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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

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

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

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

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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

13 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Quantitative Research Design

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Quantitative research design is a systematic approach used to investigate phenomena by collecting and analyzing numerical data. It involves the use of structured tools such as surveys, experiments, and statistical analysis to quantify variables and identify patterns, relationships, and cause-and-effect dynamics. This Research design emphasizes objectivity and replicability, allowing researchers to generalize findings across larger populations. By focusing on measurable data, quantitative research design aims to provide precise and reliable results.

What is Quantitative Research Design?

Quantitative research design is a structured method of inquiry that focuses on quantifying data and analyzing it using statistical techniques. This approach involves collecting numerical data through various tools such as surveys, experiments, and questionnaires to identify patterns, relationships, and causal effects. The design emphasizes objectivity, allowing researchers to generalize findings to larger populations.

Types of Quantitative Research Design

1. Descriptive Research Design

• Purpose: To describe characteristics of a population or phenomenon.
• Methods: Surveys, observational studies, case studies.
• Example: Measuring the prevalence of a particular health behavior in a community.

2. Correlational Research Design

• Purpose: To identify and measure the relationship between two or more variables without manipulating them.
• Methods: Surveys, archival data analysis.
• Example: Examining the relationship between study habits and academic performance.

3. Experimental Research Design

• Purpose: To determine cause-and-effect relationships by manipulating one or more independent variables and measuring their effect on dependent variables.
• Methods: Randomized controlled trials, laboratory experiments.
• Example: Testing the efficacy of a new drug by randomly assigning participants to treatment and control groups.

4. Quasi-Experimental Research Design

• Purpose: To estimate causal relationships when random assignment is not possible.
• Methods: Non-randomized control groups, pre-test/post-test designs.
• Example: Evaluating the impact of an educational intervention in different schools where random assignment is not feasible.

5. Cross-Sectional Research Design

• Purpose : To collect data at a single point in time to provide a snapshot of a population or phenomenon.
• Methods : Census surveys, sample surveys.
• Example : Surveying a population to assess the current prevalence of smoking.

6. Longitudinal Research Design

• Purpose : To collect data from the same subjects over an extended period to observe changes and developments.
• Methods : Panel studies, cohort studies.
• Example : Following a group of students over several years to track their academic progress.

7. Comparative Research Design

• Purpose : To compare two or more groups or variables to identify similarities and differences.
• Methods : Case-control studies, cross-national studies.
• Example : Comparing educational outcomes between students from different countries.

8. Meta-Analysis

• Purpose : To combine data from multiple studies to draw more robust conclusions about a research question.
• Methods : Systematic reviews, statistical aggregation.
• Example : Aggregating data from various studies to assess the overall effectiveness of a particular therapy.

9. Secondary Data Analysis

• Purpose : To use existing datasets collected by other researchers or organizations to answer new research questions or test different hypotheses.
• Methods : Government surveys, social surveys, market research data.
• Example : Analyzing national health survey data to investigate trends in obesity rates.

10. Archival Research

• Purpose : To analyze existing data collected for purposes other than the current study to uncover long-term trends and patterns.
• Methods : Historical documents, government records, public databases.
• Example : Examining historical voting records to understand changes in political participation over time.

Quantitative Research Design Methods

Surveys involve administering questionnaires or structured interviews to gather data from a sample of participants. Surveys can be implemented through different channels, such as conducting them in person, over the phone, via mail, or utilizing online platforms. Researchers use various question types, such as multiple-choice, Likert scales, or rating scales, to collect quantitative data on attitudes, opinions, behaviors, and demographics.

2. Experiments

Experiments involve manipulating one or more independent variables and measuring their effects on dependent variables. To compare outcomes, participants are assigned randomly to various groups, including control and experimental groups. Experimental designs allow researchers to establish cause-and-effect relationships by controlling for confounding factors.

3. Observational Studies

Observational studies involve systematically observing and recording behavior, events, or phenomena in natural settings. Researchers can use structured or unstructured quantitative observation methods, depending on the research objectives. Quantitative data can be collected by counting the frequency of specific behaviors or by using coding systems to categorize and analyze observed data.

4. Archival Research

Archival research involves analyzing existing data collected for purposes other than the current study. Researchers may use historical documents, government records, public databases, or organizational records to extract data through quantitative research. Archival research allows for large-scale data analysis and can provide insights into long-term trends and patterns.

5. Secondary Data Analysis

Similar to archival research, secondary data analysis involves using existing datasets that were collected by other researchers or organizations. Researchers analyze the data to answer new research questions or test different hypotheses. Secondary data sources can include government surveys, social surveys, or market research data.

6. Content Analysis

Content analysis is a method used to analyze textual or visual data to identify patterns, themes, or relationships. Researchers code and categorize the content of documents, interviews, articles, or media sources. The coded data is then quantified and statistically analyzed to draw conclusions. Content analysis can be both qualitative and quantitative, depending on the approach used.

7. Psychometric Testing

Psychometric testing involves the development and administration of tests or scales to measure psychological constructs, such as intelligence, personality traits, or attitudes. Researchers use statistical techniques to analyze the test data, such as factor analysis, reliability analysis, or item response theory.

Difference between Quantitative Research Design and Qualitative Research Design

How to Find Quantitative Research Design

Finding a quantitative research design involves several steps to ensure that the chosen method is suitable for your research question and objectives. Here’s a step-by-step guide:

1. Define Your Research Question

• Identify the Problem : Clearly define the problem or phenomenon you want to study.
• Specify Objectives : Determine what you aim to achieve with your research.

2. Literature Review

• Search Existing Research : Look for existing studies related to your topic in academic journals, books, and databases.
• Identify Gaps : Note any gaps in the current literature that your research could fill.

3. Choose a Suitable Research Design

• Descriptive Design : If your goal is to describe characteristics or functions, consider surveys or observational studies.
• Correlational Design : To explore relationships between variables, use correlational methods.
• Experimental Design : For establishing cause-and-effect relationships, conduct experiments with control and experimental groups.
• Quasi-Experimental Design : When random assignment isn’t feasible, use quasi-experimental designs.
• Longitudinal Design : If you need to study changes over time, choose a longitudinal approach.
• Cross-Sectional Design : For a snapshot of a population at a single point in time, use cross-sectional surveys.

4. Develop Your Hypothesis

• Formulate Hypotheses : Based on your research question, develop clear and testable hypotheses.

5. Select Your Sample

• Define the Population : Determine the population from which you will draw your sample.
• Sampling Methods : Choose an appropriate sampling method (random, stratified, cluster, etc.) to ensure representativeness.

6. Choose Data Collection Methods

• Surveys : Use questionnaires or structured interviews.
• Experiments : Design experiments with independent and dependent variables.
• Observations : Use structured or unstructured observation methods.
• Archival Research : Analyze existing records or databases.
• Secondary Data Analysis : Use pre-existing datasets for new analysis.

7. Design the Data Collection Instrument

• Create Surveys/Questionnaires : Develop questions that are clear and unbiased.
• Design Experimental Protocols : Outline the procedures for conducting experiments.
• Develop Observation Checklists : List specific behaviors or events to observe and record.

8. Pilot Testing

• Test Instruments : Conduct a pilot test to identify any issues with your data collection instruments or procedures.
• Refine Methods : Make necessary adjustments based on feedback from the pilot test.

9. Data Collection

• Implement Your Design : Collect data according to your chosen methods and protocols.
• Ensure Accuracy : Follow ethical guidelines and ensure accurate data recording.

10. Data Analysis

• Statistical Techniques : Use appropriate statistical methods to analyze your data.
• Software Tools : Utilize software such as SPSS, R, or Excel for data analysis.

FAQ’s

What is the purpose of quantitative research.

The purpose is to quantify variables, test hypotheses, and identify patterns, relationships, or causal effects through numerical data analysis.

What is a descriptive research design?

Descriptive research design aims to describe characteristics or functions of a population or phenomenon, without establishing cause-and-effect relationships.

What is correlational research design?

Correlational research design examines the relationship between two or more variables to determine if a connection exists, without implying causation.

What is causal-comparative research design?

Causal-comparative research design seeks to identify cause-and-effect relationships by comparing different groups based on varying independent variables.

What is an experimental research design?

Experimental research design involves manipulating one variable to determine its effect on another variable, establishing a cause-and-effect relationship.

What are independent and dependent variables?

Independent variables are manipulated to observe their effect on dependent variables, which are measured to see if they change due to the manipulation.

Why is random sampling important in quantitative research?

Random sampling ensures each member of a population has an equal chance of being selected, enhancing the generalizability of results.

What is the role of hypothesis in quantitative research?

A hypothesis is a testable prediction about the relationship between variables, guiding the direction and focus of the study.

What are common data collection methods in quantitative research?

Common methods include surveys, questionnaires, structured interviews, and standardized tests.

What is the significance of statistical analysis in quantitative research?

Statistical analysis helps interpret numerical data, identify trends, and test hypotheses, providing a basis for drawing conclusions.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

• Replication

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

• Direct comparisons of results

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

• Large samples

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

• Hypothesis testing

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

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

• Superficiality

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

• Narrow focus

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

• Structural bias

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

• Lack of context

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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What Is Quantitative Research? Types, Characteristics & Methods

Market Research Specialist

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

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

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

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

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

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

What Is Quantitative Research?

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

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

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

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

Let’s consider an example.

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

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

Characteristics of Quantitative Research

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

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

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

1. Measurable Variables

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

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

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

2. Sample Size

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

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

3. Normal Population Distribution

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

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

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

4. Well-Structured Data Representation

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

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

5. Predictive Outcomes

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

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

Types of Quantitative Research Methods

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

• Primary quantitative research methods
• Secondary quantitative research methods

1. Primary quantitative research methods

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

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

1.1. Techniques & Types of Studies:

• Survey Research

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

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

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

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

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys are of two types:

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

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

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

1.2. Correlational Research:

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

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

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

1.3. Causal-Comparative Research:

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

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

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

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

1.4. Experimental Research:

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

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

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

2. Data collection methodologies

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

2.1Sampling methods:

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

2.2Probability Sampling

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

There are four main types of probability sampling:

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

2.3Non-probability Sampling

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

There are five non-probability sampling models:

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

3. Data analysis techniques

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

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

2. Secondary Quantitative Research Methods

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

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

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

B. Government and Non-Government Sources:

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

C. Public Libraries:

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

D. Educational Institutions:

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

E. Commercial Information Sources:

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

Advantages of Quantitative Research Methods

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

Here are some core benefits this research methodology offers.

Direct Result Comparison

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

Replication

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

Large Samples

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

Hypothesis Testing

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

Proven Examples of Quantitative Research Methods

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

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

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

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

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

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

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

• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

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

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

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

The insights and results were:

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

8 Best Practices to Conduct Quantitative Research

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

1. Define Research Objectives

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

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

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

2. Keep Your Questions Simple

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

3. Develop a Solid Research Design

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

4. Use Reliable & Valid Instruments

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

5. Ensure Data Quality

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

6. Employ Appropriate Data Analysis Techniques

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

7. Interpret Results Objectively

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

8. Address Ethical Considerations

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

Enhance Your Quantitative Research With Cutting-Edge Software

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

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

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

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

About the author

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

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Quantitative Research Methods

Research Methods in Psychology

January 2023

This sixteen-hour course provides a comprehensive exploration of the scientific method, beginning with a detailed description of its steps, from posing a hypothesis to analyzing and reporting results. Students will learn to judge the quality of sources for literature reviews, ensuring they can discern credible information. The course covers the various types of research questions scientists pose, highlighting the differences and similarities. It also covers the definition of variables, scales of measurement, and the importance of making reliable observations.

A significant portion of the course is dedicated to comparing and contrasting major research designs such as correlational techniques and experimental methods, along with their strengths and weaknesses, which are assessed in terms of construct validity, internal validity, and external validity. Methods for establishing experimental control are reviewed in detail along with the nature of between- and within-participant designs, and factorial designs.

In another major section of the course, students will gain insights into the historical context of ethical concerns in scientific research, illustrated with specific examples. The course emphasizes the purposes served by research ethics codes and uses the Ethics Code of the American Psychological Association as a foundation for exploring ethical issues related to scientific research. Protections offered by ethics codes for human participants and animals in research are thoroughly examined. The course also addresses the implications of the overrepresentation of WEIRD (Western, Educated, Industrialized, Rich, Democratic) participants in psychological research. Students will identify the consequences of different types of research fraud and learn how replication and preregistration can mitigate issues like p-hacking and HARKing (Hypothesizing After the Results are Known).

Learning objectives

• Describe the steps of the scientific method.
• Explain how to judge the quality of a source for a literature review.
• Compare and contrast the kinds of research questions scientists ask.
• Specify how variables are defined.
• Explain what it means for an observation to be reliable.
• Compare and contrast the major research designs.
• Compare and contrast forms of validity as they apply to the major research designs.
• Illustrate the history of ethical concerns about scientific research using specific examples.
• Describe purposes served by codes of research ethics.
• Explain the five general Ethical Principles of the APA Ethics Code.
• Describe the issues addressed by the APA Ethical Standards that apply to researchers.
• Explain how IRBs and IACUCs operate.
• Outline the major ethical considerations in planning a research study using nonhuman animals.
• Explain the importance of the three Rs in animal research.
• Explain the protections ethics codes offer for human participants in research.
• Describe the implications of WEIRD participants being overrepresented in psychological research.
• Identify the consequences of the different types of fraud in research.
• Explain how replication and preregistration address the problems of p-hacking and HARKing.
• Apply your knowledge of ethical violations and propose solutions for real-life research.

This program does not offer CE credit.

More in this series

Basic qualitative methods like narrative inquiry and ethnography are introduced

January 2023 On Demand Training

A concepts-focused introduction to basic descriptive and inferential statistics

Types of Quantitative Research

Introduction to Quantitative Research

Quantitative research is outlined as a scientific investigation of phenomena by gathering quantitative information and activity applied mathematics, or procedure techniques.

Quantitative research is a research method where you gather and analyze numerical data to understand and explain various phenomena. The different types of quantitative research are survey, descriptive, experiential, correlational, and causal-comparative.

It focuses on using mathematical and statistical techniques to understand and investigate the subject. This is why the collected information must be in numerical form.

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Different Types of Quantitative Research

The following are the different types of Quantitative research types with the description of each.

1. Survey Research

Survey Research is one of the most common types of quantitative research techniques. In this research method, you can use surveys to collect numerical data from a particular group or population. After that, you can analyze the data and explain the characteristics of the group. Both small and large organizations typically employ it for a proper understanding of their customers and to understand the merchandise and product views. It is of two types:

• Cross-sectional: In a cross-sectional survey, you collect information from a large group of people at a specific time. These are mostly useful in retail stores, education, etc.
• Longitudinal: In a longitudinal survey, you collect information from a small group of people over a long period of time, usually years. These are useful in medical trials and applied sciences.

2. Descriptive Research

Descriptive research aims to explain and understand the current state of things like people, places, conditions, or events. Here, the researcher’s goal is to gather general observational data without exploring the reasons behind them. Also, you don’t need to begin with a hypothesis; instead, you gather data first and then create a hypothesis if needed. Importantly, descriptive research doesn’t try to prove or disprove the hypothesis. It is mainly about creating a research hypothesis.

3. Experimental Research

Experimental research, as the name suggests, uses the scientific method to establish the cause-effect relationship among a group of variables using experiments. Researchers can use multiple theories to conduct this research. The major components of experimental research are:

• A comparison group of participants who are randomly selected and assigned to experimental and control groups.
• An independent variable, referred to as the experimental variable that can be applied to the experimental group.
• A dependent variable, referred to as the effect or posttest variable that can be measured in an identical manner for all groups.

4. Correlational Research

Correlational research establishes a relationship between two close entities and determines how one impacts the other. For this, a researcher needs at least two separate groups. This type of research will recognize trends and patterns in data, but it does not go so far in its analysis to observe the different patterns.

5. Casual-Comparative Research

Causal-comparative research is employed to conclude the cause-effect equation between two or more variables, where one variable depends on the opposite experimental variable. The experimenter does not manipulate the independent variable and then measures the effects of the independent variable on the dependent variable.

Types of Quantitative Research Infographic

Here is a detailed infographic explaining the different types of quantitative research.

Characteristics

Here are the common characteristics of different types of quantitative research.

1. Numerical Data: Quantitative research primarily deals with numerical data, which can be quantified and measured. Researchers use structured instruments like surveys, experiments, or observations to collect this data.

2. Large Sample Sizes: Quantitative research typically involves large sample sizes to ensure statistical validity and generalize findings to a broader population. This allows for greater confidence in the results.

3. Statistical Analysis: Statistical analysis plays a central role in quantitative research. Researchers use statistical methods to analyze and interpret the data, identify patterns and relationships, and draw conclusions. Common statistical techniques include regression analysis, correlation analysis, t-tests, ANOVA, and chi-squared tests.

4. Structured Research Design: Quantitative research often employs a structured and pre-defined research design. Researchers develop hypotheses, select variables, and design experiments or surveys before data collection begins. This structured approach helps maintain consistency and rigor in the research process.

5. Generalizability: Quantitative research often aims to generalize findings to a larger population. When conducted correctly, quantitative studies can provide insights that are applicable beyond the specific sample studied.

6. Closed-Ended Questions: Surveys and questionnaires used in quantitative research often employ closed-ended questions with predetermined response options. This facilitates data analysis and allows for comparisons between participants.

Here is a step-by-step process of performing different types of quantitative research.

Step 1: Clearly define your research problem or question. Step 2: Conduct a literature review to identify relevant studies and theories related to your research topic. Step 3: Develop specific hypotheses or research questions that you aim to answer through your research. Step 4: Decide on the research design (e.g., experimental, cross-sectional, longitudinal) and your target population, and select a representative sample. Also, develop a data collection plan and choose appropriate data collection methods (e.g., surveys, experiments, observations). Step 5: Choose valid and reliable measurement instruments (e.g., questionnaires, scales) to collect data. Make sure to test your measurement tools before actual research. Step 6: Administer your selected data collection methods to your sample population. Step 7: Select appropriate statistical techniques (e.g., descriptive statistics, inferential statistics) based on your research design and hypotheses. Analyze the data to test your hypotheses or answer your research questions. Step 8: Interpret the statistical findings and summarize them to determine if they support or refute your hypothesis. Step 9: Prepare a research report or paper that includes the research process, methodology, results, and conclusions to share your findings.

Following are some of the common uses of various types of quantitative research.

Advantages and Disadvantages

Some of the key advantages and disadvantages of types of quantitative research are:

Final Thoughts

Quantitative research is an effective method that helps us build statistically reliable insights across numerous fields. However, researchers should select the appropriate quantitative approach considering the limitations and benefits of each method. Moreover, new emerging technologies like artificial intelligence (AI) can simplify data collection, analysis, and visualization of research outcomes.

Frequently Asked Questions (FAQs)

Q1. What are the methods of data analysis in quantitative research? Answer: Quantitative research uses different ways to analyze data. It uses descriptive stats, like averages and spreads, to summarize data. It also uses inferential statistics, like tests and regression, to check ideas and connections. For more complex situations, it uses multivariate methods like ANOVA and factor analysis to look at how different things interact. Researchers often use special software like SPSS or R to do these analyses.

Q2. How to interpret data in quantitative research? Answer: Interpreting quantitative data involves looking at the statistical results and assessing the present relationships or trends. You must also discuss if the data is applicable in both theoretical and practical sense. It is also crucial to present results clearly with tables and graphs and think about any problems or biases in collecting data. When interpreting, stay objective and let the data guide you, avoiding making guesses or assumptions.

Q3. What is quantitative vs. qualitative research? Answer: Quantitative research involves collecting and analyzing numerical data to uncover patterns, relationships, and trends. It relies on structured surveys, experiments, and statistical methods to draw objective conclusions. In contrast, qualitative research focuses on exploring the depth and meaning of phenomena through non-numerical data, such as interviews, observations, and open-ended questions. It aims to understand the underlying context and subjective experiences.

Recommended Articles

This is a guide to Types of Quantitative Research. Here, we also discuss the introduction and different types of quantitative research, which include survey, descriptive, experimental research, etc. You may also have a look at the following articles to learn more –

• Types of Research Methodology
• Quantitative Research Examples
• Descriptive Research
• Advantages and Disadvantages of Qualitative Research

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Types Of Quantitative Research Designs And Methods

Quantitative research design uses a variety of empirical methods to assess a phenomenon. The most common method is the experiment,…

Quantitative research design uses a variety of empirical methods to assess a phenomenon. The most common method is the experiment, but there are other types of quantitative research as well, such as correlation studies and case studies.

In contrast with qualitative research, which relies on subjective interpretations and extensive explorations, the various types of quantitative methods use objective analysis to reveal patterns and relations among data points that often have a numerical value. Quantitative research provides a mathematical summary of the results.

Let’s look at quantitative research design, the types of quantitative research methods and their respective strengths and weaknesses.

Types Of Quantitative Research

Components of quantitative research design.

If a researcher is studying a single variable, time, space, or another construct, they’re engaged in qualitative research. However, if that variable is a collection of quantitative data points—such as the number of employees that use a workplace break room compared to the number of employees who use other break rooms—the researcher is engaged in quantitative research.

Here are some methods commonly used in quantitative research design:

1. Experiment

The experiment is perhaps the most common way for quantitative researchers to gather data. In this method, researchers manipulate one variable at a time, while they hold all other variables constant. If a researcher wishes to determine which type of computer mouse is easier for employees to use, they must ensure the employees are experienced with computers, comfortable with their chairs or desks and have no issues with their eyesight. Common methods for this type of research include randomized experiments, non-randomized experiments, clinical trials and field studies.

2. Correlation

Correlation studies come in many forms, from simple correlation diagrams to the analysis of multiple variables. For instance, a researcher examining rates of depression among veterinarians could look at associations between self-perceived social status, salary and depression.

3. Cohort Studies

Cohort studies provide a way to measure the extent of change over a period of time. This type of research can lead to results that are both objective and subjective, depending on the type of study employed. For instance, a cohort study examining police officer salaries could determine what salary a police officer should make in an area. However, this same study could also delve into the subjective question of whether police officers are fairly paid compared to other professions.

Research design is a critical factor in the success of a study.

While there are many types of quantitative research methods that can be employed, the basic parts of all research designs are the same. Here are the principal components:

At the heart of every research project is a well-framed and considered question. Having a clear objective is the most important part of quantitative research design. Some examples of research questions could be:

• Which type of coffee brewing method extracts the most flavor?
• Which books are contributing most to a publisher’s profit?
• Which newspaper is the most widely read in a city?

In quantitative research design, researchers may explore the relationship between variables in a correlation study, or it could mean determining what variables are best in an experiment.

Once the aim is in place, the actual data collection method must be chosen. This will depend on the data needed to answer the research question. Some options are:

• Participant observations
• Experimental data

As long as the data is expressed numerically, it is quantitative data.

The selection process used to choose participants is a critical component of all types of quantitative research designs. Researchers need a well-defined population. This group can be as small as two people, but it could also be thousands of people as well.

Data Analysis

Once the data is collated, a researcher must decide how to analyze it. Some options at their disposal include:

• Descriptive analysis
• Content analysis
• Statistical tests

Once again, it depends on the research question and the goals of the study.

Presentation

This is sometimes referred to as dissemination. How will the research findings be shared with the world? Common choices are:

• Presentations
• Website articles and blogs

A quantitative researcher’s greatest contribution is that their work can be replicated. Because quantitative research relies on numbers, the results of the study can be exactly duplicated by other researchers.

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Mixed Method Research Design DB

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• Published: 03 September 2024

Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods

• Xiongwei Zhu 1 ,
• Dezhi Li 1 , 2 ,
• Shenghua Zhou 1 ,
• Shiyao Zhu 3 &
• Lugang Yu 1  

Scientific Reports volume  14 , Article number:  20461 ( 2024 ) Cite this article

Metrics details

• Climate-change adaptation
• Climate-change impacts
• Environmental impact
• Sustainability

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

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Introduction.

Cities, as centers of population and economy, play crucial roles in cultural exchange, social integration, transportation, communication, and disaster response in modern societal development 1 , 2 . According to the United Nations Human Settlements program’s “2022 World Cities Report”, as of 2021, the global urbanization rate has reached 56%, and it is projected that by 2050, an additional 2.2 billion people will live in cities, increasing the urbanization rate to 68% 3 . North America and European countries are approaching urbanization saturation, with little fluctuation expected, while urbanization in Asia and Africa will accelerate notably 4 . Particularly in China, the world’s second-largest economy, as of 2022, the urbanization rate is only 64.7%, ranking 96th globally, indicating significant potential for growth compared to developed countries like the USA and the UK 5 . The Chinese government places high importance on urbanization development. It was clearly stated in the “2020 State Council Government Work Report” that new urbanization is a key measure for achieving China’s modernization. Moreover, in the “14th Five-Year Plan (2021–2025) and the Long-Range Objectives Through the Year 2035”, detailed strategies are outlined for optimizing the urban layout and promoting urban–rural integration, among other policies to advance urbanization 6 . However, urbanization, as a process of continuous concentration of population and industrial elements in cities, while bringing opportunities for economic growth and social development, also presents a series of challenges such as environmental pressure, resource constraints, and increased demand for services 7 , 8 .

In 2008, the American company IBM introduced the concept of a “Smart Planet”, which garnered widespread attention globally 9 . The concept of a smart city, as a specific application within this framework, aims to enhance urban management and service efficiency through the integration and innovative application of Information and Communication Technology (ICT), thereby improving the quality of life for residents, optimizing resource use, reducing environmental impact, and promoting economic development and social progress 10 , 11 . Currently, the smart city construction model is seen as one of the effective means to advance global urbanization, improve urban governance, and solve major urban issues 12 . In 2009, IBM released the “Smart Planet: Winning in China” plan, outlining China’s five major thematic tasks in constructing a “Smart Planet” (sustainable economic development, corporate competitiveness, energy efficiency, environmental protection, and social harmony) 13 . The construction of smart cities, as a key measure to achieve these thematic tasks, has received significant attention from the Chinese government. In 2014, the Chinese government elevated smart city construction to a “national strategy”, considering it a cornerstone of China’s future economic and urban development strategies. By 2016, over 500 Chinese cities had initiated or announced smart city pilot construction plans, accounting for nearly half of all such projects planned or underway globally 14 . In recent years, with the continuous release of policy benefits related to smart city construction in China and substantial capital investment, China has become a leader in driving global smart city initiatives 15 . However, an undeniable fact is that while smart city construction models promote economic development and improve the quality of life for residents, the new infrastructure supporting the operation of smart cities, such as big data centers, 5G shared base stations, and Beidou ground-based augmentation stations, result in substantial energy consumption and significant carbon emissions 16 . Research shows that in 2018, the total electricity consumption of data centers in China supporting IT infrastructure reached 160.9 billion kilowatt-hours, exceeding the total electricity consumption of Shanghai for that year and accounting for about 2% of China’s total electricity consumption, with carbon emissions nearing 100 million tons 17 . The Environmental Defense Fund (EDF) predicts that by 2035, the total electricity consumption of China’s data centers and 5G base stations will reach 695.1–782 billion kilowatt-hours, accounting for 5–7% of China’s total electricity consumption, with total carbon emissions reaching 230–310 million tons 18 .

In 2022, global energy-related CO 2 emissions increased by 0.9%, reaching a record high of over 36.8 Gt. Concurrently, atmospheric CO 2 concentrations continued to rise, averaging 417.06 parts per million, marking the eleventh consecutive year with an increase exceeding 2 ppm 19 . According to the World Meteorological Organization (WMO), the global surface temperature in September 2023 was 1.44 °C higher than the twentieth century average, setting a new historical record 20 . The continuous rise in global temperatures has led to frequent occurrences of disastrous events such as extreme heat, torrential rains, floods, forest fires, and hurricanes in recent years, causing significant loss of life and property damage 21 . World Health Organization (WHO) data indicates that in 2022, there were at least 29 weather disaster events globally causing billions of dollars in losses, with approximately 61,672 deaths in Europe due to heatwave-related causes 22 . As global climate issues become increasingly severe, the call for global carbon emission reduction is growing louder. Cities, as highly concentrated areas of population and economic activities, according to the Global Report by the United Nations Human Settlements Programme (UN-Habitat), consume 60–80% of the global energy and contribute to over 75% of global CO 2 emissions 23 . As the largest global emitter of carbon, China’s CO 2 emissions in 2022 accounted for 27% of the global total 24 . Given China’s influence in the global economy, technological innovation, and international cooperation, international organizations and global climate policies generally believe that China’s efforts in carbon reduction are crucial to achieving the global 1.5 °C climate goal 25 . In recent years, the Chinese government has actively promoted the construction of low-carbon pilot cities. To date, three batches of low-carbon pilot cities have been implemented in China, bringing the total number of such cities to 81 26 .

However, the report “China’s Digital Infrastructure Decarburization Path: Data Centers and 5G Carbon Reduction Potential and Challenges (2020–2035)” indicates that compared to peak carbon emissions expected around 2025 in key sectors like steel, building materials, and non-ferrous metals in China, the “lock-in effect” of carbon emissions from digital infrastructure poses a significant challenge to achieving China’s peak carbon and carbon neutrality goals 27 , 28 , 29 . Given the urgency of global climate change, it raises the question of the correlation between smart cities and low-carbon cities: is it positive, negative, or non-existent? Should the pace of smart city development be slowed to achieve sustainable urban development goals, considering the significant carbon dioxide emissions resulting from current technological choices, social habits, and policy frameworks? To address these practical issues, it is first essential to conduct an objective and accurate assessment of urban SCP and LCL. However, due to the complexity and diversity of urban carbon emissions sources, current measurement and estimation techniques fail to capture all emission types. This limitation hampers the ability to obtain comprehensive, accurate, and timely city-level carbon emission data 30 , 31 . To address this challenge, this paper decomposes smart cities and low-carbon cities into their interdependent and interactive subsystems (i.e., economic, political, cultural, social, and ecological) viewed through the lens of urban complex systems. It then develops evaluation models for both city types and conducts empirical analyses in 52 representative Chinese pilot cities. Based on these analyses, the paper elucidates the coupling coordination degree between SCP and LCL and proposes a specific pathway for their coordinated development.

This paper is therefore structured as follows: “ Literature review ” section offers an overview of the relevant literature, laying the foundation for the introduction of SCP and LCL. Subsequently, SCP and LCL are identified clearly, and measurement based on a mixed method for the coupling coordination degree is established in “ Methodology ” section, followed by a case demonstration for the introduced method in “ Results ” section and the demonstration results analysis in “ Discussions and implications ” section. Finally, “ Conclusions ” section summarizes the study’s main findings and contributions, discusses its limitations, and suggests directions for future research.

Literature review

Evaluation of smart city: contents, methods, and subjects.

The evaluation of smart cities is a central research area within the smart city development field. Developing standardized evaluation criteria serves the dual purpose of defining smart city development boundaries and scientifically measuring its effectiveness. This, in turn, facilitates the achievement of development goals centered on evaluation-driven construction, improvement, and management 32 . We conducted data collection on “smart city*” AND “evaluation”, resulting in the selection of 82 articles. This involved an extensive search of the Wos Core Collection database for articles published in the period from January 2019 to January 2024.

To facilitate a clearer understanding for readers of current research on smart city evaluation, we have categorized it by evaluation contents , evaluation methods , and evaluation subjects .

Cluster1-evaluation contents (what to evaluate), including smart city evaluation dimensions and indicators. By analyzing the article content, it’s clear that most smart city evaluation approaches align with six core dimensions: economy, quality of life, governance, people, mobility, and environment 13 , 15 . Centered around these six dimensions, international organizations (ISO, ETSI, UN, and ITU) and scholars have established various sets of smart city evaluation indicators, considering the interdependencies among urban economic, environmental, and social factors, all in alignment with the goals of sustainable urban development 32 , 33 , 34 . Notably, Sharifi 35 compiled a comprehensive list of indicators incorporating a wide range of assessment schemes. This list not only covers the scope of the evaluation indicators (project/community/city) and their data types (primary/secondary) but also considers the stages of smart city development (planning/operation) and stakeholder involvement 36 . Subsequent research predominantly utilizes the same criteria as Sharifi 35 to identify indicator sets, taking into account the specific needs of each city and defining the spatial and temporal scales of the indicator sets 37 .

Cluster 2-evaluation methods (How to evaluate) , including smart city evaluation methods and tools. Research in this field focuses on three main areas: identifying evaluation indicators for smart cities, computing composite index, and developing evaluation models 38 , 39 . Methods for indicator identification mainly include literature review, case studies, brainstorming, the Delphi method, and data-driven techniques 40 , 41 . The Analytic Hierarchy Process (AHP) is commonly used for calculating composite indices, yet it faces issues like subjective biases and data size limitations 42 . Alternative methods, such as the Analytical Network Process (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL), are used to address these drawbacks by simulating inter-indicator interactions. Additionally, techniques like Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) are applied for indicator weighting. Finally, smart city evaluation models are constructed to aggregate various dimensions and indicators into a unified score, facilitating project comparison and ranking, and highlighting areas needing improvement 43 , 44 .

Cluster 3-evaluation subjects (Who performs the evaluation) , including smart city stakeholders and participants. Smart city evaluations involve various stakeholders and participants. These complex processes see each entity, including government agencies, international organizations, academic institutions, industry sectors, and NGOs, contributing to the smart cities’ planning, development, and management 45 , 46 . Key organizations in this realm are the International Organization for Standardization (ISO), International Telecommunication Union (ITU), United Nations Human Settlements Programme (UN-Habitat), Smart Cities Council, European Institute of Innovation and Technology (EIT Urban Mobility), and World Council on City Data (WCCD). Additionally, numerous countries have established their own smart city evaluation standards to direct and review smart city progress 11 . Notable examples are the “One New York: The Plan for a Strong and Just City” in the USA, the “BSI PAS 180” in the UK, Singapore's “Smart Nation Initiative”, and China’s “National New-type Smart City Evaluation Indicator System”.

Evaluation of low-carbon city: contents, methods, and subjects

As more countries integrate low-carbon city development into their national strategies and plans, conducting scientific evaluations of cities’ current low-carbon development levels to encourage them to adopt corresponding measures for improvement has become a key strategy in advancing cities towards a low-carbon future 47 . In the Wos Core Collection database, we conducted a search for studies spanning January 2018 to January 2023 with “low-carbon city*” AND “evaluation” as keywords, subsequently identifying 98 pertinent articles through two rounds of screening.

This section, maintaining the research framework of “ Evaluation of smart city: contents, methods, and subjects ” section ( evaluation contents, methods, and subjects ), organizes low-carbon city research to enable comparison with smart city evaluations.

Cluster 1-evaluation contents (what to evaluate), including low-carbon city evaluation systems, dimensions, and indicators. Current research focusing on low-carbon cities primarily spans six key domains: urban low-carbon scale, energy, behavior, policy, mobility, and carbon sinks. The evaluation dimensions for low-carbon cities are mainly divided into two types: single-criterion systems concentrating on specific low-carbon aspects (such as low-carbon economy, low-carbon energy, etc.), and comprehensive multi-criteria systems assessing the overall urban low-carbon development 48 , 49 . Compared to single-criterion evaluation systems, comprehensive and multi-criteria evaluation systems are increasingly gaining attention from scholars. These scholars share the view that low-carbon city construction is a diverse, dynamic, interconnected process that requires comprehensive consideration of various urban aspects, including economy, society, and environment, and involves coordinating the actions of different stakeholders to achieve sustainable urban development 50 , 51 . Additionally, international institutions and many national governments have also published low-carbon city evaluation frameworks from the perspective of comprehensive and multi-criteria evaluation systems. The most notable examples include the United Nations Commission on Sustainable Development, which set 30 indicators from four dimensions: social, environmental, economic, and institutional, to evaluate the level of urban low-carbon development. The Chinese Academy of Social Sciences proposed the “China Low Carbon City Indicator System”, covering 8 dimensions such as economy, energy, facilities, and 25 specific indicators including energy intensity, per capita carbon emissions, and forest coverage rate.

Cluster 2-evaluation methods (How to evaluate) , including low-carbon city evaluation methods and tools. Firstly, identifying evaluation indicators as the initial step in constructing a low-carbon city evaluation model, current research methods not only include traditional methods like literature review and expert interviews but also increasingly involve scholars using dynamic perspectives based on urban complex systems, applying models like DPSR (Driving forces-Pressures-State-Response), STIRPA (Stochastic Impacts by Regression on Population, Affluence, and Technology), the Environmental Kuznets Curve (EKC), and STEEP (Social, Technological, Economic, Ecological, and Political) for indicator identification 52 , 53 . Secondly, weighting evaluation indicators, an essential part of model construction, typically involves methods like subjective weighting (expert scoring, Delphi method, AHP) 54 , objective weighting (PCA, Entropy weight method, variance analysis), and combined weighting (DEA) 55 . Each method has its characteristics and suitable scenarios and should be selected according to specific circumstances. Additionally, quantitative assessment of regional carbon emissions using methods like carbon footprint analysis, baseline emission comparison, and Life Cycle Assessment (LCA) is also becoming a research focus 56 .

Cluster 3-evaluation subjects (Who performs the evaluation) , including low-carbon city stakeholders and participants. The evaluation of low-carbon cities also involves multiple stakeholders (government, enterprises, residents, etc.) 57 . Among them, international organizations like the International Organization for Standardization (ISO), the International Energy Agency (IEA), and the World Meteorological Organization (WMO) have played significant roles in establishing low-carbon city evaluation standards and promoting global low-carbon city development. Additionally, due to economic, policy, and perception factors, current low-carbon city construction relies primarily on government financial input, with social capital and public participation in low-carbon city construction noticeably lacking 58 . Therefore, how to enhance the awareness of enterprises and residents as main actors in low-carbon city construction has become a current research focus.

Coupling coordination analysis between SCP and LCL

Smart cities and low-carbon cities, as important urban development models for the future, have seen an increasing focus on their interrelation by scholars in recent years, becoming an emerging research hotspot in the field. In the Wos Core Collection database, we searched for studies from January 2018 to January 2024 using the keywords “smart city*” “low-carbon city*” “correlation analysis” “coupling coordination analysis” and “urban sustainability”. After two rounds of screening, 24 related studies were selected for analysis.

From the perspective of research results, the current research conclusions about the correlation between low-carbon cities and smart cities primarily include two main points: (i) SCP and LCL cannot achieve coupling coordination development. Some scholars argue that SCP and LCL differ in their focus: SCP emphasizes urban technological and economic development, while LCL focuses more on urban ecological construction 17 . Particularly, De Jong identified 12 urban development concepts, including smart city, low-carbon city, eco-city, and green city. He believes that a clear distinction must be made in the conceptual definition of these types of cities to more accurately guide future urban planning 59 . Furthermore, some scholars argue that the relationship between SMC and LCC is negatively correlated. Deakin believes that the direct environmental benefits of IoT technology are insufficient to achieve urban sustainability goals 60 . Barr et al. argue that the logic of smart cities often leads city administrations to prioritize superficial changes and promote individual behavioral shifts, detracting from the crucial task of reconfiguring urban infrastructure for low-carbon lifestyles 61 , 62 . (ii) SCP and LCL can achieve coupling coordination development. Some scholars believe there is a positive correlation between SCP and LCL, with SCP potentially promoting the development of LCL. Specifically, the intelligent systems built by SCP can effectively match urban energy supply and demand, reducing urban carbon emissions, such as through smart grids and intelligent transportation networks 18 . It is worth noting that most of the studies on the coupling coordination relationship between urban SCP and LCL are based on perspectives of individual urban subsystems such as technology, economy, management, industrial structure, and society. They lack a comprehensive consideration of the city as a complex system 59 , 61 , 63 .

From the perspective of research methodologies, coupling coordination analysis is a fundamental statistical approach for examining relationships between two or more variables. This analysis typically employs techniques such as Pearson’s correlation coefficient, Spearman’s rank correlation coefficient, Kendall’s tau, partial correlation, point-biserial correlation, and multiple correlations. Each technique offers unique insights into the nature and strength of the interdependencies among variables 61 . The selection of an appropriate method depends on the data type (continuous, ordinal, or categorical), its distribution (e.g., normal distribution), and the specific objectives of the research.

In summary, although existing research has made significant contributions to the independent evaluation and advancement of smart cities and low-carbon cities, including their relevant construction content, main actors, as well as some specific measures such as empowering cities with data intelligence for low-carbon economic development and transitioning industrial structure to low-carbon, there are still some important knowledge gaps. On the one hand, current research primarily analyzes the coupling coordination relationship between urban SCP and LCL from the micro-perspective of individual urban subsystems such as economic and energy systems. This approach lacks a macroscopic perspective from the complex urban system, which is detrimental to the comprehensive development of cities 60 , 64 , 65 . On the other hand, current studies often only conduct basic qualitative comparisons of the relationship between the development levels of urban SCP and LCL from a quantitative or qualitative perspective. They lack a comprehensive analytical approach that integrates both qualitative and quantitative analyses for further exploration of the coupling coordination relationship between urban SCP and LCL. This shortfall hinders the sustainable development of cities.

To fill these knowledge gaps, this study employs a mixed-methods approach, combining qualitative and quantitative analyses, to examine the model of coupling coordination between urban SCP and LCL. It also develops recommendations to enhance this coupling coordination, aiming to support sustainable development goals. Furthermore, this research selects 52 typical low-carbon and smart pilot cities in China as case studies, ensuring both scientific validity and practical applicability of the findings. Additionally, to enhance the logical coherence and readability of this study, we posit that a coupling coordination relationship exists between urban SCP and LCL and thus propose Hypothesis 1 .

Hypothesis 1

There is a substantial degree of coupling coordination between the overall urban system’s SCP and LCL, yet there are disparities in this coordination degree among the subsystems of economy, society, politics, culture, and ecology.

Methodology

Research framework.

The construction of low-carbon and smart cities, as key pathways to urban sustainability, necessitates examining their interplay and fostering their collaborative development for achieving sustainability goals 66 . This research employs a sequential framework, including Conceptual, Data, Analysis, and Decision-making Layers, to methodically explore the coupling coordination relationship between SCP and LCL, with the framework illustrated in Fig.  1 .

Research framework.

Firstly , in the Conceptual Layer, this study aligns with the United Nations’ objectives for sustainable cities, encompassing economic growth, social equity, better life conditions, and improved urban environments. Integrating these with China’s “Five-Sphere Integrated Plan (economy, politics, culture, society, and ecological environment construction)” for urban development, the research dissects the components of smart city systems (such as information infrastructure, information security, public welfare services) and low-carbon city systems (including low-carbon construction, transportation, and industry), with the aim to collect indicators. Secondly , in the Data Layer, this research develops smart city and low-carbon city evaluation systems, grounded in national standards and official statistics, to qualitatively examine the correlation between SCP and LCL from a macro perspective. Thirdly, in the Analysis Layer, this study selects 52 cities, both smart and low-carbon pilot cities in China, as samples for quantitative analysis. The process involves standardizing indicators, scoring and ranking the cities based on their smart performance and low-carbon levels, followed by employing Pearson’s correlation coefficient and coupling coordination degree model to scientifically analyze the correlation between SCP and LCL. Finally, in the Decision-making Layer, the study examines the coupling coordination relationship between urban smart performance, the overall low-carbon level, and the low-carbon level across five dimensions, which is key for us to test Hypothesis 1 . It also formulates development paths for the coupling coordination of smart and low-carbon cities.

SCP index system construction

Since the concept of smart cities was introduced in 2008, many national governments have established smart city evaluation standards. Due to varying national conditions, SCP evaluation indicators differ across countries. As the sample cities in this study are Chinese smart pilot cities, the selection of SCP evaluation indicators primarily references relevant Chinese national standards. As a global pioneer in smart city development, China released the “Evaluation indicators for new-type smart cities (GB/T 33356-2016)” in 2016 and revised it in 2022. This national standard, with its evaluative indicators, clearly defines the key construction content and development direction of new smart cities, aiming to specifically enhance the effectiveness and level of smart city construction, gaining significant recognition within the industry.

This study, grounded in the concept of a city’s “Five-in-One” sustainable development, is guided by three principles of “Inclusive well-being & Ecological harmony”, “Digital space & Physical space”, and “New IT technologies & Comprehensive services”. It also adheres to the “people-oriented concept” and adopts an “urban complex dynamic perspective” in the process of smart city construction. Additionally, it follows the principle of “similar attributes of evaluation objects”. Based on these foundations, the study establishes three criteria for selecting evaluation indicators, including scientific, coordination, and representation. Drawing on the Chinese government’s smart city evaluation standards and utilizing a literature review methodology, this research constructs an SCP evaluation indicator system for cities, as detailed in Supplementary Appendix Table A1 . The SCP index system includes six primary indicators, including smart public service (SPE), precise governance (PG), information infrastructure (II), digital economy (DE), innovative development environment (IDE), and citizen satisfaction (SCS). It also features 24 secondary indicators, such as traffic information services, grassroots smart governance, and spatio-temporal information platforms. Importantly, to explore the correlation between smart cities and low-carbon cities more effectively, the study deliberately omits “Internet + Green Ecology” related indicators from the smart city evaluation system. To ensure the accuracy and representativeness of these indicators, they were validated through expert consultation, public participation, and comprehensive statistical methods.

LCL index system construction

Current international organizations and academic perspectives on low-carbon city evaluation systems are predominantly based on the urban complex systems approach, considering the interplay and interaction of aspects such as low-carbon society, economy, and technology. Consistent with the principles for selecting SCP evaluation indicators, the choice of LCL evaluation indicators in this study primarily adheres to relevant Chinese national standards and related literature.

As a proactive practitioner in global low-carbon city development, in 2021, the Chinese government released the “Sustainable Cities and Communities—Guides for low-carbon development evaluation (GB/T 41152-2021)”. This national standard evaluates the level of urban low-carbon development, clarifying the key directions for such development, and serves as a current guide for low-carbon city construction in China. Thus, this study, grounded in the “Five-in-One” sustainable urban development framework and guided by the principles of “carbon reduction & pollution reduction”, “green economic growth”, and “enhanced carbon sequestration capacity”, combines the previously established principles of scientific, coordination, and representative for selecting evaluation indicators. It establishes an LCL index system based on the Chinese government’s evaluation standards and relevant literature. Specifically, the LCL evaluation index system constructed in this study includes five primary indicators, including low-carbon economic (LCE), low-carbon society (LCS), low-carbon environmental quality (LCEQ), low-carbon management (LCM), and low-carbon culture (LCC), as well as 22 secondary indicators such as energy consumption per unit of GDP and carbon emission intensity, as shown in Supplementary Appendix Table A2 . Similarly, to ensure the accuracy and representativeness of the indicators, the specific indicators were validated through expert consultation, public participation, and comprehensive statistical methods.

Analysis model construction

In this study, an Entropy-TOPSIS-Pearson correlation-Coupling coordination degree (ETPC) analysis model is constructed to quantitatively analyze the coupling coordination relationship between Urban SCP and LCL. The entropy method is first applied for objective weighting of evaluation indices, ensuring data objectivity and reducing subjective bias, thus enhancing the model’s accuracy and fairness. Next, the TOPSIS method is used to rank sample cities based on their smart performance and low-carbon levels, providing a straightforward and intuitive ranking mechanism. The Pearson correlation method then examines the correlation between SCP and LCL, offering data-driven insights into the dynamic relationships between these variables. Finally, the coupling coordination model calculates the degree of coordination between SCP and LCL, providing a theoretical basis for subsequent enhancement pathways and policy recommendations. The ETPC model constructed in this study has several advantages and complementarities, allowing for a comprehensive analysis and evaluation of the research question from various perspectives. Additionally, the ETPC model can be broadly applied to other multidimensional evaluation and decision analysis issues, such as the coupling coordination between various public health interventions and community health levels, and the comprehensive effects of different economic policies on regional economic development and environmental impact. Specific analysis steps are outlined as follows.

Step 1: Conduct the data normalization process.

where x ij and y ij represent respectively the original and standardized value for the indicator j in referring to the sample case i ( i  = 1,2,3,…, m; j  = 1,2,3,…, n ), max (x j ) and min (x j ) denote respectively the largest and smallest value among all m samples for the indicator j , P ij represents the value proportion of indicator j in the sample case i to the summation value of the indicator from all cases.

Step 2: Calculate the weight and measure the comprehensive level based on entropy method.

The entropy weight method, an objective approach deriving weights from sample characteristics, mitigates expert bias, enhancing the objectivity and credibility of indicator weighting 67 . This study employs this method, determining weights through the calculation of each indicator’s information entropy, and measure the comprehensive level of the subsystem.

where m is the total number of sample cases, \({e}_{j}\) demonstrates the entropy value of the j indicator and \({\omega }_{j}\) denotes the weight of indicator j , and V represent the comprehensive level.

Step 3: Conduct a ranking of evaluation objects based on TOPSIS method.

A key limitation of the entropy method is its tendency to neglect the significance of indicators. The TOPSIS method, addressing this issue, is an ideal-solution-based ranking technique that aids in multi-objective decision-making among finite options 68 . In this approach, the study first determines positive and negative ideal solutions, measures each objective’s distance to these ideals, and subsequently ranks the subjects by the proximity of each objective to the ideal solution.

where \({ V}^{+}\) and \({V}^{-}\) respectively represent the best ideal solution and the worst ideal solution, \({D}_{i}^{+}\) and \({D}_{i}^{-}\) represent the distances from the objective to the positive and negative ideal solutions, respectively. \({C}_{i}\) indicates the closeness of the evaluation objective to the optimal solution, with \({C}_{i}\in \left[\text{0,1}\right]\) . A larger \({C}_{i}\) value suggests stronger smart and low-carbon development capabilities of the sample city.

Step 4: Analyze the correlation based on Pearson correlation method.

The Pearson correlation method is commonly used to measure the correlation coefficient between two continuous random variables, thereby assessing the degree of correlation between them 69 . In this study, based on the results from Steps 1–3, two sets of data are obtained representing the smart development level and low-carbon development level of sample cities, \(A:\left\{{A}_{1},{A}_{2},\dots ,{A}_{n}\right\}\) and \(B:\left\{{B}_{1},{B}_{2},\dots ,{B}_{n}\right\}\) . The overall means and covariance of both data sets are calculated, resulting in the Pearson correlation coefficient between the two variables.

where \({A}_{i}\) and \({B}_{i}\) respectively represent the SCP and LCL of sample cities. \(E\left(A\right)\) and \(E\left(B\right)\) are the overall means of the two data sets, \({\sigma }_{A}\text{ and }{\sigma }_{B}\) are their respective standard deviations, \(cov(A,B)\) is the covariance, and \({\rho }_{AB}\) is the Pearson correlation coefficient. When the correlation coefficient approaches 0, the relationship weakens, as it nears − 1 or + 1, the correlation strengthens.

Step 5: Analyze the coupling coordination degree based on the coupling coordination model.

The coupling coordination degree characterizes the level of interaction between different systems and serves as a scientific model for measuring the coordinated development level of multiple subsystems or elements 70 . This study has developed a model to measure the coupling coordination degree between two systems.

where C defines the coupling degree, \({f}_{1}\) and \({f}_{2}\) are the evaluation values of SCP and LCL respectively. CPD represents the coupling coordination degree. \(\alpha\) , \(\beta\) are the coefficient to be determined, indicating the importance of the systems. This study assumes that each system is equally important. Thus \(\alpha =\beta =1/2.\)

In this study, building upon the framework established by a preceding study, a classification system for the coupling coordination degree was developed. This system delineates the various types of coupling-coordinated development among SCP, LCL, LCS, LCM, LCEQ, and LCC. Current research on the division of coupling coordination degree intervals often uses an average distribution within the [0, 1] range 70 . However, due to the large sample size and the wide distribution range of coupling coordination degrees in this study, we have categorized these types into ten distinct levels based on their rank, as detailed in Table 1 .

Selection of sample cities and data collection

The Chinese government has prioritized the development of smart and low-carbon cities. Since 2010, it has launched 290 smart city pilots and 81 low-carbon city pilots across various regions, reflecting different levels of development, resource allocations, and operational foundations. To maintain the scientific integrity of our study, we established stringent criteria for selecting sample cities: (i) each city must be concurrently identified as both a smart and a low-carbon city pilot, and (ii) their government agencies must have issued data on key performance indicators for these initiatives. Following these criteria, our research has ultimately selected 52 cities as samples, as detailed in Fig.  2 . It is noteworthy that these 52 typical case cities are almost all provincial capitals in China, mostly located within the Yangtze River Delta, Pearl River Delta, Jingjinji (Beijing–Tianjin–Hebei), and Western Triangle economic regions. Additionally, according to the “Globalization and World Cities Research Network (GaWC) World Cities Roster 2022 (GaWC2022)”, these cities are ranked within the top 200 globally. Therefore, given the scope of this research, these case cities offer significant representativeness and can serve as valuable models for promoting development in other urban areas. The data for this paper were sourced from the “China Low-Carbon Yearbook (2010–2023)”, the “China Environmental Statistics Yearbook (2010–2023)”, and low-carbon city data published by the governments of the sample cities. Additionally, this study addressed any missing data by averaging the data from adjacent years and applying exponential smoothing.

52 sample cities and their geographic locations.

Weighting values between evaluation indicators

The entropy weighting values between the 20 indicators of SCP and the 19 indicators of LCL are calculated by applying the data described in “ Weighting values between evaluation indicators ” section to formula ( 1 )–( 5 ), and the results are shown in Supplementary Appendix Tables A3 and A4 . Specifically, within the SCP evaluation framework, SPE and II are assigned the highest weights, while LCS and LCM are allocated the highest weights within the LCL evaluation framework. Conversely, SCS and LCC have attributed the lowest weights in their respective contexts.

Evaluation of SCP and LCL in sample cities

Utilizing the data from “ Selection of sample cities and data collection ” section and the weighting values derived in “ Weighting values between evaluation indicators ” section, we can determine the SCP and LCL of sample cities using the TOPSIS method, as outlined in formulas ( 6 )–( 9 ). The results are illustrated in Supplementary Appendix Table A5 and Fig.  3 . In this study, the value of the closeness coefficient (C i ) is used to indicate the relative closeness of a particular sample city to the negative ideal point 71 . The negative ideal point represents the worst solution of the ideal, where the individual attribute values reach their worst in each alternative. Therefore, a larger value of closeness indicates better smart city performance or a lower carbon level of a sample city 72 . C LCL and C SCP respectively represent the low-carbon level closeness coefficient and the smart city performance closeness coefficient. In referring to Supplementary Appendix Table A5 , the best three cities of SCP are Shenzhen, Shanghai, and Hangzhou, whilst the worst three cities are Yan’an, Jincheng, and Xining. Furthermore, Chengdu, Qingdao, and Beijing are the best there low-carbon level performers. Whilst Jincheng, Urumqi, and Huhehaote are the three worst.

TOPSIS-based analysis of SCP with LCL in 52 sample cities.

In referencing Fig.  3 , this study considers SCP data of sample cities as the control variable and ranks them in ascending order based on TOPSIS results. We then examine changes in LCL data to ascertain the correlation between these variables, yielding two key research conclusions: on one hand, analysis of 52 sample cities demonstrates a general ascending trend in both SCP and LCL data curves. This trend suggests a positive correlation between these two parameters. On the other hand, the LCL data, in contrast to the consistent rise in SCP, exhibits notable fluctuations and wider dispersion. This indicates that the positive correlation between SCP and LCL, while present, is not markedly robust.

Correlation results of SCP and LCL in sample cities

Correlation analysis of urban SCP and overall-LCL. This analysis employs the closeness coefficient (C i ) to assess SCP and overall-LCL in sample cities for Hypothesis 1 in Eqs. ( 10 ) and ( 11 ). The results are presented in Table 2 . Additionally, a linear regression analysis is conducted to determine the presence and magnitude of the relationship between SCP and LCL in these cities, as shown in Fig.  4 .

The scatter and regression of SCP and LCL: ( A ) SCP & Overall-LCL; ( B ) SCP & LCM; ( C ) SCP & LCS; ( D ) SCP & LCE; ( E ) SCP & LCQE; ( F ) SCP & LCC.

Considering the closeness coefficient range, correlation is categorized into five levels: very weak ( \(\left|{\rho }_{AB}\right|<0\) .1), weak ( \(0.1\le \left|{\rho }_{AB}\right|<0\) .3), moderate ( \(0.3\le \left|{\rho }_{AB}\right|<0\) .5), strong ( \(0.5\le \left|{\rho }_{AB}\right|<0\) .7), and very strong ( \(0.7\le \left|{\rho }_{AB}\right|<1.0\) ) 73 . Table 1 indicates a strong positive correlation between SCP and overall LCL. Linear regression analysis in Fig.  4 A demonstrates a significant correlation between SCP and urban LCL ( R 2  = 0.42, p  < 0.001), with notable differences exist among cities, consistent with Hypothesis 1 .

Correlation analysis of SCP and each low-carbon dimension. Pearson correlation analysis effectively measures the strength of linear relationships between two variables, but it does not identify causal relationships between them. To address this limitation and explore the interaction between the two variables, this study sets and solves the closeness coefficient for each low-carbon dimension, which are low-carbon economy (C LCE ), low-carbon society (C LCS ), low-carbon environmental quality (C LCEQ ), low-carbon management (C LCM ), and low-carbon culture (C LCC ). It then calculates the correlation analysis results for SCP and each low-carbon dimension for Hypothesis 1 , as shown in Table 1 . Furthermore, the results of the linear regression analysis are presented in Fig.  4 .

In detail, strong correlations exist between SCP and LCM, LCS, and LCEQ. The correlation is moderate with LCE and weak with LCC. Furthermore, linear regression analysis shows that the links between SCP and low-carbon levels across five dimensions are significant with minimal variance. Cities with higher SCP typically show higher values in LCM ( R 2  = 0.38, p  = 0.000), LCS ( R 2  = 0.35, p  = 0.000), and LCE ( R 2  = 0.32, p  = 0.000) as depicted in Fig.  4 B–D. However, this trend is less pronounced in LCEQ ( R 2  = 0.17, p  = 0.000) and LCC ( R 2  = 0.06, p  = 0.001), which exhibit greater dispersion as shown in Fig.  4 E,F. The lower R 2 values for LCEQ and LCC compared to other dimensions suggest a greater influence of factors not included in the model. Furthermore, to ensure the credibility and reliability of the research findings, this study conducted a sensitivity analysis by identifying and removing outliers from the sample dataset using the Z-score method, in addition to the previously mentioned Pearson correlation analysis. The Pearson correlation coefficient for the original dataset of city SCP and LCL is 0.65, with a significant P-value. After removing the outliers, the Pearson correlation coefficient is 0.61, and the P-value remained significant. Therefore, the correlation between city SCP and LCL proposed in Research Hypothesis 1 is robust.

Coupling coordination degree of SCP and LCL in sample cities

The degree of coupling coordination comprehensively considers multiple aspects of urban complex systems, including economic, social, and environmental dimensions. By systematically evaluating the coordinated development level of urban SCP and LCL, this approach enables the analysis of the coupling and coordination relationships between SCP and LCL, as well as among various subsystems such as LCM, LCS, LCE, LCEQ, and LCC. This reveals the dynamic interactions and causality between SCP and LCL within urban complex systems. The coupling coordination degrees of SCP and LCL, along with their subsystems, in 52 typical smart and low-carbon pilot cities in China, are illustrated in Fig.  5 .

Coupled coordination degree of SCP and LCL, LCS, LCEQ, LCE, LCM, LCC.

Characteristics of objective changes in the coupled coordination degree between SCP and LCL. Based on the coupling coordination model and Eqs. ( 12 ) to ( 14 ), the coupling coordination degree of the urban complex system in SCP and LCL regions is calculated for Hypothesis 1 , as illustrated in Fig.  5 .

From the holistic perspective of urban complex systems, as the level of urban SCP continuously improves, the coupling coordination degree between SCP and LCL among 52 pilot cities in China shows an upward trend. This indicates that as the functional indices of urban SCP and LCL both strengthen, their interaction and coordination also enhance. Among these, Jincheng has the lowest coupled coordination degree at 0.5201, while Beijing boasts the highest at 0.8622. Within the 52 pilot cities, 5.78% exhibit a barely coupling coordination level, 51.93% display a primary coupling coordination level, 25% achieve an intermediate coupling coordination level, and 17.31% reach a good coupling coordination level. Moreover, the average coupling coordination degree of the 52 pilot cities is 0.598, suggesting that the SCP and LCL of the pilot cities can achieve coupled coordinated development.

Characteristics of objective changes in the coupled coordination degree among SCP, LCM, LCS, LCE, LCEQ, and LCC for Hypothesis 1 are illustrated in Fig.  5 .

From the perspective of urban subsystems, the coupling coordination degrees of LCS & SCP, LCE & SCP, and LCM & SCP all exhibit characteristics of steady fluctuations with an upward trend, while the coupling coordination degree of LCC & SCP shows greater volatility in its upward trend. The coupling coordination degree of LCEQ & SCP demonstrates a trend of initially rising and then declining. Furthermore, the average values of the coupling coordination degrees for LCS & SCP, LCE & SCP, LCM & SCP, LCEQ & SCP, and LCC & SCP are 0.478, 0.761, 0.779, 0.710, and 0.485, respectively. Among these, the pilot cities’ subsystems of LCE, LCM, and LCEQ with SCP exhibit an intermediate level of coupling coordination, while the coupling coordination degrees of LCS and LCC with SCP are on the verge of a dysfunctional recession. This indicates that the causal relationships between urban SCP and the subsystems of urban LCM, LCS, LCE, LCEQ, and LCC vary. Overall, Hypothesis 1 holds true both from the perspective of the city's overall system and from the perspective of its various subsystems.

Discussions and implications

Relationship between scp and lcl of different cities.

Considering the evaluation results of the urban SCP and LCL, four grades of the overall points can be classified, namely, excellent (0.7–1.0), average (0.5–0.7), below average (0.4–0.5), and poor (0–0.4). Subsequently, the sample cities in Supplementary Appendix Table A5 were classified based on these gradations. In the sample, cities with excellent SCP constitute 9.62%, about double the proportion with excellent LCL. Cities with average SCP account for 48.08%, whereas those at average LCL represent only 26.92%. Notably, cities with poor LCL comprise 26.92%, nearly triple the rate of those with poor SCP. The findings suggest that China’s SCP currently outperforms its low-carbon city initiatives, largely attributable to the rapid advancement of the Internet and Information and Communication Technology (ICT) in recent years. What’s more, Fig.  4 illustrates that urban SCP significantly positively influences the urban LCL, though substantial variations exist among different cities. The relevant types can be summarized into the following four categories.

Quadrant I-high SCP and high LCL, including only six cities (Shenzhen, Shanghai, Beijing, Ningbo, Xiamen, and Qingdao). These cities are not only among China’s earliest smart city pilots but also recent focus areas for the government’s “Carbon Peak Pioneer Cities” initiative. By actively exploring innovative models, systems, and technologies for smart and low-carbon co-development, these cities provide valuable practical experiences for others. For instance, Shenzhen has developed a multi-level, multi-component greenhouse gas monitoring network and technology system for “carbon flux, carbon concentration, carbon emissions”, while Ningbo has constructed a “smart zero-carbon” comprehensive demonstration port area.

Quadrant II-poor SCP and poor LCL, numerous cities in Fig.  4 A, such as Jincheng, Lhasa, and Urumqi, exhibit poor SCP and LCL. Despite China having the most smart and low-carbon city pilots globally, its development level in these areas still lags significantly behind typical developed countries. While China’s infrastructure like networking and computing power has reached a certain scale, issues persist with insufficient integration and intensity in infrastructure construction and operation, as well as problems with aging infrastructure and low levels of intelligence. Furthermore, although China’s low-carbon pilot cities have made positive progress in promoting low-carbon development, most still have incomplete carbon emission statistical systems and inadequate operational mechanisms, leading to generally poor overall low-carbon development levels.

Quadrant III-high LCL but poor SCP, such as Kunming, Xining, and Guiyang. These cities possess resources conducive to low-carbon development, such as Kunming and Guiyang with their rich forest carbon sinks, and Xining with abundant clean energy sources like solar and wind power. However, they are mostly situated in China’s central and southwest areas with underdeveloped physical and economic conditions. Leveraging their abundant low-carbon resources, and utilizing big data and IoT technology, achieving sustainable green economic growth through carbon credits and trading markets, as well as green finance, represents a significant future development direction for these cities.

Quadrant IV-high SCP but poor LCL, including Suzhou, and Jinhua Zhongshan, decoupling economic development from carbon emissions presents a significant development challenge for these cities. Specifically, for Suzhou, one of the world’s largest industrial cities, the main challenge is achieving decarburization in the energy sector and transitioning high-emission manufacturing industries to low-carbon alternatives.

What’s more, as illustrated in Fig.  5 , the degree of interaction between SCP and LCL across the 52 pilot cities in China positively impacts the balanced and comprehensive performance of these cities. This, in turn, fosters the coordinated development of urban systems as a whole. Moreover, the continual increase in the coupled coordination degree between SCP and LCL with the enhancement of SCP in pilot cities indicates that smart city construction contributes to urban low-carbon development. Future urban development in China should fully leverage the industrial upgrading effect, carbon sequestration effect, and energy utilization effect of smart city construction. However, the increasing slope of the SCP & LCL coupled coordination degree curve in Fig.  5 suggests significant regional differences in the level of SCP & LCL coupled coordination development across Chinese cities. Smart city construction has a more pronounced decarburization effect in central and western cities, southern cities, non-environmentally focused cities, and resource-based cities, with cities in the northwest showing notably poorer levels of SCP & LCL coupled coordination development. This serves as a warning for future urban development in China.

Relationships between SCP and LCL in each urban subsystem

The relationship between urban SCP and LCL across five dimensions is illustrated in Fig.  4 B–F. There is a strong positive correlation between SCP and LCM, LCS, and LCE, while a moderate correlation is observed with LCEQ, and a weak correlation with LCC. Furthermore, the degree of coupling coordination between SCP and subsystems such as LCS, LCEQ, LCE, LCM, and LCC is examined in Fig.  5 . The results of the coupling coordination vividly illustrate the synergistic interactions and developmental harmony between urban SCP and various systems.

Among these, the coupling coordination degree curve fluctuation between SCP & LCM is stable, situated at an intermediate coupling coordination level, indicating the dominant role of the Chinese government in the construction of smart cities and low-carbon cities, as well as the effectiveness of policy implementation. However, this also suggests that in promoting urban smart and low-carbon construction, China faces the risk of adopting “one-size-fits-all” mandatory policies, neglecting to advance construction in phases with emphasis, tailored to the city's resource endowment and economic development status. The coupling coordination degree curve changes between SCP&LCE and SCP&LCL show the highest degree of fit, indicating that low-carbon economic development brought about by digital empowerment and upgrading of the urban industrial structure is a key driving factor for promoting the coupled coordination development of urban smart and low-carbon initiatives. Transforming traditional industrial structures and pursuing low-carbon upgrades of the economic structure present challenges for urban development in China today. The coupled coordination degree of SCP & LCS is on the verge of a dysfunctional recession, highlighting the imbalance in the development between China's SCP and LCS, especially in terms of new infrastructure construction, such as smart transportation and logistics facilities, smart energy systems, smart environmental resources facilities, etc. The current construction of new infrastructure in China is far from meeting the living needs of the broad masses of people.

It is noteworthy that with the continuous improvement of the SCP in sample cities, the coupling performance degree between SCP and LCEQ exhibits two phases: an initial stage of synergistic enhancement followed by a stage of diminished synergy. In the early phase of synergistic development, the SCP and LCEQ systems of cities, driven by shared goals of sustainable urban development, strategy adjustments, resource sharing, and technological progress, facilitated effective collaboration and integration between systems. However, upon reaching a certain stage, intensified resource competition, declining management efficiency, and environmental changes led to internal system fatigue, resulting in weakened synergy. This indicates that once the technological effects generated by smart city construction reach a certain level, it becomes crucial to enhance the city's capacity for autonomous innovation. Addressing the bottleneck issues of core technologies and transforming the development mode of smart low-carbon technology from “imitative innovation” represent significant breakthroughs for further promoting the coupled coordination of SCP and LCEQ in China’s future.

Moreover, as the SCP of sample cities continuously improves, the coupled coordination degree between SCP and LCC shows two phases: initial stable fluctuations and subsequent rapid growth. The turning point in the curve change occurs at a coupled coordination degree of 0.6, denoted as the primary coupling coordination point. Among these, the low-carbon awareness rate of urban residents, as a key indicator of LCC, shows that the majority of urban residents in China are still in the cognitive awakening stage regarding low-carbon consciousness. At this stage, residents begin to recognize the severity of climate change and environmental degradation, along with the importance of smart low-carbon lifestyles in mitigating these issues. The government continuously promotes this awareness through media reports, educational activities, official propaganda, and community initiatives. As residents gain a deeper understanding of the issues, their attitudes shift from initial indifference or skepticism to a stronger identification with and support for the values and concepts of smart low-carbon living. This shift encourages residents to experiment with new smart low-carbon lifestyles, gradually finding suitable smart low-carbon behavioral patterns that become habitual. Ultimately, when smart low-carbon lifestyles are fully internalized as part of residents’ values, they not only practice smart low-carbon living at the individual level but also actively participate in promoting society’s smart low-carbon construction. Therefore, this study posits that the emergence of the coupled coordination degree turning point between SCP and LCC is not only a process of individual behavioral change but also a reflection of social and cultural transformation. This process is time-consuming and influenced by multiple factors, including policy guidance, economic incentives, educational dissemination, and the social atmosphere.

Implications for promoting coupling coordination development between urban SCP and LCL

Low-carbon and smartness are vital features of modern, sustainable urban development and key supports for it. This study posits that urban low-carbon and smart development should not be disjointed but rather synergistic and complementary. To better achieve sustainable urban development goals, a model should be constructed with “low-carbon” as the cornerstone of sustainable development and “smartness” as the technological assurance for low-carbon growth. Specifically, this study proposes the “urban smart low-carbon co-development model”, which entails a deep integration of intelligent technologies such as the Internet of Things (IoT) and big data with urban construction, governance services, and economic development. This model leverages digitalization to facilitate decarburization, thereby achieving urban sustainable development goals such as energy-efficient and green urbanization, ecological and livable environments, and streamlined governance services.

Furthermore, to better coordinate smart development with low-carbon city construction, enhance low-carbon city building through digitalization, and explore exemplary practices and models of smart low-carbon city construction, this study finds it necessary to establish an evaluation system for smart and low-carbon urban co-development. Therefore, based on the aforementioned urban SCP and LCL evaluation indicator system, this study initially conducted a literature review of past research, selecting 5 primary indicators and 20 secondary indicators from 48 articles to evaluate the degree of coupling coordination development between urban SCP and LCL. Subsequently, the Delphi method was employed to finalize the list of evaluation indicators, with 10 experts from various regions and diverse backgrounds in China refining the list and determining the weights of each indicator, as shown in Supplementary Appendix Table A6 . The final Smart Low-Carbon City Coupling Coordination Development Evaluation Indicator System, as presented in Table 3 , comprises 5 primary indicators and 18 secondary indicators. This evaluation system aims to emphasize the utilization of next-generation information technologies such as 5G, artificial intelligence, cloud computing, and blockchain to expand urban green ecological spaces, strengthen ecological environment governance, and enhance the level of intelligent urban governance, meeting the development needs of smart low-carbon cities.

The policy implications from the analysis results suggest that actions should be taken by government departments in China to reduce the uneven performance between urban SCP and LCL across various cities. These actions include, for example: Firstly, guiding the innovative development of urban SCP and LCL through policies, such as enhancing government digital services and administrative platforms, continuously promoting the development of emerging industries and the upgrading of traditional industries, and actively promoting green energy technologies. Secondly, categorizing and advancing the coordinated development of smart and low-carbon cities—comprehensive development should be pursued simultaneously in large cities in eastern and central China, while in smaller cities in western China, priorities should include enhancing urban innovation capabilities and improving infrastructure to lay a solid foundation for the coupled coordination of urban SCP and LCL. Thirdly, constructing a multi-stakeholder governance system to maximize the leading role of the government, the main role of enterprises, and the active participation of residents. By fostering a positive social atmosphere and cultural attributes, this will enhance the sense of participation and achievement among different social groups, creating a sustainable development model for urban SCP and LCL coordination. Lastly, emphasizing the development of SCP and LCL coordination in county-level cities is crucial. While large Chinese cities have already begun to form a pattern of coordinated SCP and LCL development, county-level cities, though with weaker infrastructures, possess tremendous potential. Focusing on low-carbon production, circulation, and consumption, and strengthening smart and low-carbon constructions in county-level cities will be a vital task for future urban development in China.

Conclusions

The global urbanization process brings opportunities for economic growth and social development, but also presents a series of challenges, such as environmental pressures and resource constraints 3 . The evaluation of urban SCP and LCL creates a link between the policy-making in urban resources environment management and the objectives of sustainable development goals (SDGs 11.4, 11.6, and 11.b) at the city level 74 . Currently, there is no unified consensus on the coupling coordination development between urban SCP and LCL. This study proposes a method combining qualitative and quantitative analysis from the perspective of urban complex systems to analyze the coupling coordination relationship between SCP and LCL. This new method clearly interprets a strong positive correlation between urban smart performance and the overall low-carbon level. Specifically, there are strong correlations between SMC and LCM, LCS, and LCE, with a moderate correlation to LCQE and a weak correlation with LCC. Several innovative insights for this method are highlighted: (i) sustainable development based on SCP and LCL assessment; (ii) emphasizing the “people-centric” concept in urban development; (iii) analyzing from the perspective of urban complex systems.

This study selected 52 typical smart and low-carbon pilot cities in China as sample cities to analyze the coupled coordination relationship between urban SCP and LCL. And the main findings from this analysis can be summarized as follows: (i) smart city initiatives outperform low-carbon city development, with notable differences in SCP and LCL effectiveness across eastern, central, and non-resource-based cities versus western, peripheral, and resource-dependent ones in China. (ii) A strong positive link between urban SCP and low-carbon levels, especially between SCP and LCM, LCS, and LCE, with moderate and weak correlations to LCEQ and LCC, respectively. (iii) An increasing urban SCP levels enhance the coupling coordination within the urban SCP and LCL system. SCP & LCE, SCP & LCM, and SCP & LCS subsystems align well with the overall system, driving the coupled coordination of urban SCP and LCL. In contrast, SCP & LCC and SCP & LCEQ have lesser alignment, affected by factors like technology, policy, economic incentives, education, and societal attitudes. Based on the evaluation results, this study posits that the development of urban low-carbon and smart initiatives should not be disjointed but rather synergistic and complementary. This study constructs an evaluation indicator system for the co-development of smart low-carbon cities aimed at better guiding the future coupling coordination development of smart and low-carbon cities.

The novelty of this study not only addresses the practical dilemma of obtaining comprehensive, accurate, and timely urban-level carbon emission data, a challenge due to existing measurement and estimation technologies being unable to capture all types of carbon emissions, but also assesses the urban SCP and LCL. Simultaneously, by combining qualitative and quantitative analysis methods, it fills the research gap on the nature of the coupled coordination relationship between urban SCP and LCL. Moreover, from the perspective of urban complex systems, this study dissects the urban low-carbon level into LCC, LC, LCE, LCEQ, and LCS, exploring their respective coupled coordination relationships with SCP. This clarifies the impact mechanism between SCP and LCL, providing a theoretical basis for smart low-carbon city co-development. The limitations of the study are also appreciated. Firstly, the study only selected a sample of cities in China, and the limited number of samples may not fully substantiate the research conclusions. Secondly, the indicator system constructed by this study is still not perfect, leading to certain inaccuracies in the evaluation results. In this regard, future studies are recommended to conduct a more comprehensive comparison analysis on the coupled coordination relationship between SCP and LCL at city, regional, and national levels, which would be beneficial in better guiding the practice of urban sustainability.

Data availability

All data generated or analysed during this study are included in this published article [and its Supplementary Information files].

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Xiongwei Zhu, Dezhi Li, Shenghua Zhou & Lugang Yu

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Zhu, X., Li, D., Zhou, S. et al. Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods. Sci Rep 14 , 20461 (2024). https://doi.org/10.1038/s41598-024-68417-4

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