design field experiments

Field experiments, explained

Editor’s note: This is part of a series called “The Day Tomorrow Began,” which explores the history of breakthroughs at UChicago.  Learn more here.

A field experiment is a research method that uses some controlled elements of traditional lab experiments, but takes place in natural, real-world settings. This type of experiment can help scientists explore questions like: Why do people vote the way they do? Why do schools fail? Why are certain people hired less often or paid less money?

University of Chicago economists were early pioneers in the modern use of field experiments and conducted innovative research that impacts our everyday lives—from policymaking to marketing to farming and agriculture.  

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What is a field experiment, why do a field experiment, what are examples of field experiments, when did field experiments become popular in modern economics, what are criticisms of field experiments.

Field experiments bridge the highly controlled lab environment and the messy real world. Social scientists have taken inspiration from traditional medical or physical science lab experiments. In a typical drug trial, for instance, participants are randomly assigned into two groups. The control group gets the placebo—a pill that has no effect. The treatment group will receive the new pill. The scientist can then compare the outcomes for each group.

A field experiment works similarly, just in the setting of real life.

It can be difficult to understand why a person chooses to buy one product over another or how effective a policy is when dozens of variables affect the choices we make each day. “That type of thinking, for centuries, caused economists to believe you can't do field experimentation in economics because the market is really messy,” said Prof. John List, a UChicago economist who has used field experiments to study everything from how people use  Uber and  Lyft to  how to close the achievement gap in Chicago-area schools . “There are a lot of things that are simultaneously moving.”

The key to cleaning up the mess is randomization —or assigning participants randomly to either the control group or the treatment group. “The beauty of randomization is that each group has the same amount of bad stuff, or noise or dirt,” List said. “That gets differenced out if you have large enough samples.”

Though lab experiments are still common in the social sciences, field experiments are now often used by psychologists, sociologists and political scientists. They’ve also become an essential tool in the economist’s toolbox.  

Some issues are too big and too complex to study in a lab or on paper—that’s where field experiments come in.

In a laboratory setting, a researcher wants to control as many variables as possible. These experiments are excellent for testing new medications or measuring brain functions, but they aren’t always great for answering complex questions about attitudes or behavior.

Labs are highly artificial with relatively small sample sizes—it’s difficult to know if results will still apply in the real world. Also, people are aware they are being observed in a lab, which can alter their behavior. This phenomenon, sometimes called the Hawthorne effect, can affect results.

Traditional economics often uses theories or existing data to analyze problems. But, when a researcher wants to study if a policy will be effective or not, field experiments are a useful way to look at how results may play out in real life.

In 2019, UChicago economist Michael Kremer (then at Harvard) was awarded the Nobel Prize alongside Abhijit Banerjee and Esther Duflo of MIT for their groundbreaking work using field experiments to help reduce poverty . In the 1990s and 2000s, Kremer conducted several randomized controlled trials in Kenyan schools testing potential interventions to improve student performance. 

In the 1990s, Kremer worked alongside an NGO to figure out if buying students new textbooks made a difference in academic performance. Half the schools got new textbooks; the other half didn’t. The results were unexpected—textbooks had no impact.

“Things we think are common sense, sometimes they turn out to be right, sometimes they turn out to be wrong,” said Kremer on an episode of  the Big Brains podcast. “And things that we thought would have minimal impact or no impact turn out to have a big impact.”

In the early 2000s, Kremer returned to Kenya to study a school-based deworming program. He and a colleague found that providing deworming pills to all students reduced absenteeism by more than 25%. After the study, the program was scaled nationwide by the Kenyan government. From there it was picked up by multiple Indian states—and then by the Indian national government.

“Experiments are a way to get at causal impact, but they’re also much more than that,” Kremer said in  his Nobel Prize lecture . “They give the researcher a richer sense of context, promote broader collaboration and address specific practical problems.”    

Among many other things, field experiments can be used to:

Study bias and discrimination

A 2004 study published by UChicago economists Marianne Bertrand and Sendhil Mullainathan (then at MIT) examined racial discrimination in the labor market. They sent over 5,000 resumes to real job ads in Chicago and Boston. The resumes were exactly the same in all ways but one—the name at the top. Half the resumes bore white-sounding names like Emily Walsh or Greg Baker. The other half sported African American names like Lakisha Washington or Jamal Jones. The study found that applications with white-sounding names were 50% more likely to receive a callback.

Examine voting behavior

Political scientist Harold Gosnell , PhD 1922, pioneered the use of field experiments to examine voting behavior while at UChicago in the 1920s and ‘30s. In his study “Getting out the vote,” Gosnell sorted 6,000 Chicagoans across 12 districts into groups. One group received voter registration info for the 1924 presidential election and the control group did not. Voter registration jumped substantially among those who received the informational notices. Not only did the study prove that get-out-the-vote mailings could have a substantial effect on voter turnout, but also that field experiments were an effective tool in political science.

Test ways to reduce crime and shape public policy

Researchers at UChicago’s  Crime Lab use field experiments to gather data on crime as well as policies and programs meant to reduce it. For example, Crime Lab director and economist Jens Ludwig co-authored a  2015 study on the effectiveness of the school mentoring program  Becoming a Man . Developed by the non-profit Youth Guidance, Becoming a Man focuses on guiding male students between 7th and 12th grade to help boost school engagement and reduce arrests. In two field experiments, the Crime Lab found that while students participated in the program, total arrests were reduced by 28–35%, violent-crime arrests went down by 45–50% and graduation rates increased by 12–19%.

The earliest field experiments took place—literally—in fields. Starting in the 1800s, European farmers began experimenting with fertilizers to see how they affected crop yields. In the 1920s, two statisticians, Jerzy Neyman and Ronald Fisher, were tasked with assisting with these agricultural experiments. They are credited with identifying randomization as a key element of the method—making sure each plot had the same chance of being treated as the next.

The earliest large-scale field experiments in the U.S. took place in the late 1960s to help evaluate various government programs. Typically, these experiments were used to test minor changes to things like electricity pricing or unemployment programs.

Though field experiments were used in some capacity throughout the 20th century, this method didn’t truly gain popularity in economics until the 2000s. Kremer and List were early pioneers and first began experimenting with the method in the 1990s.

In 2004, List co-authored  a seminal paper defining field experiments and arguing for the importance of the method. In 2008,  he and UChicago economist Steven Levitt published another study tracing the history of field experiments and their impact on economics.

In the past few decades, the use of field experiments has exploded. Today, economists often work alongside NGOs or nonprofit organizations to study the efficacy of programs or policies. They also partner with companies to test products and understand how people use services.  

There are several  ethical discussions happening among scholars as field experiments grow in popularity. Chief among them is the issue of informed consent. All studies that involve human test subjects must be approved by an institutional review board (IRB) to ensure that people are protected.

However, participants in field experiments often don’t know they are in an experiment. While an experiment may be given the stamp of approval in the research community, some argue that taking away peoples’ ability to opt out is inherently unethical. Others advocate for stricter review processes as field experiments continue to evolve.

According to List, another major issue in field experiments is the issue of scale . Many experiments only test small groups—say, dozens to hundreds of people. This may mean the results are not applicable to broader situations. For example, if a scientist runs an experiment at one school and finds their method works there, does that mean it will also work for an entire city? Or an entire country?

List believes that in addition to testing option A and option B, researchers need a third option that accounts for the limitations that come with a larger scale. “Option C is what I call critical scale features. I want you to bring in all of the warts, all of the constraints, whether they're regulatory constraints, or constraints by law,” List said. “Option C is like your reality test, or what I call policy-based evidence.”

This problem isn’t unique to field experiments, but List believes tackling the issue of scale is the next major frontier for a new generation of economists.

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Introduction to Field Experiments and Randomized Controlled Trials

Have you ever been curious about the methods researchers employ to determine causal relationships among various factors, ultimately leading to significant breakthroughs and progress in numerous fields? In this article, we offer an overview of field experimentation and its importance in discerning cause and effect relationships. We outline how randomized experiments represent an unbiased method for determining what works. Furthermore, we discuss key aspects of experiments, such as intervention, excludability, and non-interference. To illustrate these concepts, we present a hypothetical example of a randomized controlled trial evaluating the efficacy of an experimental drug called Covi-Mapp.

Why experiments?

Every day, we find ourselves faced with questions of cause and effect. Understanding the driving forces behind outcomes is crucial, ranging from personal decisions like parenting strategies to organizational challenges such as effective advertising. This blog aims to provide a systematic introduction to experimentation, igniting enthusiasm for primary research and highlighting the myriad of experimental applications and opportunities available.

The challenge for those who seek to answer causal questions convincingly is to develop a research methodology that doesn't require identifying or measuring all potential confounders. Since no planned design can eliminate every possible systematic difference between treatment and control groups, random assignment emerges as a powerful tool for minimizing bias. In the contentious world of causal claims, randomized experiments represent an unbiased method for determining what works. Random assignment means participants are assigned to different groups or conditions in a study purely by chance. Basically, each participant has an equal chance to be assigned to a control group or a treatment group. 

Field experiments, or randomized studies conducted in real-world settings, can take many forms. While experiments on college campuses are often considered lab studies, certain experiments on campus – such as those examining club participation – may be regarded as field experiments, depending on the experimental design. Ultimately, whether a study is considered a field experiment hinges on the definition of "the field."

Researchers may employ two main scenarios for randomization. The first involves gathering study participants and randomizing them at the time of the experiment. The second capitalizes on naturally occurring randomizations, such as the Vietnam draft lottery. 

Intervention, Excludability, and Non-Interference

Three essential features of any experiment are intervention, excludability, and non-interference. In a general sense, the intervention refers to the treatment or action being tested in an experiment. The excludability principle is satisfied when the only difference between the experimental and control groups is the presence or absence of the intervention. The non-interference principle holds when the outcome of one participant in the study does not influence the outcomes of other participants. Together, these principles ensure that the experiment is designed to provide unbiased and reliable results, isolating the causal effect of the intervention under study.

Omitted Variables and Non-Compliance

To ensure unbiased results, researchers must randomize as much as possible to minimize omitted variable bias. Omitted variables are factors that influence the outcome but are not measured or are difficult to measure. These unmeasured attributes, sometimes called confounding variables or unobserved heterogeneity, must be accounted for to guarantee accurate findings.

Non-compliance can also complicate experiments. One-sided non-compliance occurs when individuals assigned to a treatment group don't receive the treatment (failure to treat), while two-sided non-compliance occurs when some subjects assigned to the treatment group go untreated or individuals assigned to the control group receive the treatment. Addressing these issues at the design level by implementing a blind or double-blind study can help mitigate potential biases.

Achieving Precision through Covariate Balance

To ensure the control and treatment groups are comparatively similar in all relevant aspects, particularly when the sample size (n) is small, it is essential to achieve covariate balance. Covariance measures the association between two variables, while a covariate is a factor that influences the outcome variable. By balancing covariates, we can more accurately isolate the effects of the treatment, leading to improved precision in our findings.

Fictional Example of Randomized Controlled Trial of Covi-Mapp for COVID-19 Management

Let's explore a fictional example to better understand experiments: a one-week randomized controlled trial of the experimental drug Covi-Mapp for managing Covid. In this case, the control group receives the standard care for Covid patients, while the treatment group receives the standard care plus Covi-Mapp. The outcome of interest is whether patients have cough symptoms on day 7, as subsidizing cough symptoms is an encouraging sign in Covid recovery. We'll measure the presence of cough on day 0 and day 7, as well as temperature on day 0 and day 7. Gender is also tracked. The control represents the standard care for COVID-19 patients, while the treatment includes standard care plus the experimental drug.

In this Covi-Mapp example, the intervention is the Covi-Mapp drug, the excludability principle is satisfied if the only difference in patient care between the groups is the drug administration, and the non-interference principle holds if one patient's outcome doesn't affect another's.

First, let's assume we have a dataset containing the relevant information for each patient, including cough status on day 0 and day 7, temperature on day 0 and day 7, treatment assignment, and gender. We'll read the data and explore the dataset:

Simple treatment effect of the experimental drug

Without any covariates, let's first look at the estimated effect of the treatment on the presence of cough on day 7. The estimated proportion of patients with a cough on day 7 for the control group (not receiving the experimental drug) is 0.847458. In other words, about 84.7% of patients in the control group are expected to have a cough on day 7, all else being equal. The estimated effect of the experimental drug on the presence of cough on day 7 is -0.23. This means that, on average, receiving the experimental drug reduces the proportion of patients with a cough on day 7 by 23.8% compared to the control group.

We know that a patient's initial condition would affect the final outcome. If the patient has a cough and a fever on day 0, they might not fare well with the treatment. To better understand the treatment's effect, let's add these covariates:

The output shows the results of a linear regression model, estimating the effect of the experimental drug (treat_covid_mapp) on the presence of cough on day 7, adjusting for cough on day 0 and temperature on day 0. The experimental drug significantly reduces the presence of cough on day 7 by approximately 16.6% compared to the control group (p-value = 0.046242). The presence of cough on day 0 does not significantly predict the presence of cough on day 7 (p-value = 0.717689). A one-unit increase in temperature on day 0 is associated with a 20.6% increase in the presence of cough on day 7, and this effect is statistically significant (p-value = 0.009859).

Should we add day 7 temperature as a covariate? By including it, we might find that the treatment is no longer statistically significant since the temperature on day 7 could be affected by the treatment itself. It is a post-treatment variable, and by including it, the experiment loses value as we used something that was affected by intervention as our covariate.

However, we'd like to investigate if the treatment affects men or women differently. Since we collected gender as part of the study, we could check for Heterogeneous Treatment Effect (HTE) for male vs. female. The experimental drug has a marginally significant effect on the outcome variable for females, reducing it by approximately 23.1% (p-value = 0.05391).

Which group, those coded as male == 0 or male == 1, have better health outcomes (cough) in control? What about in treatment? How does this help to contextualize any heterogeneous treatment effect that might have been estimated?

Stargazer is a popular R package that enables users to create well-formatted tables and reports for statistical analysis results.

Looking at this regression report, we see that males in control have a temperature of 102; females in control have a temperature of 98.6 (which is very nearly a normal temperature). So, in control, males are worse off. In treatment, males have a temperature of 102 - 2.59 = 99.41. While this is closer to a normal temperature, this is still elevated. Females in treatment have a temperature of 98.5 - .32 = 98.18, which is slightly lower than a normal temperature, and is better than an elevated temperature. It appears that the treatment is able to have a stronger effect among male participants than females because males are *more sick* at baseline.

In conclusion, experimentation offers a fascinating and valuable avenue for primary research, allowing us to address causal questions and enhance our understanding of the world around us. Covariate control helps to isolate the causal effect of the treatment on the outcome variable, ensuring that the observed effect is not driven by confounding factors. Proper control of covariates enhances the internal validity of the study and ensures that the estimated treatment effect is an accurate representation of the true causal relationship. By exploring and accounting for sub groups in data, researchers can identify whether the treatment has different effects on different groups, such as men and women or younger and older individuals. This information can be critical for making informed policy decisions and developing targeted interventions that maximize the benefits for specific groups. The ongoing investigation of experimental methodologies and their potential applications represents a compelling and significant area of inquiry. 

Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation . W. W. Norton.

“DALL·E 2.” OpenAI , https://openai.com/product/dall-e-2

“Data Science 241. Experiments and Causal Inference.” UC Berkeley School of Information , https://www.ischool.berkeley.edu/courses/datasci/241

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

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

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

When designing the experiment, you decide:

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

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

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

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

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

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

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

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

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

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

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Conducting Effective Field Experiments in Psychology

Field experiments play a crucial role in psychology research, allowing researchers to study behavior in real-world settings. But what exactly are field experiments, and why are they important? In this article, we will explore the steps involved in designing and conducting effective field experiments, from identifying the research question to collecting and analyzing data. We will also discuss the limitations of field experiments in psychology and provide strategies to overcome challenges such as ethical concerns and unforeseen variables.

Join us as we delve into the world of field experiments in psychology!

• Field experiments in psychology are valuable for studying behavior in natural settings, allowing for more accurate and generalizable results.
• To design an effective field experiment, researchers must carefully identify research questions, select appropriate settings, determine sample size, and control for confounding variables.
• To overcome challenges in conducting field experiments, researchers should address ethical concerns, anticipate and account for unforeseen variables, and strive for improved sample size and randomization.
• 1 What Are Field Experiments in Psychology?
• 2 Why Are Field Experiments Important in Psychology Research?
• 3.1 Identify the Research Question
• 3.2 Select the Appropriate Setting
• 3.3 Determine the Sample Size
• 3.4 Randomize the Participants
• 3.5 Control for Confounding Variables
• 3.6 Use Appropriate Measures and Instruments
• 3.7 Consider Ethical Considerations
• 4.1 Pre-Experiment Preparation
• 4.2 Implementation of the Experiment
• 4.3 Data Collection and Analysis
• 5 What Are the Limitations of Field Experiments in Psychology?
• 6.1 Addressing Ethical Concerns
• 6.2 Dealing with Unforeseen Variables
• 6.3 Improving Sample Size and Randomization
• 7.1 What is the purpose of conducting effective field experiments in psychology?
• 7.2 How do field experiments in psychology differ from laboratory experiments?
• 7.3 What are some examples of effective field experiments in psychology?
• 7.4 What are the benefits of conducting field experiments in psychology?
• 7.5 What are some challenges of conducting field experiments in psychology?
• 7.6 How can researchers ensure the validity and reliability of field experiments in psychology?

What Are Field Experiments in Psychology?

Field experiments in psychology refer to research studies conducted in real-life settings outside traditional laboratory environments, aiming to observe behavior and study variables in natural contexts.

These experiments play a crucial role in understanding how individuals behave authentically in their daily lives, offering insights that may be missed in controlled lab settings. By manipulating independent variables and observing the subsequent effects on dependent variables in the wild, researchers can uncover more realistic behaviors and responses. This focus on ecological validity enhances the generalizability and applicability of findings to real-world situations, providing a more comprehensive understanding of human behavior.

Why Are Field Experiments Important in Psychology Research?

Field experiments play a crucial role in psychology research by providing insights into human behavior in authentic settings, offering valuable data that enhances the understanding of psychological phenomena and their impact in real-world contexts.

By conducting experiments in natural environments, researchers can observe behaviors as they naturally occur, minimizing the artificiality of controlled lab settings. This method allows for a more ecologically valid study of behavior, leading to results that are more applicable to everyday life. Field experiments facilitate the collection of rich and diverse data, offering a broader perspective on how individuals interact with their surroundings. Researchers can also make direct observations of behavior without the influence of laboratory-induced biases, leading to more accurate interpretations of results.

How To Design an Effective Field Experiment?

Designing an effective field experiment involves careful consideration of variables, conditions, and methodological approaches to ensure the study’s validity and reliability, incorporating statistical methods for data analysis and result interpretation.

One of the key steps in designing a field experiment is selecting the appropriate variables to study, ensuring that they are both relevant to the research question and measurable. Researchers must define independent and dependent variables clearly to establish the cause-and-effect relationship. Establishing the study conditions is crucial, as controlling external factors can help isolate the impact of the manipulated variables. Methodological procedures, such as randomization and control groups, play a vital role in minimizing bias and ensuring the experiment’s internal validity.

Statistical methods such as regression analysis or ANOVA are then utilized to analyze the collected data, allowing researchers to draw meaningful conclusions and make informed decisions based on the results.

Identify the Research Question

The first step in designing a field experiment is to identify a clear research question or hypothesis that guides the study, ensuring that the recruitment process and standardization of procedures align with the null hypothesis for comprehensive analysis.

Formulating a research question serves as the backbone of any scientific investigation, providing a road map for the entire experiment. It helps researchers define the scope of their study, establish objectives, and ultimately draw meaningful conclusions. By recruiting participants that meet specific criteria related to the research question, the experiment’s results become more robust and reliable.

Maintaining standardized procedures ensures consistency in data collection and analysis, enabling researchers to accurately test the null hypothesis and draw valid inferences based on the findings.

Select the Appropriate Setting

Choosing the right setting for a field experiment is paramount, whether in a natural environment or through volunteer participants, to ensure the study’s external validity and relevance to real-world scenarios.

Conducting experiments in natural environments provides researchers with the opportunity to observe behaviors and reactions in settings that closely mirror real-world conditions, enhancing the authenticity of the findings. Involving volunteer participants can lead to a more diverse sample, increasing the generalizability of the study’s results. External validity, crucial for the application of research findings beyond the lab, hinges on how well the experiment’s setting reflects the complexities of the context being studied.

Determine the Sample Size

Determining the sample size for a field experiment involves calculating the number of participants required to achieve statistical significance and effectively represent the targeted population, ensuring the study’s reliability and validity.

One crucial aspect to consider when determining the sample size is the scale of the study. Larger studies often require a proportionally larger sample size to capture the intricacies and variations within the population accurately.

The relationship between the participants in the study can impact the sample size calculation. If the participants are expected to have similar responses or behaviors, a smaller sample size may suffice.

Identifying the level of precision desired in the results is essential. A higher level of precision usually necessitates a larger sample size to reduce the margin of error and enhance the research’s credibility.

Randomize the Participants

Randomizing participants in a field experiment through random allocation is crucial to establish control groups, enable randomization processes, and facilitate study replication for robust and reliable results.

This method ensures that each participant has an equal chance of being assigned to different groups, reducing the risk of bias and increasing the validity of research findings.

By employing random allocation, researchers can minimize the influence of confounding variables and increase the internal validity of the study.

Control groups, which remain untreated or receive a placebo, provide a baseline for comparison, allowing researchers to measure the true effects of the intervention or treatment. This comparison is vital in determining causality and distinguishing between correlation and causation.

Control for Confounding Variables

Controlling for confounding variables in a field experiment is essential to mitigate biases, address selection issues, and ensure that the study’s outcomes are attributed to the independent variables rather than external factors.

One common technique used to control for confounding variables is random assignment, where participants are allocated randomly to experimental groups. This helps ensure that any differences in outcomes are due to the treatment received and not other factors. Researchers can employ matched-pair designs, where participants are matched based on key characteristics. By doing so, the impact of these specific factors can be isolated and accounted for.

Use Appropriate Measures and Instruments

Utilizing appropriate measures and instruments in a field experiment is crucial to gather relevant data, implement study conditions effectively, and conduct thorough analysis using suitable methodologies.

Measures in a field experiment could range from basic tools like tape measures and thermometers to complex devices such as spectrophotometers or geospatial sensors, depending on the nature of the study. These instruments serve the vital role of ensuring accurate data collection, maintaining controlled study conditions, and facilitating the systematic evaluation of results. For instance, a digital spectrometer enables precise measurements of light absorption, crucial in environmental research, while data loggers automate data collection in long-term studies, reducing human error. Methodical analysis heavily relies on the quality and appropriateness of these tools, emphasizing the need for careful selection and calibration.

Consider Ethical Considerations

Ethical considerations are paramount in field experiments, necessitating informed consent from participants, addressing potential risks, and ensuring the study adheres to ethical guidelines to protect participant rights and well-being.

When conducting research involving human subjects, it is crucial to prioritize the ethical implications of the study. Informed consent serves as the cornerstone of ethical research, ensuring that participants are fully aware of the purpose, procedures, and potential risks involved in the study. This transparency allows individuals to make an informed decision about their participation, safeguarding their autonomy and right to self-determination.

Researchers must implement measures to protect the well-being and confidentiality of participants throughout the study. This includes minimizing any potential harm that participants may experience and upholding their privacy and data protection rights.

What Are the Steps Involved in Conducting a Field Experiment?

Conducting a field experiment entails several key steps, including pre-experiment preparation, implementation of the study, data collection, and analysis to examine behavioral patterns and draw meaningful conclusions.

Ahead of the actual experiment, researchers must carefully plan the study design, determine the research questions, and outline the variables to be observed. This initial phase also involves obtaining any necessary permissions, securing equipment, selecting suitable locations for data collection, and addressing any ethical considerations.

Regarding the implementation stage, it is crucial to adhere to the predefined protocols and procedures to ensure the reliability and validity of the experiment. Researchers often conduct pilot studies to fine-tune their approach and make any necessary adjustments before fully launching the field experiment.

Data collection methods in field experiments typically involve direct observation of subjects in their natural environment, recording behaviors, interactions, and responses to various stimuli. Researchers may employ tools such as checklists, behavior coding schemes, or video recordings to capture relevant data accurately.

After gathering the data, the next step is to analyze and interpret the findings. This involves organizing the collected information, identifying patterns, correlations, or trends, and drawing conclusions based on the observed behaviors. Statistical analysis and qualitative coding techniques are often utilized to make sense of the data and provide meaningful insights.

Pre-Experiment Preparation

The pre-experiment preparation phase involves conducting background research, selecting appropriate statistical methods, and establishing the methodology framework to ensure a structured and systematic approach to the field experiment.

Conducting thorough background research is essential as it provides a foundational understanding of the topic under study and helps in identifying gaps in existing literature.

Selecting the most suitable statistical methods is crucial for accurately analyzing and interpreting the data collected during the experiment.

Establishing a robust methodology framework not only ensures reproducibility but also enhances the credibility of the research findings. This comprehensive preparation stage lays the groundwork for a successful and meaningful field study.

Implementation of the Experiment

The implementation phase of a field experiment involves executing the study protocols, observing behavioral patterns, maintaining control over experimental conditions, and ensuring participant compliance to gather valid and reliable data.

During the behavior observation stage, researchers closely monitor how participants interact with the experimental setup and record any deviations from expected responses. Control mechanisms, such as random assignment or placebo groups, are put in place to minimize external influences on the outcomes of the study. Participant interactions play a crucial role as researchers engage with them to explain the study objectives and guidelines, ensuring their understanding and cooperation. For instance, in a study on consumer behavior, participants may be asked to navigate a simulated shopping experience to observe their decision-making processes.

Data Collection and Analysis

Data collection and analysis in field experiments are critical stages that involve interpreting results, assessing statistical significance, conducting thorough analysis, and validating findings through replication for robust conclusions.

During the data collection phase, researchers systematically gather information from the experimental setting, utilizing various tools such as surveys, observations, and sensor data to ensure comprehensive coverage. This raw data is then meticulously processed, organized, and cleaned to prepare it for analysis. Once the data is ready, statistical methods and analytical procedures are applied to identify patterns, relationships, and trends within the dataset.

Interpreting these results requires a deep understanding of the experimental design, the variables involved, and the potential impact of external factors on the outcomes.

What Are the Limitations of Field Experiments in Psychology?

Field experiments in psychology face inherent limitations, including criticisms regarding bias, expectancy effects, and challenges related to controlling extraneous variables that may impact the study outcomes.

One common critique of field experiments is the potential for selection bias, where participants self-select into certain groups, affecting the validity of the results. Due to the naturalistic setting of field experiments, researchers may struggle to isolate the specific variables influencing the outcomes, leading to confounding factors. Expectancy effects, where participants’ behavior is influenced by their expectations of the study’s results, can skew the data, making it challenging to draw accurate conclusions.

How to Overcome Challenges in Conducting Field Experiments?

Overcoming challenges in conducting field experiments requires addressing ethical concerns, implementing standardization protocols, and leveraging laboratory settings to enhance experimental control and reduce potential biases.

One strategy to tackle ethical concerns is to obtain informed consent from participants or stakeholders involved in the study, ensuring transparency and respect for autonomy. Establishing clear guidelines for data collection and analysis helps in standardizing practices across different research sites, leading to consistent and reliable results. Utilizing laboratory environments allows researchers to create controlled conditions, minimizing external influences on the experiment and enhancing the internal validity of the study.

Addressing Ethical Concerns

Addressing ethical concerns in field experiments involves prioritizing participant welfare, obtaining informed consent, and ensuring data confidentiality and integrity to uphold ethical standards and research integrity.

One of the key strategies to safeguard participant welfare in field experiments is by implementing thorough informed consent procedures. This involves clearly outlining the purpose of the study, potential risks and benefits, and the voluntary nature of participation.

Participant protection can also be ensured by minimizing harm through careful study design and monitoring. Another vital aspect is maintaining data privacy measures to safeguard confidential information and maintain the trust of participants. By adhering to these ethical considerations, researchers can uphold the credibility of their work and reinforce respect for participant rights.

Dealing with Unforeseen Variables

Handling unforeseen variables in field experiments necessitates controlling extraneous and confounding variables, implementing rigorous control measures, and addressing selection biases to minimize their impact on the study outcomes.

One crucial aspect of managing unexpected variables in field experiments is the meticulous design of the research protocol. By clearly defining the variables under study and establishing specific criteria for inclusion and exclusion, researchers can maintain clarity and consistency in data collection.

Employing randomization techniques can help distribute potential sources of variability evenly across treatment groups, reducing the influence of uncontrolled factors. Creating a well-structured data collection process and regularly monitoring data quality can enhance the reliability and validity of the findings.

Improving Sample Size and Randomization

Enhancing sample size and randomization techniques in field experiments involves scaling up participant numbers, refining randomization processes, conducting study replications, and employing robust statistical methods to enhance research validity and reliability.

One fundamental strategy to boost sample size in field experiments is by implementing participant scaling . This involves expanding the pool of participants to represent a more diverse population, increasing the generalizability of findings.

Advancements in randomization techniques, such as using cutting-edge algorithms and adaptive designs, can further enhance the randomness of group assignments, reducing bias and increasing the accuracy of results.

Incorporating rigorous replication practices, including multi-site studies and longitudinal designs, can help validate findings across different contexts.

Improving statistical methodologies with advanced techniques like Bayesian analysis and machine learning algorithms can provide more robust and sophisticated insights into the data collected.

Frequently Asked Questions

What is the purpose of conducting effective field experiments in psychology.

The purpose of conducting effective field experiments in psychology is to study behavior and psychological processes in natural settings, as opposed to a controlled laboratory environment, in order to enhance the generalizability of research findings and improve the understanding of real-world behaviors.

How do field experiments in psychology differ from laboratory experiments?

Field experiments in psychology differ from laboratory experiments in that they take place in natural settings, with participants’ behavior and responses being observed in their everyday environment. This allows for a more realistic and applicable understanding of behavior compared to artificial laboratory conditions.

What are some examples of effective field experiments in psychology?

Some examples of effective field experiments in psychology include studying the impact of social norms on behavior in a public setting, observing the effects of natural environments on mood and stress levels, and examining the effectiveness of interventions in real-world settings.

What are the benefits of conducting field experiments in psychology?

Conducting field experiments in psychology has several benefits, including improved external validity, increased ecological validity, and the ability to study real-world behaviors and processes in their natural context. This can lead to a better understanding of human behavior and more accurate applications of research findings.

What are some challenges of conducting field experiments in psychology?

Some challenges of conducting field experiments in psychology include difficulty controlling extraneous variables, potential ethical concerns, and limited control over the research environment. Additionally, field experiments may require more resources and time compared to laboratory experiments.

How can researchers ensure the validity and reliability of field experiments in psychology?

To ensure the validity and reliability of field experiments in psychology, researchers should carefully design their study and control for potential confounds. This may include using random assignment, pre-testing and post-testing, and developing specific protocols for data collection and analysis. It is also important to replicate the study in different settings to ensure consistent results.

Marcus Wong, a cognitive neuroscientist, explores the mysteries of the human brain and behavior. His work in experimental psychology and brain imaging techniques has contributed to our understanding of memory, decision-making, and neural mechanisms underlying cognitive functions. Marcus is committed to making complex scientific concepts accessible to a broad audience, writing about the latest trends in neuroscience, cognitive enhancement, and the intersection of technology with brain health.

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Experiments

Louis Cohen, Lawrence Manion, Keith Morrison in Research Methods in Education , 2017

The design experiment can be considered as a special case of a field experiment; it has its roots in experimental research, both in ‘true’ and quasi-experiments, and is intended to provide formative feedback on, for example, practical problems in, say, teaching and learning, and to bridge the potential gap between research and practice (Brown, 1992, p. 143; Reinking and Bradley, 2008; Bradley and Reinking, 2011; Engeström, 2011; Seel, 2011, p. 925; Anderson and Shattuck, 2012; Laurillard, 2012), in other words, to enhance the external validity of an experiment. The design experiment strives to avoid the artificial world of the laboratory and the lack of applicability to ‘real-world problems’ that follows from this artificial condition (Bradley and Reinking, 2011; Reinking and Bradley, 2008; Seel, 2011; Laurillard, 2012), and to have direct practical relevance to the complex world of teaching, learning and classrooms. Given their intended direct relevance to classrooms and the field nature – the diverse, complex, ‘real world’ of an actual classroom – design experiments may not be able to fulfil the requirements of a true experiment, for example, in randomization or in the application of controls. In these respects, design experiments are similar to action research (cf. Anderson and Shattuck, 2012).

External validity and public health

Sridhar Venkatapuram, Alex Broadbent in The Routledge Handbook of Philosophy of Public Health , 2023

The other reason they give for doubting the trade-off is because the notion of the “artificiality” of experimental settings, used to justify its existence, is vague and ambiguous (Jimenez-Buedo and Miller 2010: 307). The difficulty in blaming the artificiality of experimental environments for external validity failure is that none of the proponents of this view explain in exactly which respects the environments are meant to differ. In any case, it is clear that there is a lot more to the external validity problem than just a concern about the artificiality of experimental settings. This can be seen by considering the case of so-called field experiments or observational studies, where causes and effects are observed in real-world settings. In these studies, there is nothing artificial about the experimental context; yet, the external validity question is still a legitimate concern. This is because we can have cases where a field experiment exposes a clear causal relation evident in one context, but this causal relation does not hold in a new context because of different confounding factors, for example.

Randomization Tests or Permutation Tests? A Historical and Terminological Clarification

Vance W. Berger in Randomization, Masking, and Allocation Concealment , 2017

Interestingly, another statistical heavyweight, Jerzy Neyman, did something very similar with respect to giving credit to Fisher for the randomization design principle as Edward Pitman did with respect to giving credit to Fisher for developing the test. In his notorious paper, read before the Industrial and Agricultural Research Section of the Royal Statistical Society, Neyman (1935, p. 109) stated:Owing to the work of R. A. Fisher, “Student” and their followers, it is hardly possible to add anything essential to the present knowledge concerning local experiments…. One of the most important achievements of the English School is their method of planning field experiments known as the method of Randomized Blocks and Latin Squares.

Does an economic incentive affect provider behavior? Evidence from a field experiment on different payment mechanisms

Published in Journal of Medical Economics , 2019

Xiaoyu Xi, Ennan Wang, Qianni Lu, Piaopiao Chen, Tian Wo, Kammy Tang

We used a field experiment study design to examine the behaviors of physicians. If a laboratory experiment were performed instead of a field experiment, the conclusions might not be valid due to hyper-abstraction and simplification26. However, the participants in a field study were not restricted to college students, instead, they were adults in society. Moreover, the experimental environment was not confined to a laboratory. A field experiment, as defined by Harrison and List27, was an experiment conducted in multiple locations, including laboratories and actual environments. Its participants included both students and non-college adults. Therefore, under the real social conditional, the experiment subjects could make realistic choices. Above all, because of the differences between the experimental environment and subjects, the field experiment could represent actual conditions in a real environment, and subjects might act instinctively as they do in daily life, increasing the external validity of results28.

Employees’ Improvisational Behavior: Exploring the Role of Leader Grit and Humility

Published in Human Performance , 2022

Arménio Rego, Andreia Vitória, Miguel Pina e Cunha, Bradley P. Owens, Ana Ventura, Susana Leal, Camilo Valverde, Rui Lourenço-Gil

While providing overall support for the proposed causal direction of our model, our research is not without limitations. First, other causalities are possible. For example, employees may develop higher self-efficacy, hope, and optimism after making improvisations that are revealed to be successful. It is also possible that leaders adopt more perseverant efforts in pursuing challenging goals as a consequence of their higher employees’ PsyCap. Although the experiment was designed to enhance realism, which enhances confidence in hypothesized causality, it also suffers from modest external validity and other limitations (Lonati et al., 2018). Future studies should include covariates to rule out confounding and endogeneity effects, should adopt other experimental designs, and should be carried out in real organizational settings (Antonakis, Bendahan, Jacquart, & Lalive, 2014). A field experiment would represent a very important step forward in that endeavor, although abundant obstacles (methodological and practical) may make the endeavor unfeasible. Second, future studies may explore boundary conditions of the PsyCap-improvisation relationship. For example, is the relationship more positive when employees experience psychological safety?

Impact of safety training and interventions on training-transfer: targeting migrant construction workers

Published in International Journal of Occupational Safety and Ergonomics , 2020

Rahat Hussain, Akeem Pedro, Do Yeop Lee, Hai Chien Pham, Chan Sik Park

Notwithstanding, even though the culturally diverse nature of the work crews is a valuable aspect to evaluate migrant worker safety performance, it is hard to engage labourers for such practice. Similarly, the extent of interventions being implemented during and after training sessions is difficult to control. To address these challenges, the experiment design phase of this research has focused more on the reasons for training failures from both the literature and current migrant worker issues in industry. The approach presented in this study measures the combined impact of all interventions in a field experiment; however, in order to improve the reliability and validity in measurement, the individual effects of interventions should also be considered in further studies.

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Handbook of Field Experiments

The last 15 years have seen an explosion in the number, scope, quality, and creativity of field experiments. To take stock of this remarkable progress, we were invited to edit a Handbook of Field Experiments , published at Elsevier. We were fortunate to assemble a volume made of wonderful papers by the best experts in the field. Some chapters are more methodological, while others are focused on results. All of them provide thoughtful reflections on the advances and issues in the field, useful research tips and insights into what the next steps need to be, all of which should be very useful for graduate students. Taken together, these papers offer an incredibly rich overview of the state of literature. This page collects together all the working paper versions of the chapters, and will also link to the final versions as they become available. We hope you enjoy it.

—Abhijit Banerjee and Esther Duflo

Introduction

An Introduction to the "Handbook of Field Experiments" Abhijit Banerjee and Esther Duflo

Many (though by no means all) of the questions that economists and policymakers ask themselves are causal in nature: What would be the impact of adding computers in classrooms? What is the price elasticity of demand for preventive health products? Would increasing interest rates lead to an increase in default rates? Decades ago, the statistician Fisher (Fisher, 1925) proposed a method to answer such causal questions: Randomized Controlled Trials (RCTs) . In an RCT, the assignment of different units to different treatment groups is chosen randomly. This ensures that no unobservable characteristics of the units are reflected in the assignment, and hence that any difference between treatment and control units reflects the impact of the treatment. While the idea is simple, the implementation in the field can be more involved, and it took some time before randomization was considered to be a practical tool for answering questions in economics.

Some Historical Background

The Politics and Practice of Social Experiments: Seeds of a Revolution Judy Gueron

Between 1970 and the early 2000s, there was a revolution in support for the use of randomized experiments to evaluate social programs. Focusing on the welfare reform studies that helped to speed that transformation in the United States, this chapter describes the major challenges to randomized controlled trials (RCTs), how they emerged and were overcome, and how initial conclusions about conditions necessary to success — strong financial incentives, tight operational control, and small scale — proved to be wrong. The final section discusses lessons from this experience for other fields.

Methodology and Practice of RCTs

The Econometrics of Randomized Experiments Susan Athey and  Guido Imbens

Randomized experiments have a long tradition in agricultural and biomedical settings. In economics they have a much shorter history. Although there have been notable experiments over the years, such as the RAND health care experiment (Manning, Newhouse, Duan, Keeler and Leibowitz, 1987, see the general discussion in Rothstein and von Wachter, 2016) and the Negative Income Tax experiments (e.g., Robins, 1985), it is only recently that there has been a large number of randomized experiments in economics, and development economics in particular. See Duflo, Glennerster, and Kremer (2006) for a survey.  In this chapter we discuss some of the statistical methods that are important for the analysis and design of randomized experiments. A major theme of the chapter is the focus on statistical methods directly justified by randomization, in the spirit of Freedman who wrote “Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by ‘sophisticated’ models,” (Freedman, 2006, p. 691) We draw from a variety of literatures. This includes the statistical literature on the analysis and design of experiments, e.g., Wu and Hamada (2009), Cox and Reid (2000), Altman (1991), Cook and DeMets (2008), Kempthorne (1952, 1955), Cochran and Cox (1957), Davies (1954), and Hinkelman and Kempthorne (2005, 2008). We also draw on the literature on causal inference, both in experimental and observational settings, Rosenbaum (1995, 2002, 2009), Rubin (2006), Cox (1992), Morgan and Winship (2007), Morton Williams (2010) and Lee (2005), and Imbens and Rubin (2015). In the economics literature we build on recent guides to practice in randomized experiments in development economics, e.g., Duflo, Glennerster, and Kremer (2006), Glennerster (2016), and Glennerster and Takavarasha (2013) as well as the general empirical micro literature (Angrist and Pischke, 2008).

Decision Theoretic Approaches to Experiment Design and External Validity Abhijit Banerjee, Sylvain Chassang,  and Erik Snowberg

A modern, decision-theoretic framework can help clarify important practical questions of experimental design. Building on our recent work, this chapter begins by summarizing our framework for understanding the goals of experimenters, and applying this to re-randomization.  We then use this framework to shed light on questions related to experimental registries, pre-analysis plans, and most importantly, external validity. Our framework implies that even when large samples can be collected, external decisionmaking remains inherently subjective. We embrace this conclusion, and argue that in order to improve external validity, experimental research needs to create a space for structured speculation.

The Practicalities of Running Randomized Evaluations: Partnerships, Measurement, Ethics, and Transparency Rachel Glennerster

Economists have known for a long time that randomization could help identify causal connections by solving the problem of selection bias. Chapter 1 in this book and Gueron and Rolston (2013) describe the effort in the US to move experiments out of the laboratory into the policy world in the 1960s and 1970s.  This experience was critical in proving the feasibility of field experiments, working through some of the important ethical questions involved, showing how researchers and practitioners could work together, and demonstrating that the results of field experiments were often very different from those generated by observational studies. Interestingly, there was relatively limited academic support for this first wave of field experiments (Gueron and Rolston 2013), most of which were carried out by research groups such as MDRC, Abt, and Mathematica, to evaluate US government programs, and they primarily used individual-level randomization. In contrast, a more recent wave of field experiments starting in the mid-1990s was driven by academics, initially was focused on developing countries, often worked with nongovernmental organizations, and frequently used clustered designs.

The Psychology of Construal in the Design of Field Experiments Elizabeth Levy Paluck and Eldar Shafir

Why might you be interested in this chapter? A fair assumption is that you are reading because you care about good experimental design. To create strong experimental designs that test people’s responses to an intervention, researchers typically consider the classically recognized motivations presumed to drive human behavior.  It does not take extensive psychological training to recognize that several types of motivations could affect an individual’s engagement with and honesty during your experimental paradigm. Such motivations include strategic self-presentation, suspicion, lack of trust, level of education or mastery, and simple utilitarian motives such as least effort and optimization. For example, minimizing the extent to which your findings are attributable to high levels of suspicion among participants, or to their decision to do the least amount possible, is important for increasing the generalizability and reliability of your results.

Understanding Preferences and Preference Change

Field Experiments in Markets Omar Al-Ubaydli and  John List

This is a review of the literature of field experimental studies of markets. The main results covered by the review are as follows: (1) Generally speaking, markets organize the efficient exchange of commodities; (2) There are some behavioral anomalies that impede efficient exchange; (3) Many behavioral anomalies disappear when traders are experienced.

Field Experiments on Discrimination Marianne Bertrand and Esther Duflo

This article reviews the existing field experimentation literature on the prevalence of discrimination, the consequences of such discrimination, and possible approaches to undermine it. We highlight key gaps in the literature and ripe opportunities for future field work.  Section 1 reviews the various experimental methods that have been employed to measure the prevalence of discrimination, most notably audit and correspondence studies; it also describes several other measurement tools commonly used in lab-based work that deserve greater consideration in field research. Section 2 provides an overview of the literature on the costs of being stereotyped or discriminated against, with a focus on self-expectancy effects and self-fulfilling prophecies; section 2 also discusses the thin field-based literature on the consequences of limited diversity in organizations and groups. The final section of the paper, Section 3, reviews the evidence for policies and interventions aimed at weakening discrimination, covering role model and intergroup contact effects, as well as socio-cognitive and technological de-biasing strategies.

Field Experiments on Voter Mobilization: An Overview of a Burgeoning Literature Alan Gerber and Donald Green

In recent years the focus of empirical work in political science has begun to shift from description to an increasing emphasis on the credible estimation of causal effects. A key feature of this change has been the increasing prominence of experimental methods, and especially field experiments. In this chapter we review the use of field experiments to study political participation.  Although several important experiments address political phenomena other than voter participation (Bergan 2009; Butler and Broockman 2015; Butler and Nickerson 2011; Broockman 2013, 2014; Grose 2014), the literature measuring the effect of various interventions on voter turnout is the largest and most fully developed, and it provides a good illustration of how the use of field experiments in political science has proceeded. From an initial focus on the relative effects of different modes of communication, scholars began to explore how theoretical insights from social psychology and behavioral economics might be used to craft messages and how voter mobilization experiments could be employed to test the real world effects of theoretical claims. The existence of a large number of experimental turnout studies was essential, because it provided the background against which unusual and important results could be easily discerned.

Lab in the Field: Measuring Preferences in the Wild Uri Gneezy and Alex Imas

In this chapter, we discuss the “lab-in-the-field” methodology, which combines elements of both lab and field experiments in using standardized, validated paradigms from the lab in targeting relevant populations in naturalistic settings. We begin by examining how the methodology has been used to test economic models with populations of theoretical interest. Next, we outline how lab-in-the-field studies can be used to complement traditional Randomized Control Trials in collecting covariates to test theoretical predictions and explore behavioral mechanisms. We proceed to discuss how the methodology can be utilized to compare behavior across cultures and contexts, and test for the external validity of results obtained in the lab. The chapter concludes with an overview of lessons on how to use the methodology effectively.

Field Experiments in Marketing Duncan Simester

Marketing is a diverse field that draws from a rich array of disciplines and a broad assortment of empirical and theoretical methods. One of those disciplines is economics and one of the methods used to investigate economic questions is field experiments. The history of field experiments in the marketing literature is surprisingly long. Early examples include Curhan (1974) and Eskin and Baron (1977), who vary prices, newspaper advertising, and display variables in grocery stores.  This chapter reviews the recent history of field experiments in marketing by identifying papers published in the last 20 years (between 1995 and 2014). We report how the number of papers published has increased during this period, and evaluate different explanations for this increase. We then group the papers into five topics and review the papers by topic. The chapter concludes by reflecting on the design of field experiments used in marketing, and proposing topics for future research.

The Challenge of Improving Human Capital

Impacts and Determinants of Health Levels in Low-Income Countries Pascaline Dupas and Ted Miguel

Improved health in low-income countries could considerably improve wellbeing and possibly promote economic growth. The last decade has seen a surge in field experiments designed to understand the barriers that households and governments face in investing in health and how these barriers can be overcome, and to assess the impacts of subsequent health gains. This chapter first discusses the methodological pitfalls that field experiments in the health sector are particularly susceptible to, then reviews the evidence that rigorous field experiments have generated so far.  While the link from in utero and child health to later outcomes has increasingly been established, few experiments have estimated the impacts of health on contemporaneous productivity among adults, and few experiments have explored the potential for infrastructural programs to impact health outcomes. Many more studies have examined the determinants of individual health behavior, on the side of consumers as well as among providers of health products and services.

The Production of Human Capital in Developed Countries: Evidence from 196 Randomized Field Experiments Roland Fryer

Randomized field experiments designed to better understand the production of human capital have increased exponentially over the past several decades. This chapter summarizes what we have learned about various partial derivatives of the human capital production function, what important partial derivatives are left to be estimated, and what – together – our collective efforts have taught us about how to produce human capital in developed countries. The chapter concludes with a back of the envelope simulation of how much of the racial wage gap in America might be accounted for if human capital policy focused on best practices gleaned from randomized field experiments.

Field Experiments in Education in Developing Countries Karthik Muralidharan Perhaps no field in development economics in the past decade has benefited as much from the use of experimental methods as the economics of education. The rapid growth in high‐quality studies on education in developing countries (many of which use randomized experiments) is perhaps best highlighted by noting that there have been  several  systematic reviews of this evidence aiming to synthesize findings for research and policy in  just the past three years .   These include Muralidharan 2013 (focused on India), Glewwe et al. 2014 (focused on school inputs), Kremer et al. 2013, Krishnaratne et al. 2013, Conn 2014 (focused on sub‐Saharan Africa), McEwan 2014, Ganimian and Murnane (2016), Evans and Popova (2015), and Glewwe and Muralidharan (2016). While these are not all restricted to experimental studies, they typically provide greater weight to evidence from randomized controlled trials (RCT's).

Designing Effective Social Programs

Social Policy: Mechanism Experiments and Policy Evaluations Bill Congdon,  Jeffrey Kling, Jens Ludwig, and Sendhil Mullainathan

Policymakers and researchers are increasingly interested in using experimental methods to inform the design of social policy. The most common approach, at least in developed countries, is to carry out large-scale randomized trials of the policies of interest, or what we call here policy evaluations. In this chapter we argue that in some circumstances the best way to generate information about the policy of interest may be to test an intervention that is different from the policy being considered, but which can shed light on one or more key mechanisms through which that policy may operate.  What we call mechanism experiments can help address the key external validity challenge that confronts all policy-oriented work in two ways. First, mechanism experiments sometimes generate more policy-relevant information per dollar of research funding than can policy evaluations, which in turn makes it more feasible to test how interventions work in different contexts. Second, mechanism experiments can also help improve our ability to forecast effects by learning more about the way in which local context moderates policy effects, or expand the set of policies for which we can forecast effects. We discuss how mechanism experiments and policy evaluations can complement one another, and provide examples from a range of social policy areas including health insurance, education, labor market policy, savings and retirement, housing, criminal justice, redistribution, and tax policy. Examples focus on the U.S. context.

Field Experiments in Developing Country Agriculture Alain de Janvry, Elisabeth Sadoulet, and Tavneet Suri

This chapter provides a review of the role of field experiments in answering research questions in agriculture that ultimately let us better understand how policy can improve productivity and farmer welfare in developing economies. We first review recent field experiments in this area, highlighting the contributions experiments have already made to this area of research. We then outline areas where experiments can further fill existing gaps in our knowledge on agriculture and how future experiments can address the specific complexities in agriculture.

The Personnel Economics of the State Frederico Finan, Ben Olken, and Rohini Pande

Governments play a central role in facilitating economic development. Yet while economists have long emphasized the importance of government quality, historically they have paid less attention to the internal workings of the state and the individuals who provide the public services. This chapter reviews a nascent but growing body of field experiments that explores the personnel economics of the state.  To place the experimental findings in context, we begin by documenting some stylized facts about how public sector employment differs from that in the private sector. In particular, we show that in most countries throughout the world, public sector employees enjoy a significant wage premium over their private sector counterparts. Moreover, this wage gap is largest among low-income countries, which tends to be precisely where governance issues are most severe. These differences in pay, together with significant information asymmetries within government organizations in low-income countries, provide a prima facie rationale for the emphasis of the recent field experiments on three aspects of the state–employee relationship: selection, incentive structures, and monitoring. We review the findings on all three dimensions and then conclude this survey with directions for future research.

Designing Social Protection Programs: Using Theory and Experimentation to Understand how to Help Combat Poverty Rema Hanna and Dean Karlan

“Anti-poverty” programs come in many varieties, ranging from multi-faceted, complex programs to more simple cash transfers. Articulating and understanding the root problem motivating government and nongovernmental organization intervention is critical for choosing amongst many anti-poverty policies, or combinations thereof. Policies should differ depending on whether the underlying problem is about uninsured shocks, liquidity constraints, information failures, or some combination of all of the above.  Experimental designs and thoughtful data collection can help diagnose the root problems better, thus providing better predictions for what anti-poverty programs to employ in specific conditions and contexts. However, the more complex theories are likewise more challenging to test, requiring larger samples, and often more nuanced experimental designs, as well as detailed data on many aspects of household and community behavior and outcomes. We provide guidance on these design and testing issues for social protection programs, from how to target programs, to who should implement the program, to whether and what conditions to require for program participation. In short, careful experimentation designed testing can help provide a stronger conceptual understanding of why programs do or not work, thereby allowing one to ultimately make stronger policy prescriptions that further the goal of poverty reduction.

Social Experiments in the Labor Market Jesse Rothstein and  Till von Wachter

Large-scale social experiments were pioneered in labor economics, and are the basis for much of what we know about topics ranging from the effect of job training to incentives for job search to labor supply responses to taxation. Random assignment has provided a powerful solution to selection problems that bedevil non- experimental research. Nevertheless, many important questions about these topics require going beyond random assignment.  This applies to questions pertaining to both internal and external validity, and includes effects on endogenously observed outcomes, such as wages and hours; spillover effects; site effects; heterogeneity in treatment effects; multiple and hidden treatments; and the mechanisms producing treatment effects. In this Chapter, we review the value and limitations of randomized social experiments in the labor market, with an emphasis on these design issues and approaches to addressing them. These approaches expand the range of questions that can be answered using experiments by combining experimental variation with econometric or theoretical assumptions. We also discuss efforts to build the means of answering these types of questions into the ex ante design of experiments. Our discussion yields an overview of the expanding toolkit available to experimental researchers.

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Field Experiments

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Research output : Chapter in Book/Report/Conference proceeding › Chapter

Field experiments are experiments in settings with high degrees of naturalism. This article describes different types of field experiments, including randomized field trials, randomized rollout designs, encouragement designs, downstream field experiments, hybrid lab-field experiments, and covert population experiments, and discusses their intellectual background and benefits. It also lists methodological challenges researchers can encounter when conducting field experiments, including failure to treat, selective attrition, spillover, difficulty of replication, and black box causality, and discusses available solutions. Finally, it provides an overview over current and emerging directions in field experimentation and concludes with a brief history of field experiments.

All Science Journal Classification (ASJC) codes

• General Social Sciences
• Audit studies
• Correspondence studies
• Encouragement designs
• Experiments
• External validity
• Failure to treat
• Hybrid field experiments
• Randomized controlled trial
• Randomized rollout designs
• Social experiments
• Stepped wedge designs
• Waiting list designs

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• Field Experimentation Agricultural and Biological Sciences 100%

T1 - Field Experiments

AU - Ditlmann, Ruth

AU - Paluck, Elizabeth Levy

N1 - Publisher Copyright: © 2015 Elsevier Ltd. All rights reserved.

PY - 2015/3/26

Y1 - 2015/3/26

N2 - Field experiments are experiments in settings with high degrees of naturalism. This article describes different types of field experiments, including randomized field trials, randomized rollout designs, encouragement designs, downstream field experiments, hybrid lab-field experiments, and covert population experiments, and discusses their intellectual background and benefits. It also lists methodological challenges researchers can encounter when conducting field experiments, including failure to treat, selective attrition, spillover, difficulty of replication, and black box causality, and discusses available solutions. Finally, it provides an overview over current and emerging directions in field experimentation and concludes with a brief history of field experiments.

AB - Field experiments are experiments in settings with high degrees of naturalism. This article describes different types of field experiments, including randomized field trials, randomized rollout designs, encouragement designs, downstream field experiments, hybrid lab-field experiments, and covert population experiments, and discusses their intellectual background and benefits. It also lists methodological challenges researchers can encounter when conducting field experiments, including failure to treat, selective attrition, spillover, difficulty of replication, and black box causality, and discusses available solutions. Finally, it provides an overview over current and emerging directions in field experimentation and concludes with a brief history of field experiments.

KW - Attrition

KW - Audit studies

KW - Causality

KW - Correspondence studies

KW - Encouragement designs

KW - Experiments

KW - External validity

KW - Failure to treat

KW - Hybrid field experiments

KW - Naturalism

KW - Randomized controlled trial

KW - Randomized rollout designs

KW - Social experiments

KW - Stepped wedge designs

KW - Waiting list designs

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U2 - 10.1016/B978-0-08-097086-8.10542-2

DO - 10.1016/B978-0-08-097086-8.10542-2

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AN - SCOPUS:85043444790

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BT - International Encyclopedia of the Social & Behavioral Sciences: Second Edition

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Field Experiments: Design, Analysis, and Interpretation Illustrated Edition

A brief, authoritative introduction to field experimentation in the social sciences.

• ISBN-10 0393979954
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• Publisher W. W. Norton & Company
• Publication date May 29, 2012
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• Publisher ‏ : ‎ W. W. Norton & Company; Illustrated edition (May 29, 2012)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 512 pages
• ISBN-10 ‏ : ‎ 0393979954
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About the authors

Donald p. green.

Donald P. Green is J.W. Burgess Professor of Political Science at Columbia University, having moved there in 2011 after 22 years at Yale University. The author of four books and more than one hundred essays, Green's research interests span a wide array of topics: voting behavior, partisanship, campaign finance, hate crime, and research methods. Much of his current work uses field experimentation to study the ways in which political campaigns mobilize and persuade voters. He was elected to the American Academy of Arts and Sciences in 2003 and was awarded the Heinz I. Eulau Award for best article published in the American Political Science Review during 2009. In 2010, he helped found the Experimental Research section of the American Political Science Association and served as its first president. Don's hobbies include woodworking and game design.

Alan S. Gerber

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Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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

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The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Field Experiments

For geologists, social scientists and environmental biologists, amongst others, field experiments are an integral part of the discipline.

This article is a part of the guide:

• Research Designs
• Quantitative and Qualitative Research
• Literature Review
• Quantitative Research Design

Browse Full Outline

• 1 Research Designs
• 2.1 Pilot Study
• 2.2 Quantitative Research Design
• 2.3 Qualitative Research Design
• 2.4 Quantitative and Qualitative Research
• 3.1 Case Study
• 3.2 Naturalistic Observation
• 3.3 Survey Research Design
• 3.4 Observational Study
• 4.1 Case-Control Study
• 4.2 Cohort Study
• 4.3 Longitudinal Study
• 4.4 Cross Sectional Study
• 4.5 Correlational Study
• 5.1 Field Experiments
• 5.2 Quasi-Experimental Design
• 5.3 Identical Twins Study
• 6.1 Experimental Design
• 6.2 True Experimental Design
• 6.3 Double Blind Experiment
• 6.4 Factorial Design
• 7.1 Literature Review
• 7.2 Systematic Reviews
• 7.3 Meta Analysis

As the name suggests, a field study is an experiment performed outside the laboratory, in the 'real' world. Unlike case studies and observational studies , a field experiment still follows all of the steps of the scientific process , addressing research problems and generating hypotheses.

The obvious advantage of a field study is that it is practical and also allows experimentation , without artificially introducing confounding variables .

A population biologist examining an ecosystem could not move the entire environment into the laboratory, so field experiments are the only realistic research method in many fields of science.

In addition, they circumvent the accusation leveled at laboratory experiments of lacking external or ecological validity , or adversely affecting the behavior of the subject.

Social scientists and psychologists often used field experiments to perform blind studies , where the subject was not even aware that they were under scrutiny.

A good example of this is the Piliavin and Piliavin experiment , where the propensity of strangers to help blood covered 'victims' was measured. This is now frowned upon, under the policy of informed consent , and is only used in rare and highly regulated circumstances.

Field experiments can suffer from a lack of a discrete control group and often have many variables to try to eliminate.

For example, if the effects of a medicine are studied, and the subject is instructed not to drink alcohol, there is no guarantee that the subject followed the instructions, so field studies often sacrifice internal validity for external validity .

For fields like biology, geology and environmental science, this is not a problem, and the field experiment can be treated as a sound experimental practice, following the steps of the scientific method .

A major concern shared by all disciplines is the cost of field studies, as they tend to be very expensive.

For example, even a modestly sized research ship costs many thousands of dollars every day, so a long oceanographical research program can run into the millions of dollars.

Pilot studies are often used to test the feasibility of any long term or extensive research program before committing vast amounts of funds and resources. The changeable nature of the external environment and the often-prohibitive investment of time and money mean that field experiments are rarely replicable , so any generalization is always tenuous.

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Martyn Shuttleworth (Jun 14, 2010). Field Experiments. Retrieved Sep 26, 2024 from Explorable.com: https://explorable.com/field-experiments

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Annual Review of Sociology

Volume 43, 2017, review article, field experiments across the social sciences.

• Delia Baldassarri 1 , and Maria Abascal 2
• View Affiliations Hide Affiliations Affiliations: 1 Department of Sociology, New York University, New York, New York 10012; email: [email protected] 2 Department of Sociology, Columbia University, New York, New York 10027; email: [email protected]
• Vol. 43:41-73 (Volume publication date July 2017) https://doi.org/10.1146/annurev-soc-073014-112445
• First published as a Review in Advance on May 22, 2017
• © Annual Reviews

Using field experiments, scholars can identify causal effects via randomization while studying people and groups in their naturally occurring contexts. In light of renewed interest in field experimental methods, this review covers a wide range of field experiments from across the social sciences, with an eye to those that adopt virtuous practices, including unobtrusive measurement, naturalistic interventions, attention to realistic outcomes and consequential behaviors, and application to diverse samples and settings. The review covers four broad research areas of substantive and policy interest: first, randomized controlled trials, with a focus on policy interventions in economic development, poverty reduction, and education; second, experiments on the role that norms, motivations, and incentives play in shaping behavior; third, experiments on political mobilization, social influence, and institutional effects; and fourth, experiments on prejudice and discrimination. We discuss methodological issues concerning generalizability and scalability as well as ethical issues related to field experimental methods. We conclude by arguing that field experiments are well equipped to advance the kind of middle-range theorizing that sociologists value.

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The design of field experiments with survey outcomes: a framework for selecting more efficient, robust, and ethical designs.

Published online by Cambridge University Press:  18 September 2017

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There is increasing interest in experiments where outcomes are measured by surveys and treatments are delivered by a separate mechanism in the real world, such as by mailers, door-to-door canvasses, phone calls, or online ads. However, common designs for such experiments are often prohibitively expensive, vulnerable to bias, and raise ethical concerns. We show how four methodological practices currently uncommon in such experiments have previously undocumented complementarities that can dramatically relax these constraints when at least two are used in combination: (1) online surveys recruited from a defined sampling frame (2) with at least one baseline wave prior to treatment (3) with multiple items combined into an index to measure outcomes and, (4) when possible, a placebo control. We provide a general and extensible framework that allows researchers to determine the most efficient mix of these practices in diverse applications. Two studies then examine how these practices perform empirically. First, we examine the representativeness of online panel respondents recruited from a defined sampling frame and find that their representativeness compares favorably to phone panel respondents. Second, an original experiment successfully implements all four practices in the context of a door-to-door canvassing experiment. We conclude discussing potential extensions.

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Authors’ note : This paper previously circulated under the title “Testing Theories of Attitude Change With Online Panel Field Experiments.” Software for planning an experiment using all four practices we describe is available at http://experiments.berkeley.edu . Replication data is available as Broockman, Kalla, and Sekhon ( 2017 ), at http://dx.doi.org/10.7910/DVN/EEP5MT . This work was supported by the NARAL Pro-Choice America Foundation, the Signatures Innovations Fellows program at UC Berkeley, UC Berkeley’s Institute for Governmental Studies, and the Office of Naval Research [N00014-15-1-2367]. The studies reported herein were approved by Committee for the Protection of Human Subjects. We thank participants at the 2015 POLMETH meeting and at the University of California, Berkeley’s Research Workshop in American Politics for helpful feedback. Additional feedback was provided by Peter Aronow, Rebecca Barter, Kevin Collins, Alex Coppock, Jamie Druckman, Thad Dunning, Donald Green, Christian Fong, Seth Hill, Dan Hopkins, Gabe Lenz, Winston Lin, Chris Mann, David Nickerson, Kellie Ottoboni, Kevin Quinn, Fredrik Sävje, Yotam Shev-Tom, Bradley Spahn, and Laura Stoker. All remaining errors are our own.

Contributing Editor: R. Michael Alvarez

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• Volume 25, Issue 4
• David E. Broockman (a1) , Joshua L. Kalla (a2) and Jasjeet S. Sekhon (a3)
• DOI: https://doi.org/10.1017/pan.2017.27

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tirthajyoti.github.io

Design-of-experiment (doe) matrix generator for engineering and statistics.

Copyright Notice and Code repository

Copyright (c): 2018-2028, Dr. Tirthajyoti Sarkar, Sunnyvale, CA 94086

It uses a MIT License, so although I retain the copyright of this particular code, please feel free to exercise your rights of the free software by using and enhancing it.

Please get the codebase from here .

Table of Contents

What is a scientific experiment, what is experimental design, options for open-source doe builder package in python, limitation of the foundation packages used, simplified user interface, designs available, what supporitng packages are required, eratta for using pydoe, how to run the program, is an installer/python library available, full-factorial design, fractional-factorial design, central-composite design, latin hypercube design, acknowledgements and requirements, introduction.

Design of Experiment (DOE) is an important activity for any scientist, engineer, or statistician planning to conduct experimental analysis. This exercise has become critical in this age of rapidly expanding field of data science and associated statistical modeling and machine learning . A well-planned DOE can give a researcher meaningful data set to act upon with optimal number of experiments preserving critical resources.

After all, aim of Data Science is essentially to conduct highest quality scientific investigation and modeling with real world data. And to do good science with data, one needs to collect it through carefully thought-out experiment to cover all corner cases and reduce any possible bias.

In its simplest form, a scientific experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables , also referred to as “input variables” or “predictor variables.” The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables , also referred to as “output variables” or “response variables.” The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results.

Experimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources. There are multiple approaches for determining the set of design points (unique combinations of the settings of the independent variables) to be used in the experiment.

Main concerns in experimental design include the establishment of validity , reliability , and replicability . For example, these concerns can be partially addressed by carefully choosing the independent variable, reducing the risk of measurement error, and ensuring that the documentation of the method is sufficiently detailed. Related concerns include achieving appropriate levels of statistical power and sensitivity .

Need for careful design of experiment arises in all fields of serious scientific, technological, and even social science investigation —  computer science, physics, geology, political science, electrical engineering, psychology, business marketing analysis, financial analytics , etc…

Unfortunately, majority of the state-of-the-art DOE generators are part of commercial statistical software packages like JMP (SAS) or Minitab . However, a researcher will surely be benefited if there exists an open-source code which presents an intuitive user interface for generating an experimental design plan from a simple list of input variables. There are a couple of DOE builder Python packages but individually they don’t cover all the necessary DOE methods and they lack a simplified user API, where one can just input a CSV file of input variables’ range and get back the DOE matrix in another CSV file.

This set of codes is a collection of functions which wrap around the core packages (mentioned below) and generate design-of-experiment (DOE) matrices for a statistician or engineer from an arbitrary range of input variables.

Both the core packages, which act as foundations to this repo, are not complete in the sense that they do not cover all the necessary functions to generate DOE table that a design engineer may need while planning an experiment. Also, they offer only low-level APIs in the sense that the standard output from them are normalized numpy arrays. It was felt that users, who may not be comfortable in dealing with Python objects directly, should be able to take advantage of their functionalities through a simplified user interface.

User just needs to provide a simple CSV file with a single table of variables and their ranges (2-level i.e. min/max or 3-level). Some of the functions work with 2-level min/max range while some others need 3-level ranges from the user (low-mid-high). Intelligence is built into the code to handle the case if the range input is not appropriate and to generate levels by simple linear interpolation from the given input. The code will generate the DOE as per user’s choice and write the matrix in a CSV file on to the disk. In this way, the only API user is exposed to are input and output CSV files. These files then can be used in any engineering simulator, software, process-control module, or fed into process equipments.

• Full factorial,
• 2-level fractional factorial,
• Plackett-Burman,
• Sukharev grid,
• Box-Behnken,
• Box-Wilson (Central-composite) with center-faced option,
• Box-Wilson (Central-composite) with center-inscribed option,
• Box-Wilson (Central-composite) with center-circumscribed option,
• Latin hypercube (simple),
• Latin hypercube (space-filling),
• Random k-means cluster,
• Maximin reconstruction,
• Halton sequence based,
• Uniform random matrix

How to use it?

First make sure you have all the necessary packages installed. You can simply run the .bash (Unix/Linux) and .bat (Windows) files provided in the repo, to install those packages from your command line interface. They contain the following commands,

Please note that as installed, PyDOE will throw some error related to type conversion. There are two options

• I have modified the pyDOE code suitably and included a file with re-written functions in the repo. This is the file called by the program while executing, so you should see no error.
• If you encounter any error, you could try to modify the PyDOE code by going to the folder where pyDOE files are copied and copying the two files doe_factorial.py and doe_box_behnken.py supplied with this repo.

Note this is just a code repository and not a installer package. For the time being, please clone this repo from GitHub , store all the files in a local directory.

git clone https://github.com/tirthajyoti/Design-of-experiment-Python.git

Then start using the software by simply typing,

python Main.py

After this, a simple menu will be printed on the screen and you will be prompted for a choice of number (a DOE) and name of the input CSV file (containing the names and ranges of your variables).

You must have an input parameters CSV file stored in the same directory that you are running this code from. You should use the supplied generic CSV file as an example. Please put the factors in the columns and the levels in the row (not the other way around). Couple of example CSV files are provided in the repo. Feel free to modify them as per your needs.

At this time, No . I plan to work on turning this into a full-fledged Python library which can be installed from PyPi repository by a PIP command. But I cannot promise any timeline for that :-) If somebody wants to collaborate and work on an installer, please feel free to do so.

Let’s say the input file contains the following table for the parameters range. Imagine this as a generic example of a checmical process in a plant.

If we build a full-factorial DOE out of this, we will get a table with 81 entries because 4 factors permuted in 3 levels result in 3^4=81 combinations!

Clearly the full-factorial designs grows quickly! Engineers and scientists therefore often use half-factorial/fractional-factorial designs where they confound one or more factors with other factors and build a reduced DOE. Let’s say we decide to build a 2-level fractional factorial of this set of variables with the 4th variables as the confounding factor (i.e. not an independent variable but as a function of other variables). If the functional relationship is “A B C BC” i.e. the 4th parameter vary depending only on 2nd and 3rd parameter, the output table could look like,

A Box-Wilson Central Composite Design, commonly called ‘a central composite design,’ contains an imbedded factorial or fractional factorial design with center points that is augmented with a group of ‘star points’ that allow estimation of curvature. One central composite design consists of cube points at the corners of a unit cube that is the product of the intervals [-1,1], star points along the axes at or outside the cube, and center points at the origin. Central composite designs are of three types. Circumscribed (CCC) designs are as described above. Inscribed (CCI) designs are as described above, but scaled so the star points take the values -1 and +1, and the cube points lie in the interior of the cube. Faced (CCF) designs have the star points on the faces of the cube. Faced designs have three levels per factor, in contrast with the other types that have five levels per factor. The following figure shows these three types of designs for three factors. [Read this page] (http://blog.minitab.com/blog/understanding-statistics/getting-started-with-factorial-design-of-experiments-doe) for more information about this kind of design philosophy.

Sometimes, a set of randomized design points within a given range could be attractive for the experimenter to asses the impact of the process variables on the output. Monte Carlo simulations are close example of this approach. However, a Latin Hypercube design is better choice for experimental design rather than building a complete random matrix as it tries to subdivide the sample space in smaller cells and choose only one element out of each subcell. This way, a more ‘uniform spreading’ of the random sample points can be obtained. User can choose the density of sample points. For example, if we choose to generate a Latin Hypercube of 12 experiments from the same input files, that could look like,

Of course, there is no guarantee that you will get the same matrix if you run this function because this are randomly sampled, but you get the idea!

The code was written in Python 3.6. It uses following external packages that needs to be installed on your system to use it,

• pydoe: A package designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs. Check the docs here .
• diversipy: A collection of algorithms for sampling in hypercubes, selecting diverse subsets, and measuring diversity. Check the docs here .

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Experimental design of steel surface defect detection based on msfe-yolo—an improved yolov5 algorithm with multi-scale feature extraction.

1. Introduction

• Experimental Design Framework Based on Deep Learning: We propose a deep learning-based experimental design framework that integrates artificial intelligence with industrial applications. This framework not only provides an innovative solution for steel surface defect detection but also serves as a teaching tool aimed at guiding students to learn and master relevant technologies, equipping them with the skills necessary for real-world industrial applications.
• Introduction of Efficient Multi-Scale Attention (EMA) [ 25 ] Mechanism: By incorporating the EMA mechanism into the Backbone network of the YOLOv5 model, pixel-level relationships are captured through cross-dimensional interactions. Utilizing convolution kernels of varying sizes, the model efficiently fuses multi-scale contextual information, significantly enhancing the feature extraction capabilities and detection accuracy with only a slight increase in the computational cost.
• Proposed Novel C3DX Module: In the Neck of the network, we introduce the Convolution 3 Dilated Convolution X (C3DX) module. This module uses dilated convolutions with different dilation rates to capture diverse receptive fields and integrate multi-scale contextual information, further improving defect detection precision. In addition to boosting detection performance, this module helps students understand the concept of receptive fields, fostering their innovative thinking skills.
• Model Validation Across Multiple Datasets: The improved MSFE-YOLOv5 model has been validated on the NEU-DET, GC10-DET, Severstal Steel, and Crack500 datasets, with mean average precision (mAP) increases of 4.7%, 4.5%, 3.1% and 3.0%, respectively. These results demonstrate the model’s excellent performance in detection and generalization, while the experiments help students develop practical skills and the ability to solve real-world problems.

2. Materials and Methods

2.1. yolov5, 2.2. method, 2.2.2. c3dx, 2.2.3. eiou loss, 3.1. dataset preparation and preprocessing, 3.2. experimental setup, 3.3. evaluation metrics, 3.4. performance evaluation of each module, 3.4.1. attention effectiveness experiment, 3.4.2. comparison experiments, 3.4.3. ablation study, 4. discussion, author contributions, data availability statement, conflicts of interest.

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Li, L.; Zhang, R.; Xie, T.; He, Y.; Zhou, H.; Zhang, Y. Experimental Design of Steel Surface Defect Detection Based on MSFE-YOLO—An Improved YOLOV5 Algorithm with Multi-Scale Feature Extraction. Electronics 2024 , 13 , 3783. https://doi.org/10.3390/electronics13183783

Li L, Zhang R, Xie T, He Y, Zhou H, Zhang Y. Experimental Design of Steel Surface Defect Detection Based on MSFE-YOLO—An Improved YOLOV5 Algorithm with Multi-Scale Feature Extraction. Electronics . 2024; 13(18):3783. https://doi.org/10.3390/electronics13183783

Li, Lin, Ruopeng Zhang, Tunjun Xie, Yushan He, Hao Zhou, and Yongzhong Zhang. 2024. "Experimental Design of Steel Surface Defect Detection Based on MSFE-YOLO—An Improved YOLOV5 Algorithm with Multi-Scale Feature Extraction" Electronics 13, no. 18: 3783. https://doi.org/10.3390/electronics13183783

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Analysis and optimization of performance parameters of the 220 Rn chamber in flow-field mode using computational fluid dynamics method

• Published: 24 September 2024
• Volume 35 , article number  175 , ( 2024 )

Cite this article

• Shao-Hua Hu 1 ,
• Yong-Jun Ye   ORCID: orcid.org/0000-0002-0944-181X 2 ,
• Zheng-Zhong He   ORCID: orcid.org/0000-0003-4922-0453 1 ,
• De-Tao Xiao   ORCID: orcid.org/0000-0002-1875-6733 1 ,
• Xiang-Yu Xu 1 ,
• Jian-Kai Wang 1 &
• Qing-Zhi Zhou   ORCID: orcid.org/0009-0005-2179-4991 1  

The impact of the radiation dose produced by \({^{222}\hbox {Rn}}\) / \({^{220}{\hbox {Rn}}}\) and its progeny on human health has garnered increasing interest in the nuclear research field. The establishment of robust, regulatory, and competent \({^{220}{\hbox {Rn}}}\) chambers is crucial for accurately measuring radioactivity levels. However, studying the uniformity of the \({^{220}{\hbox {Rn}}}\) progeny through experimental methods is challenging, because measuring the concentration of \({^{220}{\hbox {Rn}}}\) and its progeny in multiple spatial locations simultaneously and in real time using experimental methods is difficult. Therefore, achieving precise control of the concentration of \({^{220}{\hbox {Rn}}}\) and its progeny as well as the reliable sampling of the progeny pose significant challenges. To solve this problem, this study uses computational fluid dynamics to obtain the flow-field data of the \({^{220}{\hbox {Rn}}}\) chamber under different wind speeds and progeny-replenishment rates. Qualitative analysis of the concentration distribution of the progeny and quantitative analysis of the progeny concentration and uniformity of the progeny concentration are conducted. The research findings indicated that the progeny concentration level is primarily influenced by wind speed and the progeny-complement rate. Wind speed also plays a crucial role in determining progeny concentration uniformity, whereas the progeny-complement rate has minimal impact on uniformity. To ensure the accuracy of \({^{220}{\hbox {Rn}}}\) progeny concentration sampling, we propose a methodology for selecting an appropriate sampling area based on varying progeny concentrations. This study holds immense importance for enhancing the regulation and measurement standards of \({^{220}{\hbox {Rn}}}\) and its progeny.

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Data availibility statement

The data that support the findings of this study are openly available in Science Data Bank at https://cstr.cn/31253.11.sciencedb.j00186.00107 and https://doi.org/10.57760/sciencedb.j00186.00107.

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School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China

Shao-Hua Hu, Zheng-Zhong He, De-Tao Xiao, Xiang-Yu Xu, Jian-Kai Wang & Qing-Zhi Zhou

School of Resources Environment and Safety Engineering, University of South China, Hengyang, 421001, China

Yong-Jun Ye

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Shao-Hua Hu, Xiang-Yu Xu, and Jian-Kai Wang. The first draft of the manuscript was written by Shao-Hua Hu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qing-Zhi Zhou .

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This work was supported by the National Natural Science Foundation of China (Nos. 12375310, 12175102, and 118750356), Youth Talent Foundation of Hunan Province of China (2022TJ-Q16), and Graduate Research and Innovation Projects of Hunan Province (CX20230964).

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Hu, SH., Ye, YJ., He, ZZ. et al. Analysis and optimization of performance parameters of the 220 Rn chamber in flow-field mode using computational fluid dynamics method. NUCL SCI TECH 35 , 175 (2024). https://doi.org/10.1007/s41365-024-01526-x

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Received : 26 December 2023

Revised : 09 March 2024

Accepted : 23 March 2024

Published : 24 September 2024

DOI : https://doi.org/10.1007/s41365-024-01526-x

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