quantitative research techniques have the disadvantage of quizlet

13 Pros and Cons of Quantitative Research Methods

Quantitative research utilizes mathematical, statistical, and computational tools to derive results. This structure creates a conclusiveness to the purposes being studied as it quantifies problems to understand how prevalent they are.

It is through this process that the research creates a projectable result which applies to the larger general population.

Instead of providing a subjective overview like qualitative research offers, quantitative research identifies structured cause-and-effect relationships. Once the problem is identified by those involved in the study, the factors associated with the issue become possible to identify as well. Experiments and surveys are the primary tools of this research method to create specific results, even when independent or interdependent factors are present.

These are the quantitative research pros and cons to consider.

List of the Pros of Quantitative Research

1. Data collection occurs rapidly with quantitative research. Because the data points of quantitative research involve surveys, experiments, and real-time gathering, there are few delays in the collection of materials to examine. That means the information under study can be analyzed very quickly when compared to other research methods. The need to separate systems or identify variables is not as prevalent with this option either.

2. The samples of quantitative research are randomized. Quantitative research uses a randomized process to collect information, preventing bias from entering into the data. This randomness creates an additional advantage in the fact that the information supplied through this research can then be statistically applied to the rest of the population group which is under study. Although there is the possibility that some demographics could be left out despite randomization to create errors when the research is applied to all, the results of this research type make it possible to glean relevant data in a fraction of the time that other methods require.

3. It offers reliable and repeatable information. Quantitative research validates itself by offering consistent results when the same data points are examined under randomized conditions. Although you may receive different percentages or slight variances in other results, repetitive information creates the foundation for certainty in future planning processes. Businesses can tailor their messages or programs based on these results to meet specific needs in their community. The statistics become a reliable resource which offer confidence to the decision-making process.

4. You can generalize your findings with quantitative research. The issue with other research types is that there is no generalization effect possible with the data points they gather. Quantitative information may offer an overview instead of specificity when looking at target groups, but that also makes it possible to identify core subjects, needs, or wants. Every finding developed through this method can go beyond the participant group to the overall demographic being looked at with this work. That makes it possible to identify trouble areas before difficulties have a chance to start.

5. The research is anonymous. Researchers often use quantitative data when looking at sensitive topics because of the anonymity involved. People are not required to identify themselves with specificity in the data collected. Even if surveys or interviews are distributed to each individual, their personal information does not make it to the form. This setup reduces the risk of false results because some research participants are ashamed or disturbed about the subject discussions which involve them.

6. You can perform the research remotely. Quantitative research does not require the participants to report to a specific location to collect the data. You can speak with individuals on the phone, conduct surveys online, or use other remote methods that allow for information to move from one party to the other. Although the number of questions you ask or their difficulty can influence how many people choose to participate, the only real cost factor to the participants involves their time. That can make this option a lot cheaper than other methods.

7. Information from a larger sample is used with quantitative research. Qualitative research must use small sample sizes because it requires in-depth data points to be collected by the researchers. This creates a time-consuming resource, reducing the number of people involved. The structure of quantitative research allows for broader studies to take place, which enables better accuracy when attempting to create generalizations about the subject matter involved. There are fewer variables which can skew the results too because you’re dealing with close-ended information instead of open-ended questions.

List of the Cons of Quantitative Research

1. You cannot follow-up on any answers in quantitative research. Quantitative research offers an important limit: you cannot go back to participants after they’ve filled out a survey if there are more questions to ask. There is a limited chance to probe the answers offered in the research, which creates fewer data points to examine when compared to other methods. There is still the advantage of anonymity, but if a survey offers inconclusive or questionable results, there is no way to verify the validity of the data. If enough participants turn in similar answers, it could skew the data in a way that does not apply to the general population.

2. The characteristics of the participants may not apply to the general population. There is always a risk that the research collected using the quantitative method may not apply to the general population. It is easy to draw false correlations because the information seems to come from random sources. Despite the efforts to prevent bias, the characteristics of any randomized sample are not guaranteed to apply to everyone. That means the only certainty offered using this method is that the data applies to those who choose to participate.

3. You cannot determine if answers are true or not. Researchers using the quantitative method must operate on the assumption that all the answers provided to them through surveys, testing, and experimentation are based on a foundation of truth. There are no face-to-face contacts with this method, which means interviewers or researchers are unable to gauge the truthfulness or authenticity of each result.

A 2011 study published by Psychology Today looked at how often people lie in their daily lives. Participants were asked to talk about the number of lies they told in the past 24 hours. 40% of the sample group reported telling a lie, with the median being 1.65 lies told per day. Over 22% of the lies were told by just 1% of the sample. What would happen if the random sampling came from this 1% group?

4. There is a cost factor to consider with quantitative research. All research involves cost. There’s no getting around this fact. When looking at the price of experiments and research within the quantitative method, a single result mist cost more than $100,000. Even conducting a focus group is costly, with just four groups of government or business participants requiring up to $60,000 for the work to be done. Most of the cost involves the target audiences you want to survey, what the objects happen to be, and if you can do the work online or over the phone.

5. You do not gain access to specific feedback details. Let’s say that you wanted to conduct quantitative research on a new toothpaste that you want to take to the market. This method allows you to explore a specific hypothesis (i.e., this toothpaste does a better job of cleaning teeth than this other product). You can use the statistics to create generalizations (i.e., 70% of people say this toothpaste cleans better, which means that is your potential customer base). What you don’t receive are specific feedback details that can help you refine the product. If no one likes the toothpaste because it tastes like how a skunk smells, that 70% who say it cleans better still won’t purchase the product.

6. It creates the potential for an unnatural environment. When carrying out quantitative research, the efforts are sometimes carried out in environments which are unnatural to the group. When this disadvantage occurs, the results will often differ when compared to what would be discovered with real-world examples. That means researchers can still manipulate the results, even with randomized participants, because of the work within an environment which is conducive to the answers which they want to receive through this method.

These quantitative research pros and cons take a look at the value of the information collected vs. its authenticity and cost to collect. It is cheaper than other research methods, but with its limitations, this option is not always the best choice to make when looking for specific data points before making a critical decision.

Institutvert.org

quantitative research advantages and disadvantages

When it comes to conducting research, there are various methods one can employ. One of the most widely used approaches is quantitative research. This method involves the collection and analysis of numerical data to answer research questions. While quantitative research offers several advantages, it also comes with a set of disadvantages that researchers should consider. In this article, we will explore the advantages and disadvantages of quantitative research and discuss why understanding them is important.

Advantages of Quantitative Research

One of the key advantages of quantitative research is its objectivity. By focusing on numerical data, researchers can minimize bias in their analysis. This makes quantitative research highly reliable and allows for more accurate comparisons between different groups or variables.

Another advantage of quantitative research is its potential for generalizability. By using large sample sizes, researchers can draw conclusions that are more likely to hold true for the larger population. This is particularly useful when studying social or psychological phenomena that affect a wide range of individuals.

Additionally, the replicability of quantitative research is worth mentioning. By using standardized procedures and statistical analyses, researchers can easily replicate studies and assess their validity. This not only helps in verifying the results but also contributes to the overall credibility of the research.

Furthermore, quantitative research enables the application of advanced statistical techniques. This allows researchers to uncover patterns, relationships, and trends in their data that may not be readily apparent. These statistical analyses provide a deeper understanding of the research question and can lead to more robust and comprehensive findings.

Disadvantages of Quantitative Research

Despite its advantages, quantitative research also has some limitations that researchers should be aware of. One of the main disadvantages is the lack of contextual understanding. Since quantitative research relies on numerical measures, it may overlook the underlying factors or contexts that contribute to the observed outcomes. This can limit the depth of understanding.

Another disadvantage is the restrictions in question design. Quantitative research generally relies on structured questionnaires or surveys, which limit the types of questions that can be asked. As a result, important nuances or complexities may be overlooked, leading to a less comprehensive understanding of the research topic.

Data validity concerns are also a significant disadvantage of quantitative research. Self-reported measures, such as surveys, are prone to biases and inaccuracies. Participants may provide socially desirable responses or misinterpret the questions, affecting the reliability and validity of the data collected.

Lastly, quantitative research has a limited scope when it comes to exploring complex social, emotional, or cultural phenomena. These phenomena often involve subjective experiences that cannot be easily quantified. Qualitative methodologies, such as interviews or observations, are better suited for capturing the richness and depth of such phenomena.

Benefits of Knowing the Quantitative Research Advantages and Disadvantages

Understanding the advantages and disadvantages of quantitative research is essential for researchers and practitioners alike. By knowing the strengths and weaknesses of this research approach, researchers can make informed decisions about the methodologies they adopt and the questions they ask. This knowledge can also guide the interpretation and communication of research findings, ensuring a more accurate representation of the data.

For practitioners, knowledge of the advantages and disadvantages of quantitative research is valuable in critically evaluating and synthesizing existing research. It helps them recognize the limitations and potential biases in studies, enabling them to make evidence-based decisions in their respective fields.

Overall, being aware of the advantages and disadvantages of quantitative research promotes a more comprehensive and nuanced understanding of research outcomes. It encourages researchers to consider alternative research approaches when necessary and highlights the importance of triangulating findings from different methodologies to gain a more holistic understanding of complex phenomena.

In conclusion, quantitative research offers several advantages, including objectivity, generalizability, replicability, and the application of advanced statistical techniques. However, it also has some disadvantages, such as the lack of contextual understanding, restrictions in question design, data validity concerns, and a limited scope for exploring certain phenomena. By understanding these advantages and disadvantages, researchers can make informed decisions, interpret findings accurately, and contribute to the development of robust research in their respective fields.

• quantitative and qualitative research advantages and disadvantages
• secondary research advantages and disadvantages
• about internet advantages and disadvantages
• total quality management advantages and disadvantages
• mixed methods research advantages and disadvantages
• authoritative leadership advantages and disadvantages
• matrix organisation advantages and disadvantages

10 Advantages & Disadvantages of Quantitative Research

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis.

Quantitative Research

When researchers look at gathering data, there are two types of testing methods they can use: quantitative research, or qualitative research. Quantitative research looks to capture real, measurable data in the form of numbers and figures; whereas qualitative research is concerned with recording opinion data, customer characteristics, and other non-numerical information.

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis. An integral component of quantitative research - and truly, all research - is the careful and considered analysis of the resulting data points.

There are several key advantages and disadvantages to conducting quantitative research that should be considered when deciding which type of testing best fits the occasion.

5 Advantages of Quantitative Research

• Quantitative research is concerned with facts & verifiable information.

Quantitative research is primarily designed to capture numerical data - often for the purpose of studying a fact or phenomenon in their population. This kind of research activity is very helpful for producing data points when looking at a particular group - like a customer demographic. All of this helps us to better identify the key roots of certain customer behaviors. 

Businesses who research their customers intimately often outperform their competitors. Knowing the reasons why a customer makes a particular purchasing decision makes it easier for companies to address issues in their audiences. Data analysis of this kind can be used for a wide range of applications, even outside the world of commerce. 

• Quantitative research can be done anonymously. 

Unlike qualitative research questions - which often ask participants to divulge personal and sometimes sensitive information - quantitative research does not require participants to be named or identified. As long as those conducting the testing are able to independently verify that the participants fit the necessary profile for the test, then more identifying information is unnecessary. 

• Quantitative research processes don't need to be directly observed.

Whereas qualitative research demands close attention be paid to the process of data collection, quantitative research data can be collected passively. Surveys, polls, and other forms of asynchronous data collection generate data points over a defined period of time, freeing up researchers to focus on more important activities. 

• Quantitative research is faster than other methods.

Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects. Less delays and a larger sample size ensures you will have a far easier go of managing your data collection process.

• Quantitative research is verifiable and can be used to duplicate results.

The careful and exact way in which quantitative tests must be designed enables other researchers to duplicate the methodology. In order to verify the integrity of any experimental conclusion, others must be able to replicate the study on their own. Independently verifying data is how the scientific community creates precedent and establishes trust in their findings.

5 Disadvantages of Quantitative Research

• Limited to numbers and figures.

Quantitative research is an incredibly precise tool in the way that it only gathers cold hard figures. This double edged sword leaves the quantitative method unable to deal with questions that require specific feedback, and often lacks a human element. For questions like, “What sorts of emotions does our advertisement evoke in our test audiences?” or “Why do customers prefer our product over the competing brand?”, using the quantitative research method will not derive a meaningful answer.

• Testing models are more difficult to create.

Creating a quantitative research model requires careful attention to be paid to your design. From the hypothesis to the testing methods and the analysis that comes after, there are several moving parts that must be brought into alignment in order for your test to succeed. Even one unintentional error can invalidate your results, and send your team back to the drawing board to start all over again.

• Tests can be intentionally manipulative.  

Bad actors looking to push an agenda can sometimes create qualitative tests that are faulty, and designed to support a particular end result. Apolitical facts and figures can be turned political when given a limited context. You can imagine an example in which a politician devises a poll with answers that are designed to give him a favorable outcome - no matter what respondents pick.

• Results are open to subjective interpretation.

Whether due to researchers' bias or simple accident, research data can be manipulated in order to give a subjective result. When numbers are not given their full context, or were gathered in an incorrect or misleading way, the results that follow can not be correctly interpreted. Bias, opinion, and simple mistakes all work to inhibit the experimental process - and must be taken into account when designing your tests. 

• More expensive than other forms of testing. 

Quantitative research often seeks to gather large quantities of data points. While this is beneficial for the purposes of testing, the research does not come free. The grander the scope of your test and the more thorough you are in it’s methodology, the more likely it is that you will be spending a sizable portion of your marketing expenses on research alone. Polling and surveying, while affordable means of gathering quantitative data, can not always generate the kind of quality results a research project necessitates. 

Key Takeaways 

Numerical data is a vital component of almost any research project. Quantitative data can provide meaningful insight into qualitative concerns. Focusing on the facts and figures enables researchers to duplicate tests later on, and create their own data sets.

To streamline your quantitative research process:

Have a plan. Tackling your research project with a clear and focused strategy will allow you to better address any errors or hiccups that might otherwise inhibit your testing. 

Define your audience. Create a clear picture of your target audience before you design your test. Understanding who you want to test beforehand gives you the ability to choose which methodology is going to be the right fit for them. 

Test, test, and test again. Verifying your results through repeated and thorough testing builds confidence in your decision making. It’s not only smart research practice - it’s good business.

About Author

Send Your First Survey Today!

Set up and begin receiving results within minutes. Sign up for free, no contract required.

Helpfull is the easiest way to get feedback from thousands of people in minutes. Our online focus group platform provides a pool of qualified panelists to give you their real detailed opinions helping you make better, more informed decisions.

Ask Jordon’s Newsletter

Exploring the benefits and limitations of quantitative research methods in higher, education and sciences: a comprehensive overview.

Introduction:

Quantitative research methods are often favoured for gathering and analysing data in higher education research because of their replicability in their experimental design. Research design is a crucial aspect of conducting research, and three commonly used designs in the quantitative analysis are experimental, quasi-experimental, and non-experimental. These Sampling and data collection are also essential steps in the research process, with probability and non-probability sampling methods and surveys/questionnaires, observation, experimentation, and secondary data analysis being commonly used data collection methods. Data analysis methods include descriptive statistics, inferential statistics, and regression analysis, which help researchers draw conclusions about their research question. This blog article will examine how to explore quantitative studies through my experiences conducting this biology and social sciences research. It will also explore the advantages and disadvantages of the various methods and approaches, discussing the scope and limitations. Quantitative research methods are valuable tools for researchers to understand various phenomena.

Exploring the Three Main Research Designs in Quantitative Analysis

In previous articles, I have outlined how research design is crucial to your project or dissertation. Three research designs are typically used in quantitative analysis: experimental, quasi-experimental, and non-experimental.

Experimental Design:

Experimental design involves manipulating one or more independent variables to observe the effect on the dependent variable. This design is helpful in higher education research to test the effectiveness of a new teaching method or instructional technology, for example, comparing the learning outcomes of traditional lectures versus online learning environments.

Quasi-experimental Design:

A quasi-experimental design is similar to an experimental procedure, but the researcher cannot randomly assign participants to groups. Instead, pre-existing groups are used for comparison purposes. This could be used by looking at flock performances on chicken or turkey growth rates based on an additive introduced to shed rations. Due to flocks having an inherently limited time through the production cycle, a measure of looking at different crops would be the only way to assess productivity changes.

Non-experimental Design:

The non-experimental design does not involve the manipulation of independent variables. Instead, it consists of the observation of naturally occurring phenomena. Often this method may be explored in behavioural research using ethograms and time budgets to measure groups or individual animal responses. This design can be used in higher education research to explore the relationship between a student's socioeconomic class and academic performance.

Sampling: The Art and Science of Participant Selection in Quantitative Research

Sampling refers to the process of selecting participants for a research study. There are two types of sampling methods: probability sampling and non-probability sampling. Probability sampling involves randomly selecting participants from a population, ensuring each member has an equal chance of being selected. Non-probability selection involves selecting participants based on specific criteria, which is useful when the population is too large to choose a random sample or when the research question requires a particular type of participant.

The process of selecting participants for a research project is called sampling. Probability sampling and non-probability sampling are the two types of sampling procedures. Probability sampling entails randomly picking members from a population, guaranteeing that each member has an equal probability of being chosen. Non-probability selection is selecting participants based on specific criteria, which is advantageous when the population is too big to pick a random sample or when the research question necessitates a particular sort of participant.

Unleashing the Full Potential of Data Collection Methods in Quantitative Research

The process of acquiring information for a research study is known as data collection. Surveys/questionnaires, observation, experimentation, and secondary data analysis are quantitative research's four standard data-gathering methods. Surveys/questionnaires entail asking participants to answer standard questions, whereas statements entail monitoring and documenting behaviour. The investigation involves modifying one or more independent factors to maintain the effect on the dependent variable, whereas secondary data analysis is analysing previously acquired data for a new reason.

Both the social and biological sciences benefit from quantitative research methodologies. Quantitative research methods can be used to investigate social phenomena and behaviour in the social sciences, such as political science, sociology, psychology, and economics. Quantitative research methods can be used to examine living creatures and their environments in biological disciplines such as agriculture, medicine, and animal studies.

From Agriculture to Politics: The Versatility of Quantitative Research Methods in Social and Biological Sciences

Quantitative research methods are widely used in many social and biological sciences areas. These strategies entail gathering and analysing numerical data to comprehend patterns and relationships. This blog article will examine concrete examples of how quantitative research methods are employed in many sectors and their benefits and drawbacks.

In political science, quantitative research methods are used to analyse political behaviour, public opinion, and voting trends. If you were studying ecology policy and wanted to gauge the public's reception to your project's proposal, such as their view on swift boxes in domestic properties proposals, a measure that has been explored in Brighton and successfully to benefit a population. Outputs such as the demographic of individuals and understanding their levels of responses to the question.

Researchers utilise surveys and experiments to better understand how individuals make political decisions and how government policies influence society. Sociologists use surveys and experiments to explore inequality, social mobility, and social change. In psychology, quantitative research methods investigate human behaviour and mental processes such as personality, emotion, and cognitive functions. Economic researchers use statistical analysis to analyse consumer behaviour, labour markets, and economic growth. Quantitative research methods are also applied in the biological sciences. Researchers employ experiments and statistical analysis to create new farming practices and boost agricultural sustainability. Whether this is in understanding new production techniques such as vertical farming using grey water, which can be examined to assess the viability of crop production against traditional measures.

Pros and Cons: Understanding the Advantages and Limitations of Quantitative Research Methods

Quantitative research methods offer various advantages, including exact variable measurement, which can lead to more accurate and dependable conclusions. These procedures are straightforward to replicate, which adds to the findings' validity. Moreover, statistical analysis enables the detection of patterns and correlations in data. Yet, quantitative research approaches have several disadvantages. They may need to be adapted for measuring or quantifying complex objects. These approaches may also limit their ability to capture the depth and complexity of human experience, and they may be prone to biases and assumptions that weaken the validity of the findings.

Conclusion:

In conclusion, quantitative research methods are valuable tools for researchers to gather and analyse data to draw conclusions about various phenomena. The research design, sampling, data collection, and data analysis are essential steps in the research process, with experimental, quasi-experimental, and non-experimental techniques commonly used in quantitative analysis. Probability and non-probability sampling methods and surveys/questionnaires, observation, experimentation, and secondary data analysis are widely used data collection methods. Descriptive statistics, inferential statistics, and regression analysis are standard data analysis methods used to draw conclusions about the research question. Quantitative research methods have advantages and disadvantages and are widely used in various fields, including social and biological sciences, to study phenomena and behaviour and develop new techniques and interventions. Despite the limitations, the precise measurement of variables, replicability, and ability to identify patterns and relationships in the data make quantitative research methods a valuable tool for researchers.

Ready for more?

Qualitative Vs Quantitative Data (Differences, Pros And Cons)

What is qualitative and quantitative data, what are the main differences between qualitative and quantitative data, advantages and disadvantages of qualitative data, advantages and disadvantages of quantitative data, qualitative vs quantitative data: real-world examples, how can fullsession’s tools help you gather customer feedback, install your first website feedback form right now, fullsession pricing plans, faqs in relation to qualitative vs quantitative data.

Qualitative vs quantitative data. These two are the essence of data analysis, and for some, there is a clear winner. But don’t be too quick to judge.

We’ll walk through what sets these two apart—and then dig into how they work in the real world. From capturing life’s complexities through qualitative means to crunching numbers for clear-cut answers quantitatively, this is where things get interesting.

In this article, we’ll see what they mean, how they differ, and, most importantly, when to use them.

Qualitative and quantitative data are fundamental for all kinds of research and data analysis. They both serve a good purpose and choosing one over another is tricky. Let’s see what each brings to the table.

What is Qualitative Data?

Qualitative data analysis involves examining non-numerical data to understand concepts, opinions, or experiences.

It often comes from interviews, open-ended survey responses , or observational studies focusing on the ‘why’ and ‘how’ of human behavior and experiences.

The data type provides insights that help understand the depth and complexity of the subject under study.

Examples of qualitative data questions:

• What are your main reasons for choosing our product over competitors?
• Can you describe your experience using our customer service?
• How do you feel about the latest changes we made to our software interface?

What is Quantitative Data?

Researchers work with numerical data to analyze quantitative data. It often comes from structured data sources like surveys with closed-ended questions , experiments, and statistical records.

Quantitative data analysis is used to quantify attitudes, opinions, behaviors, and other defined variables.

It often uses different statistical tools to identify patterns, trends, or correlations within the data set. Such analysis is essential for making general conclusions and predicting future trends based on the data.

Examples of quantitative data questions:

• How many hours per day do you use our product?
• On a scale of 1 to 10, how satisfied are you with our customer service?
• How often (in a month) do you encounter issues with our software interface?

Qualitative and quantitative data serve different purposes. Qualitative research is more about the individual; thus, you can create a better image of your ideal customer and profile your target audience more precisely.

However, quantitative data might be a powerful weapon if you can afford a considerable sample size, as you can collect many results and create in-depth charts.

Yet, both methods have pros and cons, and we will touch base in the next section.

Qualitative data is available through many methods, like in-depth interviews and observations in a natural setting. It offers broader pictures of human behavior and social phenomena. While qualitative studies excel in interpreting non-numerical data to provide depth and context, they could be better if used by others.

Advantages of Qualitative Data

• Qualitative data gives a more detailed view of people’s attitudes, behaviors, and experiences.
• Qualitative studies allow for flexibility in research methods since they adapt to changing behaviors.
• Gathering data in natural settings allows qualitative research to spot the complexities and nuances of real-life situations.
• The qualitative approach gives a voice to study participants and lets them express their perspectives and experiences in their own words.
•  Qualitative data is ideal for exploring new areas of research.

Disadvantages of Qualitative Data

• The interpretation of qualitative data can be highly subjective and depends on the researcher’s perspective so it can be biased.
• Due to typically smaller sample sizes and non-standardized data collection methods, the findings from qualitative studies may need to be more usable for colossal sample sizes.
• Collecting and analyzing qualitative data, such as transcribing and interpreting in-depth interviews, might be time-consuming and labor-intensive, requiring significant resources.

Quantitative data shines with its numerical nature and often contrasts with qualitative data collected through open-ended questions. Still, it has its own “place” in many research fields. It provides a strong foundation for statistical analysis and objective conclusions, but like any method, it has its own advantages and disadvantages.

Advantages of Quantitative Data

• Quantitative data offers a significant perk in statistical reliability and is known for its precise and objective analysis that can be replicated and verified.
• Quantitative data can be picked up from large populations, which makes it ideal for studies requiring a broad overview.
• Numerical data simplifies the process of comparing groups or variables. Doing that will help you make straightforward conclusions and trend analysis.
• Due to standardized feedback collection methods, results from quantitative research are often generalizable to a larger population.
•  Modern techniques for collecting quantitative data, like surveys and automated data capture, enable efficient and swift data collection

Disadvantages of Quantitative Data

• Quantitative data may need more depth and detail found in qualitative data, potentially overlooking the subtleties of human behavior and experience.
• The structured nature of quantitative data collection can be restrictive, limiting the ability to explore unanticipated phenomena during the research process.
• Without the contextual background of qualitative data, there’s a risk of misinterpreting quantitative data, significantly when complex human behaviors are reduced to numbers.

Qualitative and Quantitative data are both solid tools if you want to see how people see your product. Let’s see a couple of examples.

Qualitative Data Examples

• Customer Feedback Interviews : Gathering detailed opinions and feelings about a new product through individual interviews.
• Ethnographic Research : Observing and documenting the behaviors and interactions of a specific cultural group in their natural environment.
• Case Studies : In-depth analysis of a single event, situation, or individual to comprehensive insights into complex issues.

Quantitative Data Examples

• Survey Results : Analyzing responses from 1,000 participants on their product preferences, with 60% preferring Product A over Product B.
• Educational Achievement : Measuring students’ performance in a standardized test, where 75% scored above the national average.
• Market Analysis : Evaluating sales data to find that a particular product saw a 30% increase in sales following a marketing campaign.

FullSession is entirely focused on providing valuable insights that you can utilize at a later stage. Our tool will help you understand customers’ demands in much more depth. You can capture and analyze user interactions and draw result-driven conclusions, which are way more efficient than standard “guessing” methods.

With FullSession, you can quickly discover areas of improvement and bolster your strengths to increase your traffic even more.

It takes less than 5 minutes to set up your first website or app feedback form with FullSession , and it’s completely free!

The FullSession platform offers a 14-day free trial. It provides two paid plans—Basic and Business. Here are more details on each plan.

• The Basic plan costs $39/month and allows you to monitor up to 5,000 monthly sessions.
• The Business plan costs $149/month and helps you to track and analyze up to 25,000 monthly sessions.
• The Enterprise plan starts from 100,000 monthly sessions and has custom pricing.

If you need more information, you can get a demo.

So, you’ve journeyed through the maze of qualitative vs quantitative data. You’ve seen how each has its place—qualitative with its rich, detailed narratives and quantitative with its hard numbers.

Remember this : Qualitative paints the picture; quantitative frames it. One gives depth, the other scale.

Combine them, and what do you get? A complete view—a 360-degree take on whatever’s at hand. FullSession can help you blend both, so you can really see the full picture and enjoy much better results.

What is the difference between quantitative and qualitative data?

Difference between quantitative and qualitative data: Quantitative data is numerical and used for measuring and counting, while qualitative data is descriptive and categorizing and conceptualizing.

What is an example of quantitative data?

The percentage of people in a survey who rate service as “excellent,” “good,” “average,” “poor.”

How do you determine if the data is qualitative or quantitative?

If the data can be counted or measured and expressed in numbers, it’s quantitative. In case it’s descriptive and involves characteristics that can’t be counted, it’s qualitative.

Enhance Your Insights With Richer User Behavior Data

Discover FullSession's Digital Experience Intelligence solution firsthand. Explore FullSession for free

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

Related Articles

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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.

Recommended: Problems and criticism of democracy

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.

Recommended: Advantages and Disadvantages of Capitalism

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.

• Privacy Policy

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Transformative Design – Methods, Types, Guide

Observational Research – Methods and Guide

Textual Analysis – Types, Examples and Guide

Explanatory Research – Types, Methods, Guide

Research Methods – Types, Examples and Guide

Survey Research – Types, Methods, Examples

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

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.

Search form

You are here.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). 1 That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

KEY TAKEAWAYS

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading " Qualitative Interview Techniques and Considerations " on qualitative interviewing techniques. See if you can turn them into closed-ended questions.
• 10599 reads
• Relevance, Balance, and Accessibility
• Different Sources of Knowledge
• Ontology and Epistemology KEY TAKEAWAYS EXERCISES
• The Science of Sociology
• Specific Considerations for the Social Sciences KEY TAKEAWAYS EXERCISES
• Consuming Research and Living With Its Results
• Research as Employment Opportunity KEY TAKEAWAYS EXERCISES
• Design and Goals of This Text LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Sociology at Three Different Levels KEY TAKEAWAYS EXERCISES
• Paradigms in Social Science
• Sociological Theories KEY TAKEAWAYS EXERCISES
• Inductive Approaches and Some Examples
• Deductive Approaches and Some Examples
• Complementary Approaches? KEY TAKEAWAYS EXERCISES
• Revisiting an Earlier Question LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Human Research Versus Nonhuman Research
• A Historical Look at Research on Humans
• Institutional Review Boards KEY TAKEAWAYS EXERCISES
• Informed Consent
• Protection of Identities
• Disciplinary Considerations KEY TAKEAWAYS EXERCISES
• Ethics at Micro, Meso, and Macro Levels LEARNING OBJECTIVE KEY TAKEAWAYS EXERCISES
• Doing Science the Ethical Way
• Using Science the Ethical Way KEY TAKEAWAYS EXERCISES
• How Do You Feel About Where You Already Are?
• What Do You Know About Where You Already Are? KEY TAKEAWAYS EXERCISES
• Is It Empirical? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• What Is Sociology?
• What Is Not Sociology? KEY TAKEAWAYS EXERCISES
• Sociologists as Paparazzi?
• Some Specific Examples KEY TAKEAWAYS EXERCISES
• Feasibility
• Field Trip: Visit Your Library KEY TAKEAWAYS EXERCISES
• Exploration, Description, Explanation
• Idiographic or Nomothetic?
• Applied or Basic? KEY TAKEAWAYS EXERCISES
• Units of Analysis and Units of Observation
• Hypotheses KEY TAKEAWAYS EXERCISES
• Triangulation LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Searching for Literature
• Reviewing the Literature
• Additional Important Components KEY TAKEAWAYS EXERCISES
• What Do Social Scientists Measure?
• How Do Social Scientists Measure? KEY TAKEAWAYS EXERCISE
• Concepts and Conceptualization
• A Word of Caution About Conceptualization KEY TAKEAWAYS EXERCISES
• Putting It All Together KEY TAKEAWAYS EXERCISE
• Reliability
• Validity KEY TAKEAWAYS EXERCISES
• Levels of Measurement
• Indexes, Scales, and Typologies KEY TAKEAWAYS EXERCISES
• Populations Versus Samples LEARNING OBJECTIVE KEY TAKEAWAYS EXERCISES
• Nonprobability Sampling
• Types of Nonprobability Samples KEY TAKEAWAYS EXERCISES
• Probability Sampling
• Types of Probability Samples KEY TAKEAWAYS EXERCISES
• Who Sampled, How Sampled, and for What Purpose? KEY TAKEAWAYS EXERCISES
• Survey Research: What Is It and When Should It Be Used? LEARNING OBJECTIVES KEY TAKEAWAY EXERCISE
• Strengths of Survey Method
• Weaknesses of Survey Method KEY TAKEAWAYS EXERCISES
• Administration KEY TAKEAWAYS EXERCISES
• Asking Effective Questions
• Response Options
• Designing Questionnaires KEY TAKEAWAYS EXERCISES
• From Completed Questionnaires to Analyzable Data
• Identifying Patterns KEY TAKEAWAYS EXERCISES
• Interview Research: What Is It and When Should It Be Used? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Conducting Qualitative Interviews
• Analysis of Qualitative Interview Data
• Strengths and Weaknesses of Qualitative Interviews KEY TAKEAWAYS EXERCISES
• Conducting Quantitative Interviews
• Analysis of Quantitative Interview Data
• Strengths and Weaknesses of Quantitative Interviews KEY TAKEAWAYS EXERCISES
• Location, Location, Location
• Researcher-Respondent Relationship KEY TAKEAWAYS EXERCISES
• Field Research: What Is It and When to Use It? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Strengths of Field Research
• Weaknesses of Field Research KEY TAKEAWAYS EXERCISES
• Choosing a Site
• Choosing a Role KEY TAKEAWAYS EXERCISES
• Writing in the Field
• Writing out of the Field KEY TAKEAWAYS EXERCISE
• From Description to Analysis KEY TAKEAWAYS EXERCISE
• Unobtrusive Research: What Is It and When to Use It? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Strengths of Unobtrusive Research
• Weaknesses of Unobtrusive Research KEY TAKEAWAYS EXERCISES
• Content Analysis
• Indirect Measures
• Analysis of Unobtrusive Data Collected by You KEY TAKEAWAYS EXERCISES
• Analyzing Others’ Data LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Reliability in Unobtrusive Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Focus Groups LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Experiments LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Ethnomethodology and Conversation Analysis LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Sharing It All: The Good, the Bad, and the Ugly
• Knowing Your Audience KEY TAKEAWAYS EXERCISE
• Presenting Your Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Writing Up Research Results LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Disseminating Findings LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Reading Reports of Sociological Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Being a Responsible Consumer of Research LEARNING OBJECTIVE KEY TAKEAWAY EXERCISE
• Media Reports of Sociological Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Sociological Research: It’s Everywhere LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Evaluation Research
• Market Research
• Policy and Other Government Research KEY TAKEAWAY EXERCISE
• Doing Research for a Cause LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Public Sociology LEARNING OBJECTIVE KEY TAKEAWAYS EXERCISES
• Transferable Skills
• Understanding Yourself, Your Circumstances, and Your World KEY TAKEAWAYS EXERCISES
•  Back Matter

This action cannot be undo.

Choose a delete action Empty this page Remove this page and its subpages

Content is out of sync. You must reload the page to continue.

New page type Book Topic Interactive Learning Content

• Config Page
• Add Page Before
• Add Page After
• Delete Page