statements, industry position, the competence of the management team, and the economic environment in which it operates. By evaluating factors like earnings, dividends, and the financial health of a company, fundamental analysts aim to ascertain the true value of a security and whether it is undervalued or overvalued in the market. This form of analysis is more holistic and requires a deep understanding of the company and the industry in which it operates.

How Does Artificial Intelligence (AI) Influence Quantitative Analysis?

Quantitative analysis often intersects with machine learning (ML) and other forms of artificial intelligence (AI). ML and AI can be employed to develop predictive models and algorithms based on the quantitative data. These technologies can automate the analysis process, handle large datasets, and uncover complex patterns or trends that might be difficult to detect through traditional quantitative methods.

Quantitative analysis is a mathematical approach that collects and evaluates measurable and verifiable data in order to evaluate performance, make better decisions, and predict trends. Unlike qualitative analysis, quantitative analysis uses numerical data to provide an explanation of "what" happened, but not "why" those events occurred.

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Fischer, Black, and Myron Scholes, " The Pricing of Options and Corporate Liabilities ." Journal of Political Economy, vol. 81, no. 3, 1974, pp. 637-654.

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Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng Published: May 18, 2022

Related Articles

meaning of quantitative analysis data

A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

  • Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

  • Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

  • Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

  • Descriptive Statistics
  • Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

  • Mean:   This calculates the numerical average of a set of values.
  • Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
  • Mode: This is used to find the most commonly occurring value in a dataset.
  • Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
  • Frequency: This indicates the number of times a value is found.
  • Range: This shows the highest and lowest values in a dataset.
  • Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
  • Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

  • Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
  • Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
  • Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
  • Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
  • Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
  • Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
  • MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
  • Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
  • Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
  • SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

  • Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
  • Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

  • Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
  • Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

  • Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
  • This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

  • Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

  • Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

  • Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
  • The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

  • Data Analysis and Modeling: 4 Critical Differences
  • Exploratory Data Analysis Simplified 101
  • 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem Eteng

Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.

No-code Data Pipeline for your Data Warehouse

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Cloud Migration- Process, Types, and Strategy

What is a data stack: everything you need to know, 10 best cloud data services tools for 2024, i want to read this e-book.

meaning of quantitative analysis data

What is quantitative data? How to collect, understand, and analyze it

What is quantitative data? How to collect, understand, and analyze it

A comprehensive guide to quantitative data, how it differs from qualitative data, and why it's a valuable tool for solving problems.

  • Key takeaways
  • What is quantitative data?
  • Examples of quantitative data
  • Difference between quantitative and qualitative data
  • Characteristics of quantitative data
  • Types of quantitative data
  • When should I use quantitative or qualitative research?
  • Pros and cons of quantitative data
  • Collection methods

Quantitative data analysis tools

Qualitative vs. quantitative data: what's the difference, categorical vs. quantitative data: the difference plus why they’re so valuable, quantitative data examples to help you understand how to take action.

Data is all around us, and every day it becomes increasingly important. Different types of data define more and more of our interactions with the world around us—from using the internet, to buying a car, to the algorithms behind news feeds we see, and much more. 

One of the most common and well-known categories of data is quantitative data, or data that can be expressed in numbers or numerical values. 

This guide takes a deep look at what quantitative data is , what it can be used for, how it’s collected, its advantages and disadvantages, and more. 

Key takeaways: 

Quantitative data is data that can be counted or measured in numerical values.

The two main types of quantitative data are discrete data and continuous data.

Height in feet, age in years, and weight in pounds are examples of quantitative data. 

Qualitative data is descriptive data that is not expressed numerically. 

Both quantitative research and qualitative research are often conducted through surveys and questionnaires. 

What is quantitative data? 

Quantitative data is information that can be counted or measured—or, in other words, quantified—and given a numerical value.

Quantitative data in a dashboard showing signed-up users, rage clicks, fruit subscribers, and more.

Quantitative data is used when a researcher needs to quantify a problem, and answers questions like “what,” “how many,” and “how often.” This type of data is frequently used in math calculations, algorithms, or statistical analysis. 

In product management , UX design , or software engineering , quantitative data can be the rate of product adoption (a percentage), conversions (a number), or page load speed (a unit of time), or other metrics. In the context of shopping, quantitative data could be how many customers bought a certain item. When it comes to vehicles, quantitative data might be how much horsepower a car has. 

What are examples of quantitative data? 

Quantitative data is anything that can be counted in definite units and numbers . So, among many, many other things, some examples of quantitative data include: 

Revenue in dollars

Weight in kilograms or pounds

Age in months or years

Distance in miles or kilometers

Time in days or weeks

Experiment results

Website conversion rates

Website page load speed

What is the difference between quantitative and qualitative data? 

There are many differences between qualitative and quantitative data —each represent very different data sets and are used in different situations. Often, too, they’re used together to provide more comprehensive insights.

As we’ve described, quantitative data relates to numbers ; it can be definitively counted or measured.  Qualitative data, on the other hand, is descriptive data that’s expressed in words or visuals. So, where quantitative data is used for statistical analysis, qualitative data is categorized according to themes. 

Examples of qualitative vs. quantitative data

As mentioned above, examples of quantitative data include things like distance in miles or age in years. 

Qualitative data , however, is expressed by describing or labeling certain attributes, such as “chocolate milk,” “blue eyes,” and “red flowers.” In these examples, the adjectives chocolate, blue, and red are qualitative data because they tell us something about the objects that cannot be quantified. 

Qualtitative vs quantitative examples

Further reading: The differences between categorical and quantitative Data and examples of qualitative data

Characteristics of quantitative data 

Quantitative data is made up of numerical values and has numerical properties, and can easily undergo math operations like addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using math techniques. 

Specific types of quantitative data

Qualitative vs quantitative data: types of data

All quantitative data can be measured numerically, as shown above. But these data types can be broken down into more specific categories, too.

There are two types of quantitative data: discrete and continuous . Continuous data can be further divided into interval data and ratio data . 

Discrete data

In reference to quantitative data, discrete data is information that can only take certain fixed values. While discrete data doesn’t have to be represented by whole numbers, there are limitations to how it can be expressed. 

Examples of discrete data:

The number of players on a team

The number of employees at a company

The number of items eggs broken when you drop the carton

The number of outs a hitter makes in a baseball game

The number of right and wrong questions on a test

A website's bounce rate (percentages can be no less than 0 or great than 100)

Discrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, which is shown below.

A bar chart showing the total employees at the largest companies in the US, with Walmart being the largest, following by Amazon, Kroger, The Home Depot, Berkshire Hathaway, IBM, United Parcel Service, Target Corporation, UnitedHealth Group, and CVS Health,

Continuous data 

Continuous data , on the other hand, can take any value and varies over time. This type of data can be infinitely and meaningfully broken down into smaller and smaller parts. 

Examples of continuous data:

Website traffic

Water temperature

The time it takes to complete a task

Because continuous data changes over time, its insights are best expressed with a line graph or grouped into categories, shown below.

A line chart showing average New York City temperatures by month, showing July as the hottest month and January as the coldest.

Continuous data can be further broken down into two categories: interval data and ratio data. 

Interval data

Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. Interval data is always expressed in numbers where the distance between two points is standardized and equal. These numbers can also be called integers. 

Examples of interval data include temperature, since it can move below and above 0.

Ratio data has all the properties of interval data, but unlike interval data, ratio data also has a true zero. For example, weight in grams is a type of ratio data because it is measured along a continuous scale with equal space between each value, and the scale starts at 0.0.

Other examples of ratio data are weight, length, height, and concentration. 

Interval data vs. ratio data

Ratio data gets its name because the ratio of two measurements can be interpreted meaningfully, whereas two measurements cannot be directly compared with intervals.

For example, something that weighs six pounds is twice as heavy as something that weighs three pounds. However, this rule does not apply to interval data, which has no zero value. An SAT score of 700, for instance, is not twice as good as an SAT score of 350, because the scale does not begin at zero.

Similarly, 40º is not twice as hot as 20º. Saying uses 0º as a reference point to compare the two temperatures, which is incorrect.

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When should I use quantitative or qualitative research? 

Quantitative and qualitative research can both yield valuable findings, but it’s important to choose which type of data to collect based on the nature and objectives of your research. 

When to use quantitative research

If the thing you are trying to study or measure can be counted and expressed in numbers, quantitative research is likely most appropriate. For example, quantitative methods are used to calculate a city’s demographics—how many people live there, their ages, their ethnicities, their incomes, and so on. 

When to use qualitative research

Qualitative data is defined as non-numerical data such as language, text, video, audio recordings, and photographs. This data can be collected through qualitative methods and research such as interviews, survey questions, observations, focus groups, or diary accounts. 

Conducting qualitative research involves collecting, analyzing, and interpreting qualitative non-numerical data (like color, flavor, or some other describable aspect). Methods of qualitative analysis include thematic analysis, coding, and content analysis.

If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis.

To use our city example from above, determining why a city's population is happy or unhappy—something you would need to ask them to describe—requires qualitative data. 

In short: The goal of qualitative research is to understand how individuals perceive their own social realities. It's commonly used in fields like psychology, social sciences and sociology, educational research, anthropology, political science, and more. 

In some instances, like when trying to understand why users are abandoning your website, it’s helpful to assess both quantitative and qualitative data . Understanding what users are doing on your website—as well as why they’re doing it (or how they feel when they’re doing it)—gives you the information you need to make your website’s experience better. 

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What are the pros and cons of quantitative data? 

Quantitative data is most helpful when trying to understand something that can be counted and expressed in numbers. 

Pros of quantitative data: 

Quantitative data is less susceptible to bias than qualitative data.

It can be tested and checked, and anyone can replicate both an experiment and its results.

Quantitative data is relatively quick and easy to collect. 

Cons of quantitative data: 

Quantitative data typically lacks context. In other words, it tells you what something is but not why it is.

Conclusions drawn from quantitative research are only applicable to the particular case studied, and any generalized conclusions are only hypotheses

How do you collect quantitative data? 

There are many ways to collect quantitative data , with common methods including surveys and questionnaires. These can generate both quantitative data and qualitative data, depending on the questions asked. 

Once the data is collected and analyzed, it can be used to examine patterns, make predictions about the future, and draw inferences. 

For example, a survey of 100 consumers about where they plan to shop during the holidays might show that 45 of them plan to shop online, while the other 55 plan to shop in stores. 

Quantitative data collection

Questionnaires and surveys 

Surveys and questionnaires are commonly used in quantitative research and qualitative research because they are both effective and relatively easy to create and distribute. With a wide array of simple-to-use tools, conducting surveys online is a quick and convenient research method. 

These research types are useful for gathering in-depth feedback from users and customers, particularly for finding out how people feel about a certain product, service, or experience. For example, many ecommerce companies send post-purchase surveys to find out how a customer felt about the transaction — and if there are any areas that could be improved. 

Another common way to collect quantitative data is through a consumer survey, which retailers and other businesses can use to get customer feedback, understand intent, and predict shopper behavior. 

Open-source online datasets 

There are many public datasets online that are free to access and analyze. In some instances, rather than conducting original research through the methods mentioned above, researchers analyze and interpret this previously collected data in the way that suits their own research project. Examples of public datasets include: 

The Bureau of Labor Statistics Data

The Census Bureau Data

World Bank Open Data

The CIA World Factbook  

Experiments

An experiment is another common method that usually involves a  control group  and an  experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

Controlled experiments,  A/B tests ,  blind experiments , and many others fall under this category.

With large data pools, a survey of each individual person or data point may be infeasible. In this instance, sampling is used to conduct quantitative research. Sampling is the process of selecting a representative sample of data , which can save time and resources. There are two types of sampling : random sampling (also known as probability sampling) and non-random sampling (also known as non-probability sampling). 

Probability sampling allows for the randomization of the sample selection, meaning that each sample has the same probability of being selected for survey as any other sample. 

In non-random sampling, each sample unit does not have the same probability of being included in the sample. This type of sampling relies on factors other than random chance to select sample units, such as the researcher’s own subjective judgment. Non-random sampling is most commonly used in qualitative research. 

Typically, data analysts and data scientists use a variety of special tools to gather and analyze quantitative data from different sources. 

For example, many web analysts and marketing professionals use Google Analytics (pictured below) to gather data about their website’s traffic and performance. This tool can reveal how many visitors come to your site in a day or week, the length of an average session, where traffic comes from, and more. In this example, the goal of this quantitative analysis is to understand and optimize your site’s performance. 

Google Analytics screenshot

Google Analytics is just one example of the many quantitative analytics tools available for different research professionals. 

Other quantitative data tools include…

Microsoft Excel

Microsoft Power BI

Apache Spark

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

FullStory's DXI platform combines the quantitative insights of product analytics with picture-perfect session replay for complete context that helps you answer questions, understand issues, and uncover customer opportunities.

Start a free 14-day trial to see how FullStory can help you combine your most invaluable quantitative and qualitative insights and eliminate blind spots.

Frequently asked questions about quantitative data

Is quantitative data objective.

Quantitative researchers do everything they can to ensure data’s objectivity by eliminating bias in the collection and analysis process. However, there are factors that can cause quantitative data to be biased.

For example, selection bias can occur when certain individuals are more likely to be selected for study than others. Other types of bias include reporting bias , attrition bias , recall bias , observer bias , and others. 

Who uses quantitative data?

Quantitative research is used in many fields of study, including psychology, digital experience intelligence , economics, demography, marketing, political science, sociology, epidemiology, gender studies, health, and human development. Quantitative research is used less commonly in fields such as history and anthropology. 

Many people who are seeking advanced degrees in a scientific field use quantitative research as part of their studies.

What is quantitative data in statistics?

Statistics is a branch of mathematics that is commonly used in quantitative research. To conduct quantitative research with statistical methods, a researcher would collect data based on a hypothesis, and then that data is manipulated and studied as part of hypothesis testing, proving the accuracy or reliability of the hypothesis.

Is quantitative data better than qualitative data?

It depends on the researcher’s goal. If the researcher wants to measure something—for example, to understand “how many” or “how often,”—quantitative data is appropriate. However, if a researcher wants to learn the reason behind something—to understand “why” something is—qualitative research methods will better answer these questions.

Further reading: Qualitative vs. quantitative data — what's the difference?

Related resources and further reading

Qualitative vs. quantitative data: what's the difference?

Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

Categorical vs. quantitative data: The difference plus why they’re so valuable

Categorical & quantitative variables both provide vital info about a data set. But each is important for different reasons and has its own pros/cons.

Quantitative data examples to help you understand how to take action

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

What you need to know about data quality and DXI

What you need to know about data quality and DXI

Poor-quality data lacks the actionability teams need to make iterative improvements. Here's how Digital Experience Intelligence changes the game.

Finding signals through the noise

Finding signals through the noise

The qualitative + quantitative digital experience flywheel.

Here's how you can quantitatively analyze your qualitative digital experience data to unlock an entirely new workflow.

Qualitative data: Examples and how to use it

Qualitative data: Examples and how to use it

Learn how to analyze qualitative data. We show examples of how to collect, organize, and analyze qualitative data to gain insights.

Grad Coach

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Quantitative data analysis methods and techniques 101

Overview: Quantitative Data Analysis 101

  • What (exactly) is quantitative data analysis?
  • When to use quantitative analysis
  • How quantitative analysis works

The two “branches” of quantitative analysis

  • Descriptive statistics 101
  • Inferential statistics 101
  • How to choose the right quantitative methods
  • Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

  • Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
  • Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
  • And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

meaning of quantitative analysis data

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Descriptive statistics vs inferential statistics

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

  • Mean – this is simply the mathematical average of a range of numbers.
  • Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
  • Mode – this is simply the most commonly occurring number in the data set.
  • In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
  • Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
  • Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

Descriptive statistics example data

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

  • Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
  • Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
  • And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Examples of descriptive statistics

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

  • Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
  • And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Inferential statistics are used to make predictions about what you’d expect to find in the full population, based on the sample.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

Sample correlation

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations.

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

Remember that every statistical method has its own assumptions and limitations,  so you need to be aware of these.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

  • The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
  • Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

  • Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
  • The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
  • Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
  • Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
  • To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

meaning of quantitative analysis data

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73 Comments

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Thank you for the feedback. Good luck with your quantitative analysis.

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Your article is so good! However, I am still a bit lost. I am doing a secondary research on Gun control in the US and increase in crime rates and I am not sure which analysis method I should use?

Joy

Based on the given learning points, this is inferential analysis, thus, use ‘t-tests, ANOVA, correlation and regression analysis’

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Avery good presentation followed by the write up. yes you simplified statistics to make sense even to a layman like me. Thank so much keep it up. The presenter did ell too. i would like more of this for Qualitative and exhaust more of the test example like the Anova.

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VERY INTERESTING AND SUPPORTIVE TO NEW RESEARCHERS LIKE ME. AT LEAST SOME BASICS ABOUT QUANTITATIVE.

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Do you provide any assistance for other steps of research methodology like making research problem testing hypothesis report and thesis writing?

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I am doing a quality improvement project to determine if the implementation of a protocol will change prescribing habits. Would this be a t-test?

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Belayneh Kassahun

tnx. fruitful blog!

Suzanne

So I am writing exams and would like to know how do establish which method of data analysis to use from the below research questions: I am a bit lost as to how I determine the data analysis method from the research questions.

Do female employees report higher job satisfaction than male employees with similar job descriptions across the South African telecommunications sector? – I though that maybe Chi Square could be used here. – Is there a gender difference in talented employees’ actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – Is there a gender difference in the cost of actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – What practical recommendations can be made to the management of South African telecommunications companies on leveraging gender to mitigate employee turnover decisions?

Your assistance will be appreciated if I could get a response as early as possible tomorrow

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Quantitative data is information restricted to numerical values, making it quantifiable and amenable to statistical analysis. It includes objective and observable information stated in specified units—for example, height, temperature, income, sales figures, population size, test scores, and weights are the types of information that is considered quantitative data. These numerical representations allow quantitative data to be mathematically examined, allowing patterns and correlations to be identified.

This distinguishes it from qualitative data, which is descriptive, non-numerical data that captures insights into human experiences, emotions, and perceptions. A successful enterprise data strategy includes both quantitative and qualitative data, which together can provide a more holistic, nuanced picture.

Table of Contents

Importance of Quantitative Data

Quantitative data is invaluable when seeking a deeper knowledge of occurrences that can be properly quantified and stated numerically. This analytical technique enables a thorough evaluation of quantifiable properties, allowing for a more in-depth exploration of patterns, correlations, and trends within the data.

By relying on numerical values, quantitative data not only improves the precision of analysis but also offers a firm platform for statistical interpretations and evidence-based decision-making . As a result, it has become a vital tool in a variety of professions, allowing researchers to delve into the quantitative parts of complicated topics and develop meaningful conclusions supported by numerical data.

Characteristics of Quantitative Data

Quantitative data is represented numerically and is classified as discrete or continuous to allow for accurate measurement. Its intrinsic order makes it possible to rank items based on magnitude, for example, arithmetic operations may be applied for mathematical analysis. Quantitative data adheres to established scales, improving consistency and comparability across diverse measures.

Its analytical potential is further enhanced by its statistical analysis capability, which allows for examining patterns, trends, and correlations within the dataset. These characteristics allow quantitative data to be efficiently presented using visualizations to provide a clear and succinct portrayal of the underlying patterns and insights.

8 Quantitative Data Types

Quantitative data may be classified into several forms, based on the nature of the study and the measuring scales used. Each sort of quantitative data serves a particular research purpose. Which to employ is determined by the research question, design, and features of the variables being researched. The following are eight of the most commonly used types of quantitative data:

  • Descriptive Data— Entails summarizing and explaining the primary characteristics of a dataset; it is frequently analyzed using descriptive statistics such as mean, median, and mode.
  • Correlational Data— Looks at the relationships between two or more variables to see if changes in one correlate to changes in another; the strength and direction of these associations are quantified by correlation coefficients.
  • Data from Causal-Comparative/Quasi-Experimental Studies— Identifies cause-and-effect linkages, frequently in non-randomized contexts; is collected when experimental control is difficult.
  • Experimental Research Data— Data from controlled experiments in which researchers change independent factors to assess their impact on dependent variables; in experimental research, randomized control experiments are widespread.
  • Discrete Data— Made up of discrete, independent values with no in-between values; examples include counts of things or entire numbers, such as the number of pupils in a class.
  • Continuous Data— Has a range of values and may accept any value within that range; height, weight, and temperature are examples of measures with an unlimited number of potential values.
  • Interval Data— Has equal intervals between scale points but no real zero point, such as temperature recorded in degrees Celsius or Fahrenheit, for example.
  • Ratio Data— Similar to interval data, but with a genuine zero point signifying the lack of the measured amount; height, weight, and money are a few examples.

When To Use Quantitative Data

Quantitative data is an important tool for actively seeking answers to questions like “what” or “how many.” This data type uses numerical values to measure and quantify various aspects of a phenomenon or population, allowing for precise analysis and statistical interpretation.

When actively researching the quantity or extent of a certain variable, researchers use quantitative approaches that allow the application of mathematical tools to discover patterns, trends, and correlations within the data. The active use of quantitative data—whether measuring heights, weights, temperatures, or counting occurrences—offers an organized and objective method for answering particular queries and reaching relevant conclusions.

Benefits of Quantitative Data

Quantitative data offers advantages like objectivity, statistical robustness, and broad generalizations.

Objectivity

Because quantitative data is objective and less impacted by personal prejudices, the conclusions are more reliable.

Statistical Analysis

Quantitative data permits statistical analysis, allowing researchers to detect patterns, trends, and correlations with great accuracy, revealing the significance of relationships and providing a solid foundation for evidence-based decision-making.

Numerical Comparisons

Numerical comparisons between variables makes it easier to quantify and convey correlations, facilitating a better understanding of the data among researchers and stakeholders.

Generalizability

Findings are frequently more generalizable to bigger groups, yielding greater insights and extending the applicability of research findings beyond the studied sample, contributing to the development of broader theories and principles.

Rapid Data Collection

Efficient and quick collection can gather a large quantity of information in a short period, saving time and enabling researchers to promptly address research questions and adapt to dynamic situations.

Methodical and Replicable

Methodical and structured approach to study allows for a greater understanding of links and patterns, making discoveries more relevant; also conducive to replication, enabling other researchers to conduct similar studies and compare outcomes, contributing to the robustness of scientific knowledge.

Quantitative Data Limitations 

Despite its benefits, quantitative data has limitations—for example, it’s not great for expressing complex events and leans toward oversimplification.

Favors Simplicity

Quantitative data can oversimplify things, ignoring their complexity and richness and leading to a loss of subtlety and a shallow comprehension of the subject.

Neglects Subjective Nuances

By disregarding in-depth experiences and subjective interpretations in circumstances involving test-takers, quantitative data can lead to omissions or incomplete investigations.

Faulty Conclusions

Due to the difficulty of quantifying subjective experiences, quantitative data can lead to deceptive conclusions when dealing with emotions.

Misrepresentation or Misinterpretation

Numerical data is prone to deception or misinterpretation if not thoroughly studied, highlighting the significance of meticulous data interpretation to prevent incorrect conclusions.

Overgeneralization

Quantitative data tends to simplify complex phenomena, leading to sweeping conclusions that can incorrectly be applied more widely than justified.

Quantitative Data Collection Methods

The image shows four means of collecting quantitative data: interviews, structured observation, sampling for probability, and surveys/questionnaires.

Data that can be counted or categorized by its numerical value is often used to study events or levels of concurrence. Surveys, experiments, and statistical analyses are among the approaches used by researchers to efficiently gather, handle, and evaluate huge amounts of data.

Structured Observations

Structured observation involves systematically observing and recording certain actions or events. To maintain uniformity and objectivity, researchers adhere to specific standards created before the observation begins. This approach is especially beneficial for researching behaviors in natural settings since it allows for impartiality while monitoring individuals in their natural circumstances.

Sampling for Probability

Probability sampling is a research technique that involves selecting a subset (sample) from a larger population in such a way that each member of the population has a known, non-zero chance of being included in the sample. The principles of probability theory drive this sort of sampling, allowing researchers to make statistical conclusions about the total population based on the features found in the sample.

  • Simple Random Sampling— Every individual or element in the population has an equal probability of being chosen under this technique, guaranteeing that the sample is representative of the full population, lowering bias, and allowing results to be generalized.
  • Cluster Sampling— The population is split into clusters, and a random selection of clusters is chosen that includes all individuals within the designated clusters. This strategy works well for big, varied populations.
  • Systematic Sampling— After selecting a random beginning point, researchers choose every nth individual from a list. This approach is simple and guarantees that the population is represented systematically.

Interviews are a means of gathering information from individuals or groups in which the researcher asks questions either orally or in writing. This kind of dynamic data collection allows for a more in-depth knowledge of participants’ viewpoints, experiences, and attitudes.

  • Phone Interview— This approach enables effective data gathering from geographically separated individuals. It is appropriate for organized interviews that use closed-ended questions.
  • Face-to-Face Interviews— In-person interactions between researchers and participants using open-ended questioning relies on a more personal strategy and allows for a deeper comprehension of replies.
  • Computer-Assisted Interview— This strategy provides uniform questioning and data collecting by utilizing computer software. Self-administered surveys or guided interviews can be used to improve uniformity and accuracy.

Surveys and Questionnaires

Surveys and questionnaires are popular instruments for gathering quantitative data because they allow researchers to obtain numerical information from a large sample of respondents in a systematic manner.

  • Web or Online Surveys— Web-based surveys allow for extensive distribution and efficient data collecting; proper design is critical to prevent bias and promote clear comprehension.
  • Mail-Based— Surveys sent via mail generally include a paper questionnaire, a strategy that necessitates careful question design to ensure clarity and limit the risk of non-response bias.

Quantitative Data Examples

Quantitative data is anything that you can count and categorize according to its numerical expression. These examples demonstrate the breadth of quantitative data, which ranges from simple counts and measurements to more complicated experimental and survey-related data.

Frequently Asked Questions (FAQs)

What is the key difference between quantitative and qualitative data.

The primary distinction between quantitative and qualitative data is found in their nature. Quantitative data consists of numerical measures that may be tallied or measured, with an emphasis on numbers. Qualitative data , on the other hand, is non-numerical and focuses on traits, attributes, or descriptions. Statistical analysis is commonly linked with quantitative data, but theme coding or narrative analysis is often used to evaluate qualitative data.

What is the key difference between quantitative and categorical data?

Quantitative data is numerical measurements that may be classified as discrete or continuous data. Categorical data, on the other hand, is made up of different categories or groups that are frequently represented by labels. While quantitative data can be mathematically processed, categorical data is often evaluated using frequencies and percentages. In essence, quantitative data is concerned with measuring, whereas categorical data is concerned with categorizing.

What is the most common graphical representation of quantitative data?

A histogram is the most popular graphical representation of quantitative data. Histograms show the distribution of numerical data by dividing it into intervals or bins along the x-axis and showing the frequency or count of observations in each interval on the y-axis. Histograms are frequently employed in statistical analysis and data visualization because they give a visual depiction of the data’s central tendency, dispersion, and form.

Bottom Line: Using Quantitative Data in the Enterprise

Quantitative data is a cornerstone for businesses, where it serves to fuel decision-making, operational knowledge, and strategic planning. Its numerical structure allows for exact measurements and detailed statistical analysis, which is valuable in the many enterprise use cases from acquiring actionable insights to making evidence-based choices and successfully managing complicated processes.

Enterprises can derive significant information from quantitative data by defining defined objectives and using rigorous data gathering procedures, which allows for objective comparisons and lays the groundwork for better informed decision-making processes.The standardized scales and quantifiable qualities inherent in quantitative data help to reliable and repeatable studies, improving the reliability and validity of findings that are crucial for firms looking to gain a competitive advantage in their markets.

If you’re learning about quantitative and qualitative data and how businesses use them both in their overall data management strategies, read our Complete Guide to Data Analytics for Businesses to see how they can be used alongside other types of data to provide a detailed picture of essential information.

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meaning of quantitative analysis data

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Quantitative Data: What It Is, Types & Examples

Quantitative Data

When we’re asking questions like “ How many? “, “ How often? ” or “ How much? ” we’re talking about the kind of hard-hitting, verifiable data that can be analyzed with mathematical techniques. It’s the kind of stuff that would make a statistician’s heart skip a beat. Let’s discuss quantitative data.

Thankfully, online surveys are the go-to tool for collecting this kind of data in the internet age. With the ability to reach more people in less time and gather honest responses for later analysis, online surveys are the ultimate quantitative data-gathering machine. Plus, let’s be real: who doesn’t love taking a good survey?

What is Quantitative Data?

Quantitative data is the value of data in the form of counts or numbers where each data set has a unique numerical value. This data is any quantifiable information that researchers can use for mathematical calculations and statistical analysis to make real-life decisions based on these mathematical derivations.

For example, there are quantities corresponding to various parameters. For instance, “How much did that laptop cost?” is a question that will collect quantitative data. Values are associated with most measuring parameters, such as pounds or kilograms for weight, dollars for cost, etc.

It makes measuring various parameters controllable due to the ease of mathematical derivations they come with. It is usually collected for statistical analysis plans using surveys , polls, or questionnaires sent across to a specific section of a population. Researches can establish the retrieved results across a population.

Types of Quantitative Data with Examples

Quantitative data is integral to the research process, providing valuable insights into various phenomena. Let’s explore the most common types of quantitative data and their applications in various fields. The most common types are listed below:

Types of wuantitative data

  • Counter: Count equated with entities—for example, the number of people downloading a particular application from the App Store.
  • Measurement of physical objects: Calculating measurement of any physical thing. For example, the HR executive carefully measures the size of each cubicle assigned to the newly joined employees.
  • Sensory calculation: Mechanism to naturally “sense” the measured parameters to create a constant source of information. For example, a digital camera converts electromagnetic information to a string of numerical data.
  • Projection of data: Future data projections can be made using algorithms and other mathematical analysis tools. For example, a marketer will predict an increase in sales after launching a new product with a thorough analysis.
  • Quantification of qualitative entities: Identify numbers to qualitative information. For example, asking respondents of an online survey to share the likelihood of recommendation on a scale of 0-10.

Quantitative Data: Collection Methods

As quantitative data is in the form of numbers, mathematical and statistical analysis of these numbers can lead to establishing some conclusive results.

There are two main Quantitative Data Collection Methods :

01. Surveys

Traditionally, surveys were conducted using paper-based methods and have gradually evolved into online mediums. Closed-ended questions form a major part of these surveys as they are more effective in collecting data.

The survey includes answer options they think are the most appropriate for a particular question. Surveys are integral in collecting feedback from an audience larger than the conventional size. A critical factor about surveys is that the responses collected should be such that they can be generalized to the entire population without significant discrepancies.

Based on the time involved in completing surveys, they are classified into the following:

  • Longitudinal Studies: A type of observational research in which the market researcher conducts surveys from one time period to another, i.e., over a considerable course of time, is called a longitudinal survey . This survey is often implemented for trend analysis or studies where the primary objective is to collect and analyze a pattern in data.
  • Cross-sectional Studies: A type of observational research in which market research conducts surveys at a particular time period across the target sample is known as a cross-sectional survey . This survey type implements a questionnaire to understand a specific subject from the sample at a definite time period.

To administer a survey to collect quantitative data, the following principles are to be followed.

  • Fundamental Levels of Measurement – Nominal, Ordinal, Interval, and Ratio Scales: Four measurement scales are fundamental to creating a multiple-choice question in a survey in collecting quantitative data. They are  nominal, ordinal, interval, and ratio  measurement scales without the fundamentals of which no multiple-choice questions can be created.
  • Use of Different Question Types:  To collect quantitative data,  close-ended questions have to be used in a survey. They can be a mix of multiple  question types , including  multiple-choice questions  like  semantic differential scale questions ,  rating scale questions , etc., that can help collect data that can be analyzed and made sense of.
  • Email:  Sending a survey via email is the most commonly used and most effective survey distribution method. You can use the QuestionPro email management feature to send out and collect survey responses.
  • Buy respondents:  Another effective way to distribute a survey and collect quantitative data is to use a sample. Since the respondents are knowledgeable and also are open to participating in research studies, the responses are much higher.
  • Embed survey in a website:  Embedding a survey in a website increases the number of responses as the respondent is already near the brand when the survey pops up.
  • Social distribution:  Using  social media to distribute the survey  aids in collecting a higher number of responses from the people who are aware of the brand.
  • QR code: QuestionPro QR codes store the URL for the survey. You can  print/publish this code  in magazines, signs, business cards, or on just about any object/medium.
  • SMS survey:  A quick and time-effective way of conducting a survey to collect a high number of responses is the  SMS survey .
  • QuestionPro app:  The  QuestionPro App  allows the quick creation of surveys, and the responses can be collected both online and  offline .
  • API integration:  You can use the  API integration  of the QuestionPro platform for potential respondents to take your survey.

02. One-on-one Interviews

This quantitative data collection method was also traditionally conducted face-to-face but has shifted to telephonic and online platforms. Interviews offer a marketer the opportunity to gather extensive data from the participants. Quantitative interviews are immensely structured and play a key role in collecting information. There are three major sections of these online interviews:

  • Face-to-Face Interviews: An interviewer can prepare a list of important interview questions in addition to the already asked survey questions . This way, interviewees provide exhaustive details about the topic under discussion. An interviewer can manage to bond with the interviewee on a personal level which will help him/her to collect more details about the topic due to which the responses also improve. Interviewers can also ask for an explanation from the interviewees about unclear answers.
  • Online/Telephonic Interviews: Telephone-based interviews are no more a novelty but these quantitative interviews have also moved to online mediums such as Skype or Zoom. Irrespective of the distance between the interviewer and the interviewee and their corresponding time zones, communication becomes one-click away with online interviews. In case of telephone interviews, the interview is merely a phone call away.
  • Computer Assisted Personal Interview: This is a one-on-one interview technique where the interviewer enters all the collected data directly into a laptop or any other similar device. The processing time is reduced and also the interviewers don’t have to carry physical questionnaires and merely enter the answers in the laptop.

All of the above quantitative data collection methods can be achieved by using surveys , questionnaires and online polls .

Quantitative Data: Analysis Methods

Data collection forms a major part of the research process. This data, however, has to be analyzed to make sense of. There are multiple methods of analyzing quantitative data collected in surveys . They are:

Quantitative Data Analysis Methods

  • Cross-tabulation: Cross-tabulation is the most widely used quantitative data analysis methods. It is a preferred method since it uses a basic tabular form to draw inferences between different data-sets in the research study. It contains data that is mutually exclusive or have some connection with each other.
  • Trend analysis: Trend analysis is a statistical analysis method that provides the ability to look at quantitative data that has been collected over a long period of time. This data analysis method helps collect feedback about data changes over time and if aims to understand the change in variables considering one variable remains unchanged.
  • MaxDiff analysis: The MaxDiff analysis is a quantitative data analysis method that is used to gauge customer preferences for a purchase and what parameters rank higher than the others in this process. In a simplistic form, this method is also called the “best-worst” method. This method is very similar to conjoint analysis but is much easier to implement and can be interchangeably used.  
  • Conjoint analysis: Like in the above method, conjoint analysis is a similar quantitative data analysis method that analyzes parameters behind a purchasing decision. This method possesses the ability to collect and analyze advanced metrics which provide an in-depth insight into purchasing decisions as well as the parameters that rank the most important.
  • TURF analysis: TURF analysis or Total Unduplicated Reach and Basic Frequency Analysis, is a quantitative data analysis methodology that assesses the total market reach of a product or service or a mix of both. This method is used by organizations to understand the frequency and the avenues at which their messaging reaches customers and prospective customers which helps them tweak their go-to-market strategies.
  • Gap analysis: Gap analysis uses a side-by-side matrix to depict data that helps measure the difference between expected performance and actual performance. This data gap analysis helps measure gaps in performance and the things that are required to be done to bridge this gap.
  • SWOT analysis: SWOT analysis , is a quantitative data analysis methods that assigns numerical values to indicate strength, weaknesses, opportunities and threats of an organization or product or service which in turn provides a holistic picture about competition. This method helps to create effective business strategies.
  • Text analysis: Text analysis is an advanced statistical method where intelligent tools make sense of and quantify or fashion qualitative observation and open-ended data into easily understandable data. This method is used when the raw survey data is unstructured but has to be brought into a structure that makes sense.

Steps to conduct Quantitative Data Analysis

For Quantitative Data, raw information has to presented in a meaningful manner using data analysis methods. This data should be analyzed to find evidential data that would help in the research process. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

  • Relate measurement scales with variables:  Associate measurement scales such as Nominal, Ordinal, Interval and Ratio with the variables. This step is important to arrange the data in proper order. Data can be entered into an excel sheet to organize it in a specific format.
  • Mean- An average of values for a specific variable
  • Median- A midpoint of the value scale for a variable
  • Mode- For a variable, the most common value
  • Frequency- Number of times a particular value is observed in the scale
  • Minimum and Maximum Values- Lowest and highest values for a scale
  • Percentages- Format to express scores and set of values for variables
  • Decide a measurement scale:  It is important to decide the measurement scale to conclude descriptive statistics for the variable. For instance, a nominal data variable score will never have a mean or median, so the descriptive statistics will correspondingly vary. Descriptive statistics suffice in situations where the results are not to be generalized to the population.
  • Select appropriate tables to represent data and analyze collected data: After deciding on a suitable measurement scale, researchers can use a tabular format to represent data. This data can be analyzed using various techniques such as Cross-tabulation or TURF .  

Quantitative Data Examples

Listed below are some examples of quantitative data that can help understand exactly what this pertains:

  • I updated my phone 6 times in a quarter.
  • My teenager grew by 3 inches last year.
  • 83 people downloaded the latest mobile application.
  • My aunt lost 18 pounds last year.
  • 150 respondents were of the opinion that the new product feature will fail to be successful.
  • There will be 30% increase in revenue with the inclusion of a new product.
  • 500 people attended the seminar.
  • 54% people prefer shopping online instead of going to the mall.
  • She has 10 holidays in this year.
  • Product X costs $1000 .

As you can see in the above 10 examples, there is a numerical value assigned to each parameter and this is known as, quantitative data.

Advantages of Quantitative Data

Some of the advantages of quantitative data are:

  • Conduct in-depth research: Since quantitative data can be statistically analyzed, it is highly likely that the research will be detailed.
  • Minimum bias: There are instances in research, where personal bias is involved which leads to incorrect results. Due to the numerical nature of quantitative data, personal bias is reduced to a great extent.
  • Accurate results: As the results obtained are objective in nature, they are extremely accurate.

Disadvantages of Quantitative Data

Some of disadvantages of quantitative data, are:

  • Restricted information: Because quantitative data is not descriptive, it becomes difficult for researchers to make decisions based solely on the collected information.
  • Depends on question types: Bias in results is dependent on the question types included to collect quantitative data. The researcher’s knowledge of questions and the objective of research are exceedingly important while collecting quantitative data.

Differences between Quantitative and Qualitative Data

There are some stark differences between quantitative data and qualitative data . While quantitative data deals with numbers and measures and quantifies a specific phenomenon, qualitative data focuses on non-numerical information, such as opinions and observations.

The two types of data have different purposes, strengths, and limitations, which are important in understanding a given subject completely. Understanding the differences between these two forms of data is crucial in choosing the right research methods, analyzing the results, and making informed decisions. Let’s explore the differences:

Using quantitative data in an investigation is one of the best strategies to guarantee reliable results that allow better decisions. In summary, quantitative data is the basis of statistical analysis.

Data that can be measured and verified gives us information about quantities; that is, information that can be measured and written with numbers. Quantitative data defines a number, while qualitative data collection is descriptive. You can also get quantitative data from qualitative by using semantic analysis .

QuestionPro is a software created to collect quantitative data using a powerful platform with preloaded questionnaires. In addition, you will be able to analyze your data with advanced analysis tools such as cross tables, Likert scales, infographics, and much more.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research, on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

RESEARCH THEMATICANALYSISMETHOD

Key Features

  • Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
  • Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
  • The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
  • The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
  • The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

  • Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
  • The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
  • Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
  • The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

  • Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
  • Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
  • Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
  • Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

  • Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
  • The research aims for objectivity (i.e., without bias) and is separated from the data.
  • The design of the study is determined before it begins.
  • For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
  • Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

  • Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
  • Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
  • Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
  • Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

  • Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
  • Useful for testing and validating already constructed theories.
  • Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
  • Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
  • Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

  • Designing qualitative research
  • Methods of data collection and analysis
  • Introduction to quantitative and qualitative research
  • Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
  • Qualitative research in health care: Analysing qualitative data
  • Qualitative data analysis: the framework approach
  • Using the framework method for the analysis of
  • Qualitative data in multi-disciplinary health research
  • Content Analysis
  • Grounded Theory
  • Thematic Analysis

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Quantitative vs Qualitative Data: What’s the Difference?

If you’re considering a career in data—or in any kind of research field, like psychology—you’ll need to get to grips with two types of data: Quantitative and qualitative .

Quantitative data is anything that can be counted or measured ; it refers to numerical data. Qualitative data is descriptive , referring to things that can be observed but not measured—such as colors or emotions.

In this post, we’ll define both quantitative and qualitative data in more detail. We’ll then explore all the key ways in which they differ—from how they are collected and analyzed, to the advantages and disadvantages of each. We’ll also include useful examples throughout.

By the end, you’ll have a clear understanding of the difference between qualitative and quantitative data, and a good idea of when to use which. Want to skip ahead to a specific section? Just use this clickable menu:

  • Quantitative vs qualitative data: What are they, and what’s the difference between them?
  • What are the different types of quantitative and qualitative data?
  • How are quantitative and qualitative data collected?
  • Quantitative vs qualitative data: Methods of analysis
  • What are the advantages and disadvantages of quantitative vs qualitative data?
  • When should I use qualitative or quantitative data?
  • Quantitative vs. qualitative data: FAQ
  • Key takeaways 

Without further ado, let’s jump in.

1. What is the difference between quantitative and qualitative data?

When it comes to conducting research and data analysis, you’ll work with two types of data: quantitative and qualitative. Each requires different collection and analysis methods, so it’s important to understand the difference between the two.

What is quantitative data?

Quantitative data refers to any information that can be quantified. If it can be counted or measured, and given a numerical value, it’s quantitative data. Quantitative data can tell you “how many,” “how much,” or “how often”—for example, how many people attended last week’s webinar? How much revenue did the company make in 2019? How often does a certain customer group use online banking?

To analyze and make sense of quantitative data, you’ll conduct statistical analyses.

Learn more: What is quantitative data? A complete introduction

What is qualitative data?

Unlike quantitative data, qualitative data cannot be measured or counted. It’s descriptive, expressed in terms of language rather than numerical values.

Researchers will often turn to qualitative data to answer “Why?” or “How?” questions. For example, if your quantitative data tells you that a certain website visitor abandoned their shopping cart three times in one week, you’d probably want to investigate why—and this might involve collecting some form of qualitative data from the user. Perhaps you want to know how a user feels about a particular product; again, qualitative data can provide such insights. In this case, you’re not just looking at numbers; you’re asking the user to tell you, using language, why they did something or how they feel.

Qualitative data also refers to the words or labels used to describe certain characteristics or traits—for example, describing the sky as blue or labeling a particular ice cream flavor as vanilla.

What are the main differences between quantitative and qualitative data?

The main differences between quantitative and qualitative data lie in what they tell us , how they are collected , and how they are analyzed. Let’s summarize the key differences before exploring each aspect in more detail:

  • Quantitative data is countable or measurable, relating to numbers. Qualitative data is descriptive, relating to language.
  • Quantitative data tells us how many, how much, or how often (e.g. “20 people signed up to our email newsletter last week”). Qualitative data can help us to understand the “why” or “how” behind certain behaviors, or it can simply describe a certain attribute—for example, “The postbox is red” or “I signed up to the email newsletter because I’m really interested in hearing about local events.”
  • Quantitative data is fixed and “universal,” while qualitative data is subjective and dynamic. For example, if something weighs 20 kilograms, that can be considered an objective fact. However, two people may have very different qualitative accounts of how they experience a particular event.
  • Quantitative data is gathered by measuring and counting. Qualitative data is collected by interviewing and observing.
  • Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.

The difference between quantitative and qualitative data: An example

To illustrate the difference between quantitative and qualitative data, let’s use an example. Imagine you want to describe your best friend. What kind of data might you gather or use to paint a vivid picture?

First, you might describe their physical attributes, such as their height, their hair style and color, what size feet they have, and how much they weigh. Then you might describe some of their most prominent personality traits. On top of that, you could describe how many siblings and pets they have, where they live, and how often they go swimming (their favorite hobby).

All of that data will fall into either the quantitative or qualitative categories, as follows:

Quantitative data:

  • My best friend is 5 feet and 7 inches tall
  • They have size 6 feet
  • They weigh 63 kilograms
  • My best friend has one older sibling and two younger siblings
  • They have two cats
  • My best friend lives twenty miles away from me
  • They go swimming four times a week

Qualitative data:

  • My best friend has curly brown hair
  • They have green eyes
  • My best friend is funny, loud, and a good listener
  • They can also be quite impatient and impulsive at times
  • My best friend drives a red car
  • They have a very friendly face and a contagious laugh

Of course, when working as a researcher or data analyst, you’ll be handling much more complex data than the examples we’ve given. However, our “best friend” example has hopefully made it easier for you to distinguish between quantitative and qualitative data.

2. Different types of quantitative and qualitative data

When considering the difference between quantitative and qualitative data, it helps to explore some types and examples of each. Let’s do that now, starting with quantitative data.

Types of quantitative data (with examples)

Quantitative data is either discrete or continuous :

  • Discrete quantitative data takes on fixed numerical values and cannot be broken down further. An example of discrete data is when you count something, such as the number of people in a room. If you count 32 people, this is fixed and finite.
  • Continuous quantitative data can be placed on a continuum and infinitely broken down into smaller units. It can take any value; for example, a piece of string can be 20.4cm in length, or the room temperature can be 30.8 degrees.

What are some real-world examples of quantitative data?

Some everyday examples of quantitative data include:

  • Measurements such as height, length, and weight
  • Counts, such as the number of website visitors, sales, or email sign-ups
  • Calculations, such as revenue
  • Projections, such as predicted sales or projected revenue increase expressed as a percentage
  • Quantification of qualitative data—for example, asking customers to rate their satisfaction on a scale of 1-5 and then coming up with an overall customer satisfaction score

Types of qualitative data (with examples)

Qualitative data may be classified as nominal or ordinal :

  • Nominal data is used to label or categorize certain variables without giving them any type of quantitative value. For example, if you were collecting data about your target audience, you might want to know where they live. Are they based in the UK, the USA, Asia, or Australia? Each of these geographical classifications count as nominal data. Another simple example could be the use of labels like “blue,” “brown,” and “green” to describe eye color.
  • Ordinal data is when the categories used to classify your qualitative data fall into a natural order or hierarchy. For example, if you wanted to explore customer satisfaction, you might ask each customer to select whether their experience with your product was “poor,” “satisfactory,” “good,” or “outstanding.” It’s clear that “outstanding” is better than “poor,” but there’s no way of measuring or quantifying the “distance” between the two categories.

Nominal and ordinal data tends to come up within the context of conducting questionnaires and surveys. However, qualitative data is not just limited to labels and categories; it also includes unstructured data such as what people say in an interview, what they write in a product review, or what they post on social media.

What are some real-world examples of qualitative data?

Some examples of qualitative data include:

  • Interview transcripts or audio recordings
  • The text included in an email or social media post
  • Product reviews and customer testimonials
  • Observations and descriptions; e.g. “I noticed that the teacher was wearing a red jumper.”
  • Labels and categories used in surveys and questionnaires, e.g. selecting whether you are satisfied, dissatisfied, or indifferent to a particular product or service.

3. How are quantitative and qualitative data collected?

One of the key differences between quantitative and qualitative data is in how they are collected or generated.

How is quantitative data generated?

Quantitative data is generated by measuring or counting certain entities, or by performing calculations. Some common quantitative data collection methods include:

  • Surveys and questionnaires: This is an especially useful method for gathering large quantities of data. If you wanted to gather quantitative data on employee satisfaction, you might send out a survey asking them to rate various aspects of the organization on a scale of 1-10.
  • Analytics tools: Data analysts and data scientists use specialist tools to gather quantitative data from various sources. For example, Google Analytics gathers data in real-time, allowing you to see, at a glance, all the most important metrics for your website—such as traffic, number of page views, and average session length.
  • Environmental sensors: A sensor is a device which detects changes in the surrounding environment and sends this information to another electronic device, usually a computer. This information is converted into numbers, providing a continuous stream of quantitative data.
  • Manipulation of pre-existing quantitative data: Researchers and analysts will also generate new quantitative data by performing statistical analyses or calculations on existing data. For example, if you have a spreadsheet containing data on the number of sales and expenditures in USD, you could generate new quantitative data by calculating the overall profit margin.

How is qualitative data generated?

Qualitative data is gathered through interviews, surveys, and observations. Let’s take a look at these methods in more detail:

  • Interviews are a great way to learn how people feel about any given topic—be it their opinions on a new product or their experience using a particular service. Conducting interviews will eventually provide you with interview transcripts which can then be analyzed.
  • Surveys and questionnaires are also used to gather qualitative data. If you wanted to collect demographic data about your target audience, you might ask them to complete a survey where they either select their answers from a number of different options, or write their responses as freeform text.
  • Observations: You don’t necessarily have to actively engage with people in order to gather qualitative data. Analysts will also look at “naturally occurring” qualitative data, such as the feedback left in product reviews or what people say in their social media posts.

4. Quantitative vs qualitative data: methods of analysis

Another major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes.

Quantitative data analysis

How you analyze your quantitative data depends on the kind of data you’ve gathered and the insights you want to uncover. Statistical analysis can be used to identify trends in the data, to establish if there’s any kind of relationship between a set of variables (e.g. does social media spend correlate with sales), to calculate probability in order to accurately predict future outcomes, to understand how the data is distributed—and much, much more.

Some of the most popular methods used by data analysts include:

  • Regression analysis
  • Monte Carlo simulation
  • Factor analysis
  • Cohort analysis
  • Cluster analysis
  • Time series analysis

You’ll find a detailed explanation of these methods in our guide to the most useful data analysis techniques .

Qualitative data analysis

With qualitative data analysis, the focus is on making sense of unstructured data (such as large bodies of text). Given that qualitative data cannot be measured objectively, it is open to subjective interpretation and therefore requires a different approach to analysis.

The main method of analysis used with qualitative data is a technique known as thematic analysis. Essentially, the data is coded in order to identify recurring keywords or topics, and then, based on these codes, grouped into meaningful themes.

Another type of analysis is sentiment analysis , which seeks to classify and interpret the emotions conveyed within textual data. This allows businesses to gauge how customers feel about various aspects of the brand, product, or service, and how common these sentiments are across the entire customer base.

Traditionally, qualitative data analysis has had something of a bad reputation for being extremely time-consuming. However, nowadays the process can be largely automated, and there are plenty of tools and software out there to help you make sense of your qualitative data. To learn more about qualitative analysis and what you can do with it, check out this round-up of the most useful qualitative analysis tools on the market .

5. What are the advantages and disadvantages of quantitative vs qualitative data?

Each type of data comes with advantages and disadvantages, and it’s important to bear these in mind when conducting any kind of research or sourcing data for analysis. We’ll outline the main advantages and disadvantages of each now.

What are the advantages and disadvantages of quantitative data?

A big advantage of quantitative data is that it’s relatively quick and easy to collect, meaning you can work with large samples. At the same time, quantitative data is objective; it’s less susceptible to bias than qualitative data, which makes it easier to draw reliable and generalizable conclusions.

The main disadvantage of quantitative data is that it can lack depth and context. The numbers don’t always tell you the full story; for example, you might see that you lost 70% of your newsletter subscribers in one week, but without further investigation, you won’t know why.

What are the advantages and disadvantages of qualitative data?

Where quantitative data falls short, qualitative data shines. The biggest advantage of qualitative data is that it offers rich, in-depth insights and allows you to explore the context surrounding a given topic. Through qualitative data, you can really gauge how people feel and why they take certain actions—crucial if you’re running any kind of organization and want to understand how your target audience operates.

However, qualitative data can be harder and more time-consuming to collect, so you may find yourself working with smaller samples. Because of its subjective nature, qualitative data is also open to interpretation, so it’s important to be aware of bias when conducting qualitative analysis.

6. When should I use qualitative or quantitative data?

Put simply, whether you use qualitative or quantitative data (or a combination of both!) depends on the data analytics project you’re undertaking. Here, we’ll discuss which projects are better suited to which data.

Generally, you can use the following criteria to determine whether to go with qualitative data, quantitative data, or a mixed methods approach to collecting data for your project.

  • Do you want to understand something, such as a concept, experience, or opinions? Use qualitative data.
  • Do you want to confirm or test something, such as a theory or hypothesis? Use quantitative data.
  • Are you taking on research? You may benefit from a mixed methods approach to data collection.

You may find that more often than not, both types of data are used in projects, in order to gain a clear overall image—integrating both the numbers side and human side of things.

6. Quantitative vs. qualitative data: FAQ

What are the main differences between qualitative and quantitative research.

Qualitative research is primarily exploratory and uses non-numerical data to understand underlying reasons, opinions, and motivations. Quantitative research, on the other hand, is numerical and seeks to measure variables and relationships through statistical analysis. Additionally, qualitative research tends to be subjective and less structured, while quantitative research is objective and more structured.

What are examples of qualitative and quantitative data?

Examples of qualitative data include open-ended survey responses, interview transcripts, and observational notes. Examples of quantitative data include numerical survey responses, test scores, and website traffic data. Qualitative data is typically subjective and descriptive, while quantitative data is objective and numerical.

7. Key takeaways

Throughout this post, we’ve defined quantitative and qualitative data and explained how they differ. What it really boils down to, in very simple terms, is that quantitative data is countable or measurable, relating to numbers, while qualitative data is descriptive, relating to language.

Understanding the difference between quantitative and qualitative data is one of the very first steps towards becoming a data expert. If you’re considering a career in data, you’ll find links to some useful articles at the end of this post. Had enough theory and want some action? Check out our list of free data analytics courses for beginners , or cut to the chase and simply sign up for a free, five-day introductory data analytics short course .

  • A step-by-step guide to the data analysis process
  • What is the typical data analyst career path?
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Research-Methodology

Quantitative Data Analysis

In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process .

The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgement.

For example, questionnaire findings of a research titled “A study into the impacts of informal management-employee communication on the levels of employee motivation: a case study of Agro Bravo Enterprise” may indicate that the majority 52% of respondents assess communication skills of their immediate supervisors as inadequate.

This specific piece of primary data findings needs to be critically analyzed and objectively interpreted through comparing it to other findings within the framework of the same research. For example, organizational culture of Agro Bravo Enterprise, leadership style, the levels of frequency of management-employee communications need to be taken into account during the data analysis.

Moreover, literature review findings conducted at the earlier stages of the research process need to be referred to in order to reflect the viewpoints of other authors regarding the causes of employee dissatisfaction with management communication. Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner.

Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method . You have specified FDI and GDP as variables for your research and correlation tests produced correlation coefficient of 0.9.

In this case simply stating that there is a strong positive correlation between FDI and GDP would not suffice; you have to provide explanation about the manners in which the growth on the levels of FDI may contribute to the growth of GDP by referring to the findings of the literature review and applying your own critical and rational reasoning skills.

A set of analytical software can be used to assist with analysis of quantitative data. The following table  illustrates the advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.

Advantages and disadvantages of popular quantitative analytical software

Quantitative data analysis with the application of statistical software consists of the following stages [1] :

  • Preparing and checking the data. Input of data into computer.
  • Selecting the most appropriate tables and diagrams to use according to your research objectives.
  • Selecting the most appropriate statistics to describe your data.
  • Selecting the most appropriate statistics to examine relationships and trends in your data.

It is important to note that while the application of various statistical software and programs are invaluable to avoid drawing charts by hand or undertake calculations manually, it is easy to use them incorrectly. In other words, quantitative data analysis is “a field where it is not at all difficult to carry out an analysis which is simply wrong, or inappropriate for your data or purposes. And the negative side of readily available specialist statistical software is that it becomes that much easier to generate elegantly presented rubbish” [2] .

Therefore, it is important for you to seek advice from your dissertation supervisor regarding statistical analyses in general and the choice and application of statistical software in particular.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

Quantitative Data Analysis

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

[2] Robson, C. (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings (3rd edn). Chichester: John Wiley.

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Global Encyclopedia of Public Administration, Public Policy, and Governance pp 1–6 Cite as

Quantitative Methods

  • Juwel Rana 2 , 3 , 4 ,
  • Patricia Luna Gutierrez 5 &
  • John C. Oldroyd 6  
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Quantitative analysis ; Quantitative research methods ; Study design

Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations. It allows us to quantify effect sizes, determine the strength of associations, rank priorities, and weigh the strength of evidence of effectiveness.

Introduction

This entry aims to introduce the most common ways to use numbers and statistics to describe variables, establish relationships among variables, and build numerical understanding of a topic. In general, the quantitative research process uses a deductive approach (Neuman 2014 ; Leavy 2017 ), extrapolating from a particular case to the general situation (Babones 2016 ).

In practical ways, quantitative methods are an approach to studying a research topic. In research, the...

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia

John C. Oldroyd

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Correspondence to Juwel Rana .

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Florida Atlantic University, Boca Raton, FL, USA

Ali Farazmand

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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Quantitative Data: Definition, Pros and Cons & Analysis Techniques

What is Quantitative Data and how it works

  • February 1, 2024
  • FullSession Team

Researchers rely on standardized quantitative data collection to ensure consistency and reliability, and most importantly, to allow for the objective analysis of data collected to inform conclusions and predictions.

In this article, we will see which the the most popular data collection methods, see how to analyze the information we have gathered, and we will provide you with real-world examples to make it easy.

What is Quantitative Data?

Quantitative data deals with numbers and things you can measure objectively. It reflects quantities and includes values researchers can order, count, or measure. 

Such data is crucial in many fields for quantitative data analysis and applying the right data analysis methods. 

For example : in product research, quantitative data examples can be about sign-up form analysis , survey results with scaled responses, and population counts. 

What is The Purpose of Quantitative Data?

Numeric by nature, quantitative data serves as the backbone for high-stakes decision-making across industries. It tells stories through digits—from survey responses rated on a measurement scale to sales figures tallied at quarter-end.

Quantitative data is essential for conducting product analysis that are free from personal bias. It uses numerical values to objectively assess customer satisfaction, so it helps businesses pinpoint areas for improvement. 

When they use quantitative data, companies can perform a gap analysis to bolster their operations and services.

Types of Quantitative Data

Quantitative data helps us count and measure things. It gives precise results we can analyze with numerical data (math).

  • Discrete Data : This type links to things you can count. It’s about whole numbers, like the number of students in a class or cars in a lot. You can’t have half a student or car, so it fits with things that are countable and won’t split into smaller parts.
  • Continuous Data : This kind involves measurements of any value within a range. Think about height, temperature, or time. Continuous data isn’t stuck to whole numbers; it can have fractions and decimals, and show values that change smoothly without jumps.
  • Categorical Data : These are groups or categories. For example, blood types A, B, AB, and O are categorical because they sort blood into distinct groups. Shirt sizes like small, medium, and large also fall under this category because they label items without using actual measurements.
  • Nominal Measurement Scales : They name things without implying order. For instance, jersey numbers on a sports team tell us who is who but don’t rank players by skill or position.
  • Ordinal Data : Here we do have an order or rank. Grades in schools – A is higher than B – show ordinal data. We know which is better even if we don’t know how much.
  • Interval Data : With intervals, the distance between values matters. Temperature scales demonstrate interval data well because the difference between degrees is the same throughout.
  • Ratio Measurement Scales : These scales have all features of interval data plus a true zero point for comparison—like weight or height—allowing ratios to be formed (e.g., someone can be twice as tall as another person).

How To Collect Quantitative Data?

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To collect data from your web survey questions or other types and forms, you need to have a proven system in place. These methods help you find answers to their specific research questions.

  • Researchers use probability sampling to randomly choose a group of people from a larger population. This gives everyone an equal chance of being selected. They often use simple random sampling where they pick people by chance, like drawing names from a hat. Probability sampling helps get a representative sample that reflects the wider group.
  • Online survey tools make it easy for people to answer questions on computers or phones. It is capable of reaching many people quickly.
  • Questionnaires mailed to homes or handed out in public spaces are also common. They ask about things like habits, opinions, and experiences.
  • Survey software helps collect responses which data analysts then examine for patterns and trends.
  • Face-to-face interviews let researchers ask detailed questions. They record answers which are later turned into numbers for analysis.
  • Phone interviews work well when meeting in person is hard. The method still allows for direct communication between the interviewer and the participant.
  • Researchers sometimes use data collected by other organizations or studies. They look at open-source datasets that are free to use. It works perfectly with government records, health statistics, or education scores which already exist as numerical data.

Quantitative vs. Qualitative Data

Qualitative and quantitative data serve distinct roles, each with unique strengths. Qualitative data offers nuanced understanding, while quantitative data provides measurable evidence.

Qualitative data captures the depth of human experiences and provides much more context through face-to-face interviews and open narratives. It reveals the underlying reasons behind behaviors and decisions, getting into the subtleties that statistically analyzed figures may overlook. 

Such type of data is rich in details and is often presented in a tabular format to organize valuable insights.

Quantitative data’s purpose is to quantify information and generalize results from a data sample. It relies on close-ended questions to provide concrete evidence that supports predictions about future data trends and patterns. 

It’s essential for hypothesis testing and offering a broad overview that can be easily compared and statistically analyzed.

Quantitative data shines in scenarios where objectivity is key—like frequency analysis in market research or gap analysis in performance metrics. People prefer it when they present information or findings from 100s of respondents. It’s indispensable in fields like finance or medicine, where precise measurements are crucial. 

Qualitative data, with its descriptive richness, is invaluable in fields such as anthropology or user experience analysis , where understanding the depth of human interaction with physical objects or services is essential.

Advantages of Quantitative Data

Quantitative data analysis methods have their advantages, and that’s why they are so frequently used. Let’s see some of the biggest pros.

1. Objectivity in Numbers

The quantitative data collected is made to be accurate. You’re comparing two products online and one has an average star rating based on thousands of users while the other relies solely on passionate testimonials. Which seems more trustworthy? That’s right, numbers don’t lie.

Such objectivity is especially crucial when making big decisions affecting your business or research outcomes. When you put your faith in numerical evidence, you can identify website issues proactively, and take measures in no time.

2. Scalability

Sometimes size does matter—especially when it comes to data scalability. With quantitative methods, enlarging your study from 100 to 10,000 participants is easy to do. The scalability lets businesses grow their insights alongside their operations without skipping a beat.

Beyond just piling up numbers, scaling quantitatively also means maintaining consistency throughout your analysis. 

3. Easily Presentable

When you conduct quantitative data analysis, you can easily present information in front of your colleagues. You can use it on presentations, spreadsheets, etc.

  • No bias dressed as fact here – number-based insights keep things clear-cut and objective.
  • Growth potential? Absolutely – go ahead and scale those studies up.
  • Capturing widespread trends becomes easier because generalizing large groups isn’t playing guesswork anymore—it’s a strategy powered by statistics with a precision focus.

Challenges Associated with Quantitative Data

Quantitative data analysis might look perfect on the surface, but it’s not without its disadvantages. Let’s see in the next few paragraphs.

1. Limits in Capturing Complexity

Quantitative data captures everything fast and gives you the numbers quickly, but sometimes it misses the nuances—the emotions, motivations, and stories behind those figures.

Sure, we can count how many people left your website after hitting the landing page ( heatmaps might tell us that much), but why they bounced is a tale better told by qualitative insights.

If you’re not careful, you’ll make calls based on half-painted pictures.

2. Chances for Misinterpretation

Numbers can easily be twisted if not read well. You’ve got piles of collected data—great. But that’s not all. It’s how you read the data as well.

The key takeaway? Always pair stats with insights. Descriptive statistics can be both qualitative and quantitative , so it’s hard not to skip one for the other.

3. Influence of Measurement Errors

Due to the numerical nature of qualitative data, a little measurement error can throw off your entire dataset. Let’s say we’re tracking user engagement through a session recording software tool .

If the results from the online surveys are not accurate (for example, a few entries weren’t logged and the satisfaction rate is 92% instead of 77%), you might draw inaccurate conclusions.

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  • Access Feedback Widget : Use the FullSession dashboard to access the feedback widget, which can collect varied customer insights.
  • Customize the Form : Tailor your FullSession form to ask the right questions and gather the specific data you need.
  • Set Collection Parameters : Define which segments of users or customer journeys you want to collect data from, ensuring relevance and precision.
  • Analyze Collected Data : After data collection, use FullSession’s analytical tools to study the feedback, spot trends, and make informed decisions.

Yes, it is that simple.

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In conclusion, understanding quantitative data types and analysis techniques is crucial for making informed decisions. Quantitative data provides valuable insights into numerical trends and patterns.

Knowing how to collect and analyze quantitative data empowers researchers and professionals to draw accurate conclusions from their findings. And if you learn how to do it with FullSession, you can easily take advantage of any situation.

FAQs About Quantitative Data

What distinguishes quantitative data from qualitative data.

Quantitative data adds real-world evidence to support statistical analysis and predictions, unlike qualitative data, which is more about depth and context to behaviors and decisions.

Why is quantitative data important in decision-making?

Quantitative data is crucial for presenting information because you can measure unbiased results. And it’s very potent for scaling since you can easily measure results from 10 or 10,000 responses.

How does quantitative data contribute to market research?

When you apply quantitative data knowledge, you can easily spot news trends and identify patterns in user behavior. What’s more, it’s way more likely to collect many results in no time.

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Research Method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

What is Quantitative Research

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

  • Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
  • Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
  • Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
  • Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
  • Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

  • Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
  • Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
  • Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
  • Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
  • Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
  • Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
  • Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

  • Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
  • Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
  • Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
  • Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
  • Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
  • Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
  • Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

  • Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
  • Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
  • Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
  • Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
  • Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
  • Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

  • To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
  • To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
  • To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
  • To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
  • To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

  • Description : To provide a detailed and accurate description of a particular phenomenon or population.
  • Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
  • Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
  • Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

  • Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
  • Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
  • Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
  • Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
  • Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
  • Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

  • Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
  • Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
  • Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
  • Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
  • Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
  • Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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meaning of quantitative analysis data

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  • March 8, 2023

meaning of quantitative analysis data

Quantitative data analysis comes with the challenge of analyzing large datasets consisting of numeric variables and statistics. Researchers often get overwhelmed by various techniques, methods, and data sources. 

At the same time, the importance of data collection and analysis drastically increases. It helps improve current products/services, identify the potential for a new product, understand target market psychology, and plan upcoming campaigns. 

We have compiled this in-depth guide to ensure you get over the complexities of quantitative data analysis. Let’s begin with the basic meaning and importance of quantitative data analysis. 

Quantitative data analysis meaning 

Quantitative data analysis evaluates quantifiable and structured data to obtain simplified results. Analysts aim to interpret and draw conclusions from numeric variables and statistics. The entire analytical process works on algorithms and data analytics software, helping gain valuable insights. Continuous values are broken into parts for easy understanding using various tools and software. Such data is extracted through surveys and questionnaires. 

However, data analytics software also helps extract quantitative data through email campaigns, websites, and social media. 

Qualitative Vs. Quantitative research: major differences 

Qualitative research aims at extracting valuable insights through non-numerical data like the psychology of customers . The research aims at obtaining solid results and confirming assumptions on general ideas. Furthermore, the collected data presentation remains descriptive instead of numerical-centric.

On the other hand, quantitative research focuses on numbers and statistics to identify gaps in current marketing and operational methods. It successfully answers questions like how many leads are converted in a specific email campaign. 

Collecting quantitative data includes surveys, polls, questionnaires, etc. It remains efficient in identifying trends and patterns in the collected data. However, the obtained results aren’t always accurate as there are chances of numerical errors. 

Note: both quantitative and qualitative data can be obtained with surveys. However, qualitative data collection focuses on asking open-ended questions.

In contrast, quantitative research focuses on close-ended ones. 

4-Step Process of Quantitative data analysis 

Now that we understand the meaning of quantitative data analysis, let’s proceed with four simple steps for conducting it. 

Step 1: Identifying your goals and objectives.

Start by analyzing current business problems and the ones you plan to address with your analysis. 

For example , your customer churn rate significantly increased in the last month.

Do you want to identify the reasons behind it? Being clear with the objective helps in collecting and analyzing relevant data. 

Step 2: Data collection 

Now you are clear with the issue you plan to address. Let’s identify and collect data from all relevant sources. 

For example , conducting a survey of MCQs targeting all possible reasons behind the increase in the churn rate. Identify all relevant data sources and collect data for further analysis. 

Step 3: Data cleaning 

As discussed earlier, quantitative data doesn’t remain highly accurate as they are always chances of errors. Due to this, quantitative data analysis goes through many stages of cleaning. 

Firstly, analysts start with data validation to identify if the data was collected based on defined procedures. Secondly, large datasets require a lot of editing to identify errors like empty fields or wrongly inserted digits. 

Remember that the collected data consists of many duplications, unwanted data points, a lack of structure, and major gaps you must eliminate. Lastly, the collected data is presented in structured formats like tables for easy analysis. 

Step 4: Data Analysis and interpretation 

Now, you are equipped with fairly accurate data sets required for analysis. Using tools and software, data analysts interpret the collected data to draw valuable conclusions. Many techniques are used for quantitative data analysis, including time-series analysis, regression analysis, etc. However, applying the techniques correctly play a greater role than the type of technique used. 

What are the methods for Quantitative research data collection? 

Now that we know the meaning of quantitative data collection, let’s look at some methods of collecting it:

Surveys: close-ended questions

Surveys remain one of the most common methods of quantitative research data collection. These surveys include super-specific questions where respondents answer yes/no or multiple-choice questions. Most companies are going with rating questions or checklist-type survey questions. 

Conducting interviews 

Interviews remain another commonly used method for quantitative data collection. The interviews remain structured with specific questions. Telephone interviews were generally preferred until the introduction of video interviews using Skype, Zoom, etc. Some researchers also go with face-to-face interviews to collect quality data. 

Analytical tools 

Manually collecting and analyzing large datasets remain inconvenient. Many different analytical tools are available to collect, analyze, interpret, and visualize a large amount of data. For instance, tools like GrowthNirvana remain effective in efficient marketing analytics and provide relevant data without delays. All valuable insights required for business growth are easily extracted to make quicker decisions. 

Document review: analyzing primary data

Researchers often analyze the available primary data like public records. The findings support the results generated from other quantitative data collection methods. 

Methods and techniques of quantitative data analysis 

The two common methods used for quantitative data analysis are descriptive and inferential statistics. Analysts use both methods to generate valuable insights. Here’s how: 

Descriptive statistics 

This method describes a dataset and provides an initial idea to help researchers identify potential trends or patterns. It generally focuses on analyzing single variables and explains the details of specific datasets. There are two ways of analyzing data using the descriptive statistics technique- numerical and graphical representation. Let’s start with the numerical method of quantitative data analysis. 

  • Numerical Method 

The numerical method organizes and evaluates data arithmetically to obtain simpler answers to complex problems. Describing data remains the easiest using the measure of central tendency and dispersion. 

The measures of central tendency like mean, median, and mode help identify the collected data’s central position. On the flip side, the measures of dispersion, like range, standard deviation, variance, etc., help understand the extent of data distribution concerning the central point. 

  • Graphical method 

The graphical method provides a better understanding of data through visual representation. Evaluating large data sets becomes easier if presented using a bar chart, pie chart, histogram, boxplot, etc. 

Inferential statistics 

Conducting only descriptive analytics isn’t enough to draw valuable conclusions from the collected data. It only provides limited information on the datasets, emphasizing inferential statistics’ importance. 

Inferential statistics make predictions using the data generated through descriptive statistics. It helps establish relationships between different variables to make relevant predictions. The technique remains suitable for large datasets. 

Certain samples of the data are taken to represent the entire set as evaluating large data remains hectic. Therefore, the summarized samples generated with descriptive statistics are used to draw valuable conclusions. 

Let’s now focus on some commonly used methods in inferential statistics: 

  • Regression analysis 

The method establishes a relationship between a dependent and independent variable(s). It assesses their current strength and predicts future possibilities to devise enhanced strategies. The most commonly used regression models are simple linear and multiple linear. 

  • Cross tabulations 

Cross tabulation or contingency table method is one of the most used methods for market research. It assists in the easy analysis of two or more variables through systematic rows and columns. Furthermore, the major goal of cross tabs remains intact in showing the changes in a dependent variable based on different subgroups. 

  • Monte Carlo method 

The method focuses on weighing all possible outcomes of specific scenarios. Analyzing the pros and cons helps in predicting advanced risks before taking action. Therefore, forecasting future risks based on changing scenarios improves decision-making. 

  • SWOT analysis 

A SWOT analysis identifies an organization’s strengths, weaknesses, opportunities, and threats. It takes into consideration internal and external factors to make better business plans. Companies often conduct a SWOT analysis to improve their products and services or while initiating a new project. 

  • Time Series analysis 

Time series or trend analysis evaluates data sets recorded within specific intervals. Instead of taking random samples, the data is recorded in given time frames. Companies often use time series analysis for forecasting demand and supply. 

What are examples of Quantitative data?

Let’s now look at some examples of quantitative data: 

  • Total number of app downloads in a month 
  • The total number of people who loved a newly introduced product feature 
  • Number of users converted with a marketing campaign 
  • Total number of website conversions in six months 
  • Number of customers residing in a specific location 

What does a data analyst do?

A data analyst collects and interprets data to answer key questions like the potential of new product development, changes in customer purchase behavior, gaps in current marketing campaigns, etc. Many data analysts conduct exploratory analysis to identify trends and patterns during the data cleaning process. 

The job also includes communicating the findings to team members to create better strategies. One can thrive in this position with the right technical and leadership skills. Therefore, a data analyst’s two key roles include knowing how to collect and use the collected data for business growth. 

What is the typical process that a data analyst follows?

The process followed by a data analyst includes the following steps: 

  • Setting objectives 

Understanding the goals behind collecting data help target the right areas for data collection. Next, identify the type of data you need to collect to conduct a specific analysis. 

  • Collecting data 

Now you are clear with the goals and data requirements. Focus on collecting data from identified sources using different methods of data collection. Some of the top sources include surveys, polls, interviews, etc. 

  • Data cleaning 

The data collection process includes collecting a large amount of data which requires further cleaning for analysis. This step removes duplicate records and identifies omissions and other numerical errors. 

  • Data analytics and interpretation 

Now, data analysts focus on analyzing the collected data using various tools and software. Based on the analysis, they draw relevant conclusions to make the most of their findings. 

  • Data visualization 

Data analysts must also convey the acquired information to relevant team members. Data visualization refers to the graphical interpretation of collected data for easy understanding. The process also helps analysts identify hidden insights for detailed reporting. 

What techniques and tools do data analytics use?

Data analysts use various techniques, including regression analysis, the Monte Carlo method, factor analysis, cohort analysis, etc. The right blend of techniques to suit specific situations helps achieve the required results. 

The tools used for data analysis reduce the manual burden of analysts and improve overall decision-making. There are varied categories of tools in data analysis, including business intelligence, ETL tools, automation tools, data mining, data visualization tools, etc. 

Some popular choices for data analysis include Google Analytics, Growth Nirvana (marketing analytics), Improvado , Datapine , etc. 

What are the skills required to become a data analyst?

A data analyst requires the following skills to thrive in the field: 

  • Complete knowledge of python programming
  • Mathematical and statistical understanding 
  • Data decluttering, organizing, and analyzing 
  • SQL knowledge
  • Problem-solving 
  • Logical reasoning and critical thinking 
  • Sharp communication skills
  • Collaboration 

What are some of the best data analytics course?

Let’s now look at some of the best data analytics courses in 2022 to help you gain all relevant skills: 

  • Detailed learning: Data Analyst Nanodegree program by Udacity  

A 4-month program helping people develop advanced programming skills to handle complex data-related issues. It covers everything from data analysis, and visualization, to exploration. 

What are some of the best data analytics course?

        Source

  • Best data analytics course for beginners: Become A Data Analyst by LinkedIn. 

The course consists of beginner-friendly lessons suitable for people with no prior understanding of data analysis. The experts in the industry take all sessions. Furthermore, you can easily complete the course within the free 30 days LinkedIn Learning period. 

Best data analytics course for beginners

  • Bite-sized learning: Data Analyst with R by Datacamp

The entire learning experience breaks down into multiple courses to help you keep up the pace. Industry experts curate about 19 different courses with a duration of 4 hours for every course. Furthermore, it also helps students gain practical exposure by working with real-life datasets. 

Data Analyst with R by Datacamp

      Source

What does the future hold for data analytics?

Data analytics remains a constantly evolving area that will become increasingly important for businesses in the coming future. Extracting real-time insights will help enhance business operations for continuous growth. Furthermore, the increasing growth of business analytics tools makes it easier for businesses to analyze data and draw conclusions without complex coding knowledge. 

Key Takeaways 

  • Analysts must use quantitative (number-oriented) and qualitative (non-numeric) data to devise and modify business strategies. 
  • Surveys are used for obtaining both quantitative and qualitative research data. However, quantitative surveys include only close-ended questions. 
  • Data cleaning remains one of the most crucial steps of data analysis. It ensures the collected data doesn’t contain duplications, omissions, unwanted data points, etc. 
  • The top skills possessed by data analysts include python programming, statistical knowledge, data decluttering, SQL knowledge, collaboration, and communication skills. 

The top two data analytics techniques — descriptive and inferential statistics- are complementary.

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  • Niamh O’Callaghan 1 , 2 ,
  • http://orcid.org/0000-0001-6515-2310 Pauline Douglas 3 , 4 and
  • http://orcid.org/0000-0003-1369-5035 Laura Keaver 1 , 2
  • 1 Department of Health and Nutritional Science , Atlantic Technological University , Sligo , Ireland
  • 2 Health and Biomedical Strategic Research Centre (HEAL) , Atlantic Technological University (ATU) , Sligo , Ireland
  • 3 Ulster University , Coleraine , UK
  • 4 NNEdPro Global Institute for Food Nutrition and Health , Cambridge , UK
  • Correspondence to Laura Keaver; laura.keaver{at}atu.ie

Background Little is known about how cancer survivors perceive nutrition through the cancer experience and how those perceptions may influence their diet.

Aims This study aimed to capture the meaning of nutrition for cancer survivors who are post-cancer treatment using a participatory photography method known as photovoice.

Methods Wang and Burris’s photovoice procedure was followed. Recruitment took place via email through existing links with participants from a previous quantitative study. The participants were tasked with taking photographs to represent the meaning of nutrition for them post-treatment. Group workshops and semistructured interviews were conducted to facilitate reflection, dialogue and analysis. Data analysis followed Braun and Clarke’s six-phase thematic analysis.

Results One man and seven women (n=8) across the Island of Ireland were recruited. Participants identified six themes (illustrated with photographs): (1) Fresh is Best, (2) Be kind to yourself, (3) Building Blocks. Be Informed., (4) Post-Treatment Healing Changes, (5) Chemo Rituals and (6) Food for the Soul–Healthy Mind. Healthy Body.

Conclusions Participants displayed a holistic approach to a healthy lifestyle for recovery post-treatment and maintaining health. While diverse, participants made post-treatment nutritional changes by introducing and eliminating certain foods or food groups. All agreed that being informed and building nutrition knowledge are essential. It is important to clarify the implications cancer has had on diet and health when providing nutrition guidance to ensure that it is appropriate and specific.

  • Nutrition assessment

Data availability statement

Data are available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjnph-2023-000822

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Cancer survivors recognise the importance of nutrition and are motivated to seek information about dietary changes to improve their long-term nutritional and health outcomes.

WHAT THIS STUDY ADDS

Our participant-generated findings are of practical use in illustrating the perspectives on diet and health in this population.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study emphasises how crucial it is to consider and clarify cancer’s implications post-treatment when providing nutrition guidance and advice to ensure that it is appropriate and specific.

Introduction

The number of cancer survivors has increased by more than 50% in the last decade, and currently, more than 200 000 individuals in Ireland live with and beyond cancer. 1 A cancer diagnosis can catalyse change in many health behaviours, including diet. 2 Despite some studies showing improved nutritional quality and adherence to World Cancer Research Fund (WCRF) recommendations in cancer survivors, 3 other studies have shown lower diet quality, poor adherence to dietary guidelines and unwanted weight gain compared with the general population. 4–6

Nutrition can be an overlooked and under-resourced part of the cancer care continuum, particularly in Ireland. Only 39% of the cancer survivors in a recent nationwide study by Sullivan et al 7 were referred to a registered dietitian, despite 89% of the cohort rating nutrition as essential. Furthermore, over half of the participants (57%) wanted clarification regarding the frequently contradictory nutrition advice presented by others and in the media.

It is important to take into account the effects of cancer on dietary intake as well as the importance of nutrition for cancer survivors post-treatment when providing post-diagnosis nutritional support. Thus, this study aims to use a visual methodology called photovoice to understand the significance of nutrition for Irish cancer survivors.

To better understand health concerns from the perspectives of individuals who are experiencing them, to motivate intervention and clarify health issues, public health researchers have increasingly started using visual methodologies. 8 Photovoice’s four main pillars—images, group conversation, participant growth and action—can be customised to fit the demands of any project and target audience. The ultimate objectives of photovoice have been focused on comprehending health action and advocacy, individual and community empowerment, and health intervention across a variety of communities. 8–10 Participatory research techniques can improve our understanding of nutrition in cancer survival by allowing participants to actively contribute to knowledge creation rather than acting as passive subjects. 11

Materials and methods

Study design.

The researchers sought to address the question, ‘What is the meaning of nutrition for Irish cancer survivors?’ using a participatory descriptive qualitative study. Accordingly, the study objective was loosely structured so the participants could take photos that they felt reflected their relationship with nutrition and how it impacted their life. Participants were encouraged to interpret the photography task in whatever way made the most sense for them. 12 The study team did not wish to narrow the photography task as they wanted to obtain the best insight into the participants’ relationship with their dietary choices and nutrition behaviours.

This study was conducted by an academic research team collaboratively with the study participants, using guidance as outlined in Wang and Burris’s field. 13 NO’C completed photovoice’s comprehensive online training course ( https://photovoice.org ) and facilitated the group workshops and semistructured interviews. The Consolidated Criteria for Reporting Qualitative Research checklist is reported in the online supplemental material 1 .

Supplemental material

Inclusion criteria and recruitment.

Inclusion criteria included cancer survivors (aged ≥18 years) who had completed active cancer treatment (≥6 months) and were living across Ireland. Participants had to own a smartphone. Photovoice studies often have small sample numbers since their main goal is to completely understand participants’ perspectives and encourage them to freely communicate their experiences and thoughts. For recruitment, the study was circulated on social media and throughout cancer networks across Ireland. Of the eight participants who responded, five responded from a more extensive quantitative study on the nutrition practices of cancer survivors in Ireland, 14 while three responded to online recruitment calls such as e-newsletters from associated patient groups or social media platforms.

Photovoice procedure

The photovoice project was conducted in four distinct phases, occurring sequentially over 8 weeks in autumn 2021, and conducted online via Microsoft Teams.

Introductory group workshop

Introductory 1-hour workshops facilitated the participants’ preferred time schedules. To accommodate everyone’s schedule, a total of four workshops were held; therefore, some participants completed the introduction workshop in a group setting, while others did it individually. In each, the facilitator explained ground rules, defined participatory photography and gave an overview of the project. Exercises in visual literacy and photo analysis were also conducted. Photovoice’s ‘four fs’ (frame, focus, follow through and flash) to taking better photographs were demonstrated. Potential ethical issues were discussed, such as identifying appropriate and inappropriate pictures (eg, illegal, unsafe) and how to obtain consent to photograph another person. Participants were then given a photography assignment titled ‘The meaning of nutrition for Irish cancer survivors’ and asked to take a minimum of five shots each using their smartphone during the following 2 weeks. There was no maximum number of pictures. The participants were able to express any concerns on ethics or the project task during the workshop.

Taking the photographs

Participants were given 2 weeks to take photographs. The cohort submitted 52 photographic images in total (ranging from 4 to 14 photographs each).

Individual interviews

Individual semistructured interviews were conducted at a time convenient for each participant. During the interview, participants were asked to share all photographs that they captured. This interview provided an opportunity with the facilitator to caption their selected pictures for the final workshop 2 weeks later. Where required, a commonly deployed method, the ‘SHOWED’ mnemonic, was used to discuss and caption each photograph. This includes six questions for each photograph: (1) what do you See here?; (2) what is Happening here?; (3) how does this relate to Our lives?; (4) Why does this situation, concern or strength exist?; (5) how can we be Empowered by this?; (6) what can we Do about it? If participants required more time to create captions for their pictures, they had until the final group workshop. The duration of the interviews ranged from 30 to 60 minutes.

Final group workshop

To start the final group workshop, the facilitator introduced ground rules. Each participant introduced themselves to the group, discussed their cancer diagnosis and previous treatments received and explained their current perspective on nutrition. Each participant then presented their photos and captions to the broader group; this took approximately 20 minutes for each participant. A mini-exhibition of all photographs was then assembled using an unsystematic wall layout on a virtual platform called Padlet ( http://padlet.com ). The group had an opportunity to discuss the pictures collectively and then clustered the photographs into similar groupings using colour coding. The participants were asked to discuss and identify potential themes represented by these clusters; some photographs were regrouped until the cohort had agreed on the selected photographs. The participants and facilitator discussed theme names by discussing the similarities and differences within the photographs in each theme. Shortlisted images and captions were circulated to all participants by email post-workshop to ensure they had been accurately captured. All photographs and captions can be seen in the online supplemental material 2 of this paper. The final workshop was audio-recorded and transcribed for reliability.

Data analysis

In the final group workshop, participants acted as co-researchers to inductively develop six analytical themes representing the meaning of nutrition for them. These were then deductively applied by the facilitator to the rest of the data (eg, interviews and photos). This is a grounded practice known as ‘live coding’, aiming to maximise the researcher’s interaction with the data and ensure analytical rigour. 15 It involves manual coding while simultaneously listening to the audio-recording of the workshop. Braun and Clarke’s six-step thematic analysis was conducted to determine if any additional themes arose during the discussions and Microsoft Excel was used for data organisation and mapping. 15 No additional themes were found in this process.

Study participants

Eight cancer survivors were recruited (seven women (breast cancer) and one man (prostate cancer)). Age ranged from 45 to 59 years old (mean age: 51 years). The majority (n=7) had completed treatment in the last 5 years. All participants had higher education (third level) of varying levels and half were in full-time employment.

Visual and narrative themes

Participants generated six main themes from the pictures: (1) Fresh is Best, (2) Be kind to yourself, (3) Building Blocks. Be Informed., (4) Post-Treatment Healing Changes, (5) Chemo Rituals and (6) Food for the Soul–Healthy Mind. Healthy Body.

Theme 1: Fresh is Best

Participants highlighted the importance of fresh fruits and vegetables for recovery and maintaining health, with an emphasis on freshness and quality. Some only sourced locally grown produce; others purchased only organic produce, while one cancer survivor home foraged and is undertaking ‘an organic course at the moment’ to change a family farm’s status to organic ( figure 1 ). A participant described a local greengrocer as a ‘little oasis’ and explained many benefits of sourcing local: ‘the selection of fruit and vegetables is always so fresh and full of colour’. One cancer survivor mentioned nutrition post-treatment involved going ‘back to basics’, explaining, ‘I went back doing what our parents ate, everything fresh and out of the ground—no added preservatives. We eat as natural as we can, and the fewer processes that food goes through, the better it is for you.’ One participant talked about the struggle in ‘trying to follow an organic plant-based diet’.

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Living the good life! I had all my treatment in Dublin, and we moved home a year after I finished treatment. I had always loved to forage, and now I am also growing my vegetables, and we have our hens!

Theme 2: Building Blocks. Be Informed.

The participants shared the practice of self-directed research through various methods, including online, through books and local food producers. Different dietary choices and beliefs were present for the cohort; however, all agreed it is essential to be informed and build on nutrition knowledge. Some participants explicitly viewed cancer as a teachable moment for behaviour change. In figure 2 , a participant gives a ‘snapshot’ of a new diet post-cancer treatment. This participant undertook their own research and rationalised several dietary changes: ‘I took from what I’m reading, I’m trying to follow an organic plant-based diet, so in terms of white sugar, white flours, all those things are all out the window because they’re not healthy, processed meats, I steer away from them, you know, dairy is well’. Moreover, another cancer survivor presented an image of a local award-winning butcher and expressed how informed she feels on the source of the product: ‘these guys have won so many awards for their meats, they grow their meat for want of a better word, you know the meat is good. You know there are very few chemicals in it.’ While the group views on dietary choices differed, the cohort did note the importance of being adequately informed.

New diet and nutrition.

Theme 3: Be kind to yourself

Broadly, this theme captured forms of celebration, as reflected in figure 3 , and an approach to diet as ‘everything in moderation’. One participant outlined the importance of celebration: ‘now we celebrate all the milestones; I was 41 when I was diagnosed. I had three small children, now we celebrate Easter, we celebrate Christmas, we celebrate anything and everything.’ Food as a symbolic meaning for family time and social connections was also classified into this category. One individual shared an image containing ingredients for a family roast dinner highlighting: ‘an old-fashioned Sunday roast, good food and good company’.

We celebrate every milestone and festival with cake!

Theme 4: Post-Treatment Healing Changes

Participants made post-treatment changes by introducing and eliminating certain foods or food groups. While diverse, all the cohort agreed these fit the one theme, ‘Post-Treatment Healing Changes’, where the collective objective of these changes aimed to ‘heal from within’. One participant explained an intake of vitamin and mineral supplements which she described as part of a ‘post-treatment routine’. A common addition and photograph presented among the participants was an intake of foods with believed anti-inflammatory properties, such as mushrooms, garlic and turmeric. One cancer survivor described how ‘turmeric is something now that I have started putting into almost everything I’m cooking because I have neuropathy, and I just feel I’m very inflamed in general, I find I don’t know whether it’s working or if it’s in my head, but anyways, turmeric goes into everything’. Figure 4 relates to one participant cutting dairy out of her diet initially post-treatment and now only occasionally eats dairy. This theme may seem to overlap with theme 2, ‘Building Blocks. Be Informed’. However, the participants felt it was important to present them separately. The photographs in theme 2 were more informed choices than in this theme, where the participants’ preferences were individually based.

This is the only dairy I see.

Theme 5: Chemo Rituals

Among the cohort, certain foods and dietary practices were associated with receiving chemotherapy treatment. One participant presented an image of coconut water where she shared her chemotherapy experience: ‘every session before chemo, I was drinking litres of coconut water, research in the UK has shown your blood count will never be wrong for chemotherapy’. Figure 5 presents a breakfast of white toast, which participants agreed will always be identified as a ‘chemotherapy breakfast’.

Chemo breakfast.

Theme 6: Food for the Soul–Healthy Mind. Healthy Body.

When describing the theme, Food for the Soul–Healthy Mind. Healthy Body., participants remarked on regaining energy and described outside movement and exercise as ‘food for the soul’. The term ‘exercise’ was associated with life pre-cancer, with one individual now opting to use the word ‘movement’: ‘I hate the word exercise because that’s what I did pre-cancer but post-cancer, its movement and anything that you can do out in the fresh air’. Although the images within this theme embodied outside environments, including photographs of mountains and the seaside, the participants associated it with their overall nutrition and health. One participant explains: ‘These things are nutrition in their way as well, nutrition for the soul, it’s not just the food, I mean the food is important, but it can’t be in isolation, you know?’. Images within this theme had ambiguous meanings, including symbols of positivity and hope. For example, one embodiment portrayed a ray of light from the sun shining through trees, and ‘was my example of a symbol of hope’ ( figure 6 ).

This here is de-stress, but it’s more, as you see, the very middle centre, that’s the light at the end of the tunnel in a cancer journey and the broken branch across the photo signifies the barriers you may encounter, but there is always light at the end of the tunnel.

Our project sought to capture the meaning of nutrition for Irish cancer survivors using a method of participatory photography known as photovoice. Emphasis was placed on dietary intake of fresh fruits and vegetables to recover and maintain health. Participants expressed celebration through food with a reminder to ‘be kind to yourself’. Different dietary choices and beliefs were present. However, the cohort agreed it is essential to be correctly informed and build on nutrition knowledge. While diverse, participants made post-treatment ‘healing’ changes to their dietary intake by the process of introducing and eliminating certain foods or food groups. Additionally, the theme ‘chemo rituals’ was included as particular foods are associated with their time spent receiving chemotherapy. Although the focus was on ‘nutrition’, all participants had photographs embodying the natural landscapes or exercise/movement, describing it as ‘food for the soul’.

The group perspectives on the origin of products are very similar to a study by Chapman and Beagan 16 on women’s beliefs about diet and breast cancer. In this particular study, the authors defined a ‘traditional’ or ‘old-fashioned’ perspective of non-processed meats and vegetables for people with a European cultural heritage. It was distinct from the other perspectives in that it was the one that valued the consumption of meat. It also placed less emphasis on health concerns, defining ‘good food’ or ‘good eating habits’ based on tradition and enjoyment as much as health issues. In this study, another perspective was classified as an ‘alternative’ perspective which focused on the role of toxins, carcinogens and protective factors in food in affecting cancer risk. The traditional and alternative outlooks support the perspectives of our cohort, and some similar views occur. Still, others offer opposing opinions, such as the importance of locally sourced meat (traditional perspective) and eating only organic plant-based food (alternative perspective).

A cancer diagnosis led to dietary changes in a cohort, with some avoiding dairy products due to carcinogenic properties, while others added turmeric, garlic and mushrooms for anti-inflammatory benefits, similar to Irish cancer survivors’ advice on avoiding certain foods. 17 Although evidence indicates the role of inflammation in cancer risk, 18 19 modifiable lifestyle factors associated with inflammation, such as diet quality, 20 are understudied and relatively sparse. Furthermore, the WCRF/American Institute for Cancer Research (AICR) guidelines for cancer survivors for cancer prevention focus on overall dietary patterns rather than intake levels of individual nutrients and compounds. 3

The use of vitamin and mineral dietary supplements among cancer survivors is commonly reported, with prevalence rates in breast, prostate and colorectal cancer survivors ranging from 50% to 85%, 21 22 with 15–30% of cancer survivors initiating supplement use after their cancer diagnosis. 23 Similarly, the intake of dietary supplements was described as a ‘post-treatment routine’ for some participants in the present study. In general, no obvious reasons for taking them were given, and the goal seemed to be health improvement rather than particular anticancer properties. Conversely, the WCRF/AICR strongly encourages cancer survivors to obtain their nutritional needs through a healthy balanced diet instead of taking supplements, 3 as the benefit of such supplements is questionable.

Our cohort reported not receiving any nutritional support or dietary information from a health professional. They obtained their knowledge through self-directed research online and from various books. The use of informal sources such as ‘online’ may partially explain why some of our participants’ perspectives on dietary modifications were not in line with current evidence (eg, high intake of anti-inflammatory foods and nutritional supplements being beneficial). 8

In the theme ‘Chemo Rituals’, it was clear from the images that the associations with, for example, white toast, energy snack balls and coconut water, continue post-treatment. It should be noted how these associations could impact dietary intake or certain practices. When providing nutrition guidance, it is essential to consider and clarify all implications cancer has had.

The participants remarked on regaining energy and described outside movement and exercise as ‘food for the soul’. Engaging in physical activity and training has many beneficial effects on health-related quality of life domains in cancer survivors, including fear of recurrence (eg, breast cancer), emotional well-being, sexuality, sleep disturbance, social functioning, fatigue and pain. 24 25 The benefits of physical activity are further reinforced by results of observational studies reporting that regular physical activity of 3–5 days a week for a minimum of 30 min per activity is correlated with a reduction in mortality and all-cause mortality in early-stage breast 26 and prostate cancer. 27 Although research shows that nearly two-thirds of cancer survivors do not meet national physical activity guidelines, those who meet the guidelines report better quality of life in multiple domains than less active individuals. 28 29 The study found that participants in a holistic approach to physical activity, such as rowing and walking, experienced positive mental health outcomes and interpreted natural landscapes as symbols of hope. The participants shared a mutual outlook on creating therapeutic landscapes within nature, which they described as positively impacting emotional and psychological health. 30

This methodology has the potential to enhance greatly what healthcare professionals can know and understand about the lived experiences of cancer survivors regarding their nutrition practices and dietary intake. It is an illustration of how the photovoice methodology may support collaboration among healthcare professionals and cancer survivors within this topic but also to support other needs of this cohort. The methodology encourages patient and public participation in research, values life experiences, and offers a creative platform to collaborate and advocate.

As with any qualitative study, the present study has certain limitations that should be considered while interpreting the findings. The sample used here was small but standard for a photovoice study and similar to other photovoice projects with cancer survivors and patients. 31 Notably, the photovoice approach necessitates a consistent commitment from the participants; thus, a smaller sample size is more feasible. Breast cancer survivors are the primary group represented in the study. There was only one male in our study with a different cancer type. However, there were many group agreements throughout the project regardless of gender or cancer type.

Our photovoice project was carried out online due to the COVID-19 pandemic rather than in person face‐to‐face, which is more common for a photovoice project. This may have made the interviews more impersonal, but it had advantages such as less travel and commitment time for the participants. All participants were well educated; future research should seek to explore this topic with cancer survivors of varying education backgrounds. Furthermore, self‐selection bias could have occurred, where those who took part may have a long‐term interest in healthy lifestyles or have become interested since diagnosis.

Conclusions

The themes presented here are of practical use in illustrating the perspectives of Irish cancer survivors on nutrition. It is essential to consider and clarify the implications cancer has had on diet and health when providing nutrition guidance and advice to ensure that it is appropriate and specific. The photovoice method helped elicit nutrition perspectives of Irish cancer survivors and proved an easy‐to‐use and acceptable process.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s).

Ethics approval

This study involves human participants and ethical approval was granted by the Research Ethics Committee in the Institute of Technology Sligo (now the Atlantic Technological University Sligo; reference number: 2020023). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to acknowledge and thank the cancer survivors who participated in this project and generously gave their time and effort to this photovoice study. It was a pleasure to see how open and willing people were to share their experiences and support one another, especially during the group workshops.

Thank you to NNEdPro Global Institute for Food, Nutrition and Health for supporting an APC fee waiver

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

  • Data supplement 1
  • Data supplement 2

Contributors All authors contributed to the study’s conception and design. NO'C was responsible for data curation, including photovoice workshops. NO'C, LK and PD performed data analysis. LK and PD supervised the project. The first draft of the manuscript was written by NO'C and all authors commented on all versions of the manuscript. All authors have read and agreed to the published version of the manuscript. LK is the guarantor for the overall project.

Funding NO'C received an Irish Research Council Government of Ireland Postgraduate Scholarship (project ID: GOIPG/2020/1385).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed by Venus Medina-Maldonado, Pontificia Universidad Católica del Ecuador, Centro de Investigaciones para la Salud de América Latina (CISeAL), Ecuador.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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