research at the lab

Interactive Movie on Research Misconduct

Download The Lab Guide

Description

In "The Lab: Avoiding Research Misconduct," you become the lead characters in an interactive movie and make decisions about integrity in research that can have long-term consequences. The simulation addresses Responsible Conduct of Research topics such as avoiding research misconduct, mentorship responsibilities, handling of data, responsible authorship, and questionable research practices.

The Characters You assume the role of four characters confronted with the pressures of working in a research laboratory:

System Requirements:

• Speakers/headphones
• Web Browser: Chrome, Edge, Internet Explorer, Firefox, Opera, Safari

Email Updates

Home » Academics and Research » Research Integrity in the Lab

Research Integrity in the Lab

• Lisa Goffman, PhD
• October 2019

A research laboratory is the center of discovery and training of researchers at every level of experience and expertise. Research integrity pervades all aspects of lab life, beginning the moment a new student enters the lab and continuing through the development of independent research projects and programs. Human subject research in communication sciences and disorders (CSD) presents a unique array of research integrity issues—because inquiry focuses on individuals with communication difficulties and their families.

Following accepted ethical conduct and adhering to professional codes or norms is the basis of research integrity in the lab setting. The principal investigator of the lab is responsible for ensuring that all work done in that lab has ethical integrity. In a presentation on research integrity at an ASHA workshop for new investigators, Julie Washington began her talk by quoting the words of a Japanese proverb: “The reputation of a thousand years may be determined by the conduct of one hour.” This quote illustrates that research integrity is a central component of even the most established research lab. Research that has great potential to change our basic knowledge and the efficacy of our assessment and intervention paradigms could be destroyed by lack of attention to scientific integrity. In this short article, I focus on three scenarios that are central to laboratories in the CSD discipline.

Scenario 1: Introducing New Members to the Lab

When a new student, postdoctoral fellow, or other lab member enters the lab, it is important to assume that, although their intuitions and motives will be well-intentioned, they have incomplete knowledge of ethical conduct. The first course of action—usually required by the university—is formal (often online) ethics training. This introduction is critical for exposing new lab members to core features of human subject research and for explaining why we have tightly regulated policies and procedures. These policies may be the subject of complaint by even the most experienced researchers, but it only takes exposure to the Tuskegee experiments to make obvious the rationale for these regulations. These most egregious examples of human subject violations are only an introduction to ethics in the lab; the greater danger is in the less egregious examples that are more likely to occur. The ethical workings of a lab are subtle and complex, and lab-specific training is required for each individual context. New investigators must learn to comply with ethical procedures that result in high-quality and replicable data. Some effective approaches include written manuals that specify procedures that are unique to a given lab; these materials may include sign-offs verifying that new members reviewed and discussed these procedures. Because labs are dynamic, manuals require frequent revisiting and editing. Also, it is important that every member of the lab group understand the lab’s research aims. Often, along with formal ethics training during introductory phases, new lab members read papers or grant proposals that introduce them to target research questions. Pairing new lab members with more experienced researchers is essential for effective learning; new members are released to collect or analyze data independently only after they demonstrate understanding and the ability to appropriately implement lab procedures. Data collection and analysis notes need to be documented in pen or in electronic format. Weekly lab meetings should include discussion of lab-specific topics such as data management, human subject issues, authorship, and the inevitable mistakes that people make in data collection or analysis. It is critical that even the most senior members of the lab stay close to the daily workings and that all members model the ethical behavior that is required.

Scenario 2: Setting the Stage for High-Integrity Management of Data

The path to scientific results is one of discovery and exploration. Sometimes, lab members become tied to (or believe their mentor is tied to) a particular theory—all individuals in the lab must guard against over-commitment to a theory or result. It is essential to make clear at all points in the process that the laboratory group is on a hypothesis-driven road to discovery but that the initial hypothesis may well be wrong. This is the nature of scientific discovery. It is important to frequently remind all lab members that high-quality scientific results are the goal and to expose them to potential vulnerabilities, such as cherry-picking data, lacking appropriate experimental controls, or having incomplete documentation of experimental details. Blinding regarding clinical or typical control status often becomes an issue. Labs with high-tech equipment may be particularly challenging because only highly trained personnel can conduct data analysis. It is important that lab members openly discuss the fact that the data—not the original hypotheses—need to tell the story and that, in science, the most powerful results often are counter to predictions. The goal of science is truth—even if that means an arduous road to publication.

Scenario 3: Human Subject Research in CSD

Fortunately, all laboratories are required to incorporate consent procedures through which lab administrators inform of their rights. However, some unique aspects of research integrity are associated with individuals who have communication disorders—two issues in particular.

The first issue involves a frequent reality in CSD research labs: These individuals often are poor at communication—as is the case for a young child with developmental language disorder or an adult with aphasia. Lab members sometimes find it challenging to be sensitive to the line between encouragement and coercion. Their obligation is to be clear in conveying to participants their rights and in responding to the nonverbal cues of those who cannot clearly communicate.

A second issue involves the collection of detailed behavioral and physiological data. During data acquisition, we may observe areas of weakness that are not aligned with the initial rationale for the participant’s entry into the study. For example, a child may enter as a typical participant, but our diagnostic testing shows that her speech or language abilities are below expected levels. In this case, our clinical selves often emerge. However, we must remember that these families entered our lab as typical participants. One strategy is to ask families (whether typical control or clinical participants) whether they wish to receive a clinical report following their visit. If they check “yes,” then we are permitted to share our findings. Note that it is usually appropriate to suggest further evaluation rather than to provide a clinical diagnosis. It is challenging to avoid the transition from researcher to clinician. However, in the context of research integrity, this shift must be managed carefully. When recruiting research participants, strong clinician–research partnerships, where all are sensitive to the rights of human subjects, is one excellent approach.

Concluding Remark

In the CSD discipline, several specific and often nuanced considerations are related to research integrity. It is a critical ongoing exercise for all members of a research lab to identify and find solutions to points of vulnerability.

I acknowledge the faculty involved in the Lessons for Success conference for the ideas presented here. Special thanks to Elena Plante, Steve Camarata, Nancy Brady, Julie Washington, Kathy Chapman, and Bill Yost.

About the Author

Lisa Goffman is a professor at the School of Behavioral and Brain Sciences at the University of Texas at Dallas.

Share this post

• research ethics

Related Posts

Implementation Science: Bridging the Gap Between SoTL Research and CSD Education

Culturally Responsive Teaching and Learning (CRTL) Special Collection: Resources for All

What’s New in Bilingualism? An Invitation to Reconsider

Most recent posts from context.

Brain Injury Awareness Month: ASHA Members Make a Difference

Forum Highlights Best Practices for Children With DLD

World Hearing Day 2024: Changing Mindsets

See all posts >>, follow asha journals on twitter.

About the ASHA Journals

ASHA publishes four peer-reviewed scholarly journals and one peer-reviewed scholarly review journal pertaining to the general field of communication sciences and disorders (CSD) and to the professions of audiology and speech-language pathology. These journals are the  American Journal of Audiology ;  American Journal of Speech-Language Pathology ;  Journal of Speech, Language, and Hearing Research ;  Language, Speech, and Hearing Services in Schools ; and Perspectives of the ASHA Special Interest Groups . These journals have the collective mission of disseminating research findings, theoretical advances, and clinical knowledge in CSD.

Connect with the ASHA Journals

Subscribe to the asha journals, additional author services.

ASHA Author Services Portal  

© 1997-2024 American Speech-Language-Hearing Association Privacy Notice Terms of Use

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• CAREER COLUMN
• 15 March 2019

A student’s guide to undergraduate research

• Shiwei Wang 0

Shiwei Wang is a junior undergraduate student studying Integrated Science and Chemistry at Northwestern University in Evanston, Illinois. Twitter: @W_Shiwei

You can also search for this author in PubMed   Google Scholar

I have thoroughly enjoyed my experience working in a materials-chemistry laboratory at Northwestern University in Evanston, Illinois, for the past two years. Being able to mix an undergraduate education with original research in a proper laboratory has been a fantastic opportunity.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

doi: https://doi.org/10.1038/d41586-019-00871-x

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged. You can get in touch with the editor at [email protected].

Wang, S. et al. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.7824707.v2 (2019).

Download references

Related Articles

Bring training forward for undergraduate researchers

Maple-scented cacti and pom-pom cats: how pranking at work can lift lab spirits

Career Feature 27 MAR 24

Nature is committed to diversifying its journalistic sources

Editorial 27 MAR 24

The beauty of what science can do when urgently needed

Career Q&A 26 MAR 24

Superconductivity case shows the need for zero tolerance of toxic lab culture

Correspondence 26 MAR 24

Professor of Experimental Parasitology (Leishmania)

To develop an innovative and internationally competitive research program, to contribute to educational activities and to provide expert advice.

Belgium (BE)

Institute of Tropical Medicine

PhD Candidate (m/f/d)

We search the candidate for the subproject "P2: targeting cardiac macrophages" as part of the DFG-funded Research Training Group "GRK 2989: Targeti...

Dortmund, Nordrhein-Westfalen (DE)

Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.

At our location in Dortmund we invite applications for a DFG-funded project. This project will aim to structurally and spatially resolve the altere...

Postdoctoral Fellow

We are seeking a highly motivated PhD and/or MD graduate to work in the Cardiovascular research lab in the Tulane University Department of Medicine.

New Orleans, Louisiana

School of Medicine Tulane University

Posdoctoral Fellow Positions in Epidemiology & Multi-Omics Division of Network Medicine BWH and HMS

Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School are seeking applicants for 3 postdoctoral positions.

Boston, Massachusetts

Brigham and Women's Hospital (BWH)

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Academic writing
• How to write a lab report

How To Write A Lab Report | Step-by-Step Guide & Examples

Published on May 20, 2021 by Pritha Bhandari . Revised on July 23, 2023.

A lab report conveys the aim, methods, results, and conclusions of a scientific experiment. The main purpose of a lab report is to demonstrate your understanding of the scientific method by performing and evaluating a hands-on lab experiment. This type of assignment is usually shorter than a research paper .

Lab reports are commonly used in science, technology, engineering, and mathematics (STEM) fields. This article focuses on how to structure and write a lab report.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

Structuring a lab report, introduction, other interesting articles, frequently asked questions about lab reports.

The sections of a lab report can vary between scientific fields and course requirements, but they usually contain the purpose, methods, and findings of a lab experiment .

Each section of a lab report has its own purpose.

• Title: expresses the topic of your study
• Abstract : summarizes your research aims, methods, results, and conclusions
• Introduction: establishes the context needed to understand the topic
• Method: describes the materials and procedures used in the experiment
• Results: reports all descriptive and inferential statistical analyses
• Discussion: interprets and evaluates results and identifies limitations
• Conclusion: sums up the main findings of your experiment
• References: list of all sources cited using a specific style (e.g. APA )
• Appendices : contains lengthy materials, procedures, tables or figures

Although most lab reports contain these sections, some sections can be omitted or combined with others. For example, some lab reports contain a brief section on research aims instead of an introduction, and a separate conclusion is not always required.

If you’re not sure, it’s best to check your lab report requirements with your instructor.

The only proofreading tool specialized in correcting academic writing - try for free!

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Try for free

Your title provides the first impression of your lab report – effective titles communicate the topic and/or the findings of your study in specific terms.

Create a title that directly conveys the main focus or purpose of your study. It doesn’t need to be creative or thought-provoking, but it should be informative.

• The effects of varying nitrogen levels on tomato plant height.
• Testing the universality of the McGurk effect.
• Comparing the viscosity of common liquids found in kitchens.

An abstract condenses a lab report into a brief overview of about 150–300 words. It should provide readers with a compact version of the research aims, the methods and materials used, the main results, and the final conclusion.

Think of it as a way of giving readers a preview of your full lab report. Write the abstract last, in the past tense, after you’ve drafted all the other sections of your report, so you’ll be able to succinctly summarize each section.

To write a lab report abstract, use these guiding questions:

• What is the wider context of your study?
• What research question were you trying to answer?
• How did you perform the experiment?
• What did your results show?
• How did you interpret your results?
• What is the importance of your findings?

Nitrogen is a necessary nutrient for high quality plants. Tomatoes, one of the most consumed fruits worldwide, rely on nitrogen for healthy leaves and stems to grow fruit. This experiment tested whether nitrogen levels affected tomato plant height in a controlled setting. It was expected that higher levels of nitrogen fertilizer would yield taller tomato plants.

Levels of nitrogen fertilizer were varied between three groups of tomato plants. The control group did not receive any nitrogen fertilizer, while one experimental group received low levels of nitrogen fertilizer, and a second experimental group received high levels of nitrogen fertilizer. All plants were grown from seeds, and heights were measured 50 days into the experiment.

The effects of nitrogen levels on plant height were tested between groups using an ANOVA. The plants with the highest level of nitrogen fertilizer were the tallest, while the plants with low levels of nitrogen exceeded the control group plants in height. In line with expectations and previous findings, the effects of nitrogen levels on plant height were statistically significant. This study strengthens the importance of nitrogen for tomato plants.

Your lab report introduction should set the scene for your experiment. One way to write your introduction is with a funnel (an inverted triangle) structure:

• Start with the broad, general research topic
• Narrow your topic down your specific study focus
• End with a clear research question

Begin by providing background information on your research topic and explaining why it’s important in a broad real-world or theoretical context. Describe relevant previous research on your topic and note how your study may confirm it or expand it, or fill a gap in the research field.

This lab experiment builds on previous research from Haque, Paul, and Sarker (2011), who demonstrated that tomato plant yield increased at higher levels of nitrogen. However, the present research focuses on plant height as a growth indicator and uses a lab-controlled setting instead.

Next, go into detail on the theoretical basis for your study and describe any directly relevant laws or equations that you’ll be using. State your main research aims and expectations by outlining your hypotheses .

Based on the importance of nitrogen for tomato plants, the primary hypothesis was that the plants with the high levels of nitrogen would grow the tallest. The secondary hypothesis was that plants with low levels of nitrogen would grow taller than plants with no nitrogen.

Your introduction doesn’t need to be long, but you may need to organize it into a few paragraphs or with subheadings such as “Research Context” or “Research Aims.”

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

A lab report Method section details the steps you took to gather and analyze data. Give enough detail so that others can follow or evaluate your procedures. Write this section in the past tense. If you need to include any long lists of procedural steps or materials, place them in the Appendices section but refer to them in the text here.

You should describe your experimental design, your subjects, materials, and specific procedures used for data collection and analysis.

Experimental design

Briefly note whether your experiment is a within-subjects  or between-subjects design, and describe how your sample units were assigned to conditions if relevant.

A between-subjects design with three groups of tomato plants was used. The control group did not receive any nitrogen fertilizer. The first experimental group received a low level of nitrogen fertilizer, while the second experimental group received a high level of nitrogen fertilizer.

Describe human subjects in terms of demographic characteristics, and animal or plant subjects in terms of genetic background. Note the total number of subjects as well as the number of subjects per condition or per group. You should also state how you recruited subjects for your study.

List the equipment or materials you used to gather data and state the model names for any specialized equipment.

List of materials

35 Tomato seeds

15 plant pots (15 cm tall)

Light lamps (50,000 lux)

Nitrogen fertilizer

Measuring tape

Describe your experimental settings and conditions in detail. You can provide labelled diagrams or images of the exact set-up necessary for experimental equipment. State how extraneous variables were controlled through restriction or by fixing them at a certain level (e.g., keeping the lab at room temperature).

Light levels were fixed throughout the experiment, and the plants were exposed to 12 hours of light a day. Temperature was restricted to between 23 and 25℃. The pH and carbon levels of the soil were also held constant throughout the experiment as these variables could influence plant height. The plants were grown in rooms free of insects or other pests, and they were spaced out adequately.

Your experimental procedure should describe the exact steps you took to gather data in chronological order. You’ll need to provide enough information so that someone else can replicate your procedure, but you should also be concise. Place detailed information in the appendices where appropriate.

In a lab experiment, you’ll often closely follow a lab manual to gather data. Some instructors will allow you to simply reference the manual and state whether you changed any steps based on practical considerations. Other instructors may want you to rewrite the lab manual procedures as complete sentences in coherent paragraphs, while noting any changes to the steps that you applied in practice.

If you’re performing extensive data analysis, be sure to state your planned analysis methods as well. This includes the types of tests you’ll perform and any programs or software you’ll use for calculations (if relevant).

First, tomato seeds were sown in wooden flats containing soil about 2 cm below the surface. Each seed was kept 3-5 cm apart. The flats were covered to keep the soil moist until germination. The seedlings were removed and transplanted to pots 8 days later, with a maximum of 2 plants to a pot. Each pot was watered once a day to keep the soil moist.

The nitrogen fertilizer treatment was applied to the plant pots 12 days after transplantation. The control group received no treatment, while the first experimental group received a low concentration, and the second experimental group received a high concentration. There were 5 pots in each group, and each plant pot was labelled to indicate the group the plants belonged to.

50 days after the start of the experiment, plant height was measured for all plants. A measuring tape was used to record the length of the plant from ground level to the top of the tallest leaf.

In your results section, you should report the results of any statistical analysis procedures that you undertook. You should clearly state how the results of statistical tests support or refute your initial hypotheses.

The main results to report include:

• any descriptive statistics
• statistical test results
• the significance of the test results
• estimates of standard error or confidence intervals

The mean heights of the plants in the control group, low nitrogen group, and high nitrogen groups were 20.3, 25.1, and 29.6 cm respectively. A one-way ANOVA was applied to calculate the effect of nitrogen fertilizer level on plant height. The results demonstrated statistically significant ( p = .03) height differences between groups.

Next, post-hoc tests were performed to assess the primary and secondary hypotheses. In support of the primary hypothesis, the high nitrogen group plants were significantly taller than the low nitrogen group and the control group plants. Similarly, the results supported the secondary hypothesis: the low nitrogen plants were taller than the control group plants.

These results can be reported in the text or in tables and figures. Use text for highlighting a few key results, but present large sets of numbers in tables, or show relationships between variables with graphs.

You should also include sample calculations in the Results section for complex experiments. For each sample calculation, provide a brief description of what it does and use clear symbols. Present your raw data in the Appendices section and refer to it to highlight any outliers or trends.

The Discussion section will help demonstrate your understanding of the experimental process and your critical thinking skills.

In this section, you can:

• Interpret your results
• Compare your findings with your expectations
• Identify any sources of experimental error
• Explain any unexpected results
• Suggest possible improvements for further studies

Interpreting your results involves clarifying how your results help you answer your main research question. Report whether your results support your hypotheses.

• Did you measure what you sought out to measure?
• Were your analysis procedures appropriate for this type of data?

Compare your findings with other research and explain any key differences in findings.

• Are your results in line with those from previous studies or your classmates’ results? Why or why not?

An effective Discussion section will also highlight the strengths and limitations of a study.

• Did you have high internal validity or reliability?
• How did you establish these aspects of your study?

When describing limitations, use specific examples. For example, if random error contributed substantially to the measurements in your study, state the particular sources of error (e.g., imprecise apparatus) and explain ways to improve them.

The results support the hypothesis that nitrogen levels affect plant height, with increasing levels producing taller plants. These statistically significant results are taken together with previous research to support the importance of nitrogen as a nutrient for tomato plant growth.

However, unlike previous studies, this study focused on plant height as an indicator of plant growth in the present experiment. Importantly, plant height may not always reflect plant health or fruit yield, so measuring other indicators would have strengthened the study findings.

Another limitation of the study is the plant height measurement technique, as the measuring tape was not suitable for plants with extreme curvature. Future studies may focus on measuring plant height in different ways.

The main strengths of this study were the controls for extraneous variables, such as pH and carbon levels of the soil. All other factors that could affect plant height were tightly controlled to isolate the effects of nitrogen levels, resulting in high internal validity for this study.

Your conclusion should be the final section of your lab report. Here, you’ll summarize the findings of your experiment, with a brief overview of the strengths and limitations, and implications of your study for further research.

Some lab reports may omit a Conclusion section because it overlaps with the Discussion section, but you should check with your instructor before doing so.

If you want to know more about AI for academic writing, AI tools, or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

• Ad hominem fallacy
• Post hoc fallacy
• Appeal to authority fallacy
• False cause fallacy
• Sunk cost fallacy
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

A lab report conveys the aim, methods, results, and conclusions of a scientific experiment . Lab reports are commonly assigned in science, technology, engineering, and mathematics (STEM) fields.

The purpose of a lab report is to demonstrate your understanding of the scientific method with a hands-on lab experiment. Course instructors will often provide you with an experimental design and procedure. Your task is to write up how you actually performed the experiment and evaluate the outcome.

In contrast, a research paper requires you to independently develop an original argument. It involves more in-depth research and interpretation of sources and data.

A lab report is usually shorter than a research paper.

The sections of a lab report can vary between scientific fields and course requirements, but it usually contains the following:

• Abstract: summarizes your research aims, methods, results, and conclusions
• References: list of all sources cited using a specific style (e.g. APA)
• Appendices: contains lengthy materials, procedures, tables or figures

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, July 23). How To Write A Lab Report | Step-by-Step Guide & Examples. Scribbr. Retrieved March 26, 2024, from https://www.scribbr.com/academic-writing/lab-report/

Is this article helpful?

Pritha Bhandari

Other students also liked, guide to experimental design | overview, steps, & examples, how to write an apa methods section, how to write an apa results section, what is your plagiarism score.

artificial intelligence

human-machine interaction

learning + teaching

architecture

consumer electronics

human-computer interaction

wearable computing

bioengineering

machine learning

environment

social science

entertainment

computer science

storytelling

engineering

prosthetics

developing countries

civic technology

social robotics

social media

communications

augmented reality

computer vision

public health

neurobiology

urban planning

virtual reality

synthetic biology

biotechnology

biomechanics

social networks

transportation

climate change

affective computing

data visualization

social change

fabrication

behavioral science

data science

cognitive science

zero gravity

agriculture

prosthetic design

manufacturing

racial justice

neural interfacing and control

3d printing

banking and finance

electrical engineering

human augmentation

cryptocurrency

sustainability

construction

microfabrication

civic action

performance

open source

natural language processing

language learning

marginalized communities

internet of things

autonomous vehicles

collective intelligence

microbiology

interactive

clinical science

mechanical engineering

social justice

mental health

visualization

nanoscience

nonverbal behavior

long-term interaction

sports and fitness

biomedical imaging

orthotic design

pharmaceuticals

mechatronics

soft-tissue biomechanics

open access

assistive technology

autism research

member company

gender studies

real estate

womens-health

decision-making

digital currency

Building a Successful Research Lab Culture

A productive, joyful, and positive research group does not happen accidentally..

Posted July 2, 2022 | Reviewed by Vanessa Lancaster

• The difference between a positive graduate school experience and a miserable one is the quality of the research supervisor and lab environment.
• Creating a productive and humane lab culture requires planning, communication, and modeling.
• Communicating a set of values is a start to creating a productive and positive research lab culture.

A research lab in psychology is the general term for a team of students, scholars, and colleagues working together under a principal investigator's leadership .

The fall term brings new graduate students, visiting scholars, post-docs, and undergraduate research volunteers into research labs. Integrating new people into the lab and re-incorporating returning students and collaborators creates new issues. It is important to establish a culture quickly so the work can be done efficiently, cooperatively, and joyfully.

It is easy to assume that returning members of the lab remember the key features of the lab culture and that new members will somehow magically absorb key values. In the hustle of day-to-day work, values and culture can be forgotten or lost. Labs can easily find themselves adrift, unhappy, unproductive, and stagnant. The difference between a positive student experience and a miserable one is often the culture of the research lab.

There is nothing that replaces the modeling of values by the principal investigator. In addition, these values must be explicit, implemented, evaluated, and rewarded. Building a culture is a long-term process. However, a quick overview of the credo of the lab can be a starting place for setting expectations for all lab work. Below are ten values for establishing a productive lab culture.

Strive to become a professional, but do not forget to be a human

Work every single day to become a useful professional. That is, conscientious , independent, skilled, knowledgeable, ethical, and courageous, but realize that everyone will fall short some days. Always focus on being better tomorrow than you were today. Lab members will never have a problem with me if they do something every day to improve.

You will need to trust that I define my success by your success

My job is to prepare students as professionals. I know what it takes to be a successful psychologist, and the more successful members are, the more successful I am. I welcome challenges. A reasonable question lab members should ask me frequently is, “how will this task help me achieve my professional goals ?”

Consider mental and physical well-being a central part of graduate education . Lab members should feel comfortable discussing issues and concerns. Long-term development as a person and as a professional requires attention to physical and emotional well-being. At the first sign of any issues, let me know, and we will develop a plan. In addition, look after peers. We are a team and need to take care of each other. Although it may be obvious, harassment, sabotage, creating a hostile environment, or any other behaviors detrimental to the team's wellness, our clients or individuals will result in removal from the lab.

Write it down or it did not happen

Writing is an essential component of graduate school. Any thoughts, ideas, findings, notions, and other contributions are only real if they are written. This is the most effective way to remember, communicate, and create a trail of thinking that will have an important influence on open research and clinical practice. Writing in a lab diary is also a mechanism of accountability and minimizing misunderstandings.

We all do better when we all do better

There is inevitable competition for authorship, grants, fellowships, and the time and attention of senior members. However, this lab is a team. The success of any one of us reflects on all. Share credit, be generous with authorship, listen to the ideas of others, be genuinely happy for peers' success, and assist others' work. When this becomes a habit, everyone benefits.

Do more. Everything takes three times longer than you expect

Doing more than the bare minimum is an essential part of professionalism. In addition, it is nearly impossible to plan time and work accurately. No matter how much time is devoted and planned for a specific task, the number of hours can be multiplied by three. Just achieving minimum expectations will require much more time and energy than expected.

Attention to detail

I completely dismiss the concept that “idea people” are important and effective parts of the lab. Ideas are only important if paired with an intense work habit, focus on implementation, and single-minded attention to detail. The focus on detail will certainly annoy most lab members at some point. Attention to detail is the difference between a vague idea floating in the ether and high-quality research and clinical practice.

Ethical behavior

Too often, students and professionals gloss over ethics because they believe they are good people who would never do anything evil or wrong. Ethical violations are not usually due to bad actors. Ethical violations are typically committed by good people who are tired, emotionally overwhelmed, stressed , overloaded with work, up against timelines, or ignorant of the exact ethical standards and procedures to be followed. Ethical guidelines need to be memorized, automatized, and second nature.

Invest in preparation

Writing activity is the tip of the iceberg. For every hour of writing, there are at least two hours of planning and four hours of reading (not to mention: seemingly endless hours of data collection and analysis). Be prepared for every meeting by having questions or information to present. Investment in preparation allows for improved scholarship, reduced stress, clearer thinking, and improved overall productivity and success.

Develop productive habits

Inspiration comes and goes, but habit remains. To be an effective worker in the research lab, an aspirational goal should be to read 100 pages daily and write 1000 words daily. This will take time, practice, and training. Whatever habits are developed, focus on being the most productive person you can be. Positive habits create professionalism.

Developing a culture is far more than ten simplistic and vague ideas. This only becomes a culture when these ten points are modeled and lived. However, communicating goals and expectations is a good way to begin.

Steven R. Shaw, Ph.D., is an associate professor of Educational and Counselling Psychology at McGill University in Montreal, QC, Canada.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Teletherapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Therapy Center NEW
• Diagnosis Dictionary
• Types of Therapy

Understanding what emotional intelligence looks like and the steps needed to improve it could light a path to a more emotionally adept world.

• Coronavirus Disease 2019
• Affective Forecasting
• Neuroscience

• Design Objectives
• Building Types
• Space Types
• Design Disciplines
• Guides & Specifications
• Resource Pages
• Project Management
• Building Commissioning
• Operations & Maintenance
• Building Information Modeling (BIM)
• Unified Facilities Guide Specifications (UFGS)
• Unified Facilities Criteria (UFC)
• VA Master Specifications (PG-18-1)
• Design Manuals (PG-18-10)
• Department of Energy
• General Services Administration
• Department of Homeland Security
• Department of State
• Course Catalog
• Workforce Development
• Case Studies
• Codes & Standards
• Industry Organizations

Research Laboratory  

by Daniel Watch and Deepa Tolat Perkins + Will

Within This Page

Building attributes, emerging issues, relevant codes and standards, additional resources.

Research Laboratories are workplaces for the conduct of scientific research. This WBDG Building Type page will summarize the key architectural, engineering, operational, safety, and sustainability considerations for the design of Research Laboratories.

The authors recognize that in the 21st century clients are pushing project design teams to create research laboratories that are responsive to current and future needs, that encourage interaction among scientists from various disciplines, that help recruit and retain qualified scientists, and that facilitates partnerships and development. As such, a separate WBDG Resource Page on Trends in Lab Design has been developed to elaborate on this emerging model of laboratory design.

A. Architectural Considerations

Over the past 30 years, architects, engineers, facility managers, and researchers have refined the design of typical wet and dry labs to a very high level. The following identifies the best solutions in designing a typical lab.

Lab Planning Module

The laboratory module is the key unit in any lab facility. When designed correctly, a lab module will fully coordinate all the architectural and engineering systems. A well-designed modular plan will provide the following benefits:

Flexibility —The lab module, as Jonas Salk explained, should "encourage change" within the building. Research is changing all the time, and buildings must allow for reasonable change. Many private research companies make physical changes to an average of 25% of their labs each year. Most academic institutions annually change the layout of 5 to 10% of their labs. See also WBDG Productive—Design for the Changing Workplace .

• Expansion —The use of lab planning modules allows the building to adapt easily to needed expansions or contractions without sacrificing facility functionality.

A common laboratory module has a width of approximately 10 ft. 6 in. but will vary in depth from 20–30 ft. The depth is based on the size necessary for the lab and the cost-effectiveness of the structural system. The 10 ft. 6 in. dimension is based on two rows of casework and equipment (each row 2 ft. 6 in. deep) on each wall, a 5 ft. aisle, and 6 in. for the wall thickness that separates one lab from another. The 5 ft. aisle width should be considered a minimum because of the requirements of the Americans with Disabilities Act (ADA) .

Two-Directional Lab Module —Another level of flexibility can be achieved by designing a lab module that works in both directions. This allows the casework to be organized in either direction. This concept is more flexible than the basic lab module concept but may require more space. The use of a two-directional grid is beneficial to accommodate different lengths of run for casework. The casework may have to be moved to create a different type or size of workstation.

Three-Dimensional Lab Module —The three-dimensional lab module planning concept combines the basic lab module or a two-directional lab module with any lab corridor arrangement for each floor of a building. This means that a three-dimensional lab module can have a single-corridor arrangement on one floor, a two-corridor layout on another, and so on. To create a three-dimensional lab module:

• A basic or two-directional lab module must be defined.
• All vertical risers must be fully coordinated. (Vertical risers include fire stairs, elevators, restrooms, and shafts for utilities.)
• The mechanical, electrical, and plumbing systems must be coordinated in the ceiling to work with the multiple corridor arrangements.

Lab Planning Concepts

The relationship of the labs, offices, and corridor will have a significant impact on the image and operations of the building. See also WBDG Functional—Account for Functional Needs .

Do the end users want a view from their labs to the exterior, or will the labs be located on the interior, with wall space used for casework and equipment?

Some researchers do not want or cannot have natural light in their research spaces. Special instruments and equipment, such as nuclear magnetic resonance (NMR) apparatus, electron microscopes, and lasers cannot function properly in natural light. Natural daylight is not desired in vivarium facilities or in some support spaces, so these are located in the interior of the building.

Zoning the building between lab and non-lab spaces will reduce costs. Labs require 100% outside air while non-lab spaces can be designed with re-circulated air, like an office building .

Adjacencies with corridors can be organized with a single, two corridor (racetrack), or a three corridor scheme. There are number of variations to organize each type. Illustrated below are three ways to organize a single corridor scheme:

Single corridor lab design with labs and office adjacent to each other.

Single corridor lab design with offices clustered together at the end and in the middle.

Single corridor lab design with office clusters accessing main labs directly.

• Open labs vs. closed labs. An increasing number of research institutions are creating "open" labs to support team-based work. The open lab concept is significantly different from that of the "closed" lab of the past, which was based on accommodating the individual principle investigator. In open labs, researchers share not only the space itself but also equipment, bench space, and support staff. The open lab format facilitates communication between scientists and makes the lab more easily adaptable for future needs. A wide variety of labs—from wet biology and chemistry labs, to engineering labs, to dry computer science facilities—are now being designed as open labs.

Flexibility

In today's lab, the ability to expand, reconfigure, and permit multiple uses has become a key concern. The following should be considered to achieve this:

Flexible Lab Interiors

Equipment zones—These should be created in the initial design to accommodate equipment, fixed, or movable casework at a later date.

Generic labs

Mobile casework—This can be comprised of mobile tables and mobile base cabinets. It allows researchers to configure and fit out the lab based on their needs as opposed to adjusting to pre-determined fixed casework.

Mobile casework

Mobile base cabinet Photo Credit: Kewaunee Scientific Corp.

Flexible partitions—These can be taken down and put back up in another location, allowing lab spaces to be configured in a variety of sizes.

Overhead service carriers—These are hung from the ceiling. They can have utilities like piping, electric, data, light fixtures, and snorkel exhausts. They afford maximum flexibility as services are lifted off the floor, allowing free floor space to be configured as needed.

Flexible Engineering Systems

Lab designed with overhead connects and disconnects allow for flexibility and fast hook up of equipment.

Labs should have easy connects/disconnects at walls and ceilings to allow for fast and affordable hook up of equipment. See also WBDG Productive—Integrate Technological Tools .

The Engineering systems should be designed such that fume hoods can be added or removed.

Space should be allowed in the utility corridors, ceilings, and vertical chases for future HVAC, plumbing, and electric needs.

Building Systems Distribution Concepts

Interstitial space.

An interstitial space is a separate floor located above each lab floor. All services and utilities are located here where they drop down to service the lab below. This system has a high initial cost but it allows the building to accommodate change very easily without interrupting the labs.

Conventional design vs. interstitial design Image Credit: Zimmer, Gunsul, Frasca Partnership

Service Corridor

Lab spaces adjoin a centrally located corridor where all utility services are located. Maintenance personnel are afforded constant access to main ducts, shutoff valves, and electric panel boxes without having to enter the lab. This service corridor can be doubled up as an equipment/utility corridor where common lab equipment like autoclaves, freezer rooms, etc. can be located.

B. Engineering Considerations

Typically, more than 50% of the construction cost of a laboratory building is attributed to engineering systems. Hence, the close coordination of these ensures a flexible and successfully operating lab facility. The following engineering issues are discussed here: structural systems, mechanical systems, electrical systems, and piping systems. See also WBDG Functional—Ensure Appropriate Product/Systems Integration .

Structural Systems

Once the basic lab module is determined, the structural grid should be evaluated. In most cases, the structural grid equals 2 basic lab modules. If the typical module is 10 ft. 6 in. x 30 ft., the structural grid would be 21 ft. x 30 ft. A good rule of thumb is to add the two dimensions of the structural grid; if the sum equals a number in the low 50's, then the structural grid would be efficient and cost-effective.

Typical lab structural grid.

Key design issues to consider in evaluating a structural system include:

• Framing depth and effect on floor-to-floor height;
• Ability to coordinate framing with lab modules;
• Ability to create penetrations for lab services in the initial design as well as over the life of the building;
• Potential for vertical or horizontal expansion;
• Vibration criteria; and

Mechanical Systems

The location of main vertical supply/exhaust shafts as well as horizontal ductwork is very crucial in designing a flexible lab. Key issues to consider include: efficiency and flexibility, modular design, initial costs , long-term operational costs , building height and massing , and design image .

The various design options for the mechanical systems are illustrated below:

Shafts in the middle of the building

Shafts at the end of the building

Exhaust at end and supply in the middle

Multiple internal shafts

Shafts on the exterior

See also WBDG High Performance HVAC .

Electrical Systems

Three types of power are generally used for most laboratory projects:

Normal power circuits are connected to the utility supply only, without any backup system. Loads that are typically on normal power include some HVAC equipment, general lighting, and most lab equipment.

Emergency power is created with generators that will back up equipment such as refrigerators, freezers, fume hoods, biological safety cabinets, emergency lighting, exhaust fans, animal facilities, and environmental rooms. Examples of safe and efficient emergency power equipment include distributed energy resources (DER) , microturbines , and fuel cells .

An uninterruptible power supply (UPS) is used for data recording, certain computers, microprocessor-controlled equipment, and possibly the vivarium area. The UPS can be either a central unit or a portable system, such as distributed energy resources (DER) , microturbines , fuel cells , and building integrated photovoltaics (BIPV) .

See also WBDG Productive—Assure Reliable Systems and Spaces .

The following should be considered:

• Load estimation
• Site distribution
• Power quality
• Management of electrical cable trays/panel boxes
• User expectations
• Illumination levels
• Lighting distribution-indirect, direct, combination
• Luminaire location and orientation-lighting parallel to casework and lighting perpendicular to casework
• Telephone and data systems

Piping Systems

There are several key design goals to strive for in designing laboratory piping systems:

• Provide a flexible design that allows for easy renovation and modifications.
• Provide appropriate plumbing systems for each laboratory based on the lab programming.
• Provide systems that minimize energy usage .
• Provide equipment arrangements that minimize downtime in the event of a failure.
• Locate shutoff valves where they are accessible and easily understood.
• Accomplish all of the preceding goals within the construction budget.

C. Operations and Maintenance

Cost savings.

The following cost saving items can be considered without compromising quality and flexibility:

• Separate lab and non-lab zones.
• Try to design with standard building components instead of customized components. See also WBDG Functional—Ensure Appropriate Product/Systems Integration .
• Identify at least three manufacturers of each material or piece of equipment specified to ensure competitive bidding for the work.
• Locate fume hoods on upper floors to minimize ductwork and the cost of moving air through the building.
• Evaluate whether process piping should be handled centrally or locally. In many cases it is more cost-effective to locate gases, in cylinders, at the source in the lab instead of centrally.
• Create equipment zones to minimize the amount of casework necessary in the initial construction.
• Provide space for equipment (e.g., ice machine) that also can be shared with other labs in the entry alcove to the lab. Shared amenities can be more efficient and cost-effective.
• Consider designating instrument rooms as cross-corridors, saving space as well as encouraging researchers to share equipment.
• Design easy-to-maintain, energy-efficient building systems. Expose mechanical, plumbing, and electrical systems for easy maintenance access from the lab.
• Locate all mechanical equipment centrally, either on a lower level of the building or on the penthouse level.
• Stack vertical elements above each other without requiring transfers from floor to floor. Such elements include columns, stairs, mechanical closets, and restrooms.

D. Lab and Personnel Safety and Security

Protecting human health and life is paramount, and safety must always be the first concern in laboratory building design. Security-protecting a facility from unauthorized access-is also of critical importance. Today, research facility designers must work within the dense regulatory environment in order to create safe and productive lab spaces. The WBDG Resource Page on Security and Safety in Laboratories addresses all these related concerns, including:

• Laboratory classifications: dependent on the amount and type of chemicals in the lab;
• Containment devices: fume hoods and bio-safety cabinets;
• Levels of bio-safety containment as a design principle;
• Radiation safety;
• Employee safety: showers, eyewashes, other protective measures; and
• Emergency power.

See also WBDG Secure / Safe Branch , Threat/Vulnerability Assessments and Risk Analysis , Balancing Security/Safety and Sustainability Objectives , Air Decontamination , and Electrical Safety .

E. Sustainability Considerations

The typical laboratory uses far more energy and water per square foot than the typical office building due to intensive ventilation requirements and other health and safety concerns. Therefore, designers should strive to create sustainable , high performance, and low-energy laboratories that will:

• Minimize overall environmental impacts;
• Protect occupant safety ; and
• Optimize whole building efficiency on a life-cycle basis.

For more specific guidance, see WBDG Sustainable Laboratory Design ; EPA and DOE's Laboratories for the 21st Century (Labs21) , a voluntary program dedicated to improving the environmental performance of U.S. laboratories; WBDG Sustainable Branch and Balancing Security/Safety and Sustainability Objectives .

F. Three Laboratory Sectors

There are three research laboratory sectors. They are academic laboratories, government laboratories, and private sector laboratories.

• Academic labs are primarily teaching facilities but also include some research labs that engage in public interest or profit generating research.
• Government labs include those run by federal agencies and those operated by state government do research in the public interest.
• Design of labs for the private sector , run by corporations, is usually driven by the need to enhance the research operation's profit making potential.

G. Example Design and Construction Criteria

For GSA, the unit costs for this building type are based on the construction quality and design features in the following table   . This information is based on GSA's benchmark interpretation and could be different for other owners.

LEED® Application Guide for Laboratory Facilities (LEED-AGL)—Because research facilities present a unique challenge for energy efficiency and sustainable design, the U.S. Green Building Council (USGBC) has formed the LEED-AGL Committee to develop a guide that helps project teams apply LEED credits in the design and construction of laboratory facilities. See also the WBDG Resource Page Using LEED on Laboratory Projects .

The following agencies and organizations have developed codes and standards affecting the design of research laboratories. Note that the codes and standards are minimum requirements. Architects, engineers, and consultants should consider exceeding the applicable requirements whenever possible.

• 29 CFR 1910.1450: OSHA "Occupational Exposures to Hazardous Chemicals in Laboratories"
• ANSI/ASSE/AIHA Z9.5 Laboratory Ventilation
• ANSI/ISEA Z358.1 Emergency Eyewash and Shower Equipment
• Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) Standards
• Biosafety in Microbiological and Biomedical Laboratories (BMBL) 5th Edition , Department of Health and Human Services, Centers for Disease Control and Prevention and National Institutes of Health.
• GSA PBS-P100 Facilities Standards for the Public Buildings Service
• Guidelines for the Laboratory Use of Chemical Carcinogens , Pub. No. 81-2385. National Institutes of Health
• NIH Design Requirements Manual , National Institutes of Health
• NFPA 30 Flammable and Combustible Liquids Code
• NFPA 45 Fire Protection for Laboratories using Chemical
• Unified Facilities Guide Specifications (UFGS) —organized by MasterFormat™ divisions, are for use in specifying construction for the military services. Several UFGS exist for safety-related topics.

Publications

• Building Type Basics for Research Laboratories , 2nd Edition by Daniel Watch. New York: John Wiley & Sons, Inc., 2008. ISBN# 978-0-470-16333-7.
• CRC Handbook of Laboratory Safety , 5th ed. by A. Keith Furr. CRC Press, 2000.
• Design and Planning of Research and Clinical Laboratory Facilities by Leonard Mayer. New York, NY: John Wiley & Sons, Inc., 1995.
• Design for Research: Principals of Laboratory Architecture by Susan Braybrooke. New York, NY: John Wiley & Sons, Inc., 1993.
• Guidelines for Laboratory Design: Health and Safety Considerations , 4th Edition by Louis J. DiBerardinis, et al. New York, NY: John Wiley & Sons, Inc., 2013.
• Guidelines for Planning and Design of Biomedical Research Laboratory Facilities by The American Institute of Architects, Center for Advanced Technology Facilities Design. Washington, DC: The American Institute of Architects, 1999.
• Handbook of Facilities Planning, Vol. 1: Laboratory Facilities by T. Ruys. New York, NY: Van Nostrand Reinhold, 1990.
• Laboratories, A Briefing and Design Guide by Walter Hain. London, UK: E & FN Spon, 1995.
• Laboratory by Earl Walls Associates, May 2000.
• Laboratory Design from the Editors of R&D Magazine.
• Laboratory Design, Construction, and Renovation: Participants, Process, and Product by National Research Council, Committee on Design, Construction, and Renovation of Laboratory Facilities. Washington, DC: National Academy Press, 2000.
• Planning Academic Research Facilities: A Guidebook by National Science Foundation. Washington, DC: National Science Foundation, 1992.
• Research and Development in Industry: 1995-96 by National Science Foundation, Division of Science Resources Studies. Arlington, VA: National Science Foundation, 1998.
• Science and Engineering Research Facilities at Colleges and Universities by National Science Foundation, Division of Science Resources Studies. Arlington, VA, 1998.
• Laboratories for the 21st Century (Labs21) —Sponsored by the U.S. Environmental Protection Agency and the U.S. Department of Energy, Labs21 is a voluntary program dedicated to improving the environmental performance of U.S. laboratories.

WBDG Participating Agencies

National Institute of Building Sciences Innovative Solutions for the Built Environment 1090 Vermont Avenue, NW, Suite 700 | Washington, DC 20005-4950 | (202) 289-7800 © 2024 National Institute of Building Sciences. All rights reserved. Disclaimer

America's Lab Report: Investigations in High School Science (2006)

Chapter: 3 laboratory experiences and student learning, 3 laboratory experiences and student learning.

In this chapter, the committee first identifies and clarifies the learning goals of laboratory experiences and then discusses research evidence on attainment of those goals. The review of research evidence draws on three major strands of research: (1) cognitive research illuminating how students learn; (2) studies that examine laboratory experiences that stand alone, separate from the flow of classroom science instruction; and (3) research projects that sequence laboratory experiences with other forms of science instruction. 1 We propose the phrase “integrated instructional units” to describe these research and design projects that integrate laboratory experiences within a sequence of science instruction. In the following section of this chapter, we present design principles for laboratory experiences derived from our analysis of these multiple strands of research and suggest that laboratory experiences designed according to these principles are most likely to accomplish their learning goals. Next we consider the role of technology in supporting student learning from laboratory experiences. The chapter concludes with a summary.

GOALS FOR LABORATORY EXPERIENCES

Laboratories have been purported to promote a number of goals for students, most of which are also the goals of science education in general (Lunetta, 1998; Hofstein and Lunetta, 1982). The committee commissioned a paper to examine the definition and goals of laboratory experiences (Millar, 2004) and also considered research reviews on laboratory education that have identified and discussed learning goals (Anderson, 1976; Hofstein and Lunetta, 1982; Lazarowitz and Tamir, 1994; Shulman and Tamir, 1973). While these inventories of goals vary somewhat, a core set remains fairly consistent. Building on these commonly stated goals, the committee developed a comprehensive list of goals for or desired outcomes of laboratory experiences:

Enhancing mastery of subject matter . Laboratory experiences may enhance student understanding of specific scientific facts and concepts and of the way in which these facts and concepts are organized in the scientific disciplines.

Developing scientific reasoning . Laboratory experiences may promote a student’s ability to identify questions and concepts that guide scientific

investigations; to design and conduct scientific investigations; to develop and revise scientific explanations and models; to recognize and analyze alternative explanations and models; and to make and defend a scientific argument. Making a scientific argument includes such abilities as writing, reviewing information, using scientific language appropriately, constructing a reasoned argument, and responding to critical comments.

Understanding the complexity and ambiguity of empirical work . Interacting with the unconstrained environment of the material world in laboratory experiences may help students concretely understand the inherent complexity and ambiguity of natural phenomena. Laboratory experiences may help students learn to address the challenges inherent in directly observing and manipulating the material world, including troubleshooting equipment used to make observations, understanding measurement error, and interpreting and aggregating the resulting data.

Developing practical skills . In laboratory experiences, students may learn to use the tools and conventions of science. For example, they may develop skills in using scientific equipment correctly and safely, making observations, taking measurements, and carrying out well-defined scientific procedures.

Understanding of the nature of science . Laboratory experiences may help students to understand the values and assumptions inherent in the development and interpretation of scientific knowledge, such as the idea that science is a human endeavor that seeks to understand the material world and that scientific theories, models, and explanations change over time on the basis of new evidence.

Cultivating interest in science and interest in learning science . As a result of laboratory experiences that make science “come alive,” students may become interested in learning more about science and see it as relevant to everyday life.

Developing teamwork abilities . Laboratory experiences may also promote a student’s ability to collaborate effectively with others in carrying out complex tasks, to share the work of the task, to assume different roles at different times, and to contribute and respond to ideas.

Although most of these goals were derived from previous research on laboratory experiences and student learning, the committee identified the new goal of “understanding the complexity and ambiguity of empirical work” to reflect the unique nature of laboratory experiences. Students’ direct encounters with natural phenomena in laboratory science courses are inherently more ambiguous and messy than the representations of these phenomena in science lectures, textbooks, and mathematical formulas (Millar, 2004). The committee thinks that developing students’ ability to recognize this complexity and develop strategies for sorting through it is an essential

goal of laboratory experiences. Unlike the other goals, which coincide with the goals of science education more broadly and may be advanced through lectures, reading, or other forms of science instruction, laboratory experiences may be the only way to advance the goal of helping students understand the complexity and ambiguity of empirical work.

RECENT DEVELOPMENTS IN RESEARCH AND DESIGN OF LABORATORY EXPERIENCES

In reviewing evidence on the extent to which students may attain the goals of laboratory experiences listed above, the committee identified a recent shift in the research. Historically, laboratory experiences have been separate from the flow of classroom science instruction and often lacked clear learning goals. Because this approach remains common today, we refer to these isolated interactions with natural phenomena as “typical” laboratory experiences. 2 Reflecting this separation, researchers often engaged students in one or two experiments or other science activities and then conducted assessments to determine whether their understanding of the science concept underlying the activity had increased. Some studies directly compared measures of student learning following laboratory experiences with measures of student learning following lectures, discussions, videotapes, or other methods of science instruction in an effort to determine which modes of instruction were most effective.

Over the past 10 years, some researchers have shifted their focus. Assuming that the study of the natural world requires opportunities to directly encounter that world, investigators are integrating laboratory experiences and other forms of instruction into instructional sequences in order to help students progress toward science learning goals. These studies draw on principles of learning derived from the rapid growth in knowledge from cognitive research to address the question of how to design science instruction, including laboratory experiences, in order to support student learning.

Given the complexity of these teaching and learning sequences, the committee struggled with how best to describe them. Initially, the committee used the term “science curriculum units.” However, that term failed to convey the importance of integration in this approach to sequencing laboratory experiences with other forms of teaching and learning. The research reviewed by the committee indicated that these curricula not only integrate laboratory experiences in the flow of science instruction, but also integrate

student learning about both the concepts and processes of science. To reflect these aspects of the new approach, the committee settled on the term “integrated instructional units” in this report.

The following sections briefly describe principles of learning derived from recent research in the cognitive sciences and their application in design of integrated instructional units.

Principles of Learning Informing Integrated Instructional Units

Recent research and development of integrated instructional units that incorporate laboratory experiences are based on a large and growing body of cognitive research. This research has led to development of a coherent and multifaceted theory of learning that recognizes that prior knowledge, context, language, and social processes play critical roles in cognitive development and learning (National Research Council, 1999). Taking each of these factors into account, the National Research Council (NRC) report How People Learn identifies four critical principles that support effective learning environments (Glaser, 1994; National Research Council, 1999), and a more recent NRC report, How Students Learn , considers these principles as they relate specifically to science (National Research Council, 2005). These four principles are summarized below.

Learner-Centered Environments

The emerging integrated instructional units are designed to be learner-centered. This principle is based on research showing that effective instruction begins with what learners bring to the setting, including cultural practices and beliefs, as well as knowledge of academic content. Taking students’ preconceptions into account is particularly critical in science instruction. Students come to the classroom with conceptions of natural phenomena that are based on their everyday experiences in the world. Although these conceptions are often reasonable and can provide satisfactory everyday explanations to students, they do not always match scientific explanations and break down in ways that students often fail to notice. Teachers face the challenge of engaging with these intuitive ideas, some of which are more firmly rooted than others, in order to help students move toward a more scientific understanding. In this way, understanding scientific knowledge often requires a change in—not just an addition to—what students notice and understand about the world (National Research Council, 2005).

Knowledge-Centered Environments

The developing integrated instructional units are based on the principle that learning is enhanced when the environment is knowledge-centered. That is, the laboratory experiences and other instruction included in integrated instructional units are designed to help students learn with understanding, rather than simply acquiring sets of disconnected facts and skills (National Research Council, 1999).

In science, the body of knowledge with which students must engage includes accepted scientific ideas about natural phenomena as well as an understanding of what it means to “do science.” These two aspects of science are reflected in the goals of laboratory experiences, which include mastery of subject matter (accepted scientific ideas about phenomena) and several goals related to the processes of science (understanding the complexity of empirical work, development of scientific reasoning). Research on student thinking about science shows a progression of ideas about scientific knowledge and how it is justified. At the first stage, students perceive scientific knowledge as right or wrong. Later, students characterize discrepant ideas and evidence as “mere opinion.” Eventually, students recognize scientific knowledge as being justified by evidence derived through rigorous research. Several studies have shown that a large proportion of high school students are at the first stage in their views of scientific knowledge (National Research Council, 2005).

Knowledge-centered environments encourage students to reflect on their own learning progress (metacognition). Learning is facilitated when individuals identify, monitor, and regulate their own thinking and learning. To be effective problem solvers and learners, students need to determine what they already know and what else they need to know in any given situation, including when things are not going as expected. For example, students with better developed metacognitive strategies will abandon an unproductive problem-solving strategy very quickly and substitute a more productive one, whereas students with less effective metacognitive skills will continue to use the same strategy long after it has failed to produce results (Gobert and Clement, 1999). The basic metacognitive strategies include: (1) connecting new information to former knowledge, (2) selecting thinking strategies deliberately, and (3) monitoring one’s progress during problem solving.

A final aspect of knowledge-centered learning, which may be particularly relevant to integrated instructional units, is that the practices and activities in which people engage while learning shape what they learn. Transfer (the ability to apply learning in varying situations) is made possible to the extent that knowledge and learning are grounded in multiple contexts. Transfer is more difficult when a concept is taught in a limited set of contexts or through a limited set of activities. By encountering the same concept at work in multiple contexts (such as in laboratory experiences and in discussion),

students can develop a deeper understanding of the concept and how it can be used as well as the ability to transfer what has been learned in one context to others (Bransford and Schwartz, 2001).

Assessment to Support Learning

Another important principle of learning that has informed development of integrated instructional units is that assessment can be used to support learning. Cognitive research has shown that feedback is fundamental to learning, but feedback opportunities are scarce in most classrooms. This research indicates that formative assessments provide students with opportunities to revise and improve the quality of their thinking while also making their thinking apparent to teachers, who can then plan instruction accordingly. Assessments must reflect the learning goals of the learning environment. If the goal is to enhance understanding and the applicability of knowledge, it is not sufficient to provide assessments that focus primarily on memory for facts and formulas. The Thinkertools science instructional unit discussed in the following section incorporates this principle, including formative self-assessment tools that help students advance toward several of the goals of laboratory experiences.

Community-Centered Environments

Research has shown that learning is enhanced in a community setting, when students and teachers share norms that value knowledge and participation (see Cobb et al., 2001). Such norms increase people’s opportunities and motivation to interact, receive feedback, and learn. Learning is enhanced when students have multiple opportunities to articulate their ideas to peers and to hear and discuss others’ ideas. A community-centered classroom environment may not be organized in traditional ways. For example, in science classrooms, the teacher is often the sole authority and arbiter of scientific knowledge, placing students in a relatively passive role (Lemke, 1990). Such an organization may promote students’ view that scientific knowledge is a collection of facts about the world, authorized by expert scientists and irrelevant to students’ own experience. The instructional units discussed below have attempted to restructure the social organization of the classroom and encourage students and the teacher to interact and learn from each other.

Design of Integrated Instructional Units

The learning principles outlined above have begun to inform design of integrated instructional units that include laboratory experiences with other types of science learning activities. These integrated instructional units were

developed through research programs that tightly couple research, design, and implementation in an iterative process. The research programs are beginning to document the details of student learning, development, and interaction when students are given systematic support—or scaffolding—in carefully structured social and cognitive activities. Scaffolding helps to guide students’ thinking, so that they can gradually take on more autonomy in carrying out various parts of the activities. Emerging research on these integrated instructional units provides guidance about how to design effective learning environments for real-world educational settings (see Linn, Davis, and Bell, 2004a; Cobb et al., 2003; Design-Based Research Collective, 2003).

Integrated instructional units interweave laboratory experiences with other types of science learning activities, including lectures, reading, and discussion. Students are engaged in framing research questions, designing and executing experiments, gathering and analyzing data, and constructing arguments and conclusions as they carry out investigations. Diagnostic, formative assessments are embedded into the instructional sequences and can be used to gauge student’s developing understanding and to promote their self-reflection on their thinking.

With respect to laboratory experiences, these instructional units share two key features. The first is that specific laboratory experiences are carefully selected on the basis of research-based ideas of what students are likely to learn from them. For example, any particular laboratory activity is likely to contribute to learning only if it engages students’ current thinking about the target phenomena and is likely to make them critically evaluate their ideas in relation to what they see during the activity. The second is that laboratory experiences are explicitly linked to and integrated with other learning activities in the unit. The assumption behind this second feature is that just because students do a laboratory activity, they may not necessarily understand what they have done. Nascent research on integrated instructional units suggests that both framing a particular laboratory experience ahead of time and following it with activities that help students make sense of the experience are crucial in using a laboratory experience to support science learning. This “integration” approach draws on earlier research showing that intervention and negotiation with an authority, usually a teacher, was essential to help students make meaning out of their laboratory activities (Driver, 1995).

Examples of Integrated Instructional Units

Scaling up chemistry that applies.

Chemistry That Applies (CTA) is a 6-8 week integrated instructional unit designed to help students in grades 8-10 understand the law of conservation

of matter. Created by researchers at the Michigan Department of Education (Blakeslee et al., 1993), this instructional unit was one of only a few curricula that were highly rated by American Assocation for the Advancement of Science Project 2061 in its study of middle school science curricula (Kesidou and Roseman, 2002). Student groups explore four chemical reactions—burning, rusting, the decomposition of water, and the volcanic reaction of baking soda and vinegar. They cause these reactions to happen, obtain and record data in individual notebooks, analyze the data, and use evidence-based arguments to explain the data.

The instructional unit engages the students in a carefully structured sequence of hands-on laboratory investigations interwoven with other forms of instruction (Lynch, 2004). Student understanding is “pressed” through many experiences with the reactions and by group and individual pressures to make meaning of these reactions. For example, video transcripts indicate that students engaged in “science talk” during teacher demonstrations and during student experiments.

Researchers at George Washington University, in a partnership with Montgomery County public schools in Maryland, are currently conducting a five-year study of the feasibility of scaling up effective integrated instructional units, including CTA (Lynch, Kuipers, Pyke, and Szesze, in press). In 2001-2002, CTA was implemented in five highly diverse middle schools that were matched with five comparison schools using traditional curriculum materials in a quasi-experimental research design. All 8th graders in the five CTA schools, a total of about 1,500 students, participated in the CTA curriculum, while all 8th graders in the matched schools used the science curriculum materials normally available. Students were given pre- and posttests.

In 2002-2003, the study was replicated in the same five pairs of schools. In both years, students who participated in the CTA curriculum scored significantly higher than comparison students on a posttest. Average scores of students who participated in the CTA curriculum showed higher levels of fluency with the concept of conservation of matter (Lynch, 2004). However, because the concept is so difficult, most students in both the treatment and control group still have misconceptions, and few have a flexible, fully scientific understanding of the conservation of matter. All subgroups of students who were engaged in the CTA curriculum—including low-income students (eligible for free and reduced-price meals), black and Hispanic students, English language learners, and students eligible for special educational services—scored significantly higher than students in the control group on the posttest (Lynch and O’Donnell, 2005). The effect sizes were largest among three subgroups considered at risk for low science achievement, including Hispanic students, low-income students, and English language learners.

Based on these encouraging results, CTA was scaled up to include about 6,000 8th graders in 20 schools in 2003-2004 and 12,000 8th graders in 37 schools in 2004-2005 (Lynch and O’Donnell, 2005).

ThinkerTools

The ThinkerTools instructional unit is a sequence of laboratory experiences and other learning activities that, in its initial version, yielded substantial gains in students’ understanding of Newton’s laws of motion (White, 1993). Building on these positive results, ThinkerTools was expanded to focus not only on mastery of these laws of motion but also on scientific reasoning and understanding of the nature of science (White and Frederiksen, 1998). In the 10-week unit, students were guided to reflect on their own thinking and learning while they carry out a series of investigations. The integrated instructional unit was designed to help them learn about science processes as well as about the subject of force and motion. The instructional unit supports students as they formulate hypotheses, conduct empirical investigations, work with conceptually analogous computer simulations, and refine a conceptual model for the phenomena. Across the series of investigations, the integrated instructional unit introduces increasingly complex concepts. Formative assessments are integrated throughout the instructional sequence in ways that allow students to self-assess and reflect on core aspects of inquiry and epistemological dimensions of learning.

Researchers investigated the impact of Thinker Tools in 12 7th, 8th, and 9th grade classrooms with 3 teachers and 343 students. The researchers evaluated students’ developing understanding of scientific investigations using a pre-post inquiry test. In this assessment, students were engaged in a thought experiment that asked them to conceptualize, design, and think through a hypothetical research study. Gains in scores for students in the reflective self-assessment classes and control classrooms were compared. Results were also broken out by students categorized as high and low achieving, based on performance on a standardized test conducted before the intervention. Students in the reflective self-assessment classes exhibited greater gains on a test of investigative skills. This was especially true for low-achieving students. The researchers further analyzed specific components of the associated scientific processes—formulation of hypotheses, designing an experiment, predicting results, drawing conclusions from made-up results, and relating those conclusions back to the original hypotheses. Students in the reflective-self-assessment classes did better on all of these components than those in control classrooms, especially on the more difficult components (drawing conclusions and relating them to the original hypotheses).

Computer as Learning Partner

Beginning in 1980, a large group of technologists, classroom teachers, and education researchers developed the Computer as Learning Partner (CLP)

integrated instructional unit. Over 10 years, the team developed and tested eight versions of a 12-week unit on thermodynamics. Each year, a cohort of about 300 8th grade students participated in a sequence of teaching and learning activities focused primarily on a specific learning goal—enhancing students’ understanding of the difference between heat and temperature (Linn, 1997). The project engaged students in a sequence of laboratory experiences supported by computers, discussions, and other forms of science instruction. For example, computer images and words prompted students to make predictions about heat and conductivity and perform experiments using temperature-sensitive probes to confirm or refute their predictions. Students were given tasks related to scientific phenomena affecting their daily lives—such as how to keep a drink cold for lunch or selecting appropriate clothing for hiking in the mountains—as a way to motivate their interest and curiosity. Teachers play an important role in carrying out the curriculum, asking students to critique their own and each others’ investigations and encouraging them to reflect on their own thinking.

Over 10 years of study and revision, the integrated instructional unit proved increasingly effective in achieving its stated learning goals. Before the sequenced instruction was introduced, only 3 percent of middle school students could adequately explain the difference between heat and temperature. Eight versions later, about half of the students participating in CLP could explain this difference, representing a 400 percent increase in achievement. In addition, nearly 100 percent of students who participated in the final version of the instructional unit demonstrated understanding of conductors (Linn and Songer, 1991). By comparison, only 25 percent of a group of undergraduate chemistry students at the University of California at Berkeley could adequately explain the difference between heat and temperature. A longitudinal study comparing high school seniors who participated in the thermodynamics unit in middle school with seniors who had received more traditional middle school science instruction found a 50 percent improvement in CLP students’ performance in distinguishing between heat and temperature (Linn and Hsi, 2000)

Participating in the CLP instructional unit also increased students’ interest in science. Longitudinal studies of CLP participants revealed that, among those who went on to take high school physics, over 90 percent thought science was relevant to their lives. And 60 percent could provide examples of scientific phenomena in their daily lives. By comparison, only 60 percent of high school physics students who had not participated in the unit during middle school thought science was relevant to their lives, and only 30 percent could give examples in their daily lives (Linn and Hsi, 2000).

EFFECTIVENESS OF LABORATORY EXPERIENCES

Description of the literature review.

The committee’s review of the literature on the effectiveness of laboratory experiences considered studies of typical laboratory experiences and emerging research focusing on integrated instructional units. In reviewing both bodies of research, we aim to specify how laboratory experiences can further each of the science learning goals outlined at the beginning of this chapter.

Limitations of the Research

Our review was complicated by weaknesses in the earlier research on typical laboratory experiences, isolated from the stream of instruction (Hofstein and Lunetta, 1982). First, the investigators do not agree on a precise definition of the “laboratory” experiences under study. Second, many studies were weak in the selection and control of variables. Investigators failed to examine or report important variables relating to student abilities and attitudes. For example, they failed to note students’ prior laboratory experiences. They also did not give enough attention to extraneous factors that might affect student outcomes, such as instruction outside the laboratory. Third, the studies of typical laboratory experiences usually involved a small group of students with little diversity, making it difficult to generalize the results to the large, diverse population of U.S. high schools today. Fourth, investigators did not give enough attention to the adequacy of the instruments used to measure student outcomes. As an example, paper and pencil tests that focus on testing mastery of subject matter, the most frequently used assessment, do not capture student attainment of all of the goals we have identified. Such tests are not able to measure student progress toward goals that may be unique to laboratory experiences, such as developing scientific reasoning, understanding the complexity and ambiguity of empirical work, and development of practical skills.

Finally, most of the available research on typical laboratory experiences does not fully describe these activities. Few studies have examined teacher behavior, the classroom learning environment, or variables identifying teacher-student interaction. In addition, few recent studies have focused on laboratory manuals—both what is in them and how they are used. Research on the intended design of laboratory experiences, their implementation, and whether the implementation resembles the initial design would provide the understanding needed to guide improvements in laboratory instruction. However, only a few studies of typical laboratory experiences have measured the effectiveness of particular laboratory experiences in terms of both the extent

to which their activities match those that the teacher intended and the extent to which the students’ learning matches the learning objectives of the activity (Tiberghien, Veillard, Le Marchal, Buty, and Millar, 2000).

We also found weaknesses in the evolving research on integrated instructional units. First, these new units tend to be hothouse projects; researchers work intensively with teachers to construct atypical learning environments. While some have been developed and studied over a number of years and iterations, they usually involve relatively small samples of students. Only now are some of these efforts expanding to a scale that will allow robust generalizations about their value and how best to implement them. Second, these integrated instructional units have not been designed specifically to contrast some version of laboratory or practical experience with a lack of such experience. Rather, they assume that educational interventions are complex, systemic “packages” (Salomon, 1996) involving many interactions that may influence specific outcomes, and that science learning requires some opportunities for direct engagement with natural phenomena. Researchers commonly aim to document the complex interactions between and among students, teachers, laboratory materials, and equipment in an effort to develop profiles of successful interventions (Cobb et al., 2003; Collins, Joseph, and Bielaczyc, 2004; Design-Based Research Collective, 2003). These newer studies focus on how to sequence laboratory experiences and other forms of science instruction to support students’ science learning.

Scope of the Literature Search

A final note on the review of research: the scope of our study did not allow for an in-depth review of all of the individual studies of laboratory education conducted over the past 30 years. Fortunately, three major reviews of the literature from the 1970s, 1980s, and 1990s are available (Lazarowitz and Tamir, 1994; Lunetta, 1998; Hofstein and Lunetta, 2004). The committee relied on these reviews in our analysis of studies published before 1994. To identify studies published between 1994 and 2004, the committee searched electronic databases.

To supplement the database search, the committee commissioned three experts to review the nascent body of research on integrated instructional units (Bell, 2005; Duschl, 2004; Millar, 2004). We also invited researchers who are currently developing, revising, and studying the effectiveness of integrated instructional units to present their findings at committee meetings (Linn, 2004; Lynch, 2004).

All of these activities yielded few studies that focused on the high school level and were conducted in the United States. For this reason, the committee expanded the range of the literature considered to include some studies targeted at middle school and some international studies. We included stud-

ies at the elementary through postsecondary levels as well as studies of teachers’ learning in our analysis. In drawing conclusions from studies that were not conducted at the high school level, the committee took into consideration the extent to which laboratory experiences in high school differ from those in elementary and postsecondary education. Developmental differences among students, the organizational structure of schools, and the preparation of teachers are a few of the many factors that vary by school level and that the committee considered in making inferences from the available research. Similarly, when deliberating on studies conducted outside the United States, we considered differences in the science curriculum, the organization of schools, and other factors that might influence the outcomes of laboratory education.

Mastery of Subject Matter

Evidence from research on typical laboratory experiences.

Claims that typical laboratory experiences help students master science content rest largely on the argument that opportunities to directly interact with, observe, and manipulate materials will help students to better grasp difficult scientific concepts. It is believed that these experiences will force students to confront their misunderstandings about phenomena and shift toward more scientific understanding.

Despite these claims, there is almost no direct evidence that typical laboratory experiences that are isolated from the flow of science instruction are particularly valuable for learning specific scientific content (Hofstein and Lunetta, 1982, 2004; Lazarowitz and Tamir, 1994). White (1996) points out that many major reviews of science education from the 1960s and 1970s indicate that laboratory work does little to improve understanding of science content as measured by paper and pencil tests, and later studies from the 1980s and early 1990s do not challenge this view. Other studies indicate that typical laboratory experiences are no more effective in helping students master science subject matter than demonstrations in high school biology (Coulter, 1966), demonstration and discussion (Yager, Engen, and Snider, 1969), and viewing filmed experiments in chemistry (Ben-Zvi, Hofstein, Kempa, and Samuel, 1976). In contrast to most of the research, a single comparative study (Freedman, 2002) found that students who received regular laboratory instruction over the course of a school year performed better on a test of physical science knowledge than a control group of students who took a similar physical science course without laboratory activities.

Clearly, most of the evidence does not support the argument that typical laboratory experiences lead to improved learning of science content. More specifically, concrete experiences with phenomena alone do not appear to

force students to confront their misunderstandings and reevaluate their own assumptions. For example, VandenBerg, Katu, and Lunetta (1994) reported, on the basis of clinical studies with individual students, that hands-on activities with introductory electricity materials facilitated students’ understanding of the relationships among circuit elements and variables. The carefully selected practical activities created conceptual conflict in students’ minds—a first step toward changing their naïve ideas about electricity. However, the students remained unable to develop a fully scientific mental model of a circuit system. The authors suggested that greater engagement with conceptual organizers, such as analogies and concept maps, could have helped students develop more scientific understandings of basic electricity. Several researchers, including Dupin and Joshua (1987), have reported similar findings. Studies indicate that students often hold beliefs so intensely that even their observations in the laboratory are strongly influenced by those beliefs (Champagne, Gunstone, and Klopfer, 1985, cited in Lunetta, 1998; Linn, 1997). Students tend to adjust their observations to fit their current beliefs rather than change their beliefs in the face of conflicting observations.

Evidence from Research on Integrated Instructional Units

Current integrated instructional units build on earlier studies that found integration of laboratory experiences with other instructional activities enhanced mastery of subject matter (Dupin and Joshua, 1987; White and Gunstone, 1992, cited in Lunetta, 1998). A recent review of these and other studies concluded (Hofstein and Lunetta, 2004, p. 33):

When laboratory experiences are integrated with other metacognitive learning experiences such as “predict-observe-explain” demonstrations (White and Gunstone, 1992) and when they incorporate the manipulation of ideas instead of simply materials and procedures, they can promote the learning of science.

Integrated instructional units often focus on complex science topics that are difficult for students to understand. Their design is based on research on students’ intuitive conceptions of a science topic and how those conceptions differ from scientific conceptions. Students’ ideas often do not match the scientific understanding of a phenomenon and, as noted previously, these intuitive notions are resistant to change. For this reason, the sequenced units incorporate instructional activities specifically designed to confront intuitive conceptions and provide an environment in which students can construct normative conceptions. The role of laboratory experiences is to emphasize the discrepancies between students’ intuitive ideas about the topic and scientific ideas, as well as to support their construction of normative understanding. In order to help students link formal, scientific concepts to real

phenomena, these units include a sequence of experiences that will push them to question their intuitive and often inaccurate ideas.

Emerging studies indicate that exposure to these integrated instructional units leads to demonstrable gains in student mastery of a number of science topics in comparison to more traditional approaches. In physics, these subjects include Newtonian mechanics (Wells, Hestenes, and Swackhamer, 1995; White, 1993); thermodynamics (Songer and Linn, 1991); electricity (Shaffer and McDermott, 1992); optics (Bell and Linn, 2000; Reiner, Pea, and Shulman, 1995); and matter (Lehrer, Schauble, Strom, and Pligge, 2001; Smith, Maclin, Grosslight, and Davis, 1997; Snir, Smith, and Raz, 2003). Integrated instructional units in biology have enhanced student mastery of genetics (Hickey, Kindfield, Horwitz, and Christie, 2003) and natural selection (Reiser et al., 2001). A chemistry unit has led to gains in student understanding of stoichiometry (Lynch, 2004). Many, but not all, of these instructional units combine computer-based simulations of the phenomena under study with direct interactions with these phenomena. The role of technology in providing laboratory experiences is described later in this chapter.

Developing Scientific Reasoning

While philosophers of science now agree that there is no single scientific method, they do agree that a number of reasoning skills are critical to research across the natural sciences. These reasoning skills include identifying questions and concepts that guide scientific investigations, designing and conducting scientific investigations, developing and revising scientific explanations and models, recognizing and analyzing alternative explanations and models, and making and defending a scientific argument. It is not necessarily the case that these skills are sequenced in a particular way or used in every scientific investigation. Instead, they are representative of the abilities that both scientists and students need to investigate the material world and make meaning out of those investigations. Research on children’s and adults’ scientific reasoning (see the review by Zimmerman, 2000) suggests that effective experimentation is difficult for most people and not learned without instructional support.

Early research on the development of investigative skills suggested that students could learn aspects of scientific reasoning through typical laboratory instruction in college-level physics (Reif and St. John, 1979, cited in Hofstein and Lunetta, 1982) and in high school and college biology (Raghubir, 1979; Wheatley, 1975, cited in Hofstein and Lunetta, 1982).

More recent research, however, suggests that high school and college science teachers often emphasize laboratory procedures, leaving little time for discussion of how to plan an investigation or interpret its results (Tobin, 1987; see Chapter 4 ). Taken as a whole, the evidence indicates that typical laboratory work promotes only a few aspects of the full process of scientific reasoning—making observations and organizing, communicating, and interpreting data gathered from these observations. Typical laboratory experiences appear to have little effect on more complex aspects of scientific reasoning, such as the capacity to formulate research questions, design experiments, draw conclusions from observational data, and make inferences (Klopfer, 1990, cited in White, 1996).

Research developing from studies of integrated instructional units indicates that laboratory experiences can play an important role in developing all aspects of scientific reasoning, including the more complex aspects, if the laboratory experiences are integrated with small group discussion, lectures, and other forms of science instruction. With carefully designed instruction that incorporates opportunities to conduct investigations and reflect on the results, students as young as 4th and 5th grade can develop sophisticated scientific thinking (Lehrer and Schauble, 2004; Metz, 2004). Kuhn and colleagues have shown that 5th graders can learn to experiment effectively, albeit in carefully controlled domains and with extended supervised practice (Kuhn, Schauble, and Garcia-Mila, 1992). Explicit instruction on the purposes of experiments appears necessary to help 6th grade students design them well (Schauble, Giaser, Duschl, Schulze, and John, 1995).These studies suggest that laboratory experiences must be carefully designed to support the development of scientific reasoning.

Given the difficulty most students have with reasoning scientifically, a number of instructional units have focused on this goal. Evidence from several studies indicates that, with the appropriate scaffolding provided in these units, students can successfully reason scientifically. They can learn to design experiments (Schauble et al., 1995; White and Frederiksen, 1998), make predictions (Friedler, Nachmias, and Linn, 1990), and interpret and explain data (Bell and Linn, 2000; Coleman, 1998; Hatano and Inagaki, 1991; Meyer and Woodruff, 1997; Millar, 1998; Rosebery, Warren, and Conant, 1992; Sandoval and Millwood, 2005). Engagement with these instructional units has been shown to improve students’ abilities to recognize discrepancies between predicted and observed outcomes (Friedler et al., 1990) and to design good experiments (Dunbar, 1993; Kuhn et al., 1992; Schauble et al., 1995; Schauble, Klopfer, and Raghavan, 1991).

Integrated instructional units seem especially beneficial in developing scientific reasoning skills among lower ability students (White and Frederiksen, 1998).

Recently, research has focused on an important element of scientific reasoning—the ability to construct scientific arguments. Developing, revising, and communicating scientific arguments is now recognized as a core scientific practice (Driver, Newton, and Osborne, 2000; Duschl and Osborne, 2002). Laboratory experiences play a key role in instructional units designed to enhance students’ argumentation abilities, because they provide both the impetus and the data for constructing scientific arguments. Such efforts have taken many forms. For example, researchers working with young Haitian-speaking students in Boston used the students’ own interests to develop scientific investigations. Students designed an investigation to determine which school drinking fountain had the best-tasting water. The students designed data collection protocols, collected and analyzed their data, and then argued about their findings (Rosebery et al., 1992). The Knowledge Integration Environment project asked middle school students to examine a common set of evidence to debate competing hypotheses about light propagation. Overall, most students learned the scientific concept (that light goes on forever), although those who made better arguments learned more than their peers (Bell and Linn, 2000). These and other examples (e.g., Sandoval and Millwood, 2005) show that students in middle and high school can learn to argue scientifically, by learning to coordinate theoretical claims with evidence taken from their laboratory investigations.

Developing Practical Skills

Science educators and researchers have long claimed that learning practical laboratory skills is one of the important goals for laboratory experiences and that such skills may be attainable only through such experiences (White, 1996; Woolnough, 1983). However, development of practical skills has been measured in research less frequently than mastery of subject matter or scientific reasoning. Such practical outcomes deserve more attention, especially for laboratory experiences that are a critical part of vocational or technical training in some high school programs. When a primary goal of a program or course is to train students for jobs in laboratory settings, they must have the opportunity to learn to use and read sophisticated instruments and carry out standardized experimental procedures. The critical questions about acquiring these skills through laboratory experiences may not be whether laboratory experiences help students learn them, but how the experiences can be constructed so as to be most effective in teaching such skills.

Some research indicates that typical laboratory experiences specifically focused on learning practical skills can help students progress toward other goals. For example, one study found that students were often deficient in the simple skills needed to successfully carry out typical laboratory activities, such as using instruments to make measurements and collect accurate data (Bryce and Robertson, 1985). Other studies indicate that helping students to develop relevant instrumentation skills in controlled “prelab” activities can reduce the probability that important measurements in a laboratory experience will be compromised due to students’ lack of expertise with the apparatus (Beasley, 1985; Singer, 1977). This research suggests that development of practical skills may increase the probability that students will achieve the intended results in laboratory experiences. Achieving the intended results of a laboratory activity is a necessary, though not sufficient, step toward effectiveness in helping students attain laboratory learning goals.

Some research on typical laboratory experiences indicates that girls handle laboratory equipment less frequently than boys, and that this tendency is associated with less interest in science and less self-confidence in science ability among girls (Jovanovic and King, 1998). It is possible that helping girls to develop instrumentation skills may help them to participate more actively and enhance their interest in learning science.

Studies of integrated instructional units have not examined the extent to which engagement with these units may enhance practical skills in using laboratory materials and equipment. This reflects an instructional emphasis on helping students to learn scientific ideas with real understanding and on developing their skills at investigating scientific phenomena, rather than on particular laboratory techniques, such as taking accurate measurements or manipulating equipment. There is no evidence to suggest that students do not learn practical skills through integrated instructional units, but to date researchers have not assessed such practical skills.

Understanding the Nature of Science

Throughout the past 50 years, studies of students’ epistemological beliefs about science consistently show that most of them have naïve views about the nature of scientific knowledge and how such knowledge is constructed and evaluated by scientists over time (Driver, Leach, Millar, and Scott, 1996; Lederman, 1992). The general public understanding of science is similarly inaccurate. Firsthand experience with science is often seen as a key way to advance students’ understanding of and appreciation for the conventions of science. Laboratory experiences are considered the primary mecha-

nism for providing firsthand experience and are therefore assumed to improve students’ understanding of the nature of science.

Research on student understanding of the nature of science provides little evidence of improvement with science instruction (Lederman, 1992; Driver et al., 1996). Although much of this research historically did not examine details of students’ laboratory experiences, it often included very large samples of science students and thus arguably captured typical laboratory experiences (research from the late 1950s through the 1980s is reviewed by Lederman, 1992). There appear to be developmental trends in students’ understanding of the relations between experimentation and theory-building. Younger students tend to believe that experiments yield direct answers to questions; during middle and high school, students shift to a vague notion of experiments being tests of ideas. Only a small number of students appear to leave high school with a notion of science as model-building and experimentation, in an ongoing process of testing and revision (Driver et al., 1996; Carey and Smith, 1993; Smith et al., 2000). The conclusion that most experts draw from these results is that the isolated nature and rote procedural focus of typical laboratory experiences inhibits students from developing robust conceptions of the nature of science. Consequently, some have argued that the nature of science must be an explicit target of instruction (Khishfe and Abd-El-Khalick, 2002; Lederman, Abd-El-Khalick, Bell, and Schwartz, 2002).

As discussed above, there is reasonable evidence that integrated instructional units help students to learn processes of scientific inquiry. However, such instructional units do not appear, on their own, to help students develop robust conceptions of the nature of science. One large-scale study of a widely available inquiry-oriented curriculum, in which integrated instructional units were an explicit feature, showed no significant change in students’ ideas about the nature of science after a year’s instruction (Meichtry, 1993). Students engaged in the BGuILE science instructional unit showed no gains in understanding the nature of science from their participation, and they seemed not even to see their experience in the unit as necessarily related to professional science (Sandoval and Morrison, 2003). These findings and others have led to the suggestion that the nature of science must be an explicit target of instruction (Lederman et al., 2002).

There is evidence from the ThinkerTools science instructional unit that by engaging in reflective self-assessment on their own scientific investiga-

tions, students gained a more sophisticated understanding of the nature of science than matched control classes who used the curriculum without the ongoing monitoring and evaluation of their own and others’ research (White and Frederiksen, 1998). Students who engaged in the reflective assessment process “acquire knowledge of the forms that scientific laws, models, and theories can take, and of how the development of scientific theories is related to empirical evidence” (White and Frederiksen, 1998, p. 92). Students who participated in the laboratory experiences and other learning activities in this unit using the reflective assessment process were less likely to “view scientific theories as immutable and never subject to revision” (White and Frederiksen, 1998, p. 72). Instead, they saw science as meaningful and explicable. The ThinkerTools findings support the idea that attention to nature of science issues should be an explicit part of integrated instructional units, although even with such attention it remains difficult to change students’ ideas (Khishfe and Abd-el-Khalick, 2002).

A survey of several integrated instructional units found that they seem to bridge the “language gap” between science in school and scientific practice (Duschl, 2004). The units give students “extended opportunities to explore the relationship between evidence and explanation,” helping them not only to develop new knowledge (mastery of subject matter), but also to evaluate claims of scientific knowledge, reflecting a deeper understanding of the nature of science (Duschl, 2004). The available research leaves open the question of whether or not these experiences help students to develop an explicit, reflective conceptual framework about the nature of science.

Cultivating Interest in Science and Interest in Learning Science

Studies of the effect of typical laboratory experiences on student interest are much rarer than those focusing on student achievement or other cognitive outcomes (Hofstein and Lunetta, 2004; White, 1996). The number of studies that address interest, attitudes, and other affective outcomes has decreased over the past decade, as researchers have focused almost exclusively on cognitive outcomes (Hofstein and Lunetta, 2004). Among the few studies available, the evidence is mixed. Some studies indicate that laboratory experiences lead to more positive attitudes (Renner, Abraham, and Birnie, 1985; Denny and Chennell, 1986). Other studies show no relation between laboratory experiences and affect (Ato and Wilkinson, 1986; Freedman, 2002), and still others report laboratory experiences turned students away from science (Holden, 1990; Shepardson and Pizzini, 1993).

There are, however, two apparent weaknesses in studies of interest and attitude (Hofstein and Lunetta, 1982). One is that researchers often do not carefully define interest and how it should be measured. Consequently, it is unclear if students simply reported liking laboratory activities more than other classroom activities, or if laboratory activities engendered more interest in science as a field, or in taking science courses, or something else. Similarly, studies may report increased positive attitudes toward science from students’ participation in laboratory experiences, without clear description of what attitudes were measured, how large the changes were, or whether changes persisted over time.

Student Perceptions of Typical Laboratory Experiences

Students’ perceptions of laboratory experiences may affect their interest and engagement in science, and some studies have examined those perceptions. Researchers have found that students often do not have clear ideas about the general or specific purposes of their work in typical science laboratory activities (Chang and Lederman, 1994) and that their understanding of the goals of lessons frequently do not match their teachers’ goals for the same lessons (Hodson, 1993; Osborne and Freyberg, 1985; Wilkenson and Ward, 1997). When students do not understand the goals of experiments or laboratory investigations, negative consequences for learning occur (Schauble et al., 1995). In fact, students often do not make important connections between the purpose of a typical laboratory investigation and the design of the experiments. They do not connect the experiment with what they have done earlier, and they do not note the discrepancies among their own concepts, the concepts of their peers, and those of the science community (Champagne et al., 1985; Eylon and Linn, 1988; Tasker, 1981). As White (1998) notes, “to many students, a ‘lab’ means manipulating equipment but not manipulating ideas.” Thus, in considering how laboratory experiences may contribute to students’ interest in science and to other learning goals, their perceptions of those experiences must be considered.

A series of studies using the Science Laboratory Environment Inventory (SLEI) has demonstrated links between students’ perceptions of laboratory experiences and student outcomes (Fraser, McRobbie, and Giddings, 1993; Fraser, Giddings, and McRobbie, 1995; Henderson, Fisher, and Fraser, 2000; Wong and Fraser, 1995). The SLEI, which has been validated cross-nationally, measures five dimensions of the laboratory environment: student cohesiveness, open-endedness, integration, rule clarity, and material environment (see Table 3-1 for a description of each scale). Using the SLEI, researchers have studied students’ perceptions of chemistry and biology laboratories in several countries, including the United States. All five dimensions appear to be positively related with student attitudes, although the

TABLE 3-1 Descriptive Information for the Science Laboratory Environment Inventory

relation of open-endedness with attitudes seems to vary with student population. In some populations, there is a negative relation to attitudes (Fraser et al., 1995) and to some cognitive outcomes (Henderson et al., 2000).

Research using the SLEI indicates that positive student attitudes are particularly strongly associated with cohesiveness (the extent to which students know, help, and are supportive of one another) and integration (the extent to which laboratory activities are integrated with nonlaboratory and theory classes) (Fraser et al.,1995; Wong and Fraser, 1995). Integration also shows a positive relation to students’ cognitive outcomes (Henderson et al., 2000; McRobbie and Fraser, 1993).

Students’ interest and attitudes have been measured less often than other goals of laboratory experiences in studies of integrated instructional units. When evidence is available, it suggests that students who participate in these units show greater interest in and more positive attitudes toward science. For example, in a study of ThinkerTools, completion of projects was used as a measure of student interest. The rate of submitting completed projects was higher for students in the ThinkerTools curriculum than for those in traditional instruction. This was true for all grades and ability levels (White and

Frederiksen, 1998). This study also found that students’ ongoing evaluation of their own and other students’ thinking increased motivation and self-confidence in their individual ability: students who participated in this ongoing evaluation not only turned in their final project reports more frequently, but they were also less likely to turn in reports that were identical to their research partner’s.

Participation in the ThinkerTools instructional unit appears to change students’ attitudes toward learning science. After completing the integrated instructional unit, fewer students indicated that “being good at science” was a result of inherited traits, and fewer agreed with the statement, “In general, boys tend to be naturally better at science than girls.” In addition, more students indicated that they preferred taking an active role in learning science, rather than simply being told the correct answer by the teacher (White and Frederiksen, 1998).

Researchers measured students’ engagement and motivation to master the complex topic of conservation of matter as part of the study of CTA. Students who participated in the CTA curriculum had higher levels of basic engagement (active participation in activities) and were more likely to focus on learning from the activities than students in the control group (Lynch et al., in press). This positive effect on engagement was especially strong among low-income students. The researchers speculate, “perhaps as a result of these changes in engagement and motivation, they learned more than if they had received the standard curriculum” (Lynch et al., in press).

Students who participated in CLP during middle school, when surveyed years later as high school seniors, were more likely to report that science is relevant to their lives than students who did not participate (Linn and Hsi, 2000). Further research is needed to illuminate which aspects of this instructional unit contribute to increased interest.

Developing Teamwork Abilities

Teamwork and collaboration appear in research on typical laboratory experiences in two ways. First, working in groups is seen as a way to enhance student learning, usually with reference to literature on cooperative learning or to the importance of providing opportunities for students to discuss their ideas. Second and more recently, attention has focused on the ability to work in groups as an outcome itself, with laboratory experiences seen as an ideal opportunity to develop these skills. The focus on teamwork as an outcome is usually linked to arguments that this is an essential skill for workers in the 21st century (Partnership for 21st Century Skills, 2003).

There is considerable evidence that collaborative work can help students learn, especially if students with high ability work with students with low ability (Webb and Palincsar, 1996). Collaboration seems especially helpful to lower ability students, but only when they work with more knowledgeable peers (Webb, Nemer, Chizhik, and Sugrue, 1998). Building on this research, integrated instructional units engage students in small-group collaboration as a way to encourage them to connect what they know (either from their own experiences or from prior instruction) to their laboratory experiences. Often, individual students disagree about prospective answers to the questions under investigation or the best way to approach them, and collaboration encourages students to articulate and explain their reasoning. A number of studies suggest that such collaborative investigation is effective in helping students to learn targeted scientific concepts (Coleman, 1998; Roschelle, 1992).

Extant research lacks specific assessment of the kinds of collaborative skills that might be learned by individual students through laboratory work. The assumption appears to be that if students collaborate and such collaborations are effective in supporting their conceptual learning, then they are probably learning collaborative skills, too.

Overall Effectiveness of Laboratory Experiences

The two bodies of research—the earlier research on typical laboratory experiences and the emerging research on integrated instructional units—yield different findings about the effectiveness of laboratory experiences in advancing the goals identified by the committee. In general, the nascent body of research on integrated instructional units offers the promise that laboratory experiences embedded in a larger stream of science instruction can be more effective in advancing these goals than are typical laboratory experiences (see Table 3-2 ).

Research on the effectiveness of typical laboratory experiences is methodologically weak and fragmented. The limited evidence available suggests that typical laboratory experiences, by themselves, are neither better nor worse than other methods of science instruction for helping students master science subject matter. However, more recent research indicates that integrated instructional units enhance students’ mastery of subject matter. Studies have demonstrated increases in student mastery of complex topics in physics, chemistry, and biology.

Typical laboratory experiences appear, based on the limited research available, to support some aspects of scientific reasoning; however, typical laboratory experiences alone are not sufficient for promoting more sophisticated scientific reasoning abilities, such as asking appropriate questions,

TABLE 3-2 Attainment of Educational Goals in Typical Laboratory Experiences and Integrated Instructional Units

designing experiments, and drawing inferences. Research on integrated instructional units provides evidence that the laboratory experiences and other forms of instruction they include promote development of several aspects of scientific reasoning, including the ability to ask appropriate questions, design experiments, and draw inferences.

The evidence indicates that typical laboratory experiences do little to increase students’ understanding of the nature of science. In contrast, some studies find that participating in integrated instructional units that are designed specifically with this goal in mind enhances understanding of the nature of science.

The available research suggests that typical laboratory experiences can play a role in enhancing students’ interest in science and in learning science. There is evidence that engagement with the laboratory experiences and other learning activities included in integrated instructional units enhances students’ interest in science and motivation to learn science.

In sum, the evolving research on integrated instructional units provides evidence of increases in students’ understanding of subject matter, development of scientific reasoning, and interest in science, compared with students who received more traditional forms of science instruction. Studies conducted to date also suggest that the units are effective in helping diverse groups of students attain these three learning goals. In contrast, the earlier research on typical laboratory experiences indicates that such typical laboratory experiences are neither better nor worse than other forms of science instruction in supporting student mastery of subject matter. Typical laboratory experiences appear to aid in development of only some aspects of scientific reasoning, and they appear to play a role in enhancing students’ interest in science and in learning science.

Due to a lack of available studies, the committee was unable to draw conclusions about the extent to which either typical laboratory experiences or laboratory experiences incorporated into integrated instructional units might advance the other goals identified at the beginning of this chapter—enhancing understanding of the complexity and ambiguity of empirical work, acquiring practical skills, and developing teamwork skills.

PRINCIPLES FOR DESIGN OF EFFECTIVE LABORATORY EXPERIENCES

The three bodies of research we have discussed—research on how people learn, research on typical laboratory experiences, and developing research on how students learn in integrated instructional units—yield information that promises to inform the design of more effective laboratory experiences.

The committee considers the emerging evidence sufficient to suggest four general principles that can help laboratory experiences achieve the goals outlined above. It must be stressed, however, that research to date has not described in much detail how these principles can be implemented nor how each principle might relate to each of the educational goals of laboratory experiences.

Clearly Communicated Purposes

Effective laboratory experiences have clear learning goals that guide the design of the experience. Ideally these goals are clearly communicated to students. Without a clear understanding of the purposes of a laboratory activity, students seem not to get much from it. Conversely, when the purposes of a laboratory activity are clearly communicated by teachers to students, then students seem capable of understanding them and carrying them out. There seems to be no compelling evidence that particular purposes are more understandable to students than others.

Sequenced into the Flow of Instruction

Effective laboratory experiences are thoughtfully sequenced into the flow of classroom science instruction. That is, they are explicitly linked to what has come before and what will come after. A common theme in reviews of laboratory practice in the United States is that laboratory experiences are presented to students as isolated events, unconnected with other aspects of classroom work. In contrast, integrated instructional units embed laboratory experiences with other activities that build on the laboratory experiences and push students to reflect on and better understand these experiences. The way a particular laboratory experience is integrated into a flow of activities should be guided by the goals of the overall sequence of instruction and of the particular laboratory experience.

Integrated Learning of Science Concepts and Processes

Research in the learning sciences (National Research Council, 1999, 2001) strongly implies that conceptual understanding, scientific reasoning, and practical skills are three capabilities that are not mutually exclusive. An educational program that partitions the teaching and learning of content from the teaching and learning of process is likely to be ineffective in helping students develop scientific reasoning skills and an understanding of science as a way of knowing. The research on integrated instructional units, all of which intertwine exploration of content with process through laboratory experiences, suggests that integration of content and process promotes attainment of several goals identified by the committee.

Ongoing Discussion and Reflection

Laboratory experiences are more likely to be effective when they focus students more on discussing the activities they have done during their laboratory experiences and reflecting on the meaning they can make from them, than on the laboratory activities themselves. Crucially, the focus of laboratory experiences and the surrounding instructional activities should not simply be on confirming presented ideas, but on developing explanations to make sense of patterns of data. Teaching strategies that encourage students to articulate their hypotheses about phenomena prior to experimentation and to then reflect on their ideas after experimentation are demonstrably more successful at supporting student attainment of the goals of mastery of subject matter, developing scientific reasoning, and increasing interest in science and science learning. At the same time, opportunities for ongoing discussion and reflection could potentially support students in developing teamwork skills.

COMPUTER TECHNOLOGIES AND LABORATORY EXPERIENCES

From scales to microscopes, technology in many forms plays an integral role in most high school laboratory experiences. Over the past two decades, personal computers have enabled the development of software specifically designed to help students learn science, and the Internet is an increasingly used tool for science learning and for science itself. This section examines the role that computer technologies now and may someday play in science learning in relation to laboratory experiences. Certain uses of computer technology can be seen as laboratory experiences themselves, according to the committee’s definition, to the extent that they allow students to interact with data drawn directly from the world. Other uses, less clearly laboratory experiences in themselves, provide certain features that aid science learning.

Computer Technologies Designed to Support Learning

Researchers and science educators have developed a number of software programs to support science learning in various ways. In this section, we summarize what we see as the main ways in which computer software can support science learning through providing or augmenting laboratory experiences.

Scaffolded Representations of Natural Phenomena

Perhaps the most common form of science education software are programs that enable students to interact with carefully crafted models of natural phenomena that are difficult to see and understand in the real world and have proven historically difficult for students to understand. Such programs are able to show conceptual interrelationships and connections between theoretical constructs and natural phenomena through the use of multiple, linked representations. For example, velocity can be linked to acceleration and position in ways that make the interrelationships understandable to students (Roschelle, Kaput, and Stroup, 2000). Chromosome genetics can be linked to changes in pedigrees and populations (Horowitz, 1996). Molecular chemical representations can be linked to chemical equations (Kozma, 2003).

In the ThinkerTools integrated instructional unit, abstracted representations of force and motion are provided for students to help them “see” such ideas as force, acceleration, and velocity in two dimensions (White, 1993; White and Frederiksen, 1998). Objects in the ThinkerTools microworld are represented as simple, uniformly sized “dots” to avoid students becoming confused about the idea of center of mass. Students use the microworld to solve various problems of motion in one or two dimensions, using the com-

puter keyboard to apply forces to dots to move them along specified paths. Part of the key to the software’s guidance is that it provides representations of forces and accelerations in which students can see change in response to their actions. A “dot trace,” for example, shows students how applying more force affects an object’s acceleration in a predictable way. A “vector cross” represents the individual components of forces applied in two dimensions in a way that helps students to link those forces to an object’s motion.

ThinkerTools is but one example of this type of interactive, representational software. Others have been developed to help students reason about motion (Roschelle, 1992), electricity (Gutwill, Fredericksen, and White, 1999), heat and temperature (Linn, Bell, and Hsi, 1998), genetics (Horwitz and Christie, 2000), and chemical reactions (Kozma, 2003), among others. These programs differ substantially from one another in how they represent their target phenomena, as there are substantial differences in the topics themselves and in the problems that students are known to have in understanding them. They share, however, a common approach to solving a similar set of problems—how to represent natural phenomena that are otherwise invisible in ways that help students make their own thinking explicit and guide them to normative scientific understanding.

When used as a supplement to hands-on laboratory experiences within integrated instructional units, these representations can support students’ conceptual change (e.g., Linn et al., 1998; White and Frederiksen, 1998). For example, students working through the ThinkerTools curriculum always experiment with objects in the real world before they work with the computer tools. The goals of the laboratory experiences are to provide some experience with the phenomena under study and some initial ideas that can then be explored on the computer.

Structured Simulations of Inaccessible Phenomena

Various types of simulations of phenomena represent another form of technology for science learning. These simulations allow students to explore and observe phenomena that are too expensive, infeasible, or even dangerous to interact with directly. Strictly speaking, a computer simulation is a program that simulates a particular phenomenon by running a computational model whose behavior can sometimes be changed by modifying input parameters to the model. For example, the GenScope program provides a set of linked representations of genetics and genetics phenomena that would otherwise be unavailable for study to most students (Horowitz and Christie, 2000). The software represents alleles, chromosomes, family pedigrees, and the like and links representations across levels in ways that enable students to trace inherited traits to specific genetic differences. The software uses an underlying Mendelian model of genetic inheritance to gov-

ern its behavior. As with the representations described above, embedding the use of the software in a carefully thought out curriculum sequence is crucial to supporting student learning (Hickey et al., 2000).

Another example in biology is the BGuILE project (Reiser et al., 2001). The investigators created a series of structured simulations allowing students to investigate problems of evolution by natural selection. In the Galapagos finch environment, for example, students can examine a carefully selected set of data from the island of Daphne Major to explain a historical case of natural selection. The BGuILE software does not, strictly speaking, consist of simulations because it does not “run” a model; from a student’s perspective, it simulates either Daphne Major or laboratory experiments on tuberculosis bacteria. Studies show that students can learn from the BGuILE environments when these environments are embedded in a well-organized curriculum (Sandoval and Reiser, 2004). They also show that successful implementation of such technology-supported curricula relies heavily on teachers (Tabak, 2004).

Structured Interactions with Complex Phenomena and Ideas

The examples discussed here share a crucial feature. The representations built into the software and the interface tools provided for learners are intended to help them learn in very specific ways. There are a great number of such tools that have been developed over the last quarter of a century. Many of them have been shown to produce impressive learning gains for students at the secondary level. Besides the ones mentioned, other tools are designed to structure specific scientific reasoning skills, such as prediction (Friedler et al., 1990) and the coordination of claims with evidence (Bell and Linn, 2000; Sandoval, 2003). Most of these efforts integrate students’ work on the computer with more direct laboratory experiences. Rather than thinking of these representations and simulations as a way to replace laboratory experiences, the most successful instructional sequences integrate them with a series of empirical laboratory investigations. These sequences of science instruction focus students’ attention on developing a shared interpretation of both the representations and the real laboratory experiences in small groups (Bell, 2005).

Computer Technologies Designed to Support Science

Advances in computer technologies have had a tremendous impact on how science is done and on what scientists can study. These changes are vast, and summarizing them is well beyond the scope of the committee’s charge. We found, however, that some innovations in scientific practice, especially uses of the Internet, are beginning to be applied to secondary

science education. With respect to future laboratory experiences, perhaps the most significant advance in many scientific fields is the aggregation of large, varied data sets into Internet-accessible databases. These databases are most commonly built for specific scientific communities, but some researchers are creating and studying new, learner-centered interfaces to allow access by teachers and schools. These research projects build on instructional design principles illuminated by the integrated instructional units discussed above.

One example is the Center for Embedded Networked Sensing (CENS), a National Science Foundation Science and Technology Center investigating the development and deployment of large-scale sensor networks embedded in physical environments. CENS is currently working on ecosystem monitoring, seismology, contaminant flow transport, and marine microbiology. As sensor networks come on line, making data available, science educators at the center are developing middle school curricula that include web-based tools to enable students to explore the same data sets that the professional scientists are exploring (Pea, Mills, and Takeuchi, 2004).

The interfaces professional scientists use to access such databases tend to be too inflexible and technical for students to use successfully (Bell, 2005). Bounding the space of possible data under consideration, supporting appropriate considerations of theory, and promoting understanding of the norms used in the visualization can help support students in developing a shared understanding of the data. With such support, students can develop both conceptual understanding and understanding of the data analysis process. Focusing students on causal explanation and argumentation based on the data analysis process can help them move from a descriptive, phenomenological view of science to one that considers theoretical issues of cause (Bell, 2005).

Further research and evaluation of the educational benefit of student interaction with large scientific databases are absolutely necessary. Still, the development of such efforts will certainly expand over time, and, as they change notions of what it means to conduct scientific experiments, they are also likely to change what it means to conduct a school laboratory.

The committee identified a number of science learning goals that have been attributed to laboratory experiences. Our review of the evidence on attainment of these goals revealed a recent shift in research, reflecting some movement in laboratory instruction. Historically, laboratory experiences have been disconnected from the flow of classroom science lessons. We refer to these separate laboratory experiences as typical laboratory experiences. Reflecting this separation, researchers often engaged students in one or two

experiments or other science activities and then conducted assessments to determine whether their understanding of the science concept underlying the activity had increased. Some studies compared the outcomes of these separate laboratory experiences with the outcomes of other forms of science instruction, such as lectures or discussions.

Over the past 10 years, researchers studying laboratory education have shifted their focus. Drawing on principles of learning derived from the cognitive sciences, they have asked how to sequence science instruction, including laboratory experiences, in order to support students’ science learning. We refer to these instructional sequences as “integrated instructional units.” Integrated instructional units connect laboratory experiences with other types of science learning activities, including lectures, reading, and discussion. Students are engaged in framing research questions, making observations, designing and executing experiments, gathering and analyzing data, and constructing scientific arguments and explanations.

The two bodies of research on typical laboratory experiences and integrated instructional units, including laboratory experiences, yield different findings about the effectiveness of laboratory experiences in advancing the science learning goals identified by the committee. The earlier research on typical laboratory experiences is weak and fragmented, making it difficult to draw precise conclusions. The weight of the evidence from research focused on the goals of developing scientific reasoning and enhancing student interest in science showed slight improvements in both after students participated in typical laboratory experiences. Research focused on the goal of student mastery of subject matter indicates that typical laboratory experiences are no more or less effective than other forms of science instruction (such as reading, lectures, or discussion).

Studies conducted to date on integrated instructional units indicate that the laboratory experiences, together with the other forms of instruction included in these units, show greater effectiveness for these same three goals (compared with students who received more traditional forms of science instruction): improving students’ mastery of subject matter, increasing development of scientific reasoning, and enhancing interest in science. Integrated instructional units also appear to be effective in helping diverse groups of students progress toward these three learning goals . A major limitation of the research on integrated instructional units, however, is that most of the units have been used in small numbers of science classrooms. Only a few studies have addressed the challenge of implementing—and studying the effectiveness of—integrated instructional units on a wide scale.

Due to a lack of available studies, the committee was unable to draw conclusions about the extent to which either typical laboratory experiences or integrated instructional units might advance the other goals identified at the beginning of this chapter—enhancing understanding of the complexity

and ambiguity of empirical work, acquiring practical skills, and developing teamwork skills. Further research is needed to clarify how laboratory experiences might be designed to promote attainment of these goals.

The committee considers the evidence sufficient to identify four general principles that can help laboratory experiences achieve the learning goals we have outlined. Laboratory experiences are more likely to achieve their intended learning goals if (1) they are designed with clear learning outcomes in mind, (2) they are thoughtfully sequenced into the flow of classroom science instruction, (3) they are designed to integrate learning of science content with learning about the processes of science, and (4) they incorporate ongoing student reflection and discussion.

Computer software and the Internet have enabled development of several tools that can support students’ science learning, including representations of complex phenomena, simulations, and student interaction with large scientific databases. Representations and simulations are most successful in supporting student learning when they are integrated in an instructional sequence that also includes laboratory experiences. Researchers are currently developing tools to support student interaction with—and learning from—large scientific databases.

Anderson, R.O. (1976). The experience of science: A new perspective for laboratory teaching . New York: Columbia University, Teachers College Press.

Ato, T., and Wilkinson, W. (1986). Relationships between the availability and use of science equipment and attitudes to both science and sources of scientific information in Benue State, Nigeria. Research in Science and Technological Education , 4 , 19-28.

Beasley, W.F. (1985). Improving student laboratory performance: How much practice makes perfect? Science Education , 69 , 567-576.

Bell, P. (2005). The school science laboratory: Considerations of learning, technology, and scientific practice . Paper prepared for the Committee on High School Science Laboratories: Role and Vision. Available at: http://www7.nationalacademies.org/bose/July_12-13_2004_High_School_Labs_Meeting_Agenda.html [accessed June 2005].

Bell, P., and Linn, M.C. (2000). Scientific arguments as learning artifacts: Designing for learning from the web with KIE. International Journal of Science Education , 22 (8), 797-817.

Ben-Zvi, R., Hofstein, A., Kampa, R.F, and Samuel, D. (1976). The effectiveness of filmed experiments in high school chemical education. Journal of Chemical Education , 53 , 518-520.

Blakeslee, T., Bronstein, L., Chapin, M., Hesbitt, D., Peek, Y., Thiele, E., and Vellanti, J. (1993). Chemistry that applies . Lansing: Michigan Department of Education. Available at: http://www.ed-web2.educ.msu.edu/CCMS/secmod/Cluster3.pdf [accessed Feb. 2005].

Bransford, J.D., and Schwartz, D.L. (2001). Rethinking transfer: A simple proposal with multiple implications. In A. Iran-Nejad, and P.D. Pearson (Eds.), Review of research in education (pp. 61-100). Washington, DC: American Educational Research Association.

Bryce, T.G.K., and Robertson, I.J. (1985). What can they do: A review of practical assessment in science. Studies in Science Education , 12 , 1-24.

Carey, S., and Smith, C. (1993). On understanding the nature of scientific knowledge. Educational Psychologist , 28 , 235-251.

Champagne, A.B., Gunstone, R.F., and Klopfer, L.E. (1985). Instructional consequences of students’ knowledge about physical phenomena. In L.H.T. West and A.L. Pines (Eds.), Cognitive structure and conceptual change (pp. 61-68). New York: Academic Press.

Chang, H.P., and Lederman, N.G. (1994). The effect of levels of co-operation within physical science laboratory groups on physical science achievement. Journal of Research in Science Teaching , 31 , 167-181.

Cobb, P., Confrey, J., diSessa, A., Lehrer, R., and Schauble, L. (2003). Design experiments in educational research. Educational Researcher , 32 (1), 9-13.

Cobb, P., Stephan, M., McClain, K., and Gavemeijer, K. (2001). Participating in classroom mathematical practices. Journal of the Learning Sciences , 10 , 113-164.

Coleman, E.B. (1998). Using explanatory knowledge during collaborative problem solving in science. Journal of the Learning Sciences , 7 (3, 4), 387-427.

Collins, A., Joseph, D., and Bielaczyc, K. (2004). Design research: Theoretical and methodological issues. Journal of the Learning Sciences , 13 (1), 15-42.

Coulter, J.C. (1966). The effectiveness of inductive laboratory demonstration and deductive laboratory in biology. Journal of Research in Science Teaching , 4 , 185-186.

Denny, M., and Chennell, F. (1986). Exploring pupils’ views and feelings about their school science practicals: Use of letter-writing and drawing exercises. Educational Studies , 12 , 73-86.

Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher , 32 (1), 5-8.

Driver, R. (1995). Constructivist approaches to science teaching. In L.P. Steffe and J. Gale (Eds.), Constructivism in education (pp. 385-400). Hillsdale, NJ: Lawrence Erlbaum.

Driver, R., Leach, J., Millar, R., and Scott, P. (1996). Young people’s images of science . Buckingham, UK: Open University Press.

Driver, R., Newton, P., and Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education , 84 , 287-312.

Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science , 17 , 397-434.

Dupin, J.J., and Joshua, S. (1987). Analogies and “modeling analogies” in teaching: Some examples in basic electricity. Science Education , 73 , 791-806.

Duschl, R.A. (2004). The HS lab experience: Reconsidering the role of evidence, explanation and the language of science . Paper prepared for the Committee on High School Science Laboratories: Role and Vision, July 12-13, National Research Council, Washington, DC. Available at: http://www7.nationalacademies.org/bose/July_12-13_2004_High_School_Labs_Meeting_Agenda.html [accessed July 2005].

Duschl, R.A., and Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education , 38 , 39-72.

Eylon, B., and Linn, M.C. (1988). Learning and instruction: An examination of four research perspectives in science education. Review of Educational Research , 58 (3), 251-301.

Fraser, B.J., Giddings, G.J., and McRobbie, C.J. (1995). Evolution and validation of a personal form of an instrument for assessing science laboratory classroom environments. Journal of Research in Science Teaching , 32 , 399-422.

Fraser, B.J., McRobbie, C.J., and Giddings, G.J. (1993). Development and cross-national validation of a laboratory classroom environment instrument for senior high school science. Science Education , 77 , 1-24.

Freedman, M.P. (2002). The influence of laboratory instruction on science achievement and attitude toward science across gender differences. Journal of Women and Minorities in Science and Engineering , 8 , 191-200.

Friedler, Y., Nachmias, R., and Linn, M.C. (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching , 27 (2), 173-192.

Glaser, R. (1994). Learning theory and instruction. In G. d’Ydewalle, P. Eelen, and P. Bertelson (Eds.), International perspectives on science, volume 2: The state of the art (pp. 341-357). Hove, England: Erlbaum.

Gobert, J., and Clement, J. (1999). The effects of student-generated diagrams versus student-generated summaries on conceptual understanding of spatial, causal, and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching , 36 (1), 39-53.

Gutwill, J.P., Fredericksen, J.R., and White, B.Y. (1999). Making their own connections: Students’ understanding of multiple models in basic electricity. Cognition and Instruction , 17 (3), 249-282.

Hatano, G., and Inagaki, K. (1991). Sharing cognition through collective comprehension activity. In L.B. Resnick, J.M. Levine, and S.D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 331-348). Washington, DC: American Psychological Association.

Henderson, D., Fisher, D., and Fraser, B. (2000). Interpersonal behavior, laboratory learning environments, and student outcomes in senior biology classes. Journal of Research in Science Teaching , 37 , 26-43.

Hickey, D.T., Kindfield, A.C.H., Horwitz, P., and Christie, M.A. (2000). Integrating instruction, assessment, and evaluation in a technology-based genetics environment: The GenScope follow-up study. In B.J. Fishman and S.F. O’Connor-Divelbiss (Eds.), Proceedings of the International Conference of the Learning Sciences (pp. 6-13). Mahwah, NJ: Lawrence Erlbaum.

Hickey, D.T., Kindfield, A.C., Horwitz, P., and Christie, M.A. (2003). Integrating curriculum, instruction, assessment, and evaluation in a technology-supported genetics environment. American Educational Research Journal , 40 (2), 495-538.

Hodson, D. (1993). Philosophic stance of secondary school science teachers, curriculum experiences, and children’s understanding of science: Some preliminary findings. Interchange , 24 , 41-52.

Hofstein, A., and Lunetta, V.N. (1982). The role of the laboratory in science teaching: Neglected aspects of research. Review of Educational Research , 52 (2), 201-217.

Hofstein, A., and Lunetta, V.N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education , 88 , 28-54.

Holden, C. (1990). Animal rights activism threatens dissection. Science , 25 , 751.

Horowitz, P. (1996). Linking models to data: Hypermodels for science education. High School Journal , 79 (2), 148-156.

Horowitz, P., and Christie, M.A. (2000). Computer-based manipulatives for teaching scientific reasoning: An example. In M.J. Jacobson and R.B. Kozma (Eds.), Innovations in science and mathematics education: Advanced designs for technologies of learning (pp. 163-191). Mahwah, NJ: Lawrence Erlbaum.

Jovanovic, J., and King, S.S. (1998). Boys and girls in the performance-based science classroom: Who’s doing the performing? American Educational Research Journal , 35 (3), 477-496.

Kesidou, S., and Roseman, J. (2002). How well do middle school science programs measure up? Findings from Project 2061’s curriculum review. Journal of Research in Science Teaching , 39 (6), 522-549.

Khishfe, R., and Abd-El-Khalick, F. (2002). Influence of explicit and reflective versus implicit inquiry-oriented instruction on sixth graders’ views of nature of science. Journal of Research in Science Teaching , 39 (7), 551-578.

Klopfer, L.E. (1990). Learning scientific enquiry in the student laboratory. In E. Hegarty-Hazel (Ed.), The student laboratory and the science curriculum (pp. 95-118). London, England: Routledge.

Kozma, R.B. (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction , 13 , 205-226.

Kuhn, D., Schauble, L., and Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction , 9 (4), 285-327.

Lazarowitz, R., and Tamir, P. (1994). Research on using laboratory instruction in science. In D.L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 94-130). New York: Macmillan.

Lederman, N.G. (1992). Students’ and teachers’ conceptions of the nature of science: A review of the research. Journal of Research in Science Teaching , 29 (4), 331-359.

Lederman, N.G., Abd-El-Khalick, F., Bell, R.L., and Schwartz, R.S. (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of Research in Science Teaching , 39 (6), 497-521.

Lehrer, R., and Schauble, L. (2004). Scientific thinking and science literacy: Supporting development in learning contexts. In W. Damon, R. Lerner, K. Anne Renninger, and E. Sigel (Eds.), Handbook of child psychology, sixth edition, volume four: Child psychology in practice . Hoboken, NJ: John Wiley & Sons.

Lehrer, R., Schauble, L., Strom, D., and Pligge, M. (2001). Similarity of form and substance: Modeling material kind. In S.M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress . Mahwah, NJ: Lawrence Erlbaum.

Lemke, J. (1990). Talking science: Language, learning, and values . Norwood, NJ: Ablex.

Linn, M.C. (1997). The role of the laboratory in science learning. Elementary School Journal , 97 , 401-417.

Linn, M.C. (2004). High school science laboratories: How can technology contribute? Presentation to the Committee on High School Science Laboratories: Role and Vision. June. Available at: http://www7.nationalacademies.org/bose/June_3-4_2004_High_School_Labs_Meeting_Agenda.html [accessed April 2005].

Linn, M.C., Bell, P., and Hsi, S. (1998). Using the Internet to enhance student understanding of science: The knowledge integration environment. Interactive Learning Environments , 6 (1-2), 4-38.

Linn, M.C., Davis, E., and Bell, P. (2004a). Inquiry and technology. In M.C. Linn, E. Davis, and P. Bell, (Eds.), Internet environments for science education . Mahwah, NJ: Lawrence Erlbaum.

Linn, M.C., Davis, E., and Bell, P. (Eds.). (2004b). Internet environments for science education . Mahwah, NJ: Lawrence Erlbaum.

Linn, M.C., and Hsi, S. (2000). Computers, teachers, peers . Mahwah, NJ: Lawrence Erlbaum.

Linn, M.C., and Songer, B. (1991). Teaching thermodynamics to middle school children: What are appropriate cognitive demands? Journal of Research in Science Teaching , 28 (10), 885-918.

Lunetta, V.N. (1998). The school science laboratory. In B.J. Fraser and K.G. Tobin (Eds.), International handbook of science education (pp. 249-262). London, England: Kluwer Academic.

Lynch, S. (2004). What are the effects of highly rated, lab-based curriculum materials on diverse learners? Presentation to the Committee on High School Science Laboratories: Role and Vision. July 12. Available at: http://www7.nationalacademies.org/bose/July_12-13_2004_High_School_Labs_Meeting_Agenda.html [accessed Oct. 2004].

Lynch, S., Kuipers, J., Pyke, C., and Szesze, M. (In press). Examining the effects of a highly rated science curriculum unitinstructional unit on diverse populations: Results from a planning grant. Journal of Research in Science Teaching .

Lynch, S., and O’Donnell, C. (2005). The evolving definition, measurement, and conceptualization of fidelity of implementation in scale-up of highly rated science curriculum unitsintegrated instructional units in diverse middle schools . Paper presented at the annual meeting of the American Educational Research Association, April 7, Montreal, Canada.

McRobbie, C.J., and Fraser, B.J. (1993). Associations between student outcomes and psychosocial science environment. Journal of Educational Research , 87 , 78-85.

Meichtry, Y.J. (1993). The impact of science curricula on student views about the nature of science. Journal of Research in Science Teaching , 30 (5), 429-443.

Metz, K.E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction , 22 (2), 219-290.

Meyer, K., and Woodruff, E. (1997). Consensually driven explanation in science teaching. Science Education , 80 , 173-192.

Millar, R. (1998). Rhetoric and reality: What practical work in science education is really for. In J. Wellington (Ed.), Practical work in school science: Which way now? (pp. 16-31). London, England: Routledge.

Millar, R. (2004). The role of practical work in the teaching and learning of science . Paper prepared for the Committee on High School Science Laboratories: Role and Vision. Available at: http://www7.nationalacademies.org/bose/June3-4_2004_High_School_Labs_Meeting_Agenda.html [accessed April 2005].

National Research Council. (1999). How people learn: Brain, mind, experience, and school . Committee on Developments in the Science of Learning, J.D. Bransford, A.L. Brown, and R.R. Cocking (Eds.). Washington, DC: National Academy Press.

National Research Council. (2001). Eager to learn: Educating our preschoolers . Committee on Early Childhood Pedagogy. B.T. Bowman, M.S. Donovan, and M.S. Burns (Eds.). Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council. (2005). Systems for state science assessment . Committee on Test Design for K-12 Science Achievement, M.R. Wilson and M.W. Bertenthal (Eds.). Board on Testing and Assessment, Center for Education. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Osborne, R., and Freyberg, P. (1985). Learning in science: The implications of children’s science . London, England: Heinemann.

Partnership for 21st Century Skills. (2003). Learning for the 21st century . Washington, DC: Author. Available at: http://www.21stcenturyskills.org/reports/learning.asp [accessed April 2005].

Pea, R., Mills, M., and Takeuchi, L. (Eds). (2004). Making SENS: Science education networks of sensors . Report from an OMRON-sponsored workshop of the Media-X Program at Stanford University, October 3. Stanford, CA: Stanford Center for Innovations in Learning. Available at:: http://www.makingsens.stanford.edu/index.html [accessed May 2005].

Raghubir, K.P. (1979). The laboratory investigative approach to science instruction. Journal of Research in Science Teaching , 16 , 13-18.

Reif, F., and St. John, M. (1979) Teaching physicists thinking skills in the laboratory. American Journal of Physics , 47 (11), 950-957.

Reiner, M., Pea, R.D., and Shulman, D.J. (1995). Impact of simulator-based instruction on diagramming in geometrical optics by introductory physics students. Journal of Science Education and Technology , 4 (3), 199-225.

Reiser, B.J., Tabak, I., Sandoval, W.A., Smith, B.K., Steinmuller, F., and Leone, A.J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S.M. Carver and D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263-305). Mahwah, NJ: Lawrence Erlbaum.

Renner, J.W., Abraham, M.R., and Birnie, H.H. (1985). Secondary school students’ beliefs about the physics laboratory, Science Education , 69 , 649-63.

Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. Journal of the Learning Sciences , 2 (3), 235-276.

Roschelle, J., Kaput, J., and Stroup, W. (2000). SimCalc: Accelerating students’ engagement with the mathematics of change. In M.J. Jacobsen and R.B. Kozma (Eds). Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 47-75). Hillsdale, NJ: Lawrence Erlbaum.

Rosebery, A.S., Warren, B., and Conant, F.R. (1992). Appropriating scientific discourse: Findings from language minority classrooms. Journal of the Learning Sciences , 2 (1), 61-94.

Salomon, G. (1996). Studying novel learning environments as patterns of change. In S. Vosniadou, E. De Corte, R. Glaser, and H. Mandl (Eds.), International perspectives on the design of technology-supported learning environments (pp. 363-377). Mahwah, NJ: Lawrence Erlbaum.

Sandoval, W.A. (2003). Conceptual and epistemic aspects of students’ scientific explanations. Journal of the Learning Sciences , 12 (1), 5-51.

Sandoval, W.A., and Millwood, K.A. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction , 23 (1), 23-55.

Sandoval, W.A., and Morrison, K. (2003). High school students’ ideas about theories and theory change after a biological inquiry unit. Journal of Research in Science Teaching , 40 (4), 369-392.

Sandoval, W.A., and Reiser, B.J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic supports for science inquiry. Science Education , 88 , 345-372.

Schauble, L., Glaser, R., Duschl, R.A., Schulze, S., and John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. Journal of the Learning Sciences , 4 (2), 131-166.

Schauble, L., Klopfer, L.E., and Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching , 28 (9), 859-882.

Shaffer, P.S., and McDermott, L.C. (1992). Research as a guide for curriculum development: An example from introductory electricity. Part II: Design of instructional strategies. American Journal of Physics , 60 (11), 1003-1013.

Shepardson, D.P., and Pizzini, E.L. (1993). A comparison of student perceptions of science activities within three instructional approaches. School Science and Mathematics , 93 , 127-131.

Shulman, L.S., and Tamir, P. (1973). Research on teaching in the natural sciences. In R.M.W. Travers (Ed.), Second handbook of research on teaching . Chicago: Rand-McNally.

Singer, R.N. (1977). To err or not to err: A question for the instruction of psychomotor skills. Review of Educational Research , 47 , 479-489.

Smith, C.L., Maclin, D., Grosslight, L., and Davis, H. (1997). Teaching for understanding: A study of students’ pre-instruction theories of matter and a comparison of the effectiveness of two approaches to teaching about matter and density. Cognition and Instruction , 15 , 317-394.

Smith, C.L., Maclin, D., Houghton, C., and Hennessey, M. (2000). Sixth-grade students’ epitemologies of science: The impact of school science experiences on epitemological development. Cognition and Instruction , 18 , 349-422.

Snir, J., Smith, C.L., and Raz, G. (2003). Linking phenomena with competing underlying models: A software tool for introducing students to the particulate model of matter. Science Education , 87 (6), 794-830.

Songer, N.B., and Linn, M.C. (1991). How do students’ views of science influence knowledge integration? Journal of Research in Science Teaching , 28 (9), 761-784.

Tabak, I. (2004). Synergy: a complement to emerging patterns of distributed scaffolding. Journal of the Learning Sciences , 13 (3), 305-335.

Tasker, R. (1981). Children’s views and classroom experiences. Australian Science Teachers’ Journal , 27 , 33-37.

Tiberghien, A., Veillard, L., Le Marechal, J.-F., Buty, C., and Millar, R. (2000). An analysis of labwork tasks used in science teaching at upper secondary school and university levels in several European countries. Science Education , 85 , 483-508.

Tobin, K. (1987). Forces which shape the implemented curriculum in high school science and mathematics. Teaching and Teacher Education , 3 (4), 287-298.

VandenBerg, E., Katu, N., and Lunetta, V.N. (1994). The role of “experiments” in conceptual change . Paper presented at the annual meeting of the National Association for Research in Science Teaching, Anaheim, CA.

Webb, N.M., Nemer, K.M., Chizhik, A.W., and Sugrue, B. (1998). Equity issues in collaborative group assessment: Group composition and performance. American Educational Research Journal , 35 (4), 607-652.

Webb, N.M., and Palincsar, A.S. (1996). Group processes in the classroom. In D.C. Berliner and R.C. Calfee (Eds.), Handbook of educational psychology (pp. 841-873). New York: Macmillan.

Wells, M., Hestenes, D., and Swackhamer, G. (1995). A modeling method for high school physics instruction. American Journal of Physics , 63 (7), 606-619.

Wheatley, J.H. (1975).Evaluating cognitive learning in the college science laboratory. Journal of Research in Science Teaching , 12 , 101-109.

White, B.Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction , 10 (1), 1-100.

White, B.Y., and Frederiksen, J.R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction , 16 (1), 3-118.

White, R.T. (1996). The link between the laboratory and learning. International Journal of Science Education , 18 , 761-774.

White, R.T., and Gunstone, R.F. (1992). Probing understanding . London, England: Falmer.

Wilkenson, J.W., and Ward, M. (1997). The purpose and perceived effectiveness of laboratory work in secondary schools. Australian Science Teachers’ Journal , 43-55.

Wong, A.F.L., and Fraser, B.J. (1995). Cross-validation in Singapore of the science laboratory environment inventory. Psychological Reports , 76 , 907-911.

Woolnough, B.E. (1983). Exercises, investigations and experiences. Physics Education , 18 , 60-63.

Yager, R.E., Engen, J.B., and Snider, C.F. (1969). Effects of the laboratory and demonstration method upon the outcomes of instruction in secondary biology. Journal of Research in Science Teaching , 5 , 76-86.

Zimmerman, C. (2000). The development of scientific reasoning skills. Developmental Review , 20 , 99-149.

Laboratory experiences as a part of most U.S. high school science curricula have been taken for granted for decades, but they have rarely been carefully examined. What do they contribute to science learning? What can they contribute to science learning? What is the current status of labs in our nation�s high schools as a context for learning science? This book looks at a range of questions about how laboratory experiences fit into U.S. high schools:

• What is effective laboratory teaching?
• What does research tell us about learning in high school science labs?
• How should student learning in laboratory experiences be assessed?
• Do all student have access to laboratory experiences?
• What changes need to be made to improve laboratory experiences for high school students?
• How can school organization contribute to effective laboratory teaching?

With increased attention to the U.S. education system and student outcomes, no part of the high school curriculum should escape scrutiny. This timely book investigates factors that influence a high school laboratory experience, looking closely at what currently takes place and what the goals of those experiences are and should be. Science educators, school administrators, policy makers, and parents will all benefit from a better understanding of the need for laboratory experiences to be an integral part of the science curriculum—and how that can be accomplished.

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

The National Laboratories

Pushing the boundaries of science

The transformative science and technology solutions being discovered across the 17 National Laboratories are changing the way the world sees innovation.

Learn More →

Looking for the best & brightest

Internships, fellowships, and opportunities to further your research await you at the National Laboratories.

Innovation drives us

By recruiting and retaining a diverse workforce, the National Laboratories can better fulfill their mission to deliver scientific breakthroughs.

Get to know our workforce

The National Labs’ success depends on the unique insights and perspectives enabled by a diverse workforce.

Improving lives

Innovation is at the heart of everything done inside the National Laboratories. Our scientists strive to make our world a safer, better place for all.

The Department of Energy’s 17 National Laboratories are powerhouses of science and technology whose researchers tackle some of the world’s toughest challenges. The Laboratories support scientists and engineers from academia, government, and industry with access to specialized equipment, world-class research facilities, and skilled technical staff. Together, they are working to solve some of the world’s greatest scientific challenges.

From innovations in energy technologies and sustainable building design to medical discoveries and improved national security, National Laboratory scientists and engineers are inventing solutions that make America and the world safer, healthier, and more sustainable.

The U.S. Department of Energy’s 17 National Laboratories lead the nation in advancing the frontiers of scientific knowledge, keeping our nation secure, and fueling our clean energy economy. The innovation at the heart of the Laboratories’ past and future success benefits from the fusion of diverse talents and inclusive perspectives.

The Department of Energy’s 17 National Laboratories are powerhouses of science and technology.

STEM Resources

The National Laboratories are committed to advancing Science, Technology, Engineering, and Mathematics (STEM) in our nation’s schools.

The National Laboratories are a diverse and inclusive career destination for the next generation of scientists and engineers aspiring to make an impact through their research.

• Utility Menu

Psychology Undergraduate Program

• Department of Psychology

Finding a Research Lab

"When I joined a lab, I watched firsthand as graduate students navigated through different phases of the research process - designing a study, collecting data, analyzing that data - and was even able to participate in that process myself. No amount of classwork or reading can match that direct experience."

- Micaela Rodriguez, Class of 2020 Concentrator

Working in a research lab is an incredible experience and there are many options available to you! This page is your guide to getting involved in psychology research as an undergraduate at Harvard.  

What is Lab Research?  

Lab research is how psychological scientists make discoveries. When you work in a lab, you can expect to be involved in all aspects of the experimental research process: completing administrative tasks that keep the lab and its research projects running, reading and reviewing literature, collecting, coding and analyzing data, preparing written and oral reports, and participating in lab meetings and journal clubs. Labs are run by a Principal Investigator (or PI) who is usually a faculty member. Most undergraduates working in labs are closely mentored by a graduate student or postdoc in the lab in addition to the PI. Over 85% of Psychology concentrators at Harvard conduct research in a lab at some point during their academic career!

We maintain a list of past undergradauates who have been enrolled in lab courses with various supervisors and are willing to be in touch about their experience. If you would like to be put in touch with a past student, please reach out to Garth Coombs ( [email protected] ).  

How Do I Find a Lab to Join?  

There are several places you can check for lab research opportunities! Here are a few to get you started...

• The UGO's  Departmental Research Opportunities  - Each semester, labs that are actively seeking undergraduates for research assistantships post openings here. See each posting for more information about current projects and how to get involved in them.
• The UGO's  Non-Departmental Research Opportunities  - Opportunities for undergraduate research sponsored by faculty outside of the Psychology Department at Harvard.
• The UGO's  Summer Opportunities - Updated in December/January with opportunities for the following summer. Check back often for updates! 
• Faculty Lab Websites  - Each Psychology faculty member has a lab website that describes the research program of the lab and provides contact information. If you find a lab you’re interested in, contact the faculty member or lab manager to see if there are any open opportunities for undergraduates!
• Board of Honors Tutors  - This is a list of researchers in psychology and related disciplines from the broader Harvard community who may be interested in supervising undergraduate research assistants. Reach out to any faculty whose research interests you!
• The MBB Program's Research and Other Opportunities Board - Not monitored by the Department of Psychology, but these postings may be applicable to Psychology students. Check back often for updates!

For a discussion of helpful issues to consider when joining a lab and matching with a faculty mentor, check out this Neuron article on How to Pick a Graduate Advisor (the tips are equally helpful for undergraduates).

You can also check out our handout  How to Join a Lab!  

How Do I Reach Out?  

If the lab has posted on the UGO's  Departmental Research Listings , they will provide contact information and tell you what they are looking for from you (resumé, e-mail expressing interest, etc.). If you have explored their lab site and have not found any contact information, you are welcome to reach out to the faculty member or lab manager, usually via e-mail.

In your e-mail, you should briefly introduce yourself (name, status as a Harvard undergraduate), and explain what areas of their research interest you. This means you should have done your homework and know what work is being done in the lab. Finally, you can politely ask if there are any openings for a research assistant for the coming semester. Be sure to clarify whether you are seeking course credit, volunteer work, or a paid position.

Here is a fantastic resource for e-mail etiquette you might want to consult before hitting "send"!  

Frequently Asked Questions  

How much time will i spend working in the lab  .

If you’re working in the lab for course credit , you are expected to commit 8-10 hours a week to the lab.

If you want to see what it's like to work in the lab without making a semester-long commitment, you might start by volunteering in the lab for a few hours a week or asking permission to sit in on a few discussion groups or lab meetings. Some labs are not able to accommodate this type of request, but will most likely be willing to meet with you and show you the lab.

In general, faculty members are looking for people who seem interested and excited by their research and would be dedicated research assistants. Keep this in mind when inquiring!

Can I change labs?  

You certainly can! Sometimes students stay in the first lab they work in for several semesters or several years. Other students try out several different labs over the course of their time in the concentration. Some labs have commitment expectations - e.g., a two-semester minimum, and you’ll want to be sure you’re aware of this up front. You’re likely to have the most fulfilling research experience when you’re excited about the research ideas and work in the lab, and it may take time to find your true passion. Feel free to check in with your CA or the UGO for advice on changing labs, and keep the conversation open and honest with your supervisors as well!

Will I come up with my own project idea, or will I be assigned to an ongoing project ?  

It depends! This will vary by lab – typically, you’ll be assigned to an ongoing project that a graduate student or postdoc is working on. If you’re pursuing a thesis project, however, you’ll take an independent role in developing the study and making an original intellectual contribution. It’s good to start by working on a project that the lab is already equipped to conduct – that way, you’ll already have ready access to subject pools, equipment, and lab members familiar with your topic and methods.

Are you interested in getting involved in research in the Psychology Department but not sure where to start? Chat with Garth

• Research Experiences:  What does research in the Department look like? What labs might be a good fit with my particular interests? How do I find and reach out to labs?
• Post-graduate Plans:  How do I set myself up for or apply to graduate school? What about research assistant positions? 
• Honors Thesis:  Is a thesis for me? What is the thesis process like? How and when do I get started? Which faculty are eligible to supervise a thesis?

Before your meeting, think about what kinds of research interest you. Which psychology courses did you enjoy the most? Did you read an article or hear a speaker discuss a topic that made you want to learn more?

Email Garth at [email protected] with any questions or to schedule an appointment!

• Departmental Research Opportunities
• Non-Departmental Research Opportunities
• Board of Honors Tutors
• Course Credit for Research
• Post-Graduate Research Jobs
• Summer Opportunities

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Turk J Anaesthesiol Reanim
• v.44(4); 2016 Aug

What is Scientific Research and How Can it be Done?

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

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

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

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

Classification of Scientific Research

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

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

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

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

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

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

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

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

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

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

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

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

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

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

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

Clinical Research

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

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

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

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

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

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

I. Calculation of the Number of Subjects

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

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

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

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

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

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

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

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

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

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

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

b. Difference between Groups and ES

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

c. Allocation Ratio of Groups

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

d. Direction of Hypothesis (H1)

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

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

II. Blinding (Masking) and Randomisation Methods

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

Blinding Method

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

Randomisation Method

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

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

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

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

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

Bias and Chicanery

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Peer-review: Externally peer-reviewed.

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

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

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

Research Labs

UW Medicine Pathology has a strong focus on basic science and clinical research. Our research principle, followed since the establishment of the University of Washington School of Medicine, is that research based on the most modern methods and the up to date knowledge of basic sciences is an essential component of an academic department of pathology. This is particularly true at the present time when new discoveries generated by basic biomedical research are applied to pathology practice. The research done by our faculty has received high recognition both nationally and internationally.

Below is a roster of Department of Pathology research laboratories:

Akilesh Lab

The Akilesh lab uses functional genomic, 3D culture systems and digital spatial profiling methods to study kidney disease and kidney cancer.

The Alpers Laboratory of Renal Pathology has research interests which include experimental renal disease, diagnostic human renal pathology, developmental renal biology, and solid organ transplant rejection.

Bornfeldt Lab

The Bornfeldt Laboratory is dedicated to understanding the cellular and molecular mechanisms of diabetes-accelerated cardiovascular disease, so that these complications can be effectively treated or prevented.

The focus of the Byers Research Laboratory is to identify and characterize the molecular bases and disease mechanisms responsible for heritable disorders of bone, blood vessels, and skin. The major genetic disorders studied in the lab include all forms of osteogenesis imperfecta (OI) and Ehlers-Danlos syndrome (EDS); Loeys-Dietz syndrome, familial aneurysm syndromes and other similar disorders. The Byers Research Lab is housed alongside the Collagen Diagnostic Laboratory , a certified clinical laboratory that provides testing and consultation for patients and their families with suspected connective tissue disorders and the associated clinicians.

The Chen Lab's main research interest is in understanding pathogenesis of cancer using zebrafish and mammalian experimental systems. Their current research focuses on dissecting cellular and molecular mechanisms underlying the pathogenesis of pediatric rhabdomyosarcoma, in particular the key events regulating tumor differentiation, relapse and metastasis. Chen laboratory utilizes the strategies of chemical genetics, functional genomics and genome engineering to identify key driver events of rhabdomyosarcoma.

The mission of the Crispe lab is to understand liver immunology. Therefore, the Crispe lab investigates both innate immunity and T cell immunity in the liver, and the way both kinds of immune processes can lead to or protect against liver injury, cell death and liver fibrosis.

The Darvas Research Laboratory focuses on brain circuits as well as structural and molecular bases of learning and memory, the study of mouse brains in aging and Alzheimer’s disease models, investigation of pharmacological therapeutics aimed at preventing or reversing dementia and pathologic processes associated with progressive Alzheimer’s disease, and development of state-of-the-art quantitative neuropathology assays for Alzheimer’s disease pathologic changes in frozen and formalin-fixed autopsy tissue.

The Davis Lab is focused on uncovering the mechanistic basis for how the heart heals, repairs, and remodels in response to injury and disease. We are tackling the fundamental problem in which contractile muscle is replaced by fibrotic scarring by resolving the cellular and molecular basis for fibrotic scar formation. In addition, we have demonstrated that intracellular and extracellular biomechanical signals are central determinants of maladaptive cardiac remodeling. In lieu of this result, our lab seeks to understand how cell and tissue forces are sensed and transduced into changes in cell geometry, differentiation, and proliferation.

Derdeyn Lab

The Derdeyn Lab combines studies of human subjects and animal models to develop and evaluate novel approaches for vaccination against HIV and elimination of the viral reservoir. Their focus is on viral diversity, particularly within the highly variable envelope glycoproteins, and the B cell and antibody responses that are elicited during infection and vaccination.

Disteche Lab

The goal of the Disteche Lab is to understand the mechanisms of dosage compensation by X up-regulation of the single active X chromosome of males and females in terms of molecular processes.

Gonzalez-Cuyar Lab

Neurotoxicology associated with heavy metal exposure

Greninger Lab

The Greninger Lab strives to understand viruses – how they are transmitted, how they evolve, and how they affect their hosts. They use a wide range of techniques including next-generation sequencing, culture models and screens, and biochemical/biophysical characterization of viral gene products. Their research is tightly integrated with the clinical laboratories of the University of Washington Medical Center.

Horwitz Lab 

The Horwitz Lab employs genetic mapping and sequencing strategies to identify genes responsible for familial predisposition to leukemia, lymphoma, and bone marrow failure syndromes. This lab has successfully identified genes responsible for human cyclic neutropenia, canine cyclic neutropenia, severe congenital neutropenia (Kostmann syndrome), Hodgkin’s lymphoma, myelodysplasia and acute myeloid leukemia, and, recently, acute lymphocytic leukemia.

The Keene Lab focuses on Alzheimer's disease, Parkinson's disease and toxicity.

Kennedy Lab

The Kennedy Lab's main interest is the intersection of aging and the accumulation of somatic mutations (in both mitochondrial and nuclear genomes) in age-related diseases

Latimer Lab

The Latimer Lab studies the molecular mechanisms that underlie resilience and resistance to Alzheimer’s disease neuropathologic change

Mendenhall Lab

The Mendenhall Lab is working to understand the fundamental aspects of gene expression regulation. They are focused on understanding how epigenetic and stochastic differences in gene expression affect the manifestation of traits like lifespan and cancer. Understanding the basic biology of gene expression will allow us to collectively affect change in human health and disease.

The Monnat Lab is located in the UW Departments of Pathology and Genome Sciences in Seattle. Our research is focused on the mechanisms that ensure human genomic stability and determine cancer therapeutic response. We also develop and apply genome engineering tools to advance biology and disease treatment or prevention.

The Murphy Lab is dedicated to studying the biology of Plasmodium parasites that cause malaria and our immune response against them in order to make highly effective and long-lasting malaria vaccines. To study these infections, the laboratory uses specialized immunology and diagnostic techniques in mice, non-human primates, and humans.

Since 1996, the Murry Lab has worked to understand the mechanisms that underlie cardiovascular disease and to develop new treatments. We have a longstanding interest in the biology of myocardial infarction (heart attacks), and in particular, how the heart heals after infarction. The lab has a major focus in stem cell biology and tissue engineering, where we seek to understand the molecular basis for cardiovascular differentiation, and to harness the potential of stem cells to repair the heart. Recently, our group has begun to use stem cell approaches to study genetically based cardiomyopathies.

Najafian Lab

Najafian Lab research strives to better understand pathobiology of kidney diseases.

The Oshima Laboratory is interested in the genetic mechanisms of aging and age-related disorders with a special emphasis on the progeroid syndromes. In the past 15 years, our main focus has been the genetics and pathogenesis of Werner syndrome. 

Promislow Lab

The Promislow Lab works on the evolutionary genetics of life history strategies and sexual selection, bringing a systems biology perspective to the study of natural genetic variation for traits relevant to fitness and disease. We use empirical, epidemiological, bioinformatic and theoretical approaches in studies on a variety of organisms, from flies and mice to dogs, marmosets and humans.

The Rea Lab is working to define the fundamental molecular causes of aging and makes use of several model organisms ranging from worms to mice. Areas of special emphasis include mitochondrial dysfunction and retrograde signaling, human cellular senescence and life extending interventions.

Risques Lab

The Risques Lab studies the early steps of cancer progression and works on translating this knowledge into biomarkers for early cancer detection and prediction. We are especially interested in alterations that link cancer and aging including somatic mutation, telomere shortening, and mitochondrial dysfunction. We have developed and optimized genetic methods to quantify these alterations with high sensitivity with the ultimate goal of understanding early cancer progression and enabling cancer prediction and prevention.

Stewart & Shi Lab

The Stewart & Shi Lab's research focuses on understanding the molecular mechanisms of development and progression of neurodegenerative disorders (including Alzheimer's and Parkinson's diseases), and exploring unique biomarkers for diagnosing the diseases and monitoring their progression.

The focus of the lab, in collaborative projects, is to  identify and determine the clinical importance of novel phenotypes of prostate cancer, using open top light sheet microscopy and immunohistochemistry.

The Young lab is interested in determining the molecular and cellular mechanisms behind genetic risk for late-onset sporadic Alzheimer’s disease (SAD), the most common neurodegenerative disorder.

Brain researchers at Harvard perform research and teach at a wide range of departments, centers, schools, hospitals, university-wide initiatives and graduate programs at the university, including:

Harvard College Harvard Faculty of Arts and Sciences Harvard Medical School Harvard John A. Paulson School of Engineering & Applied Sciences

ACADEMIC DEPARTMENTS

Neurobiology Cell Biology Genetics Systems Biology Stem Cell & Regenerative Biology Molecular and Cellular Biology Organismic and Evolutionary Biology Psychology Chemistry and Chemical Biology Physics Computer Science

INTERFACULTY INITIATIVES

Harvard Brain Science Initiative Mind Brain Behavior Initiative Harvard Stem Cell Institute Wyss Institute

GRADUATE PROGRAMS

Program in Neuroscience (PIN) Molecules, Cells and Organisms (MCO)

HARVARD AFFILIATE HOSPITALS

HOME / PEOPLE / Our labs

Neuroscience Labs at Harvard

Harvard’s diverse neuroscience community — hundreds of basic researchers and physician-scientists, are engaged in the process of discovery across campuses and disciplines in Cambridge and the Greater Boston Area. Three major nodes are the  Department of Neurobiology  at Harvard Medical School, the  Center for Brain Science (CBS) at the Faculty of Arts and Sciences in Cambridge and the  F.M. Kirby Neurobiology Research Center  at Boston Children’s Hospital. Principal investigators from all three of these communities, complemented by other specialists across Harvard constitute the faculty of the Harvard PhD Program in Neuroscience (PIN) — whose students conduct research in labs spread all across the University and affiliated hospitals, including Boston Children’s Hospital, Massachusetts General Hospital, Mass Eye and Ear, Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, McLean Hospital and others.

To learn more, search our laboratory directory by principal investigator name below and view our faculty photo album . Also check out our Connectome directory to view profiles of current students, fellows, and staff, as well as faculty and alumni.

Banner Image: Cross-section of the mouse nasal epithelium. Image courtesy of David Brann (Lab of Bob Datta, Harvard Medical School).

MANY BRANCHES:   see more

Our research is focused on understanding neuronal metabolism and how it is influenced by – and influences – neuronal activity and excitability.

• Laboratories

Narrow your search

• Areas of Research
• active Find a research lab with a name beginning with the letter A A
• Find a research lab with a name beginning with the letter B B
• Find a research lab with a name beginning with the letter C C
• Find a research lab with a name beginning with the letter D D
• Find a research lab with a name beginning with the letter E E
• Find a research lab with a name beginning with the letter F F
• Find a research lab with a name beginning with the letter G G
• Find a research lab with a name beginning with the letter H H
• Find a research lab with a name beginning with the letter I I
• No research labs names begin with the letter J J
• Find a research lab with a name beginning with the letter K K
• Find a research lab with a name beginning with the letter L L
• Find a research lab with a name beginning with the letter M M
• Find a research lab with a name beginning with the letter N N
• Find a research lab with a name beginning with the letter O O
• Find a research lab with a name beginning with the letter P P
• No research labs names begin with the letter Q Q
• Find a research lab with a name beginning with the letter R R
• Find a research lab with a name beginning with the letter S S
• Find a research lab with a name beginning with the letter T T
• No research labs names begin with the letter U U
• Find a research lab with a name beginning with the letter V V
• No research labs names begin with the letter W W
• No research labs names begin with the letter X X
• No research labs names begin with the letter Y Y
• Find a research lab with a name beginning with the letter Z Z
• Advanced Endoscopy Innovation Translation and Clinical Trials Group: Barham Abu Dayyeh
• Advanced Medical Imaging Technology: Richard L. Ehman
• Aerospace Medicine and Vestibular Research: Michael J. Cevette, Jan Stepanek
• Aging and Dementia Imaging Research (ADIR): Clifford R. Jack Jr.; Kejal Kantarci; Prashanthi Vemuri
• Aging and Immune Restoration: Jessica N. Lancaster
• Allergic Diseases: Hirohito Kita
• Anesthesia Outcomes: Juraj Sprung
• Anesthesiology Systematic Review: W. Michael Hooten
• Anticancer Drug Action: Scott H. Kaufmann
• Artificial Liver and Liver Transplantation: Scott L. Nyberg
• Artificial Pancreas: Yogish C. Kudva
• Assistive and Restorative Technology: Kristin D. Zhao
• Atherosclerosis and Lipid Genomics: Iftikhar J. Kullo

More about research at Mayo Clinic

• Research Faculty
• Core Facilities
• Centers & Programs
• Departments & Divisions
• Clinical Trials
• Institutional Review Board
• Postdoctoral Fellowships
• Training Grant Programs
• Publications

Mayo Clinic Footer

• Request Appointment
• About Mayo Clinic
• About This Site

Legal Conditions and Terms

• Terms and Conditions
• Privacy Policy
• Notice of Privacy Practices
• Notice of Nondiscrimination
• Manage Cookies

Advertising

Mayo Clinic is a nonprofit organization and proceeds from Web advertising help support our mission. Mayo Clinic does not endorse any of the third party products and services advertised.

• Advertising and sponsorship policy
• Advertising and sponsorship opportunities

Reprint Permissions

A single copy of these materials may be reprinted for noncommercial personal use only. "Mayo," "Mayo Clinic," "MayoClinic.org," "Mayo Clinic Healthy Living," and the triple-shield Mayo Clinic logo are trademarks of Mayo Foundation for Medical Education and Research.

Brilliant leaps for brandkind

"yesterday, today was tomorrow...", george harrison.

The world is changing faster than ever. This isn’t a new or abstract concept. It’s a mercurial and exciting context. How is change changing consumers and what matters to them? How might brands change to keep up and get ahead?

The Lab decodes culture. We explore human behaviour. We mine millions of data signals. We shine a big, beautiful, bright light on change. To create opportunity – a chance to make a brilliant leap.

New World. New Insights.

Human intuition, data precision.

We're living in an exponential era, where change is happening faster than ever. It's fundamentally reshaping the way we live, think, communicate and our needs from brands and organisations. This change is saturating us in data, and we are searching for clearer direction, real insight and true competitive advantage. 

Our Elevated Tool Kit

Our new tool kit of methods, designed to solve the challenges of this new economy, empowers our partners with new layers of human insights and unparalleled audience connection.

The Australia Project

Uncovering the cultural dynamics that shape people.

The Australia Project is The Lab’s flagship cultural program. Our perennial proprietary study of life in Australia. An examination and collage of what’s in people’s heads and hearts. Attitudes, beliefs, values, motivations. What are they? How are they changing? We look, so your business can make its next brilliant leap.

2024 Outlook

We reflect on the cultural moments of 2023 to determine how they are shaping Aussies in 2024

Behavioural Science

Searching for truth in  human behaviour.

The Lab is where the irresistible force of cultural insight collides with the immovable object of behavioural science. Brilliantly. Behavioural Science allows us to see further and go deeper. Casting light on the subconscious influences of behaviour to illuminate opportunities for brands.

Uncover the Secrets to Communicating Value for Money

Unveiling the psychology of value for money and teaching you the secrets behind communicating value without dropping prices.

Masterclass On Demand

Amidst the soaring cost of living, consumers are feeling the pinch, and finding value for money has become a top priority. In this masterclass, you'll discover how to harness the power of psychology and behavioural science to enhance perceived value for money, while attracting customers who are more than willing to pay.

Sign up to stay ahead of change

Get our latest insights straight to your inbox.

We study what Australians seek on an ongoing basis & the consistent story of the past decade has been the growing demand for business to be a force for good.

ASU student team’s design selected as finalist for 2024 NASA-sponsored BIG Idea Challenge

Luminosity lab student team created an inflatable, reusable lunar landing pad system that could help future artemis missions.

An artistic rendering of the student-designed AEGIS inflatable lunar landing system, which is capable of autonomously deploying to reduce dust and debris generated by landers. Image courtesy Luminosity Lab/ASU

Arizona State University’s Luminosity Lab student team was recently selected as a finalist in NASA’s Breakthrough, Innovative, and Game-Changing (BIG) Idea Challenge . The group is one of six teams selected by NASA to present at the 2024 BIG Idea Challenge Forum, Nov. 5–7, at NASA Langley Research Center in Hampton, Virginia.  

The theme for the 2024 NASA-sponsored engineering competition, “Inflatable Systems for Lunar Operations,” challenges student teams to research, design and demonstrate novel inflatable systems configured for future lunar operations that could help future Artemis missions and beyond. 

The Luminosity Lab’s student group designed an inflatable and reusable lunar landing pad system, Aegis, which is capable of autonomously deploying to reduce dust and debris generated by landers; the system will also provide precision landing assistance to enable a safe landing. Being chosen as finalists means students will spend the next several months designing, building and testing their ideas, supported by as much as $150,000 from NASA.

“We have a phenomenal team of students working on this project, and I’m really proud of what they have accomplished by winning this proposal,” said Tyler Smith , senior director at ASU’s Luminosity Lab. “The inflatable landing pad system they designed is both innovative and solves a real issue for landers on the lunar surface.”

Presenting at the finals this fall provides students the opportunity to work with experts from NASA and the commercial space industry, and to receive guidance from faculty advisors from ASU’s School of Earth and Space Exploration (SESE) and Space Technology and Science ("NewSpace") Initiative , such as Professor Jim Bell and Jim Rice , assistant research scientist, both of whom have been actively involved with several NASA solar system exploration missions.

"The team at Luminosity Lab has done a fantastic job pitching an elegant solution to NASA's call for innovative inflatable technologies on the moon,” said Bell. “A number of us in SESE and ASU/NewSpace are eager to work with these talented students to help them mature their design and test/prototype it first here in Arizona and then later this year at NASA."

The BIG Idea Challenge is an initiative supporting NASA’s Space Technology Mission Directorate’s Game Changing Development program’s efforts to rapidly mature innovative and high-impact capabilities and technologies for infusion in a broad array of future NASA missions. This engineering design competition seeks innovative ideas from the higher education academic community for new topics each year relevant to current NASA Space Technology priorities. 

NASA’s Space Technology Mission Directorate sponsors the BIG Idea Challenge through a unique collaboration between its Game Changing Development program and the agency’s Office of STEM Engagement. It is managed by a partnership between the National Institute of Aerospace and the Johns Hopkins Applied Physics Laboratory.

More Science and technology

ASU class explores how ChatGPT Enterprise can assist in scholarly writing

Just over a month ago, Jacob Greene received a notification he’d been waiting for — his proposal to use ChatGPT Enterprise was approved. Greene is an assistant professor at Arizona State University’…

New engineering degrees at ASU aim to open pathways, empower engineering expertise

It doesn’t take an extensive internet search to discover that engineering has become one of the most rapidly and broadly expanding STEM fields. Engineering has been on an upswing in recent years,…

Study: Combining info on genes, gut bacteria enhances early disease detection

Identifying those at highest risk for developing common chronic diseases like heart disease, diabetes, Alzheimer’s disease and cancer is a core priority for preventive medicine. By catching elevated…

Growth Lab Research Seminar: Property Rights and Innovation Dynamism: The Role of Women Inventors

This event has passed, date and location.

​​​The Growth Lab's Research Seminar series is a weekly seminar that brings together researchers from across the academic spectrum who share an interest in growth and development.

Speaker: Ruveyda Gozen, Ph.D. - London School of Economics. 

Location: Online only. Please register in advance.

Abstract: How do stronger property rights for disadvantaged groups affect innovation? Dr. Ruveyda Gozen investigates the impact of strengthened property rights for women on U.S. innovation by analyzing the Married Women’s Property Acts, which granted equal property rights to women starting in 1845 in New York State. She examines the universe of granted patents from 1790 until 1901, exploiting the staggered adoption of the laws over time across states.

Additional Organizers

​Growth Lab

Research Lab Specialist Senior

The Rajapakse Lab in the Department of Computational Medicine and Bioinformatics is seeking a Research Associate with cell and molecular biology experience, with a preference given to candidates with experience in cell culture or cell reprogramming protocols and molecular protocols for next generation sequencing.

The candidate will be a team member working on research projects related to cell reprogramming and developing new protocols and technologies that complement lab projects. The position will heavily focus on molecular genetic techniques, including cell transfection/transduction, PCR, gel electrophoresis, cloning, and DNA and RNA isolation and preparation for next generation sequencing, and cell-based assays to assess attributes including cell organization and function, gene and protein expression, cell signaling, and cell differentiation.

The candidate should have strong written and oral communication skills, efficiently perform and document all procedures, protocols, methods, SOPs, materials and results in compliance with applicable regulatory standards in a CGMP compliant manner, have the ability to work under deadlines with general guidance, and contribute to manuscripts, grants and presentations.

Mission Statement

Michigan Medicine improves the health of patients, populations and communities through excellence in education, patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.  Our mission is guided by our Strategic Principles and has three critical components; patient care, education and research that together enhance our contribution to society.

Why Join Michigan Medicine?

Michigan Medicine is one of the largest health care complexes in the world and has been the site of many groundbreaking medical and technological advancements since the opening of the U-M Medical School in 1850. Michigan Medicine is comprised of over 30,000 employees and our vision is to attract, inspire, and develop outstanding people in medicine, sciences, and healthcare to become one of the world’s most distinguished academic health systems.  In some way, great or small, every person here helps to advance this world-class institution. Work at Michigan Medicine and become a victor for the greater good.

What Benefits can you Look Forward to?

• Excellent medical, dental and vision coverage effective on your very first day
• 2:1 Match on retirement savings

Responsibilities*

Specific duties may include:

• Assist in the design of molecular or cellular laboratory experiments, their execution, and the interpretation of results.
• Coordinate molecular or cellular research activities with scientists specializing in other fields.
• Evaluate new technologies to enhance or complement current research.
• Develop guidelines for procedures such as the management of viruses.
• Verify that financial, physical, and human resources assigned to research or development projects are used as planned.

Required Qualifications*

Masters or PhD in biology, genetics or a related field and significant (5+ years) laboratory research experience is required. Experience with DNA/RNA protocols, cloning, immunostaining, flow cytometry/sorting is preferred.

Background Screening

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Application Deadline

Job openings are posted for a minimum of seven calendar days.  The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

U.S. DEPARTMENT OF AGRICULTURE

USDA Completes Laboratory Modernization to Advance Pecan Breeding and Research

Contact: Maribel Alonso Email: [email protected]

SOMERVILLE, TEXAS , March 27, 2024- The Pecan Breeding and Genetics Program of the U.S. Department of Agriculture's Agricultural Research Service (USDA-ARS) recently completed a 2.5 million dollars laboratory modernization to accelerate pecan breeding through innovations in genetics and plant disease research. A ribbon-cutting ceremony was held on March 26 to commemorate the completion of the project.

Pecan trees represent North America's native nut tree and a multimillion-dollar crop. These trees have been cultivated commercially for less than 150 years. It takes an average of 28 years from planting a new seedling to releasing a new pecan cultivar with traditional methods of pecan breeding. This is due to the long waiting period for pecan trees to start producing nuts, which takes up more than half of the time. With the new modernized laboratory, the Pecan Breeding and Genetics Program will now be able to incorporate genetic techniques into pecan breeding to accurately predict mature nut traits on young seedlings, saving up to a decade in the breeding process.

Breeding new pecan cultivars is a lengthy process with long waiting periods of 7-10 years before a pecan tree can produce nuts and long testing phases to evaluate potential cultivars. It is challenging to support pecan breeding efforts due to their high resource demands and extended timelines, which make it impractical for private or commercial entities and challenging for academic programs.

The event was hosted by USDA's ARS and the Texas Pecan Grower’s Association. ARS leaders, State Representatives, scientists, and members of the pecan industry organizations toured the new laboratory with now dedicated research spaces for plant genetics, microscopy, tissue culture, controlled-environment growth chambers, and plant disease research.

"Today marks the celebration of the opening of a new genetics and pathology laboratory," said Warren Chatwin, the Lead Scientist at the Pecan Breeding and Genetics Program in Somerville, Texas. "This facility will enable ARS' researchers here at Somerville to advance pecan breeding and support modern genetics and plant disease research, which hasn't been possible since this site was established in the 1980s."

"This event also highlights the significant impact of our stakeholders, the pecan industry organizations. They have unified their voice through the National Pecan Federation to express the need for increased quality, accuracy, and speed of pecan breeding, genetics, and plant disease research, leading to the establishment of this laboratory space. The success of our research is only possible through partnerships with stakeholders including the Texas Pecan Grower’s Association, which has provided decades of support and significant contributions, most recently through the National Pecan Federation," added Chatwin.

The USDA Pecan Breeding and Genetics Program's new genetics and pathology laboratory meets biosafety containment standards and is equipped with modern research spaces that will advance pecan breeding, genetics, and plant disease studies. Photos provided by ARS Warren Chatwin.

In addition, researchers will now be able to do controlled evaluations of promising breeding lines with different regional strains of pecan scab. Pecan scab, caused by the fungal pathogen Venturia effusa , is the most economically significant disease in the pecan industry and has high diversity across the geographic range of cultivated pecans. For the first time, the Pecan Breeding and Genetics Program will now be able to screen pecan scab cultures from all areas of the country in controlled environments to identify new sources of disease resistance and incorporate those unique samples into the breeding program. Researchers will also be able to do controlled evaluations for other significant and emerging pathogens of pecan, including the heavily quarantined international pathogen, Xylella fastidiosa .

The Pecan Breeding and Genetics program has released 32 pecan cultivars to the industry, with notable releases like 'Pawnee,' which moved the commercial harvest window forward to mid-September, 'Lakota,' which has high scab resistance, and 'Wichita,' a high-yielding cultivar that performs well in the West. USDA-ARS’ most recently released cultivars include 'Pueblo,' 'Seneca,' and 'Zuni,' which were patented in 2022.

The  Agricultural Research Service  is the U.S. Department of Agriculture's chief scientific in-house research agency. Daily, ARS focuses on solutions to agricultural problems affecting America. Each dollar invested in agricultural research results in $20 of economic impact.

USDA is an equal opportunity provider, employer, and lender. 

• Skip to main content
• Keyboard shortcuts for audio player

Research News

Scientists studied how cicadas pee. their insights could shed light on fluid dynamics.

A cicada perches on a picnic table in front of Nolde Mansion in Cumru Township, PA in May 2021. New research shows that these insects urinate in a surprising way. Ben Hasty / MediaNews Group/Reading Eagle via Getty Images hide caption

A cicada perches on a picnic table in front of Nolde Mansion in Cumru Township, PA in May 2021. New research shows that these insects urinate in a surprising way.

This spring and summer, across the Midwest and Southeast United States, cicadas will crawl out of their underground burrows by the trillions to mate — due to two different broods of these wingèd insects emerging at about the same time, one on a 13-year cycle and one on a 17-year cycle.

In their brief several weeks aboveground, their mission will be to reproduce. Each male will attempt to attract females by producing a buzzing noise as loud as a lawnmower.

But beyond their prodigious numbers and raucous noise, new research published in PNAS reveals that cicadas are special in yet another way — their urination. Based on their size and diet, scientists suspected they'd urinate in droplets, but it turns out that these insects produce jets of pee.

This often-overlooked sea creature may be quietly protecting the planet's coral reefs

The surprising results have numerous applications when it comes to manipulating fluids at small scales — including 3D printing, drug delivery, disease diagnostics, and even testing compounds in outer space.

It's a striking discovery in a realm we know relatively little about, scientists say.

"Excretion in general is not very well understood," says lead author Elio Challita , a bio-inspired roboticist at Harvard University. "Cicadas are some of the smallest insects, to the best of our knowledge, that can form such jets at this small scale."

"Insects are just the perfect laboratory for exploring handling fluids at the micro-scale," says Anne Staples , a fluid dynamicist at Virginia Tech who studies the mechanics of insect respiration and wasn't involved in the research. "They fly through air, they drink water, they handle nectar. As this paper shows, they urinate — they excrete."

Be that as it may, ask Challita what motivated the study, and he says it was simple curiosity. "I think people should understand science doesn't have to be very serious," he says. "It can be fun, too."

A spectrum of pee

Not all animals pee in the same way. On the one hand, there are larger animals like humans and elephants. "They rely on the forces of inertia and gravity to pull down the fluids from their bladder," says Challita.

This results in a stream or jet of urine, like the one that might hit your toilet bowl on a regular basis. In fact, a 2014 Georgia Tech study entitled " Duration of urination does not change with body size " found that all mammals more than six or seven pounds take an average of 21 seconds (plus or minus 13 seconds) to empty their bladders. The researchers referred to this as the Law of Urination .

But when an organism is small (like the size of an insect), then fluids care less about gravity. Instead, surface tension and friction dominate — forces that are negligible for larger organisms like us.

"Surface tension is an invisible kind of force that is very significant for small insects," says Challita. "Just pushing a fluid at a small scale is challenging."

The result is that most insects and most small mammals like mice and bats urinate in droplets through smaller orifices. In fact, while in grad school at Georgia Tech, Challita studied a kind of insect called a sharpshooter, which sucks low-nutrient sap from plants (sap that's 95% water). "And then we calculated what is the energy required to form a jet versus a droplet," Challita says.

California sea otters nearly went extinct. Now they're rescuing their coastal habitat

It wasn't even a contest. Droplet urination used way less energy. So that seemed to be the general rule: if you're big, you pee in a jet. If you're small — and especially if you're feeding on nutrient-poor sap — you pee in droplets.

But Challita knew that the world rarely divides so cleanly. "We try to create theories that can explain things in nature," he says, "but nature is always finding surprises and exceptions for us."

A splash of insight

Based on a handful of YouTube videos he'd seen, Challita had a hunch that cicadas might just prove to be the exception to the rule. The only trouble is that they're hard to observe.

"They're usually very high up on trees," he says. "And even if you find them, it's hard to not disturb them and then they would fly away."

But later, on a different project in the Peruvian Amazon, a stroke of luck: Challita and a couple colleagues had wrapped up their field work and were taking the six-hour boat ride back to town when their driver made an early pit stop for lunch.

"So we started walking around," Challita recalls, "and then one of our colleagues, he felt this little sprinkle on his head. And then we looked up — and then we saw a lot of cicadas."

Challita and his colleagues couldn't believe it. There were 20 or 30 cicadas low down in the trees, feeding and peeing with abandon. The team leapt into action, rushing to collect data before their driver's lunch break ended. They climbed one of the trees. They grabbed a table to stand on. And they filmed the cicadas using the high-speed video setting on their phones.

"All the villagers over there, they were just staring at us, like, 'What the hell's wrong with these guys?'" says Challita with a chuckle.

It was a rush.

And the experiment turned out well, too.

The researchers saw cicadas defying expectations. They are insects feeding on low-nutrient sap, but there they were — peeing in jets . It was one of those streams that had splashed against Challita's colleague that had made them all look up in the first place.

Here's what Challita thinks is going on: Cicadas are big insects with a wider gut, so they're not under the exact same size constraint as, say, a sharpshooter. Plus, they have to process a huge quantity of sap to extract enough energy to power their bodies.

"Peeing one droplet at a time takes too long and it's not very efficient," says Challita. "So they have to get rid of that fluid in jets."

This means that in addition to large animals that pee in jets, and small animals that pee in droplets, Challita's found a third category: small organisms that also pee in jets.

Staples says that while the research would have benefited from studying a larger number of cicadas, it still pushes the limits of our understanding.

"They've extended the scale into the lower reaches of the animal kingdom and showed some surprising results that are counterintuitive," says Staples. "You wouldn't think that this would be the most efficient exploitation of fundamental physics to urinate at that size."

And yet it is. It's the latest leap in the development of what Challita hopes could become a kind of Grand Urinating Theory.

• surface tension
• insect biology
• microfluidics
• fluid dynamics

AACR’s 2024 Cancer and Biomedical Research Career Fair

The Frederick National Laboratory will be at the AACR Career Fair ahead of the 2024 annual meeting. Find us to learn more about our career opportunities, job openings, and benefits.

• AIDS Monitoring Laboratory
• Retroviral Evolution Section
• Retroviral Pathogenesis Section
• Retrovirus-Cell Interaction Section
• Viral Oncology Section
• Viral Persistence Section
• Biological Products Core
• Cellular Immunity Core
• HIV Monitoring Core
• Nonhuman Primate Research Support Core
• Quantitative Molecular Diagnostics Core
• Retroviral Protein Chemistry Core
• Tissue Analysis Core
• Viral Evolution Core
• Advanced Cryo-Electron Microscopy Technology Group
• Molecular Immunology Section
• HLA Immunogenetics Section
• Epigenetics Section
• Computational Structural Biology Section
• Center for Technical Operations Support Group
• Advanced Biomedical Computational Science
• Biopharmaceutical Capabilities
• GMP Training & Standard Operating Procedures
• Bioprocessing Laboratory
• Blood Processing Core
• CLIA Molecular Diagnostics Laboratory
• Clinical Support Laboratory
• Flow Cytometry Support Laboratory
• Immunological Monitoring Laboratory
• Laboratory of Human Retrovirology and Immunoinformatics
• Laboratory of Molecular Cell Biology
• Molecular Characterization Laboratory
• Neutrophil Monitoring Laboratory
• Protein Expression Laboratory
• Radiochemistry Support Group
• Vaccine Clinical Materials Program
• Virus Isolation and Serology Laboratory
• Center for Innovation and Strategic Partnerships
• RAS Initiative
• Collaborators
• Serology Standard
• AI and Data Science
• Antibody Science
• Biospecimen Assays
• Electron Microscopy
• Nanotechnology
• Protein Science
• Moonshot Pediatric Core
• NCEF Cryo-EM Training Program
• Nanotechnology Characterization Laboratory
• Repositories
• Antibody Characterization Laboratory
• Request Form
• New Vendor Registration
• Academic Engagement
• Partnership Mechanisms
• Technical Services
• Technology Showcase
• Laboratory-Directed Exploratory Research
• Subcontracting and Acquisitions
• Intellectual Property and Strategic Agreements
• How to Apply
• Health and Wellness
• Retirement and Income Security
• Paid Time-Off
• Internships and Fellowships
• Employee Life
• Diversity and Inclusion
• Living in Maryland
• Core Values
• Publications