types of data used in research studies

• Types of Data in Research: A Comprehensive Guide

This article explores the different types of research data, including qualitative vs quantitative, discrete vs continuous, nominal vs ordinal, and more. Learn how to combine qualitative and quantitative data in surveys for successful research projects.

When it comes to research, data is the foundation of any successful project. But what types of data are there? In this article, we'll explore the different types of data and when to use them. Qualitative and quantitative data are the two main types of data. Quantitative data is numerical and can be measured, while qualitative data is non-numerical and cannot be measured.

Quantitative data can be further divided into discrete and continuous data. Discrete data is a count that involves only whole numbers, while continuous data can be broken down into more precise levels. Nominal data is used only to label variables, without any type of quantitative value. Ordinal data shows where a number is in order.

Data can take many forms, from numerical values to images and sound recordings. Surveys remain one of the most effective types of core data that provide feedback directly from consumers. A good rule of thumb for defining whether a data is continuous or discrete is that if the measurement point can be halved and still makes sense, the data is continuous. If you are a businessman, marketer, data scientist , or other professional working with some types of data, you should be familiar with the key list of data types. Quantitative and qualitative data provide valuable information and do not conflict with each other.

The best way to combine qualitative and quantitative data in surveys is to include both multiple-choice and open-ended questions. In short, understanding the different types of data and when to use them is essential for any successful research project. Data variables can't be divided into smaller parts, so it's important to use the same type of currency for all values in the study. Working in the area of data management and having a good range of data science skills involves a deep understanding of various types of data and when to apply them.

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6 Types of Data in Statistics & Research: Key in Data Science

Understanding the different types of data (in statistics, marketing research, or data science) allows you to pick the data type that most closely matches your needs and goals.

Whether you are a businessman, marketer, data scientist, or another professional who works with some kinds of data, you should be familiar with the key list of data types.

Why? Because the various data classifications allow you to correctly use measurements and thus to correctly make decisions.

On this page:

• The most common data types (with examples) in statistics, research, and data science. Simply explained.
• Infographics in PDF

Qualitative vs Quantitative Data

1. Quantitative data

Quantitative data seems to be the easiest to explain. It answers key questions such as “how many, “how much” and “how often”.

Quantitative data can be expressed as a number or can be quantified. Simply put, it can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented by a wide variety of statistical types of graphs and charts such as line, bar graph, scatter plot, and etc.

Examples of quantitative data:

• Scores on tests and exams e.g. 85, 67, 90 and etc.
• The weight of a person or a subject.
• Your shoe size.
• The temperature in a room.

There are 2 general types of quantitative data: discrete data and continuous data. We will explain them later in this article.

2. Qualitative data

Qualitative data can’t be expressed as a number and can’t be measured. Qualitative data consist of words, pictures, and symbols, not numbers.

Qualitative data is also called categorical data  because the information can be sorted by category, not by number.

Qualitative data can answer questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

• Colors e.g. the color of the sea
• Your favorite holiday destination such as Hawaii, New Zealand and etc.
• Names as John, Patricia,…..
• Ethnicity such as American Indian, Asian, etc.

More you can see on our post qualitative vs quantitative data .

There are 2 general types of qualitative data: nominal data and ordinal data. We will explain them after a while.

Download the following infographic in PDF

Nominal vs Ordinal Data

3. Nominal data

Nominal data is used just for labeling variables, without any type of quantitative value. The name ‘nominal’ comes from the Latin word “nomen” which means ‘name’.

The nominal data just name a thing without applying it to order. Actually, the nominal data could just be called “labels.”

Examples of Nominal Data:

• Gender (Women, Men)
• Hair color (Blonde, Brown, Brunette, Red, etc.)
• Marital status (Married, Single, Widowed)
• Ethnicity (Hispanic, Asian)

As you see from the examples there is no intrinsic ordering to the variables.

Eye color is a nominal variable having a few categories (Blue, Green, Brown) and there is no way to order these categories from highest to lowest.

4. Ordinal data

Ordinal data shows where a number is in order. This is the crucial difference from nominal types of data.

Ordinal data is data which is placed into some kind of order by their position on a scale. Ordinal data may indicate superiority.

However, you cannot do arithmetic with ordinal numbers because they only show sequence.

Ordinal variables are considered as “in between” qualitative and quantitative variables.

In other words, the ordinal data is qualitative data for which the values are ordered.

In comparison with nominal data, the second one is qualitative data for which the values cannot be placed in an ordered.

We can also assign numbers to ordinal data to show their relative position. But we cannot do math with those numbers. For example: “first, second, third…etc.”

Examples of Ordinal Data:

• The first, second and third person in a competition.
• Letter grades: A, B, C, and etc.
• When a company asks a customer to rate the sales experience on a scale of 1-10.
• Economic status: low, medium and high.

Much more on the topic plus a quiz, you can learn in our post: nominal vs ordinal data .

Discrete vs Continuous Data

As we mentioned above discrete and continuous data are the two key types of quantitative data.

In statistics, marketing research, and data science, many decisions depend on whether the basic data is discrete or continuous.

5. Discrete data

Discrete data is a count that involves only integers. The discrete values cannot be subdivided into parts.

For example, the number of children in a class is discrete data. You can count whole individuals. You can’t count 1.5 kids.

To put in other words, discrete data can take only certain values. The data variables cannot be divided into smaller parts.

It has a limited number of possible values e.g. days of the month.

Examples of discrete data:

• The number of students in a class.
• The number of workers in a company.
• The number of home runs in a baseball game.
• The number of test questions you answered correctly

6. Continuous data

Continuous data is information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value.

For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc.

You can record continuous data at so many different measurements – width, temperature, time, and etc. This is where the key difference from discrete types of data lies.

The continuous variables can take any value between two numbers. For example, between 50 and 72 inches, there are literally millions of possible heights: 52.04762 inches, 69.948376 inches and etc.

A good great rule for defining if a data is continuous or discrete is that if the point of measurement can be reduced in half and still make sense, the data is continuous.

Examples of continuous data:

• The amount of time required to complete a project.
• The height of children.
• The square footage of a two-bedroom house.
• The speed of cars.

Much more on the topic you can see in our detailed post discrete vs continuous data : with a comparison chart.

All of the different types of data have a critical place in statistics, research, and data science.

Data types work great together to help organizations and businesses from all industries build successful data-driven decision-making process .

Working in the data management area and having a good range of data science skills involves a deep understanding of various types of data and when to apply them. If you’re looking to enhance your data analysis skills, taking the best data science courses online can provide you with a solid foundation in understanding these essential data types.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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• Sources of Data For Research: Types & Examples

Introduction

In the age of information, data has become the driving force behind decision-making and innovation. Whether in business, science, healthcare, or government, data serves as the foundation for insights and progress. 

As a researcher, you need to understand the various sources of data as they are essential for conducting comprehensive and impactful studies. In this blog post, we will explore the primary data sources, their definitions, and examples to help you gather and analyze data effectively.

Primary Data Sources

Primary data sources refer to original data collected firsthand by researchers specifically for their research purposes. These sources provide fresh and relevant information tailored to the study’s objectives. Examples of primary data sources include surveys and questionnaires, direct observations, experiments, interviews, and focus groups. As a researcher, you must be familiar with primary data sources, which are original data collected firsthand specifically for your research purposes. 

These sources hold significant value as they offer fresh and relevant information tailored to your study. Also, researchers use primary data to obtain accurate and specific insights into their research questions to confirm that the data is directly relevant to their study and meets their specific needs. Collecting primary data allows you as a researcher to control the data collection process, and monitor the data quality and reliability for their analyses and conclusions.

Examples of Primary Data Sources

• Surveys and questionnaires: Surveys and questionnaires are widely used data collection methods that allow you to gather information directly from respondents. Whether distributed online, through mail, or in person, surveys enable you to reach a large audience and collect quantitative data efficiently. However, it is crucial to design clear and unbiased questions to ensure the accuracy and reliability of responses.
• Observations: Direct observations involve systematically watching and recording events or behaviors as they occur. This method provides you with real-time data, offering unique insights into participants’ natural behavior and responses. It is particularly valuable in fields such as psychology, anthropology, and ecology, where understanding human or animal behavior is critical.
• Experiments: Experiments involve when you deliberately manipulate variables to study cause-and-effect relationships. When you control variables, your experiments provide rigorous and conclusive data, often used in scientific research. They are well-suited for hypothesis testing and determining causal relationships.
• Interviews and focus groups : Qualitative data collected through interviews and focus groups give you an in-depth exploration of participants’ opinions, beliefs, and experiences. These methods help you to understand complex issues and gain rich insights that quantitative data alone may not capture or provide for your study.

Read More: What is Primary Data? + [Examples & Collection Methods]

Secondary Data Sources

As a researcher, you should also be familiar with secondary data sources. Secondary data sources involve data collected by someone else for purposes other than your specific research. Therefore, secondary data complements primary data and can provide valuable context and insights to your research.

Examples of Secondary Data Sources

• Published literature: Published literature refers to academic papers, books, and reports published by researchers and scholars in various fields. These literatures serve as a rich source of secondary data. These sources contain valuable findings and analyses from previous studies, offering a foundation for new research and the ability to build upon existing knowledge. Reviewing published literature is essential for you to understand the current state of research in your area of study and identify gaps for further investigation.
• Government sources: Government agencies collect and maintain vast amounts of data on a wide range of topics. These datasets are often made available for public use and can be a valuable resource for researchers. For example, census data provides demographic information, economic indicators offer insights into the economy, and health records contribute to public health research. Government sources offer standardized and reliable data that can be used for various research purposes.
• Online databases: The internet has opened up access to a wealth of data through online databases, data repositories, and open data initiatives. These platforms host datasets on diverse subjects. This makes them easily accessible to you and other researchers worldwide. Online databases are particularly beneficial for conducting cross-disciplinary research or exploring topics beyond your immediate field of expertise.
• Market research reports: Market research companies conduct surveys and gather data to analyze market trends, consumer behavior, and industry insights. These reports provide valuable data for businesses and researchers seeking information on market dynamics and consumer preferences. Market research reports offer you a comprehensive view of industries and can inform you of how to make strategic decisions.

Read More: What is Secondary Data? + [Examples, Sources & Analysis]

Tertiary Data Sources

In addition to primary and secondary data, you should be aware of tertiary data sources, which play a critical role in aggregating and organizing existing data from various origins. Tertiary data sources focus on collecting, curating, and preserving data for easy access and analysis. 

Examples of Tertiary Data Sources

• Data aggregators: Data aggregators are companies or organizations that specialize in collecting and compiling data from multiple sources into centralized databases. These sources can include government agencies, research institutions, businesses, and other data providers. These aggregators offer a convenient way for you, a researcher, to access a vast amount of data on specific topics or industries. As they consolidate data from diverse sources, they provide you and other researchers with a comprehensive view of trends, patterns, and insights.
• Data brokers: The best way to describe data brokers is that they are entities that buy and sell data, often without the direct consent or knowledge of the individuals whose data is being traded. While data brokers can offer access to large datasets, their practices raise privacy and ethical concerns. As a researcher, you should be cautious when using data obtained through data brokers to ensure compliance with ethical guidelines and data protection laws.
• Data archives: Data archives serve as repositories for historical data and research findings. These archives are essential for preserving valuable information for future reference and analysis. They often contain datasets, reports, academic papers, and other research materials. Data archives ensure that data remains accessible for replication studies, verification of previous research, and the development of longitudinal analyses.

Emerging Data Sources

As you delve into the world of data collection, it’s important to know the emerging sources that have gained prominence in recent years. These newer data sources provide valuable insights and opportunities for research across various domains. Below are some of these emerging data sources:

• Internet of Things (IoT): The Internet of Things (IoT) has changed data collection in the 21st century through the everyday connection of devices and objects to the Internet. Smart devices like sensors, wearables, and home appliances generate vast amounts of data in real-time. For example, IoT devices in healthcare can monitor patients’ health metrics, while in agriculture, they can optimize irrigation and crop management. As a researcher, you can leverage IoT data to analyze patterns, predict trends, and make data-driven decisions.
• Social media and web data: Social media platforms and websites host a wealth of information generated by users worldwide. When you analyze social media posts and online reviews, and scrap the web, they provide you with valuable insights into public opinions, consumer behavior, and trends. You can study sentiment analysis, track customer preferences, and identify emerging topics using social media data. Web scraping allows for the extraction of data from websites, enabling researchers to gather large datasets for analysis.
• Sensor data: Sensor data is becoming increasingly relevant in various fields, including environmental monitoring, urban planning, and healthcare. Sensors are capable of measuring and collecting data on environmental parameters, traffic patterns, air quality, and more. This data helps you understand environmental changes, optimize urban infrastructure, and improve public health initiatives. Sensor networks offer a continuous stream of data, that provides you with real-time and accurate information.

In conclusion, we have explored the diverse sources of data for research, such as primary data sources, secondary data sources, and tertiary data sources, which all play a crucial role in getting the accurate information needed for research. It is important that you understand the strengths and limitations of each data source. 

As you embark on your research journey, explore and utilize these diverse data sources. And if you leverage a combination of primary, secondary, and tertiary data, you can make informed decisions, drive progress in your respective fields, and uncover novel insights that may not be achievable without trying out different sources.

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• Data Management

Defining Research Data

One definition of research data is: "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings." ( OMB Circular 110 ).

Research data covers a broad range of types of information (see examples below), and digital data can be structured and stored in a variety of file formats.

Note that properly managing data (and records) does not necessarily equate to sharing or publishing that data.

Examples of Research Data

Some examples of research data:

• Documents (text, Word), spreadsheets
• Laboratory notebooks, field notebooks, diaries
• Questionnaires, transcripts, codebooks
• Audiotapes, videotapes
• Photographs, films
• Protein or genetic sequences
• Test responses
• Slides, artifacts, specimens, samples
• Collection of digital objects acquired and generated during the process of research
• Database contents (video, audio, text, images)
• Models, algorithms, scripts
• Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
• Methodologies and workflows
• Standard operating procedures and protocols

Exclusions from Sharing

In addition to the other records to manage (below), some kinds of data may not be sharable due to the nature of the records themselves, or to ethical and privacy concerns. As defined by the OMB , this refers to:

• preliminary analyses,
• drafts of scientific papers,
• plans for future research,
• peer reviews, or
• communications with colleagues

Research data also do not include:

• Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and
• Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.

Some types of data, particularly software, may require special license to share.  In those cases, contact the Office of Technology Transfer to review considerations for software generated in your research.

Other Records to Manage

Although they might not be addressed in an NSF data management plan, the following research records may also be important to manage during and beyond the life of a project.

• Correspondence (electronic mail and paper-based correspondence)
• Project files
• Grant applications
• Ethics applications
• Technical reports
• Research reports
• Signed consent forms

Adapted from Defining Research Data by the University of Oregon Libraries.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Data Module #1: What is Research Data?

• Defining Research Data
• Qualitative vs. Quantitative

Types of Research Data

• Data and Statistics
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Data may be grouped into four main types based on methods for collection: observational, experimental, simulation, and derived. the type of research data you collect may affect the way you manage that data. for example, data that is hard or impossible to replace (e.g. the recording of an event at a specific time and place) requires extra backup procedures to reduce the risk of data loss. or, if you will need to combine data points from different sources, you will need to follow best practices to prevent data corruption.  .

Observational Data

Observational data are captured through observation of a behavior or activity. It is collected using methods such as human observation, open-ended surveys, or the use of an instrument or sensor to monitor and record information -- such as the use of sensors to observe noise levels at the Mpls/St Paul airport. Because observational data are captured in real time, it would be very difficult or impossible to re-create if lost. Image courtesy of  https://dorothyjoseph.com

Experimental Data

Experimental data are collected through active intervention by the researcher to produce and measure change or to create difference when a variable is altered. Experimental data typically allows the researcher to determine a causal relationship and is typically projectable to a larger population. This type of data are often reproducible, but it often can be expensive to do so.  

Simulation Data

Simulation data are generated by imitating the operation of a real-world process or system over time using computer test models. For example, to predict weather conditions, economic models, chemical reactions, or seismic activity. This method is used to try to determine what would, or could, happen under certain conditions. The test model used is often as, or even more, important than the data generated from the simulation.  

Derived / Compiled Data

Derived data involves using existing data points, often from different data sources, to create new data through some sort of transformation, such as an arithmetic formula or aggregation. For example, combining area and population data from the Twin Cities metro area to create population density data. While this type of data can usually be replaced if lost, it may be very time-consuming (and possibly expensive) to do so.  

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Common Data Types in Public Health Research

Quantitative data.

• Quantitative data is measurable, often used for comparisons, and involves counting of people, behaviors, conditions, or other discrete events (Wang, 2013).
• Quantitative data uses numbers to determine the what, who, when, and where of health-related events (Wang, 2013).
• Examples of quantitative data include: age, weight, temperature, or the number of people suffering from diabetes.

Qualitative Data

• Qualitative data is a broad category of data that can include almost any non-numerical data.
• Qualitative data uses words to describe a particular health-related event (Romano).
• This data can be observed, but not measured.
• Involves observing people in selected places and listening to discover how they feel and why they might feel that way (Wang, 2013).
• Examples of qualitative data include: male/female, smoker/non-smoker, or questionnaire response (agree, disagree, neutral).
• Measuring organizational change.
• Measures of clinical leadership in implementing evidence-based guidelines.
• Patient perceptions of quality of care.

Data Sources

Primary data sources.

• Primary data analysis in which the same individual or team of researchers designs, collects, and analyzes the data, for the purpose of answering a research question (Koziol & Arthur, nd).

Advantages to Using Primary Data

• You collect exactly the data elements that you need to answer your research question (Romano).
• You can test an intervention, such as an experimental drug or an educational program, in the purest way (a double-blind randomized controlled trial (Romano).
• You control the data collection process, so you can ensure data quality, minimize the number of missing values, and assess the reliability of your instruments (Romano).

Secondary Data Sources

• Existing data collected for another purposes, that you use to answer your research question (Romano).

Advantages of Working with Secondary Data

• Large samples
• Can provide population estimates : for example state data can be combined across states to get national estimates (Shaheen, Pan, & Mukherjee).
• Less expensive to collect than primary data (Romano)
• It takes less time to collect secondary data (Romano).
• You may not need to worry about informed consent, human subjects restriction (Romano).

Issues in Using Secondary Data

• Study design and data collection already completed (Koziol & Arthur, nd).
• Data may not facilitate particular research question o Information regarding study design and data collection procedures may be scarce.
• Data may potentially lack depth (the greater the breadth the harder it is to measure any one construct in depth) (Koziol & Arthur, nd).
• Certain fields or departments (e.g., experimental programs) may place less value on secondary data analysis (Koziol & Arthur, nd).
• Often requires special techniques to analyze statistically the data.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

Types of studies #

The central goal of data science is to answer meaningful questions using data. Here we will discuss various formal approaches to conducting research using data science techniques. A study is a focused and rigorous research effort aiming to address a specific question. A good study should be carefully designed before being carried out. There are many types of studies, and the design of any particular study is determined by the question of interest, the type of data that will be collected, and the ethical and logistical challenges that constrain data collection. We will discuss all of these issues here.

Research efforts can be broadly categorized into those that are observational , meaning that data are collected from systems behaving unperturbed in their natural way, and interventional , meaning that we manipulate the system, or create an artificial system that we can control, in order to obtain our data. Both observational and interventional approaches to research are common in all domains of science and engineering, including in academic, government, and industrial research settings.

Observational studies #

Observational studies, as the name suggests, involve mostly passive observation of unperturbed systems. A commonly encountered type of observational study is one that aims to establish risk factors or protective factors for health outcomes in humans. In the most prototypical of these studies, we have an exposure which is something that people do, or that happens to people. The exposure is then considered in relation to another variable called the outcome , which may be caused by the exposure, or associated with it. Examples below will make this more concrete.

Suppose we are interested in the relationship between the consumption of processed food and the presence of tooth decay. To understand this relationship, we might collect data on people’s processed food consumption (the exposure), and the presence or extent of tooth decay in the same subjects (the outcome). If the people who consume more processed food have more tooth decay, then we have found an association between these two variables.

The critical issue with an observational study is that the subjects with a higher level of the exposure may also have other characteristics that influence their outcome. For example, it may be that the people who consume processed food also tend to smoke, and it is actually the smoking that leads to the tooth decay. Or, it could be that people who consume a lot of processed food are less meticulous when brushing their teeth. There are an almost unlimited number of such factors that could be imagined.

Confounders #

A factor such as smoking or oral hygiene is known as a confounder , since it is potentially related to both the exposure and the outcome. As a result, we don’t know if the exposure causes the outcome directly, or if the exposure causes the confounder, which in turn causes the outcome. Other possibilities exist as well: the confounder could cause the exposure, which then causes the outcome, or another (unmeasured or unknown) factor could cause both the exposure and the outcome.

Most scientific research aims to identify mechanisms and causes. That is, we want to know not just what happens, but why it happens. Therefore, when using data analysis in the setting of scientific research, it is important to be clear about when any findings might support a causal or mechanistic relationship, or alternatively if they only identify an association. An association is generally a weaker type of scientific finding compared to a causal relationship.

An everyday example of a confounder would arise if we measure the air temperature, the intensity of sunlight, and the number of cars driving on the street. We would likely see an association between the number of cars driving on the street and the air temperature, since the traffic on most roads is higher during the day, and the air temperature is also higher during the day. But common sense tells us that traffic is not the main factor causing days to be warmer than nights (heat emitted from the vehicles would make a very small contribution compared to the warmth from the sun). The association between traffic and air temperature is a real association (it would continue to be found as we collect more data in different locations), but it is not causal. Such an association is sometimes called a spurious association . The relationship between sunlight intensity and temperature, however, is causal.

Observational research is widely used in science, but it can be the source of much controversy. Most observational research cannot, on its own, provide evidence that a relationship is causal. In the mid 20th century, some famous statisticians argued that smoking was not a cause of lung cancer, but rather people who smoke are doing other things that increase their risk of lung cancer. There is now an overwhelming consensus that smoking is a causal factor behind many types of cancer. Part (but not all) of the evidence for this comes from observational studies. Careful interpretation of multiple observational studies bearing on an important question can help support a causal relationship, although other lines of argument are generally needed to fully demonstrate that a relationship is causal.

One way to use observational analyses to support a causal claim is to carefully consider all the plausible confounders, and either directly account for their effects, or, argue that the observed relationship is too strong to be entirely attributable to the confounders. In this way, we can increase the quality of evidence that comes from an observational study. In doing this, we need to distinguish between three types of confounders: (i) known, measured confounders, (ii) known, unmeasured confounders, and (iii) unknown confounders. Note that unknown confounders must always be unmeasured.

For example, in the study of tooth decay discussed above, smoking is known to be associated with unhealthy diet (e.g. processed food consumption) and with tooth decay. Therefore, smoking is a confounder of the relationship between processed food consumption and tooth decay. But we can measure whether a person smokes. Therefore, this is a known, measured confounder. There are statistical techniques (to be discussed later in the course) that can at least partly account for a known, measured confounder such as smoking. On the other hand, it might be quite difficult to get an accurate sense of a person’s oral hygiene habits. Thus, this will be a known but unmeasured confounder.

Fortunately, improvements in technology often can turn a known, unmeasured confounder into a known, measured confounder. For example, it may be possible to develop an electric toothbrush that records its use, making it much easier to obtain accurate information about people’s oral hygiene habits. Such a device is known as a sensor . Use of advanced sensors is revolutionizing many areas of research, and is a major driver behind the demand for data science experts and new data science techniques.

The limitation of this strategy of identifying and accounting for confounders is that there can always be unknown confounders that we have not yet identified. For example, almost every health outcome can be influenced by genetics, and we are still decades away from having a comprehensive understanding of the genetic determinants of many human traits. Therefore, genetic factors will remain an unknown confounding factor for many observational relationships for the foreseeable future.

Interventional studies #

In an interventional study, the factor of interest is controlled by the researcher, rather than happening naturally to the subjects. An interventional study is most often called an experiment . Experiments are the canonical way approach to scientific investigations, although in many areas of science it is difficult to conduct experiments for ethical or logistical reasons.

For example, we could consider turning our observational study looking at the relationship between processed food consumption and tooth decay into an experiment. To do so, we would need the ability to force some people to consume processed food, and prevent other people from doing so. In a type of experiment called a randomized controlled trial (RCT), we would randomly choose some people to never consume processed food, these people would comprise the control arm of the experiment. The remaining people would be required to consume processed food, and these people would comprise the treatment arm of the experiment. Because the assignment to the treatment and control arms in a RCT is random, it is impossible for there to be any confounders – any association between processed food consumption and tooth decay seen in a properly conducted RCT is causal, not spurious. However, there are significant ethical and practical constraints in such a study that may make it quite difficult to conduct.

It is unethical to induce someone to do something that is known to be harmful to them. It may be possible to induce people to stop eating processed food, e.g. by giving them a financial reward for doing so. Thus, we could conduct a study in which people are randomly assigned to a treatment arm in which they are induced to eat no processed food, and a control arm in which they eat whatever they choose. This may be sufficient to demonstrate a relationship between processed food consumption and tooth decay. However, since the control group may choose to eat relatively small amounts of processed food, and the treatment group may also choose to each processed food (i.e. the inducement may fail), then the difference between the two groups may be attenuated. Also, since tooth decay develops somewhat slowly over time, it would be very difficult to maintain these controlled influences on people’s behaviors over, say, several years.

In some limited circumstances, it may be ethical to conduct a study in which people are induced to do something that may confer some risk, such as eating more processed food. But this could only be done for a limited time, and could never be done with a dangerous and addictive exposure such as smoking. For example, it may be possible to conduct a study in which people are provided with an unhealthy meal once a day for a month. A study like this would be reviewed very carefully before being approved by an Institutional Review Board (IRB, the panel of experts that must approve all studies within an institution that involve human subjects). Also, it would be necessary to demonstrate ahead of time that the value of performing the study is high. Such a study would almost certainly be restricted to low-risk participants (e.g. young people with no significant health problems).

Analogously, it may be possible to completely prevent people from eating processed food for a short period of time, for example, by having the subjects live in a research facility where all of their food is provided to them. But clearly this would be expensive, and could only be done for a short period of time (far too short to see an association with tooth decay).

Other aspects of study design #

The distinction between observational and interventional studies is arguably the most important aspect of a research study. But there are other aspects of study designs that are also important to be aware of. We will discuss a few such notions here.

A cross-sectional study takes a dynamic, time-evolving system, and studies it based on a snapshot taken at a specific point in time. Cross-sectional studies are somewhat simpler to conduct than other types of studies, but may be less informative. For example, a basic cross-sectional study looking at the relationship between processed food consumption and dental cavities would involve contacting people on a single occasion, and asking them about their diet and oral hygiene habits. Since we would only be contacting the subjects one time, we would only be able to directly measure the current status of these two factors. We would not know anything about processed food consumption in the past, so we would not know whether any cavities that are present preceded, or followed periods of eating a diet heavy in processed foods. Due to these and other issues, most cross-sectional studies provide less evidence for causal relationships than other types of observational studies.

A longitudinal study involves repeated interactions with the same subject over a period of time. On each occasion, we obtain additional data. In our study of dental cavities, we would see new cavities as they occur, and we would also be able to track changes in people’s dietary and oral hygiene habits. A critical advantage of a longitudinal study over a cross-sectional study is that we can see the temporal ordering of the exposure and the outcome, rather than only seeing them at one time (as would be the case for diet in a cross sectional study), or seeing them only in cumulative form (as would be the case for cavities in a cross-sectional study). Longitudinal studies require more effort and expense for those collecting the data, and can sometimes involve more advanced statistical methods for data analysis. But in most cases, a longitudinal study will provide a better quality of evidence than a cross-sectional study.

Studies can have either a prospective or a retrospective character, and this distinction is especially relevant for studies involving human subjects. A retrospective study is one in which all data are collected after the events of interest have occurred. For example, if we were interested in the relationship between dental cavities and processed food consumption in young children, but collected data from teenagers, then this would be a retrospective study. In a prospective study for the same research question, we would enroll young children into our study, and follow them as they progress through childhood. A retrospective study often asks the subjects to recall past exposures and past outcomes, and for many such characteristics recall can be inaccurate and biased.

In general, prospective studies yield more informative data than retrospective studies. But in some situations, retrospective studies can still provide high-quality evidence. In the case of dental cavities, dental records should provide accurate information about when cavities occurred. However data on food consumption obtained retrospectively would generally be far inferior to data on food consumption obtained prospectively.

A trial is a specific type of research study aiming to establish whether one way of doing something is more effective than another. Trials are especially common in medical research, where they are often called “clinical trials.” The goal of a clinical trial is usually to establish whether a drug, medical device, or therapy is safe and effective. Trials also arise in non-medical settings. For example, in education research we may have a trial to compare different approaches to teaching a subjects. In manufacturing, we may have a trial to compare the efficiency and quality of two ways of manufacturing a product.

A case/control study is used when studying a rare trait (such as a rare disease). In a standard case/control study, we identify a set of people with the trait of interest, say, people who are being treated for a form of cancer in a particular hospital, then we identify an equal number of people who don’t have the disease. The people with the disease are the cases and the people without the disease are the controls . This basic case/control design has 1:1 matching, so that there is one control for every case. Other ratios are possible, e.g. having three controls for every case. Often in a case/control study, we will attempt to match the cases to the controls on other relevant factors. For example, if we are studying cancer cases whose average age is 68, we would aim to identify controls whose average age is 68.

A cohort study is essentially the opposite of a case/control study. In a cohort study we identify a collection of individuals (say, people), and then determine who within that group has the condition of interest and who does not. For example, we could define the population of Ann Arbor residents over age 65 as the cohort, and then aim to identify within that cohort who has a particular disease of interest. Cohort studies also have cases and controls, but the “case status” is determined after the sample is obtained, rather than being part of the sampling process.

A cluster sample is a common way of collecting data on very large populations that are very dispersed and thus difficult to sample directly. A basic example of a cluster sample of the United States population would be to first randomly sample 20 counties out of all counties in the US. Then we would randomly sample, say, 100 people from each of the 20 selected counties. This would give us an overall sample size of 2000, but it is not a simple random sample of the United States population. Each person is equally likely to be included in the sample, but each subset of size 2000 is not equally likely to be chosen (this is the definition of simple random sample). Subsets that cover more than 20 counties have zero chance of being selected in the cluster sample described above.

The main reason to use a cluster sample is logistics. If you aim to interview people, or request administrative data (say medical or school records), it is much easier to work with a limited number of counties, and interview or otherwise obtain data on multiple people per county, rather than having a sample that is spread over the US, with most people in our study being the only representative of their county.

A survey is a study whose goal is to estimate the absolute level of one or more variables, with a particular focus on obtaining estimates that accurately reflect a defined target population. For example, we may wish to estimate the proportion of people who support a candidate in an upcoming election. For such an estimate to be of value, it is essential that it reflect the population of people who are eligible to vote, or who are likely to vote in the election. An estimate that reflects only a subset of people in the population is of very little value, because it is not generally a good predictor of the election’s outcome. Although accurately reflecting a target population is desirable in many forms of research, it is most essential in a survey, since that is the explicit goal of many surveys.

The main challenge of a survey is that in most cases we cannot force people to respond or participate. This leads to a form of selection bias , in which it is possible that, say, supporters of one candidate in an election are under-represented in the survey compared to supporters of another candidate. This could lead to the survey misstating which candidate is expected to win the election. A common technique in survey research for addressing this issue is to use weighting to compensate for this. For example, suppose that we conduct a survey in which men are half as likely to respond as women. A weighting adjustment would involve counting each man’s vote twice when estimating the level of support for each candidate.

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Research Methodology – Types, Examples and writing Guide

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Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Not all data are created equal; some are structured, but most of them are unstructured. Structured and unstructured data are sourced, collected and scaled in different ways and each one resides in a different type of database.

In this article, we will take a deep dive into both types so that you can get the most out of your data.

Structured data—typically categorized as quantitative data—is highly organized and easily decipherable by  machine learning algorithms .  Developed by IBM® in 1974 , structured query language (SQL) is the programming language used to manage structured data. By using a  relational (SQL) database , business users can quickly input, search and manipulate structured data.

Examples of structured data include dates, names, addresses, credit card numbers, among others. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility:

• Easily used by machine learning (ML) algorithms:  The specific and organized architecture of structured data eases the manipulation and querying of ML data.
• Easily used by business users:  Structured data do not require an in-depth understanding of different types of data and how they function. With a basic understanding of the topic relative to the data, users can easily access and interpret the data.
• Accessible by more tools:  Since structured data predates unstructured data, there are more tools available for using and analyzing structured data.
• Limited usage:  Data with a predefined structure can only be used for its intended purpose, which limits its flexibility and usability.
• Limited storage options:  Structured data are usually stored in data storage systems with rigid schemas (for example, “ data warehouses ”). Therefore, changes in data requirements necessitate an update of all structured data, which leads to a massive expenditure of time and resources.
• OLAP :  Performs high-speed, multidimensional data analysis from unified, centralized data stores.
• SQLite : (link resides outside ibm.com)  Implements a self-contained,  serverless , zero-configuration, transactional relational database engine.
• MySQL :  Embeds data into mass-deployed software, particularly mission-critical, heavy-load production system.
• PostgreSQL :  Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java,  Python , among others.).
• Customer relationship management (CRM):  CRM software runs structured data through analytical tools to create datasets that reveal customer behavior patterns and trends.
• Online booking:  Hotel and ticket reservation data (for example, dates, prices, destinations, among others.) fits the “rows and columns” format indicative of the pre-defined data model.
• Accounting:  Accounting firms or departments use structured data to process and record financial transactions.

Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in  non-relational (NoSQL) databases . Another way to manage unstructured data is to use  data lakes  to preserve it in raw form.

The importance of unstructured data is rapidly increasing.  Recent projections  (link resides outside ibm.com) indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management.

Examples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, among others. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources:

• Native format:  Unstructured data, stored in its native format, remains undefined until needed. Its adaptability increases file formats in the database, which widens the data pool and enables data scientists to prepare and analyze only the data they need.
• Fast accumulation rates:  Since there is no need to predefine the data, it can be collected quickly and easily.
• Data lake storage:  Allows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.
• Requires expertise:  Due to its undefined or non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who might not fully understand specialized data topics or how to utilize their data.
• Specialized tools:  Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.
• MongoDB :  Uses flexible documents to process data for cross-platform applications and services.
• DynamoDB :  (link resides outside ibm.com) Delivers single-digit millisecond performance at any scale through built-in security, in-memory caching and backup and restore.
• Hadoop :  Provides distributed processing of large data sets using simple programming models and no formatting requirements.
• Azure :  Enables agile cloud computing for creating and managing apps through Microsoft’s data centers.
• Data mining :  Enables businesses to use unstructured data to identify consumer behavior, product sentiment and purchasing patterns to better accommodate their customer base.
• Predictive data analytics :  Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
• Chatbots :  Perform text analysis to route customer questions to the appropriate answer sources.

While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:

• Sources:  Structured data is sourced from GPS sensors, online forms, network logs, web server logs,  OLTP systems , among others; whereas unstructured data sources include email messages, word-processing documents, PDF files, and others.
• Forms:  Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, among others.
• Models:  Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (for example, schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (for example, schema-on-read).
• Storage:  Structured data is stored in tabular formats (for example, excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes, which makes it difficult to scale.
• Uses:  Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in  natural language processing  (NLP) and text mining.

Semi-structured data (for example, JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (for example, tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

• Example of metadata usage:  An online article displays a headline, a snippet, a featured image, image alt-text, slug, among others, which helps differentiate one piece of web content from similar pieces.
• Example of semi-structured data vs. structured data:  A tab-delimited file containing customer data versus a database containing CRM tables.
• Example of semi-structured data vs. unstructured data:  A tab-delimited file versus a list of comments from a customer’s Instagram.

Recent developments in  artificial intelligence  (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models that are covered in this article are helping business users to do the following:

• Analyze digital communications for compliance:  Pattern recognition and email threading analysis software that can search email and chat data for potential noncompliance.
• Track high-volume customer conversations in social media:  Text analytics and sentiment analysis that enables monitoring of marketing campaign results and identifying online threats.
• Gain new marketing intelligence:  ML analytics tools that can quickly cover massive amounts of data to help businesses analyze customer behavior.

Furthermore, smart and efficient usage of data formats and models can help you with the following:

• Understand customer needs at a deeper level to better serve them
• Create more focused and targeted marketing campaigns
• Track current metrics and create new ones
• Create better product opportunities and offerings
• Reduce operational costs

Whether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By using structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission.

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Identifying and comparing types of social comparisons on social networking sites among mothers with infants: Differences in maternal loneliness by types

• Published: 10 May 2024

Cite this article

• Ryuta Onishi   ORCID: orcid.org/0000-0002-5146-6690 1 ,
• Hanami Tone 2 ,
• Funa Maruyama 3 ,
• Minori Kubota 3 &
• Nana Chino 3  

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Loneliness among mothers of infants is a serious problem that leads to increased stress and depression. Social networking sites (SNS) are platforms commonly used by mothers to gain information and socialize; however, the sites can also lead to social comparison. This study aimed to identify and compare the types of parental social comparisons on SNS among mothers with infants and examine their differences regarding maternal loneliness.

A random sampling cross-sectional survey of 233 Japanese mothers with infants aged 6–11 months old was conducted. The questionnaire considered the frequency of parental social comparisons on SNS and the associated positive/negative emotions, loneliness, perceived social support, SNS use, and participant characteristics. Hierarchical cluster analysis and analysis of covariance were used to examine differences in loneliness by social comparison type.

Identified types of parental social comparisons on SNS included: “Negative-leaning comparisons ( n  = 40),” “Ambivalent comparisons ( n  = 53),” “Heavy comparisons ( n  = 39),” and “Positive-leaning comparisons ( n  = 67).” The “non-comparative group (n = 34)” made no comparisons. The loneliness scores of the “Negative-leaning comparisons” group were significantly higher than those of the “Ambivalent comparisons,” “Positive-leaning comparisons,” and “Non-comparative” groups ( p  = 0.019, p  = 0.017, and p  < 0.001, respectively). Additionally, the loneliness scores of the “Heavy comparisons” group were higher than those of the “Non-comparative” group ( p  = 0.005).

Interventions aimed at enhancing digital literacy among mothers and providing tailored support based on their social comparison types are crucial for mitigating the negative effects of parental social comparisons on SNS.

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Data availability.

The datasets generated or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to express their gratitude to the mothers who participated in the study and cooperated with the research team.

This work was supported by the Japan Society for the Promotion of Science KAKENHI (grant no. 19K19728, Grant-in-Aid for Young Scientists). Japan Society for the Promotion of Science, 19K19728.

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Ryuta Onishi

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Hanami Tone

Department of Health Sciences, School of Medicine, Hokkaido University, Kita 12, Nishi 5, Kita-Ku, Sapporo-Shi, Hokkaido, 060-0812, Japan

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All authors contributed to the study conception and design. Material preparation and data collection were performed by Ryuta Onishi and Hnami Tone. Data analysis were performed by All authors. The first draft of the manuscript was written by Ryuta Onishi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Onishi, R., Tone, H., Maruyama, F. et al. Identifying and comparing types of social comparisons on social networking sites among mothers with infants: Differences in maternal loneliness by types. Soc Psychiatry Psychiatr Epidemiol (2024). https://doi.org/10.1007/s00127-024-02677-3

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Inferring causal cell types of human diseases and risk variants from candidate regulatory elements

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The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.

Competing Interest Statement

Steven Gazal reports consulting fees from Eleven Therapeutics unrelated to the present work. The other authors declare no competing interests.

Funding Statement

This research has been funded by the National Institutes of Health grant R35 GM147789.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Source data of annotations used in this study is publicly available at: http://screen-beta.wenglab.org/ https://www.engreitzlab.org/resources http://catlas.org/humanenhancer/#!/cellType The GWAS summary statistics used in this study are publicly available. The description of the GWAS files and the appropriate URL links are provided in Supplementary table 1 of this manuscript.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

Data availability S-LDSC reference files and GWAS summary statistics used in this study are available at https://zenodo.org/records/10515792. S-LDSC CTS SNP-annotations used in this study are available at https://zenodo.org/records/11194201. Code availability CT-FM/CT-FM-SNP softwares and the code to replicate our analyses are available at https://github.com/ArtemKimUSC/CTFM.

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In brief: what types of studies are there.

Last Update: September 8, 2016 ; Next update: 2024.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

• What is the cause of the condition?
• What is the natural course of the disease if left untreated?
• What will change because of the treatment?
• How many other people have the same condition?
• How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

• Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

• How long the study should last
• How many participants are needed
• How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes .

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

• Is the new drug A better than the standard treatment for medical condition X?
• Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
• Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

• How does high blood pressure affect heart health?
• Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

• Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

• Do HPV infections increase the risk of cervical cancer ?
• Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

• Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

• How tall are German men and women at age 20?
• How many people have cancer screening?
• Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

• How do women experience a Cesarean section?
• What aspects of treatment are especially important to men who have prostate cancer ?
• How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs , are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

• German Network for Evidence-based Medicine. Glossar: Qualitative Forschung.  Berlin: DNEbM; 2011. 
• Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003. 
• Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
• Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
• Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. informedhealth.org can provide support for talks with doctors and other medical professionals, but cannot replace them. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

• Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. In brief: What types of studies are there? [Updated 2016 Sep 8].

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• Association of time spent on social media with youth cigarette smoking and e-cigarette use in the UK: a national longitudinal study
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• http://orcid.org/0000-0003-3235-0454 Nicholas S Hopkinson 1 ,
• http://orcid.org/0000-0002-6187-0638 Charlotte Vrinten 2 ,
• http://orcid.org/0000-0002-4385-2153 Jennie C Parnham 2 ,
• Márta K Radó 3 ,
• http://orcid.org/0000-0002-2101-2559 Filippos Filippidis 2 ,
• Eszter P Vamos 2 ,
• http://orcid.org/0000-0003-1318-8439 Anthony A Laverty 2
• 1 National Heart and Lung Institute , Imperial College London , London , UK
• 2 Department of Primary Care and Public Health , Imperial College London School of Public Health , London , UK
• 3 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm , Sweden
• Correspondence to Dr Anthony A Laverty, Department of Primary Care and Public Health, Imperial College London School of Public Health, London, W6 8RP, UK; a.laverty{at}imperial.ac.uk

Background Social media may influence children and young people’s health behaviour, including cigarette and e-cigarette use.

Methods We analysed data from participants aged 10–25 years in the UK Household Longitudinal Study 2015–2021. The amount of social media use reported on a normal weekday was related to current cigarette smoking and e-cigarette use. Generalised estimating equation (GEE) logistic regression models investigated associations of social media use with cigarette smoking and e-cigarette use. Models controlled for possible confounders including age, sex, country of UK, ethnicity, household income and use of cigarette/e-cigarettes by others within the home.

Results Among 10 808 participants with 27 962 observations, current cigarette smoking was reported by 8.6% of participants for at least one time point, and current e-cigarette use by 2.5% of participants. In adjusted GEE models, more frequent use of social media was associated with greater odds of current cigarette smoking. This was particularly apparent at higher levels of use (eg, adjusted odds ratio (AOR) 3.60, 95% CI 2.61 to 4.96 for ≥7 hours/day vs none). Associations were similar for e-cigarettes (AOR 2.73, 95% CI 1.40 to 5.29 for ≥7 hours/day social media use vs none). There was evidence of dose–response in associations between time spent on social media and both cigarette and e-cigarette use (both p<0.001). Analyses stratified by sex and household income found similar associations for cigarettes; however, for e-cigarettes associations were concentrated among males and those from higher household income groups.

Conclusions Social media use is associated with increased risk of cigarette smoking and e-cigarette use. There is a need for greater research on this issue as well as potential policy responses.

• Tobacco control

Data availability statement

Data are available in a public, open access repository. Data available from UK Data Service https://ukdataservice.ac.uk .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/thorax-2023-220569

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WHAT IS ALREADY KNOWN ON THIS TOPIC

There is substantial use of social media among children and young people, which has had debated impacts on health outcomes. There are studies examining social media use and associations with cigarette and e-cigarette use in the US but only two such studies in the UK. One study was cross-sectional, while one previous cohort study of data from 2014 to 2018 found that social media use at age 14 years was associated with a greater likelihood of cigarette smoking at age 17 years. This study did not, however, assess the use of e-cigarettes.

WHAT THIS STUDY ADDS

This study examined daily use of social media among 10–25-year-olds from 2015 to 2021. It found that time spent on social media is associated, in a dose-dependent manner, with likelihood both of cigarette smoking and vaping. Those using social media for ≥7 hours/day were more than two and a half times more likely to use both cigarettes and e-cigarettes than those not using social media.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study highlights that more frequent social media use is associated with increased likelihood of using both cigarettes and e-cigarettes in the UK. This reinforces concerns that social media is a vector of direct and indirect marketing and promotion of these products and that policies to curtail this may be warranted.

Introduction

Understanding the mechanisms that drive uptake and use of cigarettes and e-cigarettes is key to developing strategies to prevent harm. The use of social media has been identified as a novel potential vector, with substantial increases in time spent in this activity by young people. 1–5 Social media use increases with age, and girls are more likely to spend longer periods of time on social media than boys. 6 Social media may be driving cigarette smoking and e-cigarette use through both direct, targeted advertisements and the use of paid influencers by the tobacco industry. 7 To date, most evidence on the impact of social media on cigarette and e-cigarette use has focused on America. 8–10 This has found associations with uptake, regular use and reduced perceptions of harm and has included assessment of engagement with different platforms. 11–13 The only two previous UK studies include a cohort study which found that social media use at age 14 years was associated with greater likelihood of cigarette use at age 17 years. 14 A second cross-national study from 42 countries including the UK concluded that there was a link between social media use and substance use but did not examine cigarette use separately from other substances. 15

Previous research has identified links between social media use and both cigarette and e-cigarette use. For example, analyses of Instagram have identified networks of influencers promoting e-cigarettes, often without disclosing financial relationships; while Juul has recently settled a lawsuit over marketing of e-cigarettes to teens, including on social media. 16 17 Comparative analyses in the UK have found good compliance with advertising standards for e-cigarettes on traditional media, but high levels of breaches on social media. 18 Analyses of 11 of the most popular social media platforms have highlighted high levels of tobacco promotion, with few platforms having policies to deal with novel forms of promotion such as sponsored or influencer content. 19 A systematic review of exposure to tobacco promotion and use identified 29 studies (none from the UK) and concluded that there is a need for greater regulation. 20 Any proposal to regulate social media needs to be justified and based on evidence. To contribute to this, we examined the longitudinal relationship of social media use with cigarette smoking and e-cigarette use among children and young people in the UK.

Data come from participants of the UK Household Longitudinal Study (UKHLS), also known as Understanding Society. 21 This is a longitudinal household panel study with annual surveys starting in 2009. The original sample consisted of a clustered and stratified probability sample of approximately 28 000 households in the UK. Data are collected via face-to-face interviews carried out by a trained interviewer in the respondent’s home and via online, self-completion questionnaires. Adults over the age of 16 years or above are asked to complete an individual questionnaire, including a self-completion questionnaire. Household members aged 10–15 years are asked to fill in a shorter self-completion questionnaire, with permission from their parent or carer.

We have focused on children and young adults aged 10–25 years using data from 2015/2016 to 2020/2021 (wave 7 to wave 12). Questions on e-cigarette use were added to UKHLS in 2015/2016. Participation in the panel is voluntary, with a gift voucher sent to encourage completion of questionnaires and a further gift voucher sent when these are completed. All participants provided consent to be interviewed. The University of Essex Ethics Committee approved all data collection. 22

Outcomes and exposure

We used three separate binary outcomes: current cigarette smoking, current e-cigarette use and current dual use of both products. Participants were classified as current cigarette smokers if they responded “I usually smoke between one and six cigarettes a week” or “I usually smoke more than six cigarettes a week”. All other responses were coded as non-users. The same question was used for all waves of data and for all ages.

Current e-cigarette use was first assessed in 2015/2016 with the question “Do you ever use electronic cigarettes (e-cigarettes)?” with response options “Yes” and “No”. From wave 8 (2016/2017) onwards participants were classified as current (weekly) e-cigarette users if they responded “I use e-cigarettes at least once a week”. All other responses were coded as non-users. Dual use was classified as participants currently using both products, with those using only one or no products classed as non-dual users.

The main exposure variable was social media use. Participants were first asked “Do you belong to any social networking websites?” (Yes/No), and if “Yes”, they were also asked how many hours they spend chatting or interacting with friends through a social website on a normal weekday, with the following response options: “None”, “<1 hour/day”, “1–3 hours”, “4–6 hours” and “≥7 hours”. We combined those reporting “None” along with those who were not a member of a social media website into a reference category of “Not a member or no use”. 6

We considered a range of potentially relevant sociodemographics: age, sex, country in UK, self-defined ethnic group (collapsed into White vs non-White due to low numbers in the non-White category), an indicator of living in an urban or rural areas (derived from Office for National Statistics Rural and Urban Classification of Output Areas) and equivalised household net income (based on the Organisation for Economic Co-operation and Development (OECD) equivalence scale, which was used to adjust household income by household composition 23 ).

Statistical analyses

We compared differences in sociodemographics between categories of social media use using ANOVA. We used binary generalised estimating equation (GEE) regression models (family: binomial; link: logit; correlation matrix: exchangeable) to assess relationships between social media use and product use, using separate models for each outcome: cigarette smoking, e-cigarette use and dual use. GEE models assess changes over time and account for the correlation caused by observations being from the same individuals. 24 We also present tests for trend based on frequency of social media use. Analyses were adjusted for time (categorical) as well as the sociodemographic variables listed above. Models of cigarette smoking were additionally adjusted for parental tobacco use, models of e-cigarette use were adjusted for parental e-cigarette use, and models of dual use were adjusted for both. Analyses used survey weights designed by the UKHLS survey team to account for clustered and stratified probability sampling and non-response bias. 25

We tested for interactions of social media use with age (split into above and below 18 years of age), sex and household income (in three groups). This was due to possible differences between those above and below the legal age of sale, greater social media use among women, and potentially differential effects by socioeconomic groups. All interactions were p<0.001 and so we present stratified analyses. Due to the small numbers, we did not test interactions for dual use.

Sensitivity analyses

We performed a range of sensitivity analyses to test the robustness of our findings. As it is possible that those not using social media at all are atypical, we repeated our analyses excluding these participants. Our main analyses used household income as a marker of socioeconomic status. We also performed our analyses using Index of Multiple Deprivation (IMD) (in five groups) as an alternative marker of socioeconomic status. We performed analyses categorising current e-cigarette use as participants who reported using e-cigarettes at least monthly. We also performed analyses controlling for a measure of mental health (the 12-item General Health Questionnaire (GHQ-12)) to consider whether this is a possible pathway, whereby social media impacts mental health, which is then linked to cigarette and e-cigarette use.

Finally, we used fixed effects analyses to directly test if changes in social media use corresponded to uptake of cigarette smoking and e-cigarette use. These adjusted for the time-varying variables parental cigarette/e-cigarette use and household income. These models were on a smaller subset of individuals who were not product users when entering the study and who were found to change their social media use over time.

Outcomes and covariates across categories of social media use are shown in table 1 . Overall, 8.6% of the sample reported current cigarette smoking at one or more data point, 2.5% reported current e-cigarette use, and 1.1% of participants were dual users at one or more data point. Social media use frequency broken down by covariates is shown in online supplemental appendix table 1 .

Supplemental material

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Description of sample observations by social media use 2015–2021

Cigarette smoking, e-cigarette use and dual use were all more common among participants reporting greater social media use (all p<0.001) ( figure 1 ). Some 2.0% of participants who used social media “None or not a member” reported being a current cigarette smoker compared with 15.7% among those using social media for ≥7 hours/day. Current e-cigarette use ranged from 0.8% among those not using social media to 2.5% among those using it for ≥7 hours/day.

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Cigarette smoking, e-cigarette use and dual use by social media use.

Differences between categories of social media use were apparent for all variables studied (all p<0.001). Males were less likely to be in higher social media use groups than females (57.5% of the “None or not a member” social media group compared with 39.9% of the “≥7 hours/day” group). Social media use was more frequent at older ages (mean age of “None or not a member” social media group 12.0 years vs 17.7 years for the “≥7 hours/day” group). Parental cigarette smoking was more common among those using social media more frequently (17.0% for the “None or not a member” social media group vs 25.2% for the “≥7 hours/day” group) as was parental e-cigarette use (7.3% and 10.5%, respectively).

Table 2 shows results of our GEE models of social media use and cigarette smoking. Cigarette smoking was more common among those using social media more frequently (p for trend <0.001). Those using social media for “<1 hour/day” were more likely to be current cigarette smokers than those using social media “None or not a member” (adjusted odds ratio (AOR) 1.92, 95% CI 1.43 to 2.58) ( table 2 ). Those using social media for “≥7 hours/day” were substantially more likely to be current cigarette smokers than those using social media “None or not a member” (AOR 3.60, 95% CI 2.61 to 4.96).

Associations of social media use with current cigarette use from generalised estimating equation model

Table 3 shows results for e-cigarette use. E-cigarette use was more common among those using social media more frequently (p for trend <0.001). E-cigarette use was more common among those using social media “1–3 hours per day” compared with those using it “None or not a member” (AOR 1.92, 95% CI 1.07 to 3.46). E-cigarette use was considerably more likely among participants using social media “≥7 hours/day” than those using social media “None or not a member” (AOR 2.73, CI 1.40 to 5.29).

Associations of social media use and current e-cigarette use from generalised estimating equation model

Table 4 shows results for dual cigarette and e-cigarette use. Models have wide confidence intervals reflecting low levels of dual use. Those using social media more frequently were more likely to be dual users (p for trend <0.001). Those using social media “1–3 hours per day” were more likely to be dual users compared with those using it “None or not a member” (AOR 3.28, 95% CI 1.24 to 8.70). Dual use was more likely among participants using social media “≥7 hours/day” than among those using social media “None or not a member” (AOR 4.96, 95% CI 1.71 to 14.34).

Associations of social media use with current e-cigarette and cigarette dual use from generalised estimating equation model

Interactions of social media and sex were statistically significant for both cigarettes and e-cigarettes (both p<0.001). In stratified models ( table 5 ) AORs were similar between the sexes for current cigarette smoking. For e-cigarettes, associations between social media use and e-cigarette use were statistically significant for males but not for females (AOR 4.10, 95% CI 1.90 to 8.87 for males for “≥7 hours/day” vs “None or not a member” social media use).

Associations of social media use with current e-cigarette and cigarette use from gender stratified generalised estimating equation models

Interactions with household income categories were statistically significant (p<0.001 for both cigarettes and e-cigarettes) ( table 6 ). In stratified analyses of cigarette smoking, point estimates for the richest income group were higher than for the lowest income group, although these overlapped (eg, AOR 5.22 for “≥7 hours/day”, 95% CI 2.82 to 9.67 for the richest income group vs AOR 4.17, 95% CI 2.27 to 7.65 for the lowest income group). For e-cigarette use, associations were statistically significant for the highest income groups (eg, AOR 7.85, 95% CI 1.72 to 35.82 for “≥7 hours/day” vs no social media use) but were not statistically significant for the lowest income group.

Associations of social media use with current e-cigarette and cigarette use from household income stratified generalised estimating equation models

Analyses stratified by age found similar results to main analyses for cigarettes ( online supplemental appendix table 2 ). Models for e-cigarette use were only statistically significant among those <18 years old.

GEE analyses excluding those not using any social media were similar to main analyses ( online supplemental appendix table 3 ). Analyses using IMD as a marker of socioeconomic status rather than household income also gave similar results ( online supplemental appendix table 4 ). Analyses classifying current e-cigarette use as participants using them at least monthly also gave similar results although with larger point estimates ( online supplemental appendix table 5 ). Analyses controlling for GHQ-12 as a measure of mental health were similar for cigarettes but did not find statistically significant associations between social media and e-cigarette or dual use. This may indicate that social media use impacts mental health, which in turn impacts likelihood of using cigarettes or e-cigarettes, although this result should be treated with caution ( online supplemental appendix table 6 ).

Fixed effect analyses gave similar results to main analyses for uptake of cigarette smoking ( online supplemental appendix table 7 ). It should be noted that sample size was much reduced for this model (n=864). These analyses found some evidence that changes in social media use are linked to uptake of cigarette smoking in a dose–response manner (p for trend=0.053). For example, changing to using social media for ≥7 hours/day was associated with more than double the odds of taking up cigarette smoking (AOR 2.33, 95% CI 1.28 to 4.24).

Associations between changes in social media use and uptake of e-cigarettes did not reveal associations between changes in social media use and uptake of e-cigarettes. These analysis models had even lower sample sizes (n=564). For example, AORs of e-cigarette uptake ranged from 0.71 (95% CI 0.34 to 1.48) for participants using social media “<1 hour/day” to AOR 0.84 (95% CI 0.38 to 1.85) for those using social media “≥7 hours/day”. The test for trend was not statistically significant (p=0.584).

The main finding of the present study is that in children and young adults more frequent social media use was associated with a higher likelihood of both current use of cigarettes and e-cigarettes. This association was independent of other factors associated with increasing smoking and vaping including age, gender, socioeconomic status and parental smoking and vaping. These findings were robust to sensitivity analyses, while in stratified analyses there were more consistent associations for e-cigarette use among those under the legal age of sale, males and those with higher household incomes.

While we were unable to assess use of specific social media platforms or what content was being accessed, we propose a number of possible, non-exclusive explanations for this relationship. First, and most straightforwardly, there is evidence that the corporations behind cigarette smoking and vaping make use of social media to advertise and promote their products. 8–10 16 This includes direct advertising which is algorithmically targeted and the use of paid social media influencers who present smoking and vaping as a fashionable and desirable activity. Greater time spent on social media is likely to increase exposure to these forms of influence. While cigarettes and e-cigarettes are likely promoted differently, we found association with use of both products, highlighting the need for greater understanding of such corporate behaviours. Second, social media use has been shown to have features in common with reward-seeking addictive behaviour. 26 High social media use may increase susceptibility to other addictive behaviours like smoking. Alternatively, both behaviours may be driven by a common susceptibility. Third, as a space that is largely unsupervised by parents/caregivers, social media use may encourage behaviours that are transgressive, including cigarette smoking and vaping. There is evidence that peer smoking is a strong influence on child uptake of smoking 27 and social media is one of the ways in which peer smoking and vaping will be experienced, both by seeing others’ behaviour and by sharing “influencer content” that promotes these behaviours.

Stratified analyses revealed more consistent associations for cigarettes, while for e-cigarettes statistically significant associations were only found for those under the legal age of sale, among males, and those from richer households. Analyses of cigarette smoking did not identify changes over time, which fits with other evidence that smoking prevalence has been reasonably consistent over this time frame. 28 Analyses of e-cigarette use found reduced odds of these outcomes after 2015/2016, likely caused by changes in e-cigarette use ascertainment, although our main findings were robust to reclassification to examine monthly use. Our main analyses focused on weekly use of e-cigarettes; as any health impacts are probably related to amounts of vapour inhaled, this measure of regular use is more important for health than e-cigarette experimentation.

Strengths and limitations

This study uses a nationally representative cohort to examine social media use and use of cigarettes and e-cigarettes over time. UKHLS households are sampled based on geographical areas, population densities and ethnic composition, with survey weight adjusting for differential non-response across groups. 29 We conducted a range of sensitivity analyses, although other potential factors such as education may also be important. All data are based on self-report, and specifically we do not have information about which social media platforms were being used or how individuals were using them, for example, the extent to which they are interacting socially with individuals they know or consuming content from influencers, personalities or media corporations, etc. Precise pathways remain to be fully elucidated: our sensitivity analyses point to a possible role for mental health, although it should be noted that a formal mediation analysis was outside the scope of this article. As cigarette smoking is linked to poorer mental health, these relationships could well be bidirectional. 30 This, as well as potential targeted advertising, are among pathways that should be investigated in both quantitative and qualitative research.

Policy implications

Although we do not have data on the specific platforms used or content used, there is compelling evidence that vape companies are using social media to market their products. 2–5 The content that social media users are exposed to is to a substantial extent algorithmically controlled, both through targeted advertising and by the promotion of material that maximises engagement in order to increase revenue to the platform. This can be controlled. For example, far right imagery which is otherwise widely available is largely inaccessible in Germany, as a consequence of German law which social media platforms are bound to enforce. The companies that own social media platforms have substantial power to modify exposure to material that promotes smoking and vaping if they choose to or are compelled to. Voluntary codes seem unlikely to achieve this, and the introduction and enforcement on bans on material that promote this should be considered. In general, we think that algorithms should not be promoting products to individuals that they cannot legally buy. Legislation and enforcement around this and other corporate determinants of health concerns should be considered a core part of online safety and child protection.

This longitudinal analysis of children and young people in the UK found that more frequent social media use is associated with an increased risk of cigarette and e-cigarette use.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @COPDdoc, @cvrinten, @anthonylav

Contributors AAL conceived the project. JCP, CV and AAL cleaned data and performed the analyses with guidance from FF and MKR. AAL and NSH wrote the first draft and all authors contributed to this process. AAL is guarantor

Funding This study was supported by Cancer Research UK (CRUK PPRCTAGPJT\100005).

Competing interests NSH is Chair of Action on Smoking and Health and Medical Director of Asthma and Lung UK. AAL is a Trustee of Action on Smoking and Health.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Linked Articles

• Editorial Strengthening the evidence base to support stronger regulation of social media based advertising of e-cigarette products to youth Kim L Lavoie Thorax 2024; - Published Online First: 16 May 2024. doi: 10.1136/thorax-2023-221169

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Do States with Easier Access to Guns have More Suicide Deaths by Firearm?

Heather Saunders Published: Jul 18, 2022

Nearly half a million lives (480,622) were lost to suicide from 2010 to 2020. During the same period, the suicide death rate increased by 12%, and as of 2009, the number of suicides outnumbered those caused by motor vehicle accidents. Suicides are most prevalent among people who live in rural areas, males, American Indian or Alaska Natives, and White people, but they are rising fastest in some people of color, younger individuals, and people who live in rural areas. On July 16, 2022, the federally mandated crisis number, 988, will be available to all landline and cell phone users, providing a single three-digit number to access a network of over 200 local and state-funded crisis centers. While the overall number of suicide deaths decreased slightly from 47,511 to 45,979 between 2019 to 2020, the suicides involving firearms increased over the same period (from 23,941 to 24,292). The recent mass shootings in Uvalde and Buffalo have catalyzed discussion around mental health and gun policy. In the same week that the federal Bipartisan Safer Communities Act was signed strengthening background checks for young adults, adding incentives for red flag laws, and reducing access to guns for individuals with a domestic violence history, the Supreme Court struck down New York’s “proper cause” requirement for concealed carry allowances. In this issue brief, we use the Center for Disease Control and Prevention (CDC) Wonder database and the State Firearm Law Database to examine the association between suicide deaths by firearm and the number of state-level firearm law provisions.

Suicides account for over half of all firearm deaths (54%), and over half of all suicides involve a firearm (53%). Though mass shootings are more widely covered, data reveal that suicides are a more common cause of firearm-related deaths than homicide. In 2020, a little more than half (54%) of all firearm-related deaths were suicides, 43% were homicides, and 2% were accidental discharges or undetermined causes. This represents a slight decrease from 2018 and 2019, where suicides by firearms accounted for over 60% of all firearm deaths in that period. Looking at suicides, we find that guns were involved in 53% of suicides in 2020, representing the majority of all suicides.

Variation in state-level suicide rates is largely driven by rates of suicide by firearm. Suicides involving firearms vary from the lowest rate of 1.8 per 100,000 in New Jersey and Massachusetts to a high of 20.9 per 100,000 in Wyoming, representing an absolute difference of 19.1. In contrast, the rate of suicide by other means is more stable across states, ranging from a low of 4.6 in Mississippi to a high of 11.4 in South Dakota, representing an absolute difference of 6.8.

There is a wide range of firearm law provisions across states, with Idaho having the fewest at just one and California having the most at 111. Because there is no comprehensive national firearm registry and very few state registries, it is difficult to track gun ownership in the US, so estimates of gun ownership rely on survey data or measures closely related to gun ownership–such as the number of firearm laws. The State Firearm Law Database is a catalog of the presence or absence of 134 firearm law provisions across all 50 states; this analysis uses firearm laws present in 2019. Even though state laws vary widely in detail and number, there are some common themes across states. Many states restrict firearm access to those considered high-risk, including people with felony convictions (37 states), domestic violence misdemeanors (31 states), or those deemed by the court to be a danger (28 states). A number of states regulate concealed carry permits–for example, 37 require background checks for applicants and 28 require authorities to revoke concealed carry permits under certain conditions, though some concealed carry laws may be subject to change given the recent Supreme Court decision.  Other major categories of gun laws include dealer regulations, ammunition regulations and child access prevention, among others. In 2019, the average number of firearm law provisions per state was 29 and ranged from one provision in Idaho to 111 in California ( Appendix Table 1).

More than twice as many suicides by firearm occur in states with the fewest gun laws, relative to states with the most laws. We grouped states into three categories according to the number of firearm law provisions. States with the lowest number of gun law provisions (17 states) had an average of six provisions and were placed in the “least” category; states with a moderate number of laws (16 states) had an average of 19 provisions and were placed in the “moderate” category; and states with the most firearm laws (17 states) had an average of 61 provisions and were placed in the “most” firearm provisions category. Using CDC WONDER underlying cause of death data, we calculated the age-adjusted rate of suicide by firearm for each category of states. We find that suicide by firearm is highest in states with the fewest gun laws (10.8 per 100,000), lower in states with moderate gun laws (8.4 per 100,000), and the lowest in states with the most gun laws (4.9 per 100,000) (Figure 3). The analysis is not designed to necessarily demonstrate a causal relationship between gun laws and suicides by firearm, and it is possible that there are other factors that explain the relationship.

Firearms are the most lethal method of suicide attempts, and about half of suicide attempts take place within 10 minutes of the current suicide thought, so having access to firearms is a suicide risk factor. The availability of firearms has been linked to suicides in a number of peer-reviewed studies . In one such study , researchers examined the association between firearm availability and suicide while also accounting for the potential confounding influence of state-level suicidal behaviors (as measured by suicide attempts). Researchers found that higher rates of gun ownership were associated with increased suicide by firearm deaths, but not with other types of suicide. Taking a look at suicide deaths starting from the date of a handgun purchase and comparing them to people who did not purchase handguns, another study found that people who purchased handguns were more likely to die from suicide by firearm than those who did not–with men 8 times more likely and women 35 times more likely compared to non-owners.

Non-firearm suicides rates are relatively stable across states suggesting that other types of suicides are not more likely in areas where guns are harder to access. To examine whether non-firearm suicides are higher in states where guns are more difficult to access, we used the state-level firearm law provision groups described above and calculated the age-adjusted rate for each group (states with the least, moderate, and the most firearm law provisions). The results of this analysis provide insight into whether there are other factors that may be contributing to the relationship between gun laws and firearm suicides, such as whether people in states that lack easy access to firearms have higher suicide rates by other means. The rate of non-firearm suicides is relatively stable across all groups, ranging from a low rate of 6.5 in states with the most firearm laws to a high of 6.9 in states with the lowest number of firearm laws. The absolute difference of 0.4 is statistically significant, but small. Non-firearm suicides remain relatively stable across groups, suggesting that other types of suicides are not more likely in areas where guns are harder to get (Figure 3). Though we do not observe an increase of suicide death by other means in states with less access to guns, there may still be differences across states that could explain these findings.

If the suicide rate by firearm in all states was similar to the rate in the states with the most gun laws, approximately 6,800 lives may have been saved in 2020, a reduction of about 15% of all suicide-related deaths. Applying the crude rate of 5.3 per 100,000 to the total population in 2020, we estimate that nearly 6,800 suicide deaths may have been averted if rates of suicide by firearm were similar to states with the most gun control laws.

Recent federal legislation strengthens some gun control measures, but it may take several years to impact firearm mortality. In the recently passed federal legislation, the Bipartisan Safer Communities Act , there is an emphasis on strengthening some measures of gun control including background checks for young adults and reducing gun access for those who have a history of domestic violence, among other provisions. Also included in the legislation are additional funds for mental health services in schools and for child and family mental health services. Despite federal movement toward strengthening gun control, a recent Supreme Court decision struck down state legislation that placed additional restrictions on concealed carry permits. It is not known how the Supreme Court’s decision will impact the frequency of concealed carry firearms and the rate of firearm mortality. More firearm regulations are associated with fewer homicides and suicides , but the newly passed federal gun laws may take several years to reduce firearm mortality .

If you or someone you know is considering suicide, contact the National Suicide Prevention Lifeline at the new three-digit dialing code 988 or 1-800-273-8255 (En Español: 1-888-628-9454; Deaf and Hard of Hearing: 1-800-799-4889).

This work was supported in part by Well Being Trust. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

• Mental Health
• Gun Violence
• State Level

Also of Interest

• The Impact of Gun Violence on Children and Adolescents
• Child and Teen Firearm Mortality in the U.S. and Peer Countries
• A Look at the Latest Suicide Data and Change Over the Last Decade