the other variables in an experiment

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Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

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• Types of Variables in Research | Definitions & Examples

Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts.
• Categorical data represents groupings.

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variable can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables.

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is colour-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases, you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e., the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variable are listed below.

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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Experimental Design - Independent, Dependent, and Controlled Variables

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Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature).  The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.

An experiment can have three kinds of variables: i ndependent, dependent, and controlled .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.

Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.

Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?

Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.

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Methodology

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

Guide to Experimental Design | Overview, 5 steps & Examples

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

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

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

There are five key steps in designing an experiment:

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

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

Table of contents

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

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

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

Start by simply listing the independent and dependent variables .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Randomization

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

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

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

Between-subjects vs. within-subjects

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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

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

When designing the experiment, you decide:

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

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

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

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

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

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

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

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

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

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

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

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

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variabl e. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

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

Identifying Variables

Three types of tomatoes (MOs810, Wikimedia Commons)

How does this align with my curriculum?

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Learn how scientists define independent, dependent and controlled variables in experimental inquiry.

As was mentioned in the  Asking Testable Questions  backgrounder, testable questions define the variables. In other words, what is being changed and what is to be kept constant, in an experimental inquiry.

What are variables in an experimental inquiry?

Scientists often use experimental inquiries to observe cause and effect relationships. In order to do so, scientists aim to make one change (the cause or  independent variable ) in order to determine if the variable is causing what is observed (the effect or  dependent variable ).

An experimental inquiry typically has three main types of variables: an independent variable, a dependent variable and controlled variables. We will look at each of these three types of variables and how they are related to experimental inquiries involving plants.

Independent Variables

The independent variable, also known as the experimental treatment , is the difference or change in the experimental conditions that is chosen by the scientist (the cause). To ensure a  fair test , a good experimental inquiry only has  one  independent variable and that variable should be something that can be measured quantitatively. For example, experimental inquiries about plants may include such independent variables as:

• Volume of water given to plants
• Nitrogen or phosphorus concentration in soil
• Duration, intensity or wavelength of light plants are exposed to
• Concentration or type of fertilizer

Dependent Variables

When a scientist chooses an independent variable (the cause), that person anticipates a certain response (the effect). This response is known as the dependent variable. The dependent variable should be something that is observable and measurable. Like the independent variable, an experimental inquiry should only have one dependent variable. For example, experimental inquiries about plants may include such dependent variables as:

• Days to germination
• Surface area of leaves
• Days to flowering or fruiting
• Dry mass (amount of plant material after all water has been removed)

Testable Question

How does the volume of water affect the number of days it takes for a tomato plant to flower?

Shown is a colour illustration explaining the relationship between an independent and a dependent variable. 

On the left is a blue oval with the word "Cause" inside it. This is labelled "Independent Variable" at the top, and "E.g., volume of water" below. On the right is a green rectangle with the word "Effect" inside. This is labelled "Dependent Variable" at the top, and "E.g., days to flowering" below. A red arrow points from cause on the left to the effect on the right.

Controlled Variables

In order for a scientist to ensure that only the independent variable is affecting the dependent variable, all the other factors acting upon the test situation (or test subjects) must be kept constant. The factors that must be kept the same are called the  controlled variables , or constant variables. In a given inquiry, there may be one or more variables that will need to be kept constant. For example, for an experimental inquiry in which you are interested in how the volume of water (independent variable) affects the days to flowering (dependent variable), you would want to keep constant:

• The type of seeds
• The type of soil
• The light source
• The humidity in the room
• The type of container (e.g., plastic pots vs. clay pots)
• The Temperature

Shown is a colour photograph of tomato plants in a greenhouse. 

Rows of tomato plants on both sides of the photograph stretch into the distance. Light comes in through a translucent ceiling. The plants are thick with green leaves. Tomato fruit is visible at the bottom of each plant. Most of the fruit is red and some is green.

A failure to control variables other than the independent variable will mean that you will not know which factor is actually causing the effects you see. In the example above, if some of the plants were sitting closer to the window than others, the differential exposure to light could be affecting the number of days to flowering, rather than the volume of water.

For more about designing experiments, see:  Setting Up a Fair Test

What are the variables in Tomatosphere™?

In the Seed Investigation, students investigate the germination rates of tomato seeds that have been to space (or exposed to space-like conditions) with seeds that have remained on Earth.

The  testable question  in the Seed Investigation is:

HOW DOES EXPOSURE TO THE SPACE ENVIRONMENT OR SPACE-LIKE CONDITIONS AFFECT THE GERMINATION RATE OF TOMATO SEEDS?

Independent variable:  type of seeds used - Earth seeds versus space seeds (sometimes seeds are treated to space-like conditions in years when seeds do not go to space)

Dependent variable:  number of seeds that germinate

Guided Practice

Have students read the following questions and determine the independent, dependent and potential controlled variables.

• How does the duration of light exposure affect the surface area of tomato plant leaves?
• How does the concentration of nitrogen fertilizer affect the days to flowering of tomato plants?
• How does the volume of water (mL) affect the number of days to germination of tomato plants?

In their own words, have students define the terms “Independent variable,” “Dependent variable,” and “Controlled variable.”

Have students brainstorm the variables that should be controlled in the Seed Investigation (e.g., quantity of water, type of soil, type of planting container, temperature, etc.).

Have the students think about the Seed Investigation and brainstorm variables that may not be controllable (e.g., giving plants different amounts of water, some plants being closer to a heat vent than others, using different types of soil, etc.).

• Independent variable:   duration of light (hours) Dependent variable:   surface area of plant leaves (Overall? Largest leaf? All leaves?) Controlled variable(s):   quantity of water, type of soil, depth of seeds, source of light, concentration/type of fertilizer (if any); temperature of the room, etc.
• Independent variable:   Concentration of nitrogen fertilizer Dependent variable:   days to flowering (when first flower on plants open) Controlled variable(s):   Same type of seeds, same quantity of water, same type of soil, same source of light, same duration of light, etc.
• Independent variable:   Volume of water in ml (per day) Dependent variable:   days to germination (when first seed germinates) Controlled variable(s):   Single type of seeds, same type of soil, same volume of soil, same type of pots, same source of light, same duration of light, temperature of the room, same time of day for watering, etc.

What are variables? How to use them in your science projects This page from Science Buddies explains different sorts of variables and how to use them to answer sample questions.

Controlled Variables This article by Explorable covers variables, control groups, and the value of consistency.

What are Independent and Dependent Variables?  (2019) This article by ThoughtCo explains how to tell the difference between independent and dependent variables, and how to plot variables on a graph.

Identifying and Controlling Variables in Scientific Investigations  (2015) This video (3:16 min.) from SciExperiment Basics explains how to identify and control variables in a scientific inquiry.

Related Topics

Chapter 6: Experimental Research

6.1 experiment basics, learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. Do changes in an independent variable cause changes in a dependent variable? Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions. For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables. Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.

Internal and External Validity

Internal validity.

Recall that the fact that two variables are statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise and be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The basic logic is this: If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just one difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial or unlike “real life” (Stanovich, 2010). In many psychology experiments, the participants are all college undergraduates and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had college students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998). At first, this might seem silly. When will college students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity. An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to. Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of college students in a laboratory at a selective college who merely judged the appeal of various colors presented on a computer screen. If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this is relevant to grocery shoppers’ cereal-buying decisions.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005). These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Manipulation of the Independent Variable

Again, to manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to do an experiment on the effect of early illness experiences on the development of hypochondriasis. This does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

An extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situation or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” , which makes the effect of the independent variable is easier to detect (although real data never look quite that good).

Table 6.1 Hypothetical Noiseless Data and Realistic Noisy Data

One way to control extraneous variables is to hold them constant. This can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, straight, female, right-handed, sophomore psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger straight women would apply to older gay men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 6.1 “Hypothetical Results From a Study on the Effect of Mood on Memory” shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory

Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.

Practice: For each of the following topics, decide whether that topic could be studied using an experimental research design and explain why or why not.

• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/observer/getArticle.cfm?id=1762 .

Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn & Bacon.

• Research Methods in Psychology. Provided by : University of Minnesota Libraries Publishing. Located at : http://open.lib.umn.edu/psychologyresearchmethods . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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5.1 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an  experiment  is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable  cause  a change in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse  these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate  an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as  conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions is often referred to as a  single factor two-level design.  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design.  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to  control  extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 5.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• An extraneous variable is any variable other than the independent and dependent variables. A confound is an extraneous variable that varies systematically with the independent variable.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

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What Is an Experiment? Definition and Design

The Basics of an Experiment

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Science is concerned with experiments and experimentation, but do you know what exactly an experiment is? Here's a look at what an experiment is... and isn't!

Key Takeaways: Experiments

• An experiment is a procedure designed to test a hypothesis as part of the scientific method.
• The two key variables in any experiment are the independent and dependent variables. The independent variable is controlled or changed to test its effects on the dependent variable.
• Three key types of experiments are controlled experiments, field experiments, and natural experiments.

What Is an Experiment? The Short Answer

In its simplest form, an experiment is simply the test of a hypothesis . A hypothesis, in turn, is a proposed relationship or explanation of phenomena.

Experiment Basics

The experiment is the foundation of the scientific method , which is a systematic means of exploring the world around you. Although some experiments take place in laboratories, you could perform an experiment anywhere, at any time.

Take a look at the steps of the scientific method:

• Make observations.
• Formulate a hypothesis.
• Design and conduct an experiment to test the hypothesis.
• Evaluate the results of the experiment.
• Accept or reject the hypothesis.
• If necessary, make and test a new hypothesis.

Types of Experiments

• Natural Experiments : A natural experiment also is called a quasi-experiment. A natural experiment involves making a prediction or forming a hypothesis and then gathering data by observing a system. The variables are not controlled in a natural experiment.
• Controlled Experiments : Lab experiments are controlled experiments , although you can perform a controlled experiment outside of a lab setting! In a controlled experiment, you compare an experimental group with a control group. Ideally, these two groups are identical except for one variable , the independent variable .
• Field Experiments : A field experiment may be either a natural experiment or a controlled experiment. It takes place in a real-world setting, rather than under lab conditions. For example, an experiment involving an animal in its natural habitat would be a field experiment.

Variables in an Experiment

Simply put, a variable is anything you can change or control in an experiment. Common examples of variables include temperature, duration of the experiment, composition of a material, amount of light, etc. There are three kinds of variables in an experiment: controlled variables, independent variables and dependent variables .

Controlled variables , sometimes called constant variables are variables that are kept constant or unchanging. For example, if you are doing an experiment measuring the fizz released from different types of soda, you might control the size of the container so that all brands of soda would be in 12-oz cans. If you are performing an experiment on the effect of spraying plants with different chemicals, you would try to maintain the same pressure and maybe the same volume when spraying your plants.

The independent variable is the one factor that you are changing. It is one factor because usually in an experiment you try to change one thing at a time. This makes measurements and interpretation of the data much easier. If you are trying to determine whether heating water allows you to dissolve more sugar in the water then your independent variable is the temperature of the water. This is the variable you are purposely controlling.

The dependent variable is the variable you observe, to see whether it is affected by your independent variable. In the example where you are heating water to see if this affects the amount of sugar you can dissolve , the mass or volume of sugar (whichever you choose to measure) would be your dependent variable.

Examples of Things That Are Not Experiments

• Making a model volcano.
• Making a poster.
• Changing a lot of factors at once, so you can't truly test the effect of the dependent variable.
• Trying something, just to see what happens. On the other hand, making observations or trying something, after making a prediction about what you expect will happen, is a type of experiment.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• Beveridge, William I. B., The Art of Scientific Investigation . Heinemann, Melbourne, Australia, 1950.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.
• Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr. ed.). Boston: Houghton Mifflin. ISBN 0-395-61556-9.
• Examples of Independent and Dependent Variables
• Difference Between Independent and Dependent Variables
• Null Hypothesis Examples
• Six Steps of the Scientific Method
• How To Design a Science Fair Experiment
• Independent Variable Definition and Examples
• Scientific Method Vocabulary Terms
• Understanding Experimental Groups
• Understanding Simple vs Controlled Experiments
• The Difference Between Control Group and Experimental Group
• Scientific Method Flow Chart
• Dependent Variable Definition and Examples
• Scientific Variable
• What Are the Elements of a Good Hypothesis?
• What Is a Hypothesis? (Science)

What Is Internal Validity In Research?

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

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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.

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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Internal validity refers to whether the design and conduct of a study are able to support that a causal relationship exists between the independent and dependent variables .

It ensures that no other variables except the independent variable caused the observed effect on the dependent variable.

Conducting research that has strong internal and external validity requires thoughtful planning and design from the outset.

Rather than hastening through the design process, it’s wise to invest sufficient time in structuring a study that is methodologically robust and widely applicable. 

By carefully considering factors that can compromise internal and external validity during the design phase, one can avoid having to remedy issues later. 

Research that exhibits both high internal and external validity permits drawing forceful conclusions about the findings. Though it may require more initial effort, ensuring studies have sound internal and external validity is necessary for producing meaningful and influential research.

For example, if you implement a smoking cessation program and see improvement among participants, high internal validity means you can be confident this is due to the program itself rather than other influences. 

Internal validity is not black-and-white – it’s about the level of confidence we can have in results based on how well the study controls for variables that could undermine the findings. 

The more a study avoids potential “confounding factors,” the higher its internal validity and the more faith we can place in the cause-effect relationship it uncovers. 

For the general public, internal validity is important because it means a given study’s results and takeaways can be trusted and applied.

Threats to Internal Validity

Confounding variables.

Confounding variables are extraneous factors that influence the dependent variables in an experiment, causing a misleading association and making it difficult to isolate the true effect of the independent variable. 

They threaten internal validity because they provide alternative explanations for study results, making it unclear if changes in the dependent variable are really due to manipulation of the independent variable or due to the confounding variable.

A failure to control extraneous variables undermines the ability of researchers to create causal inferences logically. Unfortunately, however, confounding variables are difficult to control outside of laboratory settings.

Nonetheless, Campbell (1957) identified several confounding variables that can threaten internal validity. 

Participant Factors

Participant reaction biases threaten internal validity because participants may act differently when they know they are being observed. These biases include participant expectancies, participant reactance, and evaluation apprehension.

Participant expectancies occur when a participant, consciously or unconsciously, attempts to behave in a way that the experimenter expects them to. The overly cooperative participant may often base their behavior on factors such as study setting and directions. 

Participant expectancies may also occur during a participant screening process. For example, a participant hoping to participate in a study about depression may exaggerate their symptoms on a screening questionnaire to appear more eligible for the study.

Participant reactance occurs when participants intentionally try to act in a way counter to the experimenter’s hypothesis.

For example, if studying the effects of daylight exposure on sleep habits, a participant may intentionally sleep at exactly the same time, regardless of whether or not they are exposed to daylight. Intentional uncooperativeness could result from a desire for autonomy or independence (Brehm, 1966).

Evaluation apprehension happens when a desire to appear consistent with social or group beliefs affects participant responses.

This response style can polarize responses and lead to inappropriate conclusions. For instance, participants asked about their opinions on a political issue in a group may feel pressure to conform to the responses of other group members. 

Broadly, researchers can reduce these biases by guaranteeing participant anonymity, using cover stories, unobtrusive observations, and indirect measures.

Sampling bias

Sampling bias occurs when the process of selecting participants for a research study results in key differences between groups that could skew the results. This threatens internal validity because it introduces systematic error in the comparisons between an experimental group and a control group.

For example, let’s say a study is testing a new math tutoring program and students are randomly assigned to either participate in the program (experiment group) or continue with normal instruction (control group).

However, the researcher unknowingly samples students for the experiment group from advanced math classes, while the control group is sampled from regular math classes.

In this case, a sampling bias is introduced because the students in the experiment group may have higher math abilities or motivation levels to begin with compared to the control group.

Any positive effects observed from the tutoring program could simply be due to these pre-existing differences rather than being an actual result of the program itself.

According to Campbell (1957), attrition, otherwise known as experimental mortality,  refers to a differential loss of study participants in experimental and control groups. 

This can threaten internal validity if the rate of attrition differs significantly between the experimental and control groups.

For example, imagine a clinical trial testing the effectiveness of a new therapy for depression. Participants are randomly assigned to either receive the therapy (experimental group) or no therapy (control group) for 8 weeks.

Over the course of the study, a number of participants from both groups drop out and are lost to follow-up. However, twice as many participants dropped out from the control group compared to the experimental group.

This differential attrition introduces bias because the participants remaining in each condition are no longer equivalent – the experimental group now contains more of its original participants compared to the smaller subset remaining in the control group.

Any observed differences in depression levels by the end of the study could be due to this systematic imbalance rather than being an actual effect of the therapy.

Experimenter bias

Experimenter bias refers to when a researcher’s expectations, perceptions, or motivations influence the outcome of an experiment in unconscious ways. This threatens internal validity because it provides an alternative explanation for results besides the independent variable being tested.

For example, a psychologist is conducting an experiment on the effects of praise on child task performance. The psychologist hypothesizes that praising children will improve their task performance.

During the experiment, she unconsciously provided more encouragement and positive body language when interacting with the praise group versus the neutral group.

Consequently, the praise group shows better task performance. However, it is unclear whether this is truly due to the predictive praise or inadvertent experimenter bias, where children picked up on the researcher’s subtle supportive cues.

This demonstrates how a researcher’s cognitive bias can unknowingly impact participant responses and behavior in a way that distorts the causal relationship between variables.

History encompasses specific events that a study participant experiences during the course of an experiment that is not part of the experiment itself. 

Specifically, it threatens the internal validity of experiments that take place over longer periods of time. For example, imagine a 12-month clinical trial testing a new psychotherapy for reducing anxiety. Participants are randomly assigned to receive either the new therapy or an existing therapy.

However, 8 months into the trial, the COVID-19 pandemic begins. This external event increases anxiety levels for people everywhere.

By the end of the trial, anxiety levels are reassessed. The new therapy group shows greater reductions in anxiety compared to the existing therapy group.

However, it is unclear whether this difference is truly due to the new therapy’s effectiveness or the confounding variable of COVID-19 raising anxiety in the control group.

Perhaps anxiety would have decreased similarly in both groups if not for the pandemic. This demonstrates how history can introduce confounds and alternative explanations that undermine internal validity.

Instrumentation 

Instrumentation refers to the ability of experimental instruments to provide consistent results throughout the course of a study. 

Instrumentation threats occur when there are changes in the calibration or administration of the tools, surveys, or measures used to collect data over the course of a study.

This can introduce systematic measurement error and provide an alternative explanation for any observed differences aside from the independent variable.

For example, a researcher using a battery-powered device to measure blood pressure in an experiment intended to investigate the effectiveness of a drug in reducing hypertension may find that the battery’s progressive decay may result in these readings appearing lower on a post-test than on the pre-tests.

Instrumentation is not limited to electronic or mechanical instruments. For example, a newly-hired researcher asked to rate the mental health status of participants over the course of a month may, with experience, be able to rate participants more accurately in the post-test than during the pre-test (Flannelly et al., 2018).

Diffusion of information between participants

The diffusion of information and treatments between patients can call internal validity into question. The latter case describes a situation in which research participants adopt a different intervention than the one they were assigned because they believe the different interventions to be more effective. 

For example, a control participant in a weight-loss study who learns that those in the treatment group are losing more weight than them may adopt the treatment group’s intervention. 

Differential diffusion of information can also occur when participants are given different instructions or instructions that can be misinterpreted by those conducting the study.

For instance, participants asked to take a medication biweekly may take it twice a week or once every two weeks (Flannelly et al., 2018; Campbell, 1957).

Maturation 

Maturation encompasses any biological changes related to age, or otherwise that occur with the passage of time. This can include becoming hungry, tired, or fatigued, wound healing, recovering from surgery, and disease progression. 

Maturation threatens internal validity because natural changes over time can provide an alternative explanation for study results rather than the independent variable itself. 

For example, in a year-long study of a new reading program for children, students may show reading gains over the course of the year. However, some of that improvement could simply be due to neural development and growing reading skills expected with age. 

The effects of maturation can also take effect over studies that have a short duration — for example, children given a repetitive computer task may lose focus within an hour, resulting in worsened performance (Flannelly et al., 2018).

Testing refers to when participants taking a test or assessment can perform better simply from having experienced it before. Familiarity with the test can influence results rather than any intervention or independent variable being studied.

For example, let’s say a researcher is testing a new method for improving memory in older adults. Participants take a memory assessment before and after completing the new memory training program.

However, participants may show memory improvements in the post-test partly just because it was their second time taking the exact same test. Their prior experience with the questions and format benefits their scores.

This demonstrates how repeated testing on the same measures can threaten internal validity. It provides an alternative explanation that improvements were due to practice effects rather than being an actual result of the intervention.

How can we prevent threats to internal validity?

Some methods for increasing the internal validity of an experiment include:

Random allocation

Random allocation is a technique that chooses individuals for treatment groups without regard to researchers’ will or patient condition and preference. This increases internal validity by reducing experimenter and selection bias (Kim & Shin, 2014).

Random Selection

Randomly selecting participants helps prevent systematic differences between groups that could provide alternative explanations.

It ensures any pre-existing factors are evenly distributed by chance, strengthening the ability to attribute results to the independent variable rather than confounds.

Blinding  (also called masking) refers to keeping trial participants, healthcare providers, and data collectors unaware of the assigned intervention so as not to be influenced by knowledge.

This minimizes bias in instrumentation, drop-out rates (attrition), and participant bias.

Control Groups

Control groups are groups for whom an experimental condition is not applied. These show whether or not there is a clear difference in outcome related to the application of the independent variable.

The use of a control group in combination with randomized allocation constitutes a randomized control trial, which scholars consider to be a “gold standard” for psychological research (Kim & Shin, 2014).

Study protocol

Study protocols are pre-defined plans that detail all aspects of a study: experimental design, methodology, data collection and analysis procedures, and so on.

This helps to ensure consistency throughout the study, reducing the effects of instrumentation and differential diffusion of information on internal validity (Kim & Shin, 2014).

Allocation concealment

In a research study comparing two treatments, participants must be randomly assigned so that neither the researchers nor participants know which treatment they will get ahead of time. 

This process of hiding the upcoming assignment is called allocation concealment. It’s crucial because if researchers or participants know or influence which treatment someone will receive, it ruins the randomness.

For example, if a researcher believes one treatment is better, they may steer sicker participants toward it rather than assigning them fairly by chance. 

Proper allocation concealment prevents this by keeping upcoming assignments hidden, ensuring unbiased random group assignments.

Internal Validity Example

What is the difference between internal and external validity.

Validity refers to how accurately a test measures what it claims to. Internal validity is a statement of causality and non-interference by extraneous factors, while external validity is a statement of an experiment’s generalizability to different situations or groups.

Why is internal validity more critical than external validity in a true experiment?

Internal validity concerns the robustness of an experiment in itself. An experiment with external but not internal validity cannot be used to conclude causality. Thus, it is generally unreliable for making any scientific inferences. On the contrary, an experiment that has only internal validity can be used, at least, to draw causal relationships in a narrow context.

American Psychological Association. Internal Validity. American Psychological Association Dictionary.

Blasco-Fontecilla, H., Delgado-Gomez, D., Legido-Gil, T., De Leon, J., Perez-Rodriguez, M. M., & Baca-Garcia, E. (2012). Can the Holmes-Rahe Social Readjustment Rating Scale (SRRS) be used as a suicide risk scale? An exploratory study. Archives of Suicide Research , 16 (1), 13-28.

Brehm, J. W. (1966). A theory of psychological reactance.

Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological bulletin , 54 (4), 297.

Gerst, M. S., Grant, I., Yager, J., & Sweetwood, H. (1978). The reliability of the Social Readjustment Rating Scale: Moderate and long-term stability. Journal of psychosomatic research , 22 (6), 519-523.

Holmes, T. H., & Rahe, R. H. (1967). The social readjustment rating scale. Journal of psychosomatic research , 11 (2), 213-218.

Kevin J. Flannelly, Laura T. Flannelly & Katherine R. B. Jankowski (2018): Threats to the Internal Validity of Experimental and Quasi-Experimental Research in Healthcare, Journal of Health Care Chaplaincy, DOI: 10.1080/08854726.2017.1421019

Kim, J., & Shin, W. (2014). How to do random allocation (randomization). Clinics in orthopedic surgery , 6 (1), 103-109.

Morse, G., & Graves, D. F. (2009). Internal Validity. The American Counseling Association Encyclopedia , 292-294.

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Effects of catch crops cultivated for green manure on soil c and n content and associated enzyme activities.

1. Introduction

2. materials and methods, 2.1. field experiment description, 2.2. soil sampling and sample preparation, 2.3. soil carbon and nitrogen content, 2.4. determination of enzymatic activity and soil respiration, 2.5. statistical analysis, 3.1. soil carbon and nitrogen, 3.2. c- and n-cycling enzyme activities and soil respiration, 3.3. relationship between the studied properties, 4. discussion, 4.1. soil carbon and nitrogen, 4.2. the effect of catch crops on soil microbial and enzymatic properties, 4.3. autumn vs. spring incorporation of field pea, 5. conclusions, author contributions, institutional review board statement, data availability statement, conflicts of interest.

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Share and Cite

Piotrowska-Długosz, A.; Wilczewski, E. Effects of Catch Crops Cultivated for Green Manure on Soil C and N Content and Associated Enzyme Activities. Agriculture 2024 , 14 , 898. https://doi.org/10.3390/agriculture14060898

Piotrowska-Długosz A, Wilczewski E. Effects of Catch Crops Cultivated for Green Manure on Soil C and N Content and Associated Enzyme Activities. Agriculture . 2024; 14(6):898. https://doi.org/10.3390/agriculture14060898

Piotrowska-Długosz, Anna, and Edward Wilczewski. 2024. "Effects of Catch Crops Cultivated for Green Manure on Soil C and N Content and Associated Enzyme Activities" Agriculture 14, no. 6: 898. https://doi.org/10.3390/agriculture14060898

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• Published: 05 June 2024

Conclusions

The HTT model eventually defined the germination response of Lens culinaris L. var. Markaz-09 (Lentil) for all Ts and Ψs, allowing it to be employed as a predictive tool in Lens culinaris L. var. Markaz-09 (Lentil) seed germination simulation models.

Peer Review reports

Lentil is a pulse that is eaten around the world [ 1 ]. It is a pulse crop that has been used in agriculture for much of human history. Canada produces 33% of the world’s lentils, whereas India produces 25%. Other important countries include the United States, Turkey, Nepal, Australia, Nepal, and Pakistan, among others [ 2 ]. Humans perceive lentils to have a high nutritious content and health advantages [ 3 ]. Lentils contain carbohydrates, minerals, vitamin B, iron, magnesium, copper, selenium, potassium, zinc, thiamin, and folate. riboflavin, pantothenic acid, niacin and fiber, in addition to a high protein content [ 4 ]. Lentils can be used to treat a lot of health problems, including Coronavirus disease 2019, managing blood sugar abnormalities, lowering blood lipids, and lowering the risk of cardiovascular disease and cancer [ 5 , 6 ].

Emergence and germination are the utmost crucial phases in a plant’s life cycle because they influence how effectively plants employ the water and nutrients resources accessible to them [ 7 ]. Temperature, pH, soil moisture and light are all known to have an impact on germination of seed [ 8 ]. The seed germination’s frequency and the dispersal of species are significantly affected by environmental temperatures [ 9 ]. Throughout the germination phase, seed is the unit of reproduction. As a result, a basic knowledge of seed germination is crucial for the production of crops, particularly in a world that is intensely aware of the fragile balance between the global population and food production [ 10 ].

Hydrothermal time model (HTT) is a mathematical model that can be used to quantify and describe the combined impacts of water potential (Ψ) and temperature (T) on biological activities (Allen 2003). The degree to which ambient temperature and water potential conditions surpass specified base or threshold values is inversely related to the time required to complete for a certain fraction of a population. When T goes below the base temperature (Tb) or Ψ falls below (i.e., is more negative than) the base water potential (Ψb), the process is hindered. Ψb and Tb heterogeneity account for differences in completion time among members of a community. HTT is a population-based threshold-type model, to put it another way. It was created to describe seed dormancy and germination and has virtually solely been utilized for that purpose thus far [ 11 ].

The present study’s goal was, (1) to determine the efficiency of the hydrothermal time model in studying seed germination of Lens culinaris L. var Markaz-09 at various Ts and Ψs, (2) to establish the SG at cardinal Ts and various water potentials.

Data analysis

The HTT, HT, and TT models were analyzed and measured using a repeated probit regression analysis [ 10 , 14 ]. The GR for the 50th percentile of germination was calculated using the inverse of germination time for each percentile at each Ψ or T.

Thermal time (TT)

There are mathematical models that describe how temperature affects germination patterns [ 11 ]. According to this model, the germination rate (GRg, or 1/tg) for a specific seed fraction, percentage, or germination period should be a linear function of temperature above base temperature. The minimum temperature at which germination may take place is known as the base temperature or minimum (Tb). Temperature on which germination proceeds most quickly is referred to as the optimum temperature. This can be written as: for sub-optimal temperatures:

For supra-optimal temperatures:

Due to the fact that germination rate (GR) is inversely proportional to radicle emergence time, which may be written as:

The thermal time constants θT1 and θT2 and T is the real temperature and Tb express the base temperature. GR stands for the population’s average germination rate (g).

NumberedHydro time (HT)

The hydro time notion was proposed first time by [ 15 ]. The base or threshold value (Ψb) will only prevent a percentage of the seed population (g) from germinating. As accordance to the hydro time model, the rate of germination is directly proportional to Ψ. The hydro time constant (θH) is represented by the following formula:

tg is the period for radicle emergence, GR (g) represents the germination rate, θH represents hydro time constant, Ψ is the real osmotic potential, and Ψb is the germination fraction’s base water potential.

Hydrothermal time model (HTT)

The HTT which may depict pattern of seed germination, was created by combining the aforementioned hydro-time models and thermal-time. Combining hydro time equations with thermal time equations allows for the definition of a hydrothermal time constant (θHT) at sub-optimal temperatures (T) [ 11 , 16 ]. The Hydrothermal time model is expressed at Ts ≤ To [ 15 ]:

Where Ψb (50) is the midpoint of Ψb. θHTT illustrate the hydrothermal time constant (MPa h). While σΨb is the standard deviation in Ψb.

Germination and agronomic parameters

The germination parameters presented below were estimated using the germination on each day, root and shoot lengths, dried and fresh weights of the germinated seeds.

Germination percentage (G%)

The formula of [ 17 ] used to calculate this germination percentage.

Where Nt shows the total number of seeds sown and Ne shows the number of seedlings that emerged.

Germination energy (GE)

The formula of [ 18 ] used to compute the germination energy.

The symbol X1. X2 and Xn in the equation above shows the count of seed that are germinated on the 1st day, 2nd day, and so 4th. Whereas Y1, Y2 and Yn stand for the first, second, and last day of germination.

Mean germination time (MGT)

MGT was calculated using the formula of [ 19 , 20 ].

Germination index (GI)

The Germination index gives info on the rate and percentage of germination. The germination index was calculated using the procedure provided by [ 21 ]

The number of seed that are germinated on day 1, 2, and 10 was shown by the symbols n1, n2,…, and n10. Whereas 10, 9, and 1 show the weighted average of seed number that germinated on day.

Germination rate index (GRI)

Greater and maximum GR are indicated by higher GRI values, which also represent the percentage of regular SG during the germination phase [ 22 ].

Where G1 shows the proportion of seed that germinated on the 1st day after sowing, G2 shows the proportion of seed that germinated on the 2nd day following sowing, and so on.

Timson germination index (TGI)

The TGI represents the daily average of germinated seeds. TGI was determined using the methodology of [ 23 ].

Timson Germination Index (TGI) = ϵG ÷ T.

Seed vigor index-I (SVI-I)

From each pot length of three seedling were measured and calculation was done using the [ 24 ] formula.

SVI-I = Seedling length (cm) × seed germination % age.

Seed vigor index-ii (SVI-II)

Using an electrical balance, the dry weight of three seedling from each pot was measured, and the percentage of seed germination was multiplied in the manner suggested by [ 25 ].

SVI-II = seedling dry weight (mg) × seed germination %age.

Mean moisture content (MMC)

The mean moisture content was calculated using the formula stated below [ 13 ].

M.M.C = (Fresh weight – Dry weight) ÷ dry weight.

Mean germination rate (MGR)

The below given formula was used to discover MGR [ 10 ].

Mean germination rate = 1/Mean germination time.

Antioxidant enzymes activities

Ascorbate peroxidase (apx) activity.

After centrifuging 0.5 g of fresh plant material with 10 milliliters of phosphate buffer, the resulting supernatant was collected. The final volume was brought down to 3.0 ml by adding deionized water after the supernatant, which was 0.1 ml in volume, was combined with 0.5 mM ascorbic acid and 0.1 mM EDTA. Following the addition of 0.1 milliliters of hydrogen peroxide to the mixture, the absorbance was measured at 290.0 nanometers using the protocol of W Shah, S Ullah, S Ali, M Idrees, MN Khan, K Ali, A Khan, M Ali and F Younas [ 26 ].

Superoxide dismutase (SOD) activity

The mixture was centrifuged after 0.5 g of fresh plant material were chopped with 5 milliliters of phosphate buffer. The supernatant was collected after the mixture was centrifuged. 1 milliliter of riboflavin was added to 0.1 milliliter of supernatant, which was then mixed with EDTA at a concentration of 3 mill molar, 25 µl of nitro tetrazolium blue chloride, 5 milliliters of methionine, and Na2CO3. After that, the mixture was stored at room temperature for protection. It was observed that the absorbance was at 560.0 nm and Superoxide dismutase activity was measured according to G Lalay, S Ullah and I Ahmed [ 27 ].

Peroxidase activity (POD)

The method of S Uddin, S Ullah and M Nafees [ 28 ] was tracked for the peroxidase activity investigation in fresh plant material. The supernatant was collected after 0.5 g of plant-fresh material was chopped and placed in 2 milliliters of 2-(N-Morpholino) ethanesulfonic acid (MES). The mixture was then centrifuged. In order to treat 0.1 ml of supernatant, 1.5 ml of 100 mM MES, 0.1 ml of phenylenediamine, and 0.04 ml of hydrogen peroxide are added. A measurement of absorbance was taken at 485.0 nm.

Catalase (CAT) activity

The Catalase activity was examined using the method described by of S Ullah and A Bano [ 29 ]. Fresh plant tissues weighing 0.5 g were combined with 10 milliliters of phosphate buffer, filtered, centrifuged, and the supernatant was then collected. 0.1 ml of supernatant was mixed with 0.5 ml of H 2 O 2 and the absorbance was measured at 240.0 nm.

Guaiacol peroxidase (GPX) activity

Fresh plant tissues weighing 0.5 g were combined with 10millilitres of phosphate buffer, centrifuged, and the supernatants was then extracted. 0.1 ml of supernatant was mixed with guaiacol (16 mM) and phosphate buffer (50 mM), then 2 mM H 2 O 2 was added. The mass of the reaction mixture was modified to 3 ml by the addition of deionized water. The absorbance was recorded at 470 nm and according to the protocol of M Nafees, S Ullah and I Ahmed [ 30 ].

Statistical analysis

We studied the impacts of thermal time, hydro time, and their interaction (hydro-thermal time model) on germination characteristics and seed germination rate using SPSS Statistic 25 (IBM) and Sigma Plot Version 10.0. Excel software was employed to do the fundamental statistical computations. The linear probit regression analysis was used in SPSS statistic 25 to compute the value of the following given parameters: σΨb, Ψb (50), R2, SE, F, T-test, and Sig. Graphs of germination fraction and germination parameter against Ψ and T were made using Origin 2021 PC Corporation. The data analysis techniques of correlation analysis, histogram generation, and principal component analysis (PCA) were carried out using the Origin Pro software.

Effect of osmotic potential and cardinal temperature on agronomic attributes

The germination rate and seed percentage were initially favoured by an increase in temperature amplitude, but this fall after T hit a particular threshold. GP was highest at 35 ° C and minimum at 15 and 40 ° C (Fig.  1 A, B, C, D, E and F). The lowest values of GP, 10% and 13.33% were recorded at 15 and 40˚C under (-1.2 MPa and 0 MPa respectively), while maximum 100% at 35˚C under (0 MPa) Lens culinaris L. var. Markaz 09 respectively. Germination energy (GE) were maximum at 25 0 C in (-0.9 MPa) and minimum at 40 0 C in (0 MPa). The value MGT was highest in 15 0 C at (-1.2 MPa) and lowest at 40 0 C in (-0.6 MPa), while the GRI was maximum at 40 0 C. (Figure  2 A, B, C, D, E and F)

The GI (Germination index) and TGI (Timson germination index) were maximum at 25 0 C in (-0.9 MPa) and lowest in 40 0 C at (0 MPa). On other hand Mean germination rate (MGR) was highest at 40 0 C in (0 MPa) and lowest at 15 0 C in (-0.6 MPa) (Fig.  3 A, B, C, D, E and F). The SVI-II and SVI-I were maximum at 30 0 C in (0 MPa) and lowest at 40 0 C in (-0.6 MPa). On the other hand, the MMC value was maximum in -0.6 MPa at 30 0 C (Fig.  4 A, B, C, D, E and F).

Germination for Lens culiunaris var. Markaz-09 at ( a ) 15 °C, ( b ) 20 °C, ( c ) 25 °C, ( d ) 30 °C, ( e ) 35 °C and ( f ) 40 °C having different osmotic potentials (0 MPa, -0.3 MPa, -0.6 MPa, -0.9 MPa and − 1.2 MPa)

Impact of water potentials and temperatures on (a and b) Germination Energy, (c and d) Mean Germination Time and (e and f) Mean Germination Rate of Lens culiunaris L. var. Markaz-09 using hydrothermal time model

Impact of water potentials and temperatures on (A and B) Germination Index, (B and C) Timson Germination Index and (E and F) Germination Rate Index of Lens culiunaris L. var. Markaz-09 using hydrothermal time model

Impact of water potentials and temperatures on (a and b) Seed Vigor Index-I, (c and d) Seed Vigor Index-II and (E and F) Mean Moisture Content of Lens culiunaris L. var. Markaz-09 using hydrothermal time model

Effect of osmotic potential and cardinal temperature on antioxidant enzymes

The findings regarding antioxidant enzyme indicated that the quantity of antioxidant enzyme in fresh plant tissues was substantially impacted by temperature and osmotic potential fluctuations. The findings presented in Fig.  5 a-f indicate that the CAT exhibited its maximum activity at 15ºC at -1.2 MPa, whereas it’s minimum value was documented at 0 MPa at 15 °C. In a similar vein, the POD activity at 30 °C peaked at -1.2 MPa, while the lowest activity was observed in the control group at 25 ºC (Fig.  5 a-f). Similarly, at 25 ºC, the SOD activity peaked at -1.2 MPa, with the lowermost value being − 0 MPa at 35 ºC (Fig.  5 a-f). As shown in Fig.  6 a-d APX and GPX have demonstrated their maximum values at -1.2 MPa and 15 °C, respectively, with APX reaching its minimum value at 0 MPa at 20 °C and GPX reaching its minimum value at 35 °C at 0 MPa. It has been observed that all enzymes exhibited normal activity within the temperatures range of 25–30 °C and 0 MPa. Nevertheless, both the greatest and lowest treated temperatures exhibited an adverse effect. When examining the thermal and osmotic responses, it was observed that APX and GPX exhibited the most notable response at 20 °C and − 1.2 MPa, respectively, as illustrated in Fig.  6 a-d. Moreover, at 0Mpa, the minimum response was observed for all antioxidant enzymes.

A negative correlation was observed between GE and GRI, MGR, SVI-II, SVI-I, and MMC, whereas GE was positively correlated with MGT, GI, and TGI (Fig.  7 ). The GI exhibits a negative correlation with SVI-I, SGR, and SVI-II, while its correlation with TGI is positive. There exists a positive correlation among all enzymes. As illustrated in Fig.  8 , two distinct clusters are observed to form between treatments. The initial cluster comprises the treatment at 0 MPa, whereas the subsequent cluster comprises the control, -0.3 MPa, -0.6 MPa, -0.9 MPa, and − 1.2 MPa. The germination dataset was analyzed using PCA. The findings indicate that every treatment is substantially dispersed across the dataset. The analysis of the treatment distribution indicates that the germination properties were significantly influenced by the osmotic potential. 73% of the total variance was accounted for by the first two components, according to the PCA results. As the variation in the first two components was the greatest, a biplot based on PCA was generated (Fig.  9 ).

Interactive effect of water potential, temperature on antioxidant enzymes ( a and b) -CAT, (c and d) -POD, e and f) -SOD under PEG induced stress at different temperatures (15 °C, 20 °C, 25 °C, 30 °C, 35 °C and 40 °C)

Interactive effect of water potential, temperature on antioxidant enzymes ( a and b -APX) and ( c and d -GPX) under PEG induced stress at different temperatures (15 °C, 20 °C, 25 °C, 30 °C, 35 °C and 40 °C)

Correlation between various germination attributes of Lens culiunaris L. var. Markaz-09 using hydrothermal time model

Heatmap histogram correlation between various germination attributes of Lens culiunaris L. var. Markaz-09 using hydrothermal time model

Loading Plot of Principal component analysis (PCA) on various germination attributes of Lens culiunaris L. var. Markaz-09 using hydrothermal time model

The results of HTT experiment forecast that the water potential and temperature had significantly control the germination parameters such as PL (plumule length), G%, radicle length, GI, TGI, MMC, SVI-I, GE, SVI-II, MGT and MGR, in comparison with control treatment of Lentil ( Lens culinaris L. var Markaz 09). According to Table  1 the temperature and water potential had significantly ( P  ≤ 0.05) affected GR (germination rate) and germination percentage (G%) of Lens culinaris L. var. Markaz 09. It showed that germination was increased from 10 to 100% with rising of temperature from 15 ˚C to 35˚C, then the value decreased second time to 13.33% as the temperature surpass 35˚C (optimum T) to 40˚C of Lens culinaris L. var. Markaz 09. The result also shows that the highest θT1 value was reported in 35˚C at -0.3 MPa and minimum at 15 ˚C in -1.2 MPa (Table  1 ). On the other side the highest value of θT2 was reported at -0.3 MPa in 15˚C and minimum in 40 ˚C at (0 MPa).

The TT theory is thought to be well suitable to germination data in distil water, with R 2 growing by 0.41. The hydrothermal time model may be applied to investigate the influence of temperature and water potential above the thermal and hydro thresholds on seed germination. The HTT concept has a higher value (R2 = 0.41 at 30 C) at sub-optimal temperature (T < T0) than at supraoptimal T (R2 = 0.24). T and Ψ interaction have significant effect on G% and GR ( P  < 0.05). According to the HTT model’s comparing results, the maximum HTT value was discovered in 35 °C at 0 MPa (Table  2 ).

The base temperature or minimum temperature (Tb) in our experiment was taken 15 ◦C, below from this temperature the growth of the seed is very slow and all plant will find it challenging to maintain its physiological functions. 25 to 30 o C was the ideal temperature range for the plant to grow at its fastest rate. The growth of the plant was reduced above the optimum temperature and the lowest growth was detected at 40 0 C in our experiment (Table  3 ).

It is possible to evaluate and quantify the effect of various abiotic variables on the time of SG in different seed lots using the TT, HT, and HTT models [ 31 ]. Among these abiotic factors, temperature as well as osmotic potential are the most influential environmental variables on seed germination in a wide variety of plants [ 31 , 32 ]. Likewise, the outcomes of our investigation demonstrated that both temperature and osmotic potential significantly impacted the process of seed germination.

Temperature response of seed can be characterized in general by theirs cardinal Temperatures (i.e., Tb, To and Tc). Our experiment’s cardinal temperature was found to be (15 o C, 30 o C and 40 o C for Tb, T0, and Tc) respectively (Table  3 ). The result showed that G% was maximum at 35 °C in 0 MPa, while the lowest germination was recorded at 40 °C in 0 MPa. The decrease in germination % may be caused by the high temperature denaturation of critical amino acids [ 33 ]. In comparison with control, the maximum GP was recorded at 30 in -1.2 MPa. This suggests that a variety of plant species’ GP and GR are influenced by temperature, which is a key element in seed germination.

An additional factor that exerts an impact on seed germination is water potential. Furthermore, our investigation revealed that water potential had a significant influence on the germination of seeds. At 35 0 C, the G% was highest in control and lowest in -1.2 MPa. At other temperature, the same effect was predicted, demonstrating that reducing in lowered in G%. Reduced in caused the water supply to the seed to be less sufficient for germination. This result is similar with the studies of [ 12 , 33 ] and [ 34 ] for wheat, watermelon and zucchini.

We found that a reduction in osmotic potential (towards negativity) significantly increased the GR values ( p  ≤ 0.01) for all cardinal temperatures (Table  1 ). GR decreased when the osmotic potential was decreased relative to the control. The experiment recorded a minimum temperature (Tb) of 15 degrees Celsius, below which the germination rate exhibited a decline. 30 degrees Celsius was the ideal temperature (To) for germination, whereas 40 degrees Celsius was the maximum temperature (Tc) that induced physiological and biochemical activity in plants. This is comparable to [ 13 ] which states that there are three cardinal temperatures (Ts) that are essential in delineating the germination characteristics of seed and determine the temperature required for germination.

Due to water stress, the concentration of antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) decreases. To mitigate cellular harm, the antioxidant system diminishes reactive oxygen species (ROS) accumulation through enzymatic scavenging of ROS and elevation of antioxidant concentrations such as APX and GPX [ 35 ]. SOD is a critical component of the antioxidant defense mechanism as it functions as the primary barrier against superoxide radicals. SOD-catalyzed dismutation of reactive oxygen species (ROS) generates H2O2 as a reaction product, which is subsequently scavenged by CAT and APOX [ 36 ]. The concentrations of guaiacol peroxides (GPX) and ascorbate peroxidase (APX) were diminished in the presence of water deficiency stress. APX, a crucial antioxidant, is accountable for the elimination of reactive oxygen species (ROS) in the presence of oxidative stress. Ascorbate peroxidase facilitates the conversion of H 2 O 2 to regular water by employing ascorbate as a donor of electrons and catalyzing the reaction. It is imperative to acknowledge that the regulation of APX expression varies as a consequence to environmental stresses and in the course of typical plant development and growth [ 37 ].

As a result, we have determined that the HTT model is a practical way to represent the way in which environmental factors (Ψ and T) impact the germination of seeds in seed lots. The hydro-time constant (HT) determined for lentil was 96.77 (MPa Ch − 1 ) as shown in Table  3 . In comparison to high T and low T, seed germination agronomic parameters including G%, TGI, GRI, GE, GI, SVI-II, and SVI-I were diminished. It is the consequence of chemical and cellular processes within the embryo that are thermo-inhibited. Based on the statistical analysis, the cardinal temperatures and θHTT provide a comprehensive explanation for the interaction effect of T and Ψ on the seed germination population.

The effect of temperature on other legumes is similar to that of lentils [ 38 ]. also reported arrow leaf clover germination was negatively affected at high temperatures, achieving 17 and 9% germination at day/night temperature treatments of 30/20 and 35/25°C, respectively. Button medic, Tifton burr medic, alfalfa, and crimson clover had the greatest germination of all entries at low (5 °C) temperatures. Germination at 35 °C was minimal, except for alfalfa and hairy vetch. 600RR alfalfa had the highest germination rate at 75%. Little burr medic, burr medic, and arrowleaf clover were particularly sensitive to high temperatures (30 °C), resulting in the lowest germination rates among all cool-season legume entries.

According to [ 39 ], crimson clover germinates well and quickly in all day/night temperature treatments ranging from 15/5 to 35/25°C. However, after 12 days at 4.5°C, ‘Yuchi’ arrowleaf and ‘Talladega’ red clovers had germination rates of more than 80% and about 20%, respectively. These studies found that high temperatures had a negative influence on bean germination, which is consistent with our findings. In the study, it is reported [ 40 ] that the final germination percentage of several annual Trifolium spp. remained constant between 5 and 20 °C, but decreased to zero as temperatures increased. On the other hand, the final germination percentage of perennial Trifolium spp. was constant from 5 to 30 °C and only declined at 35 °C. Another study measuring germination of several accessions of Medicago and Trifolium spp. found no difference in total germination between 5 and 20 °C, but there was considerably reduced germination at 0.5 and 30 °C. According to [ 41 ], the optimal temperature range for the germination of 15 accessions from six Vicia species is 18 to 23 °C. Other investigations have found variances in species germination percentage responses to temperature.

The changing temperatures and water potentials had a significant impact on the germination characteristics. The highest hydro-time constant (θH) of 105.12 was recorded at 25 °C, while the lowest of 23.52 was observed at 40 °C. Additionally, the base, optimum, and ceiling temperatures were determined as 15 °C, 30 °C, and 40 °C, respectively. The preservation of enzymatic activity serves as a crucial protective mechanism against damage caused by oxidative stress. The characteristics of germination may deteriorate as energy is allocated towards anti-stress mechanisms (antioxidant enzymes) that are indispensable for neutralizing reactive oxygen species (ROS) produced during mitochondrial respiration at the germination stage. Such studies can facilitate the establishment of the optimal temperature and water potential for the germination of crop species, as well as the comprehension of the adaptive response mechanisms during the early developmental stage of a plant, which is the most vulnerable phase. Nevertheless, the intricate physiological, biochemical, and molecular responses of the tested seed populations to abiotic factors should be meticulously considered in the model’s parameters for predicting future germination times.

Data availability

All data generated or analysed during this study are included in this published article.

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Acknowledgements

The authors extend their appreciation to the Researchers Supporting Project number (RSP2024R176) King Saud University, Riyadh, Saud Arabia.

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Department of Botany, University of Peshawar, Peshawar, 25120, Pakistan

Ibrar Ullah, Sami Ullah & Fazal Amin

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Jehad S. Al-Hawadi

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College of Life Sciences, Henan Normal University, Xinxiang, 453007, P.R. China

Henan International Joint Laboratory of Agricultural Microbial Ecology and Technology, Henan Normal University, Xinxiang, 453007, P.R. China

Xinxiang Key Laboratory of Plant Stress Biology, Xinxiang, 453000, P.R. China

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Contributions

Conceptualization: [S.U.]; Methodology: [I.U.; J.S.A.; M.K.O.; I.A.A.; H.A.]; Formal analysis and investigation: [F.A.; S.H.; T.N.]; Writing - original draft preparation: [I.U.; K.L.; M.T.H.; S.S.]; Writing - review and editing: [M.Z.; H.L.; S.F.]; Supervision: [S.U.]

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Ullah, I., Ullah, S., Amin, F. et al. Germination responses of Lens Culiunaris L. seeds to osmotic potentials at cardinal temperatures using hydrothermal time model. BMC Plant Biol 24 , 502 (2024). https://doi.org/10.1186/s12870-024-05223-0

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DOI : https://doi.org/10.1186/s12870-024-05223-0

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