discuss the weakness of quantitative research essay

Limitations and Weaknesses of Quantitative Research

• Post author: Edeh Samuel Chukwuemeka ACMC
• Post published: August 16, 2021
• Post category: Scholarly Articles

Limitations and Weaknesses of Quantitative Research: Research entails the collection of materials for  academic or other purposes. It is a process of gathering information and data to solve or existing problem or prevent future problems. Research works can be done via two methods. Qualitative research or quantitative research.

Qualitative research involves the carrying out of research by gathering non-numerical data. For example, gathering of video evidence, texts or messages for analysis. On the other hand, quantitative research is the process where by numerical data are collected and analyzed. It is effectively used to find patterns and averages as well as generalising a finding or result to a wider population. Quantitative research is mostly used in natural and social sciences such as biology, psychology, economics, among others.

Quantitative research could be carried out using any four methods of researching which are descriptive research, correlational research, experimental research or survey research. In descriptive, one seeks to know the ‘what’ of a thing rather than the ‘why’ of such thing. It tries to describe the various components of an information.

Correlational research involves the research between two variables to ascertain the relationship between the variables. It understudies the impact of one variable on the other. On the other hand, an experimental research is one that uses scientific methods to establish the relationship between groups of variables. That is, it tries to establish a cause-effect relationship between the various variables under study.

Recommended: How to become a good researcher: 5 qualities you need to have.

Finally, the survey research which is most widely used involves the preparation of set questionnaires, interviews and polls to which answers are provided by a segment of the target population and then, conclusions are drawn from such answers given. Survey research studies the relationship between various variables in a given research.

One of the major benefit of  quantitative research method is that it makes one arrive at a well considered conclusion since samples are collected from those who are directly affected by the research. The data collected are majorly converted to a numerical form which aids in statistical analysis. Also, quantitative research is more convenient for projects with scientific and social science inclinations.

Also see: Advantages and Disadvantages of quantitative and qualitative Research

Weaknesses of Quantitative Research

Notwithstanding the benefits of quantitative research, the research method has its own weaknesses and limitations. This is because the method is not applicable and convenient in all cases of research. Thus, using a quantitative research method in a research where qualitative research method should be used will not produce the needed result.

To this end, some of the weaknesses and limitations of quantitative research are highlighted below.

1. It Requires a Large Number of Respondents: In the course of carrying out a quantitative research, recourse has to be made to a large number of respondents. This is because you are sampling a section of a population to get their views, which views will be seen as that of the general population. In doing this, a huge number of respondents have to be consulted so as to get a fair view or percentage of the target population.

For example, if one wishes to carry out a quantitative research in Nigeria as to her acceptance of a policy of the government, one will need to consult wider. This is because Nigeria has a population of over 200 million people and the opinions of a few thousands cannot pass out as that of 200 million people. In the light of this, more respondents will be required to be interviewed so as to enable one get a fair view of the population.

Large number of respondents is thus, one of the weaknesses or limitations of quantitative research as a small sampling of a section of the target population might not be of much help to the research.

2. It is time consuming: Unlike qualitative research which has to do with analysis of already prepared data, quantitative research demands that you source for and collate the data yourself while converting such data collected into a numerical form for proper analysis. This process is time consuming. Again, the task of sending out questionnaires to respondents and waiting for answers to such questionnaires might be time consuming as most respondents will reply late or may not even reply at all.

Great patience is therefore needed in carrying out a quantitative research. It is therefore not always a good method of research in cases of urgencies as the time to get responses might take too long.

Also see: Major characteristics of customary laws

3. It requires huge resources: Quantitative research requires huge investment of time, money and energy. It is time consuming just as it also involve huge financial commitments.

In carrying out quantitative research, one needs to get your questions prepared, sent out and also followed up to ensure that such is answered. Also, some respondents might demand to be paid before giving their inputs to such a research. An example is the trending online surveys in which the target respondents are paid for every survey they carry out for a researcher.

4. Difficulty in Analyzing the Data Collected: Data are collected from respondents and then converted into statistics. This usually poses as a limitation to a researcher who is not an expert in statistics. Analysis of collected data is also demanding and time consuming. A researcher needs to make such information collected into numerical data and correlate them with the larger population. Where this is not properly done, it means that the outcome might be false or misleading.

Also, due to the fact that a researcher might not have control over the environment he is researching in, as any such environment is susceptible to change at any point in time, the outcome of his research might be inconsistent.

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5. Outcomes of quantitative research is usually limited: In quantitative research, the outcomes are usually limited. This is because the outcome is usually based on what the researcher wants. This limited outcome is due to the structured pattern of the questionnaires. Questionnaires usually have close ended questions which gives a respondent little or no opportunity of explanations. Thus, the answers provided are limited to the questions asked and nothing more.

6. Data outcomes are usually generalised : As noted earlier, quantitative research is usually conducted on a section of a target population and not on the whole population. The outcome of this research is then generalised as the view of the entire population. What this portends is that the views of  few respondents in that research is seen as that of the general populace. Such views from them might be biased or insincere, yet they are seen as that of the entire population.

In the light of this, the fallacy of hasty generalisation is prone to be committed in a quantitative research. Generalisation of the views of a section of the population might not be the best as their views may be biased.

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In conclusion, quantitative research is a veritable means of conducting research especially in the fields of natural sciences and social sciences. This is because it mostly has a one on one interaction between the researcher and the various respondents as it majorly studies behavior. This advantage notwithstanding, the research method has its own  limitations and weaknesses. These limitations and weaknesses often times affect the quality of a research which is done using the quantitative method of research.

Edeh Samuel Chukwuemeka, ACMC, is a lawyer and a certified mediator/conciliator in Nigeria. He is also a developer with knowledge in various programming languages. Samuel is determined to leverage his skills in technology, SEO, and legal practice to revolutionize the legal profession worldwide by creating web and mobile applications that simplify legal research. Sam is also passionate about educating and providing valuable information to people.

Strengths and Weaknesses of Quantitative and Qualitative Research

Research plays a crucial role when it comes to achieving success in the world of business. When it comes to good research, both quantitative and qualitative research matters the most in building marketing strategies. Data gained through quantitative research includes demographics, consumer growth, and market trends. All of these help businesses and marketers build new theories. Qualitative data, on the other hand, tests the existing strategies or theories based on the gathered data from open-ended sources. Organizations need both methods to run their business smoothly. Upon combining both quantitative and qualitative research, you can get more objective insights from data and achieve more impactful results.

Let’s discover more about the quantitative and qualitative research, including their strengths and weaknesses. But first let’s understand what these two types of researches are. Here we go…

What is Quantitative Research?

Quantitative research is a systematic investigation. It majorly focuses on quantifying relationships, behaviors, phenomena, or other variables by collecting and analyzing numerical data. This type of research is done to test hypotheses, measure outcomes, and determine patterns and trends. It provides theoretical analysis of statistical data, i.e. insights, calculations, and estimations. This research method significantly gathers quantifiable data to perform the systematic investigation. It employs statistical, computational, and mathematical techniques to provide accurate and reliable outcomes.

For this research study, the researcher often collects statistically authentic and valid information by conducting online surveys , questionnaires, and online polls. In addition, they use sampling methods. More often than not, this method is employed used in the fields of social sciences, economics, health, and marketing, among others to get unbiased results. It helps in drawing valid conclusions and making informed decisions to introduce transformative changes to society. Let’s now take a look at the strengths of this type of research.

Strengths of Quantitative Research

Now that you have understood what exactly quantitative research is, it’s time to look at the strengths of this research type. Here we go…

• Validity and Credibility : This type of research provide statistically valid and authentic results to help you make informed decisions.
• Objectivity and Unbiased : Data collection is structured. Therefore, researchers’ biases and preconceptions do not impact their findings.
• Broader Perspective : This allows for generalization and conclusions about a broader population. This makes the research findings more impactful and useful.
• Clear and Accurate Results : The theoretical analysis of statistical data is clear and accurate. This promotes its easy explanation to a wider audience.
• Forecast : This research study helps to forecast future trends. As a result of this, the researcher can make more informed decisions.
• Diversity : This research method allows researchers to collect quantifiable data from diverse sources such as online surveys, questionnaires, and more.
• Versatility : This is a versatile research method. It can be used in various organizations to benefit from data-driven decisions .

Let’s now take a look at the weaknesses of this type of research…

Weaknesses of Quantitative Data

Although quantitative research is versatile and its findings are very impactful, it has some weaknesses that you should know. Look at the following pointers to know the weaknesses of quantitative research:

• Alien to Real-Life Situations : Data collection is structural; however, it is often limited in nature. Since it is used to collect quantifiable data, often it is not related to real-life situations.
• Does Not Identify Causes : The objective of quantitative research is to find correlations between different variables. The researchers are concerned with how much and how many. However, they often avoid looking into the why part – why something happens.
• No New Idea : The quantitative research aims to test the hypothesis of existing concepts. It does not emphasize generating new ideas or discovering uncovered areas.
• No Subjectivity : It does not take into account human experience. There is no place for human opinions and feelings in this type of research.
• Time Consuming : It uses a larger sample size and complex data sets for analysis. Therefore, it is more time-consuming compared to the other research methods.
• Complex : The rigorous design of the study requires a high level of expertise to draw findings.

Common Types of Quantitative Research Methods

Go over the following types of quantitative research methods:

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Researchers often conduct surveys to gather a huge data set that can be analyzed to identify patterns, relationships, and trends. For example, you can conduct online surveys to analyze customers’ experience with products or services. This analysis helps you identify customer satisfaction levels. Furthermore, you can discover areas of improvement or change.

2. Correlation Research

This is a non-experimental method. As the name says, this method is used to discover a correlation between two variables. It does not let extraneous variables intervene in the research study. If the correlation is positive, it indicates both variables are in the same direction. On the other hand, the negative correlation suggests both variables go in a negative direction. Furthermore, this type of research method only uses existing sources to analyze the dataset. Therefore, it is considered cost-effective.

3. Causal-Comparative Research

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The casual-comparative method identifies cause and effect variables. Under this research method, one variable is dependent and another one is independent. Some researchers claim it to be similar to an experimental research method. However, this is not a complete experimental method.

4. Experimental Research

Experimental research method or true experimentation administers scientific techniques to test the hypothesis of the study. It aims to measure how independent variables impact dependent variables. Moreover, it controls extraneous variables to ensure the validity of the research design.

5. Result Analysis

Take a look at the following two methods to do the result analysis on the quantitative research:

Descriptive Analysis

This computes or calculates your datasets using mean, median, and mode to summarize the statistical dataset.

Inferential Analysis

As the name suggests, this includes inferences about what the data means. To ensure its effectiveness, researchers employ the three most common methods, including cross-tabulation, factor analysis, and T-tests.

Let’s now take a look at the important pointers you need to keep in mind when constructing surveys.

Pointers to Keep in Mind While Constructing Surveys

Check out the following pointers to know how to administer a perfect survey for quantitative research:

• The questions should be short and simple
• You should avoid asking for misleading information
• Images should be clear and legible
• Grammar and spelling mistakes can make data quality poor. So, avoid them.

Why Is Quantitative Research Important to Marketing?

Take a look at the following details to know how quantitative research is important to marketing:

Real-Time Insights

Quantitative research helps researchers gather real-time statistical data on market trends, consumer choice patterns, and the organization’s performance. Based on that, researchers compute or calculate complex datasets to gain insights into various aspects. Finally, the research insights help organizations understand the impact of their strategies. They can use this information to reform their business plans.

Improve Marketing Strategies

By gaining real-time data analytics reports on online marketing strategies , they can boost their brand visibility. This helps them determine new strategies for driving organic traffic to the website.

Competitor Analysis

Numeric data analysis helps organizations track competitors’ performance. Based on the in-depth analysis, they can compare their marketing strategies and performance with competitors. This helps them understand what they can do to increase their brand awareness.

Objectivity

The qualitative research method provides leaders with objective data. They can easily communicate this data with their team members. Furthermore, this objective data helps team members understand in which direction they should proceed to yield better results.

It’s now time to move on to another very crucial research type, i.e., Qualitative Research and understand it in detail. Here we go…

What is Qualitative Research

Qualitative research is an exploratory method. It primarily focuses on understanding human behavior, customer’s experiences, and social phenomena. It involves detailed and in-depth analysis. Unlike quantitative research, which emphasizes numerical data and statistical analysis, qualitative research strives to discover the causes of any problem by examining non-numerical data. Its main emphasis is on why rather than what. Essentially, it is subjective in nature because it typically relies on human experiences.

It employs open-ended techniques , including interviews, observations, focus groups, content analysis, and more, to collect rich data. This approach allows researchers to gain a deeper understanding of the context, motivations, and perspectives of participants. This method allows participants to express their issues and opinions in their own words. Based on the data, the researcher analyzes their attitudes, interests, behaviors, and motivations. It is often employed in the fields of education, sociology, psychology, and anthropology. The study focuses on the intricate and subtle aspects of human experiences. It often employs smaller sample sizes to facilitate an in-depth analysis of a problem. By capturing rich, detailed data, qualitative research offers a comprehensive view of the subject matter, highlighting themes, patterns, and relationships that cannot be gathered using quantitative research methods.

Strengths of Qualitative Research

Here are some of the noteworthy strengths of qualitative research that you must be aware of. Take a look…

• Data Collection : The qualitative research method is not restricted to participants’ pre-defined questions. It focuses on open-ended methods to enable data collection. Interviews/observations help a researcher gain a complete understanding of respondents. All in all, this research method focuses on collecting rich and detailed data.
• Novel Theories : This method allows researchers to generate new ideas/theories that can be opposite to conventional social beliefs and norms.
• Express in Numeric : The qualitative research method allows researchers to convert research findings into numeric data for a better understanding.
• Can combine with Quantitative Method : The researcher can combine qualitative research with quantitative research to gain incredible insights into the matter.
• Flexibility : This type of research is more flexible in nature than any other form of research, and it provides room for adaptability.
• Contextual Understanding : The researcher gains a deeper understanding of the social and cultural contexts of participants, resulting in more impactful findings.

Weaknesses of Qualitative Research

• Misleading Information : The researcher must adhere to rigorous standards when collecting and analyzing data. If they fail to do so, resources and expertise of low quality can lead to misleading results.
• Can Not Be Generalized : It is challenging to draw broad conclusions and generalize the data to a larger population using this research design.
• Time-consuming : In contrast to other research methods, the qualitative research method is time-consuming because it involves collecting data through multiple interviews and observations.
• Less Valid : Because of the human experience intervention, the qualitative research findings are believed less valid and less authentic.

Common Types of Qualitative Research Methods

Here are some common types of qualitative research methods to know:

One-on-one Interview

One-on-one interviews have emerged as one of the most popular qualitative research methods. It involves face-to-face or online interviews of the participants. This research method aims to understand and analyze the opinions, ideas, and experiences of the interviewee.

Focus Groups

This research method involves the researcher organizing a small discussion or interview with a group of participants. All of the participants need to discuss a specific topic under this method. The objective of this study is to gain an understanding of the beliefs and considerations of the participants regarding a particular topic.

Discussion Boards

Online discussion boards have replaced traditional discussion boards . Under this research method, researchers provide students with a set of questions to make them participate in the debate. This is a highly efficient way to understand their perspectives, beliefs, and ideas in different situations.

Case study is yet another kind of method used for qualitative research. This method is primarily employed to gain in-depth information about the subject. It is important to note that the subject can encompass a wide range of entities, including organizations, countries, events, or individuals. A lot of researchers view the case study method as highly explanatory.

Pictures and Videos

Pictures and videos are also used as qualitative research methods to understand human experience through image or video analysis. They enhance the richness of data by allowing participants to express themselves in a non-verbal way. Based on visual elements analysis, a researcher reveals insights into social, cultural, or psychological phenomena.

Record-Keeping or Logging

Under this research method, the researcher collects authentic and valid documents from various sources. Further, the information is used as data. The findings of this research method are considered valid and impactful.

Ethnographic Study

Under the ethnographic study, the researchers act as an active participant or observer to study participants in their natural settings. This allows them to understand their social context, culture, and behavior in a much better way.

Observation Method

The observation methods involve the researcher’s subjective interpretation to observe and analyze the attributes and characteristics of a phenomenon. The data collection relies heavily on the researcher’s keen senses of taste, smell, sight, and hearing. He conducts thorough data collection and carefully analyses the entire event.

Result Analysis

Here are the two methods that researchers often employ for the result analysis of the qualitative research:

Deductive Analysis

Deductive analysis is often used to test existing theories, ideas, or beliefs. In qualitative research, deductive analysis is the process of applying predetermined codes to the data. The codes are often generated from literature, theory, or propositions that the researcher has developed. Furthermore, this is a structured method because it applies already decided research design.

Inductive Analysis

Inductive analysis builds up new theories based on specific observations or patterns. The basis of these theories is what has been seen and how it has been seen. Furthermore, it is a flexible analysis that is open to new information.

Some people claim that surveys can only be used in quantitative research. But this is not true. You can conduct surveys in qualitative research as well to make informed decisions.

Check out the following pointers to learn what you should keep in mind while constructing surveys:

• Use appropriate language
• Avoid unnecessary capitalization in words or phrases
• Use the correct format of the questions
• Make sure that multiple-answer questions do not have conflicting answer choices.

Why Is Qualitative Research Important to Marketing?

Qualitative research is ideal for marketing because it helps organizations acquire trustworthy information regarding their consumers’ changing demands, preferences, or tastes. Go over the following pointers to understand why qualitative research is important in today’s marketing scenario. Take a look…

Build Strategies

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In this era of cut-throat competition, knowing about your customers is crucial. This is because based on that information only you can make right marketing decisions. Qualitative research helps organizations understand customers’ preferences and needs. Information gathered using qualitative research methods help businesses build new strategies to enhance customer’s experience. Strategies that businesses design using research data help them attract their target customers and improve their bottom lines.

Rebrand Products and Services

Often, researchers find this method very helpful. The information gathered using qualitative research helps businesses rebrand their products and services. Based on the results of the research, they come to know what their products and services lack. Also, they can determine what they can do to improve their products and services to attract their target customers.

Prevents the Risk of Customer Churning

Customer churning happens when a customer stops using a company’s products or services. However, qualitative research findings help companies understand their customer’s experience with their products and know what consumers want from their products or services. This reduces the risk of customer churning to a great extent.

Get Feedback from Customers

This method helps organizations get feedback on their products or services from customers. The feedback analysis makes a lot of sense in accelerating the organization’s growth.

The Bottom Line

So, this is all about the strengths and weaknesses of quantitative and qualitative research methodologies. Both quantitative and qualitative research methods showcase unique strengths, making them ideal for collecting data for different sectors. However, both methods do exhibit some weaknesses as well. Quantitative research excels in providing precise, measurable, and generalizable data through statistical analysis, while qualitative research offers rich, detailed insights into participants’ experiences, emotions, and social interactions. Quantitative research is considered best for testing hypotheses and identifying patterns across large populations.

At the same time, qualitative research is considered ideal for gaining a deeper understanding of underlying motivations and meanings. Quantitative research methodologies have a structured approach; however, they often avoid the complexities of human behavior and context. Well, that’s not the case with qualitative research methods. You can choose to use any of the research methods to write my essay for me online based on the industry you are serving. However, you can even combine both approaches to enjoy the benefits of both methods.

Thanks for reading!

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Quantitative method Quantitive data are pieces of information that can be counted and which are usually gathered by surveys from large numbers of respondents randomly selected for inclusion. Secondary data such as census data, government statistics, health system metrics, etc. are often included in quantitative research. Quantitative data is analysed using statistical methods. Quantitative approaches are best used to answer what, when and who questions and are not well suited to how and why questions.

Qualitative method Qualitative data are usually gathered by observation, interviews or focus groups, but may also be gathered from written documents and through case studies.  In qualitative research there is less emphasis on counting numbers of people who think or behave in certain ways and more emphasis on explaining why people think and behave in certain ways.  Participants in qualitative studies often involve smaller numbers of tools include and utilizes open-ended questionnaires interview guides.  This type of research is best used to answer how and why questions and is not well suited to generalisable what, when and who questions.

Learn more about using quantitative and qualitative approaches in various study types in the next lesson.

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Strengths and Weaknesses of Quantitative and Qualitative Research

There are few things more useful in developing and implementing strategies than reliable data. The only drawback is that this information can be difficult to understand, which results in many business owners knowing nothing about their own research.

When starting a company or building a product, most people ask themselves the question: qualitative or quantitative research? Given the importance of coming up with a good strategy, this is not an easy question to answer.

Here is a quick look at the strengths and weaknesses of quantitative research.

What Is Quantitative Research?

Quantitative research is a study of numerical data whose purpose is to measure the strength and direction of relationships between variables. Quantitative research uses statistics to make sense of numerical data.

Quantitative research is based on numerical data gathered from different types of research methods, such as questionnaires, structured interviews, and statistical analysis.

Quantitative research involves questions that can be answered by counting or measuring, such as, how many people purchased a product? How many people are satisfied with the customer service ? What are the demographics of customers in different age groups?

For your study to be quantitative, you need to use numerical data to either prove or disprove your hypothesis.

For example, a quantitative research about a new product launch could use data like the average consumption of products in the category among the target population, the number of competitors and their individual market share, pricing points, and the marketing budget required to launch a brand awareness campaign, to mention a few.

This type of research helps you to understand your market and target audience, so you can make informed decisions about your product or service.

The biggest advantage of quantitative research is the ability to analyze large volumes of data and make conclusions based on that data.

Difference Between Qualitative And Quantitative Research

The main difference is this – Qualitative research methods include the collection of data through the use of open-ended questions, unstructured interviews, or observations, whereas, Quantitative research focuses on gathering numerical data and making generalizations about groups of people, situations, or phenomena.

Understanding human behavior and its governing reasons are the ultimate goals of Qualitative research. The discipline explores the “why” and “how” of decision-making.

Quantitative data collection methods are more structured than qualitative data collection ones.

When you need to gather a large amount of information from a group of people, there are many ways to do so. In quantitative research, data can be collected using a variety of methods, including surveys, interviews, observation, and online polls.

A good researcher knows when to use qualitative research (to understand opinions) vs quantitative research (to test objectively). 

For example, if you want to know what people think about a particular topic, then qualitative research would be best; but if you want to determine how many people are aware of a particular issue, then quantitative research would be better.

When you use both qualitative and quantitative research methods in your surveys, you will gain results that reach a lot of people as well as deeper insights from those people. With the right question types and analysis, you can use quantitative research to gain statistically significant insights into your target audience’s attitudes and behaviors.

Qualitative questions are useful for gathering detailed feedback on open-ended topics like:

Customer satisfaction. Qualitative questions let customers explain how they feel about your company’s products or services, and why they feel that way.

Employee engagement. Use open-ended questions to solicit employee feedback on company culture, management practices, benefits, and more.

Service performance. Learn why customers choose your brand over competitors’ by asking for the specific reasons for their decision.

Market research. Open-ended questions help you identify the most important factors that influence customers’ purchasing decisions in your market.

Quantitative research is ideal for:

• Collecting data at scale (e.g., using survey software)
• Reaching a large number of respondents in a short period of time
• Analyzing trends that apply to large groups of people (e.g., gender differences)
• Highlighting broad patterns or relationships between variables
• Predicting likelihoods based on certain factors (e.g., age, income)
• Driving the direction of future quantitative studies (i.e., hypothesis testing)

Importance Of Quantitative Research

The importance of quantitative research is that it provides an objective way to measure things, as well as a means of testing theories. Additionally, the results of quantitative research may be more easily replicated by other researchers.

Quantitative research is conducted in an effort to find numbers and statistical analysis to determine relationships between two or more variables. The process involves taking data from various sources and then organizing it into a format that can be used for statistical analysis.

One advantage of quantitative research is its ability to measure hard numbers and facts. This makes it much simpler to analyze data. 

For example, if you wanted to know the average income of people living in a certain area, all you would have to do is calculate the number of participants in your study who earn above and below a certain amount. You could also compare this data with other areas to see which has the highest average income levels.

Another advantage is that quantitative research allows researchers to replicate their findings using different samples or methods. The ability to replicate results ensures accuracy and consistency in results obtained from different studies conducted on similar topics over time. 

Furthermore, this type of research may reveal new insights into how something works because it focuses on measurable relationships rather than just observations about what happens in nature or human behavior itself.

Characteristics Of Quantitative Research

Quantitative research is the type of research that most people think about when they hear the word “research”. It involves creating statistical models, analyzing data, and using mathematical theories to understand how things work.

Quantitative research is used to identify factors that affect relationships between variables. Quantitative research is widely used in psychology, economics, demography, and marketing. It is often used in natural sciences, such as biology and chemistry, and in social sciences, such as sociology and psychology. Quantitative research involves the use of computational, mathematical, or statistical techniques.

For example, if a researcher believes that watching television makes people more violent, he or she may use quantitative methods to test this theory by counting the number of violent acts depicted in a week’s worth of programming and comparing it with the number of violent crimes committed for the same time period.

These are some essential characteristics of Quantitative research:

• The focus is on measurement, analysis, and prediction of phenomena through the use of mathematical models and theories.
• Quantitative research’s objective is to obtain information about the current status of a given phenomenon.
• The focus is on variables and the relationships between them.
• The researcher can manipulate variables, which is why experiments are often used in quantitative research.
• Quantitative research includes formal data collection methods.
• The results are based on large sample sizes, so the results have high statistical power and are more likely to be statistically significant (i.e., not due to chance).
• Data is analyzed using statistical techniques.
• Quantitative research typically uses deductive reasoning.
• Variables must be identified and measured using reliable instruments and procedures; using multiple methods of measurement increases the reliability and validity of results (triangulation).

The design of a quantitative research question must be structured or ‘closed’ so that it can be answered using a predetermined response format (usually dichotomous or multiple choice) or scaled responses. 

The design of the quantitative research question should not allow respondents to answer in their own words. This will make it impossible to use the data in any meaningful way. 

The quantitative design will measure whether a change has occurred from a specific point in time, but will not determine why a change has occurred.

Quantitative research questions are best for giving an overview or analysis of a particular business, industry, or topic. Therefore, they need to be researched in detail so that the researcher can be confident that enough information exists to answer the questions. If there is no literature available on the topic, then it is unlikely that you will have sufficient knowledge to investigate the topic effectively.

Conducting thorough industry research is crucial in ensuring that the quantitative research questions are well-informed and grounded in existing knowledge.

Strengths Of Quantitative Research

Quantitative research is often used to ask questions that can be answered with numerical data. It has a number of strengths:

• Standardized data collection

This means that the same instruments are used with all the participants in a study, and the data is collected in a uniform way. This makes it easier to compare results across groups of participants or to test hypotheses on a larger scale.

• Objectivity

The standardization of both data collection and analysis can make results from studies more objective than those with qualitative research methods. The use of statistics and hard numbers can also give your findings authority when you publish them online or in a print journal. This objectivity makes it easier for researchers to explain why their findings are reliable and true.

• Difficult Data Collection

Quantitative studies can also provide researchers with data about phenomena that are difficult or impossible to measure directly, such as attitudes, beliefs, and values.

Quantitative research allows for larger sample sizes, which increases the reliability of your results. It also moves quickly and can produce results that are easy to share with others, because they’re often presented as percentages.

• Generalizability

You might find that what you learn applies not only to your research participants but also to people who weren’t included in your study. For example, if you ask 1,000 people what’s important to them about their job, you might find out some things about how work affects happiness that could be true for other people as well.

• Evidence Collection

The design of a quantitative study allows the researcher to collect numerical data that can be analyzed using statistical tests. This provides an opportunity for the researcher to support or refute theories by collecting evidence that is statistically significant.

Weaknesses Of Quantitative Research

Quantitative research is a useful tool for measuring and describing the world as it exists, but it has its weaknesses as well.

Quantitative data is often criticized for being too detached from real-life situations; this criticism typically stems from the fact that the data collected tends to be structured and limited in nature. 

Some have argued that quantitative analysis does not provide people with a full picture of complex issues or human behavior since it is concerned with measuring and counting specific variables.

Quantitative researchers are concerned with how much and how many, but their methods don’t allow them to understand why something happens. They can find correlations between factors, but not necessarily causes. 

For example, they might discover that people who drink more coffee have higher rates of cardiovascular disease than people who drink less coffee, but they can’t conclude that drinking coffee causes heart problems.

Quantitative research doesn’t always take into account a human element. People make decisions based on more than just mathematical calculations, and that’s an important part of the human experience. It’s also difficult to account for the subjective nature of human experience in quantitative methods such as surveys and questionnaires.

Quantitative research tends to minimize the role of the researcher in the research process, thereby reducing the amount of information that can be obtained on contextual factors.

Quantitative research tends not to generate new ideas or shed light on unexplored areas because they focus on testing hypotheses derived from existing theories and concepts.

Types Of Quantitative Research

There are five main types of Quantitative research:

• Descriptive Research

Descriptive research produces a description of what already exists in a group or population. It usually involves taking a sample from the population in order to describe a certain characteristic of the entire group. 

It does not seek to explain why things are a certain way or how they came about but rather describes what is and what is not.

• Correlational Research

Correlational research investigates relationships between variables as well as how these variables interact with one another. 

Unlike descriptive research, correlational research goes beyond description by seeking to identify the strength, direction, and nature of relationships between two or more variables. 

While it cannot be used to determine causality due to its correlational nature, it can be used to predict outcomes based on the relationship that exists between variables.

• Experimental Research

Experimental research involves testing a hypothesis by conducting experiments using various methods such as controlled laboratory-based scenarios, field experiments, and randomized trials. 

Experimental design involves the manipulation and measurement of variables to observe their effect on each other. This enables researchers to determine cause-and-effect relationships between variables.

• Survey Research

Survey research is a quantitative method that involves the usage of different research instruments such as questionnaires or schedules to gather data. 

Surveys are usually done in cases where it is difficult to conduct an experiment such as in the case of social sciences. 

The most common forms of survey research include mail surveys, telephone interviews, and face-to-face interviews.

• Causal-Comparative Research

Causal-comparative research is a type of research that is used when the researcher has limited control over variables, such as in a field experiment. This type of research does not involve randomization of participants or experimental manipulation, as in true experimental studies.

The name causal-comparative research comes from two terms, causal and comparative. Causal implies that the study attempts to determine whether one variable causes another. Comparative indicates that groups are compared but not randomly assigned to groups by the researcher.

When To Use Quantitative Research

Quantitative research is a great way to collect data on a large scale when you have many respondents. 

This can be useful when you need a lot of data points and/or want to record responses for future analysis. It’s also good for surveys that are complex and/or have any questions. 

If your audience is large (across multiple locations, or across countries) or if you have a smaller audience but want them to complete your survey in their own language, quantitative research is the way to go.

If your business is just getting started with market research, quantitative methods will give you an excellent baseline of information upon which to build later qualitative research projects.

Qualitative research gets to the heart of your problem, giving you much more detailed data than quantitative methods would. 

Qualitative research is more appropriate for projects that:

• require more in-depth answers than “yes” or “no”
• have small sample sizes
• require detailed interviews or observations
• are exploratory in nature

Is Qualitative Or Quantitative Research Better?

A good thing to keep in mind is that there isn’t really a “right” answer – it all depends on what you are trying to find out!

Qualitative and Quantitative research is often seen as opposing approaches to research, but they both have their advantages and disadvantages. While there is a lot of debate between these two types of studies, they are not mutually exclusive and can work together to generate meaningful results.

Qualitative research gathers information that seeks to describe a topic more than measure it. Qualitative research is often used to conduct market analysis and identify consumer trends, motivations and behaviors.

Quantitative research is the best way to reveal and prove a cause-and-effect relationship. If you want to make an argument about why something is happening, quantitative research can help you do that. 

For example, if you wanted to say that more guns in the hands of private citizens lowered crime rates, you could run a study with data on crime rates and gun ownership across states and find statistical correlations between them.

Qualitative research describes and interprets what people say and do. Instead of using numbers to describe some phenomenon, it uses words and pictures instead. It’s best for exploring questions that don’t have clear answers yet, like how people feel about a new product or how they respond to a new marketing campaign.

For example, if you wanted to know how people reacted when they saw your new TV commercial, the best way would probably be to show it to people in a focus group and tape their reactions. The group moderator might ask some follow-up questions and people might comment on each other’s reactions, but the goal is less about making an argument than understanding what’s happening.

Is Survey Qualitative Or Quantitative Research?

A survey can be considered qualitative or quantitative depending on the type of questions asked. 

Quantitative surveys ask closed-ended questions – those requiring a “yes” or “no”, a number rating, or a selection from a predetermined list of answers (e.g., choose from “Excellent”, “Good”, etc.). These kinds of questions allow for analysis that can be statistically inferred across the entire population being surveyed.

Qualitative surveys (also known as unstructured interviews) ask open-ended questions that require respondents to provide free-form answers, which cannot be statistically inferred across the entire population being surveyed and therefore may not scale well if the sample size is very large.

Is Questionnaire A Quantitative Research?

A questionnaire is a series of questions or other prompts for gathering information about a subject. Although many researchers use questionnaires for statistical analysis, this is not always the case. So, yes, a questionnaire can be both, qualitative as well as quantitative, depending on the type of questions it contains.

The questionnaire is an integral part of survey research. It is a written or verbal series of questions pertaining to a specific topic, to which the respondent provides answers. 

Questionnaires are usually designed to obtain information from a large number of respondents on one or more occasions. 

The structured interview is normally used where it is necessary to keep close control over the questioning and to ensure that all respondents are asked exactly the same questions in precisely the same way.

The design process can be complex and time-consuming and many aspects need to be decided by the researcher before starting to write up the questionnaire:

• How will you distribute it? By hand? By mail? Online?
• What type of language will you use? Formal? Informal? Will it be general, or will specific jargon be included?
• How long will your questionnaire be?

Is Statistics Quantitative Research?

Quantitative research involves statistical analysis, such as calculating averages or percentages in surveys. In its most basic form, you count things, and then you make conclusions based on the numbers — usually about how common something is.

Statistics is a quantitative research method. It is used to quantify opinions, attitudes, and behaviors. This method involves the statistical analysis of data collected through polls, questionnaires, or surveys. The survey could be administered through personal interviews, telephone conversations, or the use of online survey forms.

This method is the most widely used method in business research. Most businesses make decisions based on quantitative methods. It is easy to administer with a large population size by using computers for ease of calculation and preparation of reports. It is also easy to understand and implement because it uses statistical terms that are easy to understand and interpret. This method is also used in both small and large businesses to make decisions based on quantified data.

Is Quasi-Experimental Quantitative Research?

Quasi-Experimental research is another type of experimental research design. Therefore, it is quantitative research. The difference between them is that the quasi-experimental design does not include a random sample. With this type of design, a researcher will create an experimental group and a control group, but not through random selection. Instead, the researcher will identify participants in each group based on criteria such as specific characteristics or behavior.

One advantage of Quasi-Experimental research is that it is easier to carry out than randomized experiments. It can also be less expensive because it does not require random assignment to groups. 

However, the researcher may have trouble determining whether the results from these groups are credible because there could be mitigating factors impacting the results that were not controlled for in the study’s design.

Does Quantitative Research Have Hypothesis?

Yes, quantitative research methods do have hypotheses. In fact, the whole idea of quantitative research is to test a hypothesis.

The hypothesis of quantitative research must always be stated in a clear manner. This is because the hypothesis helps to explain the relationship that exists between the different variables that have been used for the study.

However, quantitative research does not have a single hypothesis; it always has more than one hypothesis. The number and nature of these hypotheses will depend on the scope and coverage of the study or even research. The researcher will use these hypotheses to conclude whether there was any correlation between the variables that were used, or rather whether one variable had an effect on another variable.

Does Quantitative Research Use Interviews?

Interviews in quantitative research are often structured. This means that the interviewer asks the same questions, in the same order, of every respondent.

This is so that researchers are able to make comparisons between groups of people and draw conclusions about them.

For example, if a survey was looking at how many hours a week people spend on homework, it would be useful to know the subject they are studying and their level of education. These questions would be asked before asking about study time specifically so that any differences between groups can be explored further.

Respondents are also given a limited number of response options to choose from, for example, 1-5 hours 6-10 hours 11-15 hours 16-20 hours 20+ hours. 

Structured interviews also make it easier for data to be analyzed by computer programs or entered into databases.

Does Quantitative Research Focus On Human Experiences?

Quantitative research focuses on human experiences and looks into why people do certain things while others do not carry out the same actions at all. 

Quantitative research is also known as positivist research. 

It is a systematic process of collecting, organizing, analyzing, and interpreting numerical data. 

Quantitative researchers are involved in the entire research process from defining the problem to shaping the findings for presentation. 

They use probability sampling techniques, which refer to selecting samples from a population in such a way that each individual has an equal chance of being selected.

How To Determine Sample Size For Quantitative Research?

There are several methods you can use to determine the sample size. Some methods include using statistic tables and online calculators. Other methods involve using formulas to estimate sample size.

1. Using Statistic Tables

The first method you can use to calculate sample size involves using statistic tables. You need two parameters to do this; they include a confidence level and margin of error.

2. Online Calculators

The second method is by using online calculators like Survey Monkey or Raosoft Sample Size Calculator. To use these calculators, you need to fill out information such as the population, confidence interval, and margin of error among others, and click on calculate button.

3. Using Formulas

A sample size formula can be used to calculate the appropriate sample size based on factors such as population size, the margin of error, and confidence level. There are various formulas you can choose from.

Cochran’s Sample Size Formula is a common one: 

This formula can be used when one needs to determine the appropriate sample size for estimating a proportion or a percentage. 

The formula is: n = (Z 2 *p*q)/e 2 ; 

where n = sample size; p = estimated proportion; q = 1-p; e = margin of error; Z = z-score for confidence level selected. For example, 0.05 for 95% confidence interval.

Is Quantitative Research Objective?

Quantitative research focuses on measurable concepts and uses precise measurements and analysis to answer a specific question. It is thoroughly objective in nature. 

This type of research aims at testing theories by examining the relationship among variables with the help of different research tools. The relationship between variables can be causal or correlational.

In other words, quantitative researchers are more interested in determining whether the data gathered shows a true representation of the population under study.

Is Quantitative Research Scientific And Measurable?

The scientific and measurable characteristic of quantitative research is one of its greatest strengths. In fact, it’s the reason why so many scientists prefer quantitative research over qualitative research. Quantitative research can be reproduced and validated by other researchers, which makes the results generalizable and very reliable.

Because quantitative research is so reliable, it can be used to create a theory or model that accurately describes a phenomenon. 

For example, because Newton’s laws of motion have been verified by countless experiments, we can use them to develop complex models for predicting how objects will behave in different situations.

The data can be obtained using various instruments such as questionnaires and surveys. Quantitative research gathers information that is measurable, such as age, number of hours worked, and so on.

The main objective of quantitative research is to measure phenomena. It allows for the collection of numerical data that can be analyzed in order to explain what is being measured. This type of research aims at verifying theories and hypotheses by means of observation and measurements of variables.

Quantitative research does not deal with subjective ideas or opinions, but with measurable facts. It uses a deductive approach to gather information from a large sample, which then can be used to infer conclusions about the population from which it was drawn.

It can be quite useful to understand what quantitative research is, particularly when you are doing some research of your own. By understanding more about the process, you will be better prepared to make quantitative research and turn it into useful information.

Quantitative research is one of the more scientific/technical forms of market research. It’s a good way to get specific and detailed data (hence quantitative). Not only will you get statistics, numbers, etc., but you’ll actually truly learn something. It’s a great way to find out exactly what your audience wants.

Ultimately, both types of research complement one another. If you don’t have enough data yet, qualitative research can help you identify potential problems in your quantitative study. Even if you have an abundance of data from a previous research project, conducting a qualitative study prior to analyzing your quantitative data and drawing conclusions can lead to better results.

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

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

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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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.

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

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

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

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

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

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

• Replication

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

• Direct comparisons of results

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

• Large samples

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

• Hypothesis testing

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

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

• Superficiality

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

• Narrow focus

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

• Structural bias

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

• Lack of context

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

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

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

Research bias

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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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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Strengths and Weaknesses of Quantitative and Qualitative Research

Insights from research, walking in your customers’ shoes.

Both qualitative and quantitative methods of user research play important roles in product development. Data from quantitative research—such as market size, demographics, and user preferences—provides important information for business decisions. Qualitative research provides valuable data for use in the design of a product—including data about user needs, behavior patterns, and use cases. Each of these approaches has strengths and weaknesses, and each can benefit from our combining them with one another. This month, we’ll take a look at these two approaches to user research and discuss how and when to apply them.

Quantitative Studies

Quantitative studies provide data that can be expressed in numbers—thus, their name. Because the data is in a numeric form, we can apply statistical tests in making statements about the data. These include descriptive statistics like the mean, median, and standard deviation, but can also include inferential statistics like t-tests, ANOVAs, or multiple regression correlations (MRC). Statistical analysis lets us derive important facts from research data, including preference trends, differences between groups, and demographics.

Multivariate statistics like the MRC or stepwise correlation regression break the data down even further and determine what factors—such as variances in preferences—we can attribute to differences between specific groups such as age groups. Quantitative studies often employ automated means of collecting data such as surveys, but we can also use other static methods—for example, examining preferences through two-alternative, forced-choice studies or examining error rates and time on task using competitive benchmarks.

Quantitative studies’ great strength is providing data that is descriptive—for example, allowing us to capture a snapshot of a user population—but we encounter difficulties when it comes to their interpretation. For example, Gallup polls commonly provide data about approval rates for the President of the United States, as shown in Figure 1, but don’t provide the crucial information that we would need to interpret that data.

In the absence of the data that would be necessary to interpret these presidential job-approval numbers, it’s difficult to say why people approve or disapprove of the job that President Obama is doing. Some respondents may feel that President Obama is too liberal, while others may feel that he is too conservative in his actions, but without the necessary data, there is no way to tell.

In a product-development environment, this data deficiency can lead to critical errors in the design of a product. For example, a survey might report that the majority of users like 3D displays, which may lead to a product team’s choosing to integrate a 3D display into their product. However, if most users like only autostereoscopic 3D displays—that is, 3D displays that don’t require their wearing glasses—or like 3D displays only for watching sports or action movies on a television, using a 3D display that requires glasses for data visualization on a mobile device might not be a sound design direction.

Basically, statistical significance tells you whether your findings are real, while effect size tells you how much they matter. For example, if you were investigating whether adding a feature would increase a product’s value, you could have a statistically significant finding, but the magnitude of the increase in value might very small—say a few cents. In contrast, a meaningful effect size might result in an increase in value of $10 per unit. Typically, if you are able to achieve statistical significance with a smaller sample size, the effect size is fairly substantial. It is important to take both statistical significance and effect size into account when interpreting your data.

Qualitative Studies

Data from qualitative studies describes the qualities or characteristics of something. You cannot easily reduce these descriptions to numbers—as you can the findings from quantitative research; though you can achieve this through an encoding process. Qualitative research studies can provide you with details about human behavior, emotion, and personality characteristics that quantitative studies cannot match. Qualitative data includes information about user behaviors, needs, desires, routines, use cases, and a variety of other information that is essential in designing a product that will actually fit into a user’s life.

While quantitative research requires the standardization of data collection to allow statistical comparison, qualitative research requires flexibility, allowing you to respond to user data as it emerges during a session. Thus, qualitative research usually takes the form of either some form of naturalistic observation such as ethnography or structured interviews. In this case, a researcher must observe and document behaviors, opinions, patterns, needs, pain points, and other types of information without yet fully understanding what data will be meaningful.

Following data collection, rather than performing a statistical analysis, researchers look for trends in the data. When it comes to identifying trends, researchers look for statements that are identical across different research participants. The rule of thumb is that hearing a statement from just one participant is an anecdote; from two, a coincidence; and hearing it from three makes it a trend. The trends that you identify can then guide product development, business decisions, and marketing strategies.

Because you cannot subject these trends to statistical analysis, you cannot validate trends by calculating a p-value or an effect size—as you could validate quantitative data—so you must employ them with care. Plus, you should continually verify such data through an ongoing qualitative research program.

Additionally, because it is not possible to automate qualitative-data collection as effectively as you can automate quantitative-data collection, it is usually extremely time consuming and expensive to gather large amounts of data, as would be typical for quantitative research studies. Therefore, it is usual to perform qualitative research with only 6 to 12 participants, while for quantitative research, it’s common for there to be hundreds or even thousands of participants. As a result, qualitative research tends to have less statistical power than quantitative research when it comes to discovering and verifying trends.

Using Quantitative and Qualitative Research Together

While quantitative and qualitative research approaches each have their strengths and weaknesses, they can be extremely effective in combination with one another. You can use qualitative research to identify the factors that affect the areas under investigation, then use that information to devise quantitative research that assesses how these factors would affect user preferences. To continue our earlier example regarding display preferences: if qualitative research had identified display type—such as TV, computer monitor, or mobile phone display—the researchers could have used that information to construct quantitative research that would let them determine how these variables might affect user preferences. At the same time, you can build trends that you’ve identified through quantitative research into qualitative data-collection methods and, thus verify the trends.

While this might sound contrary to what we’ve described above, the approach is actually quite straightforward. An example of a qualitative trend might be that younger users prefer autostereoscopic displays only on mobile devices, while older users prefer traditional displays on all devices. You may have discovered this by asking an open-ended, qualitative question along these lines: “What do you think of 3D displays?” This question would have opened up a discussion about 3D displays that uncovered a difference between stereoscopic displays, autostereoscopic displays, and traditional displays. In a subsequent quantitative study, you could address these factors through a series of questions such as: “Rate your level of preference for a traditional 3D display—which requires your using 3D glasses—on a mobile device,” with options ranging from strongly prefer to strongly dislike . An automated system assigns a numeric value to whatever option a participant chooses, allowing a researcher to quickly gather and analyze large amounts of data.

37 Comments

The quantitative approach is so vital, even in our daily lives, because in most, if not all things we do in life, we measure to see how much there is of something.

Quantitative method is part of our daily life, even from birth, data are constantly being collected, assessed, and re-assessed as we grow.

I also support the quantitative data because it is much used and almost whatever we do involves it.

Yes. Both quantitative and qualitative research are important on their own. It depends on the situation where a researcher conducts a particular research, or he can go for the mixed method, too. For now, I am in need of sampling and non-sampling errors. Please help me understand its applications and the ways that can be checked? Types of sampling and all related information on this chapter. Expecting someone will help me on this soon.

Quantitative data provides the facts, but facts about people are just another construct of our society. For example, is something luxurious because it’s expensive or is it expensive because it’s luxurious? Business understands that neither method should be relied upon exclusively, which is why they use both. Anyone who thinks this is a competition between the two methods to somehow win out needs to read the article again. If you want to find out what happens when you think the only tool you need to make decisions in the social world is statistics, just type ‘New Coke’ into Google.

I also think that the quantitative approach is more important than the qualitative approach because we use it more and more in our life time.

I would suggest using both quantitative and qualitative. Both are strong ways of getting information and hearing the views and suggestions of others. It would be wiser to go for a mixed research method.

This quantitative approach is the approach used to show the transparency that at the end shows the democracy in the Great lakes countries. Thanks

Both methods are useful in real life situations. Which to use depends on the situation, and it’s not bad to combine both methods as this gives better and more accurate results.

Quantitative research requires high levels of statistical understanding to enable the measurements of descriptive and inferential statistics to be computed and interpreted, whereas qualitative methods are critical to identifying gaps in underserved areas in the society. More significantly, the use of a combination of the two is perfect.

Hi, I am Mark Jonson, and I am from New York, USA. Thanks for the article and wonderful example.

I am more confused when a particular method is considered superior over the other. I am more at ease looking at all three methods as situational—in that, some decision making requires the use of a quantitative, qualitative, or mixed method to accomplish my goals. For instance, it is suitable to use the quantitative method in studying birth and death rates in Europe and Africa, whereas the qualitative method suits a study on students’ behaviour relating to a particular course of study.

I think both qualitative and quantitative are good to go by, because the demerits of one are settled by the merits of the other.

The lapses that one has are covered by the other, so I think, for better findings and more accurate results, a mixed method answers it all.

Wonderfully great to me

Good article, provides a good general overview. As a marketing-research consultant I want to stress that qualitative research helps you much more to collect insights for user stories—if you do SCRUM—get the reasons why that make you differ and not differ from competitors and that would allow you to positively stand out in the market. Quantification is great. I love the stats, measurements. Yet my clients get great stuff out of qual that quant could never deliver because it is tool for specific purposes—as qual is. If you have both in your toolbox and know how to handle them, you get a better product. Use them and use them wisely, know the strengths and weaknesses of both—or get someone who does—because your competitor might just do it right now.

Both methods play an equal role, especially in research, and may also influence each other. This will depend on time and the necessity for each method.

Both methods are relevant because they drive individuals to the same conclusions.

“On the other hand, if you achieve statistical significance with a small sample size, you don’t need to increase your sample size; the finding is true regardless.” This is not true! A significance level set to 0.05 (5%), implies that it is acceptable to have a 5% probability of incorrectly rejecting the null hypothesis. That is, one might observe statistical significance, regardless of sample size, but this may be a false positive—that is, the effect occurs by chance or due to the co-occurrence of other factors. Low statistical power—because of small sample sizes, small effects, or both—negatively affects the likelihood that a nominally statistically significant finding—that is, finding of a p-value of ~.05—actually reflects a true effect. See this example . In general, one should be cautious about making inferences based on results drawn from a small sample.

It must be remembered that the two methods are not competing. They complement each other. Employing both techniques is the surest way to get your research budget well spent.

Minini, Faith Harrison—In my opinion, all three research approaches—quantitative, qualitative, and mixed methods—are very useful in informing UX practice. However, I prefer qualitative research for the reasons that studies are cheaper to embark on and the means of data collection and analysis are less stressful. However, employing both research approaches in any given study—especially studies involving large populations in countries’ health issues—provides the best results.

Thanks for the article. Both methods are useful, but it depends on the goal of the research.

I think qualitative research is best because it involves face-to-face conversation with the respondents. It gives true and reliable data as compared to quantitative research, because those researchers obtain data only from a given source and quantify it.

I need the advantages and disadvantages of using the T-test data collection method for the United States Parcel Service about their competition. I am not sure which is better for this, t-test or not, since t-test deals in small samples whereas UPS is global. I still have to know some disadvantages and advantages though.

i think qualitative research gives you detailed information and really goes into knowing much about a phenomenon, unlike quantitative’s giving you statistics.

I think a qualitative approach is more imperative. It provides greater richness and more detailed information about a smaller number of people.

I think qualitative research is easier to make meaning from, as it simplifies the phenomena by giving details on the issues.

I beg to differ from most comments. I support qualitative research because of the quality of its results.

Good, indeed.

I now understand the concept of quantitative research. Thanks for your contribution.

This concept of quantitative research is good. Nice write-up. You can as well make a video of this and place it on Netflix for people to watch.

“While quantitative and qualitative research approaches each have their strengths and weaknesses, they can be extremely effective in combination with one another.” - very insightful and so true! Thanks for posting this post, it was, indeed, a very interesting read. However, I, personally, prefer the quantitative approach. It can provide a person with a higher quality of the result.

For the ultimate quality of both methods, a foolproof system has to be found to eliminate biases. It is almost impossible. This is the basic problem that has to be solved.

I think both qualitative and quantitative approaches are vital. The approach that the researcher will adopt should be informed by the research question that the researcher is trying to resolve.

Everyone’s story is unique. Where your story starts may not be up to you, but where it ends definitely is. Every twist and turn is an opportunity to choose what comes next. Make that choice authentically yours, and you can’t do anything but succeed. Your Rough Draft We all have a different way of finding out what will work for us. But no matter which route we take on the journey to success—however you define it—we have to get into the messy and the profound in equal measure. And once it all comes together, the structure will make sense: the who, the why, and the how.

I think both qualitative and quantitative approaches are vital. The approach that the researcher will adopt should be informed by the research question that the researcher is trying to solve.

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Demetrius madrigal.

VP, UX & Consumer Insights at 30sec.io

Co-Founder and VP of Research & Product Development at Metric Lab

Redwood City, California, USA

Bryan McClain

President & Co-Founder at Metric Lab

Strategic UX Adviser & Head of Business Development at 30sec.io

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The Strengths and Weaknesses of Research Methodology: Comparison and Complimentary between Qualitative and Quantitative Approaches.

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Strengths and Weaknesses of Quantitative Research

At the outset, it must be noted that when we talk about the “strengths” of quantitative research, we do not necessarily mean that it is better than qualitative research; nor we say that it is inferior to qualitative research if we talk about its weaknesses. Hence, these strengths and weaknesses depend only on a specific purpose they serve, such as in terms of the problems or gaps that it aims to address or in terms of the time needed to complete the research. This means, therefore, that quantitative research is better than qualitative research only in some respects, and vice versa.

So, what are some of the major strengths of quantitative research?

First, in terms of objectivity and accuracy . If the issue is about objectivity and accuracy, then quantitative research is strong and more preferrable because, as we may already know, quantitative research explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics. In this way, biases are reduced to the minimum and analysis and interpretations are more objective and accurate. In fact, another important point to remember in quantitative research is that it is informed by objectivist epistemology. This means that quantitative research seeks to develop explanatory universal laws, for example, in social behaviors, by statistically measuring what it assumes to be a static reality. In relative vein, a quantitative approach endorses the view that psychological and social phenomena have an objective reality that is independent of the subject, that is, the knower or the researcher and the known or subjects are viewed as relatively separate and independent. Hence, in quantitative research, reality should be studied objectively by the researchers who should put a distance between themselves and what is being studied. In other words, in quantitative research, the researcher lets the “object” speaks for itself by objectively describing rather than giving opinions about it. This explains why quantitative researchers are supposed to play a neutral role in the research process. Hence, the meaning participants ascribe to the phenomenon studied is largely ignored in quantitative studies.

Second, in terms of sample size . It must be noted that a broader study can be made with quantitative approach, which involves more subjects and enabling more generalizations of results. In fact, scholars and researchers argue that one major advantage of quantitative research is that it allows researchers to measure the responses of a large number of participants to a limited set of questions. Also, quantitative methods and procedures allow the researchers to obtain a broad and generalizable set of findings from huge sample size and present them succinctly and parsimoniously.

Third, in terms of efficiency in data gathering . In terms of data gathering, quantitative research allows researchers to use a pre-constructed standardized instrument or pre-determined response categories into which the participants’ varying perspectives and experiences are expected to fit. Hence, data gathering in quantitative research is faster and easier. In fact, data gathering in quantitative research can be automated via digital or mobile surveys which, for example, allows thousands of interviews to take place at the same time across multiple countries. As we can see, data gathering in quantitative research is efficient and requires less effort.

And fourth, in terms of cost efficiency . Since data gathering in quantitative research is efficient and requires less effort, then obviously, the cost of someone conducting quantitative research is typically far less than in qualitative research.

So much for the major strengths of quantitative research. Let me now discuss very briefly its major weaknesses.

First is that results in quantitative research are less detailed . Since results are based on numerical responses, then there is a big possibility that most results will not offer much insight into thoughts and behaviors of the respondents or participants. In this way too, results may lack proper context.

Second, because quantitative research puts too much emphasis on objectivity and accuracy , it does not consider meaning behind phenomena. Needles to say, in every phenomenon, there are always important points that cannot be fully captured by statistics or mathematical measurements. Indeed, not all phenomena can be explained by numbers alone.

Third is on the issue of artificiality . Quantitative research can be carried out in an unnatural environment so that controls can be applied. This means that results in quantitative research may differ from “real world” findings.

Fourth is that in quantitative research, there is a possibility of an improper representation of the target population . Improper representation of the target population might hinder the researcher from achieving its desired aims and objectives. Despite the application of an appropriate sampling plan, still representation of the subjects is dependent on the probability distribution of observed data. As we can see, this may lead to miscalculation of probability distribution and falsity in proposition.

Fifth, quantitative research is limiting . Quantitative research employs pre-set answers which might ask how people really behave or think, urging them to select an answer that may not reflect their true feelings. Also, quantitative research method involves structured questionnaire with close-ended questions which leads to limited outcomes outlined in the research proposal. In this way, the results, expressed in a generalized form, cannot always represent the actual occurrence or phenomenon.

And sixth is the difficulty in data analysis. Quantitative studies require extensive statistical analysis, which can be difficult to perform for researchers from non-statistical backgrounds. Statistical analysis is based on scientific discipline and, hence, difficult for non-mathematicians to perform. Also, quantitative research is a lot more complex for social sciences, education, sociology, and psychology. Effective response should depend on the research problem rather than just a simple yes or no response. For example, to understand the level of motivation perceived by Grade 12 students from the teaching approach taken by their class teachers, mere “yes” and “no” might lead to ambiguity in data collection and, hence, improper results. Instead, a detailed interview or focus group technique might develop in-depth views and perspectives of both the teachers and children.