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Table Of Contents

Data analysis at the Armstrong Flight Research Center in Palmdale, California

data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “ big data ,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Data Analysis

Introduction to Data Analysis
Quantitative Analysis Tools
Qualitative Analysis Tools
Mixed Methods Analysis
Geospatial Analysis
Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ).

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats.

Some examples of types of data are as follows:

Photographs
Hand-written notes from field observation
Machine learning training data sets
Ethnographic interview transcripts
Sheet music
Scripts for plays and musicals
Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis.

Data analysis falls within the larger research data lifecycle, as seen below.

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data.

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods.

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning.

Qualitative research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning.

Mixed methods research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4).

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Analysis is a type of primary research that involves finding and interpreting patterns in data, classifying those patterns, and generalizing the results. It is useful when looking at actions, events, or occurrences in different texts, media, or publications. Analysis can usually be done without considering most of the ethical issues discussed in the overview, as you are not working with people but rather publicly accessible documents. Analysis can be done on new documents or performed on raw data that you yourself have collected.

Here are several examples of analysis:

Recording commercials on three major television networks and analyzing race and gender within the commercials to discover some conclusion.
Analyzing the historical trends in public laws by looking at the records at a local courthouse.
Analyzing topics of discussion in chat rooms for patterns based on gender and age.

Analysis research involves several steps:

Finding and collecting documents.
Specifying criteria or patterns that you are looking for.
Analyzing documents for patterns, noting number of occurrences or other factors.

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

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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.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Is Data Analysis in Research? Why It Matters & What Data Analysts Do

Data analysis in research is the process of uncovering insights from data sets. Data analysts can use their knowledge of statistical techniques, research theories and methods, and research practices to analyze data. They take data and uncover what it’s trying to tell us, whether that’s through charts, graphs, or other visual representations. To analyze data effectively you need a strong background in mathematics and statistics, excellent communication skills, and the ability to identify relevant information.

Read on for more information about data analysis roles in research and what it takes to become one.

In this article – What is data analysis in research?

What is data analysis in research?

Why data analysis matters, what is data science, data analysis for quantitative research, data analysis for qualitative research, what are data analysis techniques in research, what do data analysts do, in related articles.

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Data analysis is looking at existing data and attempting to draw conclusions from it. It is the process of asking “what does this data show us?” There are many different types of data analysis and a range of methods and tools for analyzing data. You may hear some of these terms as you explore data analysis roles in research – data exploration, data visualization, and data modelling. Data exploration involves exploring and reviewing the data, asking questions like “Does the data exist?” and “Is it valid?”.

Data visualization is the process of creating charts, graphs, and other visual representations of data. The goal of visualization is to help us see and understand data more quickly and easily. Visualizations are powerful and can help us uncover insights from the data that we may have missed without the visual aid. Data modelling involves taking the data and creating a model out of it. Data modelling organises and visualises data to help us understand it better and make sense of it. This will often include creating an equation for the data or creating a statistical model.

Data analysis is important for all research areas, from quantitative surveys to qualitative projects. While researchers often conduct a data analysis at the end of the project, they should be analyzing data alongside their data collection. This allows researchers to monitor their progress and adjust their approach when needed.

The analysis is also important for verifying the quality of the data. What you discover through your analysis can also help you decide whether or not to continue with your project. If you find that your data isn’t consistent with your research questions, you might decide to end your research before collecting enough data to generalize your results.

Data science is the intersection between computer science and statistics. It’s been defined as the “conceptual basis for systematic operations on data”. This means that data scientists use their knowledge of statistics and research methods to find insights in data. They use data to find solutions to complex problems, from medical research to business intelligence. Data science involves collecting and exploring data, creating models and algorithms from that data, and using those models to make predictions and find other insights.

Data scientists might focus on the visual representation of data, exploring the data, or creating models and algorithms from the data. Many people in data science roles also work with artificial intelligence and machine learning. They feed the algorithms with data and the algorithms find patterns and make predictions. Data scientists often work with data engineers. These engineers build the systems that the data scientists use to collect and analyze data.

Data analysis techniques can be divided into two categories:

Quantitative approach
Qualitative approach

Note that, when discussing this subject, the term “data analysis” often refers to statistical techniques.

Qualitative research uses unquantifiable data like unstructured interviews, observations, and case studies. Quantitative research usually relies on generalizable data and statistical modelling, while qualitative research is more focused on finding the “why” behind the data. This means that qualitative data analysis is useful in exploring and making sense of the unstructured data that researchers collect.

Data analysts will take their data and explore it, asking questions like “what’s going on here?” and “what patterns can we see?” They will use data visualization to help readers understand the data and identify patterns. They might create maps, timelines, or other representations of the data. They will use their understanding of the data to create conclusions that help readers understand the data better.

Quantitative research relies on data that can be measured, like survey responses or test results. Quantitative data analysis is useful in drawing conclusions from this data. To do this, data analysts will explore the data, looking at the validity of the data and making sure that it’s reliable. They will then visualize the data, making charts and graphs to make the data more accessible to readers. Finally, they will create an equation or use statistical modelling to understand the data.

A common type of research where you’ll see these three steps is market research. Market researchers will collect data from surveys, focus groups, and other methods. They will then analyze that data and make conclusions from it, like how much consumers are willing to spend on a product or what factors make one product more desirable than another.

Quantitative methods

These are useful in quantitatively analyzing data. These methods use a quantitative approach to analyzing data and their application includes in science and engineering, as well as in traditional business. This method is also useful for qualitative research.

Statistical methods are used to analyze data in a statistical manner. Data analysis is not limited only to statistics or probability. Still, it can also be applied in other areas, such as engineering, business, economics, marketing, and all parts of any field that seeks knowledge about something or someone.

If you are an entrepreneur or an investor who wants to develop your business or your company’s value proposition into a reality, you will need data analysis techniques. But if you want to understand how your company works, what you have done right so far, and what might happen next in terms of growth or profitability—you don’t need those kinds of experiences. Data analysis is most applicable when it comes to understanding information from external sources like research papers that aren’t necessarily objective.

A brief intro to statistics

Statistics is a field of study that analyzes data to determine the number of people, firms, and companies in a population and their relative positions on a particular economic level. The application of statistics can be to any group or entity that has any kind of data or information (even if it’s only numbers), so you can use statistics to make an educated guess about your company, your customers, your competitors, your competitors’ customers, your peers, and so on. You can also use statistics to help you develop a business strategy.

Data analysis methods can help you understand how different groups are performing in a given area—and how they might perform differently from one another in the future—but they can also be used as an indicator for areas where there is better or worse performance than expected.

In addition to being able to see what trends are occurring within an industry or population within that industry or population—and why some companies may be doing better than others—you will also be able to see what changes have been made over time within that industry or population by comparing it with others and analyzing those differences over time.

Data mining

Data mining is the use of mathematical techniques to analyze data with the goal of finding patterns and trends. A great example of this would be analyzing the sales patterns for a certain product line. In this case, a data mining technique would involve using statistical techniques to find patterns in the data and then analyzing them using mathematical techniques to identify relationships between variables and factors.

Note that these are different from each other and much more advanced than traditional statistics or probability.

As a data analyst, you’ll be responsible for analyzing data from different sources. You’ll work with multiple stakeholders and your job will vary depending on what projects you’re working on. You’ll likely work closely with data scientists and researchers on a daily basis, as you’re all analyzing the same data.

Communication is key, so being able to work with others is important. You’ll also likely work with researchers or principal investigators (PIs) to collect and organize data. Your data will be from various sources, from structured to unstructured data like interviews and observations. You’ll take that data and make sense of it, organizing it and visualizing it so readers can understand it better. You’ll use this data to create models and algorithms that make predictions and find other insights. This can include creating equations or mathematical models from the data or taking data and creating a statistical model.

Data analysis is an important part of all types of research. Quantitative researchers analyze the data they collect through surveys and experiments, while qualitative researchers collect unstructured data like interviews and observations. Data analysts take all of this data and turn it into something that other researchers and readers can understand and make use of.

With proper data analysis, researchers can make better decisions, understand their data better, and get a better picture of what’s going on in the world around them. Data analysis is a valuable skill, and many companies hire data analysts and data scientists to help them understand their customers and make better decisions.

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Research vs Analysis: What's the Difference and Why It Matters

When it comes to data-driven business decisions, research and analysis are often used interchangeably. However, these terms are not synonymous, and understanding the difference between them is crucial for making informed decisions.

Here are our five key takeaways:

Research is the process of finding information, while analysis is the process of evaluating and interpreting that information to make informed decisions.
Analysis is a critical step in the decision-making process, providing context and insights to support informed choices.
Good research is essential to conducting effective analysis, but research alone is not enough to inform decision-making.
Analysis requires a range of skills, including data modeling, statistics, and critical thinking.
While analysis can be time-consuming and resource-intensive, it is a necessary step for making informed decisions based on data.

In this article, we'll explore the key differences between research and analysis and why they matter in the decision-making process.

Understanding research vs analysis, why analysis matters in the decision-making process, the role of research in analysis, skills needed for effective analysis, the time and resource requirements for analysis, the unified stack for modern data teams, get a personalized platform demo & 30-minute q&a session with a solution engineer, introduction.

This is a guest post by Bill Inmon. Bill Inmon is a pioneer in data warehousing, widely known as the “Father of Data Warehousing.” He is also the author of more than 50 books and over 650 articles on data warehousing, data management, and information technology.

The search vendors will tell you that there is no difference. Indeed, when you do analysis you have to do research. But there are some very real and very important differences.

When it comes to the methodology of data science, understanding the main difference between research and analysis is crucial.

What is Research?

Research is the process of collecting and analyzing data, information, or evidence to answer a specific question or to solve a problem. It involves identifying a research question, designing a study or experiment, collecting and analyzing data, and drawing conclusions based on the results.

Research is typically focused on gathering information through various qualitative research methods, in order to develop an understanding of a particular topic or phenomenon.

In its simplest form, it means we go look for something. We go to a library and we find some books. Or we go to the Internet and find a good restaurant to go to. Or we go to the Bible and look up the story of Cain and Abel. To research means to go to a body of elements and find the one or two that we need for our purposes.

What are some common research methods?

There are many research methods, but some common ones include surveys, experiments, observational studies, case studies, and interviews. Each method has its strengths and weaknesses, and the choice of method depends on the research question, the type of data needed, and the available resources.

What is Analysis?

Analysis is the process of breaking down complex information into smaller parts to gain a better understanding of it. Then take that information and apply statistical analysis and other methods to draw conclusions and make predictions.

Somewhat similar to research, we go to a body of elements and find one or two that are of interest to us. Then after finding what we are looking for we do further investigation.

That further investigation may take many forms.

We may compare and contrast the elements
We may simply count and summarize the elements
We may look at many elements and qualify some of them and disqualify the others

The goal of analysis is to answer questions or solve problems. Analysis often involves examining and interpreting data sets, identifying patterns and trends, and drawing predictive conclusions based on the evidence.

In contrast to research, which is focused on gathering data, analysis is focused on making sense of the data that has already been collected.

What are some common analysis methods?

In the analysis process, data scientists use a variety of techniques and tools to explore and analyze the data, such as regression analysis, clustering, and machine learning algorithms. These techniques are used to uncover patterns, relationships, and trends in the data that can help inform business decisions and strategies.

There are many analysis methods, but some common ones include descriptive statistics, inferential statistics, content analysis, thematic analysis, and discourse analysis. Each method has its strengths and weaknesses, and the choice of method depends on the type of data collected, the research question, and the available resources.

Analysis is a critical step in the decision-making process. It provides context and insights to support informed choices. Without analysis, decision-makers risk making choices based on incomplete or inaccurate information, leading to poor outcomes. Effective analysis helps decision-makers understand the impact of different scenarios, identify potential risks, and identify opportunities for improvement.

In almost every case, the analysis starts with quantitative research. So it’s almost like differentiating between baiting a hook and catching a fish. If you are going to catch a fish, you have to start by baiting a hook.

Although that might not be the best analogy, the role of research in analysis works in the same order. Good research is essential to conducting effective analysis. It provides a foundation of knowledge and understanding, helping analysts identify patterns, trends, and relationships in data collection. However, research alone is not enough to inform decision-making. Just like baiting a hook alone is not enough to catch a fish.

Effective analysis requires a range of skills, including data modeling, statistics, and critical thinking. Data modeling involves creating a conceptual framework for understanding the data, while statistics helps data analysts identify patterns and relationships in the data sets. Critical thinking is essential for evaluating data analytics and drawing insights that support informed decision-making.

Related Reading : The Best Data Modeling Tools: Advice & Comparison

Just because you search for something does not mean you are going to analyze it.

Analysis can be time-consuming and resource-intensive, requiring significant investments in technology, talent, and infrastructure. However, It is necessary to analyze something when you need to extract meaningful insights or draw conclusions based on big data or information gathered through quantitative research.

Whether you're conducting research or performing statistical analysis, having a solid understanding of your data and how to interpret it is essential for success. Data scientists play a critical role in this process, as they have the skills and expertise to apply statistical methods and other techniques to make sense of complex data sets.

Organizations that invest in effective analysis capabilities are better positioned to make predictive data-driven business decisions that support their strategic goals. Without quantitative analysis, research may remain incomplete or inconclusive, and the data gathered may not be effectively used.

Related Reading : 7 Best Data Analysis Tools

How Integrate.io Can Help

When it comes to search and analysis, having access to accurate and reliable data is essential for making informed decisions. This is where Integrate.io comes in - as a big data integration platform, it enables businesses to connect and combine data from a variety of sources, making it easier to search for and analyze the information that's most relevant to their needs. By streamlining the data integration process, Integrate.io helps businesses get the most out of their data collection, enabling them to make more informed decisions and gain a competitive edge in their respective industries.

In conclusion, the main difference between research and analysis lies in the approach to data collection and interpretation. While research is focused on gathering information through qualitative research methods, analysis is focused on drawing predictive conclusions based on statistical analysis and other techniques. By leveraging the power of data science and tools like Integrate.io , businesses can make better decisions based on data-driven insights.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative		to broader populations. .
Quantitative		.

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary	.	methods.
Secondary

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

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

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

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

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Qualitative Data Analysis: What is it, Methods + Examples

Explore qualitative data analysis with diverse methods and real-world examples. Uncover the nuances of human experiences with this guide.

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
Complexity: Analyzing huge data sets requires time and great attention to detail.
Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
Time-consuming: The study can be time-consuming, especially with large data sets.
Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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Research and Analysis

When writing an analytical essay, you will likely have to conduct research. Research is the process of investigating a topic in an in-depth, systematic manner. You will then have to analyze that research to examine its implications and support a defensible claim about the topic. Sometimes writers do not conduct research when writing an analytical essay, but they usually still analyze sources that have used research. Learning how to conduct and analyze research is thus a critical part of strengthening analytical writing skills.

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Cell Biology

Is a newspaper a primary or secondary source?

Is a letter a primary or a secondary source?

Which of the following questions is an analytical question specifically for a secondary source?

Which of the following is an element of active reading?

A _ source is an original document or first-hand account.

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Research and Analysis Definition

When people are interested in a topic and want to learn more about it, they conduct research. In academic and professional settings, research follows systematic, critical processes.

Analysis is the process of critically examining research. When analyzing a source, researchers reflect on many elements, including the following:

How the information is presented

The author's main point

The evidence the author uses

The credibility of the author and the evidence

The potential for bias

The implications of the information

Research and Analysis Types

The type of research people conduct depends on what they are interested in learning about. When writing analytical essays about literature, authors typically consult primary sources, secondary sources, or both. Then they craft an analytical argument in which they make a claim about the sources supported with direct evidence.

Analyzing Primary Sources

Writers who write about literature often have to analyze primary sources.

A primary source is an original document or first-hand account.

For instance, plays, novels, poems, letters, and journal entries are all examples of primary sources. Researchers can find primary sources in libraries, archives, and online. To analyze primary sources , researchers should follow the following st eps:

1. Observe the Source

Take a look at the source at hand and preview it. How is it structured? How long is it? What is the title? Who is the author? What are some defining details about it?

For example, imagine a student is faced with the following prompt:

Pick an 18th-century English poet to research. Evaluate how their personal lives shaped the themes of their poetry.

To address this prompt, the researcher might analyze a letter their chosen poet sent to a friend. When observing the letter, they might note that the writing is neat cursive and includes salutations such as "faithfully yours." Without even reading the letter, the researcher can already tell that this is a formal letter and infer that the writer is trying to come across as respectful.

2. Read the Source

Next, researchers should read the entire primary source. Developing the skill of active reading (discussed later in this article) will help readers engage with a primary source. While reading, readers should take notes about the most important details in the text and what they suggest about the research topic.

For instance, the researcher analyzing the historical letter should note what the main purpose of the letter is. Why was it written? Is the writer asking for anything? Does the writer recount any important stories or pieces of information that are central to the text?

Sometimes primary sources are not written texts. For example, photographs can also be primary sources. If you can't read a source, observe it and ask analytical questions.

3. Reflect on the Source

When analyzing a primary source, readers should reflect on what it shows about the research topic. Questions for analysis include:

What is the main idea of this text?

What is the purpose of the text?

What is the historical, social, or political context of this text?

How might the context shape the meaning of the text?

Who is the intended audience of the text?

What does this text reveal about the research topic?

The precise questions a reader should ask when analyzing a primary source depend on the research topic. For example, when analyzing the letter from the poet, the student should compare the main ideas in the letter to the main ideas in some of the writer's poems. This will help them develop an argument about how elements of the poet's personal life shaped the themes of their poetry.

When analyzing literary primary sources, writers should examine and reflect on the elements such as characters, dialogue, plot, narrative structure, point of view, setting, and tone. They should also analyze how the author uses literary techniques like figurative language to convey messages. For instance, you might identify an important symbol in a novel. To analyze it, you could argue that the author uses it to develop a particular theme.

Analyzing Secondary Sources

When researchers consult a source that is not original, they are consulting a secondary source. For example, scholarly journal articles, newspaper articles, and textbook chapters are all secondary sources.

A secondary source is a document that interprets information from a primary source.

Secondary sources can help researchers understand primary sources. Authors of secondary sources analyze primary sources. The elements they analyze might be elements other readers of the primary source might not have noticed. Using secondary sources also makes for credible analytical writing because writers can show their audience that other credible scholars support their points of view.

To analyze secondary sources, researchers should follow the same steps as analyzing primary sources. However, they should ask slightly different analytical questions, such as the following:

Where was this source published?

What sources does the author use? Are they credible?

Who is the intended audience?

Is it possible that this interpretation is biased?

What is the author's claim?

Is the author's argument convincing?

How does the author use their sources to support their claim?

What does this source suggest about the research topic?

For example, a writer analyzing the themes of a particular poet's body of work should search for secondary sources in which other writers interpret the poet's work. Reading other scholars' interpretations can help writers better understand the poetry and develop their own perspectives.

To find credible secondary sources, writers can consult academic databases. These databases often have trustworthy articles from peer-reviewed scholarly journals, newspaper articles, and book reviews.

Research and Analysis Writing

After conducting research, writers must then craft a cohesive argument using relevant analysis. They can use primary and secondary sources to support an analytical argument by making use of the following strategies:

Summarize Each Source

Researchers should reflect on all of the sources they consulted during the research process . Creating a short summary of each source for themselves can help them identify patterns and make connections between ideas. This will then ensure they craft a strong claim about the research topic.

Taking notes about the main ideas of each source while reading can make summarizing each source quite simple!

Develop an Argument

After making connections between sources, researchers should craft a claim about the argument that addresses the prompt. This claim is called a thesis statement, a defensible statement that the writer can support with evidence from the research process .

Synthesize the Sources

Once writers have fine-tuned the essay's thesis, they should synthesize the sources and decide how to use information from multiple sources to support their claims. For instance, perhaps three of the sources help prove one supporting point, and another three support a different one. Writers must decide how each source is applicable, if at all.

Discuss Quotations and Details

Once researchers have decided what pieces of evidence to use, they should incorporate short quotes and details to prove their point. After each quote, they should explain how that evidence supports their thesis and include a citation.

What to Include in Research and Analysis Writing	What to Avoid in Research and Analysis Writing
Formal academic language	Informal language, slang, and colloquialisms
Concise descriptions
Objective language	First-person point of view
Citations for outside sources	Unsupported personal thoughts and opinions

Research and Analysis Skills

To strengthen the ability to conduct research and analysis, researchers should work on the following skills :

Active Reading

Readers should actively read the texts that they research, as this will ensure they notice important elements for analysis.

Active reading is engaging with a text while reading it for a specific purpose.

In the case of research and analysis, the purpose is to investigate the research topic. Active reading involves the following steps.

1. Preview the Text

First, readers should skim the text and understand how the author structured it. This will help readers know what to expect when they dive in.

2. Read and Annotate the Text

Readers should read the text attentively, with a pencil or pen in hand, ready to note important elements and jot down thoughts or questions. While reading, they should also ask questions, make predictions and connections, and check for clarification by summarizing important points.

3. Recall and Review the Text

To make sure they understood the text, readers should ask themselves what the main idea was and what they learned.

Writing down a mini summary of a text's main points is useful in the research process because it will help researchers keep track of the point of all of their sources.

Critical Thinking

Researchers need to think critically in order to analyze sources. Critical thinking is the process of thinking analytically. Researchers who are critical thinkers are always ready to make connections, comparisons, evaluations, and arguments. Thinking critically allows researchers to draw conclusions from their work.

Organization

Collecting large amounts of data can be overwhelming! Creating an organized system to keep track of all of the information will streamline the research process .

Research and Analysis Example

Imagine a student is given the following prompt.

Analyze how William Shakespeare uses the image of blood to develop a theme in Macbeth (1623).

To analyze this prompt, the student should use Macbeth as well as secondary sources about the play to support an original analytical argument that addresses the prompt.

When reading Macbeth , the student should actively read, paying careful attention to instances of bloody images and what they might mean. They should also consult an academic database and search for articles about the images and themes in Macbeth . These secondary sources can provide insight into the potential meanings behind the images they are looking up.

Once the student has all of their sources, they should look them all over and consider what they suggest about the image of blood in the play. It is important that they do not repeat an argument that they found in secondary sources, and instead use those sources to come up with their own perspective on the topic. For instance, the student might state:

In Macbeth , William Shakespeare uses images of blood to represent the theme of guilt.

The student can then synthesize information from the sources in their research process and identify three supporting points for their thesis. They should carefully select short but significant quotes that prove each point and explain the implications of those points. For example, they might write something like the following:

As Lady Macbeth scrubs the hallucination of blood off her hands, she shouts, "Out, damned spot; out, I say" (Act V, Scene i). As English professor John Smith says, "her desperation is evident in the tone of the writing" (Smith, 2018). Her desperation emphasizes the guilt she feels. It is as if the murder is a stain on her soul.

Note how the student drew from both primary and secondary sources to inform their interpretation of the writing.

Finally, the student should make sure that they cited their sources from the research process to avoid plagiarism and give the original authors proper credit.

Research and Analysis - Key Takeaways

Research is the process of investigating a topic in an in-depth, systematic manner.
Analysis is the critical interpretation of research.
Researchers can collect and analyze primary sources, which are first-hand accounts or original documents.
Researchers can also collect and analyze secondary sources, which are interpretations of primary sources.
Readers should actively read their sources, note the main ideas, and reflect on how information from the sources supports a claim in response to the research topic.

Flashcards in Research and Analysis 6

Which of the following should not be included in research and analysis writing?

Colloquialisms

What evidence is the author using to support their claim?

Making predictions

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Frequently Asked Questions about Research and Analysis

What is meant by research analysis?

Research is the process of formally investigating a topic and analysis is the process of interpreting what is found in the research process.

What is the difference between research and analysis?

Research is the process of investigating a topic. Analysis is the process of using critical thinking skills to interpret sources found during research.

What is the research and analysis process?

Research involves searching for relevant information, closely reading and engaging with that information, and then analyzing that information.

What are the types of research methods?

Researchers can collect primary or secondary sources.

What is an example of analysis?

An example of analysis is identifying the intended audience of a primary source and inferring what this suggests about the author's intentions.

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Which of the following should not be included in research and analysis writing?

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Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

Inspecting : Initial examination of data to understand its structure, quality, and completeness.
Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

Calculate the mean, median, and mode of academic scores for both groups.
Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language :

Description: An open-source programming language specifically designed for statistical computing and data visualization.
Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

Description: A data mining software application used for building predictive models and conducting advanced analytics.
Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Is a newspaper a primary or secondary source?

Is a letter a primary or a secondary source?

Which of the following questions is an analytical question specifically for a secondary source?

Which of the following is an element of active reading?

A _ source is an original document or first-hand account.

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Research and Analysis Definition

When people are interested in a topic and want to learn more about it, they conduct research. In academic and professional settings, research follows systematic, critical processes.

Analysis is the process of critically examining research. When analyzing a source, researchers reflect on many elements, including the following:

How the information is presented

The author's main point

The evidence the author uses

The credibility of the author and the evidence

The potential for bias

The implications of the information

Research and Analysis Types

Analyzing Primary Sources

Writers who write about literature often have to analyze primary sources.

A primary source is an original document or first-hand account.

1. Observe the Source

Take a look at the source at hand and preview it. How is it structured? How long is it? What is the title? Who is the author? What are some defining details about it?

For example, imagine a student is faced with the following prompt:

Pick an 18th-century English poet to research. Evaluate how their personal lives shaped the themes of their poetry.

2. Read the Source

Sometimes primary sources are not written texts. For example, photographs can also be primary sources. If you can't read a source, observe it and ask analytical questions.

3. Reflect on the Source

When analyzing a primary source, readers should reflect on what it shows about the research topic. Questions for analysis include:

What is the main idea of this text?

What is the purpose of the text?

What is the historical, social, or political context of this text?

How might the context shape the meaning of the text?

Who is the intended audience of the text?

What does this text reveal about the research topic?

Analyzing Secondary Sources

A secondary source is a document that interprets information from a primary source.

To analyze secondary sources, researchers should follow the same steps as analyzing primary sources. However, they should ask slightly different analytical questions, such as the following:

Where was this source published?

What sources does the author use? Are they credible?

Who is the intended audience?

Is it possible that this interpretation is biased?

What is the author's claim?

Is the author's argument convincing?

How does the author use their sources to support their claim?

What does this source suggest about the research topic?

Research and Analysis Writing

Summarize Each Source

Taking notes about the main ideas of each source while reading can make summarizing each source quite simple!

Develop an Argument

Synthesize the Sources

Discuss Quotations and Details

What to Include in Research and Analysis Writing	What to Avoid in Research and Analysis Writing
Formal academic language	Informal language, slang, and colloquialisms
Concise descriptions
Objective language	First-person point of view
Citations for outside sources	Unsupported personal thoughts and opinions

Research and Analysis Skills

To strengthen the ability to conduct research and analysis, researchers should work on the following skills :

Active Reading

Readers should actively read the texts that they research, as this will ensure they notice important elements for analysis.

Active reading is engaging with a text while reading it for a specific purpose.

In the case of research and analysis, the purpose is to investigate the research topic. Active reading involves the following steps.

1. Preview the Text

First, readers should skim the text and understand how the author structured it. This will help readers know what to expect when they dive in.

2. Read and Annotate the Text

3. Recall and Review the Text

To make sure they understood the text, readers should ask themselves what the main idea was and what they learned.

Writing down a mini summary of a text's main points is useful in the research process because it will help researchers keep track of the point of all of their sources.

Critical Thinking

Organization

Collecting large amounts of data can be overwhelming! Creating an organized system to keep track of all of the information will streamline the research process .

Research and Analysis Example

Imagine a student is given the following prompt.

Analyze how William Shakespeare uses the image of blood to develop a theme in Macbeth (1623).

To analyze this prompt, the student should use Macbeth as well as secondary sources about the play to support an original analytical argument that addresses the prompt.

In Macbeth , William Shakespeare uses images of blood to represent the theme of guilt.

Note how the student drew from both primary and secondary sources to inform their interpretation of the writing.

Finally, the student should make sure that they cited their sources from the research process to avoid plagiarism and give the original authors proper credit.

Research and Analysis - Key Takeaways

Research is the process of investigating a topic in an in-depth, systematic manner.
Analysis is the critical interpretation of research.
Researchers can collect and analyze primary sources, which are first-hand accounts or original documents.
Researchers can also collect and analyze secondary sources, which are interpretations of primary sources.
Readers should actively read their sources, note the main ideas, and reflect on how information from the sources supports a claim in response to the research topic.

Flashcards in Research and Analysis 6

Which of the following should not be included in research and analysis writing?

Colloquialisms

What evidence is the author using to support their claim?

Making predictions

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Frequently Asked Questions about Research and Analysis

What is meant by research analysis?

Research is the process of formally investigating a topic and analysis is the process of interpreting what is found in the research process.

What is the difference between research and analysis?

Research is the process of investigating a topic. Analysis is the process of using critical thinking skills to interpret sources found during research.

What is the research and analysis process?

Research involves searching for relevant information, closely reading and engaging with that information, and then analyzing that information.

What are the types of research methods?

Researchers can collect primary or secondary sources.

What is an example of analysis?

An example of analysis is identifying the intended audience of a primary source and inferring what this suggests about the author's intentions.

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Research trends on nanomaterials in triple negative breast cancer (TNBC): a bibliometric analysis from 2010 to 2024

Affiliations.

1 Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250014, China.
2 Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369 Jingshi Road, Jinan, Shandong, 250014, China.
3 Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369 Jingshi Road, Jinan, Shandong, 250014, China. [email protected].
PMID: 39242466
DOI: 10.1007/s13346-024-01704-9

Breast cancer (BC) is an important cause of cancer-related death in the world. As a subtype of BC with the worst prognosis, triple-negative breast cancer (TNBC) is a serious threat to human life and health. In recent years, there has been an increasing amount of research aimed at designing and developing nanomaterials for the diagnosis and treatment of TNBC. The purpose of this study was to comprehensively evaluate the current status and trend of the application of nanomaterials in TNBC through bibliometric analysis. Studies focusing on nanomaterials and cancer were searched from the Web of Science core collection (WOSCC) database, and relevant literature meeting the inclusion criteria was selected for inclusion in the study. VOSviewer and CiteSpace were used to perform bibliometric and visual analysis of the included publications. A total of 2338 studies were included. Annual publications have increased from 2010 to 2024. China, the United States and India were the leading countries in the field, accounting for 66.1%, 11.5% and 7.2% of publications, respectively. The Chinese Academy of Sciences and Li Yaping were the most influential institutions and authors, respectively. Journal of Controlled Release was considered the most productive journal. Cancer Research was considered to be the most co-cited journal. Drug delivery and anti-cancer mechanisms related to nanomaterials were considered to be the most widely studied aspects, and green synthesis and anti-cancer mechanisms were also recent research hotspots. In this study, the characteristics of publications were summarized, and the most influential countries, institutions, authors, journals, hot spots and trends in the application of nanomaterials in cancer were identified. These findings provide valuable insights into the current state and future direction of this dynamic field.

Keywords: Bibliometric analysis; Breast cancer; Nanomaterials; Research trends; Triple negative.

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Verma A, Singh A, Singh MP, Nengroo MA, Saini KK, Satrusal SR, Khan MA, Chaturvedi P, Sinha A, Meena S, Singh AK, Datta D. EZH2-H3K27me3 mediated KRT14 upregulation promotes TNBC peritoneal metastasis. Nat Commun. 2022;13(1):7344. https://doi.org/10.1038/s41467-022-35059-x . - DOI - PubMed - PMC
Jin J, Tao Z, Cao J, Li T, Hu X. DNA damage response inhibitors: an avenue for TNBC treatment. Biochimica et biophysica acta. Reviews cancer. 2021;1875(2):188521. https://doi.org/10.1016/j.bbcan.2021.188521 . - DOI
Hu XE, Yang P, Chen S, Wei G, Yuan L, Yang Z, Gong L, He L, Yang L, Peng S, Dong Y, He X, Bao G. Clinical and biological heterogeneities in triple-negative breast cancer reveals a non-negligible role of HER2-low. Breast cancer Research: BCR. 2023;25(1):34. https://doi.org/10.1186/s13058-023-01639-y . - DOI - PubMed - PMC
Liu Y, Hu Y, Xue J, Li J, Yi J, Bu J, Zhang Z, Qiu P, Gu X. Advances in immunotherapy for triple-negative breast cancer. Mol Cancer. 2023;22(1):145. https://doi.org/10.1186/s12943-023-01850-7 . - DOI - PubMed - PMC
Li Y, Zhang H, Merkher Y, Chen L, Liu N, Leonov S, Chen Y. Recent advances in therapeutic strategies for triple-negative breast cancer. J Hematol Oncol. 2022;15(1):121. https://doi.org/10.1186/s13045-022-01341-0 . - DOI - PubMed - PMC

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Quantitative research questions	Quantitative research hypotheses
Descriptive research questions	Simple hypothesis
Comparative research questions	Complex hypothesis
Relationship research questions	Directional hypothesis
	Non-directional hypothesis
	Associative hypothesis
	Causal hypothesis
	Null hypothesis
	Alternative hypothesis
	Working hypothesis
	Statistical hypothesis
	Logical hypothesis
	Hypothesis-testing
Qualitative research questions	Qualitative research hypotheses
Contextual research questions	Hypothesis-generating
Descriptive research questions
Evaluation research questions
Explanatory research questions
Exploratory research questions
Generative research questions
Ideological research questions
Ethnographic research questions
Phenomenological research questions
Grounded theory questions
Qualitative case study questions

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Quantitative research questions
Descriptive research question
	- Measures responses of subjects to variables
	- Presents variables to measure, analyze, or assess
	What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training?
Comparative research question
	- Clarifies difference between one group with outcome variable and another group without outcome variable
	Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)?
	- Compares the effects of variables
	How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells?
Relationship research question
	- Defines trends, association, relationships, or interactions between dependent variable and independent variable
	Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic?

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Quantitative research hypotheses
Simple hypothesis
	- Predicts relationship between single dependent variable and single independent variable
	If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered.
Complex hypothesis
	- Foretells relationship between two or more independent and dependent variables
	The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable).
Directional hypothesis
	- Identifies study direction based on theory towards particular outcome to clarify relationship between variables
	Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects.
Non-directional hypothesis
	- Nature of relationship between two variables or exact study direction is not identified
	- Does not involve a theory
	Women and men are different in terms of helpfulness. (Exact study direction is not identified)
Associative hypothesis
	- Describes variable interdependency
	- Change in one variable causes change in another variable
	A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable).
Causal hypothesis
	- An effect on dependent variable is predicted from manipulation of independent variable
	A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient.
Null hypothesis
	- A negative statement indicating no relationship or difference between 2 variables
	There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2).
Alternative hypothesis
	- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables
	The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2).
Working hypothesis
	- A hypothesis that is initially accepted for further research to produce a feasible theory
	Dairy cows fed with concentrates of different formulations will produce different amounts of milk.
Statistical hypothesis
	- Assumption about the value of population parameter or relationship among several population characteristics
	- Validity tested by a statistical experiment or analysis
	The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2.
	There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan.
Logical hypothesis
	- Offers or proposes an explanation with limited or no extensive evidence
	If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less.
Hypothesis-testing (Quantitative hypothesis-testing research)
	- Quantitative research uses deductive reasoning.
	- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses.

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative research questions
Contextual research question
	- Ask the nature of what already exists
	- Individuals or groups function to further clarify and understand the natural context of real-world problems
	What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems)
Descriptive research question
	- Aims to describe a phenomenon
	What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities?
Evaluation research question
	- Examines the effectiveness of existing practice or accepted frameworks
	How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility?
Explanatory research question
	- Clarifies a previously studied phenomenon and explains why it occurs
	Why is there an increase in teenage pregnancy (phenomenon) in Tanzania?
Exploratory research question
	- Explores areas that have not been fully investigated to have a deeper understanding of the research problem
	What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic?
Generative research question
	- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions
	How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative?
Ideological research question
	- Aims to advance specific ideas or ideologies of a position
	Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care?
Ethnographic research question
	- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings
	What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis?
Phenomenological research question
	- Knows more about the phenomena that have impacted an individual
	What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual)
Grounded theory question
	- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups
	What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed?
Qualitative case study question
	- Assesses a phenomenon using different sources of data to answer “why” and “how” questions
	- Considers how the phenomenon is influenced by its contextual situation.
	How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan?

Qualitative research hypotheses
Hypothesis-generating (Qualitative hypothesis-generating research)
	- Qualitative research uses inductive reasoning.
	- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis.
	- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach.

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

Variables	Unclear and weak statement (Statement 1)	Clear and good statement (Statement 2)	Points to avoid
Research question	Which is more effective between smoke moxibustion and smokeless moxibustion?	“Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.”	1) Vague and unfocused questions
			2) Closed questions simply answerable by yes or no
			3) Questions requiring a simple choice
Hypothesis	The smoke moxibustion group will have higher cephalic presentation.	“Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group.	1) Unverifiable hypotheses
		Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group.	2) Incompletely stated groups of comparison
		Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.”	3) Insufficiently described variables or outcomes
Research objective	To determine which is more effective between smoke moxibustion and smokeless moxibustion.	“The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.”	1) Poor understanding of the research question and hypotheses
Research objective			2) Insufficient description of population, variables, or study outcomes

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

Variables	Unclear and weak statement (Statement 1)	Clear and good statement (Statement 2)	Points to avoid
Research question	Does disrespect and abuse (D&A) occur in childbirth in Tanzania?	How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania?	1) Ambiguous or oversimplistic questions
Research question			2) Questions unverifiable by data collection and analysis
Hypothesis	Disrespect and abuse (D&A) occur in childbirth in Tanzania.	Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania.	1) Statements simply expressing facts
Hypothesis	Disrespect and abuse (D&A) occur in childbirth in Tanzania.	Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania.	2) Insufficiently described concepts or variables
Research objective	To describe disrespect and abuse (D&A) in childbirth in Tanzania.	“This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.”	1) Statements unrelated to the research question and hypotheses
Research objective			2) Unattainable or unexplorable objectives

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

EXAMPLE 1. Descriptive research question (quantitative research)
- Presents research variables to be assessed (distinct phenotypes and subphenotypes)
“BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
EXAMPLE 2. Relationship research question (quantitative research)
- Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
“Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
EXAMPLE 3. Comparative research question (quantitative research)
- Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
“BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
EXAMPLE 4. Exploratory research question (qualitative research)
- Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
“Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
EXAMPLE 5. Relationship research question (quantitative research)
- Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
“Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

EXAMPLE 1. Working hypothesis (quantitative research)
- A hypothesis that is initially accepted for further research to produce a feasible theory
“As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
“In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
EXAMPLE 2. Exploratory hypothesis (qualitative research)
- Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
“We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
“Conclusion
Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
“We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
EXAMPLE 4. Statistical hypothesis (quantitative research)
- An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
“Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
“Statistical Analysis
( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

EXAMPLE 1. Background, hypotheses, and aims are provided
“Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
“ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
“This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
EXAMPLE 2. Background, hypotheses, and aims are provided
“We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
“ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
EXAMPLE 3. Background, aim, and hypothesis are provided
“In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
“This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
“ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Barroga E, Matanguihan GJ.
Methodology: Barroga E, Matanguihan GJ.
Writing - original draft: Barroga E, Matanguihan GJ.
Writing - review & editing: Barroga E, Matanguihan GJ.

Christoph’s Content Corner

Content Strategy Leader, Head of Content, Content Marketing

by Christoph Trappe
August 31, 2024 August 28, 2024

What is Content Analysis?

Trappe Digital LLC may earn commission from product clicks and purchases. Rest assured, opinions are mine or of the article’s author.

Is my content working? What does working even mean? That’s where content testing and content analysis come in. So, let’s dive into answering the following questions: What is content analysis, and what are its main parts?

Content analysis helps marketers understand how their content performs. It’s like a report card for marketing efforts. By looking at the right data, it’s possible to figure out what’s working and what’s not – which helps us make smart decisions about content strategy.

Article sections:

How it works in practices

The basics of content analysis

Content analysis is all about understanding content performance. It involves looking at data and metrics to see how well content is doing. This can include things like how many people view the content, how long they stay on a page, and whether they take action after reading.

All sorts of content can be analyzed. This includes blog posts, social media updates, videos, emails, and more. The goal is to get a clear picture of how the audience interacts with the content.

There are two main types of content analysis: quantitative and qualitative . Quantitative analysis looks at numbers and statistics . Qualitative analysis focuses on the meaning and context of the content . Both are important for getting a full picture of content performance.

Read next: What do marketers do on a daily basis?

Key parts of content analysis

To do content analysis well, it’s important to understand a few key components. Let’s break them down.

Setting baselines

A baseline is a starting point that can be used to measure progress.

Erin Del Ponte , a marketing executive who is a steward for accurate attribution and incrementality of investments, explained it like this on Episode 682 of “The Business Storytelling Show:”

“A lot of it is finding those baselines of your content, and then each piece of content that you create measuring against that baseline.”

To set a baseline, marketers look at how their content usually performs. This gives a standard against which to compare new content . For example, if blog posts usually get 1,000 views in the first week, that’s the baseline. If a new post gets 1,500 views, it’s clear that it’s performing above average.

Important metrics to track

When doing content analysis, there are several key metrics to pay attention to. These include:

Views and visits: How many people are seeing the content?
Sources: How are people finding the content?
Click-through rates: How many people are clicking on links?
Engagement rates: How are people interacting with the content?
Conversion rates: How many people are taking the desired action after viewing the content?

“We look at views, visits, click through engagement, and then using that content, developing those baselines for content types, can evaluate whether incoming content is performing as strong as baseline,” she said.

Collecting data

The first step in content analysis is collecting data. This involves using analytics tools to gather information about how content is performing.

Making sense of the data

Once the data is collected, it needs to be interpreted. This means looking for patterns and trends in the numbers. Are certain types of content performing better than others? Is there a particular time when the audience seems most engaged? Certain content types work better than others?

Erin suggests looking at content performance over time.

“We have content that we posted three years ago, and then, we hadn’t really had much engagement with the content, and it just it became much more relevant,” she said.

Finding useful insights

The goal of content analysis is to improve content and results. This might mean identifying topics the audience is particularly interested in, or figuring out the best time to post on social media – though this part becomes less and less important with social media algorithms doing what they do.

Taking action

Finally, these insights need to be used to take action. This might involve creating more of the content types that perform well, adjusting the posting schedule, or revising the overall content strategy.

Testing as part of content analysis

Testing is an important part of content analysis. It helps in figuring out what works best for the audience and can include

A/B testing
User testing and feedback
Analyzing content across different channels

Analyze regularly

Make content analysis a regular part of the workflow. Erin suggests making it a quarterly process.

“It becomes a quarterly process for us as we do business reviews looking back at, okay, let’s look at content from the previous quarter.”

Work together across teams

Content analysis often involves input from different teams, such as marketing , analytics, and content creation. It’s important to ensure that these teams collaborate effectively.

Challenges in content analysis

While content analysis is incredibly useful, it’s not without its challenges. Here are a few common ones:

Too much data

With so many metrics available, it’s easy to get overwhelmed by data. The key is to focus on the metrics that matter most for the goals at hand.

Conflicting data

Sometimes, different metrics might tell different stories. For example, a piece of content might get lots of views but low engagement. In these cases, it’s necessary to consider overall goals to decide which metrics are most important. Sometimes, it’s the wrong audience, geographically speaking. An OB clinic could probably care less about going viral globally as its services are very local. But a software firm that sells everywhere, that global reach is awesome.

Balancing numbers and context

While numbers are important, they don’t tell the whole story.Balance quantitative data with qualitative insights about the content and audience.

Read next: What’s a Google Optimize alternative?

Tying it all together

Let’s look at how content analysis works in practice. Erin shared insights from her work in the vitamin and supplement industry.

“We’re selling vitamins and supplements, so there’s a lot of questions that people have, and there’s a lot of solution oriented articles as well,” Erin explained. This shows how their content strategy focuses on addressing customer needs and questions.

Their content strategy involves various formats. “We have, we do have a blog, very much educational,” Erin said. She also mentioned, “We’ve started developing more video content, integrating that video content with the written content, or letting that video content live on its own. And then we rely on infographics as well.”

Interestingly, Erin highlighted how content relevance can change over time.

“We have content that we posted three years ago, and then, I would say, maybe about a year ago, all of a sudden, you know, we hadn’t really had much engagement with the content, and it just it became much more relevant.”

This observation underscores the importance of ongoing content analysis. It shows that older content can suddenly gain traction, emphasizing the need to monitor content performance continuously.

As content analysis is implemented in a marketing strategy, remember that it’s an ongoing process. Keep testing, keep analyzing, and keep improving. The content (and the results) will be better for it.

Read this book to improve content performance

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Article Contents

Introduction, materials and methods, acknowledgements, author contributions, data availability, conflict of interest, supplementary data.

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Dynamic temporal transcriptome analysis reveals grape VlMYB59- VlCKX4 regulatory module controls fruit set

Article contents
Figures & tables
Supplementary Data

Qiaofang Shi, Xufei Li, Shengdi Yang, Xiaochun Zhao, Yihan Yue, Yingjun Yang, Yihe Yu, Dynamic temporal transcriptome analysis reveals grape VlMYB59- VlCKX4 regulatory module controls fruit set, Horticulture Research , Volume 11, Issue 9, September 2024, uhae183, https://doi.org/10.1093/hr/uhae183

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Fruit set is a key stage in determining yield potential and guaranteeing quality formation and regulation. N-(2-chloro-4-pyridyl)-N′-phenylurea (CPPU) has been widely applied in grape production, the most iconic of which is the promotion of grape fruit set. However, current studies still lack the molecular mechanism of CPPU-induced grape fruit set. Here, the dynamic, high-resolution stage-specific transcriptome profiles were generated based on two different treatments and five developmental periods during fruit set in ‘Kyoho’ grape ( Vitis vinifera L. × V. labrusca L.). Pairwise comparison and functional category analysis showed that phytohormone action cytokinin was significantly enriched during the CPPU-induced grape fruit set, but not the natural one. Value differentially expressed gene (VDEG) was a newly proposed analysis strategy for mining genes related to the grape fruit set. Notably, the cytokinin metabolic process was significantly enriched among up-regulated VDEGs. Of importance, a key VDEG VlCKX4 related to the cytokinin metabolic process was identified as related to the grape fruit set. Overexpression of VlCKX4 gene promoted the Arabidopsis plants that produce more and heavier siliques. The transcription factor VlMYB59 directly bound to the promoter of VlCKX4 and activated its expression. Moreover, overexpression of VlMYB59 gene also promoted the Arabidopsis fruit set. Overall, VlMYB59 responded to CPPU treatment and directly activated the expression of VlCKX4 , thus promoting the fruit set. A regulatory pathway of the VlMYB59- VlCKX4 module in the fruit set was uncovered, which provides important insights into the molecular mechanisms of the fruit set and good genetic resources for high fruit set rate breeding.

Grape ( Vitis vinifera L.) is an important horticultural fruit crop cultivated worldwide and plays a crucial role in the development of the agricultural economy. ‘Kyoho’ grape ( V. vinifera L. × V. labrusca L.) is a European-American hybrid grape that is favored by consumers for its attractive taste and rich nutrient content [ 1 ]. Fruit set is critical for maintaining and improving fruit yields and also the foundation for the formation and regulation of fruit quality [ 2 ]. Grape fruit set usually occurs 6–12 days after full bloom (DAFB), and most young berries experience serious abscise at 9–10 DAFB [ 3 ], which is more severe during the fruit set of ‘Kyoho’ grape. In the current production, cytokinin has been widely used to alleviate the abscission of young berries in grape [ 4 , 5 ].

As an initial stage of fruit development in flowering plants, fruit set marks the activation of a new developmental process [ 6 ], which is affected by many factors. Changes in plant hormone content and its associated gene expression levels are recognized as the necessary internal environment of fruit set in horticultural crops [ 6 , 7 ]. Cytokinin is the key hormone responsible for fruit set. Cytokinin signal is transferred from the style of the stamen to the valve margin encapsulating the style after flowering and then reaches the ovary wall after fertilization [ 8 ]. Additionally, cytokinin concentration is shown to be distinctly elevated during the fruit set and earlier fruit development [ 9 , 10 ]. Plant growth regulator N-(2-chloro-4-pyridyl)-N′-phenylurea (CPPU), a phenylurea cytokinin, has been widely used in horticulture production for diverse purposes [ 11 ]. Particularly, CPPU is beneficial for promoting fruit set and fruit development in various fruit trees, including grapes [ 5 , 12 ]. Dissecting the mechanism of CPPU-induced fruit set and exploring the genes related to fruit set will be conducive to breeding new varieties of high fruit set. Multiple candidate genes related to fruit set have been explored based on sequencing data of CPPU-induced fruit set [ 13–15 ], but direct genetic functional verification of the genes is still lacking.

Cytokinin oxidase/dehydrogenase (CKX) enzymes, as central to the catabolism of cytokinin, could catalyze the irreversible degradation of cytokinin [ 11 ]. Several recent reviews have discussed the role of CKX gene family members in fruit set and yield in depth in crops such as rice, barley, and wheat [ 9 , 16 , 17 ]. In the model plant Arabidopsis , a decrease in the flower number was shown in the AtCKX3 overexpression line and an increase in flower number and silique number was observed in the double Atckx3ckx5 mutant [ 18 , 19 ]. Expression of SlCKX3 with a low level before anthesis was increased during the fruit set in tomato [ 20 ]. Additionally, four CKX genes showed significantly up-regulated expression during the CPPU-promoted fruit set in fig [ 21 ]. However, there are few reports on the function of CKX genes in regulating fruit set in fruit trees including grape.

The MYB transcription factor (TF) family was large with functional diversification, of which R2R3 MYBs, the predominant family, has been extensively characterized for their functions and characteristics [ 22 ]. In the model plants Arabidopsis , tomato, and rice, many MYB TFs have been shown to be involved in anther development to regulate pollen fertility [ 23–25 ]. Recently, the silencing of SlMYB gene was demonstrated to result in reducing pollen grain fertility, consequently inhibiting fruit set and fruit development of tomato [ 26 ]. In addition, SlGAMYB1/2 silencing in SlMIR159 -overexpressing plants exhibited precocious fruit initiation prior to anthesis, consequently promoting fruit set [ 27 ]. These studies provide strong evidence that MYB TFs participate in regulating fruit set and that different MYB TFs function differently. In grape, a comprehensive correlation analysis of bunch traits revealed the number of berries significantly associated with the polymorphism of the gene sequence for a MYB TF [ 28 ]. Grape VvMYB5b overexpression caused delayed anther dehiscence [ 29 ] and VvMYB4 gene induced male sterility in transgenic plants [ 30 ]. However, little research has been reported on grape fruit set regulated by MYB.

Recently, we validated that CPPU treatment could significantly improve the fruit set rate of ‘Kyoho’ grape [ 2 , 31 ], but the downstream regulatory pathway remains unknown. In this study, the young berries were treated with distilled water and CPPU at 5 DAFB and collected at 0, 1, 2, 4, and 8 d after treatment, respectively. The dynamic transcriptome profiles of five periods during grape fruit set were produced using second-generation sequencing technology. Finally, a key module VlMYB59- VlCKX4 for regulating fruit set was discovered and validated. Overall, this study aimed to uncover new molecular insights into CPPU-induced grape fruit set and could provide the theoretical basis and gene resources for directionally cultivated grape varieties with high fruit set rates and high yields.

CPPU treatment altered phytohormone levels in grape berries at fruit set

The fruit set rate of ‘Kyoho’ grape treated with CPPU (T) was significantly higher than that treated with distilled water (C) ( Fig. 1A and B ). Based on the important role of plant hormones in fruit set, the contents of endogenous hormones were determined at four periods after distilled water and CPPU treatments, respectively. The trend curve of auxin (IAA) content during the natural fruit set (NS, treated with distilled water) was shown as inverse V shape and the IAA level peaked at 4 d. After CPPU treatment, IAA content was significantly inhibited at 4 and 8 d ( Fig. 1C ). Compared with NS, gibberellin acid 1 (GA 1 ) content was significantly decreased at 1 and 2 d ( Fig. 1D ), and GA 4 was almost undetectable at four periods after CPPU treatment ( Fig. 1E ). CPPU treatment also resulted in changes in the levels of GA 3 and GA 7 , but not significantly ( Fig. S1A and B , see online supplementary material). The level of trans -zeatin (tZ), the most common biologically active form of cytokinin, decreased with the progression of the CPPU-promoted fruit set ( Fig. 1F ). Although the content of the cytokinin precursor tZ-riboside (tZR) at 1 d after CPPU treatment was almost identical to that in NS, it was much lower than that in other periods of NS ( Fig. 1G ). The content of ethylene precursor 1-aminocyclopropane-1-carboxylate (ACC) was not significantly affected by CPPU treatment at 1, 2, and 4 d, and only increased significantly approximately 1-fold at 8 d ( Fig. 1H ). CPPU treatment not only significantly inhibited the content of abscisic acid (ABA) and salicylic acid (SA) but made them with a highly similar trend curve ( Fig. 1I and J ). In terms of jasmonic acid (JA), cis -PODA content decreased at 1 and 2 d after CPPU treatment ( Fig. 1K ), while other forms of JA showed irregular changes with no significance ( Fig. S1C and D , see online supplementary material) and the changes in the two brassinosteroid content were similar to that of JA-Ile ( Fig. S1E and F , see online supplementary material). These results indicated that CPPU treatment could alter the content of various hormones during fruit set. Additionally, the co-expression relationship between hormone content and the four periods of fruit set was inferred based on the correlation coefficient. T1 was strongly correlated with multiple hormone contents, but not with C1 ( Fig. S1G , see online supplementary material).

Dynamic changes of hormone content at four periods during fruit set. A Development and fruit set phenotype of ‘Kyoho’ grape berry at four periods after treatment. C, control, treated with distilled water; T, treatment, treated with CCPU; 1, 2, 4, and 8 days after treatment. B Statistic analysis of grape berry set rate. Berry set rate of grape was the ratio of berry number at 8 d after treatment of the same fruit string to berry number at 0 d. C – K Content analysis of endogenous phytohormones in grape berries. ( C ) IAA, auxin; ( D and E ) GA 1 and GA 4 , gibberellin; ( F ) t Z, cytokinin trans -zeatin; ( G ) t ZR, cytokinin precursor t Z-riboside; ( H ) ACC, ethylene precursor 1-aminocyclopropane-1-carboxylate; ( I ) ABA, abscisic acid; ( J ) SA, salicylic acid; ( K ) cis -OPDA, jasmonic acid precursor cis -(+)-12-oxo-phytodienoic acid. Data shown are means ± SD ( * P < 0.05, * * P < 0.01, * * * P < 0.001, Student’s t -test).

Global analysis of grape berry transcriptome at fruit set

To reveal the potential molecular network of the CPPU-promoted grape fruit set, berry tissues at 0, 1, 2, 4, and 8 d after CPPU and distilled water treatment were selected for time-point transcriptome sequencing, respectively. Each sample contains three biological replicates, each of which consisted of pooled berries samples from multiple clusters of one independent grape plant. A total of 1.5 billion high-quality clean reads were generated. The values of Q20 (~98.71%) and Q30 (~95.56%) indicated that the quality of the sequencing data was sufficient to support further analysis. After filtering, an average of ~92.13% clean reads in each sample were uniquely mapped to the reference V. vinifera (PN40024.v4) genome ( https://plants.ensembl.org/Vitis_vinifera/Info/Index , Table S1 , see online supplementary material). The uniquely mapped reads were employed to calculate the normalized gene transcription level as transcripts per million (TPM) values, and the average TPM values of the three replicates were calculated as the transcription level of genes in each period. To decrease the influence of transcription noise, genes with average TPM value <1 were defined as not expressed. Principal component analysis (PCA) indicated that three biological replicates of each period were clustered together and basically separated from other periods ( Fig. 2A ). On average, more than 90% of the gene transcript levels were in the range of 1–100 TPM ( Fig. 2B ).

Global analysis of the grape berry transcriptomes and functional enrichment analysis of differentially expressed genes (DEGs). A Principal component analysis (PCA) of the transcriptomes of berries tissues. B Number of genes expressed in each sample with an average TPM ≥ 1. TPM, transcripts per million. 1, 2, and 3 represents three biological replicates. C Venn diagram of DEGs in T samples versus C samples at four periods of fruit set. D Venn diagram of DEGs at four periods of the natural fruit set. E Venn diagram of DEGs at four periods of CPPU-promoted fruit set. F Functional enrichment of DEGs of MapMan ontogeny groups. BIN, major functional category; C, control; T, CPPU treatment; 0, 1, 2, 4, and 8, days after treatment; TC, T samples versus C samples at the same period of the fruit set; NS, natural fruit set; TS, CPPU-induced fruit set. Enrichment scores (expressed as P value) for each BIN functional category is shown. Black boxes indicate significantly enriched BINs. NA, not available.

Functional categorization of differentially expressed genes (DEGs) with different analysis strategies

To get a more comprehensive view of the gene transcription changes during fruit set, different strategies were used to compare the transcriptome profiles of grape berries. Firstly, transcriptome profiles of T sample and C sample (TC) were compared at the same period of the fruit set to explore genes in response to CPPU treatment. There were 267, 588, 2184, and 995 DEGs at 1, 2, 4, and 8 d, respectively. The number of up-regulated DEGs has always been less than that of down-regulated DEGs during four periods ( Fig. S2 and Table S2 , see online supplementary material), indicating that CPPU treatment had more inhibitory effects on gene expression than activation. A total of 2917 genes were differentially expressed in at least one period, but only 18 DEGs were shared ( Fig. 2C ). Secondly, to explore genes in response to NS, transcriptome profiles of C1, C2, C4, and C8 were compared with that of C0, respectively. Correspondingly, 681, 122, 5532, and 4987 DEGs were identified and the number of DEGs at C4 was much higher than that at C2 ( Fig. S2 and Table S3, see online supplementary material ). This corresponded to the fact that C4 was the critical period of fruit set, in which more genes related to fruit set were activated or repressed. A total of 7195 DEGs were identified and only four genes were differentially expressed at four periods ( Fig. 2D ). Finally, the transcriptome profiles of T1, T2, T4, and T8 were compared with that of C0 (TS), respectively. And 237, 1780, 2774, and 3448 DEGs were identified. The number of DEGs gradually increased during fruit set after CPPU treatment ( Fig. S2 and Table S4 , see online supplementary material). A total of 4466 DEGs were identified, and 88 genes were differentially expressed at all four periods ( Fig. 2E ).

A total of 7894 DEGs were generated by three strategies and assigned to the MapMan categories, of which 896 DEGs were not assigned. Based on the threshold of P value ≤0.05, 66 functional subcategories (subBINs), which belong to 26 primary functional categories (BINs), were significantly overrepresented during the fruit set ( Fig. 2f ; Table S5 , see online supplementary material). Among them, multiple BINs were significantly overrepresented, including BIN3 (carbohydrate metabolism), BIN9 (secondary metabolism), BIN11 (phytohormone action), and BIN21 (cell wall organization) ( Fig. 2F ). Notably, of the BIN11 (phytohormone action), the pathways associated with cytokinin were significantly enriched during the fruit set after CPPU treatment, but not significantly during NS.

Temporal profiling of differentially expressed transcript factors (DETFs) during fruit set

The dynamic expression profiles of DETFs were performed as a visualize analysis to screen the candidate TFs that might be related to fruit set. There were 4, 5, and 5 statistically significant model profiles (colored profiles) identified in TC, NS, and TS, respectively ( Fig. 3 ; Fig. S3 and Table S6 , see online supplementary material). Profiles 5, 22, and 25 were identified in all three comparison strategies with different numbers of TFs ( Fig. 3 ). Of them, profile 5 showed down-regulated expression and profile 25 showed up-regulated expression, while profile 22 showed an increase to a peak, then a decline ( Fig. 3 ). Profiles 5, 14, 22, and 25 contained a total of 288 DETFs in TC. These DETFs mainly belonged to MYB, ERF, bHLH, WRKY, MYB_related, and NAC families ( Fig. 3A ). During NS, a total of 454 DETFs were contained in five profiles, and six TF families that were consistent with those of TC were most frequently represented ( Fig. 3B ). Profiles 3, 5, 6, 22, and 25 of TS contained a total of 300 DETFs, which most frequently represented families were ERF and MYB ( Fig. 3C ). Notably, of all TF families, MYB and ERF were most frequently represented in all three comparison strategies, implying that DETFs from MYB and ERF families might be involved in fruit set.

Expression profile analysis and family members of statistical analysis of differentially expressed transcription factors (DETFs) during fruit set. The number in the upper left corner of the colored box represents the profile name, and the number in the lower left corner represents the number of DETFs. The black line in the colored box represents the expression pattern of DETFs. Dot size represents DETFs number, and color scale represents -log 10 ( P value). TC, T samples compared to C samples at the same period; NS, natural fruit set; TS, CPPU-induced fruit set.

Cytokinin-related value DEGs (VDEGs) were closely related to grape fruit set

Since the involvement of sampling time, treatments, and control in sequencing of transcriptome samples, the expression confusion of some DEGs would interfere with exploring candidate genes related to fruit set. Here, a new strategy was proposed to explore valuable DEGs for CPPU-induced grape fruit set, known as VDEGs. VDEGs from each of the four periods were screened separately and VDEG screening of 1 d after treatment was used as an example to illustrate ( Fig. 4A ). First, DEGs shared by C0_C1, C0_T1, and C1_T1 in the overlap g were identified as VDEGs. Although these VDEGs responded to both CPPU-induced and natural fruit set, there were significant differences in their response levels. However, DEGs in overlap d were not identified as VDEGs, because their expression levels were not different between T1 and C1. Secondly, DEGs in the overlap f were identified as VDEGs. The expression levels of these VDEGs were significantly different at T1 versus C0 and C1, but not between C0 and C1. It supported that these VDEGs only responded to the CPPU-induced fruit set, not NS. Furthermore, DEGs in overlap e were identified as VDEGs, because their expression levels were significantly different at C1 versus C0 and T1, but not between C0 and T1. This supports that these VDEGs were able to respond to NS, whereas CPPU inhibited this response. Overall, the DEGs in the overlap g, f, and e were the VDEGs.

Identification and analysis of value differential expression genes (VDEGs) and dynamic expression patterns of cytokinin-related VDEGs during fruit set. A Schematic diagram of VDEGs identification. The genes of the Venn diagram were from the DEGs of C0_ C1, C0_T1, and C1_T1. The DEGs in overlap e, f, and g were identified as VDEGs. Lines inside the boxes represent possible expression levels of DEG in different samples. Oranges lines represent up-regulated expression and blue lines represent down-regulated expression. B Venn diagrams of up- and down-regulated VDEGs. T > C, the expression level of VDEGs were significantly higher in T than in C; T < C, the expression level of VDEGs were significantly higher in C than in T. C GO enrichment analyses of up- and down-regulated VDEGs common to at least three periods (bold numbers in B). The red and blue balls represent up- and down-regulated enrichment GO terms. The X-axis represents P . adjust value. D The expression levels of cytokinin-related VDEGs during fruit set.

Based on the expression levels of VDEGs at T and C of the same period, they were classified as up- and down-regulated VDEGs ( Fig. S4A , see online supplementary material). There were 146 (88 up and 58 down), 502 (126 up and 376 down), 1752 (304 up and 1448 down), and 871 (105 up and 766 down) VDEGs at 1, 2, 4, and 8 d after treatment, respectively ( Fig. 4B ; Fig. S4 , see online supplementary material). Similarly to the previous result ( Fig. S2 , see online supplementary material), the numbers of down-regulated VDEGs were much higher than that of up-regulated VDEGs at the latter three periods. Gene Ontology (GO) enrichment analyses were performed separately for up- and down-regulated VDEGs sharing at least three periods (bold numbers; Fig. 4B ) to investigate GO terms specific response to CPPU-induced fruit set. The results revealed two molecular functions (MFs) and two biological processes (BPs) were significantly enriched among up-regulated VDEGs ( Fig. 4C ). About the down-regulated VDEGs, two cellular components (CCs), two MFs, and four BPs were significantly enriched ( Fig. 4C ). Notably, two GO terms of cytokinin-related cytokinin dehydrogenase activity and cytokinin metabolic process were extremely significantly enriched, implying the regulatory roles of cytokinin-related VDEGs in CPPU-induced fruit set. These results indicated that the identification of VDEGs would be more beneficial in exploring genes that truly play important roles in the CPPU-induced fruit set.

To further investigate the roles of cytokinin-related genes in fruit set, a total of 18 VDEGs were identified that might participate in cytokinin action ( Fig. 4D ). Grape response regulators ( VlRRs ) and cytokinin oxidase/dehydrogenases ( VlCKXs ) were significantly up-regulated expression in TC and TS, whereas cytokinin nucleoside 5′-monophosphate phosphate ribose hydrolases 2 ( VlLOG2 ) and VlLOG5 were significantly down-regulated expression ( Fig. 4D ). It indicated that these VDEGs related to cytokinin were closely related to grape fruit set.

Regulatory relationships between key DETFs and cytokinin-related VDEGs during fruit set

Based on the significant enrichment of cytokinin-related metabolic process among cytokinin-related VDEGs ( Fig. 4C and D ), regulatory networks were constructed to predict the key DETFs regulating 18 cytokinin-related VDEGs. Results showed that 14 of the 18 cytokinin-related VDEGs were regulated by 10 DETFs, and all of these regulatory relationships were predicted by GENIE3 ( Fig. 5A ). Among these, the regulatory relationships between VlbZIP44 and VlRR28 , VlMYB12 and VlRR17 , as well as VlATHB-12 and VlRR2 were also predicted by the database JASPAR ( Fig. 5A , dashed lines). Notably, the regulatory relationships between VlMYB59 and VlCKX4 , VlMYB12 and VlCKX6 were predicted in both databases (JASPAR and PlantTFDB) and GENIE3 ( Fig. 5A , solid lines), indicating a high probability of regulatory relationships between these two groups of DETFs and VDEGs. Because the increased fold of VlCKX4 expression in TC and TS was higher than VlCKX6 ( Tables S2 and S4 , see online supplementary material), VlCKX4 was selected as the candidate gene mediating grape fruit set for further research.

Regulatory network analysis of cytokinin-related VDEGs and DETFs and overexpression of VlCKX4 promoted fruit set. A Lines between orange spheres and green boxes indicate that TFs might regulate VDEGs. Dotted lines, regulatory relationships predicted by GENIE3; dashed lines, regulatory relationships predicted by GENIE3 and JASPAR; solid lines, regulatory relationships predicted by GENIE3, JASPAR, and PlantTFDB; orange spheres, cytokinin-related VDEGs; green boxes, DETFs. B and C Phenotypic of growth morphology ( B ) and siliques ( C ) of six-week-old overexpression (OE) VlCKX4 plants. WT plants acted as controls. D – F Silique number (D ), silique weight ( E ), and plant height ( F ) in OE- VlCKX4 plants. Data shown are means ± SD ( * * P < 0.01, Student’s t -test).

Overexpression (OE) of VlCKX4 promoted fruit set

Protein VlCKX4 had the CKX characteristic domain, namely cytokinin binding site and FAD binding site. Phylogenetic analysis based on conserved CKX domain amino acid sequence showed that VlCKX4 was clustered with AtCKX2, AtCKX3, and AtCKX4 ( Fig. S5 , see online supplementary material). To validate the function of the VlCKX4 gene during fruit set, five independent pSAK277-mediated OE transgenic plants were generated and wild-type (WT) acted as control ( Fig. S6A and B , see online supplementary material). The transgenic lines OE-3, OE-4, and OE-5 with higher expression levels ( Fig. S6C , see online supplementary material) were selected for further research. The OE lines with better growth and development status exhibited significant phenotypic differences from WT ( Fig. 5B ). Specifically, OE lines showed a significant increase in the numbers of siliques ( Fig. 5C and D ), as well as a significant increase in silique weight and plant height ( Fig. 5E and F ). The numbers of siliques in the OE lines were about 30, nearly twice as many as the numbers of siliques in the WT ( Fig. 5D ). Additionally, the silique weight of the OE lines was nearly twice as much as that of the WT ( Fig. 5E ). In terms of plant height, the OE lines were about 40 cm, while the WT was less than 30 cm ( Fig. 5F ). The above results indicated that VlCKX4 overexpression was not only beneficial for fruit set but also positively promoted the growth of plant height and fruit.

VlMYB59 directly bound to VlCKX4 promoter and activated its expression

Based on the regulatory network analysis ( Fig. 5A ), VlMYB59 with 256 amino acid residues ( Fig. S7A , see online supplementary material) was chosen as a plausible upstream TF for VlCKX4 . The protein sequence of VlMYB59 contained a potential motif at the N-terminus that could interact with the basic-helix–loop–helix (bHLH) factor, and typical R2 and R3 conserved domains ( Fig. S7A , see online supplementary material). The analysis of subcellular localizations showed that VlMYB59 localized in the nucleus ( Fig. 6A ). In addition, VlMYB59 was found to be widely expressed in various tissues of grape ( Fig. S7B , see online supplementary material) and showed strong co-expression with VlCKX4 in grape under CPPU treatment ( Fig. 6B ). Three potential MYB binding sites were identified in the promoter (∼2000 bp) of VlCKX4 . Thus, we hypothesized that VlMYB59 might be involved in the transcriptional regulation of VlCKX4 during the fruit set.

Transcript factor VlMYB59 activates VlCKX4 expression and promotes fruit set. A Subcellular localization of VlMYB59 in N. benthamiana leaves. EV-YFP, empty vector 101LYFP; 35S:VlMYB59-YFP, vector 101LYFP containing VlMYB59. Scale bars = 40 μm. B The expression level of VlMYB59 in grape fruit set. C, control, treated with distilled water; T, treatment, treated with CCPU; 1, 2, 4, and 8 d, days after treatment. Data shown are means ± SD ( * P < 0.05, * * P < 0.01, * * * P < 0.001, Student’s t -test). C Schematic diagrams of the effectors and reporters used for the dual-luciferase assay. EV, empty vector. D Analysis of LUC/REN ratio in the dual luciferase assay. The EV_LUC + EV_pSAK277, EV_LUC + VlMYB59 _pSAK277, and pVlCKX4 _LUC + EV_pSAK277 were used as control. Data shown are means ± SD ( P < 0.05, Duncan’s multiple range test). E VlMYB59 protein directly bound to VlCKX4 promoter. pVlCKX4 , pAbAi vector containing the promoter of VlCKX4 . AD-EV, empty vector used as the negative control; AD-VlMYB59, prey vector containing VlMYB59. SD/−Leu/AbA0, selective medium without Leu; SD/−Leu/AbA 400 , selective medium without Leu supplemented with AbA at the concentration of 400 ng mL −1 . F Plant phenotype of six-week-old WT and OE- VlMYB59 plants. G and H Statistical analysis of siliques number (g) and plant height (h). Data shown are means ± SD ( * P < 0.05, Student’s t -test).

To test the hypothesis, a luciferase (LUC) reporter assay was performed in Nicotiana benthamiana leaves. Compared with the double empty group (LUC + pSAK277) and the single empty groups (LUC + VlMYB59 and ProCKX4_LUC + pSAK277), the LUC/REN activity of the experimental group (ProCKX4_LUC + VlMYB59) was significantly enhanced ( Fig. 6C and D ). This result indicated that VlMYB59 acted as a positive regulatory TF to activate the transcription of VlCKX4 . The VlCKX4 promoter sequence contained three MYB binding elements, each with a binding motif TAACCA, located between −1594 and −1589 bp, between −1311 and −1306 bp, and between −1026 and −1021 bp, respectively ( Fig. S8A , see online supplementary material). To determine the activity of MYB-binding elements in the VlCKX4 promoter sequence in response to CPPU treatment, the VlCKX4 promoter full-length sequence ( pVlCKX4 ) and the VlCKX4 promoter sequences with three ( pVlCKX4 -E3), two ( pVlCKX4 -E2), one ( pVlCKX4 -E1), and no ( pVlCKX4 -E0) MYB-binding elements were, respectively, constructed to GUS vectors ( Fig. S8A , see online supplementary material) and transferred into N. benthamiana leaves. After CPPU treatment, the leaves transiently expressing pVlCKX4 :: GUS and pVlCKX4 -E3:: GUS showed apparent and stronger GUS staining, while the leaves transiently expressing pVlCKX4 -E2:: GUS , pVlCKX4 -E1:: GUS , and pVlCKX4 -E0:: GUS showed weaker GUS staining ( Fig. S8B , see online supplementary material). Results of histochemical analysis supported that the MYB binding element located between −1594 and −1589 bp in the VlCKX4 promoter had a crucial role in response to CPPU treatment. The VlCKX4 promoter sequence containing this MYB binding element was constructed into the BD vector for yeast one-hybrid (Y1H). Bait yeast cells co-transformed with the fusion vector AD-VlMYB59 survived on the selective medium but bait yeast cells co-transformed with the AD-empty vector (EV) failed to grow ( Fig. 6E ), indicating that VlMYB59 directly bound to the VlCKX4 promoter. These results demonstrated that VlMYB59 directly binds to the MYB binding element located between −1594 and −1589 bp in the VlCKX4 promoter and activated its expression.

VlMYB59 positively regulated fruit set

To further characterize the function of VlMYB59 in the fruit set, the overexpression vector containing the sequence full-length coding region of VlMYB59 gene (OE- VlMYB59 ) was transformed into Arabidopsis ( Fig. S9A , see online supplementary material). A total of five transgenic lines were obtained and the expression levels of VlMYB59 were significantly increased ( Fig. S9B , see online supplementary material). Further study was conducted on three OE- VlMYB59 lines with higher VlMYB59 overexpression ( Fig. 6F ). Compared with WT, the siliques number in OE- VlMYB59 transgenic plants was significantly increased ( Fig. 6G ). The siliques number of OE- VlMYB59 lines was about 35 per plant, while in WT it was approximately 24. In terms of plant height, OE- VlMYB59 lines showed no significant differences from WT ( Fig. 6H ). The above results showed that the increase in VlMYB59 expression promoted fruit set.

Phytohormone level changes caused by CPPU treatment affect fruit set

Phytohormones play a crucial role in fruit set of many fruiting plants [ 6 , 7 ]. Pollination and fertilization triggered the biosynthesis of endogenous auxin, increasing auxin content, which in turn affected the fruit set [ 32 ]. In this study, CPPU treatment resulted in higher auxin content than the control at 1 d, followed by a gradual decrease as the expression of the auxin biosynthesis gene VlYUCCA10 was down-regulated ( Fig. 1C ; Table S2 , see online supplementary material). This change trend in auxin content was consistent with the recent research report [ 33 ]. CPPU brought an earlier peak in IAA content during grape fruit set, speculating that this might be the result of a combination of normal pollination and fertilization with CPPU treatment. Consistent with previous results [ 4 ], GA content was relatively high during NS ( Fig. 1D and E ; Fig. S1A and B , see online supplementary material), in agreement with the idea that the fruit set required a high endogenous bioactive GA content [ 34 ]. However, CPPU treatment significantly reduced GA 4 content during the fruit set of pear and melon [ 33 , 35 ]. In particular, GA 4 content was almost undetectable in the CPPU-promoted grape fruit set ( Fig. 1E ), which may be due to down-regulation of the GA biosynthesis gene VlGA20ox3 and up-regulation of the GA deactivation gene VlGA2ox8 . Studies on melon and grape confirmed that for unfertilized fruits, cytokinin-induced fruit set partially depended on the accumulation of gibberellin [ 4 , 33 ], while for fertilized fruits in grape, CPPU-induced fruit set required suppression of GA 4 level in this study. Previous studies had shown that ABA plays a negative regulatory role in tomato NS [ 36 ]. ABA content was significantly inhibited in CPPU-induced fruit set in grape ( Fig. 1I ), pear [ 35 ], and melon [ 33 ]. In addition, the expression of VlNCED6 , a key enzyme gene for ABA biosynthesis, was significantly down-regulated after CPPU treatment. These results confirmed that low ABA level might also be essential in CPPU-induced fruit set. The content of cis -OPDA presented a linear decreasing tendency during NS ( Fig. 1K ), indicating that cis -OPDA level might be negatively correlated with fruit set, which is consistent with the result in tomato [ 24 ]. The significant decrease in cis -OPDA level caused by CPPU treatment might be due to the fact that CPPU triggered the initiation of the fruit set, which inhibited cis -OPDA accumulation.

Insights into the cytokinin regulatory network during grape fruit set based on transcriptome data

In Arabidopsis , multiple mutants of AtLOGs resulted in fewer flower buds and flower formation [ 37 ]. In tomato, the concentration of tZ increased after pollination and the transcript level of the SlLOG2 gene remained at a high expression for 1–5 d after anthesis [ 20 ]. In addition, the expression of the LOGs gene in the highly parthenocarpic line cucumber was significantly stronger than in the weakly parthenocarpic line, as was the cytokinin concentration [ 38 , 39 ]. These reports provided evidence that LOG genes were involved in and might contribute to the fruit set. However, during the CPPU-induced grape fruit set, the expression of LOG2 and LOG5 was down-regulated, and cytokinin contents were significantly decreased ( Figs. 1F, G, and 4D ). A similar change in cytokinin content was also shown in the report of the CPPU-induce melon fruit set [ 33 ]. Combining the fact function of cytokinin in promoting cell division during fruit development [ 20 ], we indicated that CPPU treatment might create a high concentration of cytokinin environment for young fruits, which is sufficient to meet the growth of young fruits, thus inhibiting the expression of LOG genes to reduce the synthesis of endogenous cytokinin.

Our previous results showed that the transcription levels of VlCKX2 , VlCKX3 , VlCKX4 , VlCKX6 , and VlCKX8 genes with high expression in inflorescence were significantly up-regulated after CPPU treatment [ 31 ], which was corroborated in the transcriptome data of this study, and VlCKX3.1 and VlCKX9 were also significantly enriched during the CPPU-induced fruit set ( Fig. 4D ). Similarly, CPPU treatment significantly improved the fruit set in fig, a process also accompanied by a decrease in endogenous cytokinin content and an increase in the expression of four CKX genes [ 21 ]. The transcript levels of BrCKX3–2 and BrCKX5 showed a significant increase after cytokinin-treated on Chinese cabbage, while other BrCKXs decreased to various degrees [ 40 ]. Different members of the tomato SlCKX family exhibited different expression trends during fruit development [ 20 ]. In addition, a decrease in the expression levels of CsCKX genes has been reported to be an important condition for cucumbers to have parthenocarpy or strong parthenocarpic ability [ 38 , 39 ]. These findings, as well as the results in this research strongly supported that CKXs are involved in fruit set. The fact that overexpression of VlCKX4 significantly promoted fruit set in this study made it reasonable to speculate that the other six CKX genes might also act as positive regulators to promote fruit set. Combining the fact that the changes in endogenous cytokinin content and expression levels of cytokinin biosynthesis-related genes in this study, we speculated that CPPU treatment led to changes in the expression of VlLOGs related to cytokinin synthesis and VlCKXs related to cytokinin metabolism by disrupting the dynamic balance of endogenous cytokinin in young fruits. During this process, cytokinin homeostasis was reset and maintained to regulate fruit set.

Type-A RRs were regarded as markers and negative feedback regulators of cytokinin signaling [ 41 ]. The expression levels of CsRR8/9d , CsRR8/9e , and CsRR16/17 were up-regulated in the highly parthenocarpic genotype of cucumber, while CsRR3/4a , CsRR3/4b , and CsRR8/9a were strongly expressed in the non-parthenocarpic and weakly parthenocarpic genotypes during the early fruit development [ 38 , 39 ]. These findings elucidated the function of CsRRs and cytokinin signal transduction in the induction of fruit set. Similarly, the enhanced expression of five tomato SlRR genes during early fruit development [ 20 ] elucidated the positive regulation of RRs and active cytokinin signal transduction pathway in fruit set. In addition, RRs showed generally up-regulated expression during CPPU-promoted fruit set in fig and pear [ 21 , 35 ], supporting that RRs expression could be triggered and induced in response to CPPU treatment. This result was strongly proved by the significant up-regulation of eight VlRRs during CPPU-induced fruit set in this study ( Fig. 4D ), which also supported the fact that the expression of VlRRs and high activation of the cytokinin-signal transduction pathway induced by CPPU treatment was crucial for the fruit set in grape.

Role of regulatory modules composed of cytokinin-related VDEGs and TFs in CPPU-induced fruit set

Multiple studies have reported that the CKX gene family was closely related to crop fruit set and yield [ 9 , 16 , 17 ]. In this study, overexpression of grape VlCKX4 promoted the fruit set ( Fig. 5B–F ), confirming a novel function of the VlCKX4 gene in the fruit set. The expression of VlCKX4 was positively regulated by the VlMYB59 TF, and overexpression of the VlMYB59 gene could also promote the fruit set ( Fig. 6 ). These results indicate a novel mechanism by which the VlMYB59- VlCKX4 module regulates fruit set. Similarly, the regulatory relationship of VlMYB12 on VlCKX6 was predicted by PlantTFDB, JASPAR database, and GENIE3 ( Fig. 5A ), so it is reasonable to speculate that the VlMYB12- VlCKX6 module is likely to be involved in plant fruit set. Additionally, the predicted regulatory relationship of VlMYB12 on VlRR17 and VlRR31 supported that VlMYB12 might be related to cytokinin signal transduction and act as an upstream regulatory factor for the CPPU-induced grape fruit set. Multiple members of MYB family have been confirmed to regulate fruit set in previous studies [ 26 , 27 ] and VlMYB12 was an up-regulated DEG during CPPU-induced fruit set. Therefore, we speculated that VlMYB12 is likely to participate in the fruit set as a positive regulatory factor. Similarly, predicted VlbZIP44- VlRR28 and VlATHB-12- VlRR2 modules based on the JASPAR database and GENIE3 might also be involved in the fruit set.

Treatment of grape inflorescences with exogenous CPPU promoted grape fruit set. Notably, endogenous cytokinin (tZ and tZR) content was significantly reduced during this process. The proposal and application of value differential expression gene screening strategy had drawn attention to the significant enrichment of cytokinin dehydrogenase activity and cytokinin metabolic process during the CPPU-induced fruit set in grape. Among them, we noted a regulatory module consisting of a VEDG VlCKX4 and a DETF VlMYB59. The study revealed that VlMYB59 positively regulates VlCKX4 by binding to MYB binding element TAACCA that is located between −1594 and −1589 bp in the VlCKX4 promoter. Overexpression of both genes VlMYB59 and VlCKX4 significantly promoted fruit set, confirming that VlMYB59 and VlCKX4 are key regulators in promoting fruit set. These findings provided a model of how VlMYB59- VlCKX4 module responds to CPPU treatment to promote fruit set in grape ( Fig. 7 ).

A proposed model of VlMYB59- VlCKX4 regulatory module function during CPPU-induced grape fruit set. Left, under normal development conditions, grapes undergo physiological berry abscission resulting in a low fruit set rate. Without CPPU treatment, the VlMYB59- VlCKX4 module-mediated pathway for regulating the fruit set is not activated. Right, CPPU treatment induces the VlMYB59 gene expression. The binding of the VlMYB59 transcription factor to the cis-acting element TAACCA on the VlCKX4 promoter positively regulates the VlCKX4 expression. Gene VlCKX4 acts as a positive regulatory factor to promote fruit set. Arrows represent a positive regulatory action of one component on another.

Plant material and treatments

Ten-year-old ‘Kyoho’ grapes cultivated in Yanshi, Luoyang, China, were used as experimental materials. At 5 DAFB, young berries were immersed in a solution of 10 mg L −1 CPPU for 10 s. Control was treated with distilled water supplemented with 0.03% silicone wet-77 surfactant. Young berries were collected at 1, 2, 4, and 8 days after treatment for RNA-Seq and expression analysis of gene. At 13 DAFB, roots, stems, leaves, inflorescences, tendrils, young berries, and mature berries of the natural development were collected for tissue-specific expression analysis of gene.

Arabidopsis thaliana and N. benthamiana L. plants were cultivated in a growth chamber (16/8 h photoperiod), maintained at 24 ± 1°C.

Quantification of endogenous hormones

The phytohormone concentrations from grape young berries were determined as previously described [ 42 ]. Each sample with 0.2 g was ground to powder in liquid nitrogen. Internal standards were obtained from Sigma Chemical Co. The phytohormone concentrations were analysed with a mass spectrometer (AB Sciex Qtrap 5500 System, AB Sciex UK Limited, Warrington, UK) featuring an electrospray ionization detector. Solvent A in the mobile phase comprised 0.05% [v/v] formic acid dissolved in water, while solvent B consisted of 0.05% [v/v] formic acid in acetonitrile. Three biological replicates were performed.

RNA extraction and RNA-Seq analysis

Total RNA from berries was extracted using RNAprep Pure Plant Kit (Tiangen, Beijing, China) and quality evaluation on Nanodrop 2000 (Thermo Scientific, Wilmington, DE, USA). RNA-Seq libraries were prepared with Truseq TM RNA sample preparation Kit for Illumina® and analysed on an Illumina NovaSeq 6000 instrument (Novogene, Beijing, China).

Purification of RNA-Seq data was conducted following established protocols outlined in prior studies [ 43 ]. The aligning of clean, high-quality reads to the V. vinifera reference genome (PN40024.v4) was achieved utilizing HISAT2, with subsequent assembly carried out via StringTie ( http://ccb.jhu.edu/software.shtml ). PCA was performed using TPM, and visualization was performed using R package factoextra and FactoMineR. DEGs were identified using DEseq2 with the absolute value of log2-fold change (log2FC) ≥1.0 and adjusted P (Padj) value <0.05. Venn diagrams were drawn using an online website ( http://www.ehbio.com/test/venn/#/ ). MapMan BIN functional annotation classification ( https://www.plabipd.de/mercator_main.html ) of grape protein sequences was performed using Mercator4 [ 44 ]. Enrichment analysis of MapMan BINs ( P value <0.05) was carried out utilizing the clusterProfiler package in R, with visualization executed through the use of ComplexHeatmap. Heatmaps and upset plots were drawn using TBtools [ 45 ].

Expression profile analysis of DETFs

Expression modules of DEGs in different analysis strategies were performed using STEM [ 46 ] based on the log2FC values, and displaying the significant colored clustering groups. DETF annotation was performed on the grape protein sequence using PlantTFDB ( http://planttfdb.gao-lab.org/prediction.php ). The annotation results were organized as enrichment background files and enrichment analysis was performed using the R-package clusterProfiler. The ggplot2 package was utilized to conduct visualization of DETF family enrichment, with a significance value ( P < 0.05).

Regulatory network construction

Grape genes were mapped to PlantTFDB and JASPAR databases using Hmmscan to predict TF in grape and TFs were annotated by BLAST. Hmmscan E-value ≤0.05 and Blast E-value ≤0.05 were used as standards to identify and statistics TFs. Based on MEME motif information, potential TF binding sites (TFBSs) in the cytokinin-related gene promoter sequence were scanned using FIMO with a threshold of 10 −5 and TOMTOM was used to check the MEME motif belonging to the TFBS of a specific TF with an e-value of 0.05 [ 47 ]. Software Gephi0.9.2 was used for data visualization. Based on expression data, gene regulatory networks reflecting the potential TF and target gene regulatory relationship was performed using GENIE3 with weight >0.1 [ 48 ].

Phylogenetic analysis

Transcript sequence of VlCKX4 was amplified from the cDNA of the ‘Kyoho’ grape and translated into the protein sequence. The protein sequences for CKX genes in Arabidopsis , rice, and maize were sourced from Ensembl Plants ( http://plants.ensembl.org/index.html ). MEGA7 software was used to construct the phylogenetic tree, employing the neighbor-joining statistical method in addition to Bootstrap analysis with 1000 replications. Visualization of the phylogenetic tree was completed using online websites ( https://www.chiplot.online/ ).

Vector construction and genetic transformation

The full length of VlCKX4 transcript was amplified from grape cDNA using homologous arm primers and ligated into the pSAK277 vector using homologous recombination to generate VlCKX4 overexpression vector. The recombinant vector was transformed into A. thaliana using the floral-dip method [ 49 ]. The OE- VlMYB59 vector was also constructed and transformed into A. thaliana . Transgenic plants were identified by amplifying the marker gene NPT II of pSAK277 vector. The primers were listed in Table S7 (see online supplementary material).

The cDNA was synthesized through reverse transcription of mRNA with the HiScriptIIQ RT SuperMix for qPCR kit (Vazyme, Nanjing, China). RT-qPCR was performed on a CFX96 Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) with the TransStart Top Green qPCR SuperMix kit (TransGen, Beijing, China). To assess the transcript levels of genes in transgenic A. thaliana , 2 −ΔΔct method was employed, with normalization performed using the internal reference gene AtACTIN . The transcription levels of VlMYB59 in different tissues of grape were standardized using the internal reference gene Ubiquitin1 [ 31 ]. The primers were listed in Table S7 (see online supplementary material).

GUS staining

Promoter regions containing 3, 2, and 1 MYB binding sites of VlCKX4 were cloned into the vector pC0390-35S-GUS for driving GUS reporter expression, respectively. Infiltration by vacuum was used to transiently introduce the fusion vectors into N. benthamiana leaves [ 50 ]. Transformed leaves were sprayed with 40 μmol L −1 CPPU and incubated for 24 h at 25°C, immersed in GUSBlue Kit (Huayueyang Biotech Co., Beijing, China) for treatment at 37°C for 12 h, and washed with an ethanol series (70%, 80%, and 90%) until the WT tissues were completely decolorized. GUS staining of leaves was observed and photographed for documentation. The primers were listed in Table S7 (see online supplementary material).

Subcellular localization analysis

Subcellular localization was conducted following the previous methods [ 51 ]. The coding sequence of VlMYB59 , lacking the termination codon, was amplified from grape cDNA. Subsequently, it was inserted into the 101LYFP vector to produce a fusion construct 35S: VlMYB59 -YFP. EV served as the control in the experiment. The fusion construct was co-transformed briefly with the nuclear marker VirD2NLS -mCherry into N. benthamiana leaves. Following 3 d of infiltration, the fluorescence signals in the epidermal leaf cells were analysed using a laser confocal fluorescence microscope (Olympus, Tokyo, Japan). The primers are listed in Table S7 (see online supplementary material).

Dual-luciferase (dual-LUC) assay

The promoter sequence of VlCKX4 , 2075 bp in length and located before the ATG start codon was inserted into the pGreenII 0800-LUC vector for the creation of a reporter construct. The OE- VlMYB59 vector acted as an effector construct, and the EV pGreenII 0800-LUC and pSAK277 were, respectively, used as the negative control. A mixture of reporter and effector constructs carried by Agrobacterium GV3101 (pSoup-p19) was injected into N. benthamiana leaves at a ratio of 1:9. The infiltration process was carried out at 24°C for a duration of 48 h [ 33 ]. The dual-luciferase assay was conducted on the injected leaves using the Dual-Luciferase Reporter Assay System (Promega, Madison, WA, USA). The relative ratio of LUC/REN for the effector-reporter combination was used to evaluate the regulatory relationship of VlMYB59 TF and VlCKX4 . The primers were listed in Table S7 (see online supplementary material).

Y1H assay was conducted with the Matchmaker Gold Yeast One-Hybrid System Kit (Clontech, Tokyo, Japan). The full-length sequence of VlMYB59 was inserted into the pGADT7 vector by EcoR I and BamH I restriction sites to construct the prey. The promoter fragment of VlCKX4 was inserted into the pAbAi vector by Kpn I and Xho I restriction sites as the bait. The bait plasmid was linearized by the BstB I restriction site and subsequently transfected into yeast strain Y1HGold, and screened for resistance concentrations using SD/-Ura containing various concentrations of aureobasidin A (AbA). The prey plasmids were transfected into the Y1HGold strain harboring baits. Empty pGADT7 vector was also transfected into baits as the control. The co-transformed yeast cells were spotted on SD/−Leu/AbA medium to determine the interaction. The primers were listed in Table S7 (see online supplementary material).

Statistical analysis

Statistical analysis of the data was conducted using Microsoft Excel software, including at least three biological replicates and three technical replicates. Statistical significance was assessed using two-tailed and two-sample Student’s t -test ( * P < 0.05, * * P < 0.01, * * * P < 0.001), or by performing ANOVA followed by Duncan’s multiple comparisons ( P < 0.05) to determine differences.

This work was supported by National Natural Science Foundation of China (Grant No. 32072517), Program for Science & Technology Innovation Talents in Universities of Henan Province (Grant No. 21HASTIT035), Top Young Talents in Central Plains (Grant No. Yuzutong (2021)44), and PhD Research Startup Foundation of Henan University of Science and Technology (Grant No. 13480077).

Y.Yu. conceived the project; Q.S., X.L., and S.Y. performed RNA-Seq data analysis; Q.S. and X.L. performed the experiments; Q.S. wrote the manuscript; X.Z., Y.Yue., Y.Yang., and Y.Yu. contributed to the manuscript revision. All authors read and approved the final version of the manuscript. All authors read and approved the contents of this paper.

The transcriptome sequencing data reported in the present study has been deposited in the National Center for Biotechnology Information (NCBI) database under project number PRJNA589347. Accession numbers of grape genes mentioned in this article can be searched in Ensembl Plants ( https://plants.ensembl.org/Vitis_vinifera/Info/Index ): VlCKX2 (Vitvi07g02355), VlCKX3 (Vitvi07g00869), VlCKX3.1 (Vitvi07g00836), VlCKX4 (Vitvi11g01371), VlCKX6 (Vitvi18g01019), VlCKX8 (Vitvi00g01369), VlCKX9 (Vitvi00g01279), VlRR2 (Vitvi01g00857), VlRR12 (Vitvi08g02307), VlRR15 (Vitvi13g00183), VlRR17 (Vitvi13g01433), VlRR21 (Vitvi13g02327), VlRR22 (Vitvi13g02328), VlRR28 (Vitvi17g00732), VlRR31 (Vitvi18g00260), VlPRR95 (Vitvi01g00344), VlLOG2 (Vitvi08g01042), VlLOG5 (Vitvi18g00121), VlMYB12 (Vitvi07g00393), VlMYB59 (Vitvi06g00414), VlSPL7 (Vitvi15g00619), VlbHLH94 (Vitvi14g00277), VlbHLH137 (Vitvi01g01745), VlRAX1 (Vitvi01g01028), VlREM19 (Vitvi03g01537), VlREM21 (Vitvi03g00419), VlbZIP44 (Vitvi03g00292), VlATHB-12 (Vitvi16g01362), VlYUCCA10 (Vitvi07g00242), VlGA20ox3 (Vitvi04g01719), VlGA2ox8 (Vitvi19g00432), and VlNCED6 (Vitvi05g00963).

The authors declare that they have no conflicts of interest in this work.

Supplementary data is available at Horticulture Research online.

Guo DL , Xi FF , Yu YH . et al. Comparative RNA-Seq profiling of berry development between table grape ‘Kyoho’ and its early-ripening mutant ‘Fengzao’ . BMC Genomics . 2016 ; 17 : 795

Google Scholar

Yu YH , Li XF , Yang SD . et al. CPPU-induced changes in energy status and respiration metabolism of grape young berry development in relation to berry setting . Sci Hortic . 2021 ; 283 : 110084

Böttcher C , Davies C . Hormonal control of grape berry development and ripening . Biochem Grape Berry . 2012 ; 1 : 194 – 217

Lu L , Liang JJ , Zhu X . et al. Auxin- and cytokinin-induced berries set in grapevine partly rely on enhanced gibberellin biosynthesis . Tree Genet Genomes . 2016 ; 12 : 41

Wang W , Khalil-Ur-Rehman M , Feng J . et al. RNA-seq based transcriptomic analysis of CPPU treated grape berries and emission of volatile compounds . J Plant Physiol . 2017 ; 218 : 155 – 66

Ezura K , Nomura Y , Ariizumi T . Molecular, hormonal, and metabolic mechanisms of fruit set, the ovary-to-fruit transition, in horticultural crops . J Exp Bot . 2023 ; 74 : 6254 – 68

Sharif R , Su L , Chen XH . et al. Hormonal interactions underlying parthenocarpic fruit formation in horticultural crops . Hortic Res . 2022 ; 9 : uhab024

Marsch-Martínez N , Ramos-Cruz D , Reyes-Olalde JI . et al. The role of cytokinin during Arabidopsis gynoecia and fruit morphogenesis and patterning . Plant J . 2012 ; 72 : 222 – 34

Jameson PE , Song JC . Cytokinin: a key driver of seed yield . J Exp Bot . 2016 ; 67 : 593 – 606

Mariotti L , Picciarelli P , Lombardi L . et al. Fruit-set and early fruit growth in tomato are associated with increases in indoleacetic acid, cytokinin, and bioactive gibberellin contents . J Plant Growth Regul . 2011 ; 30 : 405 – 15

Aremu AO , Fawole OA , Makunga NP . et al. Applications of cytokinins in horticultural fruit crops: trends and future prospects . Biomol Ther . 2020 ; 10 : 1222

Smith RJ . Effects of CPPU, a synthetic cytokinin, on fruit set and yield . Sonoma County Grape Day . 2008 ; https://cesonoma.ucanr.edu/files/27461.pdf

Godoy F , Kühn N , Muñoz M . et al. The role of auxin during early berry development in grapevine as revealed by transcript profiling from pollination to fruit set . Hortic Res . 2021 ; 8 : 140

Li JF , Lin T , Ren DD . et al. Transcriptomic and metabolomic studies reveal mechanisms of effects of CPPU-mediated fruit-setting on attenuating volatile attributes of melon fruit . Agronomy . 2021 ; 11 : 1007

Zinelabidine LH , Torres-Pérez R , Grimplet J . et al. Genetic variation and association analyses identify genes linked to fruit set-related traits in grapevine . Plant Sci . 2021 ; 306 :110875

Chen L , Zhao JQ , Song JC . et al. Cytokinin dehydrogenase: a genetic target for yield improvement in wheat . Plant Biotechnol J . 2020 ; 18 : 614 –30

Sharma A , Prakash S , Chattopadhyay D . Killing two birds with a single stone—genetic manipulation of cytokinin oxidase/dehydrogenase ( CKX ) genes for enhancing crop productivity and amelioration of drought stress response . Front Genet . 2022 ; 13 :941595

Bartrina I , Otto E , Strnad M . et al. Cytokinin regulates the activity of reproductive meristems, flower organ size, ovule formation and thus seed yield in Arabidopsis thaliana . Plant Cell . 2011 ; 23 : 69 – 80

Werner T , Motyka V , Laucou V . et al. Cytokinin-deficient transgenic Arabidopsis plants show multiple developmental alterations indicating opposite functions of cytokinins in the regulation of shoot and root meristem activity . Plant Cell . 2003 ; 15 : 2532 – 50

Matsuo S , Kikuchi K , Fukuda M . et al. Roles and regulation of cytokinins in tomato fruit development . J Exp Bot . 2012 ; 63 : 5569 – 79

Chai P , Dong SJ , Chai LJ . et al. Cytokinin-induced parthenocarpy of San Pedro type fig ( Ficus carica L.) main crop: explained by phytohormone assay and transcriptomic network comparison . Plant Mol Biol . 2019 ; 99 : 329 – 46

Wu Y , Wen J , Xia YP . et al. Evolution and functional diversification of R2R3-MYB transcription factors in plants . Hortic Res . 2022 ; 9 : uhac058

Qi TC , Huang H , Song SS . et al. Regulation of Jasmonate-mediated stamen development and seed production by a bHLH-MYB complex in Arabidopsis . Plant Cell . 2015 ; 27 : 1620 – 33

Schubert R , Dobritzsch S , Gruber C . et al. Tomato MYB21 acts in ovules to mediate jasmonate-regulated fertility . Plant Cell . 2019 ; 31 : 1043 – 62

Wang DX , Li JB , Sun LL . et al. Two rice MYB transcription factors maintain male fertility in response to photoperiod by modulating sugar partitioning . New Phytol . 2021 ; 231 : 1612 – 29

Hassanin AA , Soliman SSA , Ismail TA . et al. The role of SlMYB gene in tomato fruit development . Zagazig J Agric Res . 2017 ; 44 : 969 – 88

da Silva EM , E Silva GFF , Bidoia DB . et al. microRNA159-targeted SlGAMYB transcription factors are required for fruit set in tomato . Plant J . 2017 ; 92 : 95 – 109

Tello J , Torres-Pérez R , Grimplet J . et al. Association analysis of grapevine bunch traits using a comprehensive approach . Theor Appl Genet . 2016 ; 129 : 227 – 42

Deluc L , Bogs J , Walker AR . et al. The transcription factor VvMYB5b contributes to the regulation of anthocyanin and proanthocyanidin biosynthesis in developing grape berries . Plant Physiol . 2008 ; 147 : 2041 – 53

Zheng H , Zhang Z , Zhang J . et al. Male sterility and repression of the phenylpropanoid pathway in transgenic tobacco plants induced by the grapevine VvMYB4 gene cloned from “Zhong Shan Hong” ( Vitis vinifera L.) . XI Int Conf Grapevine Breeding Genetics . 2014 ; 181 : 110084

Yu KK , Yu YH , Bian L . et al. Genome-wide identification of cytokinin oxidases/dehydrogenase ( CKXs ) in grape and expression during berry set . Sci Hortic . 2021 ; 280 : 109917

Guo L , Luo X , Li MZ . et al. Mechanism of fertilization-induced auxin synthesis in the endosperm for seed and fruit development . Nat Commun . 2022 ; 13 : 3985

Liu Y , Li Y , Guo HX . et al. Gibberellin biosynthesis is required for CPPU-induced parthenocarpy in melon . Hortic Res . 2023 ; 10 : uhad084

He H , Yamamuro C . Interplays between auxin and GA signaling coordinate early fruit development . Hortic Res . 2022 ; 9 : uhab078

Cong L , Wu T , Liu HT . et al. CPPU may induce gibberellin-independent parthenocarpy associated with PbRR9 in ‘Dangshansu’ pear . Hortic Res . 2020 ; 7 : 68 .

Wang XH , Cui XY , Shang S . et al. The destruction of endogenous ABA balance caused by fruit-specific SlNCED1 -RNAi affects tomato fruit set . J Plant Growth Regul . 2023 ; 42 : 7200 – 14

Kuroha T , Tokunaga H , Kojima M . et al. Functional analyses of LONELY GUY cytokinin-activating enzymes reveal the importance of the direct activation pathway in Arabidopsis . Plant Cell . 2009 ; 21 : 3152 – 69

Mandal NK , Kumari K , Kundu A . et al. Cross-talk between the cytokinin, auxin, and gibberellin regulatory networks in determining parthenocarpy in cucumber . Front Genet . 2022 ; 13 :957360

Su L , Rahat S , Ren NN . et al. Cytokinin and auxin modulate cucumber parthenocarpy fruit development . Sci Hortic . 2021 ; 282 : 110026

Liu ZN , Lv YX , Zhang M . et al. Identification, expression, and comparative genomic analysis of the IPT and CKX gene families in Chinese cabbage ( Brassica rapa ssp. pekinensis ) . BMC Genomics . 2013 ; 14 : 1 – 20

Kieber JJ , Schaller GE . Cytokinin signaling in plant development . Development . 2018 ; 145 : dev149344

Shao YL , Zhou HZ , Wu YR . et al. OsSPL3, an SBP-domain protein, regulates crown root development in rice . Plant Cell . 2019 ; 31 : 1257 – 75

Shi QF , Long JM , Yin ZP . et al. miR171 modulates induction of somatic embryogenesis in citrus callus . Plant Cell Rep . 2022 ; 41 : 1403 – 15

Bolger M , Schwacke R , Usadel B . MapMan visualization of RNA-Seq data using Mercator4 functional annotations . Methods Mol Biol . 2021 ; 2354 : 195 – 212

Chen C , Chen H , Zhang Y . et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data . Mol Plant . 2020 ; 13 : 1194 – 202

Ernst J , Bar-Joseph Z . STEM: a tool for the analysis of short time series gene expression data . BMC Bioinform . 2006 ; 7 : 191

Kuang JF , Wu CJ , Guo YF . et al. Deciphering transcriptional regulators of banana fruit ripening by regulatory network analysis . Plant Biotechnol J . 2021 ; 19 : 477 – 89

Huynh-Thu VA , Irrthum A , Wehenkel L . et al. Inferring regulatory networks from expression data using tree-based methods . PLoS One . 2010 ; 5 :e12776

Davis AM , Hall A , Millar AJ . et al. Protocol: streamlined sub-protocols for floral-dip transformation and selection of transformants in Arabidopsis thaliana . Plant Methods . 2009 ; 5 : 3

Santos-Rosa M , Poutaraud A , Merdinoglu D . et al. Development of a transient expression system in grapevine via agro-infiltration . Plant Cell Rep . 2008 ; 27 : 1053 – 63

Wei TL , Guo DL , Liu JH . PtrMYB3, a R2R3-MYB transcription factor from Poncirus trifoliata , negatively regulates salt tolerance and hydrogen peroxide scavenging . Antioxidants . 2021 ; 10 : 1388

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https://www.nist.gov/news-events/news/2024/09/new-report-highlights-economic-value-neutron-science-us-industry

New Report Highlights Economic Value of Neutron Science to U.S. Industry

Research using neutron beams provides an economic return far larger than the cost of building and operating neutron facilities, according to an economic impact analysis.
The study presented four case studies involving the impact of neutron research on different technologies, highlighting the value of neutron science to varied sectors of the economy.
The study also highlighted how the lack of domestic capacity in neutron science has negatively impacted U.S. innovation and offered recommendations on how to strengthen U.S. programs in the field.

View from near ceiling shows NCNR guide hall filled with big pieces of scientific equipment, including several long cylindrical "guides" running toward the far end of the room.

Federal facilities that offer neutron beams for U.S. industry play an outsized role in bringing new goods to market more quickly and cost-effectively, according to an analysis of neutron science’s economic impact.

The study , conducted by the nonprofit research institute RTI International , focuses on quantifying the national economic benefits derived from investments in three neutron scattering facilities operated by the U.S. government: the NIST Center for Neutron Research (NCNR), the Oak Ridge National Laboratory (ORNL) High-Flux Isotope Reactor, and the ORNL Spallation Neutron Source. The study, funded through a cooperative agreement with NIST, also provides insights into ways of keeping the United States competitive moving forward in areas involving neutron research, including infrastructure needs.

Neutrons are subatomic particles that can penetrate materials more deeply than can X-rays and other probes of matter. Researchers can use neutron beams to discover new things about the properties of materials, making neutrons important in scientific research fields from materials science to the physical and life sciences . Neutrons often reveal what X-rays cannot; for example, they can more easily image and study light atoms such as hydrogen. Hydrogen is common in living things and organic matter, making neutrons invaluable for scientific research related to drug development .

However, U.S. neutron scattering capacity has been declining since the 1990s. In 1985, the United States had five federal laboratories with neutron scattering capacity. Currently, only NIST and ORNL support neutron scattering instruments and offer large-scale “open user” programs, which allow interested parties from other institutions to conduct research using NIST’s and ORNL’s instruments.

RTI reports four different case studies of technologies influenced by research conducted at the three neutron facilities from 1998-2020. In addition to the case studies, RTI surveyed and interviewed 247 users of the facilities about their research results and analyzed the publications, patents and collaborative research networks formed at the facilities.

Among the findings:

The facilities more than pay for themselves: The combined benefits of the scientific research described in the four case studies would fully cover the construction and operating costs of NCNR and the two Oak Ridge facilities, even if only 6%-11% of the benefits were attributable to neutron scattering research.
A cost-benefit analysis of the case studies indicates a substantial economic return as well: Assuming neutron scattering accelerated the development of the selected case studies’ technologies by just two years — a conservative estimate, according to expert testimony RTI obtained — the estimated rate of return is 2.67. In other words, for every dollar invested in the facilities, they returned $2.67 to the U.S. economy. And this was just four of the dozens of technologies and products that neutron research has influenced.
The report suggests that the findings represent a small portion of total innovation influenced by neutron scattering infrastructure. RTI identified more than 22,000 scientific publications and 1,565 patents resulting from research conducted at federal neutron facilities between 1960 and 2020.

RTI further identified at least 372 U.S.-based companies that are known to have used at least one of the U.S. federal neutron sources. These include enterprises of large to small scale across nearly every industry in the United States.

Despite these benefits, most facility users RTI surveyed indicated that they had difficulty performing their research due to inadequate domestic capacity for neutron measurement science. A survey of 247 facility users identified that 77% of these respondents experienced issues due to insufficient capacity in the five years before facility shutdowns in 2020. Of the survey sample, 19% took research they could not complete in the United States to an international facility.

RTI offered recommendations for strengthening the U.S. neutron scattering system, including forming a federal leadership task force to create a decade-long plan for U.S. neutron scattering facilities, maintaining adequate funding for the three facilities that exist, and constructing new facilities to augment their capacity.

Report: A.C. Walsh, S. Nienow, J.M.S. Merker, E.C. Decker, C.N Strack, M.E. Salem, G. Martin and B. Shaw. Assessment of the retrospective and prospective economic impacts of investments in U.S. neutron research sources and facilities from 1960 to 2030: Final report . RTI International. Published May 2024.

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In 2023, 688128's revenue was 4.17 billion, an increase of 9.97% compared to the previous year's 3.79 billion. Earnings were 410.09 million, an increase of 12.87%.

IMAGES

8 Types of Analysis in Research
10 Easy Steps to Analyze a Research Article Sample
7 Types of Statistical Analysis: Definition and Explanation
Diagram of Research Analysis Stock Photo
FREE Research Analysis Templates
FREE 13+ Research Analysis Samples in Word, PDF, Google Docs, Apple Pages

VIDEO

Data Analysis in Research
Metho1: What Is Research?
Differences Between Research and Analysis
How to present research tools, procedures and data analysis techniques
Research and Analysis Wing
Analysis of Data? Some Examples to Explore

COMMENTS

Data Analysis in Research: Types & Methods
Data Analysis in Research: Types & Methods
Introduction to Research Statistical Analysis: An Overview of the
Introduction. Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology.
Data analysis
Data analysis | Definition, Research, & Methodology
What is Research? Definition, Types, Methods and Process
What is Research? Definition, Types, Methods and Process
Introduction to Data Analysis
According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" (2018, p. 4). Thus, qualitative analysis usually invokes inductive reasoning. Mixed methods research uses methods from both quantitative and qualitative research approaches.
The Beginner's Guide to Statistical Analysis
Learn how to use quantitative data to investigate trends, patterns, and relationships using statistical analysis. Follow the 5 steps from hypotheses to interpretation with examples of causal and correlational research.
Data Analysis
Learn what data analysis is, how it is done, and what types and methods are used. Explore the applications of data analysis in various fields and the tools and techniques involved.
Analysis
Analysis. Analysis is a type of primary research that involves finding and interpreting patterns in data, classifying those patterns, and generalizing the results. It is useful when looking at actions, events, or occurrences in different texts, media, or publications. Analysis can usually be done without considering most of the ethical issues ...
Learning to Do Qualitative Data Analysis: A Starting Point
Learning to Do Qualitative Data Analysis: A Starting Point
Quantitative Research
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.
What Is Data Analysis in Research? Why It Matters & What Data Analysts
Data analysis in research is the process of uncovering insights from data sets. Data analysts can use their knowledge of statistical techniques, research theories and methods, and research practices to analyze data. They take data and uncover what it's trying to tell us, whether that's through charts, graphs, or other visual representations.
Basic statistical tools in research and data analysis
Basic statistical tools in research and data analysis - PMC
Research vs Analysis: The Differences & Why It Matters
Learn how research and analysis are different processes in data-driven decision-making, and why they are both important. Research is finding information, while analysis is evaluating and interpreting it to make informed choices.
Creating a Data Analysis Plan: What to Consider When Choosing
For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2, 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to ...
A practical guide to data analysis in general literature reviews
A practical guide to data analysis in general literature reviews
Research Methods
Learn how to choose and use research methods for collecting and analyzing data. Find out the pros and cons of qualitative and quantitative, primary and secondary, descriptive and experimental methods.
PDF Basic Concepts in Research and Data Analysis
Chapter 1: Basic Concepts in Research and Data Analysis. Notice how this statement satisfies the definition for a hypothesis: it is a statement about the relationship between two variables. The first variable could be labeled Goal Difficulty, and the second, Amount of Insurance Sold.
What is Market Research Analysis? Definition, Steps ...
Market research analysis is defined as the systematic process of collecting, processing, interpreting, and evaluating data related to a specific market, industry, or business environment. Learn more about market research analysis steps, benefits, and best practices.
Qualitative Data Analysis: What is it, Methods + Examples
Qualitative Data Analysis: What is it, Methods + Examples
Research and Analysis: Definition and Example
Research is the process of investigating a topic in an in-depth, systematic manner. Analysis is the critical interpretation of research. Researchers can collect and analyze primary sources, which are first-hand accounts or original documents. Researchers can also collect and analyze secondary sources, which are interpretations of primary sources.
Data Analysis Techniques in Research
Data Analysis Techniques In Research - Methods, Tools & ...
Research and Analysis: Definition and Example
Research is the process of investigating a topic in an in-depth, systematic manner. Analysis is the critical interpretation of research. Researchers can collect and analyze primary sources, which are first-hand accounts or original documents. Researchers can also collect and analyze secondary sources, which are interpretations of primary sources.
Research trends on nanomaterials in triple negative breast ...
In recent years, there has been an increasing amount of research aimed at designing and developing nanomaterials for the diagnosis and treatment of TNBC. The purpose of this study was to comprehensively evaluate the current status and trend of the application of nanomaterials in TNBC through bibliometric analysis.
A Practical Guide to Writing Quantitative and Qualitative Research
A Practical Guide to Writing Quantitative and Qualitative ...
What is Content Analysis and Why is it Important?
Testing as part of content analysis. Testing is an important part of content analysis. It helps in figuring out what works best for the audience and can include. A/B testing; User testing and feedback; Analyzing content across different channels; Analyze regularly. Make content analysis a regular part of the workflow. Erin suggests making it a ...
Dynamic temporal transcriptome analysis reveals grape VlMYB59-VlCKX4
Introduction. Grape (Vitis vinifera L.) is an important horticultural fruit crop cultivated worldwide and plays a crucial role in the development of the agricultural economy.'Kyoho' grape (V. vinifera L. × V. labrusca L.) is a European-American hybrid grape that is favored by consumers for its attractive taste and rich nutrient content [].Fruit set is critical for maintaining and ...
New Report Highlights Economic Value of Neutron Science to U.S
Research using neutron beams provides an economic return far larger than the cost of building and operating neutron facilities, according to an economic impact analysis. The study presented four case studies involving the impact of neutron research on different technologies, highlighting the value of neutron science to varied sectors of the ...
China National Electric Apparatus Research Institute ...
About China National Electric Apparatus Research Institute Co. China National Electric Apparatus Research Institute Co., Ltd. provides solutions in the fields of quality and technology services, intelligent equipment, environmental-friendly coatings, and polyester resins in China and internationally.

Data collection

data analysis

Data Analysis

What is Data Analysis?

Why Analyze Data?

What are the Types of Data Analysis?

Welcome to the Purdue OWL

Here are several examples of analysis:

Quantitative Research – Methods, Types and Analysis

Quantitative Research

Quantitative Research Methods

Descriptive Research Design

Correlational Research Design

Quasi-experimental Research Design

Experimental Research Design

Survey Research

Quantitative Research Analysis Methods

Statistical Analysis

Regression Analysis

Factor Analysis

Structural Equation Modeling

Time Series Analysis

Multilevel Modeling

Applications of Quantitative Research

Characteristics of Quantitative Research

Examples of Quantitative Research

How to Conduct Quantitative Research

When to use Quantitative Research

Purpose of Quantitative Research

Advantages of Quantitative Research

Limitations of Quantitative Research

About the author

Muhammad Hassan

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What Is Data Analysis in Research? Why It Matters & What Data Analysts Do

In this article – What is data analysis in research?

What is data analysis in research?

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Research vs Analysis: What's the Difference and Why It Matters

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What is Research?

What are some common research methods?

What is Analysis?

What are some common analysis methods?

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Research Methods | Definitions, Types, Examples

Table of contents

Qualitative vs. quantitative data

Primary vs. secondary research

Descriptive vs. experimental data

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Qualitative analysis methods

Quantitative analysis methods

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