is quantitative research experimental

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Experimental Research: What it is + Types of designs

Any research conducted under scientifically acceptable conditions uses experimental methods. The success of experimental studies hinges on researchers confirming the change of a variable is based solely on the manipulation of the constant variable. The research should establish a notable cause and effect.

What is Experimental Research?

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods , for example, are experimental.

If you don’t have enough data to support your decisions, you must first determine the facts. This research gathers the data necessary to help you make better decisions.

You can conduct experimental research in the following situations:

• Time is a vital factor in establishing a relationship between cause and effect.
• Invariable behavior between cause and effect.
• You wish to understand the importance of cause and effect.

Experimental Research Design Types

The classic experimental design definition is: “The methods used to collect data in experimental studies.”

There are three primary types of experimental design:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

The way you classify research subjects based on conditions or groups determines the type of research design  you should use.

0 1. Pre-Experimental Design

A group, or various groups, are kept under observation after implementing cause and effect factors. You’ll conduct this research to understand whether further investigation is necessary for these particular groups.

You can break down pre-experimental research further into three types:

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

0 2. True Experimental Design

It relies on statistical analysis to prove or disprove a hypothesis, making it the most accurate form of research. Of the types of experimental design, only true design can establish a cause-effect relationship within a group. In a true experiment, three factors need to be satisfied:

• There is a Control Group, which won’t be subject to changes, and an Experimental Group, which will experience the changed variables.
• A variable that can be manipulated by the researcher
• Random distribution

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

The word “Quasi” indicates similarity. A quasi-experimental design is similar to an experimental one, but it is not the same. The difference between the two is the assignment of a control group. In this research, an independent variable is manipulated, but the participants of a group are not randomly assigned. Quasi-research is used in field settings where random assignment is either irrelevant or not required.

Importance of Experimental Design

Experimental research is a powerful tool for understanding cause-and-effect relationships. It allows us to manipulate variables and observe the effects, which is crucial for understanding how different factors influence the outcome of a study.

But the importance of experimental research goes beyond that. It’s a critical method for many scientific and academic studies. It allows us to test theories, develop new products, and make groundbreaking discoveries.

For example, this research is essential for developing new drugs and medical treatments. Researchers can understand how a new drug works by manipulating dosage and administration variables and identifying potential side effects.

Similarly, experimental research is used in the field of psychology to test theories and understand human behavior. By manipulating variables such as stimuli, researchers can gain insights into how the brain works and identify new treatment options for mental health disorders.

It is also widely used in the field of education. It allows educators to test new teaching methods and identify what works best. By manipulating variables such as class size, teaching style, and curriculum, researchers can understand how students learn and identify new ways to improve educational outcomes.

In addition, experimental research is a powerful tool for businesses and organizations. By manipulating variables such as marketing strategies, product design, and customer service, companies can understand what works best and identify new opportunities for growth.

Advantages of Experimental Research

When talking about this research, we can think of human life. Babies do their own rudimentary experiments (such as putting objects in their mouths) to learn about the world around them, while older children and teens do experiments at school to learn more about science.

Ancient scientists used this research to prove that their hypotheses were correct. For example, Galileo Galilei and Antoine Lavoisier conducted various experiments to discover key concepts in physics and chemistry. The same is true of modern experts, who use this scientific method to see if new drugs are effective, discover treatments for diseases, and create new electronic devices (among others).

It’s vital to test new ideas or theories. Why put time, effort, and funding into something that may not work?

This research allows you to test your idea in a controlled environment before marketing. It also provides the best method to test your theory thanks to the following advantages:

• Researchers have a stronger hold over variables to obtain desired results.
• The subject or industry does not impact the effectiveness of experimental research. Any industry can implement it for research purposes.
• The results are specific.
• After analyzing the results, you can apply your findings to similar ideas or situations.
• You can identify the cause and effect of a hypothesis. Researchers can further analyze this relationship to determine more in-depth ideas.
• Experimental research makes an ideal starting point. The data you collect is a foundation for building more ideas and conducting more action research .

Whether you want to know how the public will react to a new product or if a certain food increases the chance of disease, experimental research is the best place to start. Begin your research by finding subjects using  QuestionPro Audience  and other tools today.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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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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Quantitative Research

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

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

Google Scholar  

Descartes. Cited in Halverston, W. (1976). In: A concise introduction to philosophy, 3rd ed. New York: Random House; 1637.

Doll R, Hill AB. The mortality of doctors in relation to their smoking habits. BMJ. 1954;328(7455):1529–33. https://doi.org/10.1136/bmj.328.7455.1529 .

Article   Google Scholar  

Liamputtong P. Research methods in health: foundations for evidence-based practice. 3rd ed. Melbourne: Oxford University Press; 2017.

McNabb DE. Research methods in public administration and nonprofit management: quantitative and qualitative approaches. 2nd ed. New York: Armonk; 2007.

Merriam-Webster. Dictionary. http://www.merriam-webster.com . Accessed 20th December 2017.

Olesen Larsen P, von Ins M. The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics. 2010;84(3):575–603.

Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010;126(2):619–25. https://doi.org/10.1097/PRS.0b013e3181de24bc .

Petrie A, Sabin C. Medical statistics at a glance. 2nd ed. London: Blackwell Publishing; 2005.

Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. New Jersey: Pearson Publishing; 2009.

Sheehan J. Aspects of research methodology. Nurse Educ Today. 1986;6:193–203.

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

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Key Concepts in Quantitative Research

In this module, we are going to explore the nuances of quantitative research, including the main types of quantitative research, more exploration into variables (including confounding and extraneous variables), and causation.

Content includes:

• Flaws, “Proof”, and Rigor
• The Steps of Quantitative Methodology
• Major Classes of Quantitative Research
• Experimental versus Non-Experimental Research
• Types of Experimental Research
• Types of Non-Experimental Research
• Research Variables
• Confounding/Extraneous Variables
• Causation versus correlation/association

Objectives:

• Discuss the flaws, proof, and rigor in research.
• Describe the differences between independent variables and dependent variables.
• Describe the steps in quantitative research methodology.
• Describe experimental, quasi-experimental, and non-experimental research studies
• Describe confounding and extraneous variables.
• Differentiate cause-and-effect (causality) versus association/correlation

Flaws, Proof, and Rigor in Research

One of the biggest hurdles that students and seasoned researchers alike struggle to grasp, is that research cannot “ prove ” nor “ disprove ”. Research can only support a hypothesis with reasonable, statistically significant evidence.

Indeed. You’ve heard it incorrectly your entire life. You will hear professors, scientists, radio ads, podcasts, and even researchers comment something to the effect of, “It has been proven that…” or “Research proves that…” or “Finally! There is proof that…”

We have been duped. Consider the “ prove ” word a very bad word in this course. The forbidden “P” word. Do not say it, write it, allude to it, or repeat it. And, for the love of avocados and all things fluffy, do not include the “P” word on your EBP poster. You will be deducted some major points.

We can only conclude with reasonable certainty through statistical analyses that there is a high probability that something did not happen by chance but instead happened due to the intervention that the researcher tested. Got that? We will come back to that concept but for now know that it is called “statistical significance”.

All research has flaws. We might not know what those flaws are, but we will be learning about confounding and extraneous variables later on in this module to help explain how flaws can happen.

Remember this: Sometimes, the researcher might not even know that there was a flaw that occurred. No research project is perfect. There is no 100% awesome. This is a major reason why it is so important to be able to duplicate a research project and obtain similar results. The more we can duplicate research with the same exact methodology and protocols, the more certainty we have in the results and we can start accounting for flaws that may have sneaked in.

Finally, not all research is equal. Some research is done very sloppily, and other research has a very high standard of rigor. How do we know which is which when reading an article? Well, within this module, we will start learning about some things to look for in a published research article to help determine rigor. We do not want lazy research to determine our actions as nurses, right? We want the strongest, most reliable, most valid, most rigorous research evidence possible so that we can take those results and embed them into patient care. Who wants shoddy evidence determining the actions we take with your grandmother’s heart surgery?

Independent Variables and Dependent Variables

As we were already introduced to, there are measures called “variables” in research. This will be a bit of a review but it is important to bring up again, as it is a hallmark of quantitative research. In quantitative studies, the concepts being measured are called variables (AKA: something that varies). Variables are something that can change – either by manipulation or from something causing a change. In the article snapshots that we have looked at, researchers are trying to find causes for phenomena. Does a nursing intervention cause an improvement in patient outcomes? Does the cholesterol medication cause a decrease in cholesterol level? Does smoking cause  cancer?

The presumed cause is called the independent variable. The presumed effect is called the dependent variable. The dependent variable is “dependent” on something causing it to change. The dependent variable is the outcome that a researcher is trying to understand, explain, or predict.

Think back to our PICO questions. You can think of the intervention (I) as the independent variable and the outcome (O) as the dependent variable.

The independent variable is manipulated by the researcher or can be variants of influence. Whereas the dependent variable is never manipulated.

Variables do not always measure cause-and-effect. They can also measure a direction of influence.

Here is an example of that: If we compared levels of depression among men and women diagnosed with pancreatic cancer and found men to be more depressed, we cannot conclude that depression was caused by gender. However, we can note that the direction of influence   clearly runs from gender to depression. It makes no sense to suggest the depression influenced their gender.

In the above example, what is the independent variable (IV) and what is the dependent variable (DV)? If you guessed gender as the IV and depression as the DV, you are correct! Important to note in this case that the researcher did not manipulate the IV, but the IV is manipulated on its own (male or female).

Researchers do not always have just one IV. In some cases, more than one IV may be measured. Take, for instance, a study that wants to measure the factors that influence one’s study habits. Independent variables of gender, sleep habits, and hours of work may be considered. Likewise, multiple DVs can be measured. For example, perhaps we want to measure weight and abdominal girth on a plant-based diet (IV).

Now, some studies do not have an intervention. We will come back to that when we talk about non-experimental research.

The point of variables is so that researchers have a very specific measurement that they seek to study.

Let’s look at a couple of examples:

Now you try! Identify the IVs and DVs:

IV and DV Case Studies (Leibold, 2020)

Case Three:   Independent variable: Healthy Lifestyle education with a focus on physical activity; Dependent variable: Physical activity rate before and after education intervention, Heart rate before and after education intervention, Blood pressures before and after education intervention.

Case Four:   Independent variable: Playing classical music; Dependent variable:  Grade point averages post classical music, compared to pre-classical music.

Case Five: Independent variable: No independent variable as there is no intervention.  Dependent variable: The themes that emerge from the qualitative data.

The Steps in Quantitative Research Methodology

Now, as we learned in the last module, quantitative research is completely objective. There is no subjectivity to it. Why is this? Well, as we have learned, the purpose of quantitative research is to make an inference about the results in order to generalize these results to the population.

In quantitative studies, there is a very systematic approach that moves from the beginning point of the study (writing a research question) to the end point (obtaining an answer). This is a very linear and purposeful flow across the study, and all quantitative research should follow the same sequence.

• Identifying a problem and formulating a research question . Quantitative research begins with a theory . As in, “something is wrong and we want to fix it or improve it”.  Think back to when we discussed research problems and formulating a research question. Here we are! That is the first step in formulating a quantitative research plan.
• Formulate a hypothesis . This step is key. Researchers need to know exactly what they are testing so that testing the hypothesis can be achieved through specific statistical analyses.
• A thorough literature review .  At this step, researchers strive to understand what is already known about a topic and what evidence already exists.
• Identifying a framework .  When an appropriate framework is identified, the findings of a study may have broader significance and utility (Polit & Beck, 2021).
• Choosing a study design . The research design will determine exactly how the researcher will obtain the answers to the research question(s). The entire design needs to be structured and controlled, with the overarching goal of minimizing bias and errors. The design determines what data will be collected and how, how often data will be collected, what types of comparisons will be made. You can think of the study design as the architectural backbone of the entire study.
• Sampling . The researcher needs to determine a subset of the population that is to be studied. We will come back to the sampling concept in the next module. However, the goal of sampling is to choose a subset of the population that adequate reflects the population of interest.
• I nstruments to be used to collect data (with reliability and validity as a priority). Researchers must find a way to measure the research variables (intervention and outcome) accurately. The task of measuring is complex and challenging, as data needs to be collected reliably (measuring consistently each time) and valid. Reliability and validity are both about how well a method measures something. The next module will cover this in detail.
• Obtaining approval for ethical/legal human rights procedures . As we will learn in an upcoming module, there needs to be methods in place to safeguard human rights.
• Data collection . The fun part! Finally, after everything has been organized and planned, the researcher(s) begin to collect data. The pre-established plan (methodology) determines when data collection begins, how to accomplish it, how data collection staff will be trained, and how data will be recorded.
• Data analysis . Here comes the statistical analyses. The next module will dive into this.
• Discussion . After all the analyses have been complete, the researcher then needs to interpret the results and examine the implications. Researchers attempt to explain the findings in light of the theoretical framework, prior evidence, theory, clinical experience, and any limitations in the study now that it has been completed. Often, the researcher discusses not just the statistical significance, but also the clinical significance, as it is common to have one without the other.
• Summary/references . Part of the final steps of any research project is to disseminate (AKA: share) the findings. This may be in a published article, conference, poster session, etc. The point of this step is to communicate to others the information found through the study.  All references are collected so that the researchers can give credit to others.
• Budget and funding . As a last mention in the overall steps, budget and funding for research is a consideration. Research can be expensive. Often, researchers can obtain a grant or other funding to help offset the costs.

Edit: Steps in Quantitative Research video. Step 12 should say “Dissemination” (sharing the results).

Experimental, Quasi-Experimental, and Non-Experimental Studies

To start this section, please watch this wonderful video by Jenny Barrow, MSN, RN, CNE, that explains experimental versus nonexperimental research.

(Jenny Barrow, 2019)

Now that you have that overview, continue reading this module.

Experimental Research : In experimental research, the researcher is seeking to draw a conclusion between an independent variable and a dependent variable. This design attempts to establish cause-effect relationships among the variables. You could think of experimental research as experimenting with “something” to see if it caused “something else”.

A true experiment is called a Randomized Controlled Trial (or RCT). An RCT is at the top of the echelon as far as quantitative experimental research. It’s the gold standard of scientific research. An RCT, a true experimental design, must have 3 features:

• An intervention : The experiment does something to the participants by the option of manipulating the independent variable.
• Control : Some participants in the study receive either the standard care, or no intervention at all. This is also called the counterfactual – meaning, it shows what would happen if no intervention was introduced.
• Randomization : Randomization happens when the researcher makes sure that it is completely random who receives the intervention and who receives the control. The purpose is to make the groups equal regarding all other factors except receipt of the intervention.

Note: There is a lot of confusion with students (and even some researchers!) when they refer to “ random assignment ” versus “ random sampling ”. Random assignment  is a signature of a true experiment. This means that if participants are not truly randomly assigned to intervention groups, then it is not a true experiment. We will talk more about random sampling in the next module.

One very common method for RCT’s is called a pretest-posttest design .  This is when the researcher measures the outcome before and after the intervention. For example, if the researcher had an IV (intervention/treatment) of a pain medication, the DV (pain) would be measured before the intervention is given and after it is given. The control group may just receive a placebo. This design permits the researcher to see if the change in pain was caused by the pain medication because only some people received it (Polit & Beck, 2021).

Another experimental design is called a crossover design . This type of design involves exposing participants to more than one treatment. For example, subject 1 first receives treatment A, then treatment B, then treatment C. Subject 2 might first receive treatment B, then treatment A, and then treatment C. In this type of study, the three conditions for an experiment are met: Intervention, randomization, and control – with the subjects serving as their own control group.

Control group conditions can be done in 4 ways:

• No intervention is used; control group gets no treatment at all
• “Usual care” or standard of care or normal procedures used
• An alternative intervention is uses (e.g. auditory versus visual stimulation)
• A placebo or pseudo-intervention, presumed to have no therapeutic value, is used

Quasi-Experimental Research : Quasi-experiments involve an experiment just like true experimental research. However, they lack randomization and some even lack a control group.  Therefore, there is implementation and testing of an intervention, but there is an absence of randomization.

For example, perhaps we wanted to measure the effect of yoga for nursing students. The IV (intervention of yoga) is being offered to all nursing students and therefore randomization is not possible. For comparison, we could measure quality of life data on nursing students at a different university. Data is collected from both groups at baseline and then again after the yoga classes. Note, that in quasi-experiments, the phrase “comparison group” is sometimes used instead of “control group” against which outcome measures are collected.

Sometimes there is no comparison group either. This would be called a one-group pretest-posttest design .

Non-Experimental Research : Sometimes, cause-problem research questions cannot be answered with an experimental or quasi-experimental design because the IV cannot be manipulated. For example, if we want to measure what impact prerequisite grades have on student success in nursing programs, we obviously cannot manipulate the prerequisite grades. In another example, if we wanted to investigate how low birth weight impacts developmental progression in children, we cannot manipulate the birth weight. Often, you will see the word “observational” in lieu of non-experimental researcher. This does not mean the researcher is just standing and watching people, but instead it refers to the method of observing data that has already been established without manipulation.

There are various types of non-experimental research:

Correlational research : A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. In the example of prerequisites and nursing program success, that is a correlational design. Consider hypothetically, a researcher is studying a correlation between cancer and marriage. In this study, there are two variables: disease and marriage. Let us say marriage has a negative association with cancer. This means that married people are less likely to develop cancer.

Cohort design (also called a prospective design) : In a cohort study, the participants do not have the outcome of interest to begin with. They are selected based on the exposure status of the individual. They are then followed over time to evaluate for the occurrence of the outcome of interest. Cohorts may be divided into exposure categories once baseline measurements of a defined population are made. For example, the Framingham Cardiovascular Disease Study (CVD) used baseline measurements to divide the population into categories of CVD risk factors. Another example:  An example of a cohort study is comparing the test scores of one group of people who underwent extensive tutoring and a special curriculum and those who did not receive any extra help. The group could be studied for years to assess whether their scores improve over time and at what rate.

Retrospective design : In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. For example, a researcher might examine the medical histories of 1000 elderly women to identify the causes of health problems.

Case-control design : A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the condition. For example, investigators conducted a case-control study to determine if there is an association between colon cancer and a high fat diet. Cases were all confirmed colon cancer cases in North Carolina in 2010. Controls were a sample of North Carolina residents without colon cancer.

Descriptive research : Descriptive research design is a type of research design that aims to obtain information to systematically describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem, rather than the why. For example, the researcher might wish to discover the percentage of motorists who tailgate – the prevalence  of a certain behavior.

There are two other designs to mention, which are both on a time continuum basis.

Cross-sectional design : All data are collected at a single point in time. Retrospective studies are usually cross-sectional. The IV usually concerns events or behaviors occurring in the past. One cross-sectional study example in medicine is a data collection of smoking habits and lung cancer incidence in a given population. A cross-sectional study like this cannot solely determine that smoking habits cause lung cancer, but it can suggest a relationship that merits further investigation. Cross-sectional studies serve many purposes, and the cross-sectional design is the most relevant design when assessing the prevalence of disease, attitudes and knowledge among patients and health personnel, in validation studies comparing, for example, different measurement instruments, and in reliability studies.

Longitudinal design : Data are collected two or more times over an extended period. Longitudinal designs are better at showing patterns of change and at clarifying whether a cause occurred before an effect (outcome). A challenge in longitudinal studies is attrition or the loss of participants over time. In a longitudinal study subjects are followed over time with continuous or repeated monitoring of risk factors or health outcomes, or both. Such investigations vary enormously in their size and complexity. At one extreme a large population may be studied over decades. An example of a longitudinal design is a multiyear comparative study of the same children in an urban and a suburban school to record their cognitive development in depth.

Confounding and Extraneous Variables

Confounding variables  are a type of extraneous variable that occur which interfere with or influence the relationship between the independent and dependent variables. In research that investigates a potential cause-and-effect relationship, a confounding variable is an unmeasured third variable that influences both the supposed cause and the supposed effect.

It’s important to consider potential confounding variables and account for them in research designs to ensure results are valid. You can imagine that if something sneaks in to influence the measured variables, it can really muck up the study!

Here is an example:

You collect data on sunburns and ice cream consumption. You find that higher ice cream consumption is associated with a higher probability of sunburn. Does that mean ice cream consumption causes sunburn?

Here, the confounding variable is temperature: hot temperatures cause people to both eat more ice cream and spend more time outdoors under the sun, resulting in more sunburns.

To ensure the internal validity of research, the researcher must account for confounding variables. If he/she fails to do so, the results may not reflect the actual relationship between the variables that they are interested in.

For instance, they may find a cause-and-effect relationship that does not actually exist, because the effect they measure is caused by the confounding variable (and not by the independent variable).

Here is another example:

The researcher finds that babies born to mothers who smoked during their pregnancies weigh significantly less than those born to non-smoking mothers. However, if the researcher does not account for the fact that smokers are more likely to engage in other unhealthy behaviors, such as drinking or eating less healthy foods, then he/she might overestimate the relationship between smoking and low birth weight.

Extraneous variables are any variables that the researcher is not investigating that can potentially affect the outcomes of the research study. If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between IVs and DVs.

Extraneous variables can threaten the internal validity of a study by providing alternative explanations for the results. In an experiment, the researcher manipulates an independent variable to study its effects on a dependent variable.

In a study on mental performance, the researcher tests whether wearing a white lab coat, the independent variable (IV), improves scientific reasoning, the dependent variable (DV).

Students from a university are recruited to participate in the study. The researcher manipulates the independent variable by splitting participants into two groups:

• Participants in the experimental   group are asked to wear a lab coat during the study.
• Participants in the control group are asked to wear a casual coat during the study.

All participants are given a scientific knowledge quiz, and their scores are compared between groups.

When extraneous variables are uncontrolled, it’s hard to determine the exact effects of the independent variable on the dependent variable, because the effects of extraneous variables may mask them.

Uncontrolled extraneous variables can also make it seem as though there is a true effect of the independent variable in an experiment when there’s actually none.

In the above experiment example, these extraneous variables can affect the science knowledge scores:

• Participant’s major (e.g., STEM or humanities)
• Participant’s interest in science
• Demographic variables such as gender or educational background
• Time of day of testing
• Experiment environment or setting

If these variables systematically differ between the groups, you can’t be sure whether your results come from your independent variable manipulation or from the extraneous variables.

In summary, an extraneous variable is anything that could influence the dependent variable. A confounding variable influences the dependent variable, and also correlates with or causally affects the independent variable.

Cause-and-Effect (Causality) Versus Association/Correlation  

A very important concept to understand is cause-and-effect, also known as causality, versus correlation. Let’s look at these two concepts in very simplified statements. Causation means that one thing caused  another thing to happen. Correlation means there is some association between the two thing we are measuring.

It would be nice if it were as simple as that. These two concepts can indeed by confused by many. Let’s dive deeper.

Two or more variables are considered to be related or associated, in a statistical context, if their values change so that as the value of one variable increases or decreases so does the value of the other variable (or the opposite direction).

For example, for the two variables of “hours worked” and “income earned”, there is a relationship between the two if the increase in hours is associated with an increase in income earned.

However, correlation is a statistical measure that describes the size and direction of a relationship between two or more variables. A correlation does not automatically mean that the change in one variable caused the change in value in the other variable.

Theoretically, the difference between the two types of relationships is easy to identify — an action or occurrence can cause another (e.g. smoking causes an increase in the risk of developing lung cancer), or it can correlate with another (e.g. smoking is correlated with alcoholism, but it does not cause alcoholism). In practice, however, it remains difficult to clearly establish cause and effect, compared with establishing correlation.

Simplified in this image, we can say that hot and sunny weather causes an increase in ice cream consumption. Similarly, we can demise that hot and sunny weather increases the incidence of sunburns. However, we cannot say that ice cream caused a sunburn (or that a sunburn increases consumption of ice cream). It is purely coincidental. In this example, it is pretty easy to anecdotally surmise correlation versus causation. However, in research, we have statistical tests that help researchers differentiate via specialized analyses.

Here is a great Khan Academy video of about 5 minutes that shows a worked example of correlation versus causation with regard to sledding accidents and frostbite cases:

https://www.khanacademy.org/test-prep/praxis-math/praxis-math-lessons/gtp–praxis-math–lessons–statistics-and-probability/v/gtp–praxis-math–video–correlation-and-causation

References & Attribution

“ Light bulb doodle ” by rawpixel licensed CC0 .

“ Magnifying glass ” by rawpixel licensed CC0

“ Orange flame ” by rawpixel licensed CC0 .

Jenny Barrow. (2019). Experimental versus nonexperimental research. https://www.youtube.com/watch?v=FJo8xyXHAlE

Leibold, N. (2020). Research variables. Measures and Concepts Commonly Encountered in EBP. Creative Commons License: BY NC

Polit, D. & Beck, C. (2021).  Lippincott CoursePoint Enhanced for Polit’s Essentials of Nursing Research  (10th ed.). Wolters Kluwer Health.

Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Quantitative and Qualitative Research

• I NEED TO . . .

What is Quantitative Research?

• What is Qualitative Research?
• Quantitative vs Qualitative
• Step 1: Accessing CINAHL
• Step 2: Create a Keyword Search
• Step 3: Create a Subject Heading Search
• Step 4: Repeat Steps 1-3 for Second Concept
• Step 5: Repeat Steps 1-3 for Quantitative Terms
• Step 6: Combining All Searches
• Step 7: Adding Limiters
• Step 8: Save Your Search!
• What Kind of Article is This?
• More Research Help This link opens in a new window

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

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

What is the purpose of quantitative research?

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

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

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

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

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

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• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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

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

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

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

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

Its main characteristics are :

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

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

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

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

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

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

Basic Research Design for Quantitative Studies

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

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

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

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

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

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

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

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

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

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

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

Strengths of Using Quantitative Methods

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

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

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

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

Limitations of Using Quantitative Methods

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

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

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

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

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

SAGE Research Methods Online and Cases

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Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.

Description:

The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.

Advantages:

Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.

WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?

The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.

CONCLUSIONS

The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

• Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
• Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
• A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
• Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
• Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.

Acknowledgments

Thanks to Thomas A. Cappaert, PhD, ATC, CSCS, CSE, for suggesting the link between R.A. Fisher and the melding of the concepts of research design and statistics.

Part 1. Overview Information

National Institutes of Health ( NIH )

National Eye Institute ( NEI )

National Institute on Aging ( NIA )

National Institute on Alcohol Abuse and Alcoholism ( NIAAA )

Eunice Kennedy Shriver National Institute of Child Health and Human Development ( NICHD )

National Institute on Deafness and Other Communication Disorders ( NIDCD )

National Institute on Drug Abuse ( NIDA )

National Institute of Environmental Health Sciences ( NIEHS )

National Cancer Institute ( NCI )

All applications to this funding opportunity announcement should fall within the mission of the Institutes/Centers. The following NIH Offices may co-fund applications assigned to those Institutes/Centers.

Office of Research on Women's Health ( ORWH )

Office of Data Science Strategy ( ODSS )

Special Note: Not all NIH Institutes and Centers participate in Parent Announcements.Candidates should carefully note which ICs participate in this announcement and view their respective areas of research interest and requirements at the Table of IC-Specific Information, Requirements and Staff Contacts website. ICs that do not participate in this announcement will not consider applications for funding. Consultation with NIH staff before submitting an application is strongly encouraged.

• August 31, 2022 - Implementation Changes for Genomic Data Sharing Plans Included with Applications Due on or after January 25, 2023. See Notice  NOT-OD-22-198 .
• August 5, 2022 - Implementation Details for the NIH Data Management and Sharing Policy. See Notice  NOT-OD-22-189 .

See Section III. 3. Additional Information on Eligibility .

The purpose of the Mentored Quantitative Research Career Development Award (K25) is to attract to NIH-relevant research those investigators whose quantitative science and engineering research has thus far not been focused primarily on questions of health and disease. The K25 award will provide support and "protected time" for a period of supervised study and research for productive professionals with quantitative (e.g., mathematics, statistics, economics, computer science, imaging science, informatics, physics, chemistry) and engineering backgrounds to integrate their expertise with NIH-relevant research.

This Parent Announcement is for basic science experimental studies involving humans, referred to in  NOT-OD-18-212  as “prospective basic science studies involving human participants.” These studies fall within the NIH definition of a clinical trial and also meet the definition of basic research. Types of studies that should be submitted under this NOFO include studies that prospectively assign human participants to conditions (i.e., experimentally manipulate independent variables) and that assess biomedical or behavioral outcomes in humans for the purpose of understanding the fundamental aspects of phenomena without specific application towards processes or products in mind. Applicants not planning an independent clinical trial or basic experimental study with humans, or proposing to gain research experience in a clinical trial or basic experimental study with humans led by another investigator, must apply to the 'Independent Clinical Trial Not Allowed' companion NOFO.

The proposed project must be related to the programmatic interests of one or more of the participating NIH Institutes and Centers (ICs) based on their scientific missions.

Not Applicable

All applications are due by 5:00 PM local time of applicant organization.

Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

It is critical that applicants follow the instructions in the Career Development (K) Instructions in the  How to Apply - Application Guide  except where instructed to do otherwise (in this NOFO or in a Notice from the  NIH Guide for Grants and Contracts ). Conformance to all requirements (both in the How to Apply - Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the How to Apply - Application Guide as well as any program-specific instructions noted in  Section IV . When the program-specific instructions deviate from those in the How to Apply - Application Guide , follow the program-specific instructions.  Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

• Use the NIH ASSIST system to prepare, submit and track your application online.
• Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.
• Use Grants.gov Workspace to prepare and submit your application and eRA Commons to track your application.

Part 2. Full Text of Announcement

Section i. funding opportunity description.

The overall goal of the NIH Research Career Development program is to help ensure that a diverse pool of highly trained scientists is available in appropriate scientific disciplines to address the Nation's biomedical, behavioral, and clinical research needs. NIH Institutes and Centers (ICs) support a variety of mentored and non-mentored career development award programs designed to foster the transition of new investigators to research independence and to support established investigators in achieving specific objectives. Candidates should review the different career development (K) award programs to determine the best program to support their goals. More information about Career programs may be found at the  NIH Research Training and Career Development  website.

The NIH Mentored Quantitative Research Career Development Award (K25) is designed to attract to NIH-relevant research those investigators whose quantitative science and engineering research has thus far not been focused primarily on questions of health and disease. Examples of quantitative scientific and technical backgrounds considered appropriate for this award include, but are not limited to: mathematics, statistics, economics, computer science, imaging science, informatics, physics, chemistry, and engineering. The K25 award is intended to attract talented individuals with highly-developed quantitative skills to the challenges of biomedical, behavioral, and clinical research. At the completion of the award, candidates will have the knowledge and skills necessary to compete for independent research support from NIH, or to participate as leading members of multidisciplinary research teams.

The specific objectives of the K25 award are to :

• Encourage research-oriented quantitative scientists and engineers with little or no experience in biomedicine, bioengineering, bioimaging, or behavioral research to gain fundamental knowledge in these areas, develop relevant research skills, and to gain experience in current concepts, advanced methods, and experimental approaches that will allow them to conduct basic or clinical biomedical, behavioral, bioimaging, or bioengineering research, and to become independent investigators or play leading roles in multi-disciplinary research teams.
• Increase the pool of quantitative researchers who can conduct biomedical, behavioral, or bioengineering studies, capitalizing on the quantitative backgrounds of these investigators to inform new directions in biomedical, behavioral, and bioengineering research.
• Provide a unique opportunity for candidates holding degrees in quantitative science or engineering to embark on three to five years of special study, including coursework, seminars, meetings, and mentored research, to achieve the career enhancement goals outlined above.

Because of the focus on a progression toward independence as a quantitative biomedical, behavioral, bioimaging, or bioengineering researcher, the prospective candidate for the Mentored Quantitative Research Career Development Award will require enhanced skills in the experimental, theoretical and conceptual approaches used in biomedicine, behavioral science, bioimaging or bioengineering. To satisfy this requirement, the candidate should propose a period of study and career development that is complementary to his or her previous research and experience. For example, a candidate with no or very limited experience in a given field of biomedical research may find a phased developmental program lasting for five years that includes a designated period of didactic training together with a closely supervised research experience the most efficient means of attaining independence. A candidate with, for example, more research experience in biomedicine may benefit from a program with greater emphasis on appropriate laboratory research with lower levels of supervision and direction. All programs should be carefully tailored to meet the individual needs of the candidate and must include (an) active mentor(s) who is (are) competent and willing to provide the appropriate research guidance. Candidates should strongly consider incorporating into their training plan formal courses in relevant areas of biomedicine, behavioral science, bioimaging, or bioengineering; this program offers a unique opportunity to devote protected time to this activity.

All applications submitted to this Notice of Funding Opportunity must propose basic science experimental studies involving humans, otherwise referred to in NOT-OD-18-212 as “prospective basic science studies involving human participants,” that fall within the NIH definition of a clinical trial and also meet the definition of basic research.

NIH defines basic research consistent with the definition of basic research in federal code, “the systematic study directed toward greater knowledge or understanding of the fundamental aspects of phenomena and of observable facts without specific applications towards processes or products in mind.” ( 32 CFR 272.3 ).

NIH defines a clinical trial as "A research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." ( NOT-OD-15-015 ).

Types of studies that should submit under this NOFO include studies that prospectively assign human participants to conditions (i.e., experimentally manipulate independent variables) and that assess biomedical or behavioral outcomes in humans for the purpose of understanding the fundamental aspects of phenomena without specific application towards processes or products in mind.

For the purposes of this NOFO, “specific application towards processes or products” refers to the application of biomedical or behavioral products, procedures, or services intended to affect a health-related outcome of the individual or a group of individuals either by better understanding the mechanism of action of an intervention or a measurable improvement in health.

Basic experimental studies in which participants are prospectively assigned to experimental conditions and receive an intervention or experimental manipulation where the effect will be assessed for the purpose of understanding fundamental aspects of phenomena may submit under this NOFO.

Please refer to the table comparing Funding Opportunity Types by Clinical Trial Allowability for additional guidance on the most appropriate NOFO for the type of study.

Prospective studies with humans conducted with specific applications towards processes or products in mind, including FDA Phase 0 or 1 studies, mechanistic clinical trials (e.g., those that examine the mechanisms by which an intervention works or the processes that account for an intervention's effects on clinical outcome), and safety and efficacy studies should submit under the  'Clinical Trials Required' NOFO ( PA-24-190 ), but not under this NOFO.

Observational studies involving humans should submit under the ‘Clinical Trials Not Allowed’ NOFO ( PA-24-191 ).

Investigators proposing NIH-defined clinical trials may refer to the Research Methods Resources website for information about developing statistical methods and study designs.

Special Note:  Because of the differences in individual Institute and Center (IC) program requirements for this NOFO, prospective applicants are strongly encouraged to consult the  Table of IC-Specific Information, Requirements and Staff Contacts , to make sure that their application is appropriate for the requirements of one of the participating NIH ICs.

See Section VIII. Other Information for award authorities and regulations.

Section II. Award Information

Grant: A financial assistance mechanism providing money, property, or both to an eligible entity to carry out an approved project or activity.

The  OER Glossary  and the How to Apply - Application Guide  provides details on these application types.

Required - Basic Experimental Studies with Humans: Only accepting applications that propose independent clinical trial(s) that also meet the definition of basic research.

The number of awards is contingent upon NIH appropriations and the submission of a sufficient number of meritorious applications.

Other Award Budget Information

The participating NIH Institutes and Centers will provide salary and fringe benefits for the award recipient (see Table of IC-Specific Information, Requirements and Staff Contacts ). Further guidance on budgeting for career development salaries is provided in the  How to Apply - Application Guide . 

In addition, the candidate may derive additional compensation for effort associated with other Federal sources or awards provided the total salary derived from all Federal sources does not exceed the maximum legislated salary rate (see http://grants.nih.gov/grants/policy/salcap_summary.html ) and the total percent effort does not exceed 100%. See also NOT-OD-17-094 .

The participating NIH Institutes and Centers will provide research development support for the award recipient ( Table of IC-Specific Information, Requirements and Staff Contacts ). These funds may be used for the following expenses: (a) tuition and fees related to career development; (b) research-related expenses, such as supplies, equipment and technical personnel; c) travel to research meetings or training; and (d) statistical services including personnel and computer time.

Salary for mentors, secretarial and administrative assistants, etc. is not allowed.

NIH grants policies as described in the  NIH Grants Policy Statement  will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. Eligible Applicants

Higher Education Institutions

• Public/State Controlled Institutions of Higher Education
• Private Institutions of Higher Education

The following types of Higher Education Institutions are always encouraged to apply for NIH support as Public or Private Institutions of Higher Education:

• Hispanic-serving Institutions
• Historically Black Colleges and Universities (HBCUs)
• Tribally Controlled Colleges and Universities (TCCUs)
• Alaska Native and Native Hawaiian Serving Institutions
• Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)

Nonprofits Other Than Institutions of Higher Education

• Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
• Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)

For-Profit Organizations

• Small Businesses
• For-Profit Organizations (Other than Small Businesses)

Local Governments

• State Governments
• County Governments
• City or Township Governments
• Special District Governments
• Indian/Native American Tribal Governments (Federally Recognized)
• Indian/Native American Tribal Governments (Other than Federally Recognized)

Federal Governments

• U.S. Territory or Possession
• Independent School Districts
• Public Housing Authorities/Indian Housing Authorities
• Native American Tribal Organizations (other than Federally recognized tribal governments)
• Faith-based or Community-based Organizations
• Regional Organizations

Non-domestic (non-U.S.) Entities (Foreign Organizations)  are not  eligible to apply.

Non-domestic (non-U.S.) components of U.S. Organizations  are not  eligible to apply.

Foreign components, as  defined in the NIH Grants Policy Statement ,  are allowed. 

Applicant Organizations

Applicant organizations must complete and maintain the following registrations as described in the How to Apply - Application Guide to be eligible to apply for or receive an award. All registrations must be completed prior to the application being submitted. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. Failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference NIH Grants Policy Statement 2.3.9.2 Electronically Submitted Applications for additional information.

• NATO Commercial and Government Entity (NCAGE) Code – Foreign organizations must obtain an NCAGE code (in lieu of a CAGE code) in order to register in SAM.
• Unique Entity Identifier (UEI) - A UEI is issued as part of the SAM.gov registration process. The same UEI must be used for all registrations, as well as on the grant application.
• eRA Commons - Once the unique organization identifier is established, organizations can register with eRA Commons in tandem with completing their Grants.gov registration; all registrations must be in place by time of submission. eRA Commons requires organizations to identify at least one Signing Official (SO) and at least one Program Director/Principal Investigator (PD/PI) account in order to submit an application.
• Grants.gov – Applicants must have an active SAM registration in order to complete the Grants.gov registration.

Program Directors/Principal Investigators (PD(s)/PI(s))

All PD(s)/PI(s) must have an eRA Commons account.  PD(s)/PI(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in eRA Commons. If the PD/PI is also the organizational Signing Official, they must have two distinct eRA Commons accounts, one for each role. Obtaining an eRA Commons account can take up to 2 weeks.

All PD(s)/PI(s) must be registered with ORCID . The personal profile associated with the PD(s)/PI(s) eRA Commons account must be linked to a valid ORCID ID. For more information on linking an ORCID ID to an eRA Commons personal profile see the ORCID topic in our eRA Commons online help .

Any candidate with the skills, knowledge, and resources necessary to carry out the proposed research as the Program Director/Principal Investigator (PD/PI) is invited to work with their mentor and organization to develop an application for support. Individuals from diverse backgrounds, including individuals from underrepresented racial and ethnic groups, individuals with disabilities, and women are always encouraged to apply for NIH support. See, Reminder: Notice of NIH's Encouragement of Applications Supporting Individuals from Underrepresented Ethnic and Racial Groups as well as Individuals with Disabilities , NOT-OD-22-019 . Multiple PDs/PIs are not allowed.

By the time of award, the individual must be a citizen or a non-citizen national of the United States or have been lawfully admitted for permanent residence (i.e., possess a currently valid Permanent Resident Card USCIS Form I-551, or other legal verification of such status).

Current and former PDs/PIs on NIH research project (R01), program project (P01), center grants (P50), Project Leads of program project (P01), or center grants (P50), other major individual career development awards (e.g., K01, K07, K08, K22, K23, K25, K76, K99/R00), or the equivalent are not eligible. Current and former PDs/PIs of an NIH Small Grant (R03), Exploratory/Developmental Grants (R21/R33), Planning Grant (R34/U34), Dissertation Award (R36), or SBIR/STTR (R41, R42, R43, R44) remain eligible, as do PD/PIs of Transition Scholar (K38) awards and individuals appointed to institutional K programs (K12, KL2). Candidates for the K25 award must have an advanced degree in a quantitative area of science or engineering (e.g., MSEE, PhD, DSc) and have demonstrated research interests in their primary quantitative discipline (including research outside of biomedicine, behavioral sciences, bioimaging, or bioengineering). The candidate should have demonstrated professional accomplishments consonant with his or her career stage. The K25 award is intended for research-oriented investigators at any level of experience, from the postdoctoral level to senior faculty level, who have shown clear evidence of productivity and research excellence in the field of their training, and who would like to expand their research capability, with the goal of making significant contributions to behavioral, biomedical (basic or clinical), bioimaging or bioengineering research that is relevant to the NIH mission.

2. Cost Sharing

This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement Section 1.2 Definitions of Terms . 

3. Additional Information on Eligibility

Applicant organizations may submit more than one application, provided that each application is scientifically distinct, and each is from a different candidate.

NIH will not accept duplicate or highly overlapping applications under review at the same time per  NIH Grants Policy Statement Section 2.3.7.4 Submission of Resubmission Application . An individual may not have two or more competing NIH career development applications pending review concurrently. In addition, NIH will not accept:

• A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
• A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
• An application that has substantial overlap with another application pending appeal of initial peer review. (See  NIH Grants Policy Statement 2.3.9.4 Similar, Essentially Identical, or Identical Applications ).

Candidates may submit research project grant (RPG) applications concurrently with the K application. However, any concurrent RPG application may not have substantial scientific and/or budgetary overlap with the career award application. K award recipients are encouraged to obtain funding from NIH or other Federal sources either as a PD/PI on a competing research grant award or cooperative agreement, or as project leader on a competing multi-project award as described in  NOT-OD-18-157 .

At the time of award, the candidate must have a full-time appointment at the academic institution. Candidates are required to commit a minimum of 75% of full-time professional effort (i.e., a minimum of 9 person-months) to their program of career development. Candidates may engage in other duties as part of the remaining 25% of their full-time professional effort not covered by this award, as long as such duties do not interfere with or detract from the proposed career development program. 

Candidates who have VA appointments may not consider part of the VA effort toward satisfying the full time requirement at the applicant institution. Candidates with VA appointments should contact the staff person in the relevant Institute or Center prior to preparing an application to discuss their eligibility.

After the receipt of the award, adjustments to the required level of effort may be made in certain circumstances.  See NOT-OD-18-156   and NIH Grants Policy Statement , Section 12.3.6.4 Temporary Adjustments to the Percent Effort Requirement for more details.

Before submitting the application, the candidate must identify a mentor who will supervise the proposed career development and research experience. The mentor should be an active investigator in the area of the proposed research and be committed both to the career development of the candidate and to the direct supervision of the candidate’s research. The mentor must document the availability of sufficient research support and facilities. Candidates are encouraged to identify more than one mentor, i.e., a mentoring team, if this is deemed advantageous for providing expert advice in all aspects of the research career development program. In such cases, one individual must be identified as the primary mentor who will coordinate the candidate’s research. The candidate must work with the mentor(s) in preparing the application. The mentor, or a member of the mentoring team, should have a successful track record of mentoring individuals at the candidate’s career stage. The recruitment of women, individuals from underrepresented  racial and ethnic groups, and individuals with disabilities as potential mentors is encouraged.

The mentor(s) or mentoring team must demonstrate appropriate expertise, experience, and ability to guide the applicant in the organization, management and implementation of the proposed research and clinical trial.

The applicant institution must have a strong, well-established record of research and career development activities and faculty qualified to serve as mentors in biomedical, behavioral, or clinical research.

Section IV. Application and Submission Information

1. Requesting an Application Package

Buttons to access the online ASSIST system or to download application forms are available in Part 1 of this NOFO. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

2. Content and Form of Application Submission

It is critical that applicants follow the instructions in the Career Development (K) Instructions in the  How to Apply - Application Guide  except where instructed in this notice of funding opportunity to do otherwise. Conformance to the requirements in the How to Apply - Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

For information on Application Submission and Receipt, visit Frequently Asked Questions – Application Guide, Electronic Submission of Grant Applications .

Page Limitations

All page limitations described in the How to Apply - Application Guide and the Table of Page Limits must be followed.

The following section supplements the instructions found in the How to Apply - Application Guide and should be used for preparing an application to this NOFO.

SF424(R&R) Cover

All instructions in the How to Apply - Application Guide must be followed.

SF424(R&R) Project/Performance Site Locations

Other Project Information

SF424(R&R) Senior/Key Person Profile Expanded

R&R Budget

PHS 398 Cover Page Supplement

PHS 398 Career Development Award Supplemental Form

The PHS 398 Career Development Award Supplemental Form is comprised of the following sections:

Candidate Research Plan Other Candidate Information Mentor, Co-Mentor, Consultant, CollaboratorsEnvironment & Institutional Commitment to the CandidateOther Research Plan Sections Appendix

Candidate Section

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions: 

Candidate Information and Goals for Career Development

Candidate’s Background

• Describe prior training and research experience and how these relate to the objectives and long-term career plans of the candidate.  Explain how the award will contribute to their attainment. 
• Describe the candidate’s research efforts and professional accomplishments consonant with career status, including any publications that demonstrate the candidate’s experience and interest in pursuing research (including research outside of biomedicine, behavior, bioimaging, or bioengineering). 
• Provide a description of the candidate's commitment to a career in quantitative biomedical, bioimaging, behavioral, or bioengineering research that is relevant to the NIH mission. 
• Provide evidence of the candidate's potential to develop into a successful independent investigator.  Usually this is evident from publications, prior research interests and experience, and reference letters.
• If applicable, describe the candidate's ability to organize, manage, and implement the proposed clinical trial, feasibility or ancillary clinical trial.
• If applicable, describe the candidate's prior efforts, interests and experience in clinical trials research.

Career Goals and Objectives​

• Describe a systematic plan: (1) that shows a logical progression from prior research and training experiences to the research and career development experiences that will occur during the career award period and then to independent investigator status; and (2) that justifies the need for further career development to become an independent investigator. 
• The candidate must demonstrate they have received training or will participate in courses such as: data management, epidemiology, study design (including statistics), hypothesis development, drug development, etc., as well as the legal and ethical issues associated with research on human subjects and clinical trials.

Candidate’s Plan for Career Development/Training Activities During Award Period

• Provide a description of the career development plan, incorporating consideration of the candidate's goals and prior experience.  Propose a plan to obtain the necessary theoretical and conceptual background and research experience to launch an independent research career in quantitative biomedicine, bioengineering, bioimaging or behavioral research 
• Include a list of the specific course of study in which the candidate will engage, including specific coursework which is essential to gaining the required theoretical and conceptual understanding of biomedicine, behavioral science, bioimaging, or bioengineering, important to the candidate's short- and long-term research interests and the manner of integration of these studies into the career development plan. 
• The career development plan must be tailored to the needs of the individual candidate and the ultimate goal of achieving independence as a researcher in quantitative biomedicine, behavioral science, bioimaging, or bioengineering.  Less experienced candidates may require a phased developmental period in which the first one to two year(s) of the award are largely didactic in nature that is followed by a period of intense, supervised research. Candidates with more experience at the time of application may need a shorter developmental period and may already have an adequate theoretical background. 
• Describe the professional responsibilities/activities (including other research projects) beyond the minimum required 9 person months (75% full-time professional effort) commitment to the K25 award.  Explain how these responsibilities/activities relate to the career development objectives of this award and will help ensure career progression to achieve independence as an investigator. 
• The candidate and the mentor are jointly responsible for the preparation of the career development plan.  A timeline is often helpful. The candidate or mentor may form a mentoring team or  advisory committee to assist with the development of a program of study or to monitor the candidate's progress through the career development program.

Research Plan Section

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions:

Research Strategy

• Provide a sound quantitative biomedical, behavioral, or bioengineering research plan that is consistent with the candidate’s level of research development and objectives of their career development plan. 
• The application must also describe the relationship between the mentor’s research and the candidate’s proposed research plan and the benefits of that relationship including how the candidate’s project will lead to an independent line of research. For research projects requiring team-based approaches, such as large epidemiological studies explain how the research will enhance the candidate’s expertise and prepare the candidate to have a major role in designing and leading future projects.
• Applicants proposing a clinical trial, ancillary or feasibility study should describe the planned analyses and statistical approach and how the expected analytical approach is suited to the available resources, proposed study design, scope of the project, and methods used to assign trial participants and deliver interventions. 
• If proposing an ancillary clinical trial, provide a brief description of its relationship to the larger clinical trial. 
• If proposing a feasibility study, to begin to address a clinical question, provide justification why this is warranted and how it will contribute the overall goals of the research project including planning and preliminary data for future, larger scale clinical trials.
• Describe the proposed timelines for the proposed clinical trial, feasibility study or ancillary clinical trial, including any potential challenges and solutions (e.g., enrollment shortfalls or inability to attribute causal inference to the results of an intervention when performing a small feasibility study).
• Describe how the proposed clinical trial or ancillary clinical trial will test the safety, efficacy or effectiveness of an intervention that could lead to a change in clinical practice, community behaviors or health care policy (This would not apply to a feasibility study).

Training in the Responsible Conduct of Research

• All applications must include a plan to fulfill NIH requirements for instruction in the Responsible Conduct of Research (RCR). See How to Apply - Application Guide for instructions.

Mentor, Co-Mentor, Consultant, Collaborators Section

Plans and Statements of Mentor and Co-mentor(s)

• The candidate must name a primary mentor who, together with the candidate, is responsible for the planning, directing, monitoring, and executing the proposed program.  The candidate may also nominate co-mentors as appropriate to the goals of the program.   
• The mentor should have sufficient independent research support to cover the costs of the proposed research project in excess of the allowable costs of this award. 
• Include a statement that the candidate will commit at least 9 person months (75% of full-time professional effort) to the career development program and related career development activities. 
• The application must include a statement from the mentor providing: 1) information on their research qualifications and previous experience as a research supervisor; 2) a plan that describes the nature of the supervision and mentoring that will occur during the proposed award period; 3) a plan for career progression for the candidate to move from the mentored stage of their career to independent research investigator status during the project period of the award; and 4) a plan for monitoring the candidate’s research, publications, and progression towards independence. 
• Similar information must be provided by any co-mentor.  If more than one co-mentor is proposed, the respective areas of expertise and responsibility of each should be described.  Co-mentors should clearly describe how they will coordinate the mentoring of the candidate. If any co-mentor is not located at the sponsoring institution, a statement should be provided describing the mechanism(s) and frequency of communication with the candidate, including the frequency of face-to-face meetings. 
• The mentor must agree to provide annual evaluations of the candidate’s progress as required in the annual progress report.
• The mentor or mentoring team must provide evidence of expertise, experience, and ability to guide the candidate in the organization, management and implementation of the proposed clinical trial, ancillary clinical trial or feasibility study and help the candidate to meet timelines.

Letters of Support from Collaborators, Contributors and Consultants

• Signed statements must be provided by all collaborators and/or consultants confirming their participation in the project and describing their specific roles. Unless also listed as senior/key personnel, collaborators and consultants do not need to provide their biographical sketches. However, information should be provided clearly documenting the appropriate expertise in the proposed areas of consulting/collaboration. 
• Advisory committee members (if applicable): Signed statements must be provided by each member of the proposed advisory committee.  These statements should confirm their participation, describe their specific roles, and document the expertise they will contribute.  Unless also listed as senior/key personnel, these individuals do not need to provide their biographical sketches. 

Environmental and Institutional Commitment to the Candidate

Description of Institutional Environment

• The sponsoring institution must document a strong, well-established research and career development program related to the candidate's area of interest, including a high-quality research environment with key faculty members and other investigators capable of productive collaboration with the candidate. 
• Describe how the institutional research environment is particularly suited for the development of the candidate's research career and the pursuit of the proposed research plan.
• Describe the resources and facilities that will be available to the candidate, including any clinical trial-related resources, such as specialized administrative, data coordinating, enrollment, and laboratory/testing support. If applicable, include a description of the resources and facilities available at international sites.

Institutional Commitment to the Candidate’s Research Career Development

• The sponsoring institution must provide a statement of commitment to the candidate's development into a productive, independent investigator and to meeting the requirements of this award. It should be clear that the institutional commitment to the candidate is not contingent upon receipt of this career award. 
• Provide assurances that the candidate will be able to devote the required effort to activities under this award. The remaining effort should be devoted to activities related to the development of the candidate’s career as an independent scientist. 
• Provide assurances that the candidate will have access to appropriate office and laboratory space, equipment, and other resources and facilities (including access to clinical and/or other research populations, as applicable) to carry out the proposed research plan. 
• Provide assurance that appropriate time and support will be available for any proposed mentor(s) and/or other staff consistent with the career development plan.

Other Plan(s):

Note: Effective for due dates on or after January 25, 2023, the Data Management and Sharing Plan will be attached in the Other Plan(s) attachment in FORMS-H application forms packages.

• All candidates  planning research (funded or conducted in whole or in part by NIH) that results in the generation of scientific data are required to comply with the instructions for the Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

Limited items are allowed in the Appendix.  Follow all instructions for the Appendix as described in the How to Apply - Application Guide ; any instructions provided here are in addition to the How to Apply - Application Guide instructions.

PHS Human Subjects and Clinical Trials Information

When involving NIH-defined human subjects research, clinical research, and/or clinical trials (and when applicable, clinical trials research experience) follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply - Application Guide , with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or Delayed Onset Study record.

Study Record: PHS Human Subjects and Clinical Trials Information

Section 1 - Basic Information

1.4 Clinical Trial Questionnaire

Applications to this NOFO must propose a study that falls within the NIH definition of a clinical trial and also meets the definition of basic research. Consequently, applicants must answer "yes" to the four questions on 1.4 Clinical Trial Questionnaire and complete the subsequent form fields accordingly.

Delayed Onset Study

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start).

All instructions in the SF424 (R&R) Application Guide must be followed.

PHS Assignment Request Form

Reference Letters

Candidates must carefully follow the How to Apply - Application Guide , including the time period for when reference letters will be accepted . Applications lacking the appropriate required reference letters will not be reviewed. This is a separate process from submitting an application electronically. Reference letters are submitted directly through the eRA Commons Submit Referee Information link and not through Grants.gov. 

3. Unique Entity Identifier and System for Award Management (SAM)

See Part 2. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

4. Submission Dates and Times

Part I.  contains information about Key Dates and Times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday , the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies) using ASSIST or other electronic submission systems. Applicants must then complete the submission process by tracking the status of the application in the eRA Commons , NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time.  If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications .

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in the How to Apply - Application Guide .

5. Intergovernmental Review (E.O. 12372)

This initiative is not subject to intergovernmental review.

6. Funding Restrictions

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement Section 7.9.1 Selected Items of Cost .

Pre-award costs are allowable only as described in the NIH Grants Policy Statement .

7. Other Submission Requirements and Information

Applications must be submitted electronically following the instructions described in the How to Apply - Application Guide . Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit  How to Apply - Application Guide . If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Dealing with System Issues guidance. For assistance with application submission, contact the Application Submission Contacts in Section VII.

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile form . Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH. See Section III of this NOFO for information on registration requirements.

The applicant organization must ensure that the unique entity identifier provided on the application is the same identifier used in the organization’s profile in the eRA Commons and for the System for Award Management. Additional information may be found in the How to Apply - Application Guide .

See more tips for avoiding common errors.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review, NIH. Applications that are incomplete or non-compliant will not be reviewed.

Post Submission Materials

Applicants are required to follow the instructions for post-submission materials, as described in the policy .

Any instructions provided here are in addition to the instructions in the policy.

Section V. Application Review Information

1. Criteria

Only the review criteria described below will be considered in the review process.  Applications submitted to the NIH in support of the NIH mission are evaluated for scientific and technical merit through the NIH peer review system.

For this particular announcement, note the following : Reviewers should evaluate the candidate’s potential for developing an independent research program that will make important contributions to the field, taking into consideration the years of research experience and the likely value of the proposed research career development as a vehicle for developing a successful, independent research program.

Overall Impact

Reviewers should provide their assessment of the likelihood that the proposed career development and research plan will enhance the candidate’s potential for a productive, independent scientific research career in a health-related field, taking into consideration the criteria below in determining the overall impact score.

Reviewers will consider each of the review criteria below in the determination of scientific merit, and give a separate score for each. An application does not need to be strong in all categories to be judged likely to have major scientific impact.

The reviewers will consider that the clinical trial may include study design, methods, and intervention that are not by themselves innovative, but address important questions or unmet needs. Reviewers should also consider the scope of the clinical trial relative to the available resources, including the possibility that research support provided through career development awards may be sufficient to support only small feasibility studies.

  Candidate

• Does the candidate have the potential to develop as an independent and productive researcher? 
• Are the candidate's prior training and research experience appropriate for this award? 
• Is the candidate’s academic, clinical (if relevant), and research record of high quality? 
• Is there evidence of the candidate’s commitment to meeting the program objectives to become an independent investigator in research? 
• Do the reference letters address the above review criteria, and do they provide evidence that the candidate has a high potential for becoming an independent investigator.
• Does the candidate have the potential to organize, manage, and implement the proposed clinical trial, feasibility or ancillary study?
• Does the candidate have training (or plans to receive training) in data management and statistics including those relevant to clinical trials?

Career Development Plan/Career Goals and Objectives

• What is the likelihood that the plan will contribute substantially to the scientific development of the candidate and lead to scientific independence? 
• Are the candidate's prior training and research experience appropriate for this award?
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Open Access

Peer-reviewed

Research Article

Experimental research on the influence of acid on the chemical and pore structure evolution characteristics of Wenjiaba tectonic coal

Roles Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliations College of Mining, Guizhou University, Guiyang, China, Guizhou Engineering Center for Safe Mining Technology, Guiyang China

Roles Funding acquisition, Project administration

* E-mail: [email protected]

Roles Data curation, Methodology, Supervision

Roles Conceptualization, Resources, Validation

Roles Supervision, Validation

Affiliation College of Resource and Environmental Engineering, Guizhou University, Guiyang, China

Roles Data curation, Investigation, Supervision

Roles Formal analysis, Investigation, Supervision

• Xianxian Li, 
• Xijian Li, 
• Enyu Xu, 
• Honggao Xie, 
• Hao Sui, 
• Junjie Cai, 

• Published: April 23, 2024
• https://doi.org/10.1371/journal.pone.0301923
• Reader Comments

The chemical and pore structures of coal play a crucial role in determining the content of free gas in coal reservoirs. This study focuses on investigating the impact of acidification transformation on the micro-physical and chemical structure characteristics of coal samples collected from Wenjiaba No. 1 Mine in Guizhou. The research involves a semi-quantitative analysis of the chemical structure parameters and crystal structure of coal samples before and after acidification using Fourier Transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) experiments. Additionally, the evolution characteristics of the pore structure are characterized through high-pressure mercury injection (HP-MIP), low-temperature nitrogen adsorption (LT-N 2 A), and scanning electron microscopy (SEM). The experimental findings reveal that the acid solution modifies the structural features of coal samples, weakening certain vibrational structures and altering the chemical composition. Specifically, the asymmetric vibration structure of aliphatic CH 2 , the asymmetric vibration of aliphatic CH 3 , and the symmetric vibration of CH 2 are affected. This leads to a decrease in the contents of -OH and -NH functional groups while increasing aromatic structures. The crystal structure of coal samples primarily dissolves transversely after acidification, affecting intergranular spacing and average height. Acid treatment corrodes mineral particles within coal sample cracks, augmenting porosity, average pore diameter, and the ratio of macro-pores to transitional pores. Moreover, acidification increases fracture width and texture, enhancing the connectivity of the fracture structure in coal samples. These findings provide theoretical insights for optimizing coalbed methane (CBM) extraction and gas control strategies.

Citation: Li X, Li X, Xu E, Xie H, Sui H, Cai J, et al. (2024) Experimental research on the influence of acid on the chemical and pore structure evolution characteristics of Wenjiaba tectonic coal. PLoS ONE 19(4): e0301923. https://doi.org/10.1371/journal.pone.0301923

Editor: Nor Adilla Rashidi, Universiti Teknologi Petronas: Universiti Teknologi PETRONAS, MALAYSIA

Received: October 25, 2023; Accepted: March 24, 2024; Published: April 23, 2024

Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The research was funded by the National Natural Science Foundation of China (No. 52164015), the supported by Guizhou Provincial Science and Technology Projects (No. [2022] 231).

Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

1 Introduction

China possesses the third-largest recoverable coal resources and the highest coal production globally. However, the country faces serious challenges, particularly in Guizhou, where coal and gas outburst accidents are frequent [ 1 , 2 ]. Studies show that CBM extraction and utilization present an opportunity to address natural gas resource shortages and significantly reduce the risk of gas outbursts [ 3 ]. Tectonic deformation influences the original physical structure and chemical properties of coal, making tectonic coal a crucial indicator for determining the gas prominence of coal seams [ 4 ]. It is worth noting that tectonic coal contains numerous micropores and cracks, significantly impacting CBM adsorption and desorption. More specifically, tectonic coal is especially rich in medium pores (100–1000 nm) and large pores (>1000 nm) [ 5 , 6 ]. Its large porosity and good connectivity make it favorable for the CBM development [ 7 , 8 ]. China holds substantial CBM reserves, with 30.05×10 12 m 3 of CBM geological resources with an average gas content of 5.66 m 3 /t at burial depths less than 2000m [ 9 ]. Guizhou Province ranks second in the country with CBM geological resources of 3.06×10 12 m 3 . The annual CBM production in Guizhou Province increases by 0.3213×10 8 m 3 and is expected to reach 0.9×10 8 m 3 in 2022 [ 10 , 11 ]. Accordingly, understanding the pore characteristics of coal is crucial for comprehending the behavior of coal regarding CBM adsorption, desorption, and diffusion. The study of microphysical-chemical structural characteristics of tectonic coals is of significant importance in understanding CBM desorption [ 12 – 14 ].

Acid modification is a crucial chemical method for enhancing oilfield gas extraction and is particularly significant for corroding mineral-filled tectonic coal pores and fractures, thereby improving their connectivity [ 15 , 16 ]. Studies show that acidification pretreatment of coal yields several benefits, including improvements in equilibrium adsorption and enhanced Nuclear Magnetic Resonance Spectroscopy (NMR) signals in the saturated water state [ 17 , 18 ]. For instance, pretreatment of low-rank coal with acetic and hydrofluoric acid increases its hydrophobicity [ 19 ]. Hydrochloric acid has been widely used to remove minerals from lignite, increase the number of side chains, and alter pore structure and aromaticity [ 20 , 21 ]. Designing an appropriate acidification system is of significant importance in modifying the pore and fracture structure of tectonic coals [ 22 , 23 ]. After acidification treatment, the physicochemical structure of tectonic coals changes. The microscopic physicochemical structure of coal is generally categorized into pore structure (physical) and molecular structure (chemical) [ 24 , 25 ]. Current quantitative analyses of the pore structure of tectonic coals primarily involve methods such as high-pressure mercury intrusion (HP-MIP), LT-N 2 A, low-temperature carbon dioxide adsorption (LTCO 2 -GA), and nuclear magnetic resonance (NMR). These methods provide valuable information on pore volume, specific surface area, the most available number of pore diameters, and pore size distributions, enabling comprehensive and accurate characterization of the tectonic coal structure [ 26 – 29 ]. The challenge in studying the pore and fracture structure of tectonic coal lies in the diverse principles and outcomes of different experimental methods. Porosity size, determined by various techniques, may yield different results, necessitating a comprehensive approach that combines multiple experiments to analyze the complex structure of tectonic coal. Observing the connectivity and filling state of the pore and fracture structure typically involves direct visualization using transmission electron microscopy, atomic force microscopy, and SEM. While these techniques provide valuable insights into the geometrical and morphological representation of pore structure within tectonic coal, quantitative analysis remains challenging [ 30 – 33 ]. Furthermore, the molecular structure (chemistry) of the pore and cleavage structure of tectonic coal is explored through various experiments, including Fourier infrared spectroscopy, XRD, Raman spectroscopy, and photoelectron spectroscopy (XPS). These methods contribute to a comprehensive understanding of the chemical structure of tectonic coal, focusing on functional groups and mineral composition [ 34 – 37 ]. Historically, acidification treatments have been applied predominantly to low-rank coals, with limited studies on medium and high-rank tectonic coals.

Presently, acid treatment is employed to enhance pore connectivity in coal samples from low-permeability coal seams in Guizhou. Many studies have only focused on the use of a singular type of acid to investigate alterations in pore or chemical structure, while limited investigations have been conducted on the application of mixed acids for this purpose [ 38 , 39 ]. To explore the chemical and pore structure evolution of Wenjiaba tectonic coal, this study employs a mixed acid solution consisting of 15% hydrochloric acid (HCl) and 5% hydrofluoric acid (HF) for coal sample acidification. The analysis involves the use of FTIR and XRD to examine the chemical structure of tectonic coals before and after acidification, with a quantitative assessment of functional group characteristics, chemical structure parameters, and coal sample crystalline structure. The pore and fracture structure of the coal pre- and post-acidification is characterized using HP-MIP and LT-N 2 A. Additionally, changes in pore structure are visually observed using SEM, adhering to ISO/standard for physical and chemical analyses of pore structure. The study aims to analyze the evolution of the physical and chemical structure of tectonic coal under acidification, providing theoretical insights to enhance CBM extraction and gas control practices.

2 Coal samples and experimental methods

2.1 collection and preparation of coal samples, 2.1.1 coal sampling..

The coal specimens were collected from the No. 7 coal seam situated in the 110705 working face of the Wenjiaba Coal Mine, located in Zhijin County, Bijie City, Guizhou Province (Longitude: 105.76309, Latitude: 26.66049398).

Following geological tectonic actions, such as extrusion, shearing, and crushing, the initial coal seam undergoes various stresses, leading to the disruption of its original stratification and structure. This results in the pulverization of the primary structure. The pulverized primary structural coal subsequently experiences additional tectonic stresses, facilitating the formation of tectonic coal with low strength and weak adhesion. When subjected to mining activities, loose tectonic coal particles are produced, exhibiting characteristics such as low strength, easy fracturing, and a lackluster appearance. Primary coal refers to coal that retains its original primary sedimentary structure and tectonic features even after coalization and metamorphic processes. On the other hand, tectonic coal experiences changes in composition, structure, and tectonics due to tectonic stress. In this study, a comparative analysis is conducted on the macrostructure of primary coal and tectonic coal originating from the same mining area, and the results are presented in Table 1 .

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https://doi.org/10.1371/journal.pone.0301923.t001

Table 1 indicates that the structure of primary coal in the study area is relatively robust and intact compared to tectonic coal. Most of the tectonic coals exhibit a fractured and powdery granularity. Acidification experiments were conducted on the selected tectonic coals in the study area to investigate the impact of acidification on the development of the pore and fracture structure of tectonic coals.

2.1.2 Parameters of the coal sample.

Proximate analysis and ultimate analysis were conducted on the coal samples. To this end, an elemental analyzer (Elementar vario EL, Germany) was utilized and the test results are presented in Table 2 .

https://doi.org/10.1371/journal.pone.0301923.t002

Based on the coal classification standard [ 40 ], Wenjiaba tectonic coal is categorized as semi-anthracite coal. In the elemental analysis table, apart from carbon (C), the content of other elements is below 10%, indicating a relatively stable composition for each element.

2.1.3 Preparation of coal samples.

The lumpy coal samples were initially crushed, followed by grinding using an onyx mortar and pestle. Subsequently, an XSB-88 vibrating sieve machine was employed to screen particles within the range of 0.25 to 0.45 mm (Particle sieving shall be conducted in accordance with GB/T 477). Concurrently, molds were utilized to create standard coal samples measuring 1 mm × 1 mm × 1 mm [ 41 , 42 ]. The resulting samples were then placed in a drying box at 60°C.

2.2 Experimentation and methodology

2.2.1 experimental program..

The coal samples were prepared through the following procedures: (1) Grinding the coal samples collected from the study area and preparing standard samples measuring 1 mm × 1 mm × 1 mm using molds; (2) Formulating a suitable acid mixture based on research findings [ 43 , 44 ] to achieve optimal acidification; (3) Conducting acidification experiments on the coal samples; (4) Employing a FT-IR for the analysis of the microscopic molecular structure and macroscopic chemical composition of tectonic coal; conducting XRD experiments to analyze the crystal structure of coal samples before and after acidification. The infrared spectroscopy experiment parameters were set as follows: resolution 4 cm -1 , number of scans 16, scanning range 4000~400 cm -1 . Moreover, the XRD parameters were as follows: tube pressure 40 KV, diffraction width DS = SS = 1°, scanning speed 2.000 (d/min), scanning range 10°~80°; (5) Utilizing HP-MIP and LT-N 2 A experiments to analyze the pore structure of tectonic coal. The high-pressure mercury pressure test had a pressure range of 0.03–2200 MPa, and the pore size determination range was 350–0.005 μm. Cryogenic liquid nitrogen experiment parameters were set as follows: liquid nitrogen concentration 0.808 g/cc, experiment temperature: 77.350 K; (6) Using SEM to examine the changes in the pore space of coal samples before and after acidification. Adjustments to contrast and brightness were made during SEM experiments, with contrast set at about 60, brightness at about 0–20, and the bias beam current of the bulb adjusted to approximately 100 uA. The following section analyzes the experiments and their results.

2.2.2 Proportioning of acid.

Based on the XRD mapping, industrial composition, and elemental analysis results, Wenjiaba coal samples predominantly consist of quartz, kaolinite, and calcite, with carbon as the main element. A mixed acid solution comprising 15% HCL + 5% HF was chosen for the acidification process. This composition can effectively remove minerals from the pores and cracks of the coal [ 45 , 46 ]. Fig 1 illustrates the prepared acid solution. Approximately 100 ml of acid solution was prepared, and the result was kept sealed.

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2.2.3 Acidification experiments.

In acidification experiments, an extended reaction time can lead to a secondary reaction between the initial reaction product and the acid, resulting in the formation of a precipitate that may affect the experimental results [ 17 ]. Variations in temperature exert influence on the acidification effect [ 47 , 48 ]. However, due to experimental constraints, the present study focuses solely on analyzing the acidification process at a temperature of 28°C. The dried particles of the coal samples and the standard samples were immersed in the prepared acid solution for 12 hours [ 49 ]. The coal samples were then filtered and dried using a dryer at a temperature of 60°C. Subsequently, the dried coal samples were stored in a sealed bag for preservation, ready for subsequent experiments.

2.2.4 Experiments on the chemical structure of coal samples.

The coal samples were initially crushed and sieved with a filter sieve to eliminate coal dust particles with a diameter greater than 0.0750mm. Subsequently, deashing was carried out, ensuring the preservation of the coal sample structure. The samples were then dried at 60°C in a constant-temperature drying oven for 2 hours until a constant weight was achieved. The XRD analysis was employed to examine the microcrystalline structure of the materials, the XRD parameters were as follows: tube pressure 40 KV, diffraction width DS = SS = 1°, scanning speed 2.000 (d/min), scanning range 10°~80°;a technique extensively used for the investigation of amorphous materials in recent years [ 50 , 51 ]. Furthermore, FT-IR experiments were conducted to analyze the macroscopic chemical composition and microscopic molecular structure of the coal samples. The infrared spectroscopy experiment parameters were set as follows: resolution 4 cm -1 , number of scans 16, scanning range 4000~400 cm -1 .

2.2.5 Experiments to analyze the coal sample pore structure.

The capillary pressure curve obtained from the HP-MIP compression test provides fundamental parameters such as total porosity, pore size distribution, and specific surface area of the coal. The experiment was performed using an automatic mercury compression instrument (Mike AutoPore IV9500, Country), featuring a pressure range of 0.03–2200 MPa and a pore size range of 350~0.005 μm [ 52 ]. The mercury inlet pressure and pore radius r adhered to Washburn’s equation [ 53 ].

Where γ = 4.83×10 −3 N/m is the surface tension of mercury; θ = 130° denotes the contact angle between mercury and coal sample surface; p represents the pressure of mercury.

The LT-N 2 A pore measurement range spans 0.9~400 nm, but it exhibits lower accuracy for large pores. In contrast, the pressed mercury method can offer more precise pore data. Consequently, low-temperature liquid nitrogen is employed to accurately measure the pore structure of coal samples. In this study, an automatic analyzer (Kantar AUTOSORB IQ, Country) was utilized to explore specific surface area and porosity. The coal samples were dried under vacuum conditions at 383 K for 8 hours to eliminate the influence of gas in the instrument on the results. Finally, the sorption and desorption curves of the coal samples were investigated at 77 K to obtain more accurate pore data.

2.2.6 SEM experiments.

The adsorption resolution capacity of coal is closely related to the pore structure, with the development degree and connectivity of pores in coal reservoirs directly influencing the adsorption, desorption, and diffusion of CBM. In this study, an SEM (ZEISS Sigma 300, Germany) was employed to analyze coal samples. The Wenjiaba tectonic coal before acidification and the Wenjiaba tectonic coal after acidification were magnified at 500 times, 2000 times, and 5000 times, respectively. This allowed for a more intuitive observation of the development of the pore and fracture structure of coal samples before and after acidification.

3 Results and discussions

In the experimental process, temperature emerges as a pivotal factor, exerting a direct influence on the kinetic process of acidification, encompassing both the reaction rate and the extent of acidification [ 47 , 48 ]. Nevertheless, owing to constraints in our experimental setup, this study narrowly focuses on delineating the transformations in the physical pore structure and crystal structure of coal samples, both prior to and following acidification, within a 15% HCl + 5% HF acid solution maintained at a constant temperature of 28 degrees Celsius.

3.1 Characterization of the chemical structure of coal samples before and after acidification

In earlier studies, FTIR experiments were employed to investigate the alterations in functional groups before and after coal acidification, while XRD experiments were utilized to analyze the chemical composition of coal affected by acidification [ 54 ]. The content of functional groups vary with the acidification reaction time and the type of acid solution used [ 20 , 55 ]. The macro chemical composition and micro molecular structure of coal were analyzed through XRD and FTIR experiments, yielding results consistent with those obtained in prior research.

3.1.1 FT-IR experimental analysis.

The chemical bonds and functional groups within the constituent coal samples vibrate constantly, with vibration frequencies comparable to the frequency of infrared light. When infrared light irradiates the molecules in the coal samples, different chemical bonds and functional groups absorb different frequencies, manifesting at distinct positions in the infrared spectral map. Fig 2 shows the Fourier infrared spectral map of Wenjiaba tectonic coal samples before and after acidification and transformation obtained through FT-IR experiments. The spectrograms were processed based on the absorption peaks attributed to the infrared spectra of coal, as illustrated in Table 3 . The peak shapes were optimized, and the peaks, along with their corresponding functional groups were labeled.

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3.1.2 Analysis of characteristic functional groups of coal.

According to the absorption peaks of infrared spectra, hydroxyl functional groups, aliphatic hydrocarbon structures, oxygenated functional groups, and aromatic structures were primarily distributed in the wavenumber range of 3000~3800 cm -1 , 2800~3000 cm -1 , 1000~1800 cm -1 , and 700~900 cm -1 , respectively. The infrared spectral curves for the aforementioned four regions were fitted with peaks, and the fitting results are illustrated in Fig 3 .

(a) Absorption peaks of aromatic structures before and after acidification. (b) Absorption peaks of oxygen-containing functional group structures before and after acidification. (c) Absorption peaks of aliphatic hydrocarbon structures before and after acidification. (d) Absorption peak fitting of hydroxyl structures before and after acidification.

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• Aromatic structure analysis before and after acidification . Acidification has the potential to enhance the coalification process, thereby affecting the molecular structures. The ensuing decarboxylation and condensation reactions increase the aromaticity of coal seams and the number of aromatic layers in the coal. The aromatic structure segments were identified through peak fitting of the infrared spectra of coal samples before and after acidification. Fig 3(A) reveals that the percentage of the absorption peak area at 630~692 cm -1 and 741~770 cm -1 increased from 14.75% and 44.07% to 48.45% and 50.04%, respectively, while the absorption peak area at 869–875 cm -1 decreased from 6.99% to 1.50%. This observation demonstrates that acidification enhances the aromatization degree.
• Structural analysis of oxygen-containing functional groups before and after acidification . Oxygen-containing functional groups in coal are an accurate measure to determine the hydrophilicity and lipophilicity of coal. The primary regions of oxygen-containing functional groups in the infrared spectral map before and after acidification were fitted. Fig 3(B) shows that the percentage of absorption peak area at 1020~1072 cm -1 and 1320~1482 cm -1 decreased from 11.03% and 33% to 7.95% and 14%, respectively, while that at 1580~1640 cm -1 increased from 21.6% to 77%. Meanwhile, it is observed that the percentage of absorption peak area at 1640 cm -1 increased from 21.6% to 77.8%. The results demonstrate that the oxygen-containing functional groups in coal experienced varying degrees of reduction after acidification. However, the aromatic hydrocarbon C = C skeleton vibration and the structure of CH 3 and CH 2 asymmetric vibration on the alkane chain structure were strengthened.
• Structural analysis of aliphatic hydrocarbons before and after acidification . Before acidification, the 2800~3000 region is primarily associated with aliphatic CH 2 symmetric and asymmetric vibration, as well as aliphatic CH 3 asymmetric vibration. Fig 3(C) shows that the percentage of absorption peak area at 2929~2970 cm -1 decreases from 93.46% to 22. 49%, and the wave peak area at 2863~2889 cm -1 disappears after acidification. This observation indicates that the acidification process weakens the aliphatic CH 2 asymmetric vibration structure, as well as the aliphatic hydrocarbon CH 3 asymmetric vibration and CH 2 symmetric vibration.
• Structural analysis of hydroxyl groups and hydrogen bonding groups before and after acidification . The hydroxyl functional group in coal is hydrophilic and acidophilic, capable of forming hydrogen bonds and van der Waals forces with methane molecules. The content of hydroxyl groups significantly influences methane adsorption and desorption. Fig 3(D) indicates that the wave area of the fitted curve after acidification modification increased from 305.35 cm 2 to 74 cm 2 in the band 3200~3600 cm -1 . This observation demonstrates that the acidification process weakened the structure of hydroxyl and other hydroxyl groups involved in hydrogen bonding in the experimental coal samples.

In summary, the acidification and modification experiments significantly affected the internal structure of Wenjiaba tectonic coal. This transformation primarily involves the reduction of structures such as hydroxyl groups and oxygenated functional groups that participate in hydrogen bonding within the coal, along with an enhancement in the degree of aromatization.

3.1.3 Analysis of chemical structure parameters.

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Table 4 reveals that the reaction between the experimentally proportioned acid and the chemical constituents in the coal samples affects the parameters of aromaticity (f a ) and aliphatic branched-chain length ( R ), as well as the relative abundance of hydrogen in aromatic and aliphatic hydrocarbons (I), and the structural parameter of oxygen-containing functional groups ( C ). In comparison with the pre-acidification period, f a increased by 8.77%, R decreased by 58.94%, I increased by 50.80%, and C increased by 7.48%. Notably, the parameter R exhibited the most significant change, demonstrating that acidification weakened the aliphatic CH 2 asymmetric vibrational structure, as well as the aliphatic hydrocarbon CH 3 asymmetric vibration and CH 2 symmetric vibration in the coal samples.

3.1.4 X-ray diffraction experiments.

XRD experiments were performed on coal samples, and the XRD spectra of coal samples before and after acidification were analyzed using MDI (Jade 6.0) software based on the position and intensity of standardized peaks with corresponding components. Fig 4 indicates that the primary mineral components identified in coal samples included quartz, kaolinite, and calcite.

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Due to the chemical reaction between the acid solution and the minerals in the coal sample, there was a noticeable decrease in the diffraction intensity of kaolinite and quartz in the coal sample after acidification compared to before acidification. Particularly, Fig 5 shows that some diffraction peaks disappeared. The acidification process reduced calcite and kaolinite content in coal samples, while the stability of quartzite remained strong, making it resistant to complete dissolution by the acid. Consequently, a significant portion of quartzite remained in the coal samples after acidification. It is inferred that some kaolinite, quartz, and calcite were dissolved by the acid.

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3.1.5 Crystal structure analysis.

Fig 6 illustrates the peak-fitted XRD spectra of the 002 peak of the coal samples before and after acidification.

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Fig 6 reveals that the γ peak, corresponding to the diffraction peak in the fat group of coal, exhibits a positive correlation with its structure [ 61 ]. It is observed that the diffraction intensity of the γ peak reduces by 24.27% after acidification, indicating a corresponding decrease in the fat structure intensity of the samples. The data obtained through Gaussian peak fitting were incorporated into Eqs (7)- (10) to derive the inter-crystal spacing d 002 , the average diameter and height of the microcrystals, and the aromaticity. The results are presented in Table 5 .

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Table 5 indicates that acidification damaged the crystal structure of the coal samples, affecting inter-crystal spacing, diameter, height, and aromaticity of the coal samples before and after acidification. The results demonstrate that d 002 did not change significantly and fa-XRD increased by 3.7%, while L a and L c decreased by 16.23% and 4.17%, respectively. The substantial decrease in La suggests that the acid proportion damaged the crystals of the coal samples, primarily in terms of lateral corrosion. Moreover, the increase in aromaticity after acidification indicates that acidification enhances the aromatic structure of the coal samples, which is consistent with the observations from the infrared spectroscopy experiments.

3.2 Pore structure characteristics of coal before and after acidification

The porosity and fracture structure of coal significantly impacts the content of free gas in coal reservoirs. In this context, HP-MIP and low-temperature liquid nitrogen experiments are typically employed to investigate the pore and fracture structure of materials. The mercury injection curve can directly indicate the development and connectivity of pores and fractures in coal samples [ 62 ]. Accordingly, the present study conducted HP-MIP and low-temperature liquid nitrogen experiments to explore the pore structure of coal before and after acidification. The results were compared with experimental data [ 63 – 65 ].

3.2.1 High-pressure mercury compression experiments.

The coal samples before and after acidification were utilized in high-pressure mercury pressure experiments. The capillary pressure curves measured during these experiments provided essential parameters such as total porosity, pore size distribution, and specific surface area of the coal, as presented in Table 6 . It is observed that after acidification, the Wenjiaba tectonic coal exhibited significantly larger porosity and mercury input compared to those before acidification. Moreover, the average pore size of the coal samples before acidification was 205.1 nm, while it increased by 244.11% during acidification. However, the specific surface area was smaller than that of the coal samples before acidification. This relationship between porosity, specific surface area, and total area during the acidification process indicates a negative correlation between porosity and specific surface area. Consequently, when porosity increases and the total area of the coal sample remains constant after acidification, there is a negative correlation between the porosity and the specific surface area. The changes in pore structure after the acidification transformation experiment indicate enhanced pore development, with a noticeable trend of increased average pore diameter, demonstrating an increase in micropores and transition pores due to the transformation effect.

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3 . 2 . 1 . 1 Analysis of mercury supply curves . The mercury supply curve effectively and intuitively reflects the internal pore development and connectivity of coal samples. Fig 7 illustrates the mercury supply curves for coal samples before and after acidification.

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Analyze the results are as follows:

• As the pressure increases, the cumulative amount of mercury supply gradually rises and eventually reaches a smooth curve. The more complete curve represents the mercury feed curve under the condition of pressure increase, while the other curve is the mercury discharge curve.
• Fig 7 shows five stages of the mercury curve, divided by the inflection point position. Stages 1–3 primarily involve the pore capacity between coal particles, showing a gradual increase in the amount of mercury with the slow rise in pressure. In stages 3 and 4, the amount of mercury increases without a significant rise in energy consumption as the coal particles are compressed. Stages 4 and 5 involve a larger increase in pressure, where particles absorb more energy, leading to further compression of volume.
• Additionally, high-pressure mercury was injected into capillary and microscopic pores of coal samples. Beyond the fifth stage, even smaller pores were injected with high-pressure mercury, resulting in a cumulative mercury feed amount of about 0.23 mL/g. In the acidified coal samples, the mercury injection amount under the same pressure conditions was approximately 0.23 mL/g. However, after acidification and transformation, the cumulative mercury feed amount of coal samples significantly increased and reached 0.5 mL/g under the same pressure conditions. This was approximately twice the cumulative mercury feed amount observed before acidification and transformation. Moreover, the analysis of the steepness of the curve revealed that the mercury feed curve after acidification was steeper than the slope before acidification.
• Analyzing the slope curves of stages 1–3 reveals that the slope values before and after acidification are k1 = 0.01307 and k 2 = 0.03991, respectively. This indicates that acidification modification enhances the degree of pore development, leading to structural changes in the pores of coal samples. As a result, the amount of mercury absorbed in coal samples increases, and more micropores and transition pores are released, thereby improving the desorption capacity of the samples.

Fig 8 illustrates the relationship between logarithmic differential volume and pore size of coal samples before and after acidification.

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Fig 8 shows that the most available pore size of the coal sample before and after acidification is 0.55 and 1.85, respectively. This observation represents an over threefold increase compared to the pre-acidification state. The enhanced range of the most distributed pores in the coal samples after acidification indicates a significant improvement, and the desorption capacity of the coal samples primarily occurs through micropores and transition pores. It is worth noting that the number of micropores and transition pores is of significant importance in determining the desorption capacity of the coal. The results demonstrate that the acidification modification experiment increases the number of micropores and transition pores, thereby promoting the desorption capacity of coal samples.

3.2.2 Microporous structure of coal.

In this section, the experimental data of Feng Cong et al. [ 66 ]. are used for verification. The correlation between adsorbate and pressure in a specific system under constant temperature and relative equilibrium is referred to as the adsorption isotherm. The results are presented in Fig 9 .

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Before acidification, the coal samples exhibited an adsorption and desorption capacity of approximately 5.5, whereas this capacity was reduced to 1.8 after acidification. A larger adsorption capacity indicates greater pore space and stronger adsorption capabilities in coal samples. Although the adsorption capacity decreased notably after acidification, it still maintained a class IV(a) adsorption type. This suggests that acidification modification enhanced the desorption capacity of the coal samples.

The adsorption isotherm analysis revealed a hysteresis loop within the range of 0.5<P/P 0 <0.99, indicating that the evaporation in the pore differs from the condensation within it, reflecting the capillary condensation phenomenon in the presence of mesopores [ 67 ]. The hysteresis type of coal samples, classified according to the ISO 9277:2022 recommendations by IUPAC [ 68 ], was identified as H3 type. This classification indicates the presence of a significant number of open pores in the samples, providing more intuitive insight into the adsorption isotherms of coal samples.

3 . 2 . 2 . 1 Characteristics of adsorbed pore surface area and pore volume parameters of coal samples before and after acidification . According to the BJH model, the surface area of various types of pores in the coal samples, before and after acidification, can be categorized and summarized in Table 7 . Moreover, the pore volume data before and after acidification are presented in Table 8 . It is observed that the surface area of micropores and transition pores in the coal samples before acidification constituted over 93% of the total pore surface area. Following acidification, there was a significant reduction in the surface area of micropores and transition pores, but their percentage contribution did not change markedly. The maximum cumulative pore volume of the coal sample after acidification was approximately 0.0025, representing a reduction of about 68.75% compared to that before acidification. The pore size distribution curves of micropores and transition pores also demonstrated a reduction of about 70% after acidification, indicating that acidification modification, along with the coal composition, played a significant role in increasing pore volume, thereby reducing the proportion of micropores.

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Fig 10 shows the pore structure parameters and pore size distribution of the coal samples before and after acidification. It is noteworthy that the analysis in this section focuses solely on micropores and transition pores, as cryogenic liquid nitrogen is primarily employed to analyze these specific pore types.

(a) Pore size distribution of coal sample before and after acidification. (b) Pore structure parameters of coal sample before and after acidification.

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Fig 10(A) indicates that the cumulative pore volume after acidification approaches its value of 0.0025, representing a reduction of 68.75% compared to the pre-acidification period. The pore size distribution curves for micropores and transition pores also indicate a decrease of about 70% in pore size distribution after acidification compared to the pre-acidification period. This observation demonstrates that acidification modification and the material composition of the coal samples affect chemical interactions within coal, resulting in increased pore volume and a subsequent decrease in the proportion of micropores. Fig 10(B) shows the pore structure parameters of the coal samples before and after acidification. It is found that the cumulative specific surface area after acidification is reduced by approximately 67.5% compared to the pre-acidification period. This supports the conclusion that acidification modification engages in a chemical interaction with the material composition of the experimental coal samples, leading to increased pore volume and a reduction in the proportion of micropores.

3.2.3 SEM experimental analysis.

To study the effect of acidification on the evolution of pore and fissure structure of coal samples, the samples before and after acidification were magnified 500 times, 2000 times, and 5000 times, as shown in Figs 11 and 12 .

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Fig 11 presents the SEM images before acidification, revealing that in the 500X magnified image, the surface of the coal sample appears rough, and the pores and fissures are not prominently visible. In the 2000X and 5000X magnified images, the minerals filling the pores and fissures of the coal sample are evident. Conversely, the SEM images in Fig 12 demonstrate a smooth surface of the coal sample after acidification with distinct visibility of the pore and fissure structure. In the 2000X and 5000X magnified images, the cracks and pores on the surface of the coal samples are prominently observed. These observations suggest that the acid treatment corroded the mineral components that filled the pores and cracks within coal samples, leading to an increase in pores and cracks. The presence of these pores increases the specific surface area of the coal samples, aligning with the outcomes of Hg-pressure and BET experiments.

4 Conclusions

The current study endeavors to delve into the intricate pore and fracture structure, along with the molecular composition of coal samples, prior to and following the process of acidification. While variations in temperature can potentially alter the outcomes of acidification experiments, our exploration has been circumscribed to the specific outcomes achieved at a constant temperature of 28°C, owing to the constraints of our experimental setup. Drawing upon the insights garnered from our findings and comprehensive analysis, the pivotal advancements of this investigation can be distilled as follows:

• The acid-induced alteration in the coal samples involves the destruction of the crystal structure, primarily characterized by lateral corrosion. This process enhances the aromaticity of the coal samples and increases the relative abundance of hydrogen in aromatic and aliphatic hydrocarbons. Simultaneously, it weakens the structure related to aliphatic CH 2 asymmetric vibration, as well as aliphatic hydrocarbon CH 3 asymmetric vibration and CH 2 symmetric vibration in the coal samples. Additionally, the structure of hydroxyl and oxygen-containing functional groups is reduced. This reduction is particularly more pronounced in structures associated with hydrogen bonding.
• After acidification, the porosity and mercury uptake of the coal samples significantly increased compared to those before acidification. The average pore size of the coal samples after acidification was 500.66 nm, showing a 244.11% increase over the average pore size before acidification. Moreover, the most available pore size of the coal samples after acidification exhibited a 300% increase relative to the pre-acidification period, indicating a substantial enhancement in the range of pore sizes with the highest distribution in the coal samples after acidification.
• The maximum cumulative pore volume after acidification is approximately 0.0025, representing a reduction of about 68.75% compared to the pre-acidification period. The pore size distribution curves of micropores and excess pores also indicate a decrease of about 70% in the distribution of pore sizes after acidification compared to that before acidification. This observation demonstrates that the acidification process affects the material composition of coal, increasing pore volume and consequently reducing the proportion of micropores. The cumulative specific surface area after acidification decreased by about 67.5% compared to before acidification. This finding demonstrates that acidification modification interacts chemically with the material composition of the coal samples, leading to increased pore volume and a lower percentage of micropores.
• The SEM results provide a more intuitive demonstration that acidification enhanced the pore and fracture structure of the coal samples. It corroded the minerals filling the pores and fractures, resulting in increased fracture width and texture. This observation illustrates that acidification improved the connectivity of the pore and fracture structure in the coal samples. This improvement is crucial for enhancing the production capacity of CBM in subsequent processes.
• Acidification not only affects the content of chemical functional groups in coal but also corrodes minerals present in the pore and fissure structure, enhancing the connectivity of the pore and fissure network in coal. This provides a reliable theoretical approach for pumping and increasing the production of CBM.

In future research, the exploration of different acid solutions or the examination of the influence of strong alkaline solutions on coal samples will be conducted. Additionally, the investigation will focus on assessing whether the efficiency of the acidification process can be improved through the use of catalysts. These endeavors are expected to provide theoretical guidance and experimental insights for the future development of CBM in Guizhou.

Furthermore, to address the adverse effects of mixed acid on coal quality and the environment, the following four measures can be implemented. a)Implementation of coal mine emission control: This involves the efficient collection and treatment of acidic wastewater and waste gas produced during coal mine production processes to mitigate and minimize acid emissions. b)Waste Management: Ensuring proper treatment and disposal of waste generated during coal mine production to prevent soil and water pollution caused by acidic waste. c)Environmental Monitoring: Implementing a comprehensive environmental monitoring system to regularly assess the quality of soil, water, and air in the vicinity of the coal mine. This ensures timely identification and resolution of issues related to the environmental impact of acid. d)Technological Improvement: Implementing advancements and innovations in technology to minimize acid generation during coal mine production. This includes enhancing resource utilization efficiency and mitigating environmental impact.

Supporting information

https://doi.org/10.1371/journal.pone.0301923.s001

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