research and design center

Center for Design Research (CDR) is a community of scholars focused on understanding and augmenting engineering design innovation and design education.

Founded in 1984, CDR is a nexus for PhD students and researchers collaborating in the realm of design thinking, robotics, rehabilitative technologies, engineering design education, STEM education, neurodesign and business innovation.

The broad research objective of the Assistive Robotics and Manipulation Lab, directed by Professor Monroe Kennedy , is to develop robots that improve everyday life by anticipating and acting on the needs of human counterparts.  Our primary focus is collaborative robotic assistants (often mobile manipulators and humanoids) with the goal of deployment for service tasks that may be highly dynamic and require dexterity, situational awareness, and human-robot collaboration.

The Biomimetics and Dexterous Manipulation Lab (BDML), led by Professor  Mark Cutkosky , conducts research activities that include modeling and control of dextrous manipulation with robotic and teleoperated hands; force and tactile feedback in telemanipulation and virtual environments; and design and control of compliant "biomimetic" robots with embedded sensors and actuators. Towards our goal of more human centered computing, we believe that interaction must be grounded in the physical world and leverage our innate abilities for spatial cognition and dexterous manipulation with our hands.

Researchers in the Collaborative Haptics and Robotics in Medicine Lab (CHARM Lab), led by Professor  Allison Okamura , design and study haptic systems using both analytical and experimental approaches. This research has applications in robot-assisted surgery, simulation and training, rehabilitation, exploration of hazardous or remote environments, enabling technologies, manufacturing, design, mobile computing, and education. 

The Designing Education Lab (DEL), led by Professor Emerita  Sheri Sheppard , investigates a broad range of engineering education topics, from the persistence of students and alumni in engineering fields to the impact of exposure to entrepreneurship on engineering students' career interests.

The Stanford SHAPE Lab, directed by Professor Sean Follmer , develops advanced technologies in robotics, mechatronics, and sensing to create interactive, dynamic physical 3D displays and haptic interfaces that allow 3D information to be touched as well as seen. We are specifically interested in using these novel interfaces to support richer remote collaboration, computer-aided design, education, and interfaces for people with visual impairments.

“ How do we educate a new kind of engineer? ... ”

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Centers and Labs

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The School of Architecture and Planning is home to five internationally-regarded research centers through which students learn, work and collaborate with faculty and community partners.

Our faculty also hold leadership roles on university-wide research centers with transdisciplinary missions. This research infrastructure provides key applied learning, professional engagement and scholarship development opportunities for students across the disciplines of architecture, urban planning and environmental design.

Transdisciplinary research programs with architecture and planning leadership

• Research & Design Center

The BVR Research + Design Center—which opened in 2017—is a state-of-the-art building located at the center of our campus that provides our students from 6th to 12th grades the resources and support to create, innovate, explore, and build. The space includes two audio and video recording studios; a design workshop; presentation spaces; a materials library filled with an ever-evolving collection of “stuff” (fabrics, resins, malleable metals, wood, etc.), and an entire floor dedicated to research.

All students have access to the Research + Design Center (aka R+D Center), and we see a wide variety of classes using the space and the tools.

IN THE CLASSROOM On any given day, an 8th grade English class might be creating podcasts about the novel they’re reading, a 10th grade Biology class might be building models of the digestive system out of recycled materials, and an Entrepreneurship class might be having a Zoom conversation with a recent Beaver alum who just started their own business.

IN CLUBS & AFTERNOON ACTIVITIES We also offer a wide range of co-curricular activities where students can teach a robotic submarine to navigate an underwater obstacle course, learn how to hydro dip their sneakers, or write code to create their own hacks for Minecraft.

PASSION PROJECTS In addition to all of the projects happening in classes, students are frequently working on their own passion projects. You’ll often see students visiting the R+D Center during their free time to build battery-powered roller skates, laser-cut gifts for their grandparents, and more.

Tools & Resources

Here is a sampling of the tools, technology, and resources available to students in the R+D Center:

Research & Library Tools

• OverDrive subscription (ebooks and audiobooks)
• Full access to the New York Times and other news sources
• 50+ research databases covering history, science, and culture
• R+D Center website

Design Tools

• Two laser cutters
• Six 3D printers
• Jewelry station
• Two recording studios
• Four Oculus VR headsets
• Two vacuformers
• Prototyping supplies (hot glue guns, duct tape, sticky notes, etc.)
• Materials library (where students have access to everything from fabric samples to salvaged stereo equipment)
• Software (OnShape for 3D modeling, Adobe Photoshop for image editing, p5 for coding, etc.)

Examples of work from the R+D Center

• The Paper Instrument Project Project, Year Two
• Making the Podcast: Behind the scenes of the BVR Centennial mini-series
• Middle School students search for life beyond Earth
• Annual 9th Grade Physics Carnival
• RoboSub Competitions

Read more about our mindset behind technology .

• The New Basics
• DEIJ work in the Middle School
• DEIJ work in the Upper School
• Affinity Groups
• Head of School
• Board materials & Information
• Meet the Beaver Board of Trustees
• BVR Job Openings
• Professional Development Programs
• The DAM Store
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College of Design

Research Centers and Labs

Our six research centers focus on a range of crucial issues, many of which impact our daily lives. Our topics include the development of cities and regions, the healthcare environment, helping those with physical limitations, and new ways to create music.

Center for Inclusive Design and Innovation

The Center for Inclusive Design and Innovation (CIDI) is recognized as a leader for services and research in accessibility. We are dedicated to an inclusive society through innovations in assistive and universally designed technologies, with a goal of addressing the full range of needs for accessibility. We are committed to the promotion of technological innovation and development of user-centered research, products, and services for individuals with disabilities. CIDI was created by a merger of the Center for Assistive Technology and Environmental Access (CATEA) and AMAC Accessibility.

Director: Maureen Linden, PhD

Digital Building Laboratory

The  Digital Building Laboratory 's purpose is to develop a strong research and development link between the building industry and the building research-related capabilities of Georgia Tech to improve the innovation cycle.

Director: Russell Gentry

Center For Spatial Planning Analytics and Visualization

The  Center for Spatial Planning Analytics and Visualization , formerly the Center for Geographic Information Systems, is a leader in the development and application of geo-spatial technologies for nearly 20 years. With a strong track record of interdisciplinary research collaborations at Georgia Tech and across the nation, the Center has helped local, state, and national governments, not-for-profits, and private enterprises in planning, maintaining, and evaluating critical infrastructure for sustainable urban growth.

Director: Subhro Guhathakurta

Center For Quality Growth and Regional Development

The  Center for Quality Growth and Regional Development  produces, disseminates, and helps implement new ideas and technologies that improve the theory and practice of quality growth. We help society achieve a sustainable, equitable, superior quality of life through sound planning, policy, and design.

Director: Arthi V. Rao

Center for Music Technology

The  Georgia Tech Center for Music Technology  is an international center for creative and technological research in music, focusing on the development and deployment of innovative musical technologies that transform the ways in which we create and experience music. Our mission is to provide a collaborative framework for committed students, faculty, and researchers to apply their musical, technological, and scientific creativity to the development of innovative artistic and technological artifacts.

Director: Gil Weinberg

SimTigrate Design Lab

The  SimTigrate Design Lab  is an interdisciplinary research effort dedicated to driving healthcare innovation through the integration of evidence-based design and simulation. The SimTigrate team seeks to transform healthcare, predict and optimize outcomes and decrease cost.

Director: Jennifer DuBose

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Basic Research Design

What is research design.

• Definition of Research Design : A procedure for generating answers to questions, crucial in determining the reliability and relevance of research outcomes.
• Importance of Strong Designs : Strong designs lead to answers that are accurate and close to their targets, while weak designs may result in misleading or irrelevant outcomes.
• Criteria for Assessing Design Strength : Evaluating a design’s strength involves understanding the research question and how the design will yield reliable empirical information.

The Four Elements of Research Design (Blair et al., 2023)

• The MIDA Framework : Research designs consist of four interconnected elements – Model (M), Inquiry (I), Data strategy (D), and Answer strategy (A), collectively referred to as MIDA.
• Theoretical Side (M and I): This encompasses the researcher’s beliefs about the world (Model) and the target of inference or the primary question to be answered (Inquiry).
• Empirical Side (D and A): This includes the strategies for collecting (Data strategy) and analyzing or summarizing information (Answer strategy).
• Interplay between Theoretical and Empirical Sides : The theoretical side sets the research challenges, while the empirical side represents the researcher’s responses to these challenges.
• Relation among MIDA Components: The diagram above shows how the four elements of a design are interconnected and how they relate to both real-world and simulated quantities.
• Parallelism in Design Representation: The illustration highlights two key parallelisms in research design – between actual and simulated processes, and between the theoretical (M, I) and empirical (D, A) sides.
• Importance of Simulated Processes: The parallelism between actual and simulated processes is crucial for understanding and evaluating research designs.
• Balancing Theoretical and Empirical Aspects : Effective research design requires a balance between theoretical considerations (models and inquiries) and empirical methodologies (data and answer strategies).

Research Design Principles (Blair et al., 2023)

• Integration of Components: Designs are effective not merely due to their individual components but how these components work together.
• Focus on Entire Design: Assessing a design requires examining how each part, such as the question, estimator, and sampling method, fits into the overall design.
• Importance of Diagnosis: The evaluation of a design’s strength lies in diagnosing the whole design, not just its parts.
• Strong Design Characteristics: Designs with parallel theoretical and empirical aspects tend to be stronger.
• The M:I:D:A Analogy: Effective designs often align data strategies with models and answer strategies with inquiries.
• Flexibility in Models: Good designs should perform well even under varying world scenarios, not just under expected conditions.
• Broadening Model Scope: Designers should consider a wide range of models, assessing the design’s effectiveness across these.
• Robustness of Inquiries and Strategies: Inquiries should yield answers and strategies should be applicable regardless of variations in real-world events.
• Diagnosis Across Models: It’s important to understand for which models a design excels and for which it falters.
• Specificity of Purpose: A design is deemed good when it aligns with a specific purpose or goal.
• Balancing Multiple Criteria: Designs should balance scientific precision, logistical constraints, policy goals, and ethical considerations.
• Diverse Goals and Assessments: Different designs may be optimal for different goals; the purpose dictates the design evaluation.
• Early Planning Benefits: Designing early allows for learning and improving design properties before data collection.
• Avoiding Post-Hoc Regrets: Early design helps avoid regrets related to data collection or question formulation.
• Iterative Improvement: The process of declaration, diagnosis, and redesign improves designs, ideally done before data collection.
• Adaptability to Changes: Designs should be flexible to adapt to unforeseen circumstances or new information.
• Expanding or Contracting Feasibility: The scope of feasible designs may change due to various practical factors.
• Continual Redesign: The principle advocates for ongoing design modification, even post research completion, for robustness and response to criticism.
• Improvement Through Sharing: Sharing designs via a formalized declaration makes it easier for others to understand and critique.
• Enhancing Scientific Communication: Well-documented designs facilitate better communication and justification of research decisions.
• Building a Design Library: The idea is to contribute designs to a shared library, allowing others to learn from and build upon existing work.

The Basics of Social Science Research Designs (Panke, 2018)

Deductive and inductive research.

• Starting Point: Begins with empirical observations or exploratory studies.
• Development of Hypotheses: Hypotheses are formulated after initial empirical analysis.
• Case Study Analysis: Involves conducting explorative case studies and analyzing dynamics at play.
• Generalization of Findings: Insights are then generalized across multiple cases to verify their applicability.
• Application: Suitable for novel phenomena or where existing theories are not easily applicable.
• Example Cases: Exploring new events like Donald Trump’s 2016 nomination or Russia’s annexation of Crimea in 2014.
• Theory-Based: Starts with existing theories to develop scientific answers to research questions.
• Hypothesis Development: Hypotheses are specified and then empirically examined.
• Empirical Examination: Involves a thorough empirical analysis of hypotheses using sound methods.
• Theory Refinement: Results can refine existing theories or contribute to new theoretical insights.
• Application: Preferred when existing theories relate to the research question.
• Example Projects: Usually explanatory projects asking ‘why’ questions to uncover relationships.

Explanatory and Interpretative Research Designs

• Definition: Explanatory research aims to explain the relationships between variables, often addressing ‘why’ questions. It is primarily concerned with identifying cause-and-effect dynamics and is typically quantitative in nature. The goal is to test hypotheses derived from theories and to establish patterns that can predict future occurrences.
• Definition: Interpretative research focuses on understanding the deeper meaning or underlying context of social phenomena. It often addresses ‘how is this possible’ questions, seeking to comprehend how certain outcomes or behaviors are produced within specific contexts. This type of research is usually qualitative and prioritizes individual experiences and perceptions.
• Explanatory Research: Poses ‘why’ questions to explore causal relationships and understand what factors influence certain outcomes.
• Interpretative Research: Asks ‘how is this possible’ questions to delve into the processes and meanings behind social phenomena.
• Explanatory Research: Relies on established theories to form hypotheses about causal relationships between variables. These theories are then tested through empirical research.
• Interpretative Research: Uses theories to provide a framework for understanding the social context and meanings. The focus is on constitutive relationships rather than causal ones.
• Explanatory Research: Often involves studying multiple cases to allow for comparison and generalization. It seeks patterns across different scenarios.
• Interpretative Research: Typically concentrates on single case studies, providing an in-depth understanding of that particular case without necessarily aiming for generalization.
• Explanatory Research: Aims to produce findings that can be generalized to other similar cases or populations. It seeks universal or broad patterns.
• Interpretative Research: Offers detailed insights specific to a single case or context. These findings are not necessarily intended to be generalized but to provide a deep understanding of the particular case.

Qualitative, Quantitative, and Mixed-method Projects

• Definition: Qualitative research is exploratory and aims to understand human behavior, beliefs, feelings, and experiences. It involves collecting non-numerical data, often through interviews, focus groups, or textual analysis. This method is ideal for gaining in-depth insights into specific phenomena.
• Example in Education: A qualitative study might involve conducting in-depth interviews with teachers to explore their experiences and challenges with remote teaching during the pandemic. This research would aim to understand the nuances of their experiences, challenges, and adaptations in a detailed and descriptive manner.
• Definition: Quantitative research seeks to quantify data and generalize results from a sample to the population of interest. It involves measurable, numerical data and often uses statistical methods for analysis. This approach is suitable for testing hypotheses or examining relationships between variables.
• Example in Education: A quantitative study could involve surveying a large number of students to determine the correlation between the amount of time spent on homework and their academic achievement. This would involve collecting numerical data (hours of homework, grades) and applying statistical analysis to examine relationships or differences.
• Definition: Mixed-method research combines both qualitative and quantitative approaches, providing a more comprehensive understanding of the research problem. It allows for the exploration of complex research questions by integrating numerical data analysis with detailed narrative data.
• Example in Education: A mixed-method study might investigate the impact of a new teaching method. The research could start with quantitative methods, like administering standardized tests to measure learning outcomes, followed by qualitative methods, such as conducting focus groups with students and teachers to understand their perceptions and experiences with the new teaching method. This combination provides both statistical results and in-depth understanding.
• Research Questions: What kind of information is needed to answer the questions? Qualitative for “how” and “why”, quantitative for “how many” or “how much”, and mixed methods for a comprehensive understanding of both the breadth and depth of a phenomenon.
• Nature of the Study: Is the study aiming to explore a new area (qualitative), confirm hypotheses (quantitative), or achieve both (mixed-method)?
• Resources Available: Time, funding, and expertise available can influence the choice. Qualitative research can be more time-consuming, while quantitative research may require specific statistical skills.
• Data Sources: Availability and type of data also guide the methodology. Existing numerical data might lean towards quantitative, while studies requiring personal experiences or opinions might be qualitative.

References:

Blair, G., Coppock, A., & Humphreys, M. (2023).  Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign . Princeton University Press.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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Research center

Organic Research & Training Centre / Sejpal & Raje Architects

ZGC International Innovation Center / MAD Architects

Qom Meteorological Research Center / Eade Va Ejra

Division of Anatomy / Franz&Sue

Hsinchu Biotechnology Research and Incubation Center Phase III...

Forskaren Innovation Hub / 3XN

Technocampus Acoustique / ars. architectes urbanistes associés

Center for Energy and Environmental Chemistry / Telluride Arch...

B357 Research Hub / Christensen & Co. Architects

Natrium University Building in Gothenburg / Kanozi Arkitekter

The Technology Partnership (TTP) / Sheppard Robson

DTU B112 Research Hub / Christensen & Co Architects

Vertical Farm Beijing / van Bergen Kolpa architects

Svendborg International Maritime Academy / C.F. Møller + EFFEKT

Texoversum Innovation Center / allmannwappner + Menges Scheff...

Dafeng Timber Structure Research and Design Center / CATS

European Spallation Source / Henning Larsen + Cobe + Buro Happ...

Center for Work and Technology / KUU arhitektid

In’Cube Danone Research & Innovation Center / Arte Charpentier

American Museum of Natural History Richard Gilder Center / Stu...

Rob and Melani Walton Center for Planetary Health / Grimshaw ...

Youngmeyer Field Station / Hutton

Cary Institute of Ecosystem Studies / Becker+Becker

The Copper Coil / Sehw Architektur

Frequently asked questions

What is a research design.

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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

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

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

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

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

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

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

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

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

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

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

Safe and sustainable by design

What the framework is, how to get involved, test the framework, download documents. 

Give us feedback on the framework

The second feedback collection is open from 15 May until 30 August 2024 .

If you are a user of the framework, please provide your feedback.

Provide feedback

Support for the user

To help users apply the SSbD framework in practice:

• The JRC has published a Methodological Guidance that provides practical suggestions on the most commonly encountered issues when applying the framework
• the Partnership for the Assessment of Risks from Chemicals (PARC) has  developed a toolbox that provides an overview of existing tools for each step of the framework

The Commission Recommendation in a nutshell

The 'safe and sustainable by design' (SSbD framework) is a voluntary approach to guide the innovation process for chemicals and materials, announced on 8 December 2022 in a Commission Recommendation .

• steer the innovation process towards the green and sustainable industrial transition
• substitute or minimise the production and use of substances of concern, in line with, and beyond existing and upcoming regulatory obligations
• minimise the impact on health, climate and the environment during sourcing, production, use and end-of-life of chemicals, materials and products

The framework is composed of a (re-)design phase and an assessment phase that are applied iteratively as data becomes available.

The (re-)design phase consists of the application of guiding principles to steer the development process. The goal, the scope and the system boundaries – which will frame the assessment of the chemical or material – are defined in this phase.

The assessment phase comprises of 4 steps: hazard, workers exposure during production, exposure during use and life-cycle assessment. The assessment can be carried out either on newly developed chemicals and/or materials, or on existing chemicals and/or materials to improve their safety and sustainability performance during production, use and/or end-of-life.

Sign up to the SSbD stakeholder community

A European assessment framework. This Commission recommendation promotes research and innovation for safer and more sustainable chemicals and materials.

Test the framework

We are encouraging the engagement of relevant and willing stakeholders to support the progress of SSbD and adapt their innovation processes. The EU has started to implement SSbD under the Horizon Europe framework programme, but intends to continuously improve the methods, tools and data availability for ‘safe and sustainable by design’ chemicals and materials, as well as to refine the framework and make it applicable to a wide variety of substances.

The testing phase will allow us to establish a joint scientific reference base for safety and sustainability assessments that are necessary for innovation processes. It will also support the development of a fifth step on socioeconomic assessment. The engagement of the stakeholder community, and in particular the industry, is therefore crucial.

Who should participate?

The Recommendation is addressed to EU countries, industry, research and technology organisations (RTOs) and academia with each stakeholder group giving feedback on different actions.  

Expected actions by EU countries

• promote the framework in national research and innovation programmes
• increase the availability of findable, accessible, interoperable, reusable (FAIR) data for safe and sustainable by design assessment
• support the improvement of assessment methods, models and tools
• support the development of educational curricula on skills related to safety and sustainability of chemicals and materials

Expected actions by industry, academia and RTOs

• use the framework when developing chemicals and materials
• make available FAIR data for safe and sustainable by design assessment
• support the development of professional training and educational curricula on skills related to safety and sustainability of chemicals and materials

What is in there for me?

You can have your say by being part of the development of a common understanding of what safe and sustainable chemicals and materials are and how to assess them.

You will benefit from regulatory preparedness by applying 'safe and sustainable by design' in your innovation process and bring SSbD to practice by promoting the framework as a common baseline and ensure that other initiatives build on it.

You can support the design and assessment of digital tools assessing safety and sustainability early in the innovation process and increase transparency of SSbD strategies to support sustainable finance and consumer awareness.

• May - June 2023 Feedback collection
• Winter 2023 Workshop on collected feedback
• Spring 2024 Guidance report v1
• May - August 2024 Feedback collection
• Autumn 2024 Workshop on collected feedback
• Winter 2024 Guidance report v2
• 2025 Revision of framework
• 4 th SSbD Stakeholder workshop: Day 1 morning / Day 1 afternoon / Day 2
• 1st SSbD bootcamp: Day 1 / Day 2 / Day 3
• Webinar on the adoption of the SSbD Recommendation
• 3 rd SSbD Stakeholder workshop: Day 1 / Day 2

• Training and workshops
• Tuesday 22 October 2024, 13:30 - Friday 25 October 2024, 14:30 (CEST)
• Thessaloniki, Greece

• Wednesday 6 December 2023, 09:00 - Thursday 7 December 2023, 17:30 (CET)
• Brussels, Belgium

• Wednesday 25 October 2023, 14:30 - Friday 27 October 2023, 14:30 (CEST)
• Ispra, Italy

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40 Facts About Elektrostal

Written by Lanette Mayes

Modified & Updated: 01 Jun 2024

Reviewed by Jessica Corbett

Elektrostal is a vibrant city located in the Moscow Oblast region of Russia. With a rich history, stunning architecture, and a thriving community, Elektrostal is a city that has much to offer. Whether you are a history buff, nature enthusiast, or simply curious about different cultures, Elektrostal is sure to captivate you.

This article will provide you with 40 fascinating facts about Elektrostal, giving you a better understanding of why this city is worth exploring. From its origins as an industrial hub to its modern-day charm, we will delve into the various aspects that make Elektrostal a unique and must-visit destination.

So, join us as we uncover the hidden treasures of Elektrostal and discover what makes this city a true gem in the heart of Russia.

Key Takeaways:

• Elektrostal, known as the “Motor City of Russia,” is a vibrant and growing city with a rich industrial history, offering diverse cultural experiences and a strong commitment to environmental sustainability.
• With its convenient location near Moscow, Elektrostal provides a picturesque landscape, vibrant nightlife, and a range of recreational activities, making it an ideal destination for residents and visitors alike.

Known as the “Motor City of Russia.”

Elektrostal, a city located in the Moscow Oblast region of Russia, earned the nickname “Motor City” due to its significant involvement in the automotive industry.

Home to the Elektrostal Metallurgical Plant.

Elektrostal is renowned for its metallurgical plant, which has been producing high-quality steel and alloys since its establishment in 1916.

Boasts a rich industrial heritage.

Elektrostal has a long history of industrial development, contributing to the growth and progress of the region.

Founded in 1916.

The city of Elektrostal was founded in 1916 as a result of the construction of the Elektrostal Metallurgical Plant.

Located approximately 50 kilometers east of Moscow.

Elektrostal is situated in close proximity to the Russian capital, making it easily accessible for both residents and visitors.

Known for its vibrant cultural scene.

Elektrostal is home to several cultural institutions, including museums, theaters, and art galleries that showcase the city’s rich artistic heritage.

A popular destination for nature lovers.

Surrounded by picturesque landscapes and forests, Elektrostal offers ample opportunities for outdoor activities such as hiking, camping, and birdwatching.

Hosts the annual Elektrostal City Day celebrations.

Every year, Elektrostal organizes festive events and activities to celebrate its founding, bringing together residents and visitors in a spirit of unity and joy.

Has a population of approximately 160,000 people.

Elektrostal is home to a diverse and vibrant community of around 160,000 residents, contributing to its dynamic atmosphere.

Boasts excellent education facilities.

The city is known for its well-established educational institutions, providing quality education to students of all ages.

A center for scientific research and innovation.

Elektrostal serves as an important hub for scientific research, particularly in the fields of metallurgy , materials science, and engineering.

Surrounded by picturesque lakes.

The city is blessed with numerous beautiful lakes , offering scenic views and recreational opportunities for locals and visitors alike.

Well-connected transportation system.

Elektrostal benefits from an efficient transportation network, including highways, railways, and public transportation options, ensuring convenient travel within and beyond the city.

Famous for its traditional Russian cuisine.

Food enthusiasts can indulge in authentic Russian dishes at numerous restaurants and cafes scattered throughout Elektrostal.

Home to notable architectural landmarks.

Elektrostal boasts impressive architecture, including the Church of the Transfiguration of the Lord and the Elektrostal Palace of Culture.

Offers a wide range of recreational facilities.

Residents and visitors can enjoy various recreational activities, such as sports complexes, swimming pools, and fitness centers, enhancing the overall quality of life.

Provides a high standard of healthcare.

Elektrostal is equipped with modern medical facilities, ensuring residents have access to quality healthcare services.

Home to the Elektrostal History Museum.

The Elektrostal History Museum showcases the city’s fascinating past through exhibitions and displays.

A hub for sports enthusiasts.

Elektrostal is passionate about sports, with numerous stadiums, arenas, and sports clubs offering opportunities for athletes and spectators.

Celebrates diverse cultural festivals.

Throughout the year, Elektrostal hosts a variety of cultural festivals, celebrating different ethnicities, traditions, and art forms.

Electric power played a significant role in its early development.

Elektrostal owes its name and initial growth to the establishment of electric power stations and the utilization of electricity in the industrial sector.

Boasts a thriving economy.

The city’s strong industrial base, coupled with its strategic location near Moscow, has contributed to Elektrostal’s prosperous economic status.

Houses the Elektrostal Drama Theater.

The Elektrostal Drama Theater is a cultural centerpiece, attracting theater enthusiasts from far and wide.

Popular destination for winter sports.

Elektrostal’s proximity to ski resorts and winter sport facilities makes it a favorite destination for skiing, snowboarding, and other winter activities.

Promotes environmental sustainability.

Elektrostal prioritizes environmental protection and sustainability, implementing initiatives to reduce pollution and preserve natural resources.

Home to renowned educational institutions.

Elektrostal is known for its prestigious schools and universities, offering a wide range of academic programs to students.

Committed to cultural preservation.

The city values its cultural heritage and takes active steps to preserve and promote traditional customs, crafts, and arts.

Hosts an annual International Film Festival.

The Elektrostal International Film Festival attracts filmmakers and cinema enthusiasts from around the world, showcasing a diverse range of films.

Encourages entrepreneurship and innovation.

Elektrostal supports aspiring entrepreneurs and fosters a culture of innovation, providing opportunities for startups and business development .

Offers a range of housing options.

Elektrostal provides diverse housing options, including apartments, houses, and residential complexes, catering to different lifestyles and budgets.

Home to notable sports teams.

Elektrostal is proud of its sports legacy , with several successful sports teams competing at regional and national levels.

Boasts a vibrant nightlife scene.

Residents and visitors can enjoy a lively nightlife in Elektrostal, with numerous bars, clubs, and entertainment venues.

Promotes cultural exchange and international relations.

Elektrostal actively engages in international partnerships, cultural exchanges, and diplomatic collaborations to foster global connections.

Surrounded by beautiful nature reserves.

Nearby nature reserves, such as the Barybino Forest and Luchinskoye Lake, offer opportunities for nature enthusiasts to explore and appreciate the region’s biodiversity.

Commemorates historical events.

The city pays tribute to significant historical events through memorials, monuments, and exhibitions, ensuring the preservation of collective memory.

Promotes sports and youth development.

Elektrostal invests in sports infrastructure and programs to encourage youth participation, health, and physical fitness.

Hosts annual cultural and artistic festivals.

Throughout the year, Elektrostal celebrates its cultural diversity through festivals dedicated to music, dance, art, and theater.

Provides a picturesque landscape for photography enthusiasts.

The city’s scenic beauty, architectural landmarks, and natural surroundings make it a paradise for photographers.

Connects to Moscow via a direct train line.

The convenient train connection between Elektrostal and Moscow makes commuting between the two cities effortless.

A city with a bright future.

Elektrostal continues to grow and develop, aiming to become a model city in terms of infrastructure, sustainability, and quality of life for its residents.

In conclusion, Elektrostal is a fascinating city with a rich history and a vibrant present. From its origins as a center of steel production to its modern-day status as a hub for education and industry, Elektrostal has plenty to offer both residents and visitors. With its beautiful parks, cultural attractions, and proximity to Moscow, there is no shortage of things to see and do in this dynamic city. Whether you’re interested in exploring its historical landmarks, enjoying outdoor activities, or immersing yourself in the local culture, Elektrostal has something for everyone. So, next time you find yourself in the Moscow region, don’t miss the opportunity to discover the hidden gems of Elektrostal.

Q: What is the population of Elektrostal?

A: As of the latest data, the population of Elektrostal is approximately XXXX.

Q: How far is Elektrostal from Moscow?

A: Elektrostal is located approximately XX kilometers away from Moscow.

Q: Are there any famous landmarks in Elektrostal?

A: Yes, Elektrostal is home to several notable landmarks, including XXXX and XXXX.

Q: What industries are prominent in Elektrostal?

A: Elektrostal is known for its steel production industry and is also a center for engineering and manufacturing.

Q: Are there any universities or educational institutions in Elektrostal?

A: Yes, Elektrostal is home to XXXX University and several other educational institutions.

Q: What are some popular outdoor activities in Elektrostal?

A: Elektrostal offers several outdoor activities, such as hiking, cycling, and picnicking in its beautiful parks.

Q: Is Elektrostal well-connected in terms of transportation?

A: Yes, Elektrostal has good transportation links, including trains and buses, making it easily accessible from nearby cities.

Q: Are there any annual events or festivals in Elektrostal?

A: Yes, Elektrostal hosts various events and festivals throughout the year, including XXXX and XXXX.

Elektrostal's fascinating history, vibrant culture, and promising future make it a city worth exploring. For more captivating facts about cities around the world, discover the unique characteristics that define each city . Uncover the hidden gems of Moscow Oblast through our in-depth look at Kolomna. Lastly, dive into the rich industrial heritage of Teesside, a thriving industrial center with its own story to tell.

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Our commitment to delivering trustworthy and engaging content is at the heart of what we do. Each fact on our site is contributed by real users like you, bringing a wealth of diverse insights and information. To ensure the highest standards of accuracy and reliability, our dedicated editors meticulously review each submission. This process guarantees that the facts we share are not only fascinating but also credible. Trust in our commitment to quality and authenticity as you explore and learn with us.

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Facility for Rare Isotope Beams

At michigan state university, msu to expand chip-testing facility at frib to meet critical national need.

Today, the Michigan State University (MSU) Board of Trustees (BOT) authorized construction of a high-bay addition to the west end of FRIB. The addition will triple the testing capacity of the current chip-testing facility by providing two additional user vaults. The K500 Chip Testing Facility at FRIB will help meet the current national shortfall of testing capacity for advanced microelectronics, including those used for commercial spaceflight, wireless technology, and autonomous vehicles.

The 2018 National Academies report, “ Testing at the Speed of Light ,” outlined a critical national shortfall of testing capacity of space-bound electronic components. Supporting the national need for testing capacity, FRIB currently operates the  FRIB Single Events Effects (FSEE) facility , which uses heavy-ion beams to measure the response of electronic components to such ions. This simulates in a few minutes the effect of cosmic rays on electronics over decades of operation.

Cosmic rays are high-energy particles that bombard Earth from all directions in space. These include heavy ions that are known to cause serious issues with the performance and longevity of electronic systems on spacecraft and satellites, as well as electronics on land-based systems such as autonomous vehicles and medical devices.

The federal government awarded $14 million to MSU to establish the K500 Chip Testing Facility, which supports the refurbishment of the world’s first superconducting cyclotron—the  K500 built at MSU in the 1980s —into a heavy-ion chip testing facility.

The addition will also provide student opportunities through  the MSU Space Electronics Center started by FRIB and the MSU College of Engineering. The MSU Space Electronics Center—transitioning to the MSU Space Electronics Initiative—together with the K500 Chip Testing Facility at FRIB will position MSU as a national leader in chip design and testing, and it will provide additional capacity to educate students in chip design and testing. The K500 facility at FRIB will be a magnet for attracting high-tech companies to Michigan, increasing the state’s impact to the nation’s semiconductor and aerospace infrastructure. 

MSU Research Foundation Professor John Papapolymerou will transition from his role as chair of the Department of Electrical and Computer Engineering to the inaugural director role for the MSU Space Electronics Initiative effective 1 July. The change in title from “center” to ‘initiative” addresses the breadth of the aligned efforts toward expanding MSU’s impact in this essential field.

Papapolymerou emphasized that the MSU Space Electronics Initiative promotes collaboration between industry, government, and MSU's faculty, students, and researchers, creating an unparalleled framework that the K500 Chip Testing Facility at FRIB broadens and bolsters. 

"The K500 Chip Testing Facility at FRIB marks a pivotal expansion of our thriving exceptional ecosystem, dedicated to advancing radiation-hardened components and cutting-edge space electronics, and more crucially, to nurturing the next generation of talent essential for sustaining this technology's future and solidifying our nation’s leadership in the field,” said Papapolymerou. "Space electronics applications are poised to inspire a new wave of engineers and scientists, crucial to filling the anticipated 50,000 positions in semiconductor-related fields over the next five years. MSU is positioned at the forefront of molding this critical talent pipeline."

“This is an exciting next step in expanding MSU’s impact in this critical area in service to the nation—both in providing testing capacity and cultivating a skilled workforce,” said MSU College of Engineering Dean Leo Kempel. “By partnering to foster talent and innovation, MSU, FRIB, and the College of Engineering are forging a path that ensures America's leadership in this vital field for generations to come, with Spartan engineers leading the way."

The K500 Chip Testing Facility is an addition to FRIB’s west side, between FRIB and the Chemistry, Biomedical and Physical Sciences, and Biochemistry buildings. Construction is expected to last about 14 months. As part of the construction process, MSU will take the opportunity to better integrate the addition into the MSU campus scenery using vertical greenery, and will incorporate public art into the project.

“Expanding from our current FSEE facility not only enhances our capabilities, but also underscores our commitment to supporting scientific users in their efforts toward advancing our nation's technological resilience,” said FRIB Radiation Effects Facility Manager Steve Lidia. “We are eager to serve more users and support this national need, leveraging the unique capabilities of FRIB in a most essential and impactful way.”

“We are grateful for the continued trust placed in us to operate FRIB and leverage our skilled workforce to deliver significant and synergistic activities like chip testing that serve national needs,” said FRIB Laboratory Director Thomas Glasmacher. “This is truly a testament to the enduring importance of long-term vision and consistent action toward leveraging skills and assets afforded by public investment in FRIB to address new opportunities for the betterment of society.”

  Michigan State University  has been advancing the common good with uncommon will for more than 165 years. One of the world's leading research universities, MSU pushes the boundaries of discovery to make a better, safer, healthier world for all while providing life-changing opportunities to a diverse and inclusive academic community through more than 400 programs of study in 17 degree-granting colleges. 

Michigan State University operates the  Facility for Rare Isotope Beams  as a user facility for the U.S. Department of Energy Office of Science (DOE-SC) with funding from by a cooperative agreement between DOE-SC and MSU, supporting the mission of the DOE-SC Office of Nuclear Physics.

The  U.S. Department of Energy Office of Science  is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit  energy.gov/science .

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Elektrostal

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Elektrostal , city, Moscow oblast (province), western Russia . It lies 36 miles (58 km) east of Moscow city. The name, meaning “electric steel,” derives from the high-quality-steel industry established there soon after the October Revolution in 1917. During World War II , parts of the heavy-machine-building industry were relocated there from Ukraine, and Elektrostal is now a centre for the production of metallurgical equipment. Pop. (2006 est.) 146,189.

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This privacy policy has been compiled to better serve those who are concerned with how their 'Personally identifiable information' (PII) is being used online. PII, as used in US privacy law and information security, is information that can be used on its own or with other information to identify, contact, or locate a single person, or to identify an individual in context. Please read our privacy policy carefully to get a clear understanding of how we collect, use, protect or otherwise handle your Personally Identifiable Information in accordance with our website.

WHAT PERSONAL INFORMATION DO WE COLLECT FROM THE PEOPLE THAT VISIT OUR WEBSITE?

As appropriate, you may be asked to enter your name, email address, phone number or other details to help you with your experience.

WHEN DO WE COLLECT INFORMATION?

We collect information from you when you fill out a form, subscribe to our email list, submit a resume, or enter other information on our site.

HOW DO WE USE YOUR INFORMATION?

We may use the information we collect from you when you register, make a purchase, sign up for our newsletter, respond to a survey or marketing communication, surf the website, or use certain other site features in the following ways:

• To improve our website in order to better serve you.
• To allow us to better service you in responding to your customer service requests.
• To administer a contest, promotion, survey or other site feature.
• To send periodic emails regarding news and specials.

HOW DO WE PROTECT VISITOR INFORMATION?

Our website is scanned on a regular basis for malware, security holes, and known vulnerabilities in order to make your visit to our site as safe as possible.

THIRD PARTY DISCLOSURE

We do not sell, trade, or otherwise transfer to outside parties your personally identifiable information.

THIRD PARTY LINKS

We do not include or offer third party products or services on our website.

We along with third-party vendors, such as Google use first-party cookies (such as the Google Analytics cookies) and third-party cookies (such as the Facebook cookie) or other third-party identifiers together to compile data regarding user interactions with ad impressions, and other ad service functions as they relate to our website.

Opting out:

Users can set preferences for how Google advertises to you using the Google Ad Settings page. Alternatively, you can opt out by visiting the Network Advertising initiative opt out page or permanently using the Google Analytics Opt Out Browser add on.

If you don’t want Facebook to determine ads to show you based on your use of websites and apps off of Facebook, you can opt out through your settings . Learn more about online interest-based advertising from Facebook .

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CalOPPA is the first state law in the nation to require commercial websites and online services to post a privacy policy. The law's reach stretches well beyond California to require a person or company in the United States (and conceivably the world) that operates websites collecting personally identifiable information from California consumers to post a conspicuous privacy policy on its website stating exactly the information being collected and those individuals with whom it is being shared, and to comply with this policy. - See more at: http://consumercal.org/california-online-privacy-protection-act-caloppa/#st…

According to CalOPPA we agree to the following: 

Users can visit our site anonymously.

Once this privacy policy is created, we will add a link to it on our home page, or as a minimum on the first significant page after entering our website. 

Our Privacy Policy link includes the word 'Privacy', and can be easily be found on the page specified above. 

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Users are able to change their personal information By emailing or calling us.

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We honor do not track signals and do not track, plant cookies, or use advertising when a Do Not Track (DNT) browser mechanism is in place.

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It's also important to note that we do not allow third party behavioral tracking.

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When it comes to the collection of personal information from children under 13, the Children's Online Privacy Protection Act (COPPA) puts parents in control. The Federal Trade Commission, the nation's consumer protection agency, enforces the COPPA Rule, which spells out what operators of websites and online services must do to protect children's privacy and safety online. We do not specifically market to children under 13.

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The Fair Information Practices Principles form the backbone of privacy law in the United States and the concepts they include have played a significant role in the development of data protection laws around the globe. Understanding the Fair Information Practice Principles and how they should be implemented is critical to comply with the various privacy laws that protect personal information.

In order to be in line with Fair Information Practices we will take the following responsive action, should a data breach occur:

• We will notify the users via email within 7 business days.
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We also agree to the individual redress principle, which requires that individuals have a right to pursue legally enforceable rights against data collectors and processors who fail to adhere to the law. This principle requires not only that individuals have enforceable rights against data users, but also that individuals have recourse to courts or a government agency to investigate and/or prosecute non-compliance by data processors.

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The CAN-SPAM Act is a law that sets the rules for commercial email, establishes requirements for commercial messages, gives recipients the right to have emails stopped from being sent to them, and spells out tough penalties for violations.

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Last Edited On: 06-24-16

Elektrostal, Moscow Oblast, Russia

Elektrostal is a vibrant city located in the Moscow Oblast region of Russia. Situated approximately 40 kilometers east of Moscow, it is an important industrial and cultural center. The city has a rich history, dating back to its establishment in 1916 as a settlement for workers in the steel industry. Over the years, Elektrostal has grown into a bustling urban area with a population of around 150,000 residents.

In terms of safety, Elektrostal is generally considered to be a safe city. Like any urban area, it is important to exercise caution and be aware of your surroundings, but the crime rates in Elektrostal are relatively low compared to other regions. However, it is always advisable to take necessary precautions to ensure personal safety.

While specific crime statistics for Elektrostal might not be readily available, the city's overall crime rates are reported to be lower than the national average in Russia. This is partly due to the strong presence of law enforcement agencies and the proactive measures taken by local authorities to maintain public safety. However, it is important to remain vigilant and avoid situations that might put you at risk.

In terms of dangerous areas, Elektrostal doesn't have any notorious neighborhoods or specific areas known for high crime rates. However, it is generally recommended to exercise caution in less populated and poorly lit areas, especially during nighttime. It's always a good idea to stick to well-lit and busy streets, especially if you are unfamiliar with the city.

In terms of daily routines, Elektrostal follows a typical urban lifestyle. The city experiences peak traffic hours during morning and evening rush hours, so it's advisable to plan your travel accordingly to avoid congestion. Additionally, it's worth noting that winters in Elektrostal can be harsh, with temperatures dropping significantly below freezing. It's essential to dress warmly and take precautions to prevent any cold-related health issues during this time of year.

As for safe times of the day to be out, Elektrostal is generally considered safe during daylight hours. The city boasts several parks, recreational areas, and shopping centers that are popular among residents and visitors alike. During the daytime, these areas are usually well-populated and offer a safe environment for leisure activities. However, as the evening approaches, it's advisable to exercise increased caution and be aware of your surroundings, especially if you're walking alone or in dimly lit areas.

In terms of cultural habits, the people of Elektrostal are known for their warm hospitality and friendly nature. It is customary to greet others with a handshake and maintain eye contact during conversations. Russians, in general, appreciate when visitors show respect for their customs and traditions. While there are no specific cultural habits unique to Elektrostal, embracing the local customs and being respectful towards the residents will undoubtedly contribute to a positive experience while visiting the city.

Overall, Elektrostal is a charming city with a relatively low crime rate. By following common-sense safety precautions, such as being aware of your surroundings, avoiding isolated areas at night, and taking care of personal belongings, visitors can enjoy their time in Elektrostal without major concerns. Remember to stay informed about current safety recommendations and local regulations, as they may change over time.

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Establishment of GPAI Tokyo Expert Support Center

National Institute of Information and Communications Technology

GPAI was launched in June 2020 and Japan has been participating since the beginning. In November 2022, a "GPAI Summit" was held in Tokyo. In the "G7 Leaders' Statement on the Hiroshima AI Process" agreed on October 30, 2023, the G7 Leaders welcomed the "Hiroshima Process International Guiding Principles for Organizations Developing Advanced AI Systems" and the "Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems," and called on relevant ministers to further advance project-based cooperation with GPAI and other organizations. Furthermore, the "Hiroshima AI Process G7 Digital and Tech Ministers' Statement" agreed on December 1, 2023, incorporated a statement on project-based cooperation, welcoming projects on generative AI that contribute to supporting the implementation of the outcomes of the Hiroshima AI Process, including projects supported by GPAI Tokyo Center.

The Establishment of the GPAI Tokyo Expert Support Center

As GPAI is expected to promote project activities rooted in empirical knowledge, the Japanese government proposed the establishment of GPAI Tokyo Expert Support Center to support research for realizing the contents of the above international guiding principles and code of conduct, and to promote activities, including gathering evidence for policymaking on generative AI, and it was approved at the GPAI Ministerial Council on December 13, 2023. In light of this, GPAI Tokyo Expert Support Center was established at NICT and HARAYAMA Yuko, the Professor Emeritus of Tohoku University was appointed as the Secretary General of the center. Within the framework of GPAI, GPAI Tokyo Expert Support Center will provide operational and administrative assistance for research studies and projects conducted by GPAI experts, including impactful projects about generative AI promoting the Hiroshima AI Process. For more information, please visit the website below. https://www2.nict.go.jp/gpai-tokyo-esc/en/

About HARAYAMA Yuko, Secretary General

Future prospects.

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Drug & Device Pipeline News - July 1, 2024

This week’s Pipeline features a phase 2 trial approval for mild-to-moderate Alzheimer’s, a phase 3 trial start for Ataxia-telangiectasia and an FDA device approval for class III heart failure.

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Call or Text the Maternal Mental Health Hotline

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The National Maternal Mental Health Hotline provides free, confidential mental health support. Pregnant people, moms, and new parents can call or text any time, every day.

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Lois McCloskey, DrPH Phone: 617-358-3146 Email: [email protected]

Goals and objectives:

In 2020, the MCH workforce is confronted by a disturbing paradox: Even as key indicators of health for women, children, and youth are worsening and racial inequities widening, field staff feel unprepared important skill areas. Recent surveys of MCH workers point to particular needs in the areas of budget and financial management, systems and strategic thinking, change management, influencing policies related to social determinants of health, developing a vision for healthy communities, and incorporating health equity and social justice principles into programming (PHWINS, 2018). In addition, there are alarming turnover rates in the workforce, with over 70 percent staying in positions for five years or less (shorter than a Title V needs assessment process).

POPULATIONS SERVED:

Through curricular and extracurricular training activities, we will serve MCH trainees and their peers in interprofessional practice. Through T.A. initiatives we will serve the existing MCH workforce, with particular attention to Title V programs. Our initiatives in practice-based teaching and practice fellowships will serve our governmental and community partners. Ultimately the CoE is dedicated to eliminating health inequities among women, children and families locally and nationally.

PROGRAM SUMMARY:

• We will equip trainees with knowledge and skills to meet current and emerging demands. The MCH curriculum at BUSPH will support trainees to ground their practice in the life course perspective, experience in leadership and change management, systems thinking; and practice culturally humble community partnerships. Faculty research in strategic MCH priority areas (opioid/substance use an effects on mothers and infants; obesity prevention; mental health; and women’s health over the life course), forms the foundation for all MCH trainees to engage in research during their MPH.
• We will collaborate with other MCHB-funded programs at B.U. and with social work, education, pediatrics to offer a range of interprofessional (IPE) training activities, including practice based course in immigrant health (at border) and IPE conferences each year.
• We will recruit strong cohorts of MCH trainees each year- at least 30-35, with one- third from URM communities.
• The BUSPH CoE will support MCHiA, the student interest group at BU to lead a national network of emerging and early MCH professionals.
• We will provide technical assistance and coaching to Title V programs in three states (MA, NH, TN) as we co-design on line, interactive short courses for staff and community partners.
• We will engage students in our growing academic-community partnerships through MCH Practice Fellowships, and in our interchanges with other CoE’s and Catalyst Centers as we host webinars, build a practice-based teaching collaborative, and jointly evaluate a doula care initiative.

Evaluation:

We will conduct monitoring and evaluation activities for the purpose of tracking our achievement of goals and objectives, continuous quality improvement, and to evaluate program impact on key stakeholders: students/alumnae, faculty, and partners.

DISSEMINATION:

We will share our research as well as CoE-based best practices, lessons learned, and findings of evaluations through peer-reviewed journals, national MCH conferences, and in non-academic venues (popular press social media, policy-makers).

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In States That Won’t Pay for Obesity Drugs, ‘They May as Well Have Never Been Created’

Public employees in West Virginia who took the drugs lost weight and were healthier, and some are despondent that the state is canceling a program to help pay for them.

By Oliver Whang

Reporting from Pineville, Charleston and Morgantown W.Va.

Joanna Bailey, a family physician and obesity specialist, doesn’t want to tell her patients that they can’t take Wegovy, but she has gotten used to it.

Around a quarter of the people she sees in her small clinic in Wyoming County would benefit from the weight-loss medications, which also include Ozempic, Zepbound and Mounjaro, she says. The drugs have helped some of them lose 15 to 20 percent of their weight. But most people in the area she serves don’t have insurance that covers the cost, and virtually no one can afford sticker prices of $1,000 to $1,400 a month.

“Even my richest patients can’t afford it,” Dr. Bailey said. She then mentioned something that many doctors in West Virginia — among the poorest states in the country, with the highest prevalence of obesity, at 41 percent — say: “We’ve separated between the haves and the have-nots.”

Such disparities sharpened in March when West Virginia’s Public Employees Insurance Agency, which pays most of the cost of prescription drugs for more than 75,000 teachers, municipal workers and other public employees and their families, canceled a pilot program to cover weight-loss drugs.

Some private insurers help pay for medications to treat obesity, but most Medicaid programs do so only to manage diabetes, and Medicare covers Wegovy and Zepbound only when they are prescribed for heart problems.

Over the past year, states have been trying, amid rising demand, to determine how far to extend coverage for public employees. Connecticut is on track to spend more than $35 million this year through a limited weight-loss coverage initiative. In January, North Carolina announced that it would stop paying for weight-loss medications after forking out $100 million for them in 2023 — 10 percent of its spending on prescription drugs.

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