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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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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
• Evidence Synthesis/Systematic Reviews
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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.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Introduction to Research Methods in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

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Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

• Case studies
• Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

• A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

• Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
• Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

• A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
• In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In:  Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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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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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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U.S. Tightens Rules on Risky Virus Research

A long-awaited new policy broadens the type of regulated viruses, bacteria, fungi and toxins, including those that could threaten crops and livestock.

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By Carl Zimmer and Benjamin Mueller

The White House has unveiled tighter rules for research on potentially dangerous microbes and toxins, in an effort to stave off laboratory accidents that could unleash a pandemic.

The new policy, published Monday evening, arrives after years of deliberations by an expert panel and a charged public debate over whether Covid arose from an animal market or a laboratory in China.

A number of researchers worried that the government had been too lax about lab safety in the past, with some even calling for the creation of an independent agency to make decisions about risky experiments that could allow viruses, bacteria or fungi to spread quickly between people or become more deadly. But others warned against creating restrictive rules that would stifle valuable research without making people safer.

The debate grew sharper during the pandemic, as politicians raised questions about the origin of Covid. Those who suggested it came from a lab raised concerns about studies that tweaked pathogens to make them more dangerous — sometimes known as “gain of function” research.

The new policy, which applies to research funded by the federal government, strengthens the government’s oversight by replacing a short list of dangerous pathogens with broad categories into which more pathogens might fall. The policy pays attention not only to human pathogens, but also those that could threaten crops and livestock. And it provides more details about the kinds of experiments that would draw the attention of government regulators.

The rules will take effect in a year, giving government agencies and departments time to update their guidance to meet the new requirements.

“It’s a big and important step forward,” said Dr. Tom Inglesby, the director of the Johns Hopkins Center for Health Security and a longtime proponent of stricter safety regulations. “I think this policy is what any reasonable member of the public would expect is in place in terms of oversight of the world’s most transmissible and lethal organisms.”

Still, the policy does not embrace the most aggressive proposals made by lab safety proponents, such as creating an independent regulatory agency. It also makes exemptions for certain types of research, including disease surveillance and vaccine development. And some parts of the policy are recommendations rather than government-enforced requirements.

“It’s a moderate shift in policy, with a number of more significant signals about how the White House expects the issue to be treated moving forward,” said Nicholas Evans, an ethicist at University of Massachusetts Lowell.

Experts have been waiting for the policy for more than a year. Still, some said they were surprised that it came out at such a politically fraught moment . “I wasn’t expecting anything, especially in an election year,” Dr. Evans said. “I’m pleasantly surprised.”

Under the new policy, scientists who want to carry out experiments will need to run their proposals past their universities or research institutions, which will to determine if the work poses a risk. Potentially dangerous proposals will then be reviewed by government agencies. The most scrutiny will go to experiments that could result in the most dangerous outcomes, such as those tweaking pathogens that could start a pandemic.

In a guidance document , the White House provided examples of research that would be expected to come under such scrutiny. In one case, they envisioned scientists trying to understand the evolutionary steps a pathogen needed to transmit more easily between humans. The researchers might try to produce a transmissible strain to study, for example, by repeatedly infecting human cells in petri dishes, allowing the pathogens to evolve more efficient ways to enter the cells.

Scientists who do not follow the new policy could become ineligible for federal funding for their work. Their entire institution may have its support for life science research cut off as well.

One of the weaknesses of existing policies is that they only apply to funding given out by the federal government. But for years , the National Institutes of Health and other government agencies have struggled with stagnant funding, leading some researchers to turn instead to private sources. In recent years, for example, crypto titans have poured money into pandemic prevention research.

The new policy does not give the government direct regulation of privately funded research. But it does say that research institutions that receive any federal money for life-science research should apply a similar oversight to scientists doing research with support from outside the government.

“This effectively limits them, as the N.I.H. does a lot of work everywhere in the world,” Dr. Evans said.

The new policy takes into account the advances in biotechnology that could lead to new risks. When pathogens become extinct, for example, they can be resurrected by recreating their genomes. Research on extinct pathogens will draw the highest levels of scrutiny.

Dr. Evans also noted that the new rules emphasize the risk that lab research can have on plants and animals. In the 20th century, the United States and Russia both carried out extensive research on crop-destroying pathogens such as wheat-killing fungi as part of their biological weapons programs. “It’s significant as a signal the White House is sending,” Dr. Evans said.

Marc Lipsitch, an epidemiologist at Harvard and a longtime critic of the government’s policy, gave the new one a grade of A minus. “I think it’s a lot clearer and more specific in many ways than the old guidance,” he said. But he was disappointed that the government will not provide detailed information to the public about the risky research it evaluates. “The transparency is far from transparent,” he said.

Scientists who have warned of the dangers of impeding useful virus research were also largely optimistic about the new rules.

Gigi Gronvall, a biosafety specialist at the Johns Hopkins Bloomberg School of Public Health, said the policy’s success would depend on how federal health officials interpreted it, but applauded the way it recognized the value of research needed during a crisis, such as the current bird flu outbreak .

“I was cautiously optimistic in reading through it,” she said of the policy. “It seems like the orientation is for it to be thoughtfully implemented so it doesn’t have a chilling effect on needed research.”

Anice Lowen, an influenza virologist at Emory University, said the expanded scope of the new policy was “reasonable.” She said, for instance, that the decision not to create an entirely new review body helped to alleviate concerns about how unwieldy the process might become.

Still, she said, ambiguities in the instructions for assessing risks in certain experiments made it difficult to know how different university and health officials would police them.

“I think there will be more reviews carried out, and more research will be slowed down because of it,” she said.

Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

Benjamin Mueller reports on health and medicine. He was previously a U.K. correspondent in London and a police reporter in New York. More about Benjamin Mueller

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

Is this some kind of joke? A school facing shortages starts teaching standup comedy

In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

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Feeling artsy here's how making art helps your brain.

"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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Thirty years later, the Women’s Health Initiative provides researchers with key messages for postmenopausal women

Researchers from the NHLBI-supported Women’s Health Initiative , the largest women’s health study in the U.S., published findings from a 20-year review that underscores the importance of postmenopausal women moving away from a one-size-fits-all approach to making medical decisions. Through this lens, the researchers encourage women and physicians to work together to make shared and individualized decisions based on a woman’s medical history, age, lifestyle, disease risks, symptoms, and health needs and preferences, among other factors. These findings support the concept of “whole-person health” and published in  JAMA .  After reviewing decades of data following clinical trials that started between 1993 and 1998, the researchers explain that estrogen or a combination of estrogen and progestin, two types of hormone replacement therapies, had varying outcomes with chronic conditions. The evidence does not support using these therapies to reduce risks for chronic diseases, such as heart disease, stroke, cancer, and dementia. However, the authors caution that the study was not designed to assess the effects of FDA-approved hormone therapies for treating menopausal symptoms . These benefits had been established before the WHI study began.  Another finding from the study is that calcium and vitamin D supplements were not associated with reduced risks for hip fractures among postmenopausal women who had an average risk for osteoporosis. Yet, the authors note women concerned about getting sufficient intake of either nutrient should talk to their doctor. A third finding was that a low-fat dietary pattern with at least five daily servings of fruits and vegetables and increased grains did not reduce the risk of breast or colorectal cancer, but was associated with reduced risks for breast cancer deaths. 

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Navigating the Gap: Diabetes Support in Emerging Adulthood

May 16, 2024

By Daniel Simon, MD and Julia Blanchette, PhD, RN

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Daniel I Simon, MD (Host): Hello, everyone. Thank you for listening to another episode of Science@UH. I am your host, Dr. Dan Simon, and today I am happy to be joined by Julia Blanchette, nurse scientist lead for Diabetes Care and Education and Program Director of Diabetes Research at University Hospitals Diabetes and Metabolic Care Center, as well as Assistant Professor of Medicine at Case Western Reserve University.

Julia received her BSN in nursing science from the Francis Payne Bolton School of Nursing at Case Western in 2014, and her PhD from Case Western University School of Graduate Studies in 2019. She completed her postdoctoral fellowship at the University of Utah College of Nursing and Integrated Diabetes Management in 2021.

Dr. Blanchette examines the impacts of financial, psychological, and health literacy barriers on diabetes self-management behaviors and outcomes during emerging adulthood and throughout the lifespan. Welcome, Julia.

Julia Blanchette, PhD, RN: Thank you so much. I'm excited to be here.

Dr. Daniel Simon: So Julia, as a nurse scientist, you are very interested in Type 1 diabetes management for young adults age 18 to 25. Can you tell us why you are interested in this age group and what kind of resources your research has developed to educate young adults with Type 1 diabetes?

Julia Blanchette, PhD, RN: So right now, my research team and some diabetes community members are working on developing and testing a financial and health insurance toolkit for young adults with Type 1 diabetes. But the story to how we got here, it's a little bit long, but can I go back to some of my early days in nursing. Is that okay

Dr. Daniel Simon: Absolutely.

Julia Blanchette, PhD, RN: Okay, So, my first job as a registered nurse was right out of my undergrad and I was a nurse for the counselor in training group, so about 16 to 17 year olds at a diabetes camp. And it was the same camp that I went to when I grew up. And so I spent 10 weeks right out of my nursing degree with teenagers and young adults living with Type 1 diabetes, and I noticed that they had this amazing peer support from Diabetes Camp. But they still had so many barriers to diabetes self-management, and that really caught my attention. And then around the same time, I had my personal, first adult endocrinology visit for Type 1 diabetes. And I was really surprised at how unprepared I was for the adult health care system. And so these two observations are what really piqued my interest in understanding self-management behaviors during young adulthood.

Dr. Daniel Simon: So let me ask you a question. I mean, it has got to be an overwhelming shock, for an adolescent young adult to receive the diagnosis of Type 1 diabetes. Obviously, you focused on a financial aspect, but take us through a little bit more of the psychological and adaptive aspects of how do you deal with a diagnosis which is really life changing.

Julia Blanchette, PhD, RN: First of all, it depends on where you are in the stages of development. So someone who's a school aged child is going to have a different reaction and need different types of support than when you're an adolescent or a young adult. And so, what we find is that actually young adults have a lot of gaps in the support that they should receive during that period.

And, young adulthood in general, or this period of emerging adulthood, the ages 18 to 25, or even up to age 30, is when you're kind of stuck in between childhood and adulthood and you're learning to become fully independent. And so during that time, if you're diagnosed with diabetes or any other chronic health condition, you have to manage that on top of learning how to become an adult and taking on all of those responsibilities. So it becomes a lot to balance if you're diagnosed during emerging adulthood or even if you're just adjusting to self-management during emerging adulthood. It is very complex.

Dr. Daniel Simon: So let's talk a little bit about during your postdoctoral fellowship, you developed a pilot financial toolkit through community engaged research. Can you tell us about how those findings have informed what you're doing now and what patients are doing? Obviously, insulin can be very expensive and testing your blood sugar can be. How does that all factor into what you're doing?

Julia Blanchette, PhD, RN: I'm going to actually back up a little bit. So during my PhD, I had started looking at the transition from pediatric to adult diabetes care and understanding what emerging adulthood is and what it is to self-manage a chronic condition like Type I diabetes during that time. And at the same time, there were all these news stories coming out, about young adults who were dying because they couldn't access insulin or afford insulin, or they were having lapses in health insurance. And so when I started to look at the impact of financial stress on diabetes outcomes during young adulthood, and so that was what my dissertation looked at. And we found, not surprisingly, that higher levels of financial stress negatively contribute to glycemic outcomes and negatively contribute to diabetes related quality of life. And so during my postdoc, I then wanted to look at developing an intervention to target financial stress during emerging adulthood for those with Type 1 diabetes, but, you know, it's really hard to develop an intervention when people are experiencing so many of these real life factors, like inability to afford medications or access medications. So I worked with a community advisory board to help me identify what are the key factors that drive financial stress during emerging adulthood and what can we do about it. And so, what my community advisory board during my postdoc identified, is that it actually relates a lot to health insurance literacy, and learning how to navigate the healthcare system. So I developed a pilot intervention during my postdoc, and we're now testing it larger scale right now.

Dr. Daniel Simon: Great. So, Congratulations, what exciting news on your 2.25 million dollar grant from the Leona M. and Harry B. Helmsley Charitable Trust. And this three year study aims to develop, refine, and test a Type 1 diabetes toolkit in collaboration, you know, with your community advisory board. And there, I guess, are four academic medical centers were selected to participate to ensure a diverse collection of patients. Can you tell us more about the research project and how it's going and your collaboration with these other institutions?

Julia Blanchette, PhD, RN: So this project, it's really exciting. It's really expanding what I did during my postdoc project. So we, have a new community advisory board and the Diabetes Link, which is a national non-profit organization that supports young adults living with diabetes; is actually partnering with University Hospitals to develop this toolkit with a community advisory board.

So, over the past year, we have worked with 12 community advisory board members and we actually have a toolkit developed, which is really exciting. And that was phase one. So phase two is to now test this toolkit in an RCT to look at its effectiveness on improving health insurance literacy, financial stress, diabetes related quality of life, and diabetes distress and glycemic outcomes over a one year period.

And so when we were thinking about testing it, since we're looking at health insurance literacy, first of all, we want to make sure that we are including individuals who really could use this toolkit the most. And part of it is we want to make sure that it applies pretty broadly, so not just to individuals living in Ohio. We want to make sure is applicable to young adults in other states as well.

And different states have different legislation for health insurance. So because of that, we thought it was best to make sure that we recruited from multiple states. And the way we chose the other academic partners is, they have really great success in recruiting and retaining young adults who are racially and ethnically diverse and on public health insurance in their research.

So, we're partnering with Children's Hospital, L.A., University of Florida, and then Children's Hospital of Philadelphia. And so we're really excited to be partnering with them for recruitment. They all kind of have different recruitment methods because they know their populations the best and they've been really successful in the past. And so we're just very thankful that they're recruitment sites for us.

Dr. Daniel Simon: That's terrific. So, you know, as a cardiologist, we clearly understand that tight control in Type 1 diabetes is associated with reduction in cardiovascular complications. And here we are sitting at Rainbow Babies and Children's Hospital, which is one of the primary leaders of the DCCT trial, which now over 30 years ago, still continuously funded, showed us that tight control reduces retinopathy complications, blindness in the eyes, kidney complications, cardiovascular. And we have all these new technologies, to improve control, everything from insulin pumps to continuous monitoring. How accessible are these new technologies to patients? I mean, clearly having continuous insulin delivery and continuous monitoring and, having it Bluetooth to your iPhone is amazing, but is it accessible to the vast majority of populations?

Julia Blanchette, PhD, RN: So, if you live in Ohio like us, we actually have amazing state legislation where, anyone with diabetes in Ohio can access a continuous glucose monitor. When I'm saying access, it means they can get it covered by insurance, no cost. That doesn't mean they necessarily start the technology, though. And so we have to take into account there's still bias when healthcare providers are recommending these technologies. There's insurance mediated bias. There's racial, ethnic bias. And we really need to do a good job of being cognizant that we are supporting all people with diabetes and we know that these technologies help all people with diabetes. Some people may need a little more support in starting these systems and staying on these systems. But I don't know if you have heard of Dr. Sarah McLeish, but she has a really awesome NIH funded study right now that's looking at improving technology uptake and usage in adolescents with Type 1 diabetes.

And so, they're using community health workers in that study. And so I think we do need to use additional resources in certain situations to improve uptake because we know that, those that are racially and ethnically diverse and on public health insurance still do not use automated insulin delivery systems and continuous glucose monitors at the same percentage as the rest of the population.

Dr. Daniel Simon : So your toolkit was really focused on emerging adults, adolescent young adults. Do you envision that this can be helpful, for instance, in the elderly? I would imagine as you hit the Medicare age and there could be gaps, especially in Part D, does your toolkit help?

Julia Blanchette, PhD, RN: So we have a Medicaid specific module for this toolkit. We don't have a Medicare specific module because since we're working with a young adult community advisory board, of course, they were more concerned about those without insurance and those on Medicaid due to the age. But I think in the future, we can use this model that I used, working with the community advisory board to develop this resource to target health insurance literacy for diabetes for this population. We can use the same type of model to develop a toolkit for older adults on Medicaid. And I think it is so incredibly needed.

There are so many gaps in care and in diabetes technology usage, when you get to that Medicaid transition, just like when you're transitioning to new insurance during young adulthood. So they experience a lot of the same types of disruptions in health insurance. And I think it is totally needed, but I would guess that maybe working with a community advisory board of older adults, they may not want a young adult micro video series to teach them, information about Medicare. They might say they want peer support or maybe something in person. So, I think it would be excellent to use the same type of community engaged model to develop them a resource because it's so needed.

Dr. Daniel Simon: That's terrific. So, we think about, Cleveland and Cuyahoga County. Obesity is now 35 plus percent of the population. Type 2 diabetes is epidemic. In fact, the decrease in cardiovascular mortality has now up-ticked for the first time in the last few years, because really of obesity and Type 2 diabetes. So, we have a lot more Type 2 diabetics. Has your work extended into Type 2 diabetes because clearly, these individuals face enormous financial challenges, especially for those who are trying, to get Ozempic, or Mounjaro. So, what about a toolkit for Type 2?

Julia Blanchette, PhD, RN: I think applying some of the resources we already have, to Type 2, will work really well. So there are certain videos that are on how to advocate for yourself, what to do if you can't get a medication that you need. And so some of the content of the toolkit that we just developed over the past year can apply to younger adults living with Type 2 diabetes. But I do think that we need to make another iteration of the toolkit specific, or not specific, but more broad, to all people living with diabetes.

The reason this one's specific to Type 1 is because the Helmsley Charitable Trust has a Type 1 portfolio, so they're really interested in improving Type 1 diabetes, but that doesn't mean that this does not apply to Type 2 diabetes. It certainly can and it's certainly needed.

Dr. Daniel Simon: Well, that's really terrific. So, you know, it's so inspiring to listen to you today and to look at one of our up and coming research stars in our nursing space. I know Michelle Hereford, our Chief Nursing Executive, would be so very proud of you. Thank you so much for taking time to speak with us today, Julia.

To learn more about research at University Hospitals, please visit uhhospitals.org/uhresearch. Thank you.

Tags: Clinical Research , Research , Diabetes , Pediatric Diabetes

US Lawmakers seek $32 billion to keep American AI ahead of China

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French police in Rouen shot dead an armed man who set fire to the city's synagogue, Interior Minister Gerald Darmanin and local officials said on Friday.

The U.S. Central Command said on Friday that trucks carrying humanitarian assistance began moving ashore via a temporary pier in Gaza at 9 a.m. local time (0600 GMT).

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Household Debt Rose by $184 Billion in Q1 2024; Delinquency Transition Rates Increased Across All Debt Types

NEW YORK — The Federal Reserve Bank of New York’s Center for Microeconomic Data today issued its Quarterly Report on Household Debt and Credit . The report shows total household debt increased by $184 billion (1.1%) in the first quarter of 2024, to $17.69 trillion. The report is based on data from the New York Fed’s nationally representative Consumer Credit Panel .

The New York Fed also issued an accompanying Liberty Street Economics blog post examining credit card utilization and its relationship with delinquency. The Quarterly Report also includes a one-page summary of key takeaways and their supporting data points.

“In the first quarter of 2024, credit card and auto loan transition rates into serious delinquency continued to rise across all age groups,” said Joelle Scally, Regional Economic Principal within the Household and Public Policy Research Division at the New York Fed. “An increasing number of borrowers missed credit card payments, revealing worsening financial distress among some households.”

Mortgage balances rose by $190 billion from the previous quarter and was $12.44 trillion at the end of March. Balances on home equity lines of credit (HELOC) increased by $16 billion, representing the eighth consecutive quarterly increase since Q1 2022, and now stand at $376 billion. Credit card balances decreased by $14 billion to $1.12 trillion. Other balances, which include retail cards and consumer loans, also decreased by $11 billion. Auto loan balances increased by $9 billion, continuing the upward trajectory seen since 2020, and now stand at $1.62 trillion.

Mortgage originations continued increasing at the same pace seen in the previous three quarters, and now stand at $403 billion. Aggregate limits on credit card accounts increased modestly by $63 billion, representing a 1.3% increase from the previous quarter. Limits on HELOC grew by $30 billion and have grown by 14% over the past two years, after 10 years of observed declines.

Aggregate delinquency rates increased in Q1 2024, with 3.2% of outstanding debt in some stage of delinquency at the end of March. Delinquency transition rates increased for all debt types. Annualized, approximately 8.9% of credit card balances and 7.9% of auto loans transitioned into delinquency. Delinquency transition rates for mortgages increased by 0.3 percentage points yet remain low by historic standards.

Household Debt and Credit Developments as of Q1 2024

*Change from Q4 2023 to Q1 2024 ** Change from Q1 2023 to Q1 2024

Flow into Serious Delinquency (90 days or more delinquent)

About the Report

The Federal Reserve Bank of New York’s Household Debt and Credit Report provides unique data and insight into the credit conditions and activity of U.S. consumers. Based on data from the New York Fed’s Consumer Credit Panel , a nationally representative sample drawn from anonymized Equifax credit data, the report provides a quarterly snapshot of household trends in borrowing and indebtedness, including data about mortgages, student loans, credit cards, auto loans and delinquencies. The report aims to help community groups, small businesses, state and local governments and the public to better understand, monitor, and respond to trends in borrowing and indebtedness at the household level. Sections of the report are presented as interactive graphs on the New York Fed’s  Household Debt and Credit Report web page  and the full report is available for download.

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Some mice may owe their monogamy to a newly evolved type of cell

What makes the oldfield mouse steadfastly monogamous throughout its life while its closest rodent relatives are promiscuous? The answer may be a previously unknown hormone-generating cell, according to a new study published online today in Nature from scientists at Columbia's Zuckerman Institute.

"The hormone from these cells was actually first discovered in humans many decades ago, but nobody really knew what it did," said Andrés Bendesky, MD, PhD, a principal investigator at Columbia's Zuckerman Institute. "We've discovered that it can promote nurturing in mice, which gives us an idea of what it might be doing in humans."

The new study investigated two species of mice. One is the most abundant mammal in North America -- the deer mouse ( Peromyscus maniculatus ), which ranges from Alaska to Central America. The other, the oldfield mouse ( Peromyscus polionotus ), lives in Florida and Georgia, and is a bit smaller, weighing in at roughly 13 grams compared with the deer mouse's 18 grams.

More than 100 years of previous research has shown that the mice species behave in strikingly different ways. Whereas the deer mouse is promiscuous -- even a single litter of pups can have four different fathers -- the oldfield mouse mates for life.

However, prior work also suggested these species are evolutionary siblings, based on similarities in their skulls, teeth and other anatomical features, as well as their genetics. To find out why these close mouse relatives behave so differently, the scientists examined their adrenal glands.

"This pair of organs, located in the abdomen, produces many hormones important for behavior," said Dr. Bendesky, who is also an assistant professor of ecology, evolution and environmental biology at Columbia University. "These include stress hormones such as adrenaline, but also a number of sex hormones."

The adrenal glands of these mice proved startlingly different in size. In adults, the adrenals of the monogamous mice are roughly six times heavier than those of promiscuous mice (after adjusting for differences in the body weight between the species).

"This extraordinary difference in the size of an internal organ between such closely related species is unprecedented," Dr. Bendesky said.

Genetic analysis of the adrenal cells revealed that one gene, Akr1c18 , saw far more activity in the monogamous mice than in the promiscuous rodents. The enzyme this gene encodes helps create a little-studied hormone known as 20⍺-OHP, which is also found in humans and other mammals.

The researchers observed that increasing 20⍺-OHP hormone boosted nurturing behavior in both mouse species. For instance, 17 percent of the promiscuous mice who were given the hormone groomed their pups and brought them back to their nests, whereas none behaved this way if not given the hormone.

"This marks the first time we found anything that could increase parental care in the promiscuous group," Dr. Bendesky said.

Normally these glands are divided into three zones. But the scientists discovered that the adrenals of the monogamous mice possessed a fourth zone.

"We called this the zona inaudita , which is Latin for 'previously unheard-of zone,' because no one has ever observed this type of cell in another animal," said Natalie Niepoth, PhD, a co-first author on the study who is now a senior scientist at Regeneron.

In zona inaudita cells, the researchers found that 194 genes, including Akr1c18 , were far more active compared with the same genes in other adrenal cells. Their analyses also identified key genes underlying the development and function of the zona inaudita in the oldfield mice.

This completely unheard-of structure apparently evolved rapidly. Genetic mutations accumulate in genomes at roughly predictable rates over time. By measuring the number of mutations distinguishing these species, the scientists estimated this novel cell type evolved within the past 20,000 years, "which is just an eyeblink when it comes to evolution," Dr. Bendesky said.

Much remains uncertain about what drives the evolution of monogamous behavior. One argument suggests that monogamy can increase the chances that parents will cooperate to care for their offspring, since fathers are more confident the young are theirs. This kind of teamwork can improve the chances that the progeny will survive, especially when resources are limited, Dr. Bendesky said. The newly found adrenal cells promote parenting behavior typical of monogamy, the researchers noted.

The new findings could provide insights when it comes to parenting behavior and challenges in humans, Dr. Niepoth suggested. For example, in mice, 20⍺-OHP is often converted into a compound very similar to the molecule allopregnanolone, which naturally occurs in humans and has been approved by the FDA as a drug to help treat the postpartum depression that people often experience after childbirth, Dr. Bendesky said.

"I hope that our study motivates further investigation into the link between 20⍺-OHP and parenting in humans," saidJennifer R. Merritt, PhD, a co-first author on the study and postdoctoral researcher in the Bendesky lab.. "We have so much to learn about the role this hormone plays in human parental behavior."

• Huntington's Disease
• Social Psychology
• Child Development
• New Species
• Genetically Modified
• Thyroid hormone
• Endocrinology
• House mouse
• Pituitary gland
• Cell (biology)

Story Source:

Materials provided by Columbia University . Note: Content may be edited for style and length.

Journal Reference :

• Natalie Niepoth, Jennifer R. Merritt, Michelle Uminski, Emily Lei, Victoria S. Esquibies, Ina B. Bando, Kimberly Hernandez, Christoph Gebhardt, Sarah A. Wacker, Stefano Lutzu, Asmita Poudel, Kiran K. Soma, Stephanie Rudolph, Andres Bendesky. Evolution of a novel adrenal cell type that promotes parental care . Nature , 2024; DOI: 10.1038/s41586-024-07423-y

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Diabetes UK funding highlights how partnership between the University of Nottingham and the University of Adelaide is addressing global challenges

Researchers from the University Nottingham, in partnership with colleagues from the University of Adelaide, have secured a £500,000 grant from Diabetes UK as part of a £1m research project using pioneering neuroimaging techniques to identify how the tongue and digestive tract sense sweet stimuli and integrate this signalling in the brain of people with type 2 diabetes.

It is hoped that the findings will inform the development of new personalised dietary and drug treatments for people living with prediabetes or type 2 diabetes. 

The award is among successes being celebrated at an event to mark the second year of the Adelaide-Nottingham Alliance , a strategic global partnership between the University of Adelaide and the University of Nottingham.

Vice-Chancellors host symposium

The University of Nottingham’s Vice-Chancellor, Professor Shearer West CBE, is hosting a symposium at University Park on Monday 13 May 2024, when staff, students and researchers from both universities will meet to share ideas and spark new collaborations.

Dr Sally Eldeghaidy, of the School of Biosciences, the University of Nottingham, and Professor Amanda Page of the School of Biomedicine, the University of Adelaide, will update on the research supported by Diabetes UK at the symposium.

The Adelaide-Nottingham team will use a sophisticated brain imaging technique, functional magnetic resonance imaging (fMRI), to identify how sweet sensing is communicated from the tongue and gut to the brain in individuals with and without type 2 diabetes or prediabetes.

Habitually eating too much sugar can lead to weight gain, and carrying too much weight is one of a complex mix of factors that increase the risk of type 2 diabetes. The research will explore sensitivity to sweet flavours in people with prediabetes or type 2 diabetes and how this relates to reward processing in the brain, dietary intake and regulation of blood glucose levels.

The fMRI research will take place at the Sir Peter Mansfield Imaging Centre, which is to house the most  powerful MRI scanner in the UK , creating a new national facility for ultra high-resolution imaging.

The 11.7 Tesla scanner will sit alongside already world-leading MRI facilities at the Sir Peter Mansfield Imaging Centre. Expertise at University of Adelaide, including research by Assistant Professor Richard Youngs on intestinal sweet sensing in type 2 diabetes and innovations of Professor Page on animal models of gut-brain axis to nutrition, was also instrumental in securing the Diabetes UK grant.

The potential benefits of this research are profound. By understanding the neuronal pathways of sweet sensing and metabolism and how this is affected in people with type 2 diabetes, we can contribute to more tailored dietary advice that could improve the quality of life and health outcomes in people with prediabetes or type 2 diabetes. Dr Sally Eldeghaidy, School of Biosciences

She added: "Additionally, our findings may influence future guidelines on food manufacturing and public health policies related to dietary sugars, potentially impacting a wide spectrum of the population, from prevention strategies in at-risk groups to management approaches in diagnosed individuals.”

Dr Lucy Chambers, Head of Research Communications at Diabetes UK, said: “We are delighted to fund this innovative research at the University of Nottingham and the University of Adelaide that will build understanding of type 2 diabetes and potentially inform the development of new treatments that could benefit millions of people in the UK, Australia and across the world.”

Collaborative research themes

Dr Eldeghaidy and Professor Page are respectively the UK and Adelaide leads of Intelligent Health, one of the collaborative research themes of the Adelaide-Nottingham Alliance. The symposium will also hear from researchers and PhD students working across the Alliance’s other leading areas of collaborative research.

Global Food Systems researchers will highlight projects including Plants for Space, where University of Nottingham researchers are playing a key role in the University of Adelaide-led Centre for Excellence in Plants for Space , which aims to create on-demand, zero-waste, high-efficiency plants and plant products to feed future space missions, while applying this science to sustainable solutions on Earth. 

The symposium will also highlight research into building Sustainable Futures, and preview the First International Conference of Net Zero Carbon Built Environment, at the University of Nottingham in June 2024.

Professor West will welcome her counterpart, Professor Peter Høj AC, Vice-Chancellor and President of the University of Adelaide, at the symposium.

Decades of friendship

She said, “Professor Høj and I are both excited by the potential of our universities’ strategic global partnership, which has grown out of decades of friendship and collaboration between Adelaide and Nottingham. We will hear researchers from Adelaide and Nottingham talk about their collaborations on exciting projects, and how by sharing our world-leading expertise in food, sustainability, and health research, we will have even greater impact on these global challenges.”

Professor Høj added: “Our alliance with Nottingham is the most significant between two universities in the UK and Australia. We each have a global reputation and a shared passion for innovation, and for training the research leaders and change-makers of tomorrow. By encouraging collaboration between our researchers, we deepen the impact and reach of our discoveries, and by giving our students the opportunity to spend significant time in Adelaide or Nottingham we enrich their studies and encourage them develop a global mindset.”

As well as the funding award from Diabetes UK, highlights of the Adelaide-Nottingham Alliance’s first year include:

• 29 enrolments on the Alliance’s Joint PhD programme
• Collaborated on 6 externally funded research projects, attracting more than $50m/£24m in funding
• $187,000 / £98,000 seed funding to explore and kick-start new collaborations in research and education

For more information on the Adelaide-Nottingham Alliance , contact David Ouchterlonie, Associate Director of Global Engagement at the University of Nottingham, via email at [email protected] 

Notes to editors:

About the University of Nottingham

Ranked 32 in Europe and 16th in the UK by the QS World University Rankings: Europe 2024 , the University of Nottingham is a founding member of the Russell Group of research-intensive universities. Studying at the University of Nottingham is a life-changing experience, and we pride ourselves on unlocking the potential of our students. We have a pioneering spirit, expressed in the vision of our founder Sir Jesse Boot, which has seen us lead the way in establishing campuses in China and Malaysia - part of a globally connected network of education, research and industrial engagement.

Nottingham was crowned Sports University of the Year by The Times and Sunday Times Good University Guide 2024 – the third time it has been given the honour since 2018 – and by the Daily Mail University Guide 2024 .

The university is among the best universities in the UK for the strength of our research, positioned seventh for research power in the UK according to REF 2021 . The birthplace of discoveries such as MRI and ibuprofen, our innovations transform lives and tackle global problems such as sustainable food supplies, ending modern slavery, developing greener transport, and reducing reliance on fossil fuels.

The university is a major employer and industry partner - locally and globally - and our graduates are the second most targeted by the UK's top employers, according to The Graduate Market in 2022 report by High Fliers Research.

We lead the Universities for Nottingham initiative, in partnership with Nottingham Trent University, a pioneering collaboration between the city’s two world-class institutions to improve levels of prosperity, opportunity, sustainability, health and wellbeing for residents in the city and region we are proud to call home.

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