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A Comprehensive Guide to Quantitative Research: Types, Characteristics, Methods & Examples

quantitative research methods example

Step into the fascinating world of quantitative research, where numbers reveal extraordinary insights!

By gathering and studying data in a systematic way, quantitative research empowers us to understand our ever-changing world better. It helps understand a problem or an already-formed hypothesis by generating numerical data. The results don’t end here, as you can process these numbers to get actionable insights that aid decision-making.

You can use quantitative research to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, etc. The research is conducted on a larger sample population to draw predictive, average, and pattern-based insights.

Here, we delve into the intricacies of this research methodology, exploring various quantitative methods, their advantages, and real-life examples that showcase their impact and relevance.

Ready to embark on a journey of discovery and knowledge? Let’s go!

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to test theories about customer attitudes and behaviors. It helps researchers gather and analyze data systematically to gain valuable insights and draw evidence-based conclusions about customer preferences and trends.

Researchers use online surveys , questionnaires , polls , and quizzes to question a large number of people to obtain measurable and bias-free data.

In technical terms, quantitative research is mainly concerned with discovering facts about social phenomena while assuming a fixed and measurable reality.

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision is going to work out.

Let’s consider an example.

Suppose your qualitative analysis shows that your customers are looking for social media-based customer support . In that case, quantitative analysis will help you see how many of your customers are looking for this support.

If 10% of your customers are looking for such a service, you might or might not consider offering this feature. But, if 40% of your regular customers are seeking support via social media, then it is something you just cannot overlook.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

  • Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

quantitative research methods example

As you can see, all the variables are measurable. This ensures that the research is in-depth and provides less erroneous data for reliable, actionable insights.

  • Sample Size

No matter what data analysis methods for quantitative research are being used, the sample size is kept such that it represents the target market.

As the main aim of the research methodology is to get numerical insights, the sample size should be fairly large. Depending on the survey objective and scope, it might span hundreds of thousands of people.

  • Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

quantitative research methods example

This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in result interpretation. Any bias can ruin the fairness of the entire process and defeats the purpose of research.

  • Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

quantitative research methods example

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

  • Predictive Outcomes

Quantitative analysis of data can also be used for estimations and prediction outcomes. You can construct if-then scenarios and analyze the data for the identification of any upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools that come with advanced analytics capabilities.

8 Best Practices to Conduct Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Define Research Objectives

There can be many ways to collect data via quantitative research methods that are chosen as per the research objective and scope. These methods allow you to build your own observations regarding any hypotheses – unknown, entirely new, or unexplained. 

You can hypothesize a proof and build a prediction of outcomes supporting the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

Below, we explore quantitative research methods and discuss some examples to enhance your understanding of them.

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, and that includes a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

Read More: Proven Tips to Avoid Leading and Loaded Questions in Your Survey

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether it’s experimental, quasi-experimental, or correlational. You also need to pay attention to the sample size and sampling technique such that it represents the target population accurately.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Implement data collection protocols to minimize errors and bias during data gathering. Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings in a clear and unbiased manner. Avoid making unwarranted causal claims, especially in correlational studies. Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data. Comply with ethical guidelines and gain approval from a governing body if necessary.

Read More: How to Find Survey Participants & Respondents

Types of Quantitative Research Methods

Quantitative research is usually conducted using two methods. They are-

  • Primary quantitative research methods
  • Secondary quantitative research methods

1. Primary Methods

Primary quantitative research is the most popular way of conducting market research. The differentiating factor of this method is that the researcher relies on collecting data firsthand instead of relying on data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects, which are:

A. Techniques & Types of Studies:

  • Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

They help them understand their customers, products, and other brand offerings in a proper manner.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys.

Surveys are of two types:

  • Cross-Sectional Surveys Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.
  • Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.
  • Longitudinal Surveys Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes.
  • Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

Surveys can be distributed via various channels. Some of the most popular ones are listed below:

  • Email: Sending surveys via email is a popular and effective method. People recognize your brand, leading to a higher response rate. With ProProfs Survey Maker’s in-mail survey-filling feature, you can easily send out and collect survey responses.
  • Embed on a website: Boost your response rate by embedding the survey on your website. When visitors are already engaged with your brand, they are more likely to take the survey.
  • Social media: Take advantage of social media platforms to distribute your survey. People familiar with your brand are likely to respond, increasing your response numbers.
  • QR codes: QR codes store your survey’s URL, and you can print or publish these codes in magazines, signs, business cards, or any object to make it easy for people to access your survey.
  • SMS survey: Collect a high number of responses quickly with SMS surveys. It’s a time-effective way to reach your target audience.

Read More: 24 Different Types of Survey Methods With Examples

2. Correlational Research:

Correlational research aims to establish relationships between two or more variables.

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

3. Causal-Comparative Research:

Causal-comparative research, also known as quasi-experimental research, seeks to determine cause-and-effect relationships between variables.

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to draw conclusions.

Causal-comparative research is useful in situations where it’s not ethical or feasible to conduct true experiments.

Examples of questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

4. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

B. Data collection methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls.

Sampling methods:

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

  • Simple random sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
  • Stratified random sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
  • Cluster sampling: Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
  • Systematic sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

  • Convenience sampling: The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them.
  • Consecutive sampling: Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
  • Quota sampling: With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
  • Snowball sampling: This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
  • Judgmental sampling: In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

Read More: Data Collection Methods: Definition, Types & Examples

C. Data analysis techniques

To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

  • SWOT Analysis: This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
  • Conjoint Analysis: This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
  • Cross-tabulation: A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
  • TURF Analysis: Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.
  • By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error, you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

II. Secondary Quantitative Research Methods

  • Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.
  • Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports.
  • Secondary quantitative research plays a crucial role in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Advantages of Quantitative Research Methods

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings.

Here are some core benefits this research methodology offers.

Direct Result Comparison

As the studies can be replicated for different cultural settings and different times, even with different groups of participants, they tend to be extremely useful. Researchers can compare the results of different studies in a statistical manner and arrive at comprehensive conclusions for a broader understanding.

Replication

Researchers can repeat the study by using standardized data collection protocols over well-structured data sets. They can also apply tangible definitions of abstract concepts to arrive at different conclusions for similar research objectives with minor variations.

Large Samples

As the research data comes from large samples, the researchers can process and analyze the data via highly reliable and consistent analysis procedures. They can arrive at well-defined conclusions that can be used to make the primary research more thorough and reliable.

Hypothesis Testing

This research methodology follows standardized and established hypothesis testing procedures. So, you have to be careful while reporting and analyzing your research data , and the overall quality of results gets improved.

Proven Examples of Quantitative Research Methods

Below, we discuss two excellent examples of quantitative research methods that were used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time.

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases.

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages.

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

  • Family governance
  • Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations.

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns.

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days.

The insights and results were:

  • A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
  • People felt massive digitization to be a resourceful yet demanding aspect as they have to adapt every day.
  • Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid research method by opting for the methods that are most relevant to your objective.

This understanding comes gradually as you learn how to implement the correct combination of qualitative and quantitative research methods for your research projects. For the best results, we recommend investing in smart, efficient, and scalable research tools that come with delightful reporting and advanced analytics to make every research initiative a success.

These software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.

So, choose the best research partner, create the right research plan, and gather insights that drive sustainable growth for your business.

Emma David

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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  • What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

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

Quantitative research is the opposite of qualitative research , which involves collecting and analysing 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.

  • What is the demographic makeup of Singapore in 2020?
  • How has the average temperature changed globally over the last century?
  • Does environmental pollution affect the prevalence of honey bees?
  • Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, 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 generalised 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).

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Once data is collected, you may need to process it before it can be analysed. 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 visualise your data and check for any trends or outliers.

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

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 standardise data collection and generalise findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardised 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 analysed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalised 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 standardised 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.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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 organisations.

Operationalisation 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, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise 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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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

quantitative research methods example

  • Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
  • Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
  • Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
  • Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
  • Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
  • Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

  • Primary quantitative research methods
  • Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

  • Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
  • Multiple samples can be analyzed and compared using a cross-sectional survey research method.
  • Multiple variables can be evaluated using this type of survey research.
  • The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

  • In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
  • Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
  • In situations where the sequence of events is highly essential, longitudinal surveys are used.
  • Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

  • The relationship between stress and depression.
  • The equation between fame and money.
  • The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

  • The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

  • Traditional research methods are more effective than modern techniques.
  • Systematic teaching schedules help children who struggle to cope with the course.
  • It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

  • Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
  • Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
  • Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
  • Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

  • Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
  • Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
  • Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
  • Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
  • Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must 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.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

  • Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. 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 conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
  • Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to 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 that 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: The SMS survey is a quick and time-effective way to collect a high number of responses.
  • Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

  • SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
  • Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
  • Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
  • TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

  • Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
  • Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
  • Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
  • Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
  • Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

  • A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
  • Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

advantages-of-quantitative-research

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

Tips to conduct quantitative research

  • Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
  • Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
  • Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
  • Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

quantitative research methods example

Quantitative Research

  • Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
  • Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
  • Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
  • Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
  • Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
  • Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
  • Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

  • Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
  • Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
  • Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
  • Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
  • Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
  • Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
  • Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Quantitative research methods

a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time. The goal of gathering this quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Quantitative research techniques include experiments and surveys. 

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What are the strengths of quantitative research.

Professor Norma T. Mertz briefly discusses qualitative research and how it has changed since she entered the field. She emphasizes the importance of defining a research question before choosing a theoretical approach to research.

This is just one segment in a series about quantitative methods. You can find additional videos in our SAGE database, Research Methods: 

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

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Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

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9 Quantitative Research Methods With Real Client Examples

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Quantitative research is essential to developing a clear understanding of consumer engagement and how to increase satisfaction.

Primary Quantitative Research Methods

When it comes to quantitative research, many people often confuse this type of research with the methodology. The research type refers to style of research while the data collection method can be different.

Research types

These are the primary types of quantitative research used by businesses today.

  • Survey research: Ideally when conducting survey research businesses will use a statistically relevant sample to understand the sentiments and actions of a large group of people. This could be their current customers or consumers who fit into their ideal demographic.
  • Correlational research: Correlational research compares two variables to come to a conclusion about whether there is a relationship between the two. Keep in mind that correlation does not always imply causation, which is to say you need to account for external variables that could cause an apparent relationship.
  • Experimental research: This form of research takes a scientific approach, testing a hypothesis by manipulating certain variables to understand what changes this could cause. In these experiments, there is a control group and a manipulated group.

Also read:  6 Factors Influencing Customer Behaviors in 2021

Data collection methods

Launching the above research requires creating a plan to collect data. After all, quantitative research relies on data. Here are the common primary data collection methods for quantitative research.

  • Surveys: A common approach to collecting data is using a survey. This is ideal especially if the business can obtain a statistically relevant sample from their responses. Surveys are often conducted through web or email questionnaires.
  • Interviews: Yes, interviews can be used to obtain quantitative data. While this form of data collection is typically associated with qualitative research, interviewers can ask a standard set of questions to collate formal, quantitative data.
  • Documentation review: With an increasing amount of business occurring digitally, there is more documentation now than ever before to help inform quantitative conclusions. Businesses can assess website metrics such as return visits, time on page or even use a pixel to track customer movement across websites. They can also view how many times their app has been opened and actions users have taken on their platform to determine customer engagement.
Secondary research can be helpful when formulating a plan for obtaining primary quantitative data. It can help narrow areas of focus or illuminate key challenges.

Secondary Quantitative Research Methods

Secondary data is information that is already collected and not necessarily exclusive to the company but still relevant when understanding overall industry and marketplace trends. Here are a few examples of secondary data:

  • Government reports: Government research can indicate potential regulatory roadblocks, customer pain points and future opportunities. For example, a fitness company might use government data that shows an increase in use of outdoor running trials to develop a new product used to meet that specific use case.
  • Survey-based secondary data: Polls or surveys that have been conducted for a primary use could be reused for secondary purposes. This could include survey data obtained by other companies or governments.
  • Academic research: Research that has been previously conducted and published in peer-reviewed journals can help inform trends and consumer behavior, even if it doesn’t apply to a company’s specific customers.

Secondary research can be helpful when formulating a plan for obtaining primary quantitative data. It can help narrow areas of focus or illuminate key challenges. It can also help when it comes to interpreting primary data, especially when trying to understand the relationship between two variables of correlated data.

Also read:  The What, Why, & How of Customer Behavior Analysis

Real Examples of Quantitative Research

We regularly use quantitative research to help our clients understand where they can best add value to increase customer engagement. Here are three examples of quantitative research in motion.

Example 1: Leading food distribution company

We helped a leading food distribution company identify changes in the needs and values of their restaurant clients as a result of COVID-19. This helped inform opportunities to become more valuable partners.

The research plan involved creating a survey that was emailed to clients. The questions were specific and numeric. For example, respondents were asked what percentage of their weekly spend was used with the food distribution company. They were also asked to assign a percentage to the way their food ordering had changed during COVID-19 and to rate their satisfaction with the food distribution company.

The results showed changes that had occurred for clients of the food distribution company as a result of the unique stressors of the pandemic. We were able to determine changes in weekly food supply and customer count as well as menu adaptations and purchase behavior.

Example 2: Leading credit card company

Our work with a leading credit card company required us to understand what current travel card members valued about the rewards program and their preferred communication method for booking travel in order to create an omnichannel servicing strategy and ideal customer journey.

Through an online survey of younger cardholders, the target demographic for this project, we asked questions such as length of card membership, total spend and the number of annual leisure trips in addition to more specific questions that showed how members get inspiration for trip planning and where they research.

The results highlighted ways to overcome resistance to pricing by proving more value. It also illuminated ways to make the benefits of membership more tangible to card holders and how to influence travelers in the early stages of planning their journey.

Example 3: Internal research report

We’re in the business of drinking our own champagne, so to speak, which is why we conducted our own quantitative research aimed at understanding the consumer trends that were spurred by the pandemic and how these will transform behaviors in the future.

There’s no question that new customer experiences emerged from the pandemic. Think of offerings such as “buy online, pickup in store (BOPIS),” or blended restaurant meals that are cooked at home. We wanted to understand how consumers truly felt about these new experiences and which they were likely to continue using even after restrictions were lifted. We also wanted to know more about the changing expectations for branded communication and how all of these pieces of the puzzle fit together to create consumer engagement. Our method of data collection was a survey.

Our research led us to develop insights we could use to inform our customers in their decision making. For example, we found convenience is paramount for consumers who are seeking out hybrid experiences such as BOPIS to take the best of both worlds. We also found many of these changes are permanent as consumers embraced new experiences that made their lives easier.

We regularly use quantitative research to help our clients understand where they can best add value to increase customer engagement.

The Bottom Line

Quantitative research is essential to developing a clear understanding of consumer engagement and how to increase satisfaction. Though online surveys are one of the most common methods for obtaining data, research isn’t limited to this strategy. It’s important to use whatever strategies are within your scope to constantly evaluate new trends and consumer behaviors that could significantly impact your offerings. The results can show you how to re-engage customers and drive loyalty.

Interested in partnering with us to learn more about your customers needs, wants, and behaviors to inform future experience design? Contact us today !

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Research Methodologies

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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

  • What are the relationships between two or more variables? 
  • What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

  • Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
  • Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

  • Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
  • Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

  • Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
  • Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

  • Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
  • Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

  • Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

  • The data is usually gathered using structured research instruments.
  • The results are based on larger sample sizes that are representative of the population.
  • The research study can usually be replicated or repeated, given its high reliability.
  • Researcher has a clearly defined research question to which objective answers are sought.
  • All aspects of the study are carefully designed before data is collected.
  • Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
  • Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
  • Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

  • Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
  • Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
  • Explain the techniques you used to "clean" your data set.
  • Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
  • Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
  • When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
  • Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
  • Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
  • Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

  • Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
  • Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
  • Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

  • Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
  • Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
  • Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

  • Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

  • Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
  • Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
  • Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
  • Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

  • Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
  • Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
  • Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

  • Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
  • Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
  • Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
  • You can summarize vast sources of information and make comparisons across categories and over time; and,
  • Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

  • Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
  • Uses a static and rigid approach and so employs an inflexible process of discovery;
  • The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
  • Results provide less detail on behavior, attitudes, and motivation;
  • Researcher may collect a much narrower and sometimes superficial dataset;
  • Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
  • The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
  • Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

  • EXAMPLE 1. Descriptive research question (quantitative research)
  • - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
  • “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
  • RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
  • EXAMPLE 2. Relationship research question (quantitative research)
  • - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
  • “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
  • Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
  • EXAMPLE 3. Comparative research question (quantitative research)
  • - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
  • “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
  • RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
  • STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
  • EXAMPLE 4. Exploratory research question (qualitative research)
  • - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
  • “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
  • EXAMPLE 5. Relationship research question (quantitative research)
  • - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
  • “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

  • EXAMPLE 1. Working hypothesis (quantitative research)
  • - A hypothesis that is initially accepted for further research to produce a feasible theory
  • “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
  • “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
  • EXAMPLE 2. Exploratory hypothesis (qualitative research)
  • - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
  • “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
  • “Conclusion
  • Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
  • EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
  • “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
  • Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
  • EXAMPLE 4. Statistical hypothesis (quantitative research)
  • - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
  • “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
  • “Statistical Analysis
  • ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

  • EXAMPLE 1. Background, hypotheses, and aims are provided
  • “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
  • “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
  • “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
  • EXAMPLE 2. Background, hypotheses, and aims are provided
  • “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
  • “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
  • EXAMPLE 3. Background, aim, and hypothesis are provided
  • “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
  • “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
  • “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

  • Conceptualization: Barroga E, Matanguihan GJ.
  • Methodology: Barroga E, Matanguihan GJ.
  • Writing - original draft: Barroga E, Matanguihan GJ.
  • Writing - review & editing: Barroga E, Matanguihan GJ.

quantitative research methods example

Quantitative Research: Methods And Examples

Santosh took over as manager of a breakfast cereal brand at a large corporation. He soon found that sales were…

Define Quantitative Research

Santosh took over as manager of a breakfast cereal brand at a large corporation. He soon found that sales were weak compared to their competitors despite their large market penetration. He commissioned detailed research into why the product wasn’t doing well, including a survey of customer preferences. Soon, he discovered consumers weren’t enjoying the flavor, finding it too sweet.

  A survey is a kind of quantitative research frequently used to collect data.

What is quantitative research ? It involves gathering information through interviews, observation and experiments. The results are often available as charts, tables or numerical values. Collecting large amounts of information allows for the statistical analysis of data.  

Quantitative research is used to discover patterns in or differences between populations or objects of interest. It can be collected in many ways. Let’s dive in to understand the meaning of quantitative research and its place in the world of research.

What Is Quantitative Research?

Quantitative research vs. qualitative research, quantitative methods in business, quantitative analysis for strategic planning.

Quantitative research is one of two primary methods of understanding the world through research, the other being qualitative research. As the names suggest, quantitative methods look at matters through a numerical lens, while qualitative research is more subjective in scope.

  When trying to define quantitative research , there are four types we discuss most frequently:

Observation

Secondary research.

The term quantitative research covers a lot of ground. Which data collection methods researchers finally choose will depend on the subject and scope. For instance, if an organization wants to study the use of various detergents in a city, they could opt for a survey. On the other hand, if a start-up is developing a new kind of bio-fuel, they would have to conduct detailed experiments to gather data.

  Now that we’ve been able to define quantitative research , we can dig a little deeper into its characteristics.

  When presented with a question, researchers must first decide whether a qualitative approach is needed or a quantitative one. Sometimes the answer is one or the other, or it could even be a combination of both. Research design is a subject that requires thought. Here are some features of both:

  • Quantitative research uses numbers and statistical analysis
  • Quantitative methods collect data based on variables such as age, gender, occupation, marital status, ethnicity and religious affiliation
  • Quantitative methods are not appropriate for subjects that can’t be assigned a numerical value. They can be used for more subjective matters, but it takes careful research design
  • Qualitative research involves interviews, observations, or experiments to provide a detailed picture of a topic

There are some advantages and disadvantages of both methods that apply to most research situations. Some strengths of each method include:

  • Qualitative research may not produce statistics. Quantitative research can generate numbers from the data being gathered
  • Quantitative research methods can be used comparatively quickly, and the results generated can be interpreted with certainty. This is compared to qualitative research, which can take much longer and requires more resources to complete
  • A quantitative study can be extremely accurate because the numbers tell researchers when certain conditions are met. In qualitative research, there are no numbers associated with it and it uses qualities such as observation, interviews and participant feedback in determining what is happening
  • Quantitative research can be used in a variety of applications and forms. We can use it for marketing, policy making, sociology and psychology
  • Quantitative studies can involve many variables and experiments at one time compared to qualitative researchers who must limit their focus to get usable data

Choosing the right approach could mean the difference between failure and success in learning about the subject. With a clear understanding of quantitative research meaning, these decisions become easier.

  Studying some examples of quantitative research can help businesses achieve goals and objectives. Quantitative methods allow us a better understanding of customers and markets, improving sales and profitability.

  There are several types of quantitative analyses in marketing. Let’s take a closer look.

Market Segmentation

Market analysis, market forecasting, market research, operational analysis,  data analysis.

Quantitative analysis has far-ranging applications for business and management.

Quantitative research in business can be used in decision-making. It helps analyze large amounts of data, such as:

  • Changes in income, sales, inventory and capital spending
  • Profitability through cost of goods, labor costs or operating expenses
  • Competitive advantages through pricing or production costs
  • Limits of market access due to cost constraints
  • Trends in market share by analyzing sales over time
  • Product acceptance by analysis of satisfaction scores achieved by customers

Managers might commission a quantitative study to gather data on the current situation and then use this information to plan measures to course-correct where needed.

  Before embarking on a quantitative study, first identify your desired goals. There are many statistical measures to determine the success or failure of any study. The data can also be used to formulate a hypothesis that best explains the results.

For managers to achieve mastery over analytical tools, it can take significant amounts of hard work and experience. Harappa’s Thinking Critically course provides professionals with transformative information and insight into how to make the best decisions under pressure. It helps them on the way to the mature thinking needed to process information, arrive at proper conclusions and structure persuasive arguments. These traits are an asset in every workplace and as professionals grow through the stages of their careers. Strengthen your teams today with Harappa.

Explore Harappa Diaries to learn more about topics such as Main Objectives of Research , Methods Of Qualitative Research , Examples of Experimental Research and Understand Classical Conditioning Theory of Learning to upgrade your knowledge and skills.

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Quantitative Research Study: Definition, Approaches, Methods & Examples

Quantitative Research

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Quantitative research is a type of scientific study that involves the collection and analysis of numerical data. It uses mathematical and statistical techniques to identify patterns in large datasets. Analysis of numbers allows researchers to make predictions about future trends or outcomes. Quantitative methods include surveys, experiments, field studies, structured interviews, standardized assessments and questionnaires.

In this article, we will focus on what a quantitative study is and its main methods. Prepare to go through:

  • Key characteristics
  • Main types and approaches
  • Steps of conducting a quantitative study .

Our paper writers also provided the best quantitative research methods and examples to showcase the benefits of this approach.

What Is Quantitative Research: Definition

Before jumping into a detailed discussion on how to launch quantitative research, let’s outline a definition of this type of study. 

Quantitative research involves analyzing numerical data to uncover patterns and statistical information, which can be used to test hypotheses and respond to research questions . Quantitative methods often include statistical analysis, surveys, and experiments to generate measurable data and make accurate predictions. 

Quantitative research studies are usually applied to fields such as social science, economics, marketing, biology, etc. It is also commonly used for descriptive , correlational , or experimental studies .

Next, we will delve deeper into specific methods to define which one can best fit your academic work. However, before you start analysis and data collection, you need to be clear with the study purpose and research questions you will try to answer in your work.

>> Read more: Difference Between Qualitative and Quantitative Research

Characteristics of Quantitative Research

First, let’s define quantitative research characteristics to ensure that you choose the right type of data for your study. A deep understanding of key traits is the guarantee that you won’t make a mistake when conducting your own study.

  • Quantitative data is measurable. There are variables that can be easily counted and applied to statistical formulas. In other words, this is numeric data.
  • You can apply structured tools for your quantitative research. Those tools are surveys, polls, and questionnaires – structured forms you can use to collect the information.
  • The sample size should be sufficient. To get accurate results, you need to collect data from a significant portion of the target market.Obtaining only 10 survey responses, for example, would not yield any meaningful insights.
  • Your data can be represented in tables, graphs, or charts. As quantitative methods of data collection are focused on numbers, you should utilize visual aids to structure those numbers clearly for analysis. Such representation can provide valuable insights into patterns, trends, and relationships between independent and dependent variables.

If you need a thesis helper online , check our expert services. Our team of professional writers and editors can provide valuable guidance and support in all stages of research and writing.

Quantitative Research Examples

Quite often, it is challenging to apply all the knowledge about this type of research to your specific field of study. However, we want to share examples of quantitative research that illustrate that it can be used for any purpose.

Quantitative research example 1

One common example for students is an evaluation survey after they finish a course at the university. Students usually answer some questions on a likable scale. For instance, they evaluate the quality of lectures on a scale from 1 to 10, where 10 is the highest grade. These numbers help universities to see general satisfaction from this course, define an average number of people who like the course, and run a correlation between student satisfaction by course and their grades.

Example of quantitative research 2

Another common example is customer surveys you get after purchasing something online. After the purchase, you will get an email from a retailer or brand with questions about your satisfaction. For example, you will grade on a scale from 1 to 5 how easy you could find the right size or get customer support. As a result, they get numeric data to evaluate how well their online shop works and what can be improved, make some predictions about future purchases, and use the insights for marketing purposes.

Types of Quantitative Research

There are a few different types of quantitative research that can be used for various studies. Let’s overview each type of quantitative research to understand in what circumstances and for what goals you can use each of them.

We would focus on 4 main types of quantitative approaches in data collection and analysis:

  • Descriptive study Descriptive research is used to measure variables for understanding the situation. It does not involve any manipulation of variables. In other words, descriptive studies focus on defining key statistical measures, without testing for specific data insights.
  • Correlation Correlational study is used in the quantitative research process when you need to measure a relationship between two variables and understand how one variable (for example, the age of respondents) is related to positive answers in the customer survey. Ensure you understand the difference between correlation and causation while making this kind of research.
  • Causal-comparative research Causal-comparative research is a type of non-experimental study that aims to investigate the causes behind differences in behavior among multiple groups. It is one of the commonly used types of quantitative studies for investigating  causal relationships between variables.
  • Experimental research. Experimental research involves manipulating independent variables to observe how they affect dependent variables under controlled conditions. Put simply, the researcher will need to alter the situation to measure various outcomes that may occur.

Primary Quantitative Research Methods

When discussing types of quantitative research methods, we also need to define primary and secondary methods. Primary methods are used to collect data directly from the source, such as through surveys , experiments , or systematic observations . 

Below we will explain each of these methods in detail.

1. Survey Research

Surveys are a widely used quantitative research methodology across various disciplines and fields of study. They are organized both online or offline to gather data from different audiences. In recent years, online surveys have become increasingly popular, replacing traditional methods such as phone or in-person questioning

Surveys can be applied to achieve various study aims, such as understanding attitudes, behaviors, opinions, preferences, or demographic characteristics.

We would like to define 2 main quantitative survey methods:

  • Cross-sectional surveys Cross sectional survey that analyzes data across a sample population at a specific point in time. It means you may send this survey to various different groups of people, but you will need a one-time point you are researching for your study. It is a common method in such fields like economy, epidemiology, or medicine.
  • Longitudinal surveys In longitudinal research , you will measure the same group of people, but the data should be collected repeatedly over time. This method requires repeated measurements at regular intervals, such as days, months, or even years, to track changes in dependent variables over time.

Survey quantitative research method example

One of the common examples is the survey for measuring how citizens are satisfied with local politicians. For this purpose, the sociology group used to develop a questionnaire sample and define target audiences – people living in specific areas or some age groups. Based on their answers, different methods can be applied to answer defined questions, make predictions for the next election or just measure the general attitude of the selected group to political ideas.

2. Experiment

Experimentation is one of the quantitative approaches to research that assumes testing various theories to prove or disprove them, or to identify their limitations. This is another powerful quantitative research method that is often used in psychology, biology, physics, and sociology, among others. 

Experimentation is a systematic quantitative research approach to testing hypotheses and understanding the causal relationships between variables. Researchers manipulate independent variables while holding all other variables constant to observe changes in their dependent variable. By comparing the outcomes of the experimental group to those of the control group , researchers can determine if intervention was effective.

There are two main types of experiments:

  • Laboratory experiments A laboratory experiment is conducted in a controlled environment, such as a laboratory or research center, where researchers have complete control over the variables they manipulate. Such experiments are carefully designed to ensure that all resulting data is carefully analyzed in a lab report .
  • Field experiments Field experiment is a quantitative methodology conducted in real-world settings, such as schools, businesses, or communities, where researchers have less control over variables.

Example of experiment method

You may know about such famous experiments in psychology, such as the marshmallow experiment, when children need to wait some time to eat the marshmallow. The psychologists test how the child's behavior and motivation are related to endurance. For this experiment, scientists measured an independent variable which is the number of sweeteners and dependent variables as time and children's attitude to the task.

>> Read more: How to Design an Experiment 

3. Systematic Observation

One of the most reliable types of quantitative research methodologies is systematic observation. It requires researchers to observe specific situations, behavior, or case and collect numeric data based on predefined forms. Those forms are based on the theoretical framework for a specific quantitative study. Usually, this method involves one or more observers and can be applied to different events or behavioral observations. 

Systematic observation relies on accurate coding and the proper recording of data onto the structured forms used in the study. This quantitative research method is commonly employed in fields such as sociology, medicine, education, and psychology, and requires precise numeric data to be collected. Although observations can be documented through video or audio recordings, researchers using systematic observation focus specifically on measuring specific variables of interest. 

Observation: quantitative research design example

Great example is the observation of children's behavior in the classroom. For study proposals, observers can keep an eye on a classroom during different activities. Then they add countable information into the form - how many times the teacher asked for a specific action or raised a question? How many times do children speak during the class?

Quantitative Methods for Data Collection

When we discuss various analytical techniques, we have already mentioned some types of quantitative data collection methods . However, let’s go deeper and identify key methods to gather numeric data. We will speak about sampling methods , surveys , and polls . Also, our experts prepared examples of quantitative methods for data collection to help students and researchers work accurately. You also should consider using specific tools for working with this type of data and what is the most important to understand your study purpose. 

1. Sampling Method

Let’s imagine you are conducting research about teens and their usage of social media. There is no way you can send a survey, and they analyze data from all teens in the world. However, you will need to choose a reliable number of teens and then create quantitative research designs for this group. 

There are two main sampling methods we are going to discuss – probability and non-probability sampling . 

Probability Sampling

This quantitative method of data collection can be applied to cases when you need to analyze a specific group of people. For instance, you need to learn what the chance is that a city will vote for a chosen politician. It means you need to understand the age and gender percentages in a city and choose people for the survey based on this information. If your city has 34% of women age 55+, using a quantitative approach, you need to have the same percentage in your sampling. 

There are 4 types of probability sampling:

  • Simple random sampling
  • Systematic sampling
  • Stratified sampling
  • Cluster sampling .

Non-Probability Sampling

These quantitative data collection methods consider that the choice of samples depends on a researcher's experience and knowledge. In other words, not everyone can be selected for this data collection procedure - not everyone has an equal probability of being a part of your study. 

Quantitative researchers use five models for non-probability data gathering:

  • Convenience sampling: the only reason to choose study participants is their proximity to researchers.
  • Quota sampling: scientists use their knowledge and experience to form a quota.
  • Consecutive sampling: similar to the conventional method but can be applied to the same situation during some period of time.
  • Snowball sampling: researchers ask their target audience whom they can recommend for the same study.
  • Judgmental samplings: it is usually chosen based on the researcher's skills.

The next and quite popular method to collect data is the quantitative survey method. The design of your question will depend on the theory you are using for your study. For instance, If you are looking at how customer awareness about product features influence their engagement with a brand, you will apply relationship communication theory. In other words, you can’t put any questions you want into the survey.

You can conduct surveys for your quantitative research using the following ways:

  • Social media
  • Survey on your website
  • Offline surveys, and other methods.

As quantitative studies are focused on numeric data, you need to use a likable scale for answers and not open questions.

Polls are a commonly used quantitative research method, particularly in election and exit polls. Conducting quantitative research for the election means you can ask simple questions with multiple choices. For instance, you may offer a few demographic questions (e.g., age, employment),as well as questions about voting behavior (e.g., candidate they voted for).

Applying quantitative design for polls, researchers need to ensure that the answers can be analyzed with statistical formulas. That is why the questions for the polls are often quite simple and comprise up to 5 questions. However, in some cases, polls may include even less queries. 

A quantitative research study can use benchmarks, brushfire, and tracking polls.

Data Analysis Methods

After you collect the results of your polls, samplings, or surveys, you need to analyze quantitative data. As we are discussing primary data, researchers have raw information that can be analyzed and later interpreted in different ways. 

Based on quantitative research approaches, we identify 2 key methods for the analysis of numeric data:

  • Descriptive statistics Descriptive statistics allows us to get average data on questions or measure variability. It helps to overview the data with statistical evaluation. Applying descriptive statistics, you can count the average mean or standard deviation.
  • Inferential statistics Inferential statistics helps to design predictions and understand the relations between variables. You can run a T-test to measure the relation between two variables. Likewise, you may arrange a Pearson correlation test and measure how one variable depends on another one.

Using these statistical instruments, scientists can go deeper into result discussion and test hypotheses.

Secondary Quantitative Research Methods

Secondary methods of quantitative research are based on the analysis of existing data – the information already gathered by someone or presented in other papers. In this case, we do not need to collect data. Instead, scientists conduct their own quantitative research applying statistical analysis methods and formulas to gain new insights from existing data. 

There are 5 most commonly used types of secondary quantitative research methods:

  • Data from open online sources This is probably one of the most frequently used resources for quantitative study is the internet. A lot of companies and government institutions share the data on their own work, like the number of mobile users or a number of people using state health insurance.
  • Official data from government and non-government organizations Some data can exist in official reports but are not published online. In this case, you can ask for data that can be shared without breaking privacy protection laws. You may need to make an official request for the information you want to use for your study.
  • Public libraries You may think that no one uses public libraries. However, this is where you can find old studies conducted by someone else. The library also has a dataset for the papers that can be used for your own study.
  • Educational institutions A lot of educational institutions are also conducting research. While you can find the analytics published in open sources, a data set can be shared with you after the request.
  • Commercial sources These sources typically include information from private research firms or companies that collect and analyze data on specific industries, markets, or consumer behavior. Researchers can access this data through websites, reports, or journals, or by requesting access directly from the companies themselves.

How to Conduct Quantitative Research?

If you are working in the academic field or going to get a master's or Ph.D. degree, you definitely will need to conduct various types of studies to write a dissertation . Let’s look at the common ways to conduct quantitative research. Make sure you keep these important considerations in mind:

  • Determine the type of research you need to conduct. Will you be testing a hypothesis? If so, you will likely need to analyze numerical data.
  • Identify the appropriate sample size for your study. Do you need a large sample size to obtain reliability of outcomes, or will a smaller sample size suffice?
  • Be clear about your research goals. It's important to define your research objectives and ensure that your study design aligns with these goals.
  • Simplify your research questions. If your questions are clear and concise, it will be easier to determine the appropriate type of analysis needed to answer them.

Adhering to these recommendations ensures that research is targeted and generates valuable findings.

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Advantages of Quantitative Research

Before choosing this analytical type for your work, you need to be aware of the advantages of quantitative research methods. 

Here are the pros of using quantitative research methodologies for the research:

  • Time efficiency Gathering and analyzing numerical data usually takes less time than collecting and analyzing non-numerical data.
  • Reliable data Working with numbers allows for precise statistical analysis, resulting in more reliable results.
  • Objectivity The absence of personal comments or interpretation in quantitative data collection reduces the possibility of bias in the results.
  • Scientific approach The quantitative method is considered one of the most scientific research methods, which helps to establish credibility and believability of the results.
  • Verifiability The results can be easily checked and verified by repeating the formula or analysis, ensuring the accuracy of the data.

Disadvantages of Quantitative Research

It may look like working with quantitative research can bring only pros to your study. However, there are a few cons you need to be aware of before starting your data collection. How can methodology in quantitative research become a disadvantage for your study?

  • Risk of bias We mentioned that there is no way you will put your emotions into statistical formulas. But researcher experience and personal feelings can be used to form samplings. Even the daytime for data collection can influence the final results.
  • Narrow focus It is possible that you can be so focused on numbers that you miss the bigger picture. Anytime you are running the numeric study, you need to look at your questions broadly. You may also need some qualitative methods to answer your research questions.
  • Complexity For people who are not very good at math and statistics, it can be problematic to identify what type of numbers they need and what test should be conducted to get results.

Bottom Line on Quantitative Research

In the few paragraphs, we tried to guide you through key principles of numeric research and answer the question of what is a quantitative study and how to conduct it correctly. We identified critical approaches in collecting data for this type of analysis and outlined limitations you need to have in mind running this study. 

You also can find the best quantitative methods examples that will definitely help you with your own study. Try your best to launch a valuable and reliable study using all the knowledge on how to work with numbers!

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FAQ About Quantitative Research Studies & Methods

1. what is the purpose of quantitative research.

The primary purpose of quantitative research is to test the hypothesis you may have in your study. This is one of the most frequently used types of data analysis, but before start working with numbers, you need to be clear with your goal. For example, for answering research questions, you may need only qualitative data.

2. What is a quantitative research method?

Quantitative research methods are types of data collection and analysis that focus on numeric information. In other words, this is the research when you work with numbers instead of words. You may need to apply some statistical formulas to those numbers to get results, while in a qualitative study, you will deal with content analysis mostly.

3. When is quantitative research used?

You may need to conduct quantitative research in case you are going to test the hypothesis by running statistical formulas. In most cases, you understand what type of research you need to conduct when you are clear with the study's aims and purpose. After you define hypotheses or questions, you may focus on the methodology that will help you get results.

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Qualitative Research

Quantitative research methods are opposite to approaches applied in a  qualitative study , where you are dealing with descriptions instead of numbers. In the latter case, analysis is focused on non-numerical data, like texts from interviews or focus groups, videos, or audio.

Note that a single study may require the use of multiple methods to gather different types of data. As such, researchers may need to employ a variety of methods to gain a comprehensive understanding of their research topics .

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500+ Quantitative Research Titles and Topics

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Quantitative Research Topics

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

  • “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
  • “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
  • “Predicting Stock Market Trends Using Machine Learning Algorithms”
  • “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
  • “Impact of Economic Policies on Small Businesses: A Regression Analysis”
  • “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
  • “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
  • “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
  • “Price Elasticity of Demand for Luxury Goods: A Case Study”
  • “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
  • “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
  • “Examining the Correlation Between Interest Rates and Consumer Spending”
  • “Standardized Testing and Academic Performance: A Quantitative Evaluation”
  • “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
  • “The Relationship Between Extracurricular Activities and Academic Success”
  • “Influence of Parental Involvement on Children’s Educational Achievements”
  • “Digital Literacy in Primary Schools: A Quantitative Assessment”
  • “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
  • “Correlation Between Teacher Experience and Student Success Rates”
  • “Analyzing the Impact of Classroom Technology on Reading Comprehension”
  • “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
  • “The Relationship Between Homework Load and Academic Burnout”
  • “Assessment of Special Education Programs in Public Schools”
  • “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

  • “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
  • “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
  • “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
  • “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
  • “Correlations Between Stress Levels and Immune System Functioning”
  • “Smoking and Lung Function: A Quantitative Analysis”
  • “Influence of Physical Activity on Mental Health in Older Adults”
  • “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
  • “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
  • “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
  • “Impact of Hospital Design on Patient Recovery Rates”
  • “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

  • “Examining Social Inequality through Wage Distribution: A Quantitative Study”
  • “Impact of Parental Divorce on Child Development: A Longitudinal Study”
  • “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
  • “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
  • “Influence of Socioeconomic Status on Educational Achievement”
  • “Quantifying the Effects of Community Programs on Crime Reduction”
  • “Public Opinion and Immigration Policies: A Quantitative Exploration”
  • “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
  • “Impact of Mass Media on Public Opinion: A Regression Analysis”
  • “Influence of Urban Design on Social Interactions in Communities”
  • “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
  • “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

  • “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
  • “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
  • “Impacts of Data Center Cooling Solutions on Energy Consumption”
  • “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
  • “Optimization of 5G Network Performance: A Quantitative Assessment”
  • “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
  • “The Relationship Between Software Complexity and Bug Frequency”
  • “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
  • “Wearable Technologies and their Impact on Healthcare Monitoring”
  • “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
  • “Analysis of Noise Pollution from Urban Transportation Systems”
  • “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

  • The effects of social media on self-esteem among teenagers.
  • A comparative study of academic achievement among students of single-sex and co-educational schools.
  • The impact of gender on leadership styles in the workplace.
  • The correlation between parental involvement and academic performance of students.
  • The effect of mindfulness meditation on stress levels in college students.
  • The relationship between employee motivation and job satisfaction.
  • The effectiveness of online learning compared to traditional classroom learning.
  • The correlation between sleep duration and academic performance among college students.
  • The impact of exercise on mental health among adults.
  • The relationship between social support and psychological well-being among cancer patients.
  • The effect of caffeine consumption on sleep quality.
  • A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
  • The relationship between physical attractiveness and job opportunities.
  • The correlation between smartphone addiction and academic performance among high school students.
  • The impact of music on memory recall among adults.
  • The effectiveness of parental control software in limiting children’s online activity.
  • The relationship between social media use and body image dissatisfaction among young adults.
  • The correlation between academic achievement and parental involvement among minority students.
  • The impact of early childhood education on academic performance in later years.
  • The effectiveness of employee training and development programs in improving organizational performance.
  • The relationship between socioeconomic status and access to healthcare services.
  • The correlation between social support and academic achievement among college students.
  • The impact of technology on communication skills among children.
  • The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
  • The relationship between employee turnover and organizational culture.
  • The correlation between job satisfaction and employee engagement.
  • The impact of video game violence on aggressive behavior among children.
  • The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
  • The relationship between bullying and academic performance among middle school students.
  • The correlation between teacher expectations and student achievement.
  • The impact of gender stereotypes on career choices among high school students.
  • The effectiveness of anger management programs in reducing violent behavior.
  • The relationship between social support and recovery from substance abuse.
  • The correlation between parent-child communication and adolescent drug use.
  • The impact of technology on family relationships.
  • The effectiveness of smoking cessation programs in promoting long-term abstinence.
  • The relationship between personality traits and academic achievement.
  • The correlation between stress and job performance among healthcare professionals.
  • The impact of online privacy concerns on social media use.
  • The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
  • The relationship between teacher feedback and student motivation.
  • The correlation between physical activity and academic performance among elementary school students.
  • The impact of parental divorce on academic achievement among children.
  • The effectiveness of diversity training in improving workplace relationships.
  • The relationship between childhood trauma and adult mental health.
  • The correlation between parental involvement and substance abuse among adolescents.
  • The impact of social media use on romantic relationships among young adults.
  • The effectiveness of assertiveness training in improving communication skills.
  • The relationship between parental expectations and academic achievement among high school students.
  • The correlation between sleep quality and mood among adults.
  • The impact of video game addiction on academic performance among college students.
  • The effectiveness of group therapy in treating eating disorders.
  • The relationship between job stress and job performance among teachers.
  • The correlation between mindfulness and emotional regulation.
  • The impact of social media use on self-esteem among college students.
  • The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
  • The impact of renewable energy policies on carbon emissions
  • The relationship between employee motivation and job performance
  • The effectiveness of psychotherapy in treating eating disorders
  • The correlation between physical activity and cognitive function in older adults
  • The effect of childhood poverty on adult health outcomes
  • The impact of urbanization on biodiversity conservation
  • The relationship between work-life balance and employee job satisfaction
  • The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
  • The correlation between parenting styles and child behavior
  • The effect of social media on political polarization
  • The impact of foreign aid on economic development
  • The relationship between workplace diversity and organizational performance
  • The effectiveness of dialectical behavior therapy in treating borderline personality disorder
  • The correlation between childhood abuse and adult mental health outcomes
  • The effect of sleep deprivation on cognitive function
  • The impact of trade policies on international trade and economic growth
  • The relationship between employee engagement and organizational commitment
  • The effectiveness of cognitive therapy in treating postpartum depression
  • The correlation between family meals and child obesity rates
  • The effect of parental involvement in sports on child athletic performance
  • The impact of social entrepreneurship on sustainable development
  • The relationship between emotional labor and job burnout
  • The effectiveness of art therapy in treating dementia
  • The correlation between social media use and academic procrastination
  • The effect of poverty on childhood educational attainment
  • The impact of urban green spaces on mental health
  • The relationship between job insecurity and employee well-being
  • The effectiveness of virtual reality exposure therapy in treating anxiety disorders
  • The correlation between childhood trauma and substance abuse
  • The effect of screen time on children’s social skills
  • The impact of trade unions on employee job satisfaction
  • The relationship between cultural intelligence and cross-cultural communication
  • The effectiveness of acceptance and commitment therapy in treating chronic pain
  • The correlation between childhood obesity and adult health outcomes
  • The effect of gender diversity on corporate performance
  • The impact of environmental regulations on industry competitiveness.
  • The impact of renewable energy policies on greenhouse gas emissions
  • The relationship between workplace diversity and team performance
  • The effectiveness of group therapy in treating substance abuse
  • The correlation between parental involvement and social skills in early childhood
  • The effect of technology use on sleep patterns
  • The impact of government regulations on small business growth
  • The relationship between job satisfaction and employee turnover
  • The effectiveness of virtual reality therapy in treating anxiety disorders
  • The correlation between parental involvement and academic motivation in adolescents
  • The effect of social media on political engagement
  • The impact of urbanization on mental health
  • The relationship between corporate social responsibility and consumer trust
  • The correlation between early childhood education and social-emotional development
  • The effect of screen time on cognitive development in young children
  • The impact of trade policies on global economic growth
  • The relationship between workplace diversity and innovation
  • The effectiveness of family therapy in treating eating disorders
  • The correlation between parental involvement and college persistence
  • The effect of social media on body image and self-esteem
  • The impact of environmental regulations on business competitiveness
  • The relationship between job autonomy and job satisfaction
  • The effectiveness of virtual reality therapy in treating phobias
  • The correlation between parental involvement and academic achievement in college
  • The effect of social media on sleep quality
  • The impact of immigration policies on social integration
  • The relationship between workplace diversity and employee well-being
  • The effectiveness of psychodynamic therapy in treating personality disorders
  • The correlation between early childhood education and executive function skills
  • The effect of parental involvement on STEM education outcomes
  • The impact of trade policies on domestic employment rates
  • The relationship between job insecurity and mental health
  • The effectiveness of exposure therapy in treating PTSD
  • The correlation between parental involvement and social mobility
  • The effect of social media on intergroup relations
  • The impact of urbanization on air pollution and respiratory health.
  • The relationship between emotional intelligence and leadership effectiveness
  • The effectiveness of cognitive-behavioral therapy in treating depression
  • The correlation between early childhood education and language development
  • The effect of parental involvement on academic achievement in STEM fields
  • The impact of trade policies on income inequality
  • The relationship between workplace diversity and customer satisfaction
  • The effectiveness of mindfulness-based therapy in treating anxiety disorders
  • The correlation between parental involvement and civic engagement in adolescents
  • The effect of social media on mental health among teenagers
  • The impact of public transportation policies on traffic congestion
  • The relationship between job stress and job performance
  • The effectiveness of group therapy in treating depression
  • The correlation between early childhood education and cognitive development
  • The effect of parental involvement on academic motivation in college
  • The impact of environmental regulations on energy consumption
  • The relationship between workplace diversity and employee engagement
  • The effectiveness of art therapy in treating PTSD
  • The correlation between parental involvement and academic success in vocational education
  • The effect of social media on academic achievement in college
  • The impact of tax policies on economic growth
  • The relationship between job flexibility and work-life balance
  • The effectiveness of acceptance and commitment therapy in treating anxiety disorders
  • The correlation between early childhood education and social competence
  • The effect of parental involvement on career readiness in high school
  • The impact of immigration policies on crime rates
  • The relationship between workplace diversity and employee retention
  • The effectiveness of play therapy in treating trauma
  • The correlation between parental involvement and academic success in online learning
  • The effect of social media on body dissatisfaction among women
  • The impact of urbanization on public health infrastructure
  • The relationship between job satisfaction and job performance
  • The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
  • The correlation between early childhood education and social skills in adolescence
  • The effect of parental involvement on academic achievement in the arts
  • The impact of trade policies on foreign investment
  • The relationship between workplace diversity and decision-making
  • The effectiveness of exposure and response prevention therapy in treating OCD
  • The correlation between parental involvement and academic success in special education
  • The impact of zoning laws on affordable housing
  • The relationship between job design and employee motivation
  • The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
  • The correlation between early childhood education and social-emotional learning
  • The effect of parental involvement on academic achievement in foreign language learning
  • The impact of trade policies on the environment
  • The relationship between workplace diversity and creativity
  • The effectiveness of emotion-focused therapy in treating relationship problems
  • The correlation between parental involvement and academic success in music education
  • The effect of social media on interpersonal communication skills
  • The impact of public health campaigns on health behaviors
  • The relationship between job resources and job stress
  • The effectiveness of equine therapy in treating substance abuse
  • The correlation between early childhood education and self-regulation
  • The effect of parental involvement on academic achievement in physical education
  • The impact of immigration policies on cultural assimilation
  • The relationship between workplace diversity and conflict resolution
  • The effectiveness of schema therapy in treating personality disorders
  • The correlation between parental involvement and academic success in career and technical education
  • The effect of social media on trust in government institutions
  • The impact of urbanization on public transportation systems
  • The relationship between job demands and job stress
  • The correlation between early childhood education and executive functioning
  • The effect of parental involvement on academic achievement in computer science
  • The effectiveness of cognitive processing therapy in treating PTSD
  • The correlation between parental involvement and academic success in homeschooling
  • The effect of social media on cyberbullying behavior
  • The impact of urbanization on air quality
  • The effectiveness of dance therapy in treating anxiety disorders
  • The correlation between early childhood education and math achievement
  • The effect of parental involvement on academic achievement in health education
  • The impact of global warming on agriculture
  • The effectiveness of narrative therapy in treating depression
  • The correlation between parental involvement and academic success in character education
  • The effect of social media on political participation
  • The impact of technology on job displacement
  • The relationship between job resources and job satisfaction
  • The effectiveness of art therapy in treating addiction
  • The correlation between early childhood education and reading comprehension
  • The effect of parental involvement on academic achievement in environmental education
  • The impact of income inequality on social mobility
  • The relationship between workplace diversity and organizational culture
  • The effectiveness of solution-focused brief therapy in treating anxiety disorders
  • The correlation between parental involvement and academic success in physical therapy education
  • The effect of social media on misinformation
  • The impact of green energy policies on economic growth
  • The relationship between job demands and employee well-being
  • The correlation between early childhood education and science achievement
  • The effect of parental involvement on academic achievement in religious education
  • The impact of gender diversity on corporate governance
  • The relationship between workplace diversity and ethical decision-making
  • The correlation between parental involvement and academic success in dental hygiene education
  • The effect of social media on self-esteem among adolescents
  • The impact of renewable energy policies on energy security
  • The effect of parental involvement on academic achievement in social studies
  • The impact of trade policies on job growth
  • The relationship between workplace diversity and leadership styles
  • The correlation between parental involvement and academic success in online vocational training
  • The effect of social media on self-esteem among men
  • The impact of urbanization on air pollution levels
  • The effectiveness of music therapy in treating depression
  • The correlation between early childhood education and math skills
  • The effect of parental involvement on academic achievement in language arts
  • The impact of immigration policies on labor market outcomes
  • The effectiveness of hypnotherapy in treating phobias
  • The effect of social media on political engagement among young adults
  • The impact of urbanization on access to green spaces
  • The relationship between job crafting and job satisfaction
  • The effectiveness of exposure therapy in treating specific phobias
  • The correlation between early childhood education and spatial reasoning
  • The effect of parental involvement on academic achievement in business education
  • The impact of trade policies on economic inequality
  • The effectiveness of narrative therapy in treating PTSD
  • The correlation between parental involvement and academic success in nursing education
  • The effect of social media on sleep quality among adolescents
  • The impact of urbanization on crime rates
  • The relationship between job insecurity and turnover intentions
  • The effectiveness of pet therapy in treating anxiety disorders
  • The correlation between early childhood education and STEM skills
  • The effect of parental involvement on academic achievement in culinary education
  • The impact of immigration policies on housing affordability
  • The relationship between workplace diversity and employee satisfaction
  • The effectiveness of mindfulness-based stress reduction in treating chronic pain
  • The correlation between parental involvement and academic success in art education
  • The effect of social media on academic procrastination among college students
  • The impact of urbanization on public safety services.

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  1. Quantitative Research Methods

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COMMENTS

  1. What Is Quantitative Research?

    Quantitative research methods; Research method How to use Example; Experiment: Control or manipulate an independent variable to measure its effect on a dependent variable.: To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task.

  2. What is Quantitative Research? Definition, Methods, Types, and Examples

    Quantitative research methods are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data.

  3. 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 ...

  4. What is Quantitative Research? Definition, Examples, Key Advantages

    There are two main methods used to conduct quantitative research: 1. Primary Method. There are several methods of primary quantitative research, each with its own strengths and limitations. Surveys: Surveys are a common method of quantitative research and involve collecting data from a sample of individuals using standardized questionnaires or ...

  5. A Comprehensive Guide to Quantitative Research Methods: Design, Data

    Large Sample Size: Quantitative research typically involves collecting data from a large sample size to increase statistical power and generalizability of the findings to a larger population. ... Quantitative research methods provide a systematic framework for investigating phenomena and making evidence-based decisions. By mastering the design ...

  6. Quantitative Research: Types, Characteristics, Methods & Examples

    After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls. Sampling methods: In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

  7. What Is Quantitative Research?

    Quantitative research methods; Research method How to use Example; Experiment: Control or manipulate an independent variable to measure its effect on a dependent variable.: To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task.

  8. Quantitative Research: What It Is, Practices & Methods

    Sample size: Quantitative research is conducted on a significant sample size representing the target market. Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.

  9. Quantitative Research Methods

    Quantitative research methods. a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time.

  10. Quantitative Research

    Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. . High-quality quantitative research is ...

  11. 9 Quantitative Research Methods With Real Client Examples

    The research type refers to style of research while the data collection method can be different. Research types. These are the primary types of quantitative research used by businesses today. Survey research: Ideally when conducting survey research businesses will use a statistically relevant sample to understand the sentiments and actions of a ...

  12. Quantitative Research: Methods and Examples

    Quantitative research involves gathering numerical data - that is, data that can be turned into numbers. It's objective and involves hard facts. This data is then analysed using statistics to draw conclusions. Quantitative research is typified by closed-ended questions and a fixed set of answer options, while open-ended questions are ...

  13. Quantitative Research Methodologies

    Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as: ... The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might ...

  14. Quantitative Methods

    Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

  15. Quantitative and Qualitative Research

    Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population. Allen, M. (2017). The SAGE encyclopedia ...

  16. A Practical Guide to Writing Quantitative and Qualitative Research

    Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...

  17. Quantitative Research: Methods And Examples

    Quantitative methods can be used in operations and production management. Some examples of quantitative research can be seen in analyzing cash flow, purchasing, sales, distribution and scheduling of jobs, among others. Data Analysis . Quantitative research is used in data mining, or data gathering. Data mining involves researching large amounts ...

  18. Quantitative Data

    Quantitative Data Types. There are two main types of quantitative data: discrete and continuous. Discrete data: Discrete data refers to numerical values that can only take on specific, distinct values. This type of data is typically represented as whole numbers and cannot be broken down into smaller units. Examples of discrete data include the ...

  19. What Is Quantitative Research Study: Methods & Examples

    Survey quantitative research method example. One of the common examples is the survey for measuring how citizens are satisfied with local politicians. For this purpose, the sociology group used to develop a questionnaire sample and define target audiences - people living in specific areas or some age groups. Based on their answers, different ...

  20. 500+ Quantitative Research Titles and Topics

    Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology, economics, and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas ...

  21. (PDF) Quantitative Research Method

    2.0 Quantitative Research. Quantitative research is regarded as the organized inquiry about phenomenon through collection. of numer ical data and execution of statistical, mathematical or ...