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Non-experimental research: What it is, overview & advantages

Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information.

Unlike experimental research , where the variables are held constant, non-experimental research happens during the study when the researcher cannot control, manipulate or alter the subjects but relies on interpretation or observations to conclude.

This means that the method must not rely on correlations, surveys , or case studies and cannot demonstrate an actual cause and effect relationship.

Characteristics of non-experimental research

Some of the essential characteristics of non-experimental research are necessary for the final results. Let’s talk about them to identify the most critical parts of them.

Most studies are based on events that occurred previously and are analyzed later.
In this method, controlled experiments are not performed for reasons such as ethics or morality.
No study samples are created; on the contrary, the samples or participants already exist and develop in their environment.
The researcher does not intervene directly in the environment of the sample.
This method studies the phenomena exactly as they occurred.

Types of non-experimental research

Non-experimental research can take the following forms:

Cross-sectional research : Cross-sectional research is used to observe and analyze the exact time of the research to cover various study groups or samples. This type of research is divided into:

Descriptive: When values are observed where one or more variables are presented.
Causal: It is responsible for explaining the reasons and relationship that exists between variables in a given time.

Longitudinal research: In a longitudinal study , researchers aim to analyze the changes and development of the relationships between variables over time. Longitudinal research can be divided into:

Trend: When they study the changes faced by the study group in general.
Group evolution: When the study group is a smaller sample.
Panel: It is in charge of analyzing individual and group changes to discover the factor that produces them.

LEARN ABOUT: Quasi-experimental Research

When to use non-experimental research

Non-experimental research can be applied in the following ways:

When the research question may be about one variable rather than a statistical relationship about two variables.
There is a non-causal statistical relationship between variables in the research question.
The research question has a causal research relationship, but the independent variable cannot be manipulated.
In exploratory or broad research where a particular experience is confronted.

Advantages and disadvantages

Some advantages of non-experimental research are:

It is very flexible during the research process
The cause of the phenomenon is known, and the effect it has is investigated.
The researcher can define the characteristics of the study group.

Among the disadvantages of non-experimental research are:

The groups are not representative of the entire population.
Errors in the methodology may occur, leading to research biases .

Non-experimental research is based on the observation of phenomena in their natural environment. In this way, they can be studied later to reach a conclusion.

Difference between experimental and non-experimental research

Experimental research involves changing variables and randomly assigning conditions to participants. As it can determine the cause, experimental research designs are used for research in medicine, biology, and social science.

Experimental research designs have strict standards for control and establishing validity. Although they may need many resources, they can lead to very interesting results.

Non-experimental research, on the other hand, is usually descriptive or correlational without any explicit changes done by the researcher. You simply describe the situation as it is, or describe a relationship between variables. Without any control, it is difficult to determine causal effects. The validity remains a concern in this type of research. However, it’s’ more regarding the measurements instead of the effects.

LEARN MORE: Descriptive Research vs Correlational Research

Whether you should choose experimental research or non-experimental research design depends on your goals and resources. If you need any help with how to conduct research and collect relevant data, or have queries regarding the best approach for your research goals, contact us today! You can create an account with our survey software and avail of 88+ features including dashboard and reporting for free.

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Chapter 7: Nonexperimental Research

Overview of Nonexperimental Research

Learning Objectives

Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This distinction is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this inability does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in Chapter 6 , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which preferring nonexperimental research can be the case.

The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behaviour have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] . Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [2] .

Types of Nonexperimental Research

Nonexperimental research falls into three broad categories: single-variable research, correlational and quasi-experimental research, and qualitative research. First, research can be nonexperimental because it focuses on a single variable rather than a statistical relationship between two variables. Although there is no widely shared term for this kind of research, we will call it single-variable research . Milgram’s original obedience study was nonexperimental in this way. He was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of single-variable research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the research asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.)

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This detail is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this design would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied compares with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied thereby introducing another variable.

Research can also be nonexperimental because it focuses on a statistical relationship between two variables but does not include the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. This kind of research takes two basic forms: correlational research and quasi-experimental research. In correlational research , the researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them. A research methods student who finds out whether each of several middle-school students has been bullied and then measures each student’s self-esteem is conducting correlational research. In quasi-experimental research , the researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions. For example, a researcher might start an antibullying program (a kind of treatment) at one school and compare the incidence of bullying at that school with the incidence at a similar school that has no antibullying program.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256). [3] Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 7.1 shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends to be highest because it addresses the directionality and third-variable problems through manipulation and the control of extraneous variables through random assignment. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Correlational research is lowest because it fails to address either problem. If the average score on the dependent variable differs across levels of the independent variable, it could be that the independent variable is responsible, but there are other interpretations. In some situations, the direction of causality could be reversed. In others, there could be a third variable that is causing differences in both the independent and dependent variables. Quasi-experimental research is in the middle because the manipulation of the independent variable addresses some problems, but the lack of random assignment and experimental control fails to address others. Imagine, for example, that a researcher finds two similar schools, starts an antibullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” There is no directionality problem because clearly the number of bullying incidents did not determine which school got the program. However, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying.

Notice also in Figure 7.1 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Key Takeaways

Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyses the data nonstatistically.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.

Discussion: For each of the following studies, decide which type of research design it is and explain why.

A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

Research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

Research that focuses on a single variable rather than a statistical relationship between two variables.

The researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them.

The researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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6.1 Overview of Non-Experimental Research

Learning objectives.

Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

Most researchers in psychology consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research.

When to Use Non-Experimental Research

As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach will suffice. But the two approaches can also be used to address the same research question in complementary ways. For example, Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into three broad categories: cross-sectional research, correlational research, and observational research.

First, cross-sectional research involves comparing two or more pre-existing groups of people. What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a cross-sectional study because the researcher did not manipulate the students’ nationalities. As another example, if we wanted to compare the memory test performance of a group of cannabis users with a group of non-users, this would be considered a cross-sectional study because for ethical and practical reasons we would not be able to randomly assign participants to the cannabis user and non-user groups. Rather we would need to compare these pre-existing groups which could introduce a selection bias (the groups may differ in other ways that affect their responses on the dependent variable). For instance, cannabis users are more likely to use more alcohol and other drugs and these differences may account for differences in the dependent variable across groups, rather than cannabis use per se.

Cross-sectional designs are commonly used by developmental psychologists who study aging and by researchers interested in sex differences. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect) rather than a direct effect of age. For this reason, longitudinal studies in which one group of people is followed as they age offer a superior means of studying the effects of aging. Once again, cross-sectional designs are also commonly used to study sex differences. Since researchers cannot practically or ethically manipulate the sex of their participants they must rely on cross-sectional designs to compare groups of men and women on different outcomes (e.g., verbal ability, substance use, depression). Using these designs researchers have discovered that men are more likely than women to suffer from substance abuse problems while women are more likely than men to suffer from depression. But, using this design it is unclear what is causing these differences. So, using this design it is unclear whether these differences are due to environmental factors like socialization or biological factors like hormones?

When researchers use a participant characteristic to create groups (nationality, cannabis use, age, sex), the independent variable is usually referred to as an experimenter-selected independent variable (as opposed to the experimenter-manipulated independent variables used in experimental research). Figure 6.1 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a cross-sectional study because it is unclear whether the independent variable was manipulated by the researcher or simply selected by the researcher. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then the independent variable was experimenter-manipulated and it is a true experiment. If the researcher simply asked participants whether they made daily to-do lists or not, then the independent variable it is experimenter-selected and the study is cross-sectional. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a cross-sectional study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead. Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed. The crucial point is that what defines a study as experimental or cross-sectional l is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 6.1 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Second, the most common type of non-experimental research conducted in Psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two continuous variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related. Correlational research is very similar to cross-sectional research, and sometimes these terms are used interchangeably. The distinction that will be made in this book is that, rather than comparing two or more pre-existing groups of people as is done with cross-sectional research, correlational research involves correlating two continuous variables (groups are not formed and compared).

Third, observational research is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 6.2 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) is in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Figure 7.1 Internal Validity of Correlational, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Figure 6.2 Internal Validity of Correlation, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlation studies lower still.

Notice also in Figure 6.2 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Key Takeaways

Non-experimental research is research that lacks the manipulation of an independent variable.
There are two broad types of non-experimental research. Correlational research that focuses on statistical relationships between variables that are measured but not manipulated, and observational research in which participants are observed and their behavior is recorded without the researcher interfering or manipulating any variables.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.
A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

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7.1 Overview of Nonexperimental Research

Learning objectives.

Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This is because while experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in Chapter 6 “Experimental Research” , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which this can be the case.

The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001). Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974).

Types of Nonexperimental Research

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied compares with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In qualitative research , the data are usually nonnumerical and are analyzed using nonstatistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256).

Internal Validity Revisited

Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Key Takeaways

Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyzes the data nonstatistically.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.

Discussion: For each of the following studies, decide which type of research design it is and explain why.

A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.

Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage.

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row.

Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258.

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

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Overview of Non-Experimental Research

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

When to Use Non-Experimental Research

the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., how accurate are people’s first impressions?).
the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach is appropriate. But the two approaches can also be used to address the same research question in complementary ways. For example, in Milgram’s original (non-experimental) obedience study, he was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. However, Milgram subsequently conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into two broad categories: correlational research and observational research.

The most common type of non-experimental research conducted in psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related.

Observational research is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories).

Cross-Sectional, Longitudinal, and Cross-Sequential Studies

When psychologists wish to study change over time (for example, when developmental psychologists wish to study aging) they usually take one of three non-experimental approaches: cross-sectional, longitudinal, or cross-sequential. Cross-sectional studies involve comparing two or more pre-existing groups of people (e.g., children at different stages of development). What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect ) rather than a direct effect of age. For this reason, longitudinal studies , in which one group of people is followed over time as they age, offer a superior means of studying the effects of aging. However, longitudinal studies are by definition more time consuming and so require a much greater investment on the part of the researcher and the participants. A third approach, known as cross-sequential studies , combines elements of both cross-sectional and longitudinal studies. Rather than measuring differences between people in different age groups or following the same people over a long period of time, researchers adopting this approach choose a smaller period of time during which they follow people in different age groups. For example, they might measure changes over a ten year period among participants who at the start of the study fall into the following age groups: 20 years old, 30 years old, 40 years old, 50 years old, and 60 years old. This design is advantageous because the researcher reaps the immediate benefits of being able to compare the age groups after the first assessment. Further, by following the different age groups over time they can subsequently determine whether the original differences they found across the age groups are due to true age effects or cohort effects.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in psychiatric wards was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 6.1 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the inability to randomly assign children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Figure 6.1 Internal Validity of Correlational, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Notice also in Figure 6.1 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational (non-experimental) studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

A research that lacks the manipulation of an independent variable.

Research that is non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

Studies that involve comparing two or more pre-existing groups of people (e.g., children at different stages of development).

Differences between the groups may reflect the generation that people come from rather than a direct effect of age.

Studies in which one group of people are followed over time as they age.

Studies in which researchers follow people in different age groups in a smaller period of time.

Overview of Non-Experimental Research Copyright © 2022 by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is non-experimental research: Definition, types & examples

What is non-experimental research: Definition, types & examples

Defne Çobanoğlu

The experimentation method is very useful for getting information on a specific subject. However, when experimenting is not possible or practical, there is another way of collecting data for those interested. It's a non-experimental way, to say the least.

In this article, we have gathered information on non-experimental research, clearly defined what it is and when one should use it, and listed the types of non-experimental research. We also gave some useful examples to paint a better picture. Let us get started.

What is non-experimental research?

Non-experimental research is a type of research design that is based on observation and measuring instead of experimentation with randomly assigned participants.

What characterizes this research design is the fact that it lacks the manipulation of independent variables . Because of this fact, the non-experimental research is based on naturally occurring conditions, and there is no involvement of external interventions. Therefore, the researchers doing this method must not rely heavily on interviews, surveys , or case studies.

When to use non-experimental research?

An experiment is done when a researcher is investigating the relationship between one or two phenomena and has a theory or hypothesis on the relationship between two variables that are involved. The researcher can carry out an experiment when it is ethical, possible, and feasible to do one.

However, when an experiment can not be done because of a limitation, then they decide to opt for a non-experimental research design . Non-experimental research is considered preferable in some conditions, including:

When the manipulation of the independent variable is not possible because of ethical or practical concerns
When the subjects of an experimental design can not be randomly assigned to treatments.
When the research question is too extensive or it relates to a general experience.
When researchers want to do a starter research before investing in more extensive research.
When the research question is about the statistical relationship between variables , but in a noncausal context.
Characteristics of non-experimental research

Non-experimental research has some characteristics that clearly define the framework of this research method. They provide a clear distinction between experimental design and non-experimental design. Let us see some of them:

Non-experimental research does not involve the manipulation of variables .
The aim of this research type is to explore the factors as they naturally occur .
This method is used when experimentation is not possible because of ethical or practical reasons .
Instead of creating a sample or participant group, the existing groups or natural thresholds are used during the research.
This research method is not about finding causality between two variables.
Most studies are done on past events or historical occurrences to make sense of specific research questions.
Types of non-experimental research

Non-experimental research types

What makes research non-experimental research is the fact that the researcher does not manipulate the factors, does not randomly assign the participants, and observes the existing groups. But this research method can also be divided into different types. These types are:

Correlational research:

In correlation studies, the researcher does not manipulate the variables and is not interested in controlling the extraneous variables. They only observe and assess the relationship between them. For example, a researcher examines students’ study hours every day and their overall academic performance. The positive correlation this between study hours and academic performance suggests a statistical association.

Quasi-experimental research:

In quasi-experimental research, the researcher does not randomly assign the participants into two groups. Because you can not deliberately deprive someone of treatment, the researcher uses natural thresholds or dividing points . For example, examining students from two different high schools with different education methods.

Cross-sectional research:

In cross-sectional research, the researcher studies and compares a portion of a population at the same time . It does not involve random assignment or any outside manipulation. For example, a study on smokers and non-smokers in a specific area.

Observational research:

In observational research, the researcher once again does not manipulate any aspect of the study, and their main focus is observation of the participants . For example, a researcher examining a group of children playing in a playground would be a good example.

Non-experimental research examples

Non-experimental research is a good way of collecting information and exploring relationships between variables. It can be used in numerous fields, from social sciences, economics, psychology, education, and market research. When gathering information using secondary research is not enough and an experiment can not be done, this method can bring out new information.

Non-experimental research example #1

Imagine a researcher who wants to see the connection between mobile phone usage before bedtime and the amount of sleep adults get in a night . They can gather a group of individuals to observe and present them with some questions asking about the details of their day, frequency and duration of phone usage, quality of sleep, etc . And observe them by analyzing the findings.

Non-experimental research example #2

Imagine a researcher who wants to explore the correlation between job satisfaction levels among employees and what are the factors that affect this . The researcher can gather all the information they get about the employees’ ages, sexes, positions in the company, working patterns, demographic information, etc .

The research provides the researcher with all the information to make an analysis to identify correlations and patterns. Then, it is possible for researchers and administrators to make informed predictions.

Frequently asked questions about non-experimental research

When not to use non-experimental research?

There are some situations where non-experimental research is not suitable or the best choice. For example, the aim of non-experimental research is not about finding causality therefore, if the researcher wants to explore the relationship between two variables, then this method is not for them. Also, if the control over the variables is extremely important to the test of a theory, then experimentation is a more appropriate option.

What is the difference between experimental and non-experimental research?

Experimental research is an example of primary research where the researcher takes control of all the variables, randomly assigns the participants into different groups, and studies them in a pre-determined environment to test a hypothesis.

On the contrary, non-experimental research does not intervene in any way and only observes and studies the participants in their natural environments to make sense of a phenomenon

What makes a quasi-experiment a non-experiment?

The same as true experimentation, quasi-experiment research also aims to explore a cause-and-effect relationship between independent and dependent variables. However, in quasi-experimental research, the participants are not randomly selected. They are assigned to groups based on non-random criteria .

Is a survey a non-experimental study?

Yes, as the main purpose of a survey or questionnaire is to collect information from participants without outside interference, it makes the survey a non-experimental study. Surveys are used by researchers when experimentation is not possible because of ethical reasons, but first-hand data is needed

What is non-experimental data?

Non-experimental data is data collected by researchers via using non-experimental methods such as observations, interpretation, and interactions. Non-experimental data could both be qualitative or quantitative, depending on the situation.

Advantages of non-experimental research

Non-experimental research has its positive sides that a researcher should have in mind when going through a study. They can start their research by going through the advantages. These advantages are:

It is used to observe and analyze past events .
This method is more affordable than a true experiment .
As the researcher can adapt the methods during the study, this research type is more flexible than an experimental study.
This method allows the researchers to answer specific questions .

Disadvantages of non-experimental research

Even though non-experimental research has its advantages, it also has some disadvantages a researcher should be mindful of. Here are some of them:

The findings of non-experimental research can not be generalized to the whole population. Therefore, it has low external validity .
This research is used to explore only a single variable .
Non-experimental research designs are prone to researcher bias and may not produce neutral results.
Final words

A non-experimental study differs from an experimental study in that there is no intervention or change of internal or extraneous elements. It is a smart way to collect information without the limitations of experimentation. These limitations could be about ethical or practical problems. When you can not do proper experimentation, your other option is to study existing conditions and groups to draw conclusions. This is a non-experimental design .

In this article, we have gathered information on non-experimental research to shed light on the details of this research method. If you are thinking of doing a study, make sure to have this information in mind. And lastly, do not forget to visit our articles on other research methods and so much more!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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Experimental Vs Non-Experimental Research: 15 Key Differences

There is a general misconception around research that once the research is non-experimental, then it is non-scientific, making it more important to understand what experimental and experimental research entails. Experimental research is the most common type of research, which a lot of people refer to as scientific research.

Non experimental research, on the other hand, is easily used to classify research that is not experimental. It clearly differs from experimental research, and as such has different use cases.

In this article, we will be explaining these differences in detail so as to ensure proper identification during the research process.

What is Experimental Research?

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables of the research subject(s) and measuring the effect of this manipulation on the subject. It is known for the fact that it allows the manipulation of control variables.

This research method is widely used in various physical and social science fields, even though it may be quite difficult to execute. Within the information field, they are much more common in information systems research than in library and information management research.

Experimental research is usually undertaken when the goal of the research is to trace cause-and-effect relationships between defined variables. However, the type of experimental research chosen has a significant influence on the results of the experiment.

Therefore bringing us to the different types of experimental research. There are 3 main types of experimental research, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research

Pre-experimental research is the simplest form of research, and is carried out by observing a group or groups of dependent variables after the treatment of an independent variable which is presumed to cause change on the group(s). It is further divided into three types.

One-shot case study research
One-group pretest-posttest research
Static-group comparison

Quasi-experimental Research

The Quasi type of experimental research is similar to true experimental research, but uses carefully selected rather than randomized subjects. The following are examples of quasi-experimental research:

Time series
No equivalent control group design
Counterbalanced design.

True Experimental Research

True experimental research is the most accurate type, and may simply be called experimental research. It manipulates a control group towards a group of randomly selected subjects and records the effect of this manipulation.

True experimental research can be further classified into the following groups:

The posttest-only control group
The pretest-posttest control group
Solomon four-group

Pros of True Experimental Research

Researchers can have control over variables.
It can be combined with other research methods.
The research process is usually well structured.
It provides specific conclusions.
The results of experimental research can be easily duplicated.

Cons of True Experimental Research

It is highly prone to human error.
Exerting control over extraneous variables may lead to the personal bias of the researcher.
It is time-consuming.
It is expensive.
Manipulating control variables may have ethical implications.
It produces artificial results.

What is Non-Experimental Research?

Non-experimental research is the type of research that does not involve the manipulation of control or independent variable. In non-experimental research, researchers measure variables as they naturally occur without any further manipulation.

This type of research is used when the researcher has no specific research question about a causal relationship between 2 different variables, and manipulation of the independent variable is impossible. They are also used when:

subjects cannot be randomly assigned to conditions.
the research subject is about a causal relationship but the independent variable cannot be manipulated.
the research is broad and exploratory
the research pertains to a non-causal relationship between variables.
limited information can be accessed about the research subject.

There are 3 main types of non-experimental research , namely; cross-sectional research, correlation research, and observational research.

Cross-sectional Research

Cross-sectional research involves the comparison of two or more pre-existing groups of people under the same criteria. This approach is classified as non-experimental because the groups are not randomly selected and the independent variable is not manipulated.

For example, an academic institution may want to reward its first-class students with a scholarship for their academic excellence. Therefore, each faculty places students in the eligible and ineligible group according to their class of degree.

In this case, the student’s class of degree cannot be manipulated to qualify him or her for a scholarship because it is an unethical thing to do. Therefore, the placement is cross-sectional.

Correlational Research

Correlational type of research compares the statistical relationship between two variables .Correlational research is classified as non-experimental because it does not manipulate the independent variables.

For example, a researcher may wish to investigate the relationship between the class of family students come from and their grades in school. A questionnaire may be given to students to know the average income of their family, then compare it with CGPAs.

The researcher will discover whether these two factors are positively correlated, negatively corrected, or have zero correlation at the end of the research.

Observational Research

Observational research focuses on observing the behavior of a research subject in a natural or laboratory setting. It is classified as non-experimental because it does not involve the manipulation of independent variables.

A good example of observational research is an investigation of the crowd effect or psychology in a particular group of people. Imagine a situation where there are 2 ATMs at a place, and only one of the ATMs is filled with a queue, while the other is abandoned.

The crowd effect infers that the majority of newcomers will also abandon the other ATM.

You will notice that each of these non-experimental research is descriptive in nature. It then suffices to say that descriptive research is an example of non-experimental research.

Pros of Observational Research

The research process is very close to a real-life situation.
It does not allow for the manipulation of variables due to ethical reasons.
Human characteristics are not subject to experimental manipulation.

Cons of Observational Research

The groups may be dissimilar and nonhomogeneous because they are not randomly selected, affecting the authenticity and generalizability of the study results.
The results obtained cannot be absolutely clear and error-free.

What Are The Differences Between Experimental and Non-Experimental Research?

Definitions

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables and measuring their defect on the dependent variables, while non-experimental research is the type of research that does not involve the manipulation of control variables.

The main distinction in these 2 types of research is their attitude towards the manipulation of control variables. Experimental allows for the manipulation of control variables while non-experimental research doesn’t.

Examples of experimental research are laboratory experiments that involve mixing different chemical elements together to see the effect of one element on the other while non-experimental research examples are investigations into the characteristics of different chemical elements.

Consider a researcher carrying out a laboratory test to determine the effect of adding Nitrogen gas to Hydrogen gas. It may be discovered that using the Haber process, one can create Nitrogen gas.

Non-experimental research may further be carried out on Ammonia, to determine its characteristics, behaviour, and nature.

There are 3 types of experimental research, namely; experimental research, quasi-experimental research, and true experimental research. Although also 3 in number, non-experimental research can be classified into cross-sectional research, correlational research, and observational research.

The different types of experimental research are further divided into different parts, while non-experimental research types are not further divided. Clearly, these divisions are not the same in experimental and non-experimental research.

Characteristics

Experimental research is usually quantitative, controlled, and multivariable. Non-experimental research can be both quantitative and qualitative , has an uncontrolled variable, and also a cross-sectional research problem.

The characteristics of experimental research are the direct opposite of that of non-experimental research. The most distinct characteristic element is the ability to control or manipulate independent variables in experimental research and not in non-experimental research.

In experimental research, a level of control is usually exerted on extraneous variables, therefore tampering with the natural research setting. Experimental research settings are usually more natural with no tampering with the extraneous variables.

Data Collection/Tools

The data used during experimental research is collected through observational study, simulations, and surveys while non-experimental data is collected through observations, surveys, and case studies. The main distinction between these data collection tools is case studies and simulations.

Even at that, similar tools are used differently. For example, an observational study may be used during a laboratory experiment that tests how the effect of a control variable manifests over a period of time in experimental research.

However, when used in non-experimental research, data is collected based on the researcher’s discretion and not through a clear scientific reaction. In this case, we see a difference in the level of objectivity.

The goal of experimental research is to measure the causes and effects of variables present in research, while non-experimental research provides very little to no information about causal agents.

Experimental research answers the question of why something is happening. This is quite different in non-experimental research, as they are more descriptive in nature with the end goal being to describe what .

Experimental research is mostly used to make scientific innovations and find major solutions to problems while non-experimental research is used to define subject characteristics, measure data trends, compare situations and validate existing conditions.

For example, if experimental research results in an innovative discovery or solution, non-experimental research will be conducted to validate this discovery. This research is done for a period of time in order to properly study the subject of research.

Experimental research process is usually well structured and as such produces results with very little to no errors, while non-experimental research helps to create real-life related experiments. There are a lot more advantages of experimental and non-experimental research , with the absence of each of these advantages in the other leaving it at a disadvantage.

For example, the lack of a random selection process in non-experimental research leads to the inability to arrive at a generalizable result. Similarly, the ability to manipulate control variables in experimental research may lead to the personal bias of the researcher.

Disadvantage

Experimental research is highly prone to human error while the major disadvantage of non-experimental research is that the results obtained cannot be absolutely clear and error-free. In the long run, the error obtained due to human error may affect the results of the experimental research.

Some other disadvantages of experimental research include the following; extraneous variables cannot always be controlled, human responses can be difficult to measure, and participants may also cause bias.

In experimental research, researchers can control and manipulate control variables, while in non-experimental research, researchers cannot manipulate these variables. This cannot be done due to ethical reasons.

For example, when promoting employees due to how well they did in their annual performance review, it will be unethical to manipulate the results of the performance review (independent variable). That way, we can get impartial results of those who deserve a promotion and those who don’t.

Experimental researchers may also decide to eliminate extraneous variables so as to have enough control over the research process. Once again, this is something that cannot be done in non-experimental research because it relates more to real-life situations.

Experimental research is carried out in an unnatural setting because most of the factors that influence the setting are controlled while the non-experimental research setting remains natural and uncontrolled. One of the things usually tampered with during research is extraneous variables.

In a bid to get a perfect and well-structured research process and results, researchers sometimes eliminate extraneous variables. Although sometimes seen as insignificant, the elimination of these variables may affect the research results.

Consider the optimization problem whose aim is to minimize the cost of production of a car, with the constraints being the number of workers and the number of hours they spend working per day.

In this problem, extraneous variables like machine failure rates or accidents are eliminated. In the long run, these things may occur and may invalidate the result.

Cause-Effect Relationship

The relationship between cause and effect is established in experimental research while it cannot be established in non-experimental research. Rather than establish a cause-effect relationship, non-experimental research focuses on providing descriptive results.

Although it acknowledges the causal variable and its effect on the dependent variables, it does not measure how or the extent to which these dependent variables change. It, however, observes these changes, compares the changes in 2 variables, and describes them.

Experimental research does not compare variables while non-experimental research does. It compares 2 variables and describes the relationship between them.

The relationship between these variables can be positively correlated, negatively correlated or not correlated at all. For example, consider a case whereby the subject of research is a drum, and the control or independent variable is the drumstick.

Experimental research will measure the effect of hitting the drumstick on the drum, where the result of this research will be sound. That is, when you hit a drumstick on a drum, it makes a sound.

Non-experimental research, on the other hand, will investigate the correlation between how hard the drum is hit and the loudness of the sound that comes out. That is, if the sound will be higher with a harder bang, lower with a harder bang, or will remain the same no matter how hard we hit the drum.

Quantitativeness

Experimental research is a quantitative research method while non-experimental research can be both quantitative and qualitative depending on the time and the situation where it is been used. An example of a non-experimental quantitative research method is correlational research .

Researchers use it to correlate two or more variables using mathematical analysis methods. The original patterns, relationships, and trends between variables are observed, then the impact of one of these variables on the other is recorded along with how it changes the relationship between the two variables.

Observational research is an example of non-experimental research, which is classified as a qualitative research method.

Cross-section

Experimental research is usually single-sectional while non-experimental research is cross-sectional. That is, when evaluating the research subjects in experimental research, each group is evaluated as an entity.

For example, let us consider a medical research process investigating the prevalence of breast cancer in a certain community. In this community, we will find people of different ages, ethnicities, and social backgrounds.

If a significant amount of women from a particular age are found to be more prone to have the disease, the researcher can conduct further studies to understand the reason behind it. A further study into this will be experimental and the subject won’t be a cross-sectional group.

A lot of researchers consider the distinction between experimental and non-experimental research to be an extremely important one. This is partly due to the fact that experimental research can accommodate the manipulation of independent variables, which is something non-experimental research can not.

Therefore, as a researcher who is interested in using any one of experimental and non-experimental research, it is important to understand the distinction between these two. This helps in deciding which method is better for carrying out particular research.

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Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

The type of knowledge you aim to produce
The type of data you will collect and analyze
The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

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Non-Experimental Research

29 Correlational Research

Learning objectives.

Define correlational research and give several examples.
Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
Interpret the strength and direction of different correlation coefficients.
Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance.

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variabl e do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated.

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

Figure 6.3 Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms

The strength of a correlation between quantitative variables is typically measured using a statistic called Pearson’s Correlation Coefficient (or Pearson's r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s r is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s r is unrelated to its strength. Pearson’s r values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

Figure 6.4 Range of Pearson’s r, From −1.00 (Strongest Possible Negative Relationship), Through 0 (No Relationship), to +1.00 (Strongest Possible Positive Relationship)

There are two common situations in which the value of Pearson’s r can be misleading. Pearson’s r is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s r would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

Figure 6.5 Hypothetical Nonlinear Relationship Between Sleep and Depression

The other common situations in which the value of Pearson’s r can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s r here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s r for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s r in light of it. (There are also statistical methods to correct Pearson’s r for restriction of range, but they are beyond the scope of this book).

Figure 6.6 Hypothetical Data Showing How a Strong Overall Correlation Can Appear to Be Weak When One Variable Has a Restricted Range

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the directionality problem . Two variables, X and Y , can be statistically related because X causes Y or because Y causes X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the third-variable problem . Two variables, X and Y , can be statistically related not because X causes Y , or because Y causes X , but because some third variable, Z , causes both X and Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com (Figure 6.7 provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

Nicholas Cage and Pool Drownings © Tyler Viegen is licensed under a CC BY (Attribution) license
Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X and Y , are statistically related either because X causes Y, or because Y causes X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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1.11: Experimental and non-experimental research

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Matthew J. C. Crump
Brooklyn College of CUNY

One of the big distinctions that you should be aware of is the distinction between “experimental research” and “non-experimental research”. When we make this distinction, what we’re really talking about is the degree of control that the researcher exercises over the people and events in the study.

Experimental research

The key features of experimental research is that the researcher controls all aspects of the study, especially what participants experience during the study. In particular, the researcher manipulates or varies something (IVs), and then allows the outcome variable (DV) to vary naturally. The idea here is to deliberately vary the something in the world (IVs) to see if it has any causal effects on the outcomes. Moreover, in order to ensure that there’s no chance that something other than the manipulated variable is causing the outcomes, everything else is kept constant or is in some other way “balanced” to ensure that they have no effect on the results. In practice, it’s almost impossible to think of everything else that might have an influence on the outcome of an experiment, much less keep it constant. The standard solution to this is randomization : that is, we randomly assign people to different groups, and then give each group a different treatment (i.e., assign them different values of the predictor variables). We’ll talk more about randomization later in this course, but for now, it’s enough to say that what randomization does is minimize (but not eliminate) the chances that there are any systematic difference between groups.

Let’s consider a very simple, completely unrealistic and grossly unethical example. Suppose you wanted to find out if smoking causes lung cancer. One way to do this would be to find people who smoke and people who don’t smoke, and look to see if smokers have a higher rate of lung cancer. This is not a proper experiment, since the researcher doesn’t have a lot of control over who is and isn’t a smoker. And this really matters: for instance, it might be that people who choose to smoke cigarettes also tend to have poor diets, or maybe they tend to work in asbestos mines, or whatever. The point here is that the groups (smokers and non-smokers) actually differ on lots of things, not just smoking. So it might be that the higher incidence of lung cancer among smokers is caused by something else, not by smoking per se. In technical terms, these other things (e.g. diet) are called “confounds”, and we’ll talk about those in just a moment.

In the meantime, let’s now consider what a proper experiment might look like. Recall that our concern was that smokers and non-smokers might differ in lots of ways. The solution, as long as you have no ethics, is to control who smokes and who doesn’t. Specifically, if we randomly divide participants into two groups, and force half of them to become smokers, then it’s very unlikely that the groups will differ in any respect other than the fact that half of them smoke. That way, if our smoking group gets cancer at a higher rate than the non-smoking group, then we can feel pretty confident that (a) smoking does cause cancer and (b) we’re murderers.

Non-experimental research

Non-experimental research is a broad term that covers “any study in which the researcher doesn’t have quite as much control as they do in an experiment”. Obviously, control is something that scientists like to have, but as the previous example illustrates, there are lots of situations in which you can’t or shouldn’t try to obtain that control. Since it’s grossly unethical (and almost certainly criminal) to force people to smoke in order to find out if they get cancer, this is a good example of a situation in which you really shouldn’t try to obtain experimental control. But there are other reasons too. Even leaving aside the ethical issues, our “smoking experiment” does have a few other issues. For instance, when I suggested that we “force” half of the people to become smokers, I must have been talking about starting with a sample of non-smokers, and then forcing them to become smokers. While this sounds like the kind of solid, evil experimental design that a mad scientist would love, it might not be a very sound way of investigating the effect in the real world. For instance, suppose that smoking only causes lung cancer when people have poor diets, and suppose also that people who normally smoke do tend to have poor diets. However, since the “smokers” in our experiment aren’t “natural” smokers (i.e., we forced non-smokers to become smokers; they didn’t take on all of the other normal, real life characteristics that smokers might tend to possess) they probably have better diets. As such, in this silly example they wouldn’t get lung cancer, and our experiment will fail, because it violates the structure of the “natural” world (the technical name for this is an “artifactual” result; see later).

One distinction worth making between two types of non-experimental research is the difference between quasi-experimental research and case studies . The example I discussed earlier – in which we wanted to examine incidence of lung cancer among smokers and non-smokers, without trying to control who smokes and who doesn’t – is a quasi-experimental design. That is, it’s the same as an experiment, but we don’t control the predictors (IVs). We can still use statistics to analyse the results, it’s just that we have to be a lot more careful.

The alternative approach, case studies, aims to provide a very detailed description of one or a few instances. In general, you can’t use statistics to analyse the results of case studies, and it’s usually very hard to draw any general conclusions about “people in general” from a few isolated examples. However, case studies are very useful in some situations. Firstly, there are situations where you don’t have any alternative: neuropsychology has this issue a lot. Sometimes, you just can’t find a lot of people with brain damage in a specific area, so the only thing you can do is describe those cases that you do have in as much detail and with as much care as you can. However, there’s also some genuine advantages to case studies: because you don’t have as many people to study, you have the ability to invest lots of time and effort trying to understand the specific factors at play in each case. This is a very valuable thing to do. As a consequence, case studies can complement the more statistically-oriented approaches that you see in experimental and quasi-experimental designs. We won’t talk much about case studies in these lectures, but they are nevertheless very valuable tools!

Difference Between Experimental and Non-Experimental Research

When designing research , you must take into account the different methods and experimental research types that are available.

There are two main types of research: non-experimental and experimental. It’s critical to understand the advantages and disadvantages of each type in order to choose which is best for your research goals.

Experimental research is a type of quantitative research that controls the variables in order to test a hypothesis. Non-experimental research does not control the variables but instead looks at naturally occurring factors without manipulating them to test a hypothesis.

This article explains more about the benefits and drawbacks of experimental versus non-experimental research so you can decide which one is right for your project.

Table of Contents

What is Experimental Research?

Experimental research is a scientific method where the researcher manipulates the variables in the experiment to see how this influences the findings. In experimental research, the research subject(s) are subjected to one or more manipulations of their control variables while the effects of these manipulations are being monitored. Because it enables the adjustment of control variables, it is well known.

The goal of the experiment is to test a hypothesis . The data collected during the experiment will help you understand how accurate your hypothesis is.

Experiments are a type of quantitative research that aims to control all the variables in order to accurately test a hypothesis. Experiments are typically used in social sciences such as psychology, economics, or sociology. Other fields like biology also use experimental research, but they often use a different method called “controlled laboratory experiments”.

Experiments are also used in business research , but businesses often employ a different type of experimental research called “field experiments”.

What is Non-Experimental Research?

Non-experimental research does not experimentally manipulate the variables directly. Instead, it looks at naturally occurring factors without experimenting with them. Research that is non-experimental does not manipulate a control variable or an independent variable. In non-experimental research, variables are measured as they are without any additional manipulation.

These types of research are usually quantitative because they don’t manipulate the variables, but they don’t employ the same experimental approach as an experiment. To conduct non-experimental research, you must first define the population that you want to study. Then, you would take sample data from that population to find out what the results are.

The goal of non-experimental research is to find out what naturally occurs in the environment without directly manipulating anything. This means that the researcher does not change any variables during the research process. Non-experimental research is often used in fields like social sciences or economics where it is hard to experimentally manipulate the variables.

Benefits of Experimental Research

Experimental research has several advantages.

First, it allows you to control many different factors to test the accuracy of your hypothesis. This means that you can completely control the outcome of your research if you use the right methods.
Experimental research also allows you to conduct the research in a short amount of time, meaning you can get accurate results quickly. This is important for businesses that need to make quick decisions about products or services.
Experimental research often leads to the creation of new products or solutions because researchers can test multiple solutions at the same time. Many businesses use experimental research to create new products or services because it allows them to test different variables that would otherwise be difficult to control in a non-experimental research setting.

Disadvantages of Experimental Research

Experimental research does come with a few disadvantages.

First, it can be very expensive to conduct and may require specialized equipment or employees. You also need to hire professionals to create a hypothesis and conduct the experiment.
Experimental research can also be time consuming. You must create the right conditions to ensure that the right variables are being tested. This can take a long time.
Experimental research can also be difficult to control. If you don’t have the right conditions, you may not get accurate results. Researchers also have to make sure that other factors don’t influence the outcome of the experiment.
Experimental research can also lead to inaccurate findings if the researcher makes a mistake or if the conditions are not right for the experiment.
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Benefits of Non-Experimental Research

Non-experimental research does not have the same financial or time requirements as experimental research. You can conduct non-experimental research with minimal resources and you don’t need to create a hypothesis in order to conduct it. This means that you don’t have to hire specialists or spend as much money as you would on an experiment.
Non-experimental research is also easier to control than an experiment because you don’t have to create a controlled setting. Instead, you simply observe what naturally occurs in the environment. This means that you don’t have to worry about outside factors influencing the outcome of the research.
Non-experimental research is also less risky than an experiment because you don’t have to worry about making a mistake. If something goes wrong during the research, you don’t have to start the experiment over again. Instead, you can simply make a note of it and continue with the research .

Disadvantages of Non-Experimental Research

Non-experimental research does have a few disadvantages.

First, it does not allow you to test your hypothesis as accurately as experimental research does. This means that you don’t really know if your product or idea will work in the real world.
Non-experimental research is also limited by the number of variables that are naturally occurring in the environment. This means that you might not be able to find the right data or factors to test your hypothesis.
If you want to test a lot of different variables at once, non-experimental research can become difficult to conduct. You must then separate the data in order to find out which factor is influencing your findings.

The primary difference between these two forms of research is how they approach manipulating control variables. Non-experimental research does not allow for the manipulation of control variables.

Example: Experimental vs Non-Experimental Research

Laboratory experiments in which various chemical elements are mixed together to observe how one element affects another are instances of experimental research, whereas studies into the properties of various chemical elements are examples of non-experimental research.

Which Type of Research is Right for You?

When choosing between experimental and non-experimental research, you should remember that both methods have their benefits and drawbacks. No one research method is better than the other. Instead, different research types are better for different projects.

You should choose experimental research if you want to test a hypothesis as accurately as possible. This type of research is good for testing new products or services because you can control the variables directly.

You should choose non-experimental research if you want to observe naturally occurring factors. This type of research is good for projects where you can’t experimentally manipulate the variables or where it’s difficult to do so.

Non-Experimental Research: Designs, Characteristics, Types and Examples

The non-experimental research is one in which the variables of the study are not controlled or manipulated. To develop the research, the authors observe the phenomena to be studied in their natural environment, obtaining the data directly to analyze them later.

The difference between non-experimental and experimental research is that variables are manipulated in the latter and the study is carried out in controlled environments. So, for example, you experience gravity by intentionally dropping a stone from several heights.

On the other hand, in non-experimental research, researchers go, if necessary, to the place where the phenomenon to be studied happens. For example, to know the drinking habits of young people, surveys are conducted or observed directly as they do, but no drink is offered.

This type of research is very common in fields such as psychology, the measurement of unemployment rates, consumption studies or opinion polls. In general, these are pre-existing facts, developed under their own laws or internal rules

1.1 Differences with experimental designs
2 characteristics
3.1 Transverse or transectional design
3.2 Longitudinal design
4.1 Effects of alcohol
4.2 Opinion polls
4.3 School performance
5 References

Non-experimental research designs

As opposed to what happens with experimental research, in the non-experimental the variables studied are not deliberately manipulated. The way to proceed is to observe the phenomena to be analyzed as they are presented in their natural context.

In this way, there are no stimuli or conditions for the subjects that are being studied. These are found in their natural environment, without being transferred to any laboratory or controlled environment.

The existing variables are of two different types. The first are the independent calls, while the so-called dependents are a direct consequence of the previous ones. In this type of research, the relationships between causes and effects are investigated in order to draw valid conclusions.

Given that no exprofeso situations are created to investigate them, it can be affirmed that the non-experimental designs study the already existing situations developed under their own internal rules. In fact, another denomination that is given is that of investigations ex post facto ; that is, about facts fulfilled.

Differences with experimental designs

The main difference between both types of research is that in the experimental designs there is a manipulation of the variables by the researcher. Once the desired conditions have been created, the studies measure the effects of them.

For its part, in non-experimental investigations this manipulation does not exist, but rather the data is collected directly in the environment in which the events take place.

It can not be said that one method is better than the other. Each one is equally valid depending on what is going to be studied and / or on the perspective that the researcher wants to give to his work.

By its own characteristics, if the research is experimental it will be much easier to repeat it to ensure the results. However, the control of the environment makes some variables that may appear spontaneously more difficult to measure. It is just the opposite of what happens with non-experimental designs.

characteristics

As previously mentioned, the first characteristic of this type of research is that there is no manipulation of the variables studied.

Normally, these are phenomena that have already occurred and are analyzed a posteriori. Apart from this characteristic, other peculiarities present in these designs can be pointed out:

- Non-experimental research is widely used when, for ethical reasons (such as giving drink to young people), there is no option to conduct controlled experiments.

- No groups are formed to study them, but these are already pre-existing in their natural environments.

-The data is collected directly, and then analyzed and interpreted. There is no direct intervention on the phenomenon.

- It is very common that non-experimental designs are used in applied research, since they study the facts as they occur naturally.

- Given the characteristics presented, this type of research is not valid to establish unequivocal causal relationships.

Transverse or transectional design

This type of non-experimental research design is used to observe and record the data at a specific time and, by its very nature, unique. In this way, the analysis is focused on the effects of a phenomenon that occurs at a particular time.

As an example, we can mention the study of the consequences of an earthquake on the housing in a city or the school failure rates in a given year. You can also take more than one variable, turning the study into a more complex one.

The transversal design allows to cover diverse groups of individuals, objects or phenomena. At the time of developing them, they can be divided into two different groups:

Descriptive

The objective is to investigate those incidents and their values, in which one or more variables appear. Once the data is obtained, a description of them is simply made.

In these designs, we try to establish the relationships between several variables that have occurred at a given moment. These variables are not described one by one, but rather they try to explain how they are related.

Longitudinal design

Contrary to what happens with the previous design, in the longitudinal the researchers intend to analyze the changes suffered by certain variables over time. You can also investigate how the relationships between these variables evolve during this period.

To achieve this goal it is necessary to collect data at different time points. There are three types within this design:

They study the changes that happen in some population in general.

Of group evolution

The subjects studied are smaller groups or subgroups.

Similar to the previous ones but with specific groups that are measured at all times. These investigations are useful to analyze the individual changes together with the group, allowing to know what element has produced the changes in question.

In general terms, these designs are prepared for the study of events that have already happened and, therefore, it is impossible to control the variables. They are very frequent in statistical fields of all kinds, both to measure the incidence of some factors and for opinion studies.

Effects of alcohol

A classic example of non-experimental research is studies on the effects of alcohol on the human body. Since it is not ethical to give drink to the subjects studied, these designs are used to obtain results.

The way to achieve this would be to go to the places where alcohol is habitually consumed. There is measured the degree that this substance reaches in blood (or you can take data from the police or a hospital). With this information, we will proceed to compare the different individual reactions, extracting the conclusions about it.

Opinion polls

Any survey that tries to measure the opinion of a certain group on a topic is done through non-experimental designs. For example, electoral polls are very common in most countries.

School performance

It would only be necessary to collect the statistics of the results of the school children offered by the schools themselves. If, in addition, you want to complete the study, you can search for information on the socioeconomic status of the students.

Analyzing each data and relating them to each other, a study is obtained about how the socioeconomic level of families affects the performance of school children.

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Mathematics > Analysis of PDEs

Title: existence results for problems involving non local operator with an asymmetric weight and with a critical nonlinearity.

Abstract: Recently, a great attention has been focused on the study of fractional and non-local operators of elliptic type, both for the pure mathematical research and in view of concrete real-world applications. We consider the following non local problem on $\mathbb{H}_0^s(\Omega) \subset L^{q_s}(\Omega)$, with $q_s :=\frac{2n}{n-2s}$, $s\in ]0, 1[$ and $n\geq 3$ \begin{equation}\int_{\mathbb{R}^n}p(x) \bigg(\int_{\mathbb{R}^n}\frac{|u(x)-u(y)|^2}{|x-y|^{n+2s}}dy\bigg)dx-\lambda \int_\Omega |u(x)|^q dx, \qquad(1)\end{equation} where $\Omega$ is a bounded domain in $\mathbb{R}^n, p :\mathbb{R}^n \to \mathbb{R}$ is a given positive weight presenting a global minimum $p_0 >0$ at $a \in \Omega$ and $\lambda$ is a real constant. In this work we show that for $q=2$ the infimum of (1) over the set $\{u\in \mathbb{H}_0^s(\Omega), ||u||_{L^{q_s}(\Omega)}=1\}$ does exist for some $k, s, \lambda$ and $n$ and for $q\geq 2$ we study non ground state solutions using the Mountain Pass Theorem.

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6.7: Non-Experimental Research (Summary)

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Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton

Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton
Kwantlen Polytechnic U., Washington State U., & Texas A&M U.—Texarkana

Key Takeaways

Non-experimental research is research that lacks the manipulation of an independent variable.
There are two broad types of non-experimental research. Correlational research that focuses on statistical relationships between variables that are measured but not manipulated; and observational research in which participants are observed and their behavior is recorded without the researcher interfering or manipulating any variables.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.
Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
Correlation does not imply causation. A statistical relationship between two variables, X and Y , does not necessarily mean that X causes Y . It is also possible that Y causes X , or that a third variable, Z , causes both X and Y .
While correlational research cannot be used to establish causal relationships between variables, correlational research does allow researchers to achieve many other important objectives (establishing reliability and validity, providing converging evidence, describing relationships, and making predictions)
Correlation coefficients can range from -1 to +1. The sign indicates the direction of the relationship between the variables and the numerical value indicates the strength of the relationship.
Researchers often use complex correlational research to explore relationships among several variables in the same study.
Complex correlational research can be used to explore possible causal relationships among variables using techniques such as partial correlation and multiple regression. Such designs can show patterns of relationships that are consistent with some causal interpretations and inconsistent with others, but they cannot unambiguously establish that one variable causes another.
Qualitative research is an important alternative to quantitative research in psychology. It generally involves asking broader research questions, collecting more detailed data (e.g., interviews), and using non-statistical analyses.
Many researchers conceptualize quantitative and qualitative research as complementary and advocate combining them. For example, qualitative research can be used to generate hypotheses and quantitative research to test them.
There are several different approaches to observational research including naturalistic observation, participant observation, structured observation, case studies, and archival research.
Naturalistic observation is used to observe people in their natural setting; participant observation involves becoming an active member of the group being observed; structured observation involves coding a small number of behaviors in a quantitative manner; case studies are typically used to collect in-depth information on a single individual; and archival research involves analyzing existing data.

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A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.
People who eat more lobster tend to live longer.
People who exercise more tend to weigh less.
College students who drink more alcohol tend to have poorer grades.
Practice: Construct a correlation matrix for a hypothetical study including the variables of depression, anxiety, self-esteem, and happiness. Include the Pearson’s r values that you would expect.
Discussion: Imagine a correlational study that looks at intelligence, the need for cognition, and high school students’ performance in a critical thinking course. A multiple regression analysis shows that intelligence is not related to performance in the class but that the need for cognition is. Explain what this study has shown in terms of what is related to good performance in the critical thinking course.
Discussion: What are some ways in which a qualitative study of girls who play youth baseball would likely differ from a quantitative study on the same topic? How would the data differ by interviewing girls one-on-one rather than conducting focus groups or surveys?
Describe one problem related to internal validity.
Describe one problem related to external validity.
Generate one hypothesis suggested by the case study that might be interesting to test in a subsequent study.

Open access
Published: 15 April 2023

Understanding the roles and regulation patterns of circRNA on its host gene in tumorigenesis and tumor progression

Jianxia Wei 1 , 2 , 3 ,
Mengna Li 1 , 2 , 3 ,
Changning Xue 1 , 2 , 3 ,
Shipeng Chen 1 , 2 , 3 ,
Lemei Zheng 1 , 2 , 3 ,
Hongyu Deng 1 , 2 ,
Faqing Tang 1 ,
Guiyuan Li 1 , 2 , 3 ,
Wei Xiong 1 , 2 , 3 ,
Zhaoyang Zeng 1 , 2 , 3 &
Ming Zhou ORCID: orcid.org/0000-0001-5288-7430 1 , 2 , 3

Journal of Experimental & Clinical Cancer Research volume 42 , Article number: 86 ( 2023 ) Cite this article

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Circular RNAs (circRNAs) are a novel type of endogenous non-coding RNAs, which are covalently closed loop structures formed by precursor mRNAs (pre-mRNAs) through back-splicing. CircRNAs are abnormally expressed in many tumors, and play critical roles in a variety of tumors as oncogenes or tumor suppressor genes by sponging miRNAs, regulating alternative splicing and transcription, cis-regulating host genes, interacting with RNA binding proteins (RBPs) or encoding polypeptides. Among them, the regulation of circRNAs on their corresponding host genes is a critical way for circRNAs to exit their functions. Accumulating evidence suggests that circRNAs are able to regulate the expression of host genes at the transcriptional level, post-transcriptional level, translational level, post-translational level, or by encoding polypeptides. Therefore, this paper mainly summarized the roles and association of circRNAs and their corresponding host genes in tumorigenesis and tumor progression, generalized the circRNAs that function synergistically or antagonistically with their host genes, and elaborated the mechanisms of mutual regulation between circRNAs and their host genes. More importantly, this review provides specific references for revealing the potential application of circRNAs combined with their host genes in tumor diagnosis, treatment and prognosis.

Increasing evidence on non-coding RNAs (ncRNAs) has revealed their critical roles in tumorigenesis [ 1 ]. Circular RNAs are a novel type of non-coding RNAs, which are covalently closed loop structures formed by back-splicing through different mechanisms [ 2 , 3 ]. Most human exonic circRNAs are less than 1500 nt in length, with a median length of around 500 nt [ 4 ]. In recent years, circular RNAs have become a new hotspot in the field of non-coding RNAs. With the development and improvement of deep sequencing and bioinformatics methods, the biogenesis and function of circRNAs have been widely studied. More importantly, clinical data showed that the expression of circRNAs was different in a variety of diseases, including tumors, suggesting that circRNAs have regulatory roles in carcinogenesis and tumor progression [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Additionally, the functions and mechanisms of circRNAs involved in different tumors may be rather diverse. Increasing evidence shows that circRNAs exert their oncogenic or tumor suppressor roles by acting as miRNA sponges, binding to RNA-binding proteins (RBPs), regulating alternative splicing or transcription, encoding peptides, regulating the expression of host genes, or acting as exosomal circRNAs [ 12 , 13 ]. Among them, the regulation of circRNAs on their corresponding host genes is a critical mechanism for their function. Increasing studies have clarified that circRNAs can participate in tumor progression by positively or negatively regulating the expression and function of their host genes. Moreover, circRNAs are highly stable compared with linear RNAs, resistant to RNase R, and have tissue and cell specificity and high abundance, so circRNAs can be detected in human body fluids such as plasma and saliva [ 14 , 15 ]. Therefore, targeting circRNAs and their host genes might be novel strategies for early diagnosis, effective treatment and prognostic evaluation of tumors [ 16 , 17 , 18 , 19 ].

In this review, we summarized the roles and association of circRNAs and their host genes in tumorigenesis and tumor progression, generalized the circRNAs that function synergistically or antagonistically with their host genes, and elaborated the mechanisms of mutual regulation between circRNAs and their host genes. We also discussed the clinical potential of circRNAs combined with their host genes in tumor diagnosis, treatment and prognosis as biomarkers and therapeutic targets.

Functional relationship between circRNAs and their host genes

Circrnas that are functionally consistent with their host genes.

Circular RNAs, which are derived from their corresponding host genes [ 16 , 17 , 18 , 19 , 20 ], have been proved to be important regulators of human tumors. In addition, according to previous studies, most circRNAs have been found to have the same functions as their host genes and play synergistic roles in tumors, of which some of them are highly expressed in tumor tissues and can promote the expression of the host genes through a variety of mechanisms, thus to promote tumor proliferation, migration, invasion, stemness, drug resistance and radiation resistance of tumor cells as oncogenes, such as circ-EGFR [ 21 ], circ-ENO1 [ 22 ] and circ-Amotl1 [ 23 , 24 ]. While other of them are lowly expressed in tumor tissues, which can continuously activate their host genes and inhibit the malignant phenotype of tumors as tumor suppressors, such as circ-Foxo3 [ 16 , 25 ], circ-ITCH [ 26 , 27 , 28 ] and circ-FBXW7 [ 29 , 30 ]. All of the circRNAs that function synergistically with their host genes in tumor tissues or cells were generalized based on their expression patterns and functions, as shown in Table 1 [ 16 , 17 , 18 , 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. As circRNA is derived from its host gene, the host gene always promotes the formation and expression of its circRNA, therefore, the circRNA/host gene regulation axis could form a positive feedback loop to synergistically play a critical role in tumorigenesis and tumor progression.

CircRNAs with opposite functions to their host genes

As indicated in the above that most circRNAs have the same expression patterns as their host genes, however, some studies have found that the expression patterns of a few of circRNAs are opposite to that of their host genes, proving that some circRNAs have different functions from the linear products encoded by their host genes [ 6 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. For example, circGSK3β was upregulated in the tumor tissues compared to the normal tissues, which was confirmed to act as an oncogene to promote cell migration, invasion and EMT by inhibiting GSK3β/β-catenin signaling axis activity in esophageal squamous cell carcinoma (ESCC), while its host gene GSK3β presented low expression and tumor suppressor role in ESCC [ 80 ]. Meanwhile, Circ-Ccnb1 was found to bind to both Ccnb1 and Cdk1 proteins to dissociate the formation of the Ccnb1-Cdk1 complex and inhibit the tumor-promoting function of its host gene Ccnb1 by forming a large complex containing circ-Ccnb1, Ccnb1 and Cdk1, thereby inhibiting breast cancer cell proliferation, migration, invasion and tumor growth in vivo [ 83 ]. All of the circRNAs that function oppositely to their host genes in tumors were generalized based on their expression patterns and functions, as shown in Table 2 . As the expression patterns of these circRNAs are different from that of their host genes, they antagonize the functions of their host genes and play negative feedback roles. Thus, circRNAs and their host genes constitute complex regulatory mechanisms and action networks of organisms [ 89 ].

Mechanisms of circRNAs regulating host genes

Regulation at the transcriptional level.

Promoter regions are the most widely studied specific regions in transcriptional regulation [ 90 , 91 ]. CircRNAs have been reported to positively or negatively regulate the transcription of their host genes by binding to RNA polymerase II (Pol II) [ 92 , 93 ], recruiting proteins [ 34 , 35 , 36 , 82 ], or by forming an R-loop [ 87 , 94 , 95 ] to target the transcriptional regulatory regions of their host genes (Fig. 1 ).

CircRNAs regulate their host genes expression at the transcriptional level. A CircRNAs can be classified into three subtypes: exonic circRNAs (ecircRNAs), intronic circRNAs (ciRNAs), and exon-intron circRNAs (EIciRNAs). Some ciRNAs and EIciRNAs can promote the transcriptional activity of their host genes by binding to RNA polymerase II (Pol II). B CircRNAs can function as protein decoys, scaffolds or recruiters to promote or inhibit their host genes expression. C Some circRNAs, such as circSMARCA5, can increase the cleavage efficiency of homologous exon-defective mRNA by forming R-loops, which in turn terminated transcription, and affect their host genes expression

Promoting the transcriptional activity of their host genes by binding to poly II

According to the source and location of the circRNAs sequences, the currently discovered circRNAs can be classified into three subtypes, exonic circRNAs (ecircRNAs), intronic circRNAs (ciRNAs) and exon-intron circRNAs (EIciRNAs) [ 1 , 96 ]. Detailed studies have clarified that some ciRNAs are mainly distributed in the nucleus and interact with RNA Pol II to regulate the transcriptional activity of host genes in a cis-acting manner. Ci-ankrd52, ci-mcm5 and ci-sirt7 have been reported to be mainly enriched in the transcription sites of their host genes, which are related to the transcription extension mediated by RNA Pol II and act as positive transcription regulators of host genes to enhance the expression of host genes [ 92 ]. Exon-intron circRNAs, which are formed by cyclization of RNAs with intron retention, are also enriched in the nucleus and associated with Pol II in human cells [ 93 , 97 , 98 ]. It was found that circEIF3J and circPAIP2, two exon-intron circRNAs, were able to interact with RNA polymerase II, U1 snRNP and host gene promoters to enhance the transcription of their host genes in a cis-acting manner by forming a positive feedback loop, and the deletion of these circRNAs reduced the transcription level of the corresponding EIF3J or PAIP2 host genes [ 93 ] (Fig. 1 A).

Regulating the expression of their host genes by recruiting proteins

Many studies have reported that there are highly specific RNA-binding protein binding sites on circRNAs, therefore circRNAs can function as protein decoys, scaffolds and recruiters to recruit single or multiple proteins to the specific regions of the target promoter, thereby regulating transcription activation and expression of the host genes, which may also be an important mechanism for circRNAs participating in tumor progression [ 34 , 35 , 36 , 82 ]. So far, these protein types have been found to include RNA-binding proteins (RBPs), DNA demethylase and DNA methyltransferase.

Some circRNAs were confirmed to transcriptionally activate the expression of their host genes and downstream target genes by recruiting proteins, thus promoting or inhibiting tumor progression (Fig. 1 B). For example, Feng et al. determined that circ0005276 is a new circRNA formed by back-splicing of its host gene XIAP, which could recruit the RNA-binding protein FUS to the promoter region of the host gene XIAP and transcriptionally activate the expression of XIAP, thus promoting the occurrence and development of prostate cancer (PCa) [ 34 ]. Li et al. found that circ-CUX1, encoded by CUX1, is highly expressed in neuroblastoma and could bind to EWS RNA-binding protein 1 (EWSR1), thus promoting the interaction between EWSR1 and MYC-associated zinc finger protein (MAZ), leading to transactivation of MAZ and transcriptional alterations of its host gene CUX1 and other genes associated with tumor progression, thus promoting aerobic glycolysis and malignant progression of neuroblastoma [ 36 ]. In addition to the above two circRNAs, FECR1, a circRNA identified in the FLI1 promoter chromatin complex, was found to induce DNA demethylation by recruiting TET1 demethylase to bind to the promoter region of its host gene FLI1. Moreover, FECR1 also bound and downregulated DNA methyltransferase DNMT1, activating FLI1 transcription by inducing DNA hypomethylation of the promoter CpG islands, thereby promoting the invasion ability of breast cancer cells [ 35 ].

In addition to recruiting proteins to promote the transcription of host genes, some circRNAs can also inhibit the transcription of their host genes by sponging and binding RNA-binding proteins, thereby inhibiting the occurrence and progression of tumors. For example, circ-HUR was found to be down-regulated in gastric cancer tissues and cell lines, and interacted with the RGG domain of CCHC-type zinc finger nucleic acid binding protein (CNBP) to restrain its binding to the HuR promoter, thereby inhibiting the transcription of HuR, resulting in the down-regulation of its host gene HuR and repression of gastric cancer growth and aggressiveness in vitro and in vivo [ 82 ]. In summary, circRNAs can transcriptionally activate or inhibit the expression of their host genes by recruiting proteins, which is a critical mechanism of circRNAs involved tumorigenesis and tumor progression.

Regulating the expression of their host genes by forming an R-loop

R-loops are specialized chromatin structures, consisting of an RNA-DNA hybrid and a displaced single-stranded DNA, which are usually generated by RNA polymerase pause or RNA biogenesis dysfunction [ 99 , 100 ]. R-loops have been shown to play critical roles in genome stabilization, and in general, R-loops may interfere with DNA replication, repair and transcription [ 101 ]. Recent studies show that circRNAs can increase the cleavage efficiency of homologous exon-defective mRNA by forming DNA hybrids or R-loops, which not only affects linear transcript abundance but also provides an mRNA trap to suspend transcription and improve splicing factors, which is also a critical mechanism of circRNAs to regulate host genes [ 94 , 95 ]. So far, only circSMARCA5 has been found to regulate host gene expression through R-loop formation during tumor development. For example, Xu et al. found that circSMARCA5 was recruited to its host gene SMARCA5 locus to form an R-loop, which in turn terminated transcription, produced a truncated nonfunctional ΔSMARCA5 protein, and reduced the expression of SMARCA5 in breast cancer [ 87 ]. As SMARCA5 is a member of the SWI/SNF chromatin remodeling complex, which can be recruited to DNA damage sites during the process of DNA damage repair to induce the ubiquitination and phosphorylation of histone H2A, and promote chromatin remodeling and DNA damage repair [ 102 ]. Therefore, circSMARCA5 inhibits the expression of its host gene by forming an R-loop, which leads to a decrease of the DNA damage repair ability of its host gene and an improvement of the sensitivity of breast cancer cells to cytotoxic drugs, thus providing evidence that circSMARCA5 may be a therapeutic target for drug-resistant breast cancer patients (Fig. 1 C). We believe that the regulation of host genes expression by circRNAs through R-loops formation will play a critical role in deciphers the mechanisms of tumorigenesis and progression in the future.

Regulation at the post-transcriptional level

Microrna sponges.

Competitive endogenous RNAs (ceRNAs) are transcripts that can regulate target genes at the post-transcriptional level through competitively binding to the shared miRNAs [ 103 ], which is also an essential way for circRNAs to participate in post-transcriptional regulation of target genes [ 104 , 105 , 106 ]. Several studies have demonstrated that circRNAs can bind to miRNAs as ceRNAs [ 26 , 104 , 105 , 106 , 107 , 108 ], since miRNAs have an inhibitory effect on their target genes, the sponges and binding of miRNAs by circRNAs will lead to the upregulation of miRNA target genes, increase the expression of protein-coding genes, and then participate in the regulation of specific cellular pathways. Therefore, circRNAs may promote or inhibit tumor progression by indirectly regulating mRNA translation [ 109 , 110 , 111 ].

Previous studies have suggested that circRNAs and their host genes contain one or more of the same microRNAs binding sites [ 25 , 44 , 78 , 79 ]. Therefore, circRNAs can remove the inhibitory effect of microRNAs on their host genes by binding the shared microRNAs, and then participating in tumor growth and metastasis (Fig. 2 A). For example, Li and others have shown that circ-ITCH shared the same miRNA binding sites with the 3’-untranslated region (3’-UTR) of the transcript from its host gene ITCH, and that circ-ITCH increased the expression of its host gene ITCH by sponging several miRNAs including miR-7, miR-17, and miR-214, thus inhibiting the Wnt/β-catenin pathway and the proliferation of esophageal squamous cell carcinoma cells and tumor growth in vivo by promoting ubiquitin-mediated Dvl2 degradation and decreasing the expression of oncogene c-Myc [ 26 ]. Circ-ENO1, also acting as a ceRNA, interacted with miR-22-3p to upregulate the expression of its host gene ENO1, and promoted glycolysis and tumor progression in lung adenocarcinoma (LUAD) [ 22 ]. Liu et al. found and confirmed that circ_MMP2 functions as a ceRNA to sequester miR-136-5p, and then positively regulated the expression of its host gene MMP2, which is transmitted to living cells in adjacent tissues through secreted exosomes, ultimately promoting the metastasis of hepatocellular carcinoma (HCC) [ 32 ]. In addition to the circRNAs mentioned above, many circRNAs as shown in Tables 1 and 2 can also establish circRNA-miRNA-host gene networks to participate in tumorigenesis and tumor progression in different tumors. In addition, a large number of circRNAs have been reported to regulate the expression of non-parental target genes by acting as ceRNAs and participate in the occurrence and development of tumors [ 104 , 105 ], such as circEZH2/miR-133b/IGF2BP2/CREB1 [ 112 ], circBCAR3/miR-27a-3p/TNPO1 [ 113 ], which is also a critical mechanism of circRNAs involved in the cancer development.

CircRNAs regulate the post-transcriptional modification of their host genes. A CircRNAs act as competing endogenous RNAs (ceRNAs) to relieve the adsorption of miRNAs on host genes and indirectly regulate the expression of their host genes. B CircRNAs, such as circ_0004296, function as protein sponges or decoys to regulate host gene expression and participate in the change of tumor malignant phenotype through post-transcriptional regulation. C CircRNAs enhance the stability of the host gene mRNA or induce the instability of the mRNA by directly interacting with the host gene mRNA or binding to RNA-binding proteins

Protein sponges

RNA-binding proteins also play a key role in post-transcriptional regulatory processes associated with different biological activities [ 114 ], and increasing evidence shows that circRNAs can act as protein sponges or decoys to participate in tumorigenesis and tumor progression through post-transcriptional regulation by binding to RNA-binding proteins to form a complex [ 88 , 115 ] (Fig. 2 B). Mao et al. found that the expression of circ_0004296, which is derived from back-splicing of exons 4, 5, 6, and 7 of host gene ETS1, was decreased in prostate cancer tissue, blood and urine. In addition, circ_0004296 was identified to be mainly distributed in the nucleus and interacted with the RNA-binding protein EIF4A3 to promote the retention of EIF4A3 in the nucleus and effectively inhibit the nuclear export of its host gene ETS1 mRNA, leading to the downregulation of ETS1 expression, thereby significantly suppressing proliferation, migration, invasion and EMT of prostate cancer (PCa) cells [ 88 ]. Altogether, circRNAs act as protein sponges to regulate the binding between proteins and nucleic acids and thus achieve certain biological functions.

mRNA stability

CircRNAs also can enhance the stability of the host genes mRNAs or induce the instability of the mRNAs by binding to RNA-binding proteins or directly interacting with the host genes mRNAs (Fig. 2 C). It has been reported that circular RNA ciRS-7/CDR1-AS enhances the expression of the host gene CDR1 by directly interacting with the host gene to stabilize the mRNA of CDR1 [ 116 ]. In addition to circRNAs that can directly bind to the mRNAs of the host genes, studies have found that a variety of circRNAs, such as hsa_circ_0062270 [ 55 ], circE2F3 [ 40 ] and circDNMT1 [ 33 ], can regulate the stability of host gene mRNA by interacting with RNA-binding proteins, thereby participating in the tumorigenesis and development of tumors. For example, the study demonstrated that hsa_circ_0062270 was significantly upregulated in melanoma cells and could interact with RNA-binding protein EIF4A3 to positively regulate the expression of CDC45 by enhancing the stability of its host gene CDC45 mRNA, thereby promoting the proliferation, invasion and inhibiting the apoptosis of melanoma cells [ 55 ]. The study by Zhao et al. reported that circ_0075804 was upregulated in retinoblastoma (RB), which improved the stability of its host gene E2F3 mRNA and promoted the proliferation of RB by binding to the nucleic acid binding protein heterogeneous nuclear ribonucleoprotein K (HNRNPK) [ 40 ]. Circ-DNMT1 was reported to interact with both p53 and AUF1 (AU-rich element-binding factor 1) and promote the nuclear translocation of both proteins, and nuclear translocation of p53 induced autophagy, while nuclear translocation of AUF1 increased the stability of DNMT1 mRNA, leading to an increased translation of DNMT1, which ultimately increases the proliferation of breast cancer cells by stimulating cellular autophagy [ 33 ]. Taken together, circRNAs can regulate the expression of their host genes by promoting or inhibiting mRNA stability.

Regulating the translation process of their host genes

The translation of messenger RNA into protein and the folding of the resulting protein into an active form is one of the most complex processes in the cell. The complex nature of this process makes it susceptible to deregulation at multiple levels. Studies have shown that circRNAs can regulate the translation process of host genes by binding to translation initiation-related proteins, thus increasing or decreasing protein synthesis, which in turn leads to tumorigenesis or progression (Fig. 3 ). YAP is a key component of the Hippo pathway [ 117 , 118 ]. Inhibition of YAP activity could promote apoptosis, and inhibit proliferation and metastasis of tumor cells, suggesting that YAP as an important oncoprotein participates in the occurrence and development of tumors [ 119 , 120 , 121 ]. Wu et al. showed that circYAP was downregulated in breast cancer cells, which played a tumor suppressor role and significantly reduced YAP protein levels but had no effect on its mRNA levels. CircYAP was further found to bind with YAP mRNA and translation initiation related proteins eIF4G and PABP (poly(A) binding protein), which abolished the interaction of PABP on the poly(A) tail and eIF4G on the 5’-cap of the YAP mRNA translation initiation complex, and thus circYAP functions as a tumor suppressor gene by functionally inhibiting the translation initiation process of its host gene YAP [ 84 ] (Fig. 3 A).

CircRNAs regulate the translation process of their host genes. A Some circRNAs, such as circYap, regulate the translation process of host genes by binding to translation initiation related proteins, increasing or decreasing protein synthesis. B Some circRNAs, such as circPABPN1, act as translation inhibitors or activators to regulate the binding of RNA-binding proteins to the mRNA of host genes, inhibit or promote the translation process of host genes, and affect the synthesis of proteins

On the other hand, circRNAs can act as translation inhibitors or activators to regulate the binding of RNA-binding proteins to the mRNA of their host genes, thus inhibiting or promoting the translation process of their host genes. Abdelmohsen and colleagues [ 85 ] found that circPABPN1 was a circRNA derived from its host gene PABPN1, and PABPN1 was confirmed to be the target of HuR, which positively regulates PABPN1 protein translation by binding to PABPN1 mRNA. Furthermore, circPABPN1 inhibited the binding of HuR to PABPN1 mRNA, and therefore circPABPN1 reduced the translation of its host gene PABPN1 mRNA by competing with the translation activator (HuR), thus leading to metabolism disorders and tumorigenesis [ 85 ]. Therefore, we summarized the pathogenesis of circRNAs involvement in tumorigenesis by affecting translation dysregulation of host genes and described how translation dysregulation generates the phenotypic variability observed in tumors (Fig. 3 B).

Regulating the post-translational modification of their host genes

Post-translational modifications are essential for protein activity and degradation, such as acetylation, ubiquitination or deubiquitination and phosphorylation [ 122 , 123 ]. Some studies have shown that circRNAs may regulate the activity and degradation of parental proteins by directly interacting with them or by recruiting proteins to regulate the post-translational modifications of parental proteins (Fig. 4 ). For example, Foxo3 gene is downregulated in many tumors and is considered as a tumor suppressor [ 124 ]. CircFoxo3 is a circular RNA spliced from Foxo3. Previous studies have shown that MDM2 can poly-ubiquitinate p53 and Foxo3 and down-regulate their expression in a proteasome-dependent manner [ 125 ]. Therefore, MDM2 plays a vital role in inhibiting apoptosis by inhibiting p53, Foxo3 and their downstream molecule Puma. Du et al. showed that circFoxo3 may interact with both p53 and MDM2 to promote MDM2-induced p53 ubiquitination and subsequent degradation in breast cancer, and avoid MDM2-induced Foxo3 ubiquitination and degradation [ 126 ]. Therefore, circFoxo3 promoted the expression of Foxo3 protein as well as its downstream target PUMA, thus inducing cell apoptosis [ 126 ] (Fig. 4 A). At present, multiple myeloma (MM) is still an incurable disease, and revealing its pathogenesis will provide new targets for clinical diagnosis and treatment. Circ-MYBL2 was reported to be downregulated in multiple myeloma tissues, which could inhibit the phosphorylation and activation of its host gene encoding protein MYBL2 by promoting the binding of Cyclin F to MYBL2, thereby inhibiting the transcription of some critical proliferation-related oncogenes, and playing a tumor suppressor role [ 86 ] (Fig. 4 B). Whether circRNAs can regulate other post-translational modifications in addition to the ubiquitination and phosphorylation of the parental proteins to regulate the expression of the host genes remains to be further explored.

CircRNAs regulate post-translational modification of their host genes. A Some circRNAs, such as circFoxo3, regulate the activity and degradation of host proteins by recruiting proteins to regulate the ubiquitination of host proteins. B CircRNAs, such as circ-MYBL2, regulate the activity and degradation of host proteins by directly interacting with them to regulate the phosphorylation of host proteins

Regulating the expression of their host genes by encoding polypeptides

For a long time, circRNAs have been thought to be directly involved in various biological processes as non-coding RNAs. In recent years, a variety of circRNAs have been found to have translation functions, and their encoded peptides have different functions similar to or opposite to the parental proteins, and also play biological roles in the occurrence and progression of tumors. Previous studies have demonstrated that the proteins encoded by circRNAs may regulate the stability of the host gene mRNA or host proteins at the post-transcriptional and post-translational levels (Fig. 5 ).

CircRNAs regulate their host genes expression by encoding polypeptides. A Small peptides encoded by circRNAs regulate the expression of host genes at the post-transcriptional level, thus participating in the malignant phenotype of tumors. B Small peptides encoded by circRNAs regulate endocytosis and degradation, cholesterol modification, ubiquitination and deubiquitination of host proteins at the post-translational level to regulate the stability of host proteins

Several studies have confirmed that small peptides encoded by circRNAs can regulate the expression of host genes at the post-transcriptional level, and then participate in the malignant phenotype of tumors. For example, Zhang et al. revealed that the hsa_circ_0006401 generated from col6a3 that contains an open reading frame (ORF) and encodes a novel 198-aa functional peptide, and the encoded hsa_circ_0006401 peptides could promote the stability of the host gene col6a3 mRNA at the post-transcriptional level, thereby promoting colorectal cancer (CRC) proliferation and metastasis [ 50 ] (Fig. 5 A).

Regulation at the post-translational level

In addition to the regulation at the post-transcriptional level, more and more studies have shown that proteins encoded by circRNAs can also regulate endocytosis and degradation, cholesterol modification, ubiquitination and deubiquitination of host proteins at the post-translational level, thus to regulate the stability of host proteins, and then participate in tumorigenesis and progression (Fig. 5 B). Liu et al. found that circ-EGFR can encode a polymetric novel protein complex, called rolling-translated EGFR (rtEGFR). When rtEGFR co-localized with EGFR on the cell membrane, rtEGFR directly interacted with EGFR to maintain EGFR stability and membrane localization, and attenuated EGFR endocytosis and degradation. Therefore, abnormal activation of the EGFR signaling pathway promoted the malignant progression of glioblastoma (GBM) [ 21 ].

In addition, some circRNAs can regulate cholesterol modification of proteins encoded by their host genes at the post-translational level. SMO-193a.a is a nascent protein with 193 amino acids generated from circSMO (hsa_circ_0001742), which is crucial for the Hedgehog (HH) signaling pathway [ 127 ]. Cholesterol modification is essential for full-length smoothened (SMO) activation, while PTCH1 in the HH signaling pathway can block SMO cholesterol modification [ 128 , 129 ]. The authors further found that SMO-193a.a directly binds to the N-terminal of SMO, acts as a scaffold to transport cholesterol to full-length SMO, promotes cholesterol modification of full-length SMO, and releases SMO by inhibiting PTCH1, functionally maintaining the self-renewal ability of cancer stem cells and the tumorigenicity of GBM [ 127 ].

Another typical function of circRNAs is that they can regulate the ubiquitination and deubiquitination of their host genes encoding proteins at the post-translational level [ 29 , 37 , 68 ]. For example, Zhang et al. found that circ-SHPRH translated a new protein of 146-aa by overlapping genetic codes in glioblastoma. Both SHPRH and SHPRH-146aa can be used as ubiquitin targets of DTL, and SHPRH-146aa has a strong affinity. Mechanistically, SHPRH-146aa acts as a decoy to competitively bind DTL to protect the host SHPRH from degradation by the ubiquitin-proteasome [ 68 ]. Stabilized SHPRH, as an E3 ligase, ubiquitinates proliferating cell nuclear antigen (PCNA) [ 130 , 131 ], thereby inhibiting cell proliferation and tumorigenicity [ 68 ]. Yang et al. reported that FBXW7-185aa is a new protein with 185 amino acids encoded by circ-FBXW7 [ 29 ]. So far, three FBXW7 isoforms, FBXW7a, b and c, have been reported, and the N-terminus of these isoforms is capable of being driven by the isoform-specific promoter [ 132 ]. The deubiquitination enzyme USP28 reportedly binds to the N-terminus of FBXW7a for deubiquitinating degradation, and then induces c-Myc to promote the development of GBM [ 29 , 133 ]. Although FBXW-185aa is shorter than the above three subtypes, it has a stronger affinity to USP28 and binds to USP28 as a decoy, thereby inhibiting the proliferation of glioblastoma by releasing FBXW7a and reducing the half-life of c-Myc [ 132 ]. In addition to the two circRNAs mentioned above, the protein encoded by circβ-catenin, β-catenin-370aa, also promoted the growth of HCC cells by ubiquitination modification of its parental protein [ 37 ].

In summary, the discovery of these circRNAs and their encoded peptides enriches genomics and helps us to study the causes of tumorigenesis. The complex regulatory networks between the circRNAs encoded peptides and their host genes provide a new direction for the discovery of biomarkers for tumor diagnosis, prognosis and therapeutic targets.

Regulatory network of circRNAs and their host genes

Diversity of host genes regulation by circrnas.

In recent years, circRNAs have been reported to play dual functions in different types of tumors through different mechanisms, among which the regulation of circRNAs on their host genes is an important mechanism for participating in tumorigenesis and tumor progression [ 16 , 25 , 68 , 69 , 134 ]. For example, circ-Foxo3 functions as a tumor suppressor gene by positively regulating the expression of its host gene Foxo3 in breast cancer and non-small cell lung cancer [ 16 , 25 ], however, it functions as an oncogene through a circ-Foxo3-miR-143-3p-USP44 axis independent of its host gene in gastric carcinoma [ 134 ]. Moreover, in recent years, the same circRNA could simultaneously regulate the expression of host genes through multiple mechanisms in the same tumor, supporting the specific and complex regulation of circRNAs on their host genes. For example, circ-CCND1 could not only combine with HuR protein to enhance the stability of CCND1 mRNA, but also act as a sponge for miR-646 to alleviate the inhibitory effect of miR-646 on CCND1 mRNA. Therefore, circ-CCND1 promotes the tumorigenesis of laryngeal squamous cell carcinoma (LSCC) by increasing mRNA stability and expression of CCND1 at the post-transcriptional [ 41 ]. CircMMP9 could interact with both AUF1 and miR-149, and block the inhibitory effect of AUF1 and miR-149 on the 3’-UTR of MMP9 to enhance the stability of MMP9 mRNA, thereby promoting the metastasis of oral squamous cell carcinoma [ 42 ]. FBXW7-185aa encoded by circFBXW7 inhibits the proliferation and migration of triple-negative breast cancer (TNBC) cells by increasing the abundance of FBXW7, inducing c-Myc degradation [ 30 ], and acting by the same mechanism as in glioblastoma, which has been described above [ 133 ]. Moreover, circFBXW7 could also upregulate the expression of FBXW7 by sponge of miR-197-3p to inhibit the progression of TNBC [ 30 ]. The above results show that, circRNAs function as oncogenes or tumor suppressor genes largely depending on tissue or cell type due to the diversity of target genes and mechanisms regulated by circRNAs.

In conclusion, circRNAs regulate the expression of their host genes through a variety of mechanisms at the transcriptional, post-transcriptional, translational, and post-translational levels, which forming a complex network to reveal the mechanisms of tumor malignant progression (Fig. 6 ).

Regulatory network of circRNAs and their host genes. Schematic diagram of the molecular mechanism by which circRNAs regulate the expression of host genes and then participate in the tumorigenesis and development at transcriptional, post-transcriptional, translational, and post-translational levels

A complex regulatory network between circRNAs and their host genes

As shown in Tables 1 and 2 , we enumerated some circRNAs with the same and opposite functions as their host genes, and detailed the mechanisms by which circRNAs regulate the expression of the host genes at the transcriptional, post-transcriptional, translational, and post-translational levels. The production of circRNAs can affect the accumulation of linear mRNA, thus regulating genes expression [ 33 , 40 , 50 , 55 ]. Therefore, the regulation between circRNAs and the host genes not only affects the linear transcript abundance of the host gene, but also provides a feedback loop that can regulate the formation of circRNA. For example, circMbl is derived from exon 2 of MBL and can directly bind to MBL, and MBL is prevented from binding to other targets. Moreover, MBL can also interact with flanking introns to regulate the formation of circMbl [ 135 ]. The regulation of MBL levels strongly affects the biosynthesis of circMbl, which is dependent on MBL binding sites, forming a positive feedback network.

CircRNAs are formed by back splicing of precursor mRNAs. Similarly, pre-mRNA requires further splicing modification to form mature mRNA after transcription [ 136 ]. For most host genes, the production of circRNAs is usually incompatible with functional mRNA formation, and there is a passive competition between them, with circRNAs production coming at the expense of a reduction in its corresponding mRNA isoform. Under certain circumstances, such as when pre-mRNA processing is slowed down, the nascent RNA can be directed to alternative pathways that promote back-splicing [ 137 , 138 , 139 ]. On the other hand, some circRNAs can compete with linear alternative splicing (AS) targets, and logically, back-splicing is less efficient than canonical splicing due to suboptimal spliceosome assembly at the back-splicing site. However, due to core damage, which refers to the depletion or pharmacological inhibition of core spliceosome components that control the RNA outputs of reporter and endogenous genes, splicing factors were inhibited, leading to the suppression of pre-mRNA splicing and enhancement of back-splicing [ 140 , 141 , 142 ]. Comparison of back-splicing and linear splicing further suggests that although splicing factors can control both processes, the splicing regulation rules of circular RNA biogenesis are different from those of linear splicing [ 143 ]. In addition, it has been proposed that linear splicing and back splicing may compete for limited splicing factors, introducing flanking exons with strong 5’ and 3’ splice sites, greatly reducing looping efficiency [ 135 ], and in addition to canonical splicing signals, important signal sequences in the spliceosome machinery (such as polypyrimidine tracts) also affect looping. Therefore, the abnormal increase and decrease of circRNAs will break the balance between the linear host genes and the circRNAs, forming a double negative or double positive feedback regulatory loop to regulate the expression of the host genes (Fig. 6 ).

Combination of circRNAs and their host genes is a potential molecular target for tumor diagnosis and treatment

The lack of effective diagnostic markers and therapeutic targets in tumor patients is part of the reason for their poor prognosis. Therefore, it is urgent to find biomarkers or therapeutic targets to improve the clinical prognosis of tumors. CircRNAs have been proved to have great potential in tumor diagnosis and prognostic biomarkers, and are becoming an emerging field of tumor diagnosis and treatment research [ 144 , 145 , 146 ].

Combination of circRNAs and their host genes might be a set of biomarkers for tumor diagnosis and prognosis

The expression patterns and characteristics of circRNAs make them ideal biomarkers. Firstly, circRNAs are highly stable and have a long half-life due to their circular structure, which makes them more resistant to RNase R exonuclease degradation than the corresponding linear RNAs. This stability makes circRNAs more easily detectable and thus are applied to clinical diagnosis [ 12 , 20 , 33 ]. Secondly, the expression of many circRNAs is tissue-specific and developmental stage specific, which plays an important role in diagnosis and prognosis. Moreover, circRNAs have been reported to perform their biological functions inside cells, or can be identified in human blood and urine through exosomes export, used for non-invasive detection [ 2 , 147 , 148 , 149 ], and to be taken up by adjacent (paracrine) or distant cells (endocrine), and affect many aspects of the physiological and pathological conditions of recipient cells [ 150 , 151 ].

The study showed that circITGA7 and ITGA7 were low expressed in colorectal cancer tissues. The receiver operating characteristic (ROC) curve analysis, which is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker [ 152 , 153 , 154 ], showed that the area under the curve (AUC) of circITGA7 was 0.8791 with a sensitivity (true-positive rate = true positives/[true positives + false negatives]) of 0.9275 and a specificity (true-negative rate = true negatives/[true negatives + false positives]) of 0.6667, which was much higher than that of ITGA7 (AUC = 0.7402) [ 18 ]. AUC (takes values from 0 to 1) is an effective way to summarize the overall diagnostic accuracy of the test. Generally, the higher AUC test may be considered better [ 155 , 156 ]. In conclusion, circITGA7 has the potential as a biomarker for the diagnosis of colorectal cancer. In addition, it was also found that the expression level of circITGA7 was negatively correlated with tumor size, lymph node metastasis, distant metastasis and TNM stage [ 18 ]. The study found that circZKSCAN1 and linear ZKSCAN1 were low expressed in liver cancer tissues, and the area under the curve (AUC) of cirZKSCAN1 was 0.834, with a sensitivity of 82.2% and specificity of 72.4%, which was much higher than that of ZKSCAN1. In addition, it was found that among all clinical parameters, the low expression level of ZKSCAN1 was correlated with tumor size [ 19 ]. In addition to circITGA7 and circZKSCAN1, there are many circRNAs, such as circGSK3β [ 80 ], circ-CCNB1 [ 83 ], circ_MMP2 [ 32 ], circ-ITCH [ 17 ], circCOL6A3 [ 31 ] and circ-SHPRH [ 68 ] are also abnormally expressed in tumor tissues, which are related to the occurrence and progression of tumors and can be used as biomarkers for clinical diagnosis and prognosis.

Combination of circRNAs and their host genes is a set of potential molecular targets for cancer therapy

Although there are no clinical reports of circRNAs for targeted therapy, their low molecular weight, stability, conservation, and regulatory effect on tumor cell activity make it possible to become a molecular drug or target for tumor therapy [ 157 , 158 ]. With the gradual maturity of artificial circRNAs construction and circRNAs interference technology, it is possible to regulate circRNAs, which will provide a new way for tumor treatment.

In tumor progression, circRNAs stimulate or stabilize the expression of host genes through positive or negative feedback mechanisms, and then play a role in promoting or inhibiting the occurrence and development of tumors. Numerous studies have shown that in tumors, interactions between circRNAs and their host genes are involved in regulating the downstream pathways of host genes, increasing the richness and complexity of potential mechanisms. Therefore, linking the expression of circRNAs with the expression changes of host genes plays a role of signal amplification and is more helpful for clinical treatment. CircRNAs such as circ-ENO1 [ 22 ], circGFRA1 [ 39 ], circCCDC66 [ 64 ], circ-Amotl1 [ 24 ] and circ-Foxo3 [ 16 ] have been found to participate in tumorigenesis and metastasis by regulating the expression of host genes. Therefore, we anticipate that targeting the circRNAs/host genes regulatory axis will provide information for innovative therapeutic targets, indicating the important role of the regulatory networks of circRNAs as well as their host genes as biomarkers in tumors.

Conclusions

With the continuous progress of the RNA field, circRNAs have become a new research hotspot. In recent years, a large number of studies have deepened our understanding of circRNAs, and their interaction with tumors has gradually attracted people’s attention. CircRNAs are derived from host genes, and in human tumors, similar to the regulatory effect of circRNAs on other targets, they can regulate the transcription, post-transcription, translation, protein activity and degradation of host genes. Emerging studies have demonstrated that circRNAs, as biomarkers or regulators, participate in human diseases and may improve clinical treatment in the future in combination with the currently widely used diagnostic and therapeutic methods. Because it is likely that the complex functional networks composed of circRNAs, rather than a single circRNA, affect tumorigenesis, a reasonable research advance should be to screen circRNAs and then investigate the function of a group or a single of significantly differentiated circRNAs. The combination of circRNAs and their host genes plays a role in signal amplification, which is helpful for later diagnosis and treatment, thus further exploration of circRNAs will help us better understand their heterogeneity.

Data Availability

Not applicable.

Abbreviations

Circular RNAs

RNA binding proteins

Non-coding RNAs

Esophageal squamous cell carcinoma

Epithelial-mesenchymal transition

Precursor mRNA

Exonic circRNAs

Intronic circRNAs

Exon-intron circRNAs

Polymerase II

EWS RNA-binding protein 1

MYC-associated zinc finger protein

CCHC-type zinc finger nucleic acid binding protein

Competitive endogenous RNAs

3’-untranslated region

Lung adenocarcinoma

Hepatocellular carcinoma

Prostate cancer

Retinoblastoma

Heterogeneous nuclear ribonucleoprotein K

AU-rich element-binding factor 1

Poly(A) binding protein

Multiple myeloma

Open reading frame

Rolling-translated EGFR

Glioblastoma

Proliferating cell nuclear antigen

Alternative splicing

Receiver operating characteristic

Area under the curve

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Acknowledgements

This work was supported by the grants from the National Natural Science Foundation of China (grant nos. 82172592 and 81772990), the Free Exploration Program of Central South University (grant no. 2021zzts0934), the program of Introducing Talents of Discipline to Universities (grant no. 111-2-12).

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NHC Key Laboratory of Carcinogenesis, Hunan Key Laboratory of Oncotarget Gene, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, China

Jianxia Wei, Mengna Li, Changning Xue, Shipeng Chen, Lemei Zheng, Hongyu Deng, Faqing Tang, Guiyuan Li, Wei Xiong, Zhaoyang Zeng & Ming Zhou

Cancer Research Institute, Central South University, Changsha, 410078, China

Jianxia Wei, Mengna Li, Changning Xue, Shipeng Chen, Lemei Zheng, Hongyu Deng, Guiyuan Li, Wei Xiong, Zhaoyang Zeng & Ming Zhou

The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Central South University, Changsha, 410078, China

Jianxia Wei, Mengna Li, Changning Xue, Shipeng Chen, Lemei Zheng, Guiyuan Li, Wei Xiong, Zhaoyang Zeng & Ming Zhou

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MZ, JXW, FQT, GYL, WX, and ZYZ designed and conducted this study. JXW and MNL drafted the manuscript. CNX, SPC, LMZ, and HYD revised the manuscript. All authors read and approved the final manuscript.

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Wei, J., Li, M., Xue, C. et al. Understanding the roles and regulation patterns of circRNA on its host gene in tumorigenesis and tumor progression. J Exp Clin Cancer Res 42 , 86 (2023). https://doi.org/10.1186/s13046-023-02657-6

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