experimental psychology sentence example

19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

Related posts:

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• 40+ Famous Psychologists (Images + Biographies)
• 11+ Psychology Experiment Ideas (Goals + Methods)
• The Little Albert Experiment
• 41+ White Collar Job Examples (Salary + Path)

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Psychological Research

Experiments

Learning objectives.

• Describe the experimental process, including ways to control for bias
• Identify and differentiate between independent and dependent variables

Causality: Conducting Experiments and Using the Data

Experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 1).

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 2).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 3). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 4). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

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group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

using a probability-based method to select a subset of individuals for the sample from the population.

using a probability-based method to divide a sample into treatment groups.

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6.2 Experimental Design

Learning objectives.

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 college students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Table 6.2 “Block Randomization Sequence for Assigning Nine Participants to Three Conditions” shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Table 6.2 Block Randomization Sequence for Assigning Nine Participants to Three Conditions

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right (see Note 6.28 “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Figure 6.2 Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This is what is shown by a comparison of the two outer bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?”

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999). There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002). The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Research has shown that patients with osteoarthritis of the knee who receive a “sham surgery” experience reductions in pain and improvement in knee function similar to those of patients who receive a real surgery.

Army Medicine – Surgery – CC BY 2.0.

Within-Subjects Experiments

In a within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in carryover effects. A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This is called a context effect . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is counterbalancing , which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this, he asked one group of participants to rate how large the number 9 was on a 1-to-10 rating scale and another group to rate how large the number 221 was on the same 1-to-10 rating scale (Birnbaum, 1999). Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often do exactly this.

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
• Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

Discussion: For each of the following topics, list the pros and cons of a between-subjects and within-subjects design and decide which would be better.

• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g., dog ) are recalled better than abstract nouns (e.g., truth ).
• Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.

Birnbaum, M. H. (1999). How to show that 9 > 221: Collect judgments in a between-subjects design. Psychological Methods, 4 , 243–249.

Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88.

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590.

Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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10 Cognitive Psychology Examples (Most Famous Experiments)

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Cognitive psychology is the scientific study of mental processes. This includes trying to understand how people perceive the world around them, store and recall memories, acquire and use language, and engage in problem-solving.

Although not the first to study mental processes, Ulric Neisser helped cement the term in the field of psychology in his 1967 book Cognitive Psychology .

He offered an elaborate definition of cognitive psychology, with key points quoted below:

“ The term cognition refers to all processes by which sensory input is transformed, reduced, elaborated, recovered, and used…Giving such a sweeping definition, it is apparent that cognition is involved in everything a human being might possibly do” (p. 4).

In the mid-20 th century, there was significant divide in psychology between behaviorism and cognitive psychologists.

The behaviorists, such as Skinner, argued that only observable phenomena should be studied. Since mental processes could not be observed, they could not be studied scientifically.

Neisser countered, stating that:

“Cognitive processes surely exist, so it can hardly be unscientific to study them” (p. 5).  

Cognitive Psychology Examples (Famous Studies)

1. the forgetting curve and the serial position effect.

The contributions of Hermann Ebbinghaus to cognitive psychology were so significant that his individual studies could consume all 10 examples in this article.

Some believe that his book Über das Gedächtnis (1902) “…records one of the most remarkable research achievements in the history of psychology” (Roediger, 1985, p. 519).

Two of his most influential discoveries on memory include: the forgetting curve and the serial position effect .

To make his research on memory scientific, he created a list of over 2,000 nonsense syllables (e.g., BOK, YAT). Using commonly used vocabulary words would be too heavily associated with meaning, but nonsense syllables had no prior associations.

By conducting testing on himself, he was able to eliminate numerous other variables that would result from using people with varied backgrounds, experiences, and mental acuities.

So, he would present himself with lists of nonsense syllables and then test his memory at various intervals afterward.

This led to the discovery of the forgetting curve : forgetting begins right after the initial presentation of information and continues to degrade from then on.

The serial-position effect is the tendency to remember the first and last items in a list more so than the items in the middle.

2. The Magical Number 7 

One of the most often cited papers in psychology was written by cognitive psychologist George Miller of Harvard University in 1956.

The paper did not describe a series of experiments conducted by Miller himself. Instead, Miller outlines the work of several researchers that point to the magical number 7 as the capacity of short-term memory.

He made the case that this capacity is the same no matter what form the stimuli takes; whether talking about tones or words.

He also suggested that information is organized in “chunks,” not individual bits. A word is just one chunk for a native speaker, but for someone learning the language, the word consists of several bits of information in the form of individual letters.

Therefore, the capacity of the native speaker is 7 words, but for the beginner, it may only be two, or just 7 letters.

Miller concludes the paper by making a point about the number 7 itself:

“And finally, what about the magical number seven? What about the seven wonders of the world, the seven seas, the seven deadly sins, the seven daughters of Atlas in the Pleiades, the seven ages of man, the seven levels of hell, the seven primary colors, the seven notes of the musical scale, and the seven days of the week?” (p. 96).

See Also: Short-Term Memory Examples

3. The Framing Bias 

Tversky and Kahneman (1981) discovered the framing bias , which occurs when a person’s decision is influenced by the way information is presented. 

A typical study involved presenting information to participants, but varying one or two words in how the information was described.

For example:

“Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. If Program C is adopted 400 people will die. [22 percent] If Program D is adopted there is 1/3 probability that nobody will die, and 2/3 probability that 600 people will die. [78 percent] Which of the two programs would you favor?” (p. 453).

Although both programs lead to the same mortality rate, most research participants preferred Program D.

As the researchers explain, “the certain death of 400 people is less acceptable than the two-in-three chance that 600 will die” (p. 453).

Moreover, the effects were far from trivial:

“They occur when the outcomes concern the loss of human lives as well as in choices about money; they are not restricted to hypothetical questions and are not eliminated by monetary incentives” (p.  457).

4. Schema: Assimilation and Accommodation 

Jean Piaget’s research in the 1950’s and 60’s on cognitive development had a profound impact on our understanding of children. He detailed the way in which children perceive and make sense of the world and identified the stages of that developmental sequence which we still follow today.  

According to Piaget, children develop a schema , usually defined as a mental framework that organizes information about a concept.

As the child grows and experiences the world, everything they encounter will be processed within that schema. This is called assimilation . When the schema is altered or a new schema is developed, it is called accommodation .

He conducted a great deal of his research by observing his own three children and taking excruciatingly detailed notes on their behavior.

During the sensorimotor stage (birth to 2 years old), Piaget highlights a milestone that demonstrates the infant is now exploring their environment with intent.

“…the definitive conquest of the mechanisms of grasping marks the beginning of the complex behavior patterns which we shall call “assimilations through secondary schemata” and which characterize the first forms of deliberate action” (Piaget, 1956, p. 88).

Although this milestone takes place in the sensorimotor stage, it is much more than a sensory experience. It is driven by intent, a purely cognitive construct.

Priming occurs when exposure to a stimulus has an effect on our behavior or how we respond to information presented subsequently. It can occur outside of conscious awareness.  

Priming affects how we process all kinds of information and is a widely used concept in marketing.

Meyer and Schvaneveldt (1971) were among the first to study priming.

They presented research participants with various pairs of associated words (Bread/Butter), unassociated words (Bread/Doctor), or nonwords.

The participants were instructed to indicate “yes” if both words were real words or “no” if one was not a real word.

The results revealed that participants were able to make this decision much faster when the pair of words were associated than when they were unassociated.

Although not conclusive and in need of further research, this pattern indicated that words that have strong connections in memory are activated more easily than words that are less connected.

Research since has identified numerous types of priming, including: perceptual, semantic, associative, affective, and cultural.

6. Semantic Memory Network and Spreading Activation

Further research on priming was conducted by Collins and Loftus (1975). Their studies led to more conclusive evidence that information is stored in a memory network of linked concepts.

When one concept is activated, that activation spreads throughout the network and activates other concepts.

The stronger the connection between concepts, the more likely one will activate the other. Eventually, the activation loses energy and dissipates.

Collins and Loftus provide a thorough explanation of the semantic memory network :

“The more properties two concepts have in common, the more links there are between the two nodes via these properties and the more closely related are the concepts…When a concept is processed (or stimulated), activation spreads out along the paths of the network in a decreasing gradient” (p. 411).

This research led to a more complete understanding of how information is stored and organized in memory. This has helped us understand a wide range of psychological phenomena such as how we form impressions of others and make decisions.

7. The ELM Model of Persuasion

Understanding how people form an attitude has been an area of study in cognitive psychology for more than 50 years.

Researchers Petty and Cacioppo (1986) formulated the Elaboration Likelihood Model (ELM) of persuasion to explain how message factors and personality characteristics affect attitude formation.

The ELM identifies two routes to persuasion: central and peripheral.

The central route to persuasion is activated when the message recipient engages in a critical analysis of the message content. This occurs when the message is about an issue considered important by the recipient.

In this scenario, a person will be persuaded by the quality of arguments in the message.

The central route results

“…from a person’s careful and thoughtful consideration of the true merits of the information presented…” (1986, p. 125).

The peripheral route to persuasion involves very little cognitive processing of the message content. This occurs when the issue is unimportant to the recipient.

In this scenario, a person will be persuaded by the status of the person expressing their opinion.

The peripheral route results from:

“…some simple cue in the persuasion context (e.g., an attractive source) that induces change without necessitating scrutiny of the true merits of the information presented” (p. 125).

Findings from ELM research apply to everything from product advertising, to public health campaigns, to political debate.

Go Deeper: The Six Types of Persuasion

8. The Bobo Doll Study

The Bobo Doll study by Albert Bandura in 1963 may be one of the most famous studies in psychology and a founding study for the social cognitive theory . It had a tremendous impact on society as well.

It took place at a time in the U. S. in which there was great concern and debate over the growing prevalence of violence depicted on television.

In the study, children watched a video of an adult either playing violently or not violently with a Bobo doll.

Afterwards, each child was placed in a room with a Bobo doll. Their behavior was carefully observed by trained raters.

Children that watched the violent video were more aggressive towards the doll than those that watched the non-violent video.

This type of study was among the first demonstrate the powerful effect of television on children’s behavior. It led to decades of research and intense debate throughout society.

9. Bystander Intervention: The First Study

In 1964 in New York City, late at night, a young woman was murdered just steps away from her apartment.

The newspapers reported that nearly 40 residents heard her pleas for help, but that no one actually did anything. That reporting has now been found to have many inaccuracies.

However, the story created a national debate about crime and helping those in need.

This was the impetus for a study conducted by Latané and Darley (1968) on “ the bystander effect .”

The methodology was simple. Over 60 college students at New York University were taken to individual rooms to discuss an issue via an intercom system.

The students knew that several people would be participating in the discussion simultaneously.

One “participant” spoke about their difficulties adjusting to college life and their medical condition which sometimes led to seizures. This was a pre-recorded script and included a part where the “participant” acted as if they were feeling physical distress. They eventually stopped communicating with the other participants.

The results revealed that:

“The number of bystanders that the subject perceived to be present had a major effect on the likelihood with which she would report the emergency. Eighty-five percent of the subjects who thought they alone knew of the victim’s plight reported the seizure before the victim was cut off, only 31% of those who thought four other bystanders were present did so” (p. 379).

This was the beginning of a long program of research that identified the decision-making steps that determine the likelihood of a bystander intervening in an emergency situation.

10. The Car Crash Experiment: Leading Questions

Dr. Elizabeth Loftus and her undergraduate student John Palmer designed a study in 1974 that shook our confidence in eyewitness testimony.

Research participants watched videos that depicted accidents between two cars. Afterward, participants were asked to estimate how fast the two cars were traveling upon impact.  

“How fast were the two cars going when they ______ into each other?”

However, the word in the blank varied. For some participants the word in the blank was “smashed” and for other participants the word was “contacted.”

The results showed that estimates varied depending on the word.

When the word “smashed” was used, estimates were much higher than when the “contacted” was used. 

This was the first in a long line of research conducted on how phrasing can result in leading questions that affect the memory of eyewitnesses.

It has had a tremendous impact on law enforcement interrogation practices, line-up procedures, and the credibility of eyewitness testimony .

Today’s article was about 10 famous studies in cognitive psychology. Ten is actually a low number given how many studies have had substantial impact on the field.

The studies described above include the famous work of Ebbinghaus, who used himself as a test subject. This entire article could have consisted of his work.

Also included above was just one study by Tversky and Kahneman. The two researchers have identified so many heuristics and cognitive biases that only choosing one was just unfair.

Two studies by Loftus were included because they were both groundbreaking: one in memory and the other in eyewitness testimony.

Of course, Bandura’s Bobo Doll study was included because of its fame and impact on public discourse.

The ELM model and the earliest study on bystander intervention were also included. Both have had profound impacts in not just our understanding about the given subjects, but have also had substantial practical applications in various professions and matters in real-life.

Bandura, A. (1977).  Social Learning Theory . Prentice Hall.

Bandura, A., Ross, D., & Ross, S. A. (1963). Imitation of film-mediated aggressive models. The Journal of Abnormal and Social Psychology, 66 (1), 3–11. https://doi.org/10.1037/h0048687

Ebbinghaus, H. (1902). Grundz Üge der Psychologic. Leipzig, Germany: von Veit.

Ebbinghaus, H. (1964). Memory: A contribution to experimental psychology (H. A. Ruger, C. E. Bussenius translators). New York: Dover.

Ebbinghaus, H. (1913). On memory: A contribution to experimental psychology . New York: Teachers College.

Kitchen, P., Kerr, G., Schultz, D., Mccoll, R., & Pals, H. (2014). The elaboration likelihood model: Review, critique and research agenda. European Journal of Marketing, 48 (11/12), 2033-2050. https://doi.org/10.1108/EJM-12-2011-0776

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13 (5), 585–589.

Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review , 63 (2), 81–97.

Neisser, U. (1967). Cognitive psychology . Englewood Cliffs, NJ: Prentice Hall.

Roediger, H. (1985). Remembering Ebbinghaus. PsycCRITIQUES, 30(7), 519-523.

Petty, R.E. & Cacioppo, J.T. (1986). The Elaboration Likelihood Model of persuasion. Advances in Experimental Social Psychology, 19 , 123-205. Doi: https://doi.org/10.1016/S0065-2601(08)60214-2

Piaget, J. (1956; 1965). The origins of intelligence in children . International Universities Press Inc. New York.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice .  Science ,  211 (4481), 453-458.

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Independent and Dependent Variables

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, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

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Ideas for Psychology Experiments

Inspiration for psychology experiments is all around if you know where to look

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

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

Psychology experiments can run the gamut from simple to complex. Students are often expected to design—and sometimes perform—their own experiments, but finding great experiment ideas can be a little challenging. Fortunately, inspiration is all around if you know where to look—from your textbooks to the questions that you have about your own life.

Always discuss your idea with your instructor before beginning your experiment—particularly if your research involves human participants. (Note: You'll probably need to submit a proposal and get approval from your school's institutional review board.)

At a Glance

If you are looking for an idea for psychology experiments, start your search early and make sure you have the time you need. Doing background research, choosing an experimental design, and actually performing your experiment can be quite the process. Keep reading to find some great psychology experiment ideas that can serve as inspiration. You can then find ways to adapt these ideas for your own assignments.

15 Ideas for Psychology Experiments

Most of these experiments can be performed easily at home or at school. That said, you will need to find out if you have to get approval from your teacher or from an institutional review board before getting started.

The following are some questions you could attempt to answer as part of a psychological experiment:

• Are people really able to "feel like someone is watching" them ? Have some participants sit alone in a room and have them note when they feel as if they are being watched. Then, see how those results line up to your own record of when participants were actually being observed.
• Can certain colors improve learning ? You may have heard teachers or students claim that printing text on green paper helps students read better, or that yellow paper helps students perform better on math exams. Design an experiment to see whether using a specific color of paper helps improve students' scores on math exams.
• Can color cause physiological reactions ? Perform an experiment to determine whether certain colors cause a participant's blood pressure to rise or fall.
• Can different types of music lead to different physiological responses ? Measure the heart rates of participants in response to various types of music to see if there is a difference.
• Can smelling one thing while tasting another impact a person's ability to detect what the food really is ? Have participants engage in a blind taste test where the smell and the food they eat are mismatched. Ask the participants to identify the food they are trying and note how accurate their guesses are.
• Could a person's taste in music offer hints about their personality ? Previous research has suggested that people who prefer certain styles of music tend to exhibit similar  personality traits. Administer a personality assessment and survey participants about their musical preferences and examine your results.
• Do action films cause people to eat more popcorn and candy during a movie ? Have one group of participants watch an action movie, and another group watch a slow-paced drama. Compare how much popcorn is consumed by each group.
• Do colors really impact moods ? Investigate to see if the  color blue makes people feel calm, or if the color red leaves them feeling agitated.
• Do creative people see  optical illusions  differently than more analytical people ? Have participants complete an assessment to measure their level of creative thinking. Then ask participants to look at optical illusions and note what they perceive.
• Do people rate individuals with perfectly symmetrical faces as more beautiful than those with asymmetrical faces ? Create sample cards with both symmetrical and asymmetrical faces and ask participants to rate the attractiveness of each picture.
• Do people who use social media exhibit signs of addiction ? Have participants complete an assessment of their social media habits, then have them complete an addiction questionnaire.
• Does eating breakfast help students do better in school ? According to some, eating breakfast can have a beneficial influence on school performance. For your experiment, you could compare the test scores of students who ate breakfast to those who did not.
• Does sex influence short-term memory ? You could arrange an experiment that tests whether men or women are better at remembering specific types of information.
• How likely are people to conform in groups ? Try this experiment to see what percentage of people are likely to conform . Enlist confederates to give the wrong response to a math problem and then see if the participants defy or conform to the rest of the group.
• How much information can people store in short-term memory ? Have participants study a word list and then test their memory. Try different versions of the experiment to see which memorization strategies, like chunking or mnemonics, are most effective.

Once you have an idea, the next step is to learn more about  how to conduct a psychology experiment .

Psychology Experiments on Your Interests

If none of the ideas in the list above grabbed your attention, there are other ways to find inspiration for your psychology experiments.

How do you come up with good psychology experiments? One of the most effective approaches is to look at the various problems, situations, and questions that you are facing in your own life.

You can also think about the things that interest you. Start by considering the topics you've studied in class thus far that have really piqued your interest. Then, whittle the list down to two or three major areas within psychology that seem to interest you the most.

From there, make a list of questions you have related to the topic. Any of these questions could potentially serve as an experiment idea.

Use Textbooks for Inspiration for Psychology Experiments

Your psychology textbooks are another excellent source you can turn to for experiment ideas. Choose the chapters or sections that you find particularly interesting—perhaps it's a chapter on  social psychology  or a section on child development.

Start by browsing the experiments discussed in your book. Then think of how you could devise an experiment related to some of the questions your text asks. The reference section at the back of your textbook can also serve as a great source for additional reference material.

Discuss Psychology Experiments with Other Students

It can be helpful to brainstorm with your classmates to gather outside ideas and perspectives. Get together with a group of students and make a list of interesting ideas, subjects, or questions you have.

The information from your brainstorming session can serve as a basis for your experiment topic. It's also a great way to get feedback on your own ideas and to determine if they are worth exploring in greater depth.

Study Classic Psychology Experiments

Taking a closer look at a classic psychology experiment can be an excellent way to trigger some unique and thoughtful ideas of your own. To start, you could try conducting your own version of a famous experiment or even updating a classic experiment to assess a slightly different question.

Famous Psychology Experiments

Examples of famous psychology experiments that might be a source of further questions you'd like to explore include:

• Marshmallow test experiments
• Little Albert experiment
• Hawthorne effect experiments
• Bystander effect experiments
• Robbers Cave experiments
• Halo effect experiments
• Piano stairs experiment
• Cognitive dissonance experiments
• False memory experiments

You might not be able to replicate an experiment exactly (lots of classic psychology experiments have ethical issues that would preclude conducting them today), but you can use well-known studies as a basis for inspiration.

Review the Literature on Psychology Experiments

If you have a general idea about what topic you'd like to experiment, you might want to spend a little time doing a brief literature review before you start designing. In other words, do your homework before you invest too much time on an idea.

Visit your university library and find some of the best books and articles that cover the particular topic you are interested in. What research has already been done in this area? Are there any major questions that still need to be answered? What were the findings of previous psychology experiments?

Tackling this step early will make the later process of writing the introduction  to your  lab report  or research paper much easier.

Ask Your Instructor About Ideas for Psychology Experiments

If you have made a good effort to come up with an idea on your own but you're still feeling stumped, it might help to talk to your instructor. Ask for pointers on finding a good experiment topic for the specific assignment. You can also ask them to suggest some other ways you could generate ideas or inspiration.

While it can feel intimidating to ask for help, your instructor should be more than happy to provide some guidance. Plus, they might offer insights that you wouldn't have gathered on your own. Your instructor probably has lots of ideas for psychology experiments that would be worth exploring.

If you need to design or conduct psychology experiments, there are plenty of great ideas (both old and new) for you to explore. Consider an idea from the list above or turn some of your own questions about the human mind and behavior into an experiment.

Before you dive in, make sure that you are observing the guidelines provided by your instructor and always obtain the appropriate permission before conducting any research with human or animal subjects.

Frequently Asked Questions

Finding a topic for a research paper is much like finding an idea for an experiment. Start by considering your own interests, or browse though your textbooks for inspiration. You might also consider looking at online news stories or journal articles as a source of inspiration.

Three of the most classic social psychology experiments are:

• The Asch Conformity Experiment : This experiment involved seeing if people would conform to group pressure when rating the length of a line.
• The Milgram Obedience Experiment : This experiment involved ordering participants to deliver what they thought was a painful shock to another person.
• The Stanford Prison Experiment : This experiment involved students replicating a prison environment to see how it would affect participant behavior. 

Jakovljević T, Janković MM, Savić AM, et al. The effect of colour on reading performance in children, measured by a sensor hub: From the perspective of gender .  PLoS One . 2021;16(6):e0252622. doi:10.1371/journal.pone.0252622

Greenberg DM, et al. Musical preferences are linked to cognitive styles . PLoS One. 2015;10(7). doi:10.1371/journal.pone.0131151

Kurt S, Osueke KK. The effects of color on the moods of college students . Sage. 2014;4(1). doi:10.1177/2158244014525423

Hartline-Grafton H, Levin M. Breakfast and School-Related Outcomes in Children and Adolescents in the US: A Literature Review and its Implications for School Nutrition Policy .  Curr Nutr Rep . 2022;11(4):653-664. doi:10.1007/s13668-022-00434-z

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

CogniFit Blog: Brain Health News

Brain Training, Mental Health, and Wellness

Experimental Psychology: Learn everything about its history

The field of experimental psychology branches out into many various sub-fields and directions with people believing in various things. Even now scientists do not have a clear picture of the connection between the mind and the body. There have been many different attempts to unravel and end the dilemma. Understanding even the majority of the connection and the brain by itself will be a major development in today’s science. The attempt has brought on many big collaborative initiatives with big names like the Human Brain Project coming to mind. Psychology in itself has had a long history and has shaped itself in various ways and directions. To understand it, one needs to look at the first mentions of what we now call psychology from centuries ago.

History of Experimental Psychology

Experimental psychology today is completely different from what the discipline looked like years and centuries ago.  Back then we didn’t have the technology and the infrastructure available to us today. The question of mind and body was on the lips of many prominent philosophers. Names like Plato and Aristotle come to mind when the first mentions of the mind-body problem arise. The arguments and debates over free will and determinism and nature vs. nurture take roots centuries ago. These debates are still prevalent nowadays. They turn into years long research projects in the fields of experimental psychology and neuroscience.

Philosophical beginnings: nature vs. nurture & free will vs. determinism

Famous philosophers like Plato, Aristotle, and René Descartes made the first references to experimental psychology. Plato and Aristotle both contemplated the famous nature vs. nurture question. They disagreed on the fundamental point of the origin of what makes us human comes from. Plato argued from the genetic point of view, saying that certain things are a part of our biological configuration. He believed that everything is set in stone from the very beginning. Aristotle, on the other hand, put the emphasis on the nurture side of the debate. He preached that humans are sponges that soak up the information with every new experience and learning opportunity.

Descartes looked at a different question that boggles the minds of scientists and researchers nowadays. He believed that actions and behaviors of people are predetermined and free will in itself does not exist. According to Descartes, pineal gland controls every behavior in the brain. His view formed a very popular belief called the mind-body dualism. The pineal gland being the master gland for all actions was proven wrong at a later point. The free will vs. determinism debate, however, still remains open in the 21st century.

Research into decision making has become one of the hottest topics in neuroscience nowadays. We now have different research studies that show neuronal spiking activity before a decision is made (1). This sparked a lot of controversy in favor of determinism. Many started proclaiming that if there is neuronal activity before a behavior, that means, that all actions are predetermined beforehand. All the philosophical questions are still very present today and experimental psychology tries to answer the questions with various methods. It does so by looking at the problem in hand from various perspectives.

First steps to science

The beginning of psychology as a discipline emerged in Leipzig, Germany. In 1879 Wilhelm Wundt built his first experimental laboratory on the grounds of the University of Leipzig. Wundt governed the term introspection. Wundt believed that by asking subjects to talk in detail about the experience during an assigned task, he will be able to develop a guideline for the consciousness elements. That became the ultimate goal for introspection. Wundt believed that since conscious experiences could be described by people, there was a possibility to explore and observe these experiences and create a map of them.

Nowadays, looking back, the approach that Wundt had was a bit naïve. Despite that, it became the first milestone in creating what is now known as cognitive psychology . Wundt and his colleagues have discovered that there is a difference in realizing that something is happening or sensing it and understanding what that something is or,  perceiving it. He noted a time difference between this notion of sensation and perception. Perception seemed to occur later than sensation.

Wundt’s impact on science today

Nowadays, in cognitive psychology, measuring reaction times happening during various mental tasks is a regular occurrence. Scientists try to show exactly which events happen in the brain first and which ones occur later. Researchers are attempting to acquire the answer to the origin of consciousness. They want to unravel where and when the very first series of neuronal spikes occur in the brain with the introduction of a new stimulus. Researchers trace it back to that same question of free will and determinism. They are still trying to figure out what happens first, the behavior or the action itself or a certain event that happens in the brain.

Of course, nowadays, scientists have a lot more advanced tools to measure these time lapses and series of events. Despite that fact, we seem to not be a lot closer to the truth. We are still trying to figure out the truth behind the conscious experiences and the external behaviors and actions.

Functionalism: evolutionary psychology

Another branch of experimental psychology went into quite the opposite direction from what Wundt and his colleagues were doing. It solidified the ground for what later would become behavioral psychology. Behavioral psychology would dominate the field of the entire discipline for quite some time.

The functionalists, as they called themselves, tried to understand why humans and nonhuman animals behaved in the way they do. Functionalism thesis moved onto to what is also known as evolutionary psychology . It quite heavily operates upon the principles of Darwin’s natural selection. The notion that the best genetic components survived and the not useful ones have disappeared over the years. All actions intend to pass our genes on to our descendants with the goal of keeping our species alive.

Evolutionary psychology is still quite a prominent part of the discipline right now. Despite that it poses a slight problem in the face of experimental psychology. Experimental psychology values reliable and valid experiments. Evolutionary psychology experiments are quite difficult to arrange. Because of this, it is not as popular as some other branches of psychology.

Psychoanalysis: what do you dream of?

After Wundt’s laboratory and the waves of functionalism have died off, a new branch of psychology developed. It is the branch that the majority of the population associated with psychology nowadays. Despite the fact that not many practitioners use it nowadays, it is still quite popular.

Sigmund Freud created the psychodynamic approach was created and it focuses a lot on the unconscious. Id (the unconscious), desires, feelings , memories, and dreams are prime targets for psychodynamic therapists. Compared to other branches of psychology this one does not have very reliable results when it comes to proving its theories. Despite that fact, it came as a result of Freud’s observations of his many patients and their behaviors. Ordinary public associates it with clinical psychology and the methods of treatments for various psychological disorders up to this day.

Freud focused a lot on experiences that a patient cannot remember that could result in various disorders and dysfunctions in the adult life. Freud governed concepts like Oedipal complex, ego, superego, and interpretations of dreams. As mentioned above, not a lot of research went into the psychodynamic theory. Sometimes experimental psychology doesn’t consider the psychodynamic approach a part of it. Despite that, the contributions that the psychodynamic approach provided to the discipline still resonate to this day.

Behaviorism

Behaviorism is one of the prime examples of experimental psychology. Behaviorists believe that the true way to study the mind is by the actions and behaviors themselves and they attempt to do so in an objective and a clear way.

Ivan Pavlov and B.F. Skinner are the big names for behaviorism. Their experiments in classical and operational conditioning are popular in classes to this day. The experiments that they did became the premise for behaviorism. This approach understands everything as results of things happening in the environment – stimuli – and the actions that these stimuli produce – responses.

John. B. Watson was one of the famous American behaviorists with his experiments involving fear stimuli. His experiments were highly unethical and would be quite illegal today, but, despite that, they were the ones that brought quite a lot of light into the concepts of learning and developed phobias. Nowadays, the treatment for various phobias comes exclusively from the behaviorist point of view. Clinicians use exposure therapy to treat phobias and are quite successful in curing the majority of them.

Revolution of cognition

After behaviorism, the cognitive approach became popular as well. It did so due to the fact that scientists at that time became more and more interested in the brain and how the brain influences the behaviors that we do. The development of computers was a big step forward. Researchers saw the potential of how the brain is similar to a computer and how they can utilize information technologies in order to measure the brain and see the anatomy and functions and be able to model different events that happen in the nervous system . Cognitive psychology studies mental processes , memory, learning , attention , judgment, language and uses a variety of different methods including eye tracking and both, non-invasive and invasive neuroimaging methods.

Collaboration of all

Overall, the entire field of experimental psychology encompasses many different sub-disciplines and fields. It developed quite a bit from the first laboratory that Wundt created to hundreds upon hundreds experimental laboratories around the world today. Modern state-of-the art machinery and popular technology methods equip these laboratories in an attempt to help objectively study the mind and the body and the relationship between the two.

Marcos E, Genovesio A. Determining Monkey Free Choice Long before the Choice Is Made: The Principal Role of Prefrontal Neurons Involved in Both Decision and Motor Processes. Front Neural Circuits [Internet]. 2016;10:75. Available from: http://journal.frontiersin.org/Article/10.3389/fncir.2016.00075/abstract

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• Tag: brain and Neuroscience , cognition , cognitive behavioral therapy , cognitive psychology , evolution , sigmund freud

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Experimental Psychology Studies Humans and Animals

Understanding Experimental Psychology

Our personalities, and to some degree our life experiences, are defined by the way we behave. But what influences the way we behave in the first place? How does our behavior shape our experiences throughout our lives? 

Experimental psychologists are interested in exploring theoretical questions, often by creating a hypothesis and then setting out to prove or disprove it through experimentation. They study a wide range of behavioral topics among humans and animals, including sensation, perception, attention, memory, cognition and emotion.

Experimental Psychology Applied

Experimental psychologists use scientific methods to collect data and perform research. Often, their work builds, one study at a time, to a larger finding or conclusion. Some researchers have devoted their entire career to answering one complex research question. 

These psychologists work in a variety of settings, including universities, research centers, government agencies and private businesses. The focus of their research is as varied as the settings in which they work. Often, personal interest and educational background will influence the research questions they choose to explore. 

In a sense, all psychologists can be considered experimental psychologists since research is the foundation of the discipline, and many psychologists split their professional focus among research, patient care, teaching or program administration. Experimental psychologists, however, often devote their full attention to research — its design, execution, analysis and dissemination. 

Those focusing their careers specifically on experimental psychology contribute work across subfields . For example, they use scientific research to provide insights that improve teaching and learning, create safer workplaces and transportation systems, improve substance abuse treatment programs and promote healthy child development.

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experimental psychology

• the branch of psychology dealing with the study of emotional and mental activity, as learning, in humans and other animals by means of experimental methods.
• the scientific study of the individual behaviour of man and other animals, esp of perception, learning, memory, motor skills, and thinking

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Word history and origins.

Origin of experimental psychology 1

Example Sentences

Although experimental psychology originated in Germany in 1879, Watson’s notorious study foreshadowed a messy, contentious approach to the “science of us” that has played out over the last 100 years.

The effect’s existence has since become one of the most robust findings in all of experimental psychology.

Experimental psychology began about twenty-five years ago; at that time there existed one psychological laboratory.

Experimental psychology is but a half-century old; educational psychology, less than a quarter-century old.

His demonstrations were conducted along lines familiar to all students of experimental psychology.

The early history of experimental psychology in America once occasioned discussion.

So experimental psychology needs as its starting point an exact definition of the technique to be used in making the experiment.

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# 10 Popular Linguistic Experiment Examples in Labvanced

Language and speech researchers use online experiment platforms like Labvanced for running their various studies because it’s a way to gather both participants and data quickly.

By running experiments in a virtual language lab, publishing studies online and sharing them through the web, linguists and cognitive psychologists not only complete their research faster but also create their experiment quickly and without code.

Below we highlight 10 popular linguistic experiments that can be performed in Labvanced for studying speech perception and language comprehension, all which demonstrate a different capability or feature of the platform.

# 1. Multimodal Stroop Effect Task open in new window

The Multimodal Stroop Effect Task is a classic task that challenges participants’ cognitive associations.

In the study, words like ‘blue’ or ‘green’ are shown one by one with a varying text color, only sometimes corresponding to what the written word indicates. This incongruence challenges the participant.

The study prompts the participant to focus on the text color and ignore the text meaning. During the experiment, there are also distracting auditory words spoken, a voice that says one of the 4 featured colors.

In the training session, the participant practices focusing on the text color and clicking the corresponding button. The other two dimensions (spoken word and written-text) are congruent and reflect the target color.

In the example below in the training session, the correct response is ‘F’ because the text color is blue. But the participant is also reinforced because the actual written word is blue and the audio playing automatically also says ‘blue.’

In the experiment, things become more challenging as the three dimensions are incongruent.

In the example below, the correct response is ‘D’ because the color is red, but the written word says ‘yellow’ and the audio voice prompts ‘blue.’

Thus, the participant is challenged to focus and limit the various cognitive associations in order to pick the correct response and override the language written- and spoken- language cues.

Fun Fact: Did you know being bilingual predicts a Stroop Effect? A study with Spanish-English bilinguals shows a language stroop effect (Suarez et al., 2014)!

# 2. Finish the Sentence open in new window

This study, published by the UCLA Linguistics Department aims to test how adult native American English speakers finish sentences.

Participants must listen to sentence fragments and then provide a response where their voice is recorded using their computer microphone, completing the sentence fragment into a full sentence.

The participants are prompted to provide an answer using the first thing that comes to mind and without any hesitation.

The general study progress is illustrated below:

• The participant tests the recording feature of Labvanced to ensure their recording works.
• The participant moves to the next screen and clicks ‘Play’ to hear the sentence fragment.
• Then, the participant is prompted to think of a way complete the sentence starting with the fragment they just heard
• The participant clicks the record button and says the whole sentence out loud.

The study aims to increase scientific knowledge about speech and human language. The researchers state that the gathered insights will have positive implications for several areas, including: implementing computer technology, language teaching, and speech pathology treatment.

# 3. Dimensions & Sounds open in new window

The participants begin by filling out a simple questionnaire about themselves. Then, they are instructed to listen to sounds and vocalizations. After perceiving the audio stimuli, the participants are asked to rate the sound on 2 scales.

This experiment demonstrates how to incorporate a questionnaire at the beginning of the study and then use audio to study human sound perception of vocalizations.

# 4. Spanish Pronunciation Study open in new window

In this study, the participant goes through information about the experimental procedure. Then, there are 2 short tasks to be completed, about 10 minutes each. The first task is about speaking and reading and the second task is about listening.

At the end, there is a questionnaire so the participant can provide basic information about themselves, as well as any relevant information about their language learning background.

Labvanced is used for many language learning and bilingual studies. Researchers can design their experiment in any language, choose to limit a study only to specific speakers, and share the study internationally so different language speakers can participate from around the globe or keep the study local to examine language learning in a specific group, such as students in a university learning a second language.

# 5. Voice & Well-being open in new window

In this study, the relationship between sound perception and feelings is assessed. The participants are prompted to listen to 21 human sounds from all over the world. After hearing this clip, the participant must rate how the sound made them feel using 5-point Likert scales.

The experimental screen opens with instructions of the experiment. Towards the end of the explanation, there is a sound volume adjuster where the participant can adjust and calibrate the audio that will proceed to a comfortable level:

After calibrating and adjusting the sound, the experiment begins.

The participant hears a sound that plays and lasts for about 30 seconds:

Then, after the sound has been played, the participant is prompted to indicate on a 5-point Likert to what extent certain emotions and feelings (like confidence, sadness, or alertness) were invoked by the audio:

This experiment is a great example of how to present audio recordings and then a questionnaire so the participant can provide a response to the sound, language, or vocalization they perceived.

# 6. Song or Speech? open in new window

This study is interested in auditory perception and how participants classify sounds based on 2 scales.

For each sound, the participant must rate how they perceived the sound, whether it sounds like a song or like speech and whether it sounds natural or artificially-produced.

The response is recorded on a continuous range using slider scales, also known as visual analogous scales (VASs). These scales are sometimes preferred over Likert scales because they record a continuous value as opposed to discrete values (Chyung et al., 2018).

Before the experiment begins, there is a sound calibration process that checks whether the participant is using speakers of headphones to play the audio. During this process, there are three sounds presented and the participant must pick which tone was the quietest.

If you go through this calibration process using speakers, you will not pass because the answers will be wrong, indicating that headphones were not used and a prompt will appear:

The Song or Speech study is a great example of not only how to calibrate sound and objectively ensure your participants are following instructions (like using headphones), but also to use continuous slider scales for recording responses.

# 7. Semantic Networking open in new window

The participants see a word, then they see a letter sequence. If the letter sequence means something in the English language, the participant is asked to click ‘Y’ on the keyboard but if the letter sequence does not mean anything, then ‘N’ should be pressed.

The design is simple and straightforward, but it demonstrates how to collect participant responses using button presses after presenting words visually in a particular sequence.

# 8. Text Presentation open in new window

The results of this study aim to suggest best practice for educational practitioners and businesses using online fonts since reading text online has become a commonplace behavior. By establishing which fonts are associated with the highest language comprehension and user performance, the researchers are helping increase the efficiency of how language is used online for communication and learning.

# 9. Adult Reading Test open in new window

Before starting the training session, the study also asks the participants to provide their email address so that responses from a previous section in Labvanced can be linked.

In the training session, the participant must record themselves reading the prompted passage out loud:

After the voice recording has been completed, a series of questions about the passage follow:

The Adult Reading Test captures several different types of measurements, from voice recordings to answers from questionnaires. It’s a great way to measure language comprehension and mastery and can be adapted to other languages and population groups.

# 10. Semantic Learning for Toddlers open in new window

The Semantic Learning for Toddlers study aims to look at how different speakers can influence semantic connections in children between the ages of 22 and 36 months that are monolingual (English-only) or bilingual (English + another language).

The study combines several different features that can be used in virtual language labs:

• Video presentation of speakers using target words
• Video recording of the participant
• Questionnaires

Through these features, the researchers can determine how a toddler is looking at the screen for each trial and where their attention is while learning new words in different conversational settings.

Child sees a video of two speakers, each teaching two new words. Then, in one type of trial, the child will hear two words repeated (for about 20 seconds) from the same speaker. In the second type of trial, the child will hear two words again, but one word per speaker.

With this set-up, the experiment aims to investigate how semantic connections are formed between newly acquired words and if the speakers that taught those words influence in any way the semantic connections with the newly learned words.

# Conclusion

Together these 10 linguistic experiments are great examples not only of what you can do in Labvanced but also how researchers from various universities are studying speech and language but also perception using online experiments to record data and responses.

open in new window

# References

Chyung, S. Y., Swanson, I., Roberts, K., & Hankinson, A. (2018). Evidence‐based survey design: The use of continuous rating scales in surveys. Performance Improvement, 57(5), 38-48.

Suarez, P. A., Gollan, T. H., Heaton, R., Grant, I., Cherner, M., & HNRC Group. (2014). Second-language fluency predicts native language Stroop effects: Evidence from Spanish–English bilinguals. Journal of the International Neuropsychological Society, 20(3), 342-348.

5 Science-Backed Ways to Write Clearly

If you want to become a better writer, ignore the lore and follow the science..

Posted June 24, 2024 | Reviewed by Devon Frye

• We read sentences written with active voice faster and comprehend content better than passive sentences.
• Studies document that we read and recall sentences with less effort when they turn content into micro-stories.
• Pronouns as subjects send readers backward, but readers comprehend sentences through prediction.
• Action verbs activate the brain's motor systems, creating semantic richness and enabling rapid comprehension.

Most writers assume they write well. Yet most writers grapple with the reality of writing as a black box.

That is, we know that writing works, but we’re a bit fuzzy on what makes readers grasp the meaning of some sentences instantly and without noticeable effort, while we find others difficult to understand after repeat re-readings. And contrary to popular belief, clear writing has virtually nothing to do with content, sentence length, or writing style.

Instead, we perceive sentences as clear when they map onto the methods our reading brains use to make sense of writing. Knowing the most important ones, including the below, could help make you a better writer.

1. Active voice makes sentences easier to read.

In dozens of studies, researchers have found that readers comprehend sentences more rapidly when sentences reflect the causal order of events. Two factors determine these outcomes.

First, human brains naturally perceive cause and effect, a likely survival mechanism. In fact, infants as young as six months can identify cause and effect, registered as spikes in heart rate and blood pressure.

Second, English sentence structure reflects causes and effects in its ordering of words: subject-verb-object order. In key studies, participants read sentences with active voice at speeds one-third faster than they read sentences in passive voice. More significantly, these same participants misunderstood even simple sentences in passive voice about 25 percent of the time.

As readers, we also perceive active sentences as both shorter and easier to read because active voice typically makes sentences more efficient. Consider the difference between the first sentence below, which relies on passive voice, and the second, which uses active voice.

• Passive: Among board members, there was an instant agreement to call for a pause in negotiations.
• Active: Board members instantly agreed to call for a pause in negotiations.

2. Actors or concrete objects turn sentences into micro-stories.

We read sentences with less effort—or cognitive load—when we can clearly see cause and effect, or, “who did what to whom,” as Ina Bornkessel-Schlesewsky puts it.

Bornkessel-Schlesewsky, a professor of cognitive neuroscience at the University of South Australia, used functional Magnetic Resonance Imaging (fMRI), to spot brains reacting to meaning and word order in sentences. Unsurprisingly, when the subjects of sentences are nouns clearly capable of performing actions, readers process sentences with greater speed and less effort. For actors, writers can choose people, organizations, publications—any individual, group, or item, intentionally created, that generates impact.

In addition to our unconsciously perceiving these sentences as easy to read and recall, we can also more readily identify actors in sentences. Furthermore, these nouns enhance the efficiency of any sentence by paring down its words. Take the examples below:

• Abstract noun as subject: Virginia Woolf’s examination of the social and economic obstacles female writers faced due to the presumption that women had no place in literary professions and so were instead relegated to the household, particularly resonated with her audience of young women who had struggled to fight for their right to study at their colleges, even after the political successes of the suffragettes.
• Actor as subject: In A Room of One’s Own , Virginia Woolf examined social and economic obstacles female writers faced. Despite the political success of the suffragettes, writers like Woolf battled the perception that women had no place in the literary professions. Thus Woolf’s book resonated with her audience, young women who had to fight for the right to study at their colleges.

3. Pronouns send readers backward, but readers make sense of sentences by anticipating what comes next.

Writers typically love to use pronouns as the subjects of sentences, especially the demonstrative pronouns this, that, these, those, and it , believing that these pronouns help link their sentences. Instead, pronouns save writers time and effort—but significantly cost readers for two likely reasons.

First, readers assume that pronouns refer to a singular noun, rather than a cluster of nouns, a phrase, or even an entire sentence. Second and more importantly, when writers use these pronouns without anchoring nouns, readers slow down and frequently misidentify the pronoun referents. In fact, readers rated writing samples with high numbers of sentences using demonstrative pronouns as being less well-written than sentences that used actors as subjects or pronouns anchored by nouns.

Pronoun as subjects: [Katie Ledecky] estimated that she swims more than 65,000 yards—or about 37 miles—a week. That adds up to 1,900 miles a year, and it means eons of staring at the black line that runs along the bottom of a pool. Actor as subject: [Katie] Ledecky swims up to 1,900 miles a year, mileage that entails seeming aeons of staring at the black line that runs along the bottom of a pool.

4. Action verbs make sentences more concrete, memorable, and efficient.

For years, old-school newspaper and magazine editors urged writers to use action verbs to enliven sentences.

However, action verbs also offer readers and writers significant benefits in terms of their memorability, as revealed in one study of readers’ recall of verbs. Of the 200 verbs in the study, readers recalled concrete verbs and nouns more accurately than non-action verbs.

In fact, when we read concrete verbs, our brains recruit the sensory-motor system, generating faster reaction times than abstract or non-action verbs, processed outside that system . Even in patients with dementia , action verbs remain among the words patients can identify with advanced disease, due to the richness of semantic associations that action verbs recruit in the brain.

• Non-action verbs: That the electric trolleys being abandoned in Philadelphia were greener and more efficient was not an insight available at that time.
• Action Verbs: Philadelphia scrapped its electric trolleys, decades before urban planners turned to greener, more efficient forms of transport.

5. Place subjects and verbs close together.

Over the past 20 years, researchers have focused on models of reading that rely on our understanding of sentence structure, a focus validated by recent studies.

As we read, we predict how sentence structure or syntax unfolds, based on our encounters with thousands of sentences. We also use the specific words we encounter in sentences to verify our predictions, beginning with grammatical subjects, followed by verbs.

As a result, readers struggle to identify subjects and verbs when writers separate them—the more distance between subjects and verbs, the slower the process of identifying them correctly. Moreover, readers make more errors in identifying correct subjects and verbs—crucial to understanding sentences—with increases in the number of words between subjects and verbs, even with relatively simple sentence structure.

Ironically, as writers tackle increasingly complex topics, they typically modify their subjects with phrases and adjective clauses that can place subjects at one end of the sentence and verbs at the opposite end. This separation strains working memory , as readers rely on subject-verb-object order in English to understand the sentence’s meaning. Consider, for example, this sentence from an online news organization:

In Florida, for instance, a bill to eliminate a requirement that students pass an Algebra I end-of-course and 10th-grade English/language arts exams in order to graduate recently cleared the Senate’s education committee.

On the other hand, when we place the subject and verb close together and use modifiers after the verb, we ease readers’ predictions and demands on working memory:

In Florida, the Senate’s education committee recently cleared a bill to eliminate two graduation requirements: an Algebra I end-of-course and 10th-grade English language arts.

Jane Yellowlees Douglas, Ph.D. , is a consultant on writing and organizations. She is also the author, with Maria B. Grant, MD, of The Biomedical Writer: What You Need to Succeed in Academic Medicine .

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