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How to Write Quantitative Research Questions: Types With Examples

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Want to create a quantitative research survey hassle-free? Explore our library of 1,000,000+ readymade questions.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive 

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

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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98 Quantitative Research Questions & Examples

As researchers, we know how powerful quantitative research data can be in helping answer strategic questions. Here, I’ve detailed 23 use cases and curated 98 quantitative market research questions with examples – making this a post you should add to your bookmark list , so you can quickly refer back.

I’ve formatted this post to show you 10-15 questions for each use case. At the end of each section, I also share a quicker way to get similar insights using modern market research tools like Similarweb.

What is a quantitative research question?

Quantitative market research questions tell you the what, how, when, and where of a subject. From trendspotting to identifying patterns or establishing averages– using quantitative data is a clear and effective way to start solving business problems.

Types of quantitative research questions

Quantitative market research questions are divided into two main types: descriptive and causal.

• Descriptive research questions seek to quantify a phenomenon by focusing on a certain population or phenomenon to measure certain aspects of it, such as frequency, average, or relationship.
• Causal research questions explore the cause-and-effect relationship between two or more variables.

The ultimate list of questions for quantitative market research

Get clear explanations of the different applications and approaches to quantitative research–with the added bonus of seeing what questions to ask and how they can impact your business.

Examples of quantitative research questions for competitive analysis

A powerful example of quantitative research in play is when it’s used to inform a competitive analysis . A process that’s used to analyze and understand how industry leaders and companies of interest are performing.

Pro Tip: Collect data systematically, and use a competitive analysis framework to record your findings. You can refer back to it when you repeat the process later in the year.

• What is the market share of our major competitors?
• What is the average purchase price of our competitors’ products?
• How often do our competitors release new products?
• What is the total number of customer reviews for our competitors’ products?
• What is the average rating of our competitors’ products?
• What is the average customer satisfaction score for our competitors?
• What is the average return rate of our competitors’ products?
• What is the average shipping time for our competitors’ products?
• What is the average price discount offered by our competitors?
• What is the average lifespan of our competitors’ products?

With this data, you can determine your position in the market and benchmark your performance against rival companies. It can then be used to improve offerings, service standards, pricing, positioning, and operational effectiveness. Notice that all questions can be answered with a numerical response , a key component of all successful examples of quantitative market research questions.

Quantitative research question example: market analysis

‍♀️ Question: What is the market share of our major competitors?

Insight sought: Industry market share of leaders and key competitors.

Challenges with traditional quantitative research methods: Outdated data is a major consideration; data freshness remains critical, yet is often tricky to obtain using traditional research methods. Markets shift fast, so being able to obtain and track market share in real time is a challenge many face.

A new approach: Similarweb enables you to track this key business KPI in real-time using digital data directly from the platform. On any day, you can see what your market share is, along with any players in your market. Plus, you get to see rising stars showing significant growth, who may pose a threat through market disruption or new tactics.

⏰ Time to insight: 30 seconds

✅ How it’s done: Using Similarweb’s Web Industry Analysis, two digital metrics give you the intel needed to decipher the market share in any industry. I’m using the Banking, Credit, and Lending market throughout these examples. I’ve selected the US market, analyzing the performance of the previous 3 months.

• Share of visits 

Here, I can see the top players in my market based on the number of unique visitors to their sites. On top of the raw data that shows me the volume of visitors as a figure, I can quickly see the two players ( Capital One and Chase ) that have grown and by what percentage. On the side, you can see rising players in the industry. Now, while my initial question was to establish the market share of my major competitors, I can see there are a few disruptive players in my market who I’d want to track too; Synchrony.com being one of particular interest, given their substantial growth and traffic numbers.

• Share of search 

Viewing the overall market size based on total search volumes, you can explore industry leaders in more detail. The top websites are the top five players, ranking by traffic share . You can also view the month-over-month change in visits, which shows you who is performing best at any given time . It’s the same five names, with Paypal and Chase leading the pack. However, I see Wells Fargo is better at attracting repeat visitors, while Capital One and Bank of America perform better at drawing in unique visitors.

In answer to my question, what is the market share of my major competitors, I can quickly use Similarweb’s quantitative data to get my answer.

This traffic share visual can be downloaded from the platform. It plots the ten industry leader’s market share and allocates the remaining share to the rest of the market.

I can also download a market quadrant analysis, which takes two key data points, traffic share and unique visitors, and plots the industry leaders. All supporting raw data can be downloaded in .xls format or connected to other business intelligence platforms via the API.

Quantitative research questions for consumer behavior studies

These studies measure and analyze consumer behavior, preferences, and habits . Any type of audience analysis helps companies better understand customer intent, and adjust offerings, messaging, campaigns, SEO, and ultimately offer more relevant products and services within a market.

• What is the average amount consumers spend on a certain product each month?
• What percentage of consumers are likely to purchase a product based on its price?
• How do the demographics of the target audience affect their purchasing behavior?
• What type of incentive is most likely to increase the likelihood of purchase?
• How does the store’s location impact product sales and turnover?
• What are the key drivers of product loyalty among consumers?
• What are the most commonly cited reasons for not buying a product?
• How does the availability of product information impact purchasing decisions?
• What is the average time consumers spend researching a product before buying it?
• How often do consumers use social media when making a purchase decision?

While applying a qualitative approach to such studies is also possible, it’s a great example of quantitative market research in action. For larger corporations, studies that involve a large, relevant sample size of a target market deliver vital consumer insights at scale .

Read More: 83 Qualitative Research Questions & Examples

Quantitative research question and answer: content strategy and analysis

‍♀️ Question: What type of content performed best in the market this past month?

Insight sought: Establish high-performing campaigns and promotions in a market.

Challenges with traditional quantitative research methods: Whether you consider putting together a panel yourself, or paying a company to do it for you, quantitative research at scale is costly and time-consuming. What’s more, you have to ensure that sampling is done right and represents your target audience.

A new approach: Data analysis is the foundation of our entire business. For over 10 years, Similarweb has developed a unique , multi-dimensional approach to understanding the digital world. To see the specific campaigns that resonate most with a target audience, use Similarweb’s Popular Pages feature. Key metrics show which campaigns achieve the best results for any site (including rival firms), campaign take-up, and periodic changes in performance and interest.

✅ How it’s done: I’ve chosen Capital One and Wells Fargo to review. Using the Popular Pages campaign filter, I can view all pages identified by a URL parameter UTM. For clarity, I’ve highlighted specific campaigns showing high-growth and increasing popularity. I can view any site’s trending, new, or best-performing pages using a different filter.

In this example, I have highlighted three campaigns showing healthy growth, covering teen checking accounts, performance savings accounts, and add-cash-in-store. Next, I will perform the same check for another key competitor in my market.

Here, I can see financial health tools campaigns with over 300% month-over-month growth and smarter credit and FICO campaigns showing strong performance. This tells me that campaigns focussing on education and tools are growing in popularity within this market. 

Examples of quantitative research questions for brand tracking

These studies are designed to measure customers’ awareness, perceptions, behaviors, and attitudes toward a brand over time. Different applications include measuring brand awareness , brand equity, customer satisfaction, and purchase or usage intent.

These types of research surveys ask questions about brand knowledge, brand attributes, brand perceptions, and brand loyalty . The data collected can then be used to understand the current state of a brand’s performance, identify improvements, and track the success of marketing initiatives.

• To what extent is Brand Z associated with innovation?
• How do consumers rate the quality of Brand Z’s products and services?
• How has the awareness of Brand Z changed over the past 6 months?
• How does Brand Z compare to its competitors in terms of customer satisfaction?
• To what extent do consumers trust Brand Z?
• How likely are consumers to recommend Brand Z?
• What factors influence consumers’ purchase decisions when considering Brand Z?
• What is the average customer satisfaction score for equity?
• How does equity’s customer service compare to its competitors?
• How do customer perceptions of equity’s brand values compare to its competitors?

Quantitative research question example and answer: brand tracking

‍♀️ Question: How has the awareness of Brand Z changed over the past 6 months?

Insight sought: How has brand awareness changed for my business and competitors over time.

⏰ Time to insight: 2 minutes

✅ How it’s done: Using Similarweb’s search overview, I can quickly identify which brands in my chosen market have the highest brand awareness over any time period or location. I can view these stats as a custom market or examine brands individually.

Here, I’ve chosen a custom view that shows me five companies side-by-side. In the top right-hand corner, under branded traffic, you get a quick snapshot of the share of website visits that were generated by branded keywords. A branded keyword is when a consumer types the brand name + a search term.

Below that, you will see the search traffic and engagement section. Here, I’ve filtered the results to show me branded traffic as a percentage of total traffic. Similarweb shows me how branded search volumes grow or decline monthly. Helping me answer the question of how brand awareness has changed over time.

Quantitative research questions for consumer ad testing

Another example of using quantitative research to impact change and improve results is ad testing. It measures the effectiveness of different advertising campaigns. It’s often known as A/B testing , where different visuals, content, calls-to-action, and design elements are experimented with to see which works best. It can show the impact of different ads on engagement and conversions.

A range of quantitative market research questions can be asked and analyzed to determine the optimal approach.

• How does changing the ad’s headline affect the number of people who click on the ad?
• How does varying the ad’s design affect its click-through rate?
• How does altering the ad’s call-to-action affect the number of conversions?
• How does adjusting the ad’s color scheme influence the number of people who view the ad?
• How does manipulating the ad’s text length affect the average amount of time a user spends on the landing page?
• How does changing the ad’s placement on the page affect the amount of money spent on the ad?
• How does varying the ad’s targeting parameters affect the number of impressions?
• How does altering the ad’s call-to-action language impact the click-through rate?

Quantitative question examples for social media monitoring

Quantitative market research can be applied to measure and analyze the impact of social media on a brand’s awareness, engagement, and reputation . By tracking key metrics such as the number of followers, impressions, and shares, brands can:

• Assess the success of their social media campaigns
• Understand what content resonates with customers
• Spot potential areas for improvement
• How often are people talking about our brand on social media channels?
• How many times has our brand been mentioned in the past month?
• What are the most popular topics related to our brand on social media?
• What is the sentiment associated with our brand across social media channels?
• How do our competitors compare in terms of social media presence?
• What is the average response time for customer inquiries on social media?
• What percentage of followers are actively engaging with our brand?
• What are the most popular hashtags associated with our brand?
• What types of content generate the most engagement on social media?
• How does our brand compare to our competitors in terms of reach and engagement on social media?

Example of quantitative research question and answer: social media monitoring

‍♀️ Question: How does our brand compare to our competitors in terms of reach and engagement on social media?

Insight sought: The social channels that most effectively drive traffic and engagement in my market

✅ How it’s done: Similarweb Digital Research Intelligence shows you a marketing channels overview at both an industry and market level. With it, you can view the most effective social media channels in any industry and drill down to compare social performance across a custom group of competitors or an individual company.

Here, I’ve taken the five closest rivals in my market and clicked to expand social media channel data. Wells Fargo and Bank of America have generated the highest traffic volume from social media, with over 6.6 million referrals this year. Next, I can see the exact percentage of traffic generated by each channel and its relative share of traffic for each competitor. This shows me the most effective channels are YouTube, Facebook, LinkedIn, and Reddit – in that order.

In 30-seconds, I’ve discovered the following:

• YouTube is the most popular social network in my market.
• Facebook and LinkedIn are the second and third most popular channels.
• Wells Fargo is my primary target for a more in-depth review, with the highest performance on the top two channels.
• Bank of America is outperforming all key players significantly on LinkedIn.
• American Express has found a high referral opportunity on Reddit that others have been unable to match.

Power-up Your Market Research with Similarweb Today

Examples of quantitative research questions for online polls

This is one of the oldest known uses of quantitative market research. It dates back to the 19th century when they were first used in America to try and predict the outcome of the presidential elections.

Polls are just short versions of surveys but provide a point-in-time perspective across a large group of people. You can add a poll to your website as a widget, to an email, or if you’ve got a budget to spend, you might use a company like YouGov to add questions to one of their online polls and distribute it to an audience en-masse.

• What is your annual income?
• In what age group do you fall?
• On average, how much do you spend on our products per month?
• How likely are you to recommend our products to others?
• How satisfied are you with our customer service?
• How likely are you to purchase our products in the future?
• On a scale of 1 to 10, how important is price when it comes to buying our products?
• How likely are you to use our products in the next six months?
• What other brands of products do you purchase?
• How would you rate our products compared to our competitors?

Quantitative research questions for eye tracking studies

These research studies measure how people look and respond to different websites or ad elements. It’s traditionally an example of quantitative research used by enterprise firms but is becoming more common in the SMB space due to easier access to such technologies.

• How much time do participants spend looking at each visual element of the product or ad?
• How does the order of presentation affect the impact of time spent looking at each visual element?
• How does the size of the visual elements affect the amount of time spent looking at them?
• What is the average time participants spend looking at the product or ad as a whole?
• What is the average number of fixations participants make when looking at the product or ad?
• Are there any visual elements that participants consistently ignore?
• How does the product’s design or advertising affect the average number of fixations?
• How do different types of participants (age, gender, etc.) interact with the product or ad differently?
• Is there a correlation between the amount of time spent looking at the product or ad and the participants’ purchase decision?
• How does the user’s experience with similar products or ads affect the amount of time spent looking at the current product or ad?

Quantitative question examples for customer segmentation

Segmentation is becoming more important as organizations large and small seek to offer more personalized experiences. Effective segmentation helps businesses understand their customer’s needs–which can result in more targeted marketing, increased conversions, higher levels of loyalty, and better brand awareness.

If you’re just starting to segment your market, and want to know the best quantitative research questions to ask to help you do this, here are 20 to choose from.

Examples of quantitative research questions to segment customers

• What is your age range?
• What is your annual household income?
• What is your preferred online shopping method?
• What is your occupation?
• What types of products do you typically purchase?
• Are you a frequent shopper?
• How often do you purchase products online?
• What is your typical budget for online purchases?
• What is your primary motivation for purchasing products online?
• What factors influence your decision to purchase a product online?
• What device do you use most often when shopping online?
• What type of product categories are you most interested in?
• Do you prefer to shop online for convenience or for a better price?
• What type of discounts or promotions do you look for when making online purchases?
• How do you prefer to receive notifications about product promotions or discounts?
• What type of payment methods do you prefer when shopping online?
• What methods do you use to compare different products and prices when shopping online?
• What type of customer service do you expect when shopping online?
• What type of product reviews do you consider when making online purchases?
• How do you prefer to interact with a brand when shopping online?

Examples of quantitative research questions for analyzing customer segments

• What is the average age of customers in each segment?
• How do spending habits vary across customer segments ?
• What is the average length of time customers spend in each segment?
• How does loyalty vary across customer segments?
• What is the average purchase size in each segment?
• What is the average frequency of purchases in each segment?
• What is the average customer lifetime value in each segment?
• How does customer satisfaction vary across customer segments?
• What is the average response rate to campaigns in each segment?
• How does customer engagement vary across customer segments?

These questions are ideal to ask once you’ve already defined your segments. We’ve written a useful post that covers the ins and outs of what market segmentation is and how to do it.

Additional applications of quantitative research questions

I’ve covered ten use cases for quantitative questions in detail. Still, there are other instances where you can put quantitative research to good use.

Product usage studies: Measure how customers use a product or service.

Preference testing: Testing of customer preferences for different products or services.

Sales analysis: Analysis of sales data to identify trends and patterns.

Distribution analysis: Analyzing distribution channels to determine the most efficient and effective way to reach customers.

Focus groups: Groups of consumers brought together to discuss and provide feedback on a particular product, service, or marketing campaign.

Consumer interviews: Conducted with customers to understand their behavior and preferences better.

Mystery shopping: Mystery shoppers are sent to stores to measure customer service levels and product availability.

Conjoint analysis: Analysis of how consumers value different attributes of a product or service.

Regression analysis: Statistical analysis used to identify relationships between different variables.

A/B testing: Testing two or more different versions of a product or service to determine which one performs better.

Brand equity studies: Measure, compare and analyze brand recognition, loyalty, and consumer perception.

Exit surveys: Collect numerical data to analyze employee experience and reasons for leaving, providing insight into how to improve the work environment and retain employees.

Price sensitivity testing: Measuring responses to different pricing models to find the optimal pricing model, and identify areas if and where discounts or incentives might be beneficial.

Quantitative market research survey examples

A recent GreenBook study shows that 89% of people in the market research industry use online surveys frequently–and for good reason. They’re quick and easy to set up, the cost is minimal, and they’re highly scalable too.

Questions are always formatted to provide close-ended answers that can be quantified. If you wish to collect free-text responses, this ventures into the realm of qualitative research . Here are a few examples.

Brand Loyalty Surveys: Companies use online surveys to measure customers’ loyalty to their brand. They include questions about how long an individual has been a customer, their overall satisfaction with the service or product, and the likelihood of them recommending the brand to others.

Customer Satisfaction Surveys: These surveys may include questions about the customer’s experience, their overall satisfaction, and the likelihood they will recommend a product or service to others.

Pricing Studies: This type of research reveals how customers value their products or services. These surveys may include questions about the customer’s willingness to pay for the product, the customer’s perception of the price and value, and their comparison of the price to other similar items.

Product/Service Usage Studies: These surveys measure how customers use their products or services. They can include questions about how often customers use a product, their preferred features, and overall satisfaction.

Here’s an example of a typical survey we’ve used when testing out potential features with groups of clients. After they’ve had the chance to use the feature for a period, we send a short survey, then use the feedback to determine the viability of the feature for future release.

Employee Experience Surveys: Another great example of quantitative data in action, and one we use at Similarweb to measure employee satisfaction. Many online platforms are available to help you conduct them; here, we use Culture AMP . The ability to manipulate the data, spot patterns or trends, then identify the core successes and development areas are astounding.

How to answer quantitative research questions with Similarweb

For the vast majority of applications I’ve covered in this post, there’s a more modern, quicker, and more efficient way to obtain similar insights online. Gone are the days when companies need to use expensive outdated data or pay hefty sums of money to market research firms to conduct broad studies to get the answers they need.

By this point, I hope you’ve seen how quick and easy it is to use Similarweb to do market research the modern way. But I’ve only scratched the surface of its capabilities.

Take two to watch this introductory video and see what else you can uncover.

Added bonus: Similarweb API

If you need to crunch large volumes of data and already use tools like Tableau or PowerBI, you can seamlessly connect Similarweb via the API and pipe in the data. So for faster analysis of big data, you can leverage Similarweb data to use alongside the visualization tools you already know and love.

Similarweb’s suite of market intelligence solutions offers unbiased, accurate, honest insights you can trust. With a world of data at your fingertips, use Similarweb Research Intelligence to uncover facts that help inform your research and strengthen your position.

Take a look at:

• Our Market Research suite
• Our Benchmarking tools
• Our Audience Insights tool
• Our Company Research module
• Our Consumer Journey Tracker
• Our Competitive Analysis Tool

Wrapping up

Today’s markets change at lightning speed. To keep up and succeed, companies need access to insights and intel they can depend on to be timely and on-point. While quantitative market research questions can and should always be asked, it’s important to leverage technology to increase your speed to insight, and thus improve reaction times and response to market shifts.

What is quantitative market research?

Quantitative market research is a form of research that uses numerical data to gain insights into the behavior and preferences of customers. It is used to measure and track the performance of products, services, and campaigns.

How does quantitative market research help businesses?

Quantitative market research can help businesses identify customer trends, measure customer satisfaction, and develop effective marketing strategies. It can also provide valuable insights into customer behavior, preferences, and attitudes.

What types of questions should be included in a quantitative market research survey?

Questions in a quantitative market research survey should be focused, clear, and specific. Questions should be structured to collect quantitative data, such as numbers, percentages, or frequency of responses.

What methods can be used to collect quantitative market research data?

Common methods used to collect quantitative market research data include surveys, interviews, focus groups, polls, and online questionnaires.

What are the advantages and disadvantages of using quantitative market research?

The advantages of using quantitative market research include the ability to collect data quickly, the ability to analyze data in a structured way, and the ability to identify trends. Disadvantages include the potential for bias, the cost of collecting data, and the difficulty in interpreting results.

by Liz March

Digital Research Specialist

Liz March has 15 years of experience in content creation. She enjoys the outdoors, F1, and reading, and is pursuing a BSc in Environmental Science.

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• Quantitative research questions: Types, tips & examples

Defne Çobanoğlu

Deciding on your next survey’s goal gives you a starting point as to what kind of questions you will use on your survey. And if you want to do concrete market research, give a data summary to your supervisors, or make informed decisions based on the data you collect, you should use quantitative survey questions.

In this article, we have gathered more than 100 survey question examples about gender, marketing, stress, psychology, academic performance, social media, and mental health to get you started. You can add these questions to your next research survey, or you can use them to get inspiration to write many more. Let us get started!

• What is a quantitative research question?

The quantitative research question is a type of question where the person asking the question wants to obtain a numeric answer that will provide them with a tangible answer. It involves collecting objective, measurable data about a particular subject or topic, often through surveys, experiments, or other structured methods.

The definition of a quantitative research question

The data collected is typically numerical in nature, such as ratings, counts, measurements, or percentages . So, an answer to this type of question can be confidentially used when creating a quantitative analysis.

Quantitative vs. qualitative research questions

The main difference between quantitative and qualitative questions is what you want to achieve from the question and methods of data collection. Qualitative research focuses on exploring and understanding complex phenomena, experiences, and perspectives . And qualitative research questions aim to gather detailed descriptions and subjective experiences to gain insights.

On the other hand, quantitative research aims to answer questions that involve measuring and quantifying variables, examining relationships, and making statistical deductions. It mainly relies on structured data collection methods, such as surveys, experiments, observations, and existing datasets, in order to collect numerical data .

• How to write a quantitative research question

If you want to obtain concrete data on a research topic, you should use quantitative research questions. They give you numerical answers such as ratings, measurements, counts, or percentages. That makes it easier to conclude a quantitative analysis. Therefore, use questions that will give you answers like; “three times a week”, “about 11”, “20% of the students”, etc. Here are some question starters to have in mind to give you quantitative research questions ideas:

• How frequently?
• What percentage?
• To what extent?
• What proportion?
• On a scale of…

Here are some simple examples:

• How often do you go to the gym in a week?
• How much do you spend on groceries?
• How many phone calls do you make a day?
• Types of quantitative questions

When you try to get numerical answers, the only option is not the multiple-choice one. You can use different types of quantitative research questions to make the form more interesting, visually appealing, and detailed if you use a smart survey creator, such as forms.app, you can make use of its multiple smart form fields to build your form. Let us see what are some good options to use on your next survey.

Star rating:

It is a good way to ask people their opinions, and the survey takers can rate criteria based on different categories. Each star represents an equivalent numeric value, and they typically range from 1 to 5. Even if they are clicking on stars, you get numeric data in the end.

A star rating question example

Opinion scale:

It is basically the same thing with the stars but instead, the survey takers rate criteria as numbers from 1-5 or 1-10. It is better to keep in mind the best way for this is using a 1-5 scale, with 5 being the best and 1 being the worst rating.

An opinion scale question example

Picture selection:

Having people choose their opinions in a picture selection form is a good way to go. It is a good option to use when you are creating a survey for market research and such.

A picture selection question example

Multiple-choice:

When you ask people a question such as; “what are the reasons that negatively affect your mental health?” it is better to let them choose multiple reasons rather than a single one. You would not want to limit the target audience by making them choose only one thing on the list.

A multiple-choice question example

Selection matrix:

In this type of question, you can make multiple sentences, categories, and statements, and survey takers can answer them accordingly. They allow you to get the answers as one question rather than setting up multiple questions.

A selection matrix example

• 100+ Quantitative research questions to ask in your research surveys

In your next survey, you can use any of the questions below, or you can create your own. If you use smart questions focused on a subject or aspect, it will make it easier for you to make an informed analysis at the end. Now, let us start with the first one:

Quantitative research questions about gender

A question example about quantitative research about gender

Quantitative research questions about gender aim to gather numerical data to quantify and analyze gender-related patterns, differences, and associations. They focus on exploring gender-related issues and investigating gender influences on several aspects of life.

1 - What is the difference in average earnings between male and female employees in a specific industry?

2 - How does gender affect academic achievement in STEM subjects among high school students?

3 - What is the percentage of women in leadership positions in Fortune 500 companies?

4 - What is the impact of gender on access to and utilization of health services?

5 - What is the percentage of female students speaking in a classroom as opposed to male students?

6 - How does gender influence consumer preferences and purchasing behavior in the fashion industry?

7 - What are the gender differences in response to specific marketing strategies for a particular product?

8 - What is the correlation between gender and mental health outcomes in a specific population?

9 - How does gender influence the perception of work-life balance among working professionals?

10 - How often do you feel discriminated against in a work environment because of your gender?

11 - What is the effect of gender on smoking at the ages 14-18?

Quantitative research questions about stress

A question example about quantitative research about stress

Research questions about stress aim to investigate different aspects of stress, its causes, and its consequences. Researchers can measure stress levels and examine the relationships between stress and other variables. Also, they can analyze patterns and trends associated with stress after collecting appropriate data.

12 - On a scale of 1 to 10, how often do you feel stressed?

13 - What is the prevalence of stress among college students?

14 - How does stress impact academic achievement among high school students?

15 - How does mindfulness meditation training impact stress levels in university students?

16 - What are the primary sources of work-related stress among employees?

17 - What is the relationship between stress levels and job performance among healthcare professionals?

18 - Who are the people in your life that cause you the most stress?

19 - In the last month, how often have you felt that you were unable to control important things in your life?

20 - How does workplace stress influence employee turnover rates in a specific organization?

21 - What is the correlation between stress levels and physical health in young people?

22 - What are the demographic factors (such as age, gender, or income) associated with higher levels of stress?

23 - What is the impact of stress on sleep quality and duration among adults?

24 - What are the stress levels experienced by parents of children with special needs compared to parents of typically developing children?

25 - What is the effectiveness of stress management interventions in reducing stress levels among individuals with chronic illnesses?

26 - What is the impact of daily meditation helping stress levels?

27 - What are the factors contributing to job-related stress among healthcare professionals in a specific specialty?

Quantitative research questions in Psychology

A question example about quantitative research in psychology

Quantitative research questions in psychology cover a range of psychological topics, including mental health, personality, behavior, and social dynamics. The aim of these questions is to collect quantitative data to examine relationships, assess the effectiveness of interventions, and identify factors associated with psychological events.

28 - What is the relationship between self-esteem and academic performance in high school students?

29 - How does exposure to violent media affect aggressive behavior in children?

30 - What is the prevalence of depression among college students?

31 - How is parental attachment style associated with the development of anxiety disorders in children?

32 - How many times a month should one use professional therapy?

33 - What are the factors influencing job satisfaction among employees in a specific industry?

34 - What are the predictors of job performance among healthcare professionals?

35 - Generally, at what age do children start getting psychological help?

36 - What is the effect of cognitive-behavioral therapy on reducing symptoms of post-traumatic stress disorder?

37 - How does the classroom environment affect academic motivation and achievement in elementary school students?

38 - What is the effectiveness of a cognitive training program in improving memory function in older adults?

39 - How do exercise frequency and intensity impact symptoms of anxiety and depression in individuals with diagnosed mental health conditions?

40 - What is the correlation between sleep duration and academic performance in college students?

41 - How does parental divorce during childhood impact the development of attachment styles in adulthood?

42 - What is the relationship between self-esteem and job satisfaction among working professionals?

43 - What are the predictors of eating disorder symptoms in adolescent females?

44 - At what age the teenage girls prone to depression?

45 - What is the correlation between young adults and suicide rates?

46 - What is the effect of a specific cognitive training program on improving cognitive functioning in elders?

47 - How does the presence of social support networks impact resilience levels in individuals who have experienced traumatic events?

48 - What are the effects of a specific therapeutic intervention on reducing symptoms of anxiety in individuals with a generalized anxiety disorder?

49 - What is the correlation between social media use and symptoms of depression in young adults?

50 - How does mindfulness meditation training influence stress levels in individuals with high-stress occupations?

51 - How does exposure to violent video games affect aggressive behavior in adolescents?

Quantitative research questions about mental health

A question example about quantitative research about mental health

Quantitative research questions about mental health focus on various aspects of mental health, including the prevalence of disorders, risk factors, treatment interventions, and the impact of lifestyle factors. 

52 - How does the frequency of social media use relate to levels of depressive symptoms in adolescents?

53 - What is the correlation between sleep quality and mental health outcomes in adults with diagnosed mental health conditions?

54 - What is the percentage of people diagnosed with anxiety disorder that has a college education?

55 - What kind of activities helps with your mental health?

56 - How many times a week do you spare time for your mental well-being?

57 - What is the effect of a specific psychotherapy intervention on reducing symptoms of depression?

58 - What are the factors determining treatment adherence in patients with schizophrenia?

59 - How do exercise frequency and intensity relate to anxiety levels?

60 - What is the relationship between social support and endurance in individuals with a history of trauma?

61 - How does stigma surrounding mental illness influence help-seeking behavior among college students?

62 - What is the prevalence of anxiety disorders among college students?

Quantitative research questions about social media

A question example about quantitative research about social media

Quantitative research questions about social media try to explore various aspects of social media, including its impact on psychological well-being, behavior, relationships, and society. They aim to collect quantitative data to analyze relations, examine effects, and measure the influence of social media.

63 - How many times a day do you check your social media accounts?

64 - How much time do you spend on social media every day?

65 - How many social media accounts do you own?

66 - What is the correlation between social media engagement and academic performance in high school students?

67 - What are the most used social media accounts among teenagers?

68 - What is the psychological effect of social media accounts on young people?

69 - What is the relationship between social media use and self-esteem among adolescents?

70 - How does the frequency of social media use relate to levels of loneliness in young adults?

71 - How does exposure to idealized body images on social media impact body dissatisfaction in women?

72 - What are the predictors of problematic social media use among college students?

73 - How does social media use influence political attitudes and behaviors among young adults?

74 - What is the effect of social media advertising on consumer purchasing behavior and brand loyalty?

75 - What is the association between cyberbullying on social media and mental health outcomes among teenagers?

76 - How does social media use affect sleep quality and duration in adults?

77 - How does social media use impact interpersonal relationships and social support among individuals in long-distance relationships?

Quantitative research questions about academic performance

A question example about quantitative research about academic performance

Quantitative research questions about academic performance focus on academic performance, the predictors, and the elements affecting it negatively and positively. They aim to collect quantitative data to figure out the relation between academic performance and the environment of the students and make informed decisions.

78 - What is the correlation between student attendance rates and academic achievement in a specific grade level?

79 - How does parental involvement in education relate to students' academic performance?

80 - What is the impact of classroom size on student academic outcomes?

81 - What are the predictors of academic success among undergraduate students in a specific major?

82 - How many times were you absent during the last semester?

83 - What is the correlation between student engagement in extracurricular activities and their academic performance?

84 - What is the effect of peer tutoring programs on student grades and test scores?

85 - How do student motivation and self-efficacy influence academic achievement in a specific academic setting?

86 - What is the relationship between study habits and academic performance among high school students?

87 - How does the implementation of a specific teaching methodology or instructional approach impact student achievement in a particular subject?

Quantitative research questions about marketing

A question example about quantitative research about marketing

Quantitative research questions about marketing explore various aspects of marketing, including advertising effectiveness, consumer behavior, branding, pricing, and customer satisfaction. They involve collecting quantitative data to analyze relationships and assess the impact of marketing strategies. 

88 - What is the correlation between advertising expenditure and sales revenue for a specific product?

89 - As a consumer, how often do you make purchasing decisions based on marketing exposure?

90 - What are the top 5 brands that stand out to you because of ads of their quality?

91 - How does brand loyalty relate to customer satisfaction and repeat purchase behavior?

92 - What is the impact of pricing strategies on consumer purchase intentions and price sensitivity?

93 - When making a purchase, how important is the packaging of the product to you?

94 - What is the effectiveness of different marketing channels (e.g., social media, television, email marketing) in reaching and engaging the target audience?

95 - How does product packaging design influence consumer perception and purchase decisions?

96 - What are the key factors influencing customer loyalty in the retail industry?

97 - What is the relationship between online customer reviews and purchase decisions in e-commerce?

98 - How do brand reputation and perception affect consumer trust and willingness to recommend a product or service?

99 - What are the channels you visit to ensure the quality of the product you will purchase?

100 - How does the personalization of marketing messages impact customer engagement and response rates?

101 - What is the effect of promotional offers (e.g., discounts, coupons) on consumer purchase behavior?

102 - What is the effect of ad placement on popular social media accounts on teenagers?

• Tips for creating quantitative research questions

When you want to create your survey, you should be professional and collect the data systematically. That will help you have clear results. In order to achieve this: 

• Use clear and unambiguous language
• Avoid leading or biased questions 
• Use different question types 
• Keep the length of your survey at an appropriate level

After you create your survey in a systematic manner and use a competitive analysis framework to record your findings, you can achieve the concrete results you want. Also, always remember to obtain the necessary ethical approvals and informed consent required for your research study.

• How to create a quantitative research survey

When you are creating your next survey, you can go old-fashion and write everything down on a piece of paper and try to get people to fill them out. However, there is a much easier option thanks to online survey tools. And a great survey maker you can use is forms.app. It has over 1000 ready-to-use templates, and each of them is as useful. Now, let us go through the steps to creating a quantitative survey using forms.app:

1 - Go to forms.app and log in to your account (or create one for free).

2 - Go to the dropdown menu and click on the templates option .

3 - Choose one of the survey templates and click on the “use template” button and customize it as much as you want by adding question fields and changing the visuals as much as you want.

4 - Or, you can decide on starting from scratch and build everything from the start in a matter of minutes.

5 - Save your changes, and by clicking on the “eye” icon on the upper left side of the page, see the final result.

6 - Copy the unique link and share it with your audience. If you want, you can also embed the survey on the page of your choosing.

• Key points to take away

Creating a simple survey to collect numerical values to make informed and supported plans is very easy. It can be done with a simple and effective form creator, such as forms.app. It has many functional form fields and is also completely adjustable.

You can easily create your own research survey with the questions we have gathered for you. It should be mentioned that you should keep in mind to have a structured plan to go with. Because only then can you analyze your results effectively and repeat the research if it is needed.

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

• Form Features
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What Are Quantitative Survey Questions? Types and Examples

Learn all about quantitative research surveys, including types of quantitative survey questions, question formats, and quantitative question examples.

quantilope is the Consumer Intelligence Platform for all end-to-end research needs

Table of contents: 

• Types of quantitative survey questions - with examples 
• Quantitative question formats
• How to write quantitative survey questions 
• Examples of quantitative survey questions 

Leveraging quantilope for your quantitative survey 

In a quantitative research study brands will gather numeric data for most of their questions through formats like numerical scale questions or ranking questions. However, brands can also include some non-quantitative questions throughout their quantitative study - like open-ended questions, where respondents will type in their own feedback to a question prompt. Even so, open-ended answers can be numerically coded to sift through feedback easily (e.g. anyone who writes in 'Pepsi' in a soda study would be assigned the number '1', to look at Pepsi feedback as a whole).  One of the biggest benefits of using a quantitative research approach is that insights around a research topic can undergo statistical analysis; the same can’t be said for qualitative data like focus group feedback or interviews. Another major difference between quantitative and qualitative research methods is that quantitative surveys require respondents to choose from a limited number of choices in a close-ended question - generating clear, actionable takeaways. However, these distinct quantitative takeaways often pair well with freeform qualitative responses - making quant and qual a great team to use together.  The rest of this article focuses on quantitative research, taking a closer look at quantitative survey question types and question formats/layouts. 

Back to table of contents 

Types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions - with examples 

Quantitative questions come in many forms, each with different benefits depending on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139784">your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives. Below we’ll explore some of these dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139785">survey question dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139785" data-dropdown-placement-param="top" data-term-id="281139785"> types, which are commonly used together in a single survey to keep things interesting for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . The style of questioning used during dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139739">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139750">data dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139750" data-dropdown-placement-param="top" data-term-id="281139750"> collection is important, as a good mix of the right types of questions will deliver rich data, limit dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent fatigue, and optimize the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139757">response rate . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">Questionnaires should be enjoyable - and varying the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755">types of dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139755" data-dropdown-placement-param="top" data-term-id="281139755">quantitative research dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755"> questions used throughout your survey will help achieve that. 

Descriptive survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139763">Descriptive research questions (also known as usage and attitude, or, U&A questions) seek a general indication or prediction about how a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139773">group of people behaves or will behave, how that group is characterized, or how a group thinks.

For example, a business might want to know what portion of adult men shave, and how often they do so. To find this out, they will survey men (the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience ) and ask descriptive questions about their frequency of shaving (e.g. daily, a few times a week, once per week, and so on.) Each of these frequencies get assigned a numerical ‘code’ so that it’s simple to chart and analyze the data later on; daily might be assigned ‘5’, a few times a week might be assigned ‘4’, and so on. That way, brands can create charts using the ‘top two’ and ‘bottom two’ values in a descriptive question to view these metrics side by side.

Another business might want to know how important local transit issues are to residents, so dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions will allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to indicate the degrees of opinion attached to various transit issues. Perhaps the transit business running this survey would use a sliding numeric scale to see how important a particular issue is.

Comparative survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139782">Comparative research questions are concerned with comparing individuals or groups of people based on one or more variables. These questions might be posed when a business wants to find out which segment of its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience might be more profitable, or which types of products might appeal to different sets of consumers.

For example, a business might want to know how the popularity of its chocolate bars is spread out across its entire customer base (i.e. do women prefer a certain flavor? Are children drawn to candy bars by certain packaging attributes? etc.). Questions in this case will be designed to profile and ‘compare’ segments of the market.

Other businesses might be looking to compare coffee consumption among older and younger consumers (i.e. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic segments), the difference in smartphone usage between younger men and women, or how women from different regions differ in their approach to skincare.

Relationship-based survey questions

As the name suggests, relationship-based survey questions are concerned with the relationship between two or more variables within one or more dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic groups. This might be a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link between one thing and the other - for example, the consumption of caffeine and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents ’ reported energy levels throughout the day. In this case, a coffee or energy drink brand might be interested in how energy levels differ between those who drink their caffeinated line of beverages and those who drink decaf/non-caffeinated beverages.

Alternatively, it might be a case of two or more factors co-existing, without there necessarily being a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link - for example, a particular type of air freshener being more popular amongst a certain dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic (maybe one that is controlled wirelessly via Bluetooth is more popular among younger homeowners than one that’s plugged into the wall with no controls). Knowing that millennials favor air fresheners which have options for swapping out scents and setting up schedules would be valuable information for new product development.

Advanced method survey questions

Aside from descriptive, comparative, and relationship-based survey questions, brands can opt to include advanced methodologies in their quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire for richer depth. Though advanced methods are more complex in terms of the insights output, quantilope’s Consumer Intelligence Platform automates the setup and analysis of these methods so that researchers of any background or skillset can leverage them with ease.

With quantilope’s pre-programmed suite of 12 advanced methodologies , including MaxDiff , TURF , Implicit , and more, users can drag and drop any of these into a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire and customize for their own dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives.

For example, consider a beverage company that’s looking to expand its flavor profiles. This brand would benefit from a MaxDiff which forces dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to make tradeoff decisions between a set of flavors. A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent might say that coconut is their most-preferred flavor, and lime their least (when in a consideration set with strawberry), yet later on in the MaxDiff that same dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent may say Strawberry is their most-preferred flavor (over black cherry and kiwi). While this is just one example of an advanced method, instantly you can see how much richer and more actionable these quantitative metrics become compared to a standard usage and attitude question .

Advanced methods can be used alongside descriptive, comparison, or relationship questions to add a new layer of context wherever a business sees fit. Back to table of contents 

Quantitative question formats  

So we’ve covered the kinds of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative research questions you might want to answer using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research , but how do these translate into the actual format of questions that you might include on your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire ?

Thinking ahead to your reporting process during your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire setup is actually quite important, as the available chart types differ among the types of questions asked; some question data is compatible with bar chart displays, others pie charts, others in trended line graphs, etc. Also consider how well the questions you’re asking will translate onto different devices that your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents might be using to complete the survey (mobile, PC, or tablet).

Single Select questions

Single select questions are the simplest form of quantitative questioning, as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents are asked to choose just one answer from a list of items, which tend to be ‘either/or’, ‘yes/no’, or ‘true/false’ questions. These questions are useful when you need to get a clear answer without any qualifying nuances.

Multi-select questions

Multi-select questions (aka, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139767">multiple choice ) offer more flexibility for responses, allowing for a number of responses on a single question. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents can be asked to ‘check all that apply’ or a cap can be applied (e.g. ‘select up to 3 choices’).

For example:

Aside from asking text-based questions like the above examples, a brand could also use a single or multi-select question to ask respondents to select the image they prefer more (like different iterations of a logo design, packaging options, branding colors, etc.). 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139766">scale dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139766" data-dropdown-placement-param="top" data-term-id="281139766"> questions

A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert scale   is widely used as a convenient and easy-to-interpret rating method. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents find it easy to indicate their degree of feelings by selecting the response they most identify with.

Slider scales

Slider scales are another good interactive way of formatting questions. They allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to customize their level of feeling about a question, with a bit more variance and nuance allowed than a numeric scale:

One particularly common use of a slider scale in a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139770">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139770" data-dropdown-placement-param="top" data-term-id="281139770"> study is known as a NPS (Net Promoter Score) - a way to measure dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139775">customer experience and loyalty . A 0-10 scale is used to ask customers how likely they are to recommend a brand’s product or services to others. The NPS score is calculated by subtracting the percentage of ‘detractors’ (those who respond with a 0-6) from the percentage of promoters (those who respond with a 9-10). dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents who select 7-8 are known as ‘passives’.

For example: 

Drag and drop questions

Drag-and-drop question formats are a more ‘gamified’ approach to survey capture as they ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to do more than simply check boxes or slide a scale. Drag-and-drop question formats are great for ranking exercises - asking dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to place answer options in a certain order by dragging with their mouse. For example, you could ask survey takers to put pizza toppings in order of preference by dragging options from a list of possible answers to a box displaying their personal preferences:

Matrix questions

Matrix   questions are a great way to consolidate a number of questions that ask for the same type of response (e.g. single select yes/no, true/false, or multi-select lists). They are mutually beneficial - making a survey look less daunting for the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent , and easier for a brand to set up than asking multiple separate questions.

Items in a matrix question are presented one by one, as respondents cycle through the pages selecting one answer for each coffee flavor shown. 

While the above example shows a single-matrix question - meaning a respondent can only select one answer per element (in this case, coffee flavors), a matrix setup can also be used for multiple-choice questions - allowing respondents to choose multiple answers per element shown, or for rating questions - allowing respondents to assign a rating (e.g. 1-5) for a list of elements at once.  Back to table of contents 

How to write dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

We’ve reviewed the types of questions you might ask in a quantitative survey, and how you might format those questions, but now for the actual crafting of the content.

When considering which questions to include in your survey, you’ll first want to establish what your research goals are and how these relate to your business goals. For example, thinking about the three types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions explained above - descriptive, comparative, and relationship-based - which type (or which combination) will best meet your research needs? The questions you ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents may be phrased in similar ways no matter what kind of layout you leverage, but you should have a good idea of how you’ll want to analyze the results as that will make it much easier to correctly set up your survey.

Quantitative questions tend to start with words like ‘how much,’ ‘how often,’ ‘to what degree,’ ‘what do you think of,’ ‘which of the following’ - anything that establishes what consumers do or think and that can be assigned a numerical code or value. Be sure to also include ‘other’ or ‘none of the above’ options in your quant questions, accommodating those who don’t feel the pre-set answers reflect their true opinion. As mentioned earlier, you can always include a small number of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139748">open-ended questions in your quant survey to account for any ideas or expanded feedback that the pre-coded questions don’t (or can’t) cover. Back to table of contents 

Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">Quantitative survey questions impose limits on the answers that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents can choose from, and this is a good thing when it comes to measuring consumer opinions on a large scale and comparing across dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . A large volume of freeform, open-ended answers is interesting when looking for themes from qualitative studies, but impractical to wade through when dealing with a large dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139756">sample size , and impossible to subject to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139774">statistical analysis .

For example, a quantitative survey might aim to establish consumers' smartphone habits. This could include their frequency of buying a new smartphone, the considerations that drive purchase, which features they use their phone for, and how much they like their smartphone.

Some examples of quantitative survey questions relating to these habits would be:

Q. How often do you buy a new smartphone?

[single select question]

More than once per year

Every 1-2 years

Every 3-5 years

Every 6+ years

Q. Thinking about when you buy a smartphone, please rank the following factors in order of importance:

[drag and drop ranking question]

screen size

storage capacity

Q. How often do you use the following features on your smartphone?

[matrix question]

Q. How do you feel about your current smartphone?

[sliding scale]

I love it <-------> I hate it

Answers from these above questions, and others within the survey, would be analyzed to paint a picture of smartphone usage and attitude trends across a population and its sub-groups. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">Qualitative research might then be carried out to explore those findings further - for example, people’s detailed attitudes towards their smartphones, how they feel about the amount of time they spend on it, and how features could be improved. Back to table of contents 

quantilope’s Consumer Intelligence Platform specializes in automated, advanced survey insights so that researchers of any skill level can benefit from quick, high-quality consumer insights. With 12 advanced methods to choose from and a wide variety of quantitative question formats, quantilope is your one-stop-shop for all things dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research (including its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139776">in-depth dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">qualitative research solution - inColor ).

When it comes to building your survey, you decide how you want to go about it. You can start with a blank slate and drop questions into your survey from a pre-programmed list, or you can get a head start with a survey dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139765">template for a particular business use case (like concept testing ) and customize from there. Once your survey is ready to launch, simply specify your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience , connect any panel (quantilope is panel agnostic), and watch as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139783">answer questions in your survey in real-time by monitoring the fieldwork section of your project. AI-driven dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139764">data analysis takes the raw data and converts it into actionable findings so you never have to worry about manual calculations or statistical testing.

Whether you want to run your quantitative study entirely on your own or with the help of a classically trained research team member, the choice is yours on quantilope’s platform. For more information on how quantilope can help with your next dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139768">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139768" data-dropdown-placement-param="top" data-term-id="281139768"> project , get in touch below!

Get in touch to learn more about quantitative research with quantilope!

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Quantitative Research Questions: What It Is Types & Examples

If you’re struggling to create the perfect research question that will lead to meaningful insights, look no further than this article.

We’ll show you how to craft compelling quantitative research questions to take your research to the next level.

We all know that asking the right questions is crucial to the success of any study.

Quantitative research questions are essential in determining the direction of your research, data, and final insights.

However, crafting the perfect question can be a daunting task. How do you ensure your question is specific, measurable, and relevant to your goals?

How do you avoid bias?

In this article, we’ll answer all of these questions and more. Also, you’ll learn the following:

What are Quantitative Research Questions?

Importance of quantitative research questions, when to use quantitative research questions, characteristics of quantitative research questions, types of quantitative research questions, quantitative research questions examples, how to conduct quantitative research questions, how to analyze quantitative research questions.

Before diving into the blog’s core, we’ll address the following question: what is quantitative research?

Quantitative research questions are inquiries crafted to gather numerical data and quantify phenomena within a research study. These questions seek to understand relationships between variables, measure frequencies, or assess the extent of certain behaviors, attitudes, or trends within a defined population. They are structured and precise, often aiming to elicit specific responses that can be quantified and analyzed statistically. Quantitative research questions play a fundamental role in studies that require numerical data to draw objective conclusions, establish patterns, and correlations, or make predictions. These questions provide a framework for designing surveys, experiments, or data collection methods that generate quantifiable data, facilitating rigorous analysis and empirical validation of findings across various disciplines.

Quantitative research questions play a pivotal role in structured data collection, providing a framework to gather precise, measurable information. These questions are crucial as they enable researchers to quantify phenomena, trends, or behaviors within a population, offering statistical insights and objective findings. By formulating clear, specific questions, researchers can design surveys or experiments that yield numerical data, facilitating rigorous analysis and comparisons. This approach is instrumental in uncovering patterns, correlations, and trends, allowing for generalizations and predictions within a given context. Moreover, quantitative research questions are vital in validating hypotheses, informing decision-making processes, and contributing to evidence-based conclusions across various fields, from social sciences to business and beyond. Their importance lies in their ability to offer structured, quantifiable data that enhances understanding and drives informed actions.

Quantitative research questions are best employed when seeking precise, numerical data to answer specific queries or test hypotheses. These questions find their place in scenarios where a structured approach is necessary to measure, quantify, or statistically analyze phenomena within a defined population or sample. They are particularly valuable when aiming to identify patterns, relationships, or correlations among variables that can be objectively measured or quantified. Additionally, quantitative research questions are well-suited for large-scale studies, surveys, or experiments where numerical data is essential for drawing statistically valid conclusions. Their use is prominent in fields such as economics, psychology, sociology, marketing, and other sciences, providing a robust framework to gather data that can be analyzed rigorously using statistical methods. Overall, quantitative research questions excel in scenarios requiring precise measurement, numerical analysis, and statistical validation of findings.

For a research question to be effective, several key attributes must be adhered to. Clarity and specificity are paramount; the question should be crystal clear, leaving no room for ambiguity. Additionally, it should directly align with the research objectives and hypotheses, ensuring a seamless connection between the inquiry and the study’s purpose. Testability and measurability are equally crucial; the question should lend itself to empirical testing and quantifiable outcomes.

Quantitative research questions typically fall into several categories, each serving a specific purpose within a study:

Descriptive Questions: Aim to describe characteristics, frequencies, or trends within a population or sample. For example, “What percentage of customers prefer product A over product B?”

Comparative Questions: Focus on comparing two or more groups, variables, or conditions to identify differences or similarities. For instance, “Is there a significant difference in sales between urban and rural markets?”

Relationship Questions: Investigate the correlation or association between two or more variables. An example is, “Is there a relationship between advertising expenditure and sales revenue?”

Predictive Questions: Seek to forecast or predict future outcomes based on identified patterns or relationships. For instance, “Can customer satisfaction scores predict future purchase intentions?”

Causal Questions: Explore cause-and-effect relationships between variables. These questions aim to determine if changes in one variable cause changes in another. For example, “Does increased training lead to higher employee productivity?”

These types of quantitative research questions guide the design of studies and the collection of numerical data to address specific research objectives within various fields of inquiry.

Quantitative research questions can be a lifesaver in creating surveys .

Below are two examples of questionnaires you can use to gather quantitative data regarding customer service and product usage.

Customer Service Questions

If you’re looking to improve your customer service , these questions can help you identify areas of strength.

They’re designed to be quick and easy for customers to answer.

For instance, you might ask your customers to rate their overall satisfaction on a scale of 1 to 5 .

You could also ask how easy it was for them to contact customer service and how helpful and knowledgeable the representatives were.

Product and Usage Questions

If you’re interested in improving your product or understanding your customers’ usage patterns, these questions can help.

They’re designed to be straightforward to answer.

For instance, you might ask customers how satisfied they are with the product’s performance or how often they use it.

You could also ask how easy it was to set up and start using the product or whether they’ve encountered any issues.

The tool we recommend you use to gather qualitative research questions’ responses is Google Forms .

Follow the easy steps below to get started with Google Forms application.

• Open Google Sheets and click the Tools Click the Create a new form , as shown below.

• Fill in your question in the Untitled Question Fill in the multiple-choice questions in Option 1, 2, etc.

• Click the three dots in the Responses Tab to link your Google Form survey template to Google Sheets.
• Download your qualitative research questions data by clicking the Download Responses (CSV) in the dropdown.

Excel is a great tool for creating charts.

But it lacks survey-specific charts and graphs, like Likert Scale Chart .

Fortunately, there’s a solution: ChartExpo.

ChartExpo is a powerful data visualization tool that offers a range of benefits, like access to the best charts for survey analysis. With ChartExpo, you can easily and quickly create effective charts and graphs that will help you gain a deeper understanding.

One of the biggest merits of the ChartExpo add-in for Excel is its user-friendly interface.

You don’t need coding experience to use it. This makes it accessible to everyone.

Plus, the visualizations provided by ChartExpo can help you uncover hidden patterns and trends in your data.

This means you’ll have unlimited access to valuable insights into your customers’ experiences .

ChartExpo takes your data security seriously.  Your data does not leave your environment.

ChartExpo is affordable at just $10 monthly, with a 7-day free trial.

Sign up for ChartExpo today and start gaining insights like never before!

Let’s assume we’ve downloaded our survey data into Excel by following the easy steps below.

We’ll use ChartExpo’s Likert Scale Chart to visualize the data below

Before we visualize the data above, we’ll show you how to install and use ChartExpo add-in.

To get started with ChartExpo in Excel, follow the steps below:

• Open your Microsoft Excel.
• Open the worksheet and click the Insert button to  access the  My Apps

• Click the Insert button to initiate the ChartExpo engine.

• Click the Search box and type “Likert Scale Chart.”

• Highlight your data and click the Create Chart From Selection button, as shown below.

• Use the multiple-choice responses you deployed in your survey to gather responses to map your Likert Scale.

In our case, we’ll use the following multiple-choice responses:

• Very Unlikely = 1
• Unlikely = 2
• Not Sure = 3
• Very Likely = 5

• To include the chart header, click the Edit Chart

• Once the Chart Header Properties window shows, fill in your header in Line 1, as shown.

• Toggle the small button below Line 2 to the right side to activate the header.
• Click the Apply button, as shown above.
• Click the Save Changes button to preserve all the changes.

• Check out the final chart below.

• According to the survey results, 60% of customers indicated that they would buy the product again.
• However, 25% said they would not.
• The remaining 15% were unsure about their future purchasing intentions.
• Regarding the discount code, 55% of customers said they would use it. Only 35% said they would not.
• Regarding recommending the product to their friends, 55% of customers said they would. However, 20% said they would not.
• Overall, 56% of respondents expressed satisfaction with both the product and store. Only 27% expressed dissatisfaction.

What are examples of quantitative research questions?

Examples of quantitative research questions include:

• What is the relationship between your education level and your income?
• How does your age affect memory recall?
• “What is the impact of exercise on your blood pressure?”

These questions are measurable and objective and typically involve the collection and visualization of numerical data.

What is the main purpose of quantitative research?

The main purpose of quantitative research is to provide objective answers to research questions through the analysis of numerical data.

Use it to identify patterns and relationships between variables and to provide evidence-based solutions.

In conclusion, quantitative research questions are a powerful tool for gathering accurate and reliable insights.

By using well-crafted questionnaires and data visualization tools like ChartExpo, you can gain valuable insights into your customers’ experiences and preferences.

ChartExpo offers numerous benefits, including a user-friendly interface and the ability to create compelling charts that uncover hidden patterns and trends in survey data.

Whether you’re looking to improve customer service, understand product usage patterns, or gain insights into any other area of your business, quantitative research questions can help.

 So why not use them today?

If you need a tool to help you visualize your data, give ChartExpo a try. It has an affordable monthly subscription and 7-day free trial.

Start making data-driven decisions today with the help of quantitative research questions and ChartExpo.

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How to structure quantitative research questions

There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps : (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2) Identifying the different types of variables you are trying to measure, manipulate and/or control, as well as any groups you may be interested in; (3) Selecting the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved; and (4) Writing out the problem or issues you are trying to address in the form of a complete research question. In this article, we discuss each of these four steps , as well as providing examples for the three types of quantitative research question you may want to create: descriptive , comparative and relationship-based research questions .

• STEP ONE: Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
• STEP TWO: Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
• STEP THREE: Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
• STEP FOUR: Write out the problem or issues you are trying to address in the form of a complete research question

STEP ONE Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create

The type of quantitative research question that you use in your dissertation (i.e., descriptive , comparative and/or relationship-based ) needs to be reflected in the way that you write out the research question; that is, the word choice and phrasing that you use when constructing a research question tells the reader whether it is a descriptive, comparative or relationship-based research question. Therefore, in order to know how to structure your quantitative research question, you need to start by selecting the type of quantitative research question you are trying to create: descriptive, comparative and/or relationship-based.

STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in

Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure , manipulate and/or control . If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable , should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal , dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables , which you need to understand to create quantitative research questions.

To provide a brief explanation; a variable is not only something that you measure , but also something that you can manipulate and control for. In most undergraduate and master's level dissertations, you are only likely to measure and manipulate variables. You are unlikely to carry out research that requires you to control for variables, although some supervisors will expect this additional level of complexity. If you plan to only create descriptive research questions , you may simply have a number of dependent variables that you need to measure. However, where you plan to create comparative and/or relationship-based research questions , you will deal with both dependent and independent variables . An independent variable (sometimes called an experimental or predictor variable ) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable ). For example, if we were interested in investigating the relationship between gender and attitudes towards music piracy amongst adolescents , the independent variable would be gender and the dependent variable attitudes towards music piracy . This example also highlights the need to identify the group(s) you are interested in. In this example, the group of interest are adolescents .

Once you identifying the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in, it is possible to start thinking about the way that the three types of quantitative research question can be structured . This is discussed next.

STEP THREE Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved

The structure of the three types of quantitative research question differs, reflecting the goals of the question, the types of variables, and the number of variables and groups involved. By structure , we mean the components of a research question (i.e., the types of variables, groups of interest), the number of these different components (i.e., how many variables and groups are being investigated), and the order that these should be presented (e.g., independent variables before dependent variables). The appropriate structure for each of these quantitative research questions is set out below:

Structure of descriptive research questions

• Structure of comparative research questions
• Structure of relationship-based research questions

There are six steps required to construct a descriptive research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the group(s) you are interested in; (4) decide whether dependent variable or group(s) should be included first, last or in two parts; (5) include any words that provide greater context to your question; and (6) write out the descriptive research question. Each of these steps is discussed in turn:

Choose your starting phrase

Identify and name the dependent variable

Identify the group(s) you are interested in

Decide whether the dependent variable or group(s) should be included first, last or in two parts

Include any words that provide greater context to your question

Write out the descriptive research question

FIRST Choose your starting phrase

You can start descriptive research questions with any of the following phrases:

How many? How often? How frequently? How much? What percentage? What proportion? To what extent? What is? What are?

Some of these starting phrases are highlighted in blue text in the examples below:

How many calories do American men and women consume per day?

How often do British university students use Facebook each week?

What are the most important factors that influence the career choices of Australian university students?

What proportion of British male and female university students use the top 5 social networks?

What percentage of American men and women exceed their daily calorific allowance?

SECOND Identify and name the dependent variable

All descriptive research questions have a dependent variable. You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .

The first two examples highlight that while the name of the dependent variable is the same, namely daily calorific intake , the way that this dependent variable is written out differs in each case.

THIRD Identify the group(s) you are interested in

All descriptive research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .

What are the most important factors that influence the career choices of Australian university students ?

The examples illustrate the difference between the use of a single group (e.g., British university students ) and multiple groups (e.g., American men and women ).

FOURTH Decide whether the dependent variable or group(s) should be included first, last or in two parts

Sometimes it makes more sense for the dependent variable to appear before the group(s) you are interested in, but sometimes it is the opposite way around. The following examples illustrate this, with the group(s) in green text and the dependent variable in blue text :

Group 1st; dependent variable 2nd:

How often do British university students use Facebook each week ?

Dependent variable 1st; group 2nd:

Sometimes, the dependent variable needs to be broken into two parts around the group(s) you are interested in so that the research question flows. Again, the group(s) are in green text and the dependent variable is in blue text :

How many calories do American men and women consume per day ?

Of course, you could choose to restructure the question above so that you do not have to split the dependent variable into two parts. For example:

How many calories are consumed per day by American men and women ?

When deciding whether the dependent variable or group(s) should be included first or last, and whether the dependent variable should be broken into two parts, the main thing you need to think about is flow : Does the question flow? Is it easy to read?

FIFTH Include any words that provide greater context to your question

Sometimes the name of the dependent variable provides all the explanation we need to know what we are trying to measure. Take the following examples:

In the first example, the dependent variable is daily calorific intake (i.e., calories consumed per day). Clearly, this descriptive research question is asking us to measure the number of calories American men and women consume per day. In the second example, the dependent variable is Facebook usage per week. Again, the name of this dependent variable makes it easy for us to understand that we are trying to measure the often (i.e., how frequently; e.g., 16 times per week) British university students use Facebook.

However, sometimes a descriptive research question is not simply interested in measuring the dependent variable in its entirety, but a particular component of the dependent variable. Take the following examples in red text :

In the first example, the research question is not simply interested in the daily calorific intake of American men and women, but what percentage of these American men and women exceeded their daily calorific allowance. So the dependent variable is still daily calorific intake, but the research question aims to understand a particular component of that dependent variable (i.e., the percentage of American men and women exceeding the recommend daily calorific allowance). In the second example, the research question is not only interested in what the factors influencing career choices are, but which of these factors are the most important.

Therefore, when you think about constructing your descriptive research question, make sure you have included any words that provide greater context to your question.

SIXTH Write out the descriptive research question

Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the group(s) you are interested in, and (4) any potential joining words ? you can write out the descriptive research question in full. The example descriptive research questions discussed above are written out in full below:

In the section that follows, the structure of comparative research questions is discussed.

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• Market Research

Quantitative research question examples

One of the best ways to determine how your target audience feels about your company or organization is through quantitative research. Once you understand user opinions, attitudes, behaviors, preferences, and market trends, you can make informed decisions that help you improve your products, services, and every aspect of the customer experience.

In this post, we’ll review what a quantitative research question is, cover the types of quantitative research questions, share examples of quantitative research questions across various fields, and highlight tips for creating a quantitative research survey.

Some background on quantitative research questions

Quantitative research questions collect objective, measurable, numerical data through

• Surveys and questionnaires
• Controlled observations
• Reviewing existing research to produce sound statistical analysis

The data includes ratings, counts, measurements, and percentages. Because this data is objective, it’s considered more reliable than qualitative research data.

Quantitative data helps researchers identify trends and patterns. They can use these insights to make informed decisions about company or organizational goals, targets, and strategic improvements to undertake.

Quantitative research questions are useful for measuring many things, but businesses commonly use them to determine overall customer satisfaction , gather feedback on existing products and services, gauge the demand for new products and services, and decide on business improvements to roll out.

Some examples of quantitative research questions include

• How many times per week do you use social media?
• How often do you visit our website?
• How many mobile shopping apps do you use?

Types of quantitative research questions

The three main types of quantitative research questions are descriptive, comparative, and relationship-based. Which type or types you use will depend on the kind of data you want to collect and your research objective.

Descriptive research questions are usually closed-ended, and they elicit participants’ opinions about a specific variable. With these questions, you may ask how often someone uses your product, when they use your product, or how much they’d be willing to pay for a specific product.

Comparative research questions consider differences between groups based on dependable variables. With these questions, you may want to compare brand preferences among men versus women, compare how often individuals use similar products, or assess how your products stack up against competitors’ offerings.

Relationship-based research questions are helpful for gauging trends, causal relationships, or connections between variables. You may develop questions that help you explore how color influences buying decisions for a product or assess the relationship between employee turnover and workplace environment.

Examples of quantitative research questions

Now let’s take a look at some examples of quantitative research questions in the fields of education, health, marketing, and social sciences.

Examples of quantitative research questions in education

• On a scale of 1 to 10, how much does parental participation in education impact student academic achievement?
• What impact does classroom size have on academic performance? Choose from the following: no impact, limited impact, high impact.
• How many times were you (the student) absent last semester?
• Is the relationship between extracurricular activities and student performance positive, negative, or neutral?
• On a scale of 1 to 5, how much do study habits impact student grades and test scores?

Examples of quantitative research questions in the mental and physical health fields

• On a scale of 1–10, how often do you feel stressed?
• How many times per week do you engage in activities to improve your mental well-being?
• How frequently do you exercise?
• Do you have a health insurance plan?
• How would you rate the care you received on your last visit with a primary care provider?
• What is the relationship between stress levels and physical health in retirees?
• On average, how many times per year do you visit a healthcare provider or facility?

Examples of quantitative research questions in marketing

• How often do you make buying decisions based on advertising or marketing campaigns?
• How often do you use products in this category?
• On a scale of 1–10, how satisfied are you with the quality of this product?
• On a scale of 1–10, how likely are you to recommend this product to others?
• How much are you willing to pay for this product?
• Which product features are the most important to you when making buying decisions in this category?
• How much do customer reviews impact your buying decisions?
• What is your preferred way to purchase products in this category (online or in the store)?

Examples of quantitative research questions in social sciences

• On a scale of 1 to 10, how much does income inequality impact academic performance?
• To what extent is there still a gender imbalance in pay/wages? Rate your answer on a scale of 1 to 5.
• To what degree does race impact rates of mental health diagnosis in adults? Rate your answer on a scale of 1 to 5.
• Does gender affect an individual’s contribution to household tasks?

Tips for creating quantitative research questions

Now that we’ve seen some examples, let’s review a few tips for creating your own quantitative research questions.

Since you’re looking for concrete data, ask questions such as

• What percentage?
• What proportion?

Let’s look at some concrete examples:

• How much is your weekly grocery budget?
• How many times per month do you visit a brick-and-mortar store?
• What percentage of your monthly income is spent on housing?

To increase the quality of your questions and ensure the best results

• Use different question types (i.e., descriptive, comparative, relationship-based).
• Keep the survey or questionnaire as short as you can without sacrificing data collection.
• Don’t use leading or biased questions.
• Use clear language and avoid jargon.
• Address one topic per question, starting with easier questions first to build momentum.
• Be sure to get approvals and informed consent before proceeding.

How to create a quantitative research survey

• Select the type of quantitative research question or questions from among the three discussed above — descriptive, comparative, or relationship-based — based on your research objective.
• Identify the type of variable you’re trying to measure — either independent (the variable being manipulated) or dependent (the outcome variable) — and the target audience. Measurement variables include nominal, ordinal, interval, and ratio.
• Decide on the structure of your research questions based on the type of questions you’ll be presenting. Structure pertains to variables, groups, and the order of the variables and groups in the questions.
• Draft your research questions and finalize your survey.

If you’re interested in learning more, we offer a more in-depth look at quantitative market research best practices . Also, check out our detailed, step-by-step guide on how to do market research .

You can build beautiful, easy-to-use, fully customizable surveys using Jotform’s premade survey templates or create them from scratch — no coding required. Tailor your surveys to match your business and your specific goals, and even share, collect, and analyze your survey results with our free online survey maker .

If you want to gather invaluable insights into user behavior, opinions, attitudes, and preferences, quantitative research is a great way to go about it. Jotform’s robust survey and questionnaire tools make it easy to get started.

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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 .

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.

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.

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Research Questions Tutorial

What is a Quantitative Research Question?

A research question is the driving question(s) behind your research. It should be about an issue that you are genuinely curious and/or passionate about. A good research question is:

Clear :  The purpose of the study should be clear to the reader, without additional explanation.

Focused :  The question is specific. Narrow enough in scope that it can be thoroughly explored within the page limits of the research paper. It brings the common thread that weaves throughout the paper.

Concise :  Clarity should be obtained in the fewest possible words. This is not the place to add unnecessary descriptors and fluff (i.e. “very”).

Complex :  A true research question is not a yes/no question. It brings together a collection of ideas obtained from extensive research, without losing focus or clarity.

Arguable :  It doesn’t provide a definitive answer. Rather, it presents a potential position that future studies could debate.

The format of a research question will depend on a number of factors, including the area of discipline, the proposed research design, and the anticipated analysis.

Unclear:   Does loneliness cause the jitters? Clear:   What is the relationship between feelings of loneliness, as measured by the Lonely Inventory, and uncontrollable shaking?

Unfocused:   What’s the best way to learn? Focused:   In what ways do different teaching styles affect recall and retention in middle schoolers?

Verbose :  Can reading different books of varying genres influence a person’s performance on a test that measures familiarity and knowledge of different words?

Concise:   How does exposure to words through reading novels influence a person’s language development?

Definitive:   What is my favorite color? Arguable:   What is the most popular color amongst teens in America?

Developing a Quantitative Research Question

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

Statistical Research Questions: Five Examples for Quantitative Analysis

Table of contents, introduction.

How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.

In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.

Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the  four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.

Five Examples of Statistical Research Questions

In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.

Topic 1: Physical Fitness and Academic Achievement

Statistical research question no. 1.

Is there a significant relationship between physical fitness and academic achievement?

Notice that this study correlated two variables, namely 1) physical fitness, and 2) academic achievement.

On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the  number of passing scores  in both Mathematics and English.

Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about  Education 4.0 .

Topic 2: Climate Conditions and Consumption of Bottled Water

Statistical research question no. 2.

Is there a significant relationship between average temperature and amount of bottled water consumed?

Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled  Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.

Topic 3: Nursing Home Staff Size and Number of COVID-19 Cases

Statistical research question no. 3.

Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.

A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced  regression models  that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population.  Stepwise multiple regression models  take care of those redundancies. Using this statistical test requires further study and experience.

Topic 4: Surrounding Greenness, Stress, and Memory

Statistical research question no. 4.

As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.

Topic 5: Income and Happiness

This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.

An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 5

I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.

References:

Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?

© P. A. Regoniel 12 October 2021 | Updated 08 January 2024

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 

• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Methodology

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

What Is Quantitative Research? | Definition, Uses & Methods

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

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

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

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

• 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, other interesting articles, frequently asked questions about quantitative research.

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

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

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

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

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

Prevent plagiarism. Run a free check.

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

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

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

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

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

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

• Replication

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

• Direct comparisons of results

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

• Large samples

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

• Hypothesis testing

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

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

• Superficiality

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

• Narrow focus

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

• Structural bias

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

• Lack of context

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

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

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

Research bias

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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Quantitative Research Questions Examples & Types

CEO Plerdy — expert in SEO&CRO with over 14 years of experience.

• Post date Feb 02, 2024
• No Comments on Quantitative Research Questions Examples & Types

Quantitative research questions form the backbone of solid data analysis, a crucial step in understanding market trends and consumer behavior. As we delve into the art of crafting precise, measurable questions, remember: the clarity of your inquiry directly impacts the quality of your findings. It’s not just about asking; it’s about asking right. Here at Plerdy, where data-driven insights are paramount, we recognize the power of well-structured questions in revealing actionable truths. In this article, we’ll guide you through shaping questions that bring clear, objective results, ensuring your research strikes the perfect balance between depth and simplicity. Discover the keys of effective quantitative inquiry and transform your data analysis with Plerdy.

Understanding Quantitative Research Questions

Quantitative research questions are the gateway to unlocking a world of data-driven insights. Central to effective research, these questions help us quantify variables, compare groups, and establish relationships in a structured, objective manner.

Definition: At their core, quantitative research questions seek measurable, numeric answers. They are designed to collect data that can be statistically analyzed, ensuring precise, objective outcomes. This approach is ideal for studies that require definitive results rather than subjective interpretations.

Characteristics:

• Specificity: They are clear and focused, aiming at specific variables or groups.
• Measurability: These questions ensure that responses can be quantified in numerical terms.
• Objectivity: They maintain neutrality, avoiding any bias in phrasing.
• Identifying Trends: By quantifying responses, these questions help in spotting patterns and trends in data.
• Making Comparisons: They allow for the comparison of different groups or variables.
• Predicting Outcomes: They assist in forecasting future trends based on current data.

Quantitative research questions are a vital tool for researchers and analysts. They provide a structured path to gaining valuable insights, crucial for making informed decisions. Whether you’re exploring market dynamics or investigating social trends, crafting these questions with precision is key to obtaining reliable, actionable data. As we journey through the nuances of these questions, keep in mind their potential to transform your understanding of the world around us.

Types of Quantitative Research Questions

In the realm of data analysis, understanding the types of quantitative research questions is pivotal for conducting robust research. These questions are classified based on their objective, leading to distinct approaches in data collection and interpretation.

Descriptive Questions:

• Objective: These questions aim to describe characteristics or functions.
• Structure: Often begin with “What is” or “How many.”
• What is the average income of a family in a specific region?
• How many hours per week do teenagers spend on social media?

Comparative Questions:

• Objective: Designed to compare two or more groups or variables.
• Structure: Typically structured as “How does X compare to Y?”
• How does the customer satisfaction level differ between Brand A and Brand B?
• What is the difference in test scores between students who study online and those who attend traditional classes?

Relationship-based Questions:

• Objective: Explore the relationship between variables.
• Structure: Often phrased as “What is the relationship between X and Y?”
• What is the relationship between diet and heart health?
• How does exercise frequency relate to stress levels in working adults?

These types of questions are the bedrock of quantitative research, providing a clear path to analyze and interpret data. Descriptive questions lay the foundation by establishing basic facts. Comparative questions build on this by highlighting differences or similarities, while relationship-based questions delve deeper into how variables interact and influence each other.

To effectively employ these questions, researchers must be clear and precise in their phrasing, ensuring each question aligns with their specific research goals. By mastering these types, you can unlock a wealth of information and insights, critical for making informed decisions in any field. Remember that quantitative research may simplify complex data into usable knowledge.

Crafting Effective Quantitative Research Questions

Crafting effective quantitative research questions is a crucial step in any data-driven study, setting the stage for meaningful and reliable results. To ensure precision and clarity, following a structured approach is essential.

• Identifying Variables: Start by pinpointing the independent and dependent variables. The dependent variable is measured, while the independent variable is changed. For example, in a study on education, “teaching methods” could be your independent variable, and “student performance” could be the dependent variable. Understanding these variables helps in formulating a focused question.
• Question Structure: A well-structured question is clear and to the point. It directly addresses the relationship or comparison you’re investigating. Use phrases like “What impact does…,” “How does…,” or “What is the correlation between…” to structure your question. Keep it concise to avoid confusion.
• Ensuring Clarity and Precision: Avoid ambiguity. Your question should be understandable to someone outside your field. This means avoiding technical jargon and being as specific as possible about what you are investigating.

For instance:

• Unclear: How does technology affect learning?
• Clear: What is the impact of interactive digital textbooks on high school students’ math test scores?

Crafting effective quantitative research questions involves a balance of specificity, clarity, and structure. Begin by identifying your variables, then structure your question in a way that clearly conveys your investigative aim. Finally, ensure the wording is precise and free from ambiguity. This approach will not only refine your research focus but also enhance the comprehensibility and relevancy of your study, making it a valuable contribution to your field.

Real-world Examples of Quantitative Research Questions

Exploring quantitative research problems in real-world settings shows their practicality across fields. These examples not only demonstrate the diversity of these questions but also provide insight into how they drive specific, measurable outcomes.

• Education: In the educational sector, a common focus is on evaluating teaching methods and their effectiveness. An example question could be, “What is the impact of blended learning on the mathematics achievement of high school students compared to traditional teaching methods?” This question targets a specific teaching approach and measurable student performance.
• Healthcare: Healthcare research often revolves around patient outcomes and treatment efficacy. A question like, “How does a 6-week physical therapy program affect the recovery rate of post-operative knee surgery patients?” precisely addresses a treatment duration and a measurable patient outcome.
• Social Sciences: In social sciences, research questions might explore societal trends or behaviors. An example could be, “What is the correlation between social media usage and anxiety levels among young adults in urban areas?” This question is aimed at understanding the relationship between a widespread modern habit and a specific psychological condition.

Some real-world quantitative research questions on marketing strategy and social media monitoring:

Marketing Strategy Research Questions

• “How does varying the headline of an online advertisement influence its CTR?”
• “What impact does the use of different images in ads have on viewer engagement rates?”
• “Does incorporating video content in ads increase the conversion rate compared to static images?”
• “How does the integration of user testimonials in ad layouts affect viewer response rates?”
• “What effect does changing the color palette of an ad have on viewer attention span?”
• “Does the use of brighter colors in ads lead to an increased number of views and interactions?”
• “How does modifying the length and tone of ad copy influence the time users spend on the corresponding landing page?”
• “What is the effect of using direct vs. suggestive call-to-actions in ad texts on the user response rate?”
• “How does the positioning of an ad on a webpage influence the advertising cost per click?”
• “Does the placement of ads above the fold result in better engagement compared to below the fold?”
• “What is the effect of using demographic-based targeting on the total number of ad impressions?”
• “How does altering location targeting in digital ads influence the audience reach and diversity?”

Social Media Monitoring Research Questions

• “How frequently is our brand mentioned on social media platforms within a given time frame?”
• “What is the ratio of positive to negative brand mentions on social media during product launch periods?”
• “Which types of social media posts (images, videos, text) generate the highest engagement for our brand?”
• “What are the prevalent themes in user-generated content related to our brand on social platforms?”
• “How does the introduction of a new hashtag influence engagement and sharing rates on our social media channels?”
• “What impact do social media promotional campaigns have on follower growth and interaction rates?”

Advanced Ad Analysis Questions:

• “What is the click-through rate for ads with interactive elements like quizzes or polls compared to standard ads?”
• “How does the inclusion of interactive features in ads influence the time spent by users on the website?”
• “How do ad engagement rates vary during different seasons or major holidays?”
• “What impact does season-specific ad theming have on conversion rates?”
• “What is the optimal frequency for displaying retargeting ads to maximize conversions without causing ad fatigue?”
• “How does the timing of ad displays (time of day/week) affect user engagement and click rates?”

Deep Dive into Social Media Dynamics Questions:

• “What is the change in brand mentions and engagement rates after collaborating with social media influencers?”
• “How does influencer marketing affect the demographic profile of the brand’s social media followers?”
• “What is the average time spent by users on our social media pages before and after specific campaign launches?”
• “Which types of content (live videos, stories, posts) lead to the highest user interaction rates on our social media platforms?”
• “How does the engagement rate for our brand differ across various social media platforms?”
• “What are the differences in audience demographics and interaction patterns across different social media channels?”
• “What is the overall sentiment (positive, negative, neutral) expressed in user comments on our social media posts?”
• “How do product launches or service updates influence the sentiment of discussions around the brand on social media?”

Optimizing Digital Presence Questions:

• “How does social media traffic contribute to user behavior and conversion rates on the company’s website?”
• “What is the correlation between social media activity and lead generation on the company’s digital platforms?”
• “Which content strategies lead to the highest growth in followers and engagement on our social media channels?”
• “How does the frequency and type of content posted on social media influence brand perception and customer loyalty?”

These quantitative research questions are designed to provide concrete data that can help businesses refine their marketing strategies and social media presence for maximum effectiveness and engagement.

These real-world examples demonstrate the value of concise, targeted, and measurable quantitative research topics. By following this approach, researchers can effectively investigate and draw significant conclusions in their respective fields. Whether it’s understanding educational techniques, medical treatments, or societal behaviors, well-structured quantitative research questions are instrumental in uncovering valuable insights and contributing to informed decision-making.

Common Mistakes to Avoid

In the process of formulating quantitative research questions, certain common mistakes can significantly hinder the effectiveness of your study. Being aware of these pitfalls is essential for conducting meaningful research.

• Vague Wording: Ambiguity is the enemy of clarity. Questions like “How does social media influence behavior?” are too broad. Instead, specify the aspect of behavior, such as “How does social media use impact the attention span of teenagers?”
• Over-complicating Questions: Simplicity is key. Avoid convoluted questions that might confuse respondents. For instance, instead of asking “What are the various factors that affect the decision-making process of consumers purchasing technological gadgets?” simplify it to “What key factors influence consumer decisions when buying technological gadgets?”

Crafting clear, concise, and focused quantitative research questions is crucial. Avoid vague wording and over-complication. By steering clear of these common mistakes, you ensure that your research questions are robust and yield valuable, actionable data. This approach not only enhances the quality of your research but also increases its relevance and applicability to your target audience.

Quantitative research question writing is essential for gaining insights in any discipline. Through clarity, specificity, and focus, these questions become powerful tools in your analytical arsenal. Remember, the precision of your inquiry shapes the depth of your understanding. As we’ve explored various facets of quantitative questioning, the potential for data-driven decision-making becomes evident. For more insights and strategies to elevate your research, explore other articles on the Plerdy blog. Ready to dive deeper into data analytics? Plerdy offers an array of tools to enhance your digital strategy. Check out Plerdy’s solutions for your next project – a step towards transforming data into actionable insights.

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

40 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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4.3 Quantitative research questions

Learning objectives.

• Describe how research questions for exploratory, descriptive, and explanatory quantitative questions differ and how to phrase them
• Identify the differences between and provide examples of strong and weak explanatory research questions

Quantitative descriptive questions

The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.

Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about debt load, or they may include multiple variables. Because these are descriptive questions, we cannot investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.

Quantitative explanatory questions

Most studies you read in the academic literature will be quantitative and explanatory. Why is that? Explanatory research tries to build something called nomothetic causal explanations.Matthew DeCarlo says “com[ing]up with a broad, sweeping explanation that is universally true for all people” is the hallmark of nomothetic causal relationships (DeCarlo, 2018, chapter 7.2, para 5 ). They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.

Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relation between these variables. A standard format for an explanatory quantitative research question is: “What is the relation between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising it as you see fit. The goal is to make the research question reflect what you really want to know in your study.

Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 4.1 does just that. While reading the table, keep in mind that it only includes some of the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table.

Making it more specific

A good research question should also be specific and clear about the concepts it addresses. A group of students investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the students’ definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researchers are clear on what they mean by “household tasks.”

Table 4.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.

It can be challenging in social work research to be this specific, particularly when you are just starting out your investigation of the topic. If you’ve only read one or two articles on the topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project. However, social work research demands that you examine the literature on the topic and refine your question over time to be more specific and clear before you begin your study. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.

Quantitative exploratory questions

In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 4.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.

Key Takeaways

• Quantitative descriptive questions are helpful for community scans but cannot investigate causal relationships between variables.
• Quantitative explanatory questions must include an independent and dependent variable.

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Guidebook for Social Work Literature Reviews and Research Questions Copyright © 2020 by Rebecca Mauldin and Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Aug 15, 2024 • 13 minutes read

Mixed methods research explained: Combine data like a pro

From heatmaps to interviews, here’s how to blend qualitative and quantitative data for holistic user insights.

Ella Webber

Mixed methods research is one of the most popular and powerful UX research approaches—blending numbers with narrative to garner a holistic understanding of your product or research question.

Whether you’re in UX research and design, education, healthcare, or social sciences, mixed methods research can help you find insights and make better decisions.

Read on for a breakdown of what mixed methods are, their strengths and weaknesses, when to use them, and how to analyze the data.

UX research made easy

Explore the power of combining quantitative and qualitative research to discover new insights and test final solutions.

What is mixed methods research and when should you use it?

Mixed methods research involves collecting, analyzing, and integrating both quantitative and qualitative UX research methods within a single study. It is unique to other UX research techniques in that it combines data types, encouraging product teams to use qualitative feedback to explain the story behind quantitative numbers.

• Quantitative data can come from UX surveys , product analytics , usability testing , experiments, or statistical databases and provide broad numerical insights
• Qualitative data is gathered through user interviews , focus groups, or contextual inquiries and offers a deep, contextual understanding of the subject matter

Why use a mixed methods approach?

The power of mixed methods research is simple: it allows you to combine the best parts of both types of data—quantitative research methods, like surveys, give you broad trends, while qualitative methods, such as interviews, dig deep into personal experiences.

Anthony J. Onwuegbuzie and R. Burke Johnson, in Mixed Methods Research: A Research Paradigm Whose Time Has Come , highlight how blending these methods allows researchers to leverage the strengths of both approaches. They identify mixed methods research as one of the “three core research paradigms: qualitative, quantitative, and mixed methods.”

Like any technique, however, mixed methods research has both strengths and weaknesses to consider.

When should you use mixed methods research?

Mixed UX research methods are useful when neither qualitative nor quantitative data alone can fully answer your research question . Evaluative research further helps to assess the effectiveness of your mixed method research findings and ensure they meet user needs.

For example, use mixed methods research when:

• You need to go beyond numbers (generalizability): Quantitative methods, like surveys, provide broad trends and patterns that are relevant to a wider population. For example, a survey might show that most users enjoy a new app feature, but it won’t capture why some users might dislike it.
• The why matters (contextualization): Mixed methods allow you to put numerical findings in context, adding rich detail to your conclusions. For example, if analytics show that users are spending less time on your app (quantitative), interviews can help you understand the reasons behind this behavior, such as frustration with a recent update or a lack of engaging content (qualitative).
• Credibility is important (credibility and triangulation): When both data types converge on the same conclusion, it strengthens your findings. For example, the combined evidence is more credible if survey data indicates that most users prefer a particular software interface and focus groups echo this preference.
• You need to track changes (developmental purposes): Mixed methods are invaluable when one type of data informs the other. For example, initial qualitative research with a small group of beta testers can uncover key issues and user needs, which can then be explored quantitatively with a larger user base to see how widespread these issues are.
• Understand complex issues (complementary insights): Different data types can offer complementary insights. For example, in a study on software usability, quantitative data might show a drop in task completion rates, while qualitative data reveals specific pain points and user frustrations. This combined approach can guide more effective design improvements.

What are the types of mixed methods research design?

The type of mixed methods research design you choose depends on your research goals, the timing of data collection, and each data type. Here are some key factors to consider:

• Your research approach: Are you trying to understand existing findings (explanatory) or dig deeper into a topic (exploratory)?
• Your research questions: Do your questions need big-picture answers (like how many users are happy) and detailed explanations (like why some users are unhappy)?
• Existing data availability: Is there any existing information you can use from previous studies or a research repository (like user demographics)?
• Data you can collect yourself: What kind of in-depth information do you need to gather from users (through interviews, testing, etc.)?

Whether you're a data diver or a narrative novelist, understanding these research methods can make your studies more dynamic and insightful.

📚 A UX research repository is crucial for keeping track of research findings. You need a centralized database to store and manage all your qualitative and quantitative data. This ensures that your research is organized, accessible, and reusable for future studies.

Let’s look at the most common types of mixed methods research design:

Convergent parallel

Convergent parallel design involves collecting qualitative and quantitative data simultaneously but analyzing them separately. The primary goal is to merge the two datasets to provide a complete understanding of the research problem.

For example, let’s say you want to study user satisfaction with a new mobile app. Here’s how you might use the convergent parallel design:

• Qualitative results: Conduct in-depth user interviews with 30 participants to gather detailed insights into their experiences and perceptions of the app. Plus, analyze 200 user reviews from app stores. You might use prompts like, "What features do you find most valuable?" and "Please describe any difficulties you've experienced while using the app."
• Quantitative study: Use analytics data to measure user engagement metrics like session duration and feature usage, then distribute UX surveys to gather quantitative satisfaction scores.

Concurrent embedded design

Embedded design is a mixed methods research approach where qualitative and quantitative data are collected simultaneously, but one type of data is supplementary to the other.

The secondary data provides additional context and can help explain or clarify the primary findings. This approach is particularly beneficial when time or resources are limited, as it allows for a more comprehensive analysis without doubling the workload.

Explanatory sequential design

Explanatory sequential design is a popular mixed methods research approach introduced by John W. Creswell and Vicki L. Plano Clark. This research design involves collecting and analyzing quantitative data first, followed by qualitative data collection and analysis.

According to Creswell, this approach is particularly useful when researchers need to explain relationships found in quantitative data.

The process typically involves two phases:

• Quantitative phase: This involves collecting numerical data through methods like surveys or experiments. The goal here is to identify patterns, trends, or relationships.
• Qualitative phase: Qualitative phase: After analyzing the quantitative data, researchers collect qualitative data with qualitative approaches, like interviews or focus groups, to provide deeper insights. This phase helps explain the ‘why’ or ‘how’ behind the quantitative findings.

Creswell emphasizes that one of the strengths of this design is its simple structure, making it easy for researchers to manage and for audiences to understand the research process and findings.

Exploratory sequential design

Exploratory sequential design begins with qualitative data collection and analysis, followed by quantitative data collection. This immersive approach helps generate rich, detailed data that lays a strong foundation for the subsequent quantitative analysis.

For example, let’s say a researcher wants to understand why people don't meditate regularly. They could start with generative research techniques , like conducting workshops where participants discuss their daily routines and barriers to meditation. These qualitative insights reveal underlying themes and patterns, like time constraints and lack of motivation.

Next, the researcher analyzes these qualitative data to identify key factors impacting wellbeing habits. Based on these insights, they develop a survey to quantitatively measure how widespread these barriers are among a larger population.

So, that’s how you collect data. But how do you analyze it? Unsurprisingly, there are multiple analysis and interpretation methods commonly used in mixed methods research. Let’s look at some.

How to analyze mixed methods research data: 3 Ways to combine qualitative and quantitative data

Combining different types of research data can add credibility to your research findings. Let’s look at how to conduct mixed methods research:

Triangulation protocol

Following a thread, mixed methods matrix.

The triangulation protocol in mixed methods research is a systematic way to use multiple data sources, techniques, or perspectives to get a clear understanding of a research problem. The goal is to capitalize on the strengths of both types of data while minimizing their individual weaknesses.

Let's say you want to conduct a study aiming to evaluate the effectiveness of a new educational program on student performance, and you arrive at the following datasets:

• Quantitative finding: 80% of students improved their math scores after the program
• Qualitative finding: Students reported that interactive activities helped them understand math concepts better

When you merge these findings, the research concludes that the interactive activities (identified qualitatively) are likely a significant factor contributing to the improved scores (quantitatively).

The following a thread method allows researchers to trace a specific theme or concept across both qualitative and quantitative data sets.

Here’s how it works:

• Identify key themes: Begin by identifying key themes or variables that are central to your research questions. These themes will serve as the ‘threads’ you’ll follow through your data.
• Extracting data: Extract relevant data segments related to each theme from qualitative (e.g. interviews, focus groups) and quantitative (e.g. surveys, statistical data) sources. This involves coding qualitative data and identifying relevant quantitative measures.
• Mapping data: Create a map or matrix that links data segments from different sources according to the identified themes. This matrix helps visualize how different data points converge or diverge on the same theme.
• Comparative analysis: Compare the data segments within each theme to identify patterns, consistencies, and discrepancies. Look for how qualitative narratives support or contradict quantitative findings.
• Synthesis and interpretation: Synthesize the findings to develop an understanding of each theme. Interpret the data by integrating the qualitative insights with the quantitative results, explaining how they complement or contrast with each other.

A mixed methods matrix is a visual tool used to integrate and compare qualitative and quantitative data in mixed methods research. It helps researchers organize data according to key themes or variables, facilitating a comprehensive analysis and interpretation.

The matrix consists of several rows and columns:

• Rows represent key themes or research questions
• Columns represent different data sources or methods (e.g. interviews, surveys, observations)

By populating each cell with relevant data segments, researchers can easily identify areas of convergence, divergence, and complementarity. Let’s say you want to answer this research question: How does a new health intervention impact patient satisfaction and health outcomes?

You would populate the matrix as follows:

How to conduct mixed methods research: A mixed method research example

Let’s say you own a project management app and want to understand user satisfaction and identify areas for improvement. Here are eight steps to apply mixed methods research—using the convergent parallel technique—to discover user pain points and create a better user experience.

Step 1: Define your research objectives

In UX research , asking the right questions is crucial for identifying user needs and pain points effectively. But in order to write the right user research questions , you need to define a clear objective. What are you looking to understand?

Defining a clear UX research objective helps guide all other research decisions and acts as a lighthouse that guides your research project.

In our example , our research objective could be ‘to explore user experience and identify areas for improvement within our project management app’.

Step 2: Design your study and recruit participants

Ensure your study is designed to allow integration of both quantitative and qualitative data. There are various mixed method research designs to choose from—the right one for you depends on your research objectives and preferences.

At this stage, you should also establish a clear strategy for data integration and decide how you’ll combine the qualitative and quantitative data during the UX reporting and analysis phase. This might involve merging data sets for comparative analysis , or embedding one data set within the other to provide additional context.

The integration plan should reflect your research goals and ensure that the combined data offers a clear understanding. For our study, we’ll design a convergent parallel mixed methods study and triangulate our data during the analysis phase. This enables us to find our what and our why.

This is also when you need to recruit research participants for your study. Consider what you’re studying and identify your target test audience. You then need to create a call-out for your research study—either on socials, via email, or with In-Product Prompts .

Alternatively, you can find and filter research participants using Maze Panel , then manage your participant relationships using Maze Reach .

Step 3: Collect quantitative data

Next up, you want to start gathering your quantitative data. A good way to do this is with a survey to collect numerical data that can be statistically analyzed. For example, a user satisfaction survey that includes rating scales (1–10) for various aspects of the software.

For our research into app user satisfaction, we asked:

• Please rate your overall satisfaction with the app (1–10)
• How often do you use the app per week?
• How easy is the app to use on a scale of 1 to 10?
• How likely are you to recommend the app to a friend or colleague (1–10)?

❓ Need a quick and easy way to create and manage surveys? Maze Feedback Surveys simplify your feedback collection process so you can focus on making the changes your customers want to see. You can quickly create surveys tailored to your needs with Maze's survey templates .

Step 4: Collect qualitative data

Once you’ve got your quantitative data, it’s time to collect your qualitative data. Consider conducting user interviews or focus groups to obtain detailed, descriptive data that provides context and deep understanding.

For our study, we selected 20 users from the survey who gave varied ratings and conducted 30-minute interviews, asking:

• What do you like most about the app?
• What features do you find difficult to use?
• Can you describe a recent experience using the app?
• What improvements would you suggest?

💬 User interviews are resource-intensive and time-consuming. Speed them up with Maze’s end-to-end user interview solution: Interview Studies .

Step 5: Quantitative data analysis

Now you’ve got all your data—it’s time to dig in. For your quantitative data, this involves using statistical methodology to identify trends and patterns.

When we looked at our example data, we calculated:

• CSAT score: 75%
• Frequency of use: 70% use the app daily
• Ease of use average score: 6.8/10
• Net Promoter Score (NPS): 65

Step 6: Qualitative data analysis

Analyzing qualitative data involves coding and categorizing qualitative responses to uncover themes and patterns. Identify recurring themes in user feedback, such as ease of use, functionality, and improvement areas. If you’re using Maze Interview Studies to analyze your findings, you can automatically extract key themes and summaries to speed this process up.

When reviewing qualitative data, we found a number of interesting nuggets in our qualitative data:

• Users express dissatisfaction with the app’s usability, specifically the navigation between different functionalities
• Users wish they could access their billing details via the app, instead of solely via the web
• User find the core functionality—the project management features—to be highly valuable to their day-to-day, but also report finding the interface to be clunky and unintuitive

Step 7: Integrate data and interpret findings

Following your analysis, combine the findings from both data sets and draw conclusions. Look for correlations and insights that span both types of data.

Example integration:

• High satisfaction scores (75%) but lower ease of use (6.8/10) prove a strong product market fit but call for a more intuitive experience
• Further qualitative research agreed with this conclusion and identified specific areas for improvement, such as adding additional functionalities and improving the interface

Step 8: Report findings to stakeholders for buy-in

Present the integrated results to highlight how qualitative insights support or explain quantitative trends.

The format of your report will depend on your audience:

• Internal stakeholders (project managers, designers): Consider a concise report with clear visuals like charts, graphs, and user quotes to highlight key findings and actionable recommendations
• External stakeholders (clients, investors): Create a formal report with a clear introduction, methodology section, and comprehensive results presentation, summarizing key findings and highlighting the impact on user satisfaction and app usage

Always strive to go beyond what the data says and explain why it matters.

For example, once we’d conducted our research and drawn conclusions, we compiled this into a report that shared:

• Research methods: We used mixed methods research (surveys and interviews) to explore existing user pain points and satisfaction levels.
• Overall findings: User satisfaction is moderately high (7.5/10), indicating a generally positive reception. However, the ease of use score (6.8/10) and qualitative feedback highlight significant usability issues for new users.
• Actionable next steps based on findings: Simplify the user interface to improve the experience for new users, potentially increasing overall satisfaction and ease of use scores.

Conducting mixed methods research with Maze

Mixed methods research is one of the most effective ways to boost your UX insights, and gather a more rounded understanding of your users’ problems and perspectives. Combining research methods and types of data can uncover insights you may otherwise miss. And while there are ideal times to conduct qualitative, quantitative, or mixed methods research, ultimately it really is as simple as more research = more insights .

If you’re looking for the ideal research companion to help conduct mixed methods research, consider Maze. Maze is the user research platform that empowers all teams with the research methods they need to get game-changing insights. Whether it’s a mixed methods study or a one-off test—Maze helps you gather accurate insights, faster, for more informed decision-making.

Frequently asked questions about mixed methods research

What is the purpose of mixed methods research?

The purpose of mixed methods research is to combine quantitative and qualitative data to provide a more complete understanding of a research problem. This approach helps validate findings, explore complex issues from multiple perspectives, and produce more reliable and actionable results.

What’s the difference between qualitative and quantitative research?

• Qualitative research explores non-numerical data to understand concepts, opinions, or experiences. It uses methods like interviews, focus groups, and observations to gather in-depth insights.
• Quantitative research focuses on numerical data to quantify variables and uncover patterns. It uses methods like surveys, experiments, and statistical analysis to measure and analyze data.

What is the difference between mixed methods and multiple methods?

Mixed methods research integrates qualitative (e.g. interviews) and quantitative (e.g. surveys) data within a single study. Multiple methods research uses various research approaches, but they can be either qualitative or quantitative. For example, it might use surveys and experiments (quantitative) or interviews and focus groups (qualitative) in different parts of a study without combining the data.