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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
• Get Data, Get Help!

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 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

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The contents of these databases are only partially included in HOLLIS. To make sure you're really seeing everything, you need to search in multiple places. Use Database Search to identify and connect to the best databases for your topic.

In addition to digital content, you will find specialized search engines used in specific scholarly domains.

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The top list of academic research databases

2. Web of Science

5. ieee xplore, 6. sciencedirect, 7. directory of open access journals (doaj), get the most out of your academic research database, frequently asked questions about academic research databases, related articles.

Whether you are writing a thesis , dissertation, or research paper it is a key task to survey prior literature and research findings. More likely than not, you will be looking for trusted resources, most likely peer-reviewed research articles.

Academic research databases make it easy to locate the literature you are looking for. We have compiled the top list of trusted academic resources to help you get started with your research:

Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Besides searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator .

• Coverage: 90.6 million core records
• References: N/A
• Discipline: Multidisciplinary
• Access options: Limited free preview, full access by institutional subscription only
• Provider: Elsevier

Web of Science also known as Web of Knowledge is the second big bibliographic database. Usually, academic institutions provide either access to Web of Science or Scopus on their campus network for free.

• Coverage: approx. 100 million items
• References: 1.4 billion
• Access options: institutional subscription only
• Provider: Clarivate (formerly Thomson Reuters)

PubMed is the number one resource for anyone looking for literature in medicine or biological sciences. PubMed stores abstracts and bibliographic details of more than 30 million papers and provides full text links to the publisher sites or links to the free PDF on PubMed Central (PMC) .

• Coverage: approx. 35 million items
• Discipline: Medicine and Biological Sciences
• Access options: free
• Provider: NIH

For education sciences, ERIC is the number one destination. ERIC stands for Education Resources Information Center, and is a database that specifically hosts education-related literature.

• Coverage: approx. 1.6 million items
• Discipline: Education
• Provider: U.S. Department of Education

IEEE Xplore is the leading academic database in the field of engineering and computer science. It's not only journal articles, but also conference papers, standards and books that can be search for.

• Coverage: approx. 6 million items
• Discipline: Engineering
• Provider: IEEE (Institute of Electrical and Electronics Engineers)

ScienceDirect is the gateway to the millions of academic articles published by Elsevier, 1.4 million of which are open access. Journals and books can be searched via a single interface.

• Coverage: approx. 19.5 million items

The DOAJ is an open-access academic database that can be accessed and searched for free.

• Coverage: over 8 million records
• Provider: DOAJ

JSTOR is another great resource to find research papers. Any article published before 1924 in the United States is available for free and JSTOR also offers scholarships for independent researchers.

• Coverage: more than 12 million items
• Provider: ITHAKA

Start using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with PubMed and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Beside searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator .

PubMed is the number one resource for anyone looking for literature in medicine or biological sciences. PubMed stores abstracts and bibliographic details of more than 30 million papers and provides full text links to the publisher sites or links to the free PDF on PubMed Central (PMC)

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Creating a Corporate Social Responsibility Program with Real Impact

• Emilio Marti,
• David Risi,
• Eva Schlindwein,
• Andromachi Athanasopoulou

Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

• EM Emilio Marti is an associate professor at the Rotterdam School of Management, Erasmus University. His research focuses on corporate sustainability with a specific focus on sustainable investing.”
• DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
• ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
• AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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Sea horse research gets a boost from volunteer citizen-scientists

Members of the public are helping to advance research on sea horses, the tiny fish that can be found in coral reefs, shallow waters and estuaries around the world, according to a study.

When researchers looked at the results of public contributions to the iSeahorse science project between 2013 and 2022, they found the community effort enabled scientific advances in the field.

Citizen contributions provided new information on 10 of 17 sea horse species with data once considered deficient and helped update knowledge about the geographic distribution of nine species, researchers wrote in the Journal of Fish Biology . Some of the observations even helped scientists better understand when and how sea horses breed.

Founded in 2013, the iSeahorse project asks the public to record sea horse sightings and observe the animals’ behavior. According to the project website , iSeahorse has amassed about 11,000 observations from more than 1,900 contributors to date.

Overall, the researchers were able to validate 7,794 of the observations from 96 countries and 35 sea horse species. The volunteer observers even noted rare species that traditional monitoring probably would not detect, they write.

“Seahorses are very much the sort of fascinating species that benefit from community science, as they are cryptic enough to make even formal research challenging,” Heather Koldewey, the project’s co-founder and the lead on the Bertarelli Foundation’s marine science program, said in a news release . Koldewey, who co-wrote the study, said the new findings underscore the importance of community science efforts in raising awareness and achieving conservation goals.

Want to get involved? Visit https://projectseahorse.org/iseahorse/ to learn more.

• Sea horse research gets a boost from volunteer citizen-scientists March 30, 2024 Sea horse research gets a boost from volunteer citizen-scientists March 30, 2024
• Climate change threatens snow cover at ski destinations worldwide March 23, 2024 Climate change threatens snow cover at ski destinations worldwide March 23, 2024
• Scientists discover fossilized remnants of earliest known forest March 16, 2024 Scientists discover fossilized remnants of earliest known forest March 16, 2024

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

• Observational
• Experimental
• Descriptive
• Retrospective
• Prospective
• Cross-sectional
• Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

• Observational studies: cohort, case control and cross- sectional research
• Interventional research: quasi-experimental and clinical research
• Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
• Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
• Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

• Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

• Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
• Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

• H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
• H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

• Type-I (α) and type-II (β) error levels
• Difference between groups (d-difference) and effect size (ES)
• Distribution ratio of groups
• Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

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CJRP Co-Directors Dr. Bryanna Fox (left) and Dr. Edelyn Verona (right) at the celebratory launch of their new book, “Routledge Handbook of Evidence-Based Criminal Justice Practices.” (Photo by Corey Lepak)

Center for Justice Research and Policy directors release new book on evidence-based criminal justice practices

• Anna Mayor, USF College of Arts and Sciences
• March 28, 2024

Accomplishments , Community Engagement , Events , Research

The University of South Florida’s  Center for Justice Research and Policy  (CJRP)  hosted a book release celebration in February honoring the work of CJRP co-directors Dr. Edelyn Verona , professor in the  Department of Psychology  in the College of Arts and Sciences and Dr. Bryanna Fox , professor in the Department of Criminology in the College of Behavioral and Community Sciences.

Panelists from various disciplines joined the conversation about the work of the CJPR and the importance of the new book. (From left) Mateus Rennó Santos, Assistant Professor in the Department of Criminology; Chae Jaynes, Associate Professor in the Department of Criminology; Christine Ruva, Professor and Chair of the Department of Psychology at USF Sarasota Manatee; Ali Andrew Shakoor, Senior Attorney for the Capital Collateral Regional Counsel; Michael T. Baglivio, Vice President of Research & Development at Youth Opportunity; and Joan Reid, Professor in the Department of Criminology. (Photo by Corey Lepak)

Their new book, “Routledge Handbook of Evidence-Based Criminal Justice Practices,” aims to provide evidence-based solutions to the important questions in today’s criminal justice system.

The CJRP, which is the first of its kind in Florida and approved by the Board of Governors as a state-level center, consists of an interdisciplinary team of scholars and practitioners focused on the study of crime, violence, and criminal and social justice policy, with a goal of applying research to enhance the evidence base of policing, corrections and court practices.

“We thought about bringing something to the table that’s concrete and to have a physical manifestation of the goals that we at the CJRP have in mind,” Verona explained.

Fox explained the connection between researchers and practitioners is an area that is often diluted, but through partnering with their colleagues who are highly involved with the work of the CJRP, such as police chiefs, judges, corrections officers, juvenile justice system employees, and more, they were able to write a book in a way that was practical and applicable for those working in the field.

The book provides not only a practice perspective, but insight from research, Fox emphasized.

“This is a unique and wonderful edition, and we are so appreciative of our contributors,” Fox said.

“This is the only book of its kind that addresses all levels of the system,” Verona added. “We don’t only talk about once people are wrapped in the system, but how do we prevent that from happening in the first place?”

Learn more about the CJRP and opportunities to support the work of the center.

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CAS Chronicles is the monthly newsletter for the University of South Florida's College of Arts and Sciences, your source for the latest news, research, and events at CAS.

Physics-based predictive tool will speed up battery and superconductor research

From lithium-ion batteries to next-generation superconductors, the functionality of many modern, advanced technologies depends on the physical property known as intercalation. Unfortunately, it's difficult to identify in advance which of the many possible intercalated materials are stable, which necessitates a lot of trial-and-error lab work in product development.

Now, in a study recently published in ACS Physical Chemistry Au , researchers from the Institute of Industrial Science, The University of Tokyo, and collaborating partners have devised a straightforward equation that correctly predicts the stability of intercalated materials. The systematic design guidelines enabled by this work will speed up the development of upcoming high-performance electronics and energy-storage devices.

To appreciate the research team's achievement, we need to understand the context of this research. Intercalation is the reversible insertion of guests (atoms or molecules) into hosts (for example, 2D-layered materials). The purpose of intercalation is commonly to modify the host's properties or structure for improved device performance, as seen in, for example, commercial lithium-ion batteries. Although many synthetic methods are available for preparing intercalated materials, researchers have had no reliable means of predicting which host-guest combinations are stable. Therefore, much lab work has been needed to devise new intercalated materials for imparting next-generation device functionalities. Minimizing this lab work by proposing a straightforward predictive tool for host-guest stability was the goal of the research team's study.

"We are the first to develop accurate predictive tools for host-guest intercalation energies, and the stability of intercalated compounds," explains Naoto Kawaguchi, lead author of the study. "Our analysis, based on a database of 9,000 compounds, uses straightforward principles from undergraduate first-year chemistry."

A particular highlight of the work is that only two guest properties and eight host-derived descriptors were necessary for the researchers' energy and stability calculations. In other words, initial 'best guesses' weren't necessary; only the underlying physics of the host-guest systems. Furthermore, the researchers validated their model against nearly 200 sets of regression coefficients.

"We're excited because our regression model formulation is straightforward and physically reasonable," says Teruyasu Mizoguchi, senior author. "Other computational models in the literature lack a physical basis or validation against unknown intercalated compounds."

This work is an important step forward in minimizing the laborious lab work that's typically required to prepare intercalated materials. Given that many current and upcoming energy storage and electronic devices depend on such materials, the time and expense required for corresponding research and development will be minimized. Consequently, products with advanced functionalities will reach the market faster than what has been previously possible.

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Materials provided by Institute of Industrial Science, The University of Tokyo . Note: Content may be edited for style and length.

Journal Reference :

• Naoto Kawaguchi, Kiyou Shibata, Teruyasu Mizoguchi. Unraveling the Stability of Layered Intercalation Compounds through First-Principles Calculations: Establishing a Linear Free Energy Relationship with Aqueous Ions . ACS Physical Chemistry Au , 2024; DOI: 10.1021/acsphyschemau.3c00063

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Writing a Research Paper Introduction | Step-by-Step Guide

Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.

The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your topic and get the reader interested
• Provide background or summarize existing research
• Position your own approach
• Detail your specific research problem and problem statement
• Give an overview of the paper’s structure

The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.

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Table of contents

Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.

The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.

The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.

For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:

A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:

Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.

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This part of the introduction differs depending on what approach your paper is taking.

In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.

Argumentative paper: Background information

After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.

Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .

Empirical paper: Describing previous research

For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.

This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.

Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.

The next step is to clarify how your own research fits in and what problem it addresses.

Argumentative paper: Emphasize importance

In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.

Empirical paper: Relate to the literature

In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:

• What research gap is your work intended to fill?
• What limitations in previous work does it address?
• What contribution to knowledge does it make?

You can make the connection between your problem and the existing research using phrases like the following.

Now you’ll get into the specifics of what you intend to find out or express in your research paper.

The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).

Argumentative paper: Thesis statement

The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.

Empirical paper: Research question and hypothesis

The research question is the question you want to answer in an empirical research paper.

Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.

A research question can be framed either directly or indirectly.

• This study set out to answer the following question: What effects does daily use of Instagram have on the prevalence of body image issues among adolescent girls?
• We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls.

If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.

For example, the following hypothesis might respond to the research question above:

The final part of the introduction is often dedicated to a brief overview of the rest of the paper.

In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.

If included, the overview should be concise, direct, and written in the present tense.

• This paper will first discuss several examples of survey-based research into adolescent social media use, then will go on to …
• This paper first discusses several examples of survey-based research into adolescent social media use, then goes on to …

Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.

The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

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Selected Projects Will Address Energy Security Research and Training Needs for Each Unique Region

WASHINGTON, D.C. -- Today, the U.S. Department of Energy (DOE) announced $15 million in funding to establish six university-based electric power cybersecurity centers that will foster collaborations across the energy sector to address gaps in energy security research and provide cybersecurity education programs. Each university, selected by the DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), will partner with energy sector owners and operators, vendors, and DOE National Laboratories to conduct innovative cybersecurity research and develop cybersecurity trainings that will meet the needs of the energy workforce in their regions. The university-based cybersecurity centers directly align with the Biden-Harris Administration’s commitment to bolstering secure energy infrastructure and a skilled energy workforce to tackle the existing and emerging cyber threats facing the sector. 

"This investment in university-based cybersecurity centers will enable us to simultaneously grow the U.S. cyber workforce and build the expertise we need to take on the evolving cyber threats to our nation's energy systems," said Director of CESER, Puesh M. Kumar. "The U.S. competitive advantage has always depended on cutting-edge research and a high-skilled workforce. Through these projects, we are advancing our economic and national security." 

The industry partnerships will help the university-based cybersecurity centers research cybersecurity capabilities that consider the distinctive characteristics of each region's electricity system, network of infrastructure, and workforce skills. Their research will combine multidisciplinary expertise, such as power system engineering and cybersecurity, to find solutions that will reduce the risk of power disruption resulting from a cyber-incident in an energy delivery system. The selected universities, listed below, will conduct the following research projects at their new cybersecurity centers: 

• The University of Connecticut (Mansfield, Connecticut) will develop tools to isolate and mitigate the effects of cyber attacks on distributed energy resources (DER) to quickly restore devices to operational states. The project will use data from across the Northeast region’s urban, suburban, and rural environments, making this work scalable to a variety of system operators and utilities. Project title: CyberCARED: Northeast University Cybersecurity Center for Advanced and Resilient Energy Delivery 
• The Iowa State University (Ames, Iowa) will focus on improving the security and resilience of the distribution grid that includes DER and microgrids, including real-time cyber situational awareness for distribution management systems. Several project partners bring expertise in rural energy issues, which is key for implementing next-generation grid infrastructure. Project title: CyDERMS: Center for Cybersecurity & Resiliency of DERs and Microgrids-integrated Distribution Systems    
• The University of Pittsburgh (Pittsburgh, Pennsylvania) will use digital twins to evaluate the effectiveness of protections for cyber attacks and assess the impact of cybersecurity compromises. Their diverse project partners will help focus the project on existing gaps in cybersecurity research. Project title: University of Pittsburgh Cyber Energy Center   
• The Illinois Institute of Technology (Chicago, Illinois) will address cybersecurity issues in DER and microgrids and understand how system operators can contribute to efficient cyber attack detection and response. They plan to distribute their findings through workshops, symposia, and trainings. Project title: 2MC: Midwest Center for Microgrid Cybersecurity   
• The Texas Tech University (Lubbock, Texas) will develop a single framework that addresses the various stages of cyber-physical attacks such as attack detection, prevention, impact analysis, and recovery plans. It will focus on DER integration on rural utilities within the Texas power grid. Project title: A Clean Energy and Rural Electric Industry-focused University Cyber-Physical Security Center   
• The Florida International University (Miami, Florida) will explore the moving target defense (MTD) technique as a way to prevent cyber attacks by “hiding” the system from the adversary. This could be especially useful in systems with communications channels that can easily be rerouted or switched. Project title: Center for Agile and Intelligent Power Systems (CAIPS): Cybersecurity Research, Development, and Workforce Training   

In addition to the research projects, the centers will also host a collection of cybersecurity education programs to equip energy professionals with the knowledge to safeguard the energy sector’s critical infrastructure from cybersecurity threats. 

For more information about DOE’s efforts to secure and protect America’s energy sector, visit www.energy.gov/ceser .

Computer Security Group

ZenHammer: Rowhammer Attacks on AMD Zen-based Platforms

Our work shows that it is possible to trigger Rowhammer bit flips on DDR4 devices on AMD Zen 2 and Zen 3 systems despite deployed TRR mitigations. This result proves that AMD systems are equally vulnerable to Rowhammer as Intel systems , which greatly increases the attack surface, considering today’s AMD market share of around 36% on x86 desktop CPUs. This poses a significant risk as DRAM devices in the wild cannot easily be fixed, and previous work showed that Rowhammer attacks are practical, for example,  in the browser ,  on smartphones ,  across VMs , and even  over the network . Furthermore, we show that ZenHammer can trigger Rowhammer bit flips on a DDR5 device for the first time .

Our results show a high number of bit flips on Zen 2 and Zen 3 systems. Also, devices are more vulnerable on Zen 3 than Coffee Lake, simplifying exploitation. We can build the page table, RSA public key corruption, and sudo exploits from prior work on 7/6/4 of these devices, taking, on average, just 164/267/209 seconds.

How did you do it?

We reverse-engineered the secret DRAM address functions by adopting the DRAMA technique for AMD systems. We found that the timing routine must be changed for more reliable results. We make the key observation that a physical offset must be applied to physical addresses before recovering the DRAM address functions due to system address remapping (see Figure 1 ). This allows us to recover the address functions completely. However, using these address functions gives us very few bit flips on 5 and 0 of 10 devices on AMD Zen 2 and Zen 3, respectively, as we show in Table 1 .

We started by looking at the refresh synchronization , which previous work (e.g., SMASH , Blacksmith ) showed to be important for triggering bit flips. We demonstrate that continuous timing measurements using non-repeating rows are effective for precise and reliable refresh synchronization on AMD. The result in Listing 1 shows our improved refresh synchronization method.

We found out that the activation rate of non-uniform Rowhammer patterns on AMD Zen+/3 systems is significantly lower than on Intel Coffee Lake (see Figure 2 ). To investigate this, we conducted a series of experiments to find the optimal hammering instruction sequence . Our results showed that regular loads ( MOV ) with CLFLUSHOPT for flushing aggressors from the cache, issued immediately after accessing an aggressor (“scatter” style), is optimal. It further revealed that, unlike on Zen 2, explicit fencing after flushing is not required on Zen 3.

We further investigated how different fence types and fence scheduling policies affect Rowhammer patterns on AMD Zen systems. For this, we came up with six different pattern-aware and cache-avoiding fence scheduling policies (see Table 2 ) and tested them on our devices for 6 hours each to determine the device’s optimal policy. We found that on most devices on Zen 2 the policy SP_none is optimal while SP_pair is in most cases better suited on Zen 3.

How bad is it?

For our evaluation , we considered a test pool of 10x DDR4 DRAM devices covering the three major manufacturers (Samsung, Micron, SK Hynix). We let our ZenHammer fuzzer run for 3 hours with each fence type (mfence, sfence) and fence scheduling policy. After each run, the best pattern is determined by using a minisweep over 4 MiB with all found patterns. Thereafter, we swept the best pattern of the best policy over a contiguous memory area of 256 MB and reported the number of bit flips. The results in Table 3 show that our ZenHammer fuzzer is able to trigger bit flips on 7 (Zen 2) and 6 (Zen 3) of 10 DDR4 DRAM devices.

We evaluated the exploitability of these bit flips based on three attacks from previous work : (i) an attack targeting the page frame number of a page table entry (PTE) to pivot it to an attacker-controlled page table page, (ii) an attack on the RSA-2048 public key that allows recovering the associated private key used to authenticate to an SSH host, (iii) and an attack on the password verification logic of the sudoers.so library that enables gaining root privileges. We report the results in Table 4 .

What about DDR5?

Finally, we tried ZenHammer on DDR5 by reverse engineering the DRAM functions on AMD Zen 4 and evaluating ten DDR5 devices. Out of these ten devices, ZenHammer could trigger ~42k flips on one device. This is the first public report of DDR5 bit flips on commodity systems in the wild . However, given that ZenHammer could not trigger flips on nine out of ten devices, we conclude that more research is necessary to find more effective patterns for DDR5 devices.

Further Information

For full details and more information about our work, please have a look at our  paper , which is to appear at  USENIX Security 2024 in August 2024. Our ZenHammer fuzzer is available on Github .

Following, we provide answers to the most frequently asked questions about our work.

Why has nobody paid any attention to AMD systems before? We believe that AMD received little attention as the original work on Rowhammer by Kim et al. showed a much higher number of bit flips on Intel systems and subsequent work also focused primarily on Intel. Further, there is much more known about the microarchitecture of Intel CPUs compared to AMD CPUs.

Are there any DDR4 devices that are safe? Not likely. We could not trigger any bit flips on 3 and 4 of our 10 devices on Zen 2 and Zen 3, respectively. However, given that we triggered only a few bit flips on them on Intel Coffee Lake as well, we think that tuning the fuzzer further may expose bit flips on these devices too.

Why does your evaluation consider ten devices only? Because we have a limited number of AMD Zen 2/3 machines in our lab and some of our experiments took a long time, we have decided to reduce the number of devices to ten. We made sure that our randomly chosen subset includes devices from all three major DRAM vendors.

How can I check whether my DRAM is vulnerable? The code of our ZenHammer fuzzer, which you can use to assess your DRAM device for bit flips on AMD Zen 2/3/4 CPUs, is available on  GitHub .

Why has JEDEC not fixed this issue yet? By now we know, thanks to a better understanding, that solving Rowhammer is hard but not impossible, as we show in our previous work ProTRR and REGA . We believe that there is a lot of bureaucracy inside JEDEC that makes it difficult to properly address Rowhammer.

What about DIMMs with Error Correction Codes (ECC)? Previous work  on DDR3 showed that ECC cannot provide protection against Rowhammer. Due to the even larger number of bit flips in current DDR4 devices, we believe that ECC cannot provide complete protection against Rowhammer but just makes exploitation harder.

What if my system runs with a double refresh rate? Besides an increased performance overhead and power consumption, previous work (e.g.,  Mutlu et al.  and  Frigo et al. ) showed that doubling the refresh rate is a weak solution not providing complete protection.

Responsible Disclosure

Rowhammer is a known industry-wide issue, and we did not see a need to go through the typical disclosure process. Nonetheless, we informed AMD on February 26, 2024, and at their request, we did not publicly disclose the issue until March 25, 2024. This page was briefly online by mistake on March 21.

Acknowledgments

This research was supported by the Swiss National Science Foundation (SNSF) under NCCR Automation, grant agreement 51NF40_180545, by the Swiss State Secretariat for Education, Research and Innovation under contract number MB22.00057 (ERC-StG PROMISE), and by a Microsoft Swiss JRC grant.