2. variables
3. variables
4. variables
5. variables
6. variables
7. variables
8. variables
The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.
Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:
As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…
Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:
Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.
For example:
It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.
While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.
Keeping with the previous example, let’s look at some dependent variables in action:
In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.
As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.
To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!
As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.
In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂
As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.
Some examples of variables that you may need to control include:
Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.
Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!
As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.
Let’s jump into it…
A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).
For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.
It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.
Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.
Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.
In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.
A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:
Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.
Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.
Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.
For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:
One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!
In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .
To recap, we’ve explored:
If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .
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Published on 6 May 2022 by Shona McCombes .
A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.
What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).
Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.
In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .
Step 1: ask a question.
Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.
Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.
At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.
Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.
You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:
To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.
In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.
If you are comparing two groups, the hypothesis can state what difference you expect to find between them.
If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .
Research question | Hypothesis | Null hypothesis |
---|---|---|
What are the health benefits of eating an apple a day? | Increasing apple consumption in over-60s will result in decreasing frequency of doctor’s visits. | Increasing apple consumption in over-60s will have no effect on frequency of doctor’s visits. |
Which airlines have the most delays? | Low-cost airlines are more likely to have delays than premium airlines. | Low-cost and premium airlines are equally likely to have delays. |
Can flexible work arrangements improve job satisfaction? | Employees who have flexible working hours will report greater job satisfaction than employees who work fixed hours. | There is no relationship between working hour flexibility and job satisfaction. |
How effective is secondary school sex education at reducing teen pregnancies? | Teenagers who received sex education lessons throughout secondary school will have lower rates of unplanned pregnancy than teenagers who did not receive any sex education. | Secondary school sex education has no effect on teen pregnancy rates. |
What effect does daily use of social media have on the attention span of under-16s? | There is a negative correlation between time spent on social media and attention span in under-16s. | There is no relationship between social media use and attention span in under-16s. |
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).
A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.
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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/
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Formulating a strong hypothesis is a fundamental step in the scientific method and essential for conducting meaningful research. A well-crafted hypothesis not only guides your research design but also ensures that your study is focused and testable. In this article, we will explore the key elements that make a good hypothesis and provide practical tips for developing one.
A good hypothesis is a foundational element of any scientific research. It serves as a tentative explanation or prediction that can be tested through study and experimentation. Crafting a well-defined hypothesis is crucial for guiding your research and ensuring that your findings are valid and reliable.
Understanding the role of variables is crucial in formulating a strong hypothesis. Variables are the elements that you manipulate, measure, and control in your research. They help establish the relationship between different factors and ensure that your hypothesis is testable and measurable.
Formulating a strong hypothesis is a critical step in the research process. It involves several key stages that ensure your hypothesis is both testable and meaningful.
Designing experiments.
Designing experiments is a critical step in hypothesis testing. You must ensure that your experimental design is robust and capable of isolating the variables of interest. A well-designed experiment will allow you to draw clear conclusions about the relationship between the independent and dependent variables. Consider using control groups and randomization to minimize biases and enhance the validity of your results.
Selecting appropriate data collection methods is essential for obtaining reliable and valid data. Depending on your research question, you might use surveys, experiments, or observational studies. Ensure that your data collection methods are consistent and repeatable to maintain the integrity of your data. Operational definitions of variables should be clear to avoid any ambiguity during data collection.
Once data is collected, the next step is to analyze the results. Statistical analysis helps in determining whether the data supports or refutes the hypothesis. Use appropriate statistical tests to evaluate the significance of your findings. Remember, the goal is to provide evidence that can either support or challenge your hypothesis. By rigorously analyzing your data, you contribute to the broader field of knowledge and help in the construct development and validation in your area of study.
Hypotheses in natural sciences.
In the natural sciences, hypotheses often predict relationships between variables based on empirical evidence. For instance, a hypothesis might state, "If the amount of sunlight is increased, then the growth rate of the plant will increase." This hypothesis is testable and clearly defines the independent variable (amount of sunlight) and the dependent variable (growth rate of the plant).
Social science hypotheses frequently address human behavior and societal trends. An example could be, "Individuals who experience higher levels of thesis anxiety are less likely to complete their thesis on time." This hypothesis is grounded in past knowledge and is designed to be tested through surveys or observational studies.
Applied research often involves practical problem-solving. A well-formulated hypothesis in this field might be, "Implementing a new software tool will reduce the time required for data analysis by 20%." This hypothesis is specific, measurable, and directly applicable to real-world scenarios.
A robust theoretical framework is essential for developing a strong hypothesis. It serves as the foundation upon which your research is built, linking your research question to existing theories and literature. The importance of theoretical frameworks cannot be overstated, as they provide the context and rationale for your study, guiding the formulation of your hypothesis and ensuring it is grounded in established knowledge.
When formulating and testing hypotheses, it is crucial to adhere to ethical standards to ensure the integrity and credibility of your research. Ethical considerations are not just about following rules; they are about respecting the dignity, rights, and welfare of participants and maintaining the trustworthiness of your findings.
Ethical considerations in hypothesis testing are crucial for maintaining the integrity and credibility of research. Researchers must ensure that their methods are transparent, their data is accurately reported, and their conclusions are drawn without bias. For students embarking on their thesis journey, understanding these ethical principles is essential. At Research Rebels, we provide comprehensive guides and resources to help you navigate these challenges effectively. Claim your special offer now and take the first step towards a stress-free thesis experience.
In conclusion, crafting a good hypothesis is a fundamental step in the scientific research process. A well-constructed hypothesis not only provides a clear direction for research but also ensures that the study is grounded in empirical evidence and theoretical frameworks. Key elements of a good hypothesis include clarity, testability, and specificity, all of which contribute to the robustness and reliability of the research findings. By adhering to these principles, researchers can formulate hypotheses that are both meaningful and impactful, ultimately advancing knowledge in their respective fields. As we have discussed, the process of developing a hypothesis involves careful consideration and refinement, underscoring the importance of precision and critical thinking in scientific inquiry.
What is a hypothesis.
A hypothesis is a testable statement that predicts an outcome based on certain conditions or variables. It serves as a starting point for scientific research.
Clarity ensures that the hypothesis is easily understood and can be tested accurately. A clear hypothesis helps in designing appropriate experiments and in interpreting results correctly.
Independent variables are the factors that are manipulated or changed in an experiment, while dependent variables are the outcomes that are measured to see the effect of the independent variables.
Refining a hypothesis involves reviewing preliminary research, seeking feedback, and conducting initial tests. This process helps in making the hypothesis more precise and testable.
Common pitfalls include being too vague, making untestable statements, and not basing the hypothesis on existing research or theory. Avoiding these pitfalls can lead to a stronger and more reliable hypothesis.
Control variables are important because they help to isolate the effect of the independent variable on the dependent variable. By keeping control variables constant, researchers can ensure that any observed changes are due to the manipulation of the independent variable.
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Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.
It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .
Table of Contents
A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.
Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”
A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.
Here are the characteristics of a good hypothesis :
A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.
Let’s look at each step for creating an effective, testable, and good research hypothesis :
Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.
When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.
An example of a research hypothesis in this format is as follows:
“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”
Population: athletes
Independent variable: daily cold water showers
Dependent variable: endurance
You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.
Following from above, here is a 10-point checklist for a good research hypothesis :
By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.
Different types of research hypothesis are used in scientific research:
A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.
Example: “ The newly identified virus is not zoonotic .”
This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.
Example: “ The newly identified virus is zoonotic .”
This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.
Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”
While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.
Example, “ Cats and dogs differ in the amount of affection they express .”
A simple hypothesis only predicts the relationship between one independent and another independent variable.
Example: “ Applying sunscreen every day slows skin aging .”
A complex hypothesis states the relationship or difference between two or more independent and dependent variables.
Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)
An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.
Example: “ There is a positive association between physical activity levels and overall health .”
A causal hypothesis proposes a cause-and-effect interaction between variables.
Example: “ Long-term alcohol use causes liver damage .”
Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.
Here are some good research hypothesis examples :
“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”
“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”
“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”
“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”
Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.
Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:
“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)
“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)
“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)
If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.
To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.
1. What is the difference between research question and research hypothesis ?
A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.
2. When to reject null hypothesis ?
A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.
3. How can I be sure my hypothesis is testable?
A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:
4. How do I revise my research hypothesis if my data does not support it?
If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.
5. I am performing exploratory research. Do I need to formulate a research hypothesis?
As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.
6. How is a research hypothesis different from a research question?
A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.
7. Can a research hypothesis change during the research process?
Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.
8. How many hypotheses should be included in a research study?
The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.
9. Can research hypotheses be used in qualitative research?
Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.
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Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.
Updated on April 27, 2022
General hypothesis.
Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.
A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.
A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.
Once you've written a possible hypothesis, make sure it checks the following boxes:
Pose it as a question first.
Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.
A possible initial question could be: Why is the sky blue?
Once you have a question in mind, read research around your topic. Collect research from academic journals.
If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.
Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.
Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.
Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.
Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.
Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.
In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.
One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.
According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”
Characteristics that make a hypothesis weak include:
A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.
A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .
In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.
Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.
“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”
There can be overlap in these types of hypotheses.
A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.
Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).
A complex hypothesis shows the relationship of two or more independent and dependent variables.
Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).
A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.
Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.
A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.
An associative hypothesis says that when one variable changes, so does the other variable.
An alternative hypothesis states that the variables have a relationship.
Example: An apple a day keeps the doctor away.
A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.
Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:
Example: An apple a day does not keep the doctor away.
A logical hypothesis is a suggested explanation while using limited evidence.
Example: Bats can navigate in the dark better than tigers.
In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.
An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.
In this case, the research changes the hypothesis as the researcher learns more about his/her research.
A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.
Example: 70% of people who live in Illinois are iron deficient.
A causal hypothesis states that the independent variable will have an effect on the dependent variable.
Example: Using tobacco products causes cancer.
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Methodology
Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.
What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.
An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.
There are two main types of independent variables.
In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.
You can apply just two levels in order to find out if an independent variable has an effect at all.
You can also apply multiple levels to find out how the independent variable affects the dependent variable.
You have three independent variable levels, and each group gets a different level of treatment.
You randomly assign your patients to one of the three groups:
A true experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.
Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.
It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .
Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.
Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.
A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.
In statistics , dependent variables are also called:
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.
Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.
Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .
A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .
Here are some tips for identifying each variable type.
Use this list of questions to check whether you’re dealing with an independent variable:
Check whether you’re dealing with a dependent variable:
Professional editors proofread and edit your paper by focusing on:
See an example
Independent and dependent variables are generally used in experimental and quasi-experimental research.
Here are some examples of research questions and corresponding independent and dependent variables.
Research question | Independent variable | Dependent variable(s) |
---|---|---|
Do tomatoes grow fastest under fluorescent, incandescent, or natural light? | ||
What is the effect of intermittent fasting on blood sugar levels? | ||
Is medical marijuana effective for pain reduction in people with chronic pain? | ||
To what extent does remote working increase job satisfaction? |
For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .
The type of test is determined by:
You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.
In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).
The type of visualization you use depends on the variable types in your research questions:
To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.
You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.
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.
Research bias
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Bhandari, P. (2023, June 22). Independent vs. Dependent Variables | Definition & Examples. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/methodology/independent-and-dependent-variables/
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General Education
Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.
But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including:
So let’s get started!
Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid.
As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.
Hypotheses are one part of what’s called the scientific method . Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):
You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!
After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.
There are two types of variables: independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable.
Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets.
Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.
The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.
As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.
Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.
Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.
When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.
The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement!
In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.
Additionally, keep in mind that hypotheses can range from very specific to very broad. These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.
Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.
First of all, if-then hypotheses typically follow this formula:
If ____ happens, then ____ will happen.
The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life:
In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades).
You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:
“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”
It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.
Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .
One null hypothesis for the cell phone and sleep study from the last section might say:
“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.”
In this case, this is a null hypothesis because it’s asking the opposite of the original thesis!
Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:
“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”
In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:
“If people have many followers on Instagram, they will spend more time on the app than people who have less.”
You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship.
If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.
At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think.
Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.
Improbable hypotheses generally go against science, as well. Take this hypothesis example:
“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.”
This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.
The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.
Here’s what we mean. Which of the following sentences makes more sense to the common person?
If the kerning is greater than average, more words will be read per minute.
If the space between letters is greater than average, more words will be read per minute.
For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible.
Good hypotheses ensure that you can observe the results.
In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.
Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable.
In writing your hypothesis, always keep in mind how you'll execute the experiment.
Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.
Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?
We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.
You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?
You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:
If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”
Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”
These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.
To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come and how many leave. You also write down the temperature on the hour.
After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.
Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?
Here’s what your hypotheses might look like:
If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”
Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”
This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).
While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment.
However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this:
If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”
Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”
Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.
You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while:
If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.
Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.
You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?
This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again!
The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.
Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.
If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)
If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.
If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home
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Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.
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Hypothesis Definition, Format, Examples, and Tips
Verywell / Alex Dos Diaz
Falsifiability of a hypothesis.
Hypotheses examples.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.
Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."
A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.
In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:
The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.
Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.
In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.
Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.
In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.
In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."
In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."
So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:
Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.
To form a hypothesis, you should take these steps:
In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.
Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.
One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.
A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.
Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.
For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.
These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.
One of the basic principles of any type of scientific research is that the results must be replicable.
Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.
Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.
To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.
The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:
A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .
The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."
Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.
Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.
Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.
What Are Independent and Dependent Variables?
Both the independent variable and dependent variable are examined in an experiment using the scientific method , so it's important to know what they are and how to use them.
In a scientific experiment, you'll ultimately be changing or controlling the independent variable and measuring the effect on the dependent variable. This distinction is critical in evaluating and proving hypotheses.
Below you'll find more about these two types of variables, along with examples of each in sample science experiments, and an explanation of how to graph them to help visualize your data.
An independent variable is the condition that you change in an experiment. In other words, it is the variable you control. It is called independent because its value does not depend on and is not affected by the state of any other variable in the experiment. Sometimes you may hear this variable called the "controlled variable" because it is the one that is changed. Do not confuse it with a control variable , which is a variable that is purposely held constant so that it can't affect the outcome of the experiment.
The dependent variable is the condition that you measure in an experiment. You are assessing how it responds to a change in the independent variable, so you can think of it as depending on the independent variable. Sometimes the dependent variable is called the "responding variable."
If you are having a hard time identifying which variable is the independent variable and which is the dependent variable, remember the dependent variable is the one affected by a change in the independent variable. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won't make sense.
Independent variable causes an effect on the dependent variable.
Example : How long you sleep (independent variable) affects your test score (dependent variable).
This makes sense, but:
Example : Your test score affects how long you sleep.
This doesn't really make sense (unless you can't sleep because you are worried you failed a test, but that would be a different experiment).
There is a standard method for graphing independent and dependent variables. The x-axis is the independent variable, while the y-axis is the dependent variable. You can use the DRY MIX acronym to help remember how to graph variables:
D = dependent variable R = responding variable Y = graph on the vertical or y-axis
M = manipulated variable I = independent variable X = graph on the horizontal or x-axis
Test your understanding with the scientific method quiz .
The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.
The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.
The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”
Here are several examples of independent and dependent variables in experiments:
If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).
This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.
Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
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In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.
In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.
Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.
In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.
It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.
For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).
In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.
By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.
For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.
In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.
In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.
An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).
In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.
For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).
For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.
In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).
For the following hypotheses, name the IV and the DV.
1. Lack of sleep significantly affects learning in 10-year-old boys.
IV……………………………………………………
DV…………………………………………………..
2. Social class has a significant effect on IQ scores.
DV……………………………………………….…
3. Stressful experiences significantly increase the likelihood of headaches.
4. Time of day has a significant effect on alertness.
To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.
Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).
For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.
Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.
In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.
The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.
If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.
Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .
For the following hypotheses, name the IV and the DV and operationalize both variables.
1. Women are more attracted to men without earrings than men with earrings.
I.V._____________________________________________________________
D.V. ____________________________________________________________
Operational definitions:
I.V. ____________________________________________________________
2. People learn more when they study in a quiet versus noisy place.
I.V. _________________________________________________________
D.V. ___________________________________________________________
3. People who exercise regularly sleep better at night.
Yes, it is possible to have more than one independent or dependent variable in a study.
In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.
Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.
Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.
Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.
Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.
Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.
Yes, both quantitative and qualitative data can have independent and dependent variables.
In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.
The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.
So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.
Yes, the same variable can be independent in one study and dependent in another.
The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.
However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.
The role of a variable as independent or dependent can vary depending on the research question and study design.
Now it's time to state your hypothesis . The hypothesis is an educated guess as to what will happen during your experiment.
The hypothesis is often written using the words "IF" and "THEN." For example, " If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables .
The hypothesis should relate back to your original question and must be testable .
A word about variables...
Your experiment will include variables to measure and to explain any cause and effect. Below you will find some useful links describing the different types of variables.
Statistics By Jim
Making statistics intuitive
By Jim Frost 15 Comments
In this post, learn the definitions of independent and dependent variables, how to identify each type, how they differ between different types of studies, and see examples of them in use.
Independent variables (IVs) are the ones that you include in the model to explain or predict changes in the dependent variable. The name helps you understand their role in statistical analysis. These variables are independent . In this context, independent indicates that they stand alone and other variables in the model do not influence them. The researchers are not seeking to understand what causes the independent variables to change.
Independent variables are also known as predictors, factors , treatment variables, explanatory variables, input variables, x-variables, and right-hand variables—because they appear on the right side of the equals sign in a regression equation. In notation, statisticians commonly denote them using Xs. On graphs, analysts place independent variables on the horizontal, or X, axis.
In machine learning, independent variables are known as features.
For example, in a plant growth study, the independent variables might be soil moisture (continuous) and type of fertilizer (categorical).
Statistical models will estimate effect sizes for the independent variables.
Relate post : Effect Sizes in Statistics
The nature of independent variables changes based on the type of experiment or study:
Controlled experiments : Researchers systematically control and set the values of the independent variables. In randomized experiments, relationships between independent and dependent variables tend to be causal. The independent variables cause changes in the dependent variable.
Observational studies : Researchers do not set the values of the explanatory variables but instead observe them in their natural environment. When the independent and dependent variables are correlated, those relationships might not be causal.
When you include one independent variable in a regression model, you are performing simple regression. For more than one independent variable, it is multiple regression. Despite the different names, it’s really the same analysis with the same interpretations and assumptions.
Determining which IVs to include in a statistical model is known as model specification. That process involves in-depth research and many subject-area, theoretical, and statistical considerations. At its most basic level, you’ll want to include the predictors you are specifically assessing in your study and confounding variables that will bias your results if you don’t add them—particularly for observational studies.
For more information about choosing independent variables, read my post about Specifying the Correct Regression Model .
Related posts : Randomized Experiments , Observational Studies , Covariates , and Confounding Variables
The dependent variable (DV) is what you want to use the model to explain or predict. The values of this variable depend on other variables. It is the outcome that you’re studying. It’s also known as the response variable, outcome variable, and left-hand variable. Statisticians commonly denote them using a Y. Traditionally, graphs place dependent variables on the vertical, or Y, axis.
For example, in the plant growth study example, a measure of plant growth is the dependent variable. That is the outcome of the experiment, and we want to determine what affects it.
If you’re reading a study’s write-up, how do you distinguish independent variables from dependent variables? Here are some tips!
How statisticians discuss independent variables changes depending on the field of study and type of experiment.
In randomized experiments, look for the following descriptions to identify the independent variables:
In observational studies, independent variables are a bit different. While the researchers likely want to establish causation, that’s harder to do with this type of study, so they often won’t use the word “cause.” They also don’t set the values of the predictors. Some independent variables are the experiment’s focus, while others help keep the experimental results valid.
Here’s how to recognize independent variables in observational studies:
Regardless of the study type, if you see an estimated effect size, it is an independent variable.
Dependent variables are the outcome. The IVs explain the variability or causes changes in the DV. Focus on the “depends” aspect. The value of the dependent variable depends on the IVs. If Y depends on X, then Y is the dependent variable. This aspect applies to both randomized experiments and observational studies.
In an observational study about the effects of smoking, the researchers observe the subjects’ smoking status (smoker/non-smoker) and their lung cancer rates. It’s an observational study because they cannot randomly assign subjects to either the smoking or non-smoking group. In this study, the researchers want to know whether lung cancer rates depend on smoking status. Therefore, the lung cancer rate is the dependent variable.
In a randomized COVID-19 vaccine experiment , the researchers randomly assign subjects to the treatment or control group. They want to determine whether COVID-19 infection rates depend on vaccination status. Hence, the infection rate is the DV.
Note that a variable can be an independent variable in one study but a dependent variable in another. It depends on the context.
For example, one study might assess how the amount of exercise (IV) affects health (DV). However, another study might study the factors (IVs) that influence how much someone exercises (DV). The amount of exercise is an independent variable in one study but a dependent variable in the other!
Regression analysis and ANOVA mathematically describe the relationships between each independent variable and the dependent variable. Typically, you want to determine how changes in one or more predictors associate with changes in the dependent variable. These analyses estimate an effect size for each independent variable.
Suppose researchers study the relationship between wattage, several types of filaments, and the output from a light bulb. In this study, light output is the dependent variable because it depends on the other two variables. Wattage (continuous) and filament type (categorical) are the independent variables.
After performing the regression analysis, the researchers will understand the nature of the relationship between these variables. How much does the light output increase on average for each additional watt? Does the mean light output differ by filament types? They will also learn whether these effects are statistically significant.
Related post : When to Use Regression Analysis
As I mentioned earlier, graphs traditionally display the independent variables on the horizontal X-axis and the dependent variable on the vertical Y-axis. The type of graph depends on the nature of the variables. Here are a couple of examples.
Suppose you experiment to determine whether various teaching methods affect learning outcomes. Teaching method is a categorical predictor that defines the experimental groups. To display this type of data, you can use a boxplot, as shown below.
The groups are along the horizontal axis, while the dependent variable, learning outcomes, is on the vertical. From the graph, method 4 has the best results. A one-way ANOVA will tell you whether these results are statistically significant. Learn more about interpreting boxplots .
Now, imagine that you are studying people’s height and weight. Specifically, do height increases cause weight to increase? Consequently, height is the independent variable on the horizontal axis, and weight is the dependent variable on the vertical axis. You can use a scatterplot to display this type of data.
It appears that as height increases, weight tends to increase. Regression analysis will tell you if these results are statistically significant. Learn more about interpreting scatterplots .
April 2, 2024 at 2:05 am
Hi again Jim
Thanks so much for taking an interest in New Zealand’s Equity Index.
Rather than me trying to explain what our Ministry of Education has done, here is a link to a fairly short paper. Scroll down to page 4 of this (if you have the inclination) – https://fyi.org.nz/request/21253/response/80708/attach/4/1301098%20Response%20and%20Appendix.pdf
The Equity Index is used to allocate only 4% of total school funding. The most advantaged 5% of schools get no “equity funding” and the other 95% get a share of the equity funding pool based on their index score. We are talking a maximum of around $1,000NZD per child per year for the most disadvantaged schools. The average amount is around $200-$300 per child per year.
My concern is that I thought the dependent variable is the thing you want to explain or predict using one or more independent variables. Choosing the form of dependent variable that gets a good fit seems to be answering the question “what can we predict well?” rather than “how do we best predict the factor of interest?” The factor is educational achievement and I think this should have been decided upon using theory rather than experimentation with the data.
As it turns out, the Ministry has chosen a measure of educational achievement that puts a heavy weight on achieving an “excellence” rating on a qualification and a much lower weight on simply gaining a qualification. My reading is that they have taken what our universities do when looking at which students to admit.
It doesn’t seem likely to me that a heavy weighting on excellent achievement is appropriate for targeting extra funding to schools with a lot of under-achieving students.
However, my stats knowledge isn’t extensive and it’s definitely rusty, so your thoughts are most helpful.
Regards Kathy Spencer
April 1, 2024 at 4:08 pm
Hi Jim, Great website, thank you.
I have been looking at New Zealand’s Equity Index which is used to allocate a small amount of extra funding to schools attended by children from disadvantaged backgrounds. The Index uses 37 socioeconomic measures relating to a child’s and their parents’ backgrounds that are found to be associated with educational achievement.
I was a bit surprised to read how they had decided on the dependent variable to be used as the measure of educational achievement, or dependent variable. Part of the process was as follows- “Each measure was tested to see the degree to which it could be predicted by the socioeconomic factors selected for the Equity Index.”
Any comment?
Many thanks Kathy Spencer
April 1, 2024 at 9:20 pm
That’s a very complex study and I don’t know much about it. So, that limits what I can say about it. But I’ll give you a few thoughts that come to mind.
This method is common in educational and social research, particularly when the goal is to understand or mitigate the impact of socioeconomic disparities on educational outcomes.
There are the usual concerns about not confusing correlation with causation. However, because this program seems to quantify barriers and then provide extra funding based on the index, I don’t think that’s a problem. They’re not attempting to adjust the socioeconomic measures so no worries about whether they’re directly causal or not.
I might have a small concern about cherry picking the model that happens to maximize the R-squared. Chasing the R-squared rather than having theory drive model selecting is often problematic. Chasing the best fit increases the likelihood that the model fits this specific dataset best by random chance rather than being truly the best. If so, it won’t perform as well outside the dataset used to fit the model. Hopefully, they validated the predicted ability of the model using other data.
However, I’m not sure if the extra funding is determined by the model? I don’t know if the index value is calculated separately outside the candidate models and then fed into the various models. Or does the choice of model affect how the index value is calculated? If it’s the former, then the funding doesn’t depend on a potentially cherry picked model. If the latter, it does.
So, I’m not really clear on the purpose of the model. I’m guessing they just want to validate their Equity Index. And maximizing the R-squared doesn’t really say it’s the best Index but it does at least show that it likely has some merit. I’d be curious how the took the 37 measures and combined them to one index. So, I have more questions than answers. I don’t mean that in a critical sense. Just that I know almost nothing about this program.
I’m curious, what was the outcome they picked? How high was the R-squared? And what were your concerns?
February 6, 2024 at 6:57 pm
Excellent explanation, thank you.
February 5, 2024 at 5:04 pm
Thank you for this insightful blog. Is it valid to use a dependent variable delivered from the mean of independent variables in multiple regression if you want to evaluate the influence of each unique independent variable on the dependent variables?
February 5, 2024 at 11:11 pm
It’s difficult to answer your question because I’m not sure what you mean that the DV is “delivered from the mean of IVs.” If you mean that multiple IVs explain changes in the DV’s mean, yes, that’s the standard use for multiple regression.
If you mean something else, please explain in further detail. Thanks!
February 6, 2024 at 6:32 am
What I meant is; the DV values used as parameters for multiple regression is basically calculated as the average of the IVs. For instance:
From 3 IVs (X1, X2, X3), Y is delivered as :
Y = (Sum of all IVs) / (3)
Then the resulting Y is used as the DV along with the initial IVs to compute the multiple regression.
February 6, 2024 at 2:17 pm
There are a couple of reasons why you shouldn’t do that.
For starters, Y-hat (the predicted value of the regression equation) is the mean of the DV given specific values of the IV. However, that mean is calculated by using the regression coefficients and constant in the regression equation. You don’t calculate the DV mean as the sum of the IVs divided by the number of IVs. Perhaps given a very specific subject-area context, using this approach might seem to make sense but there are other problems.
A critical problem is that the Y is now calculated using the IVs. Instead, the DVs should be measured outcomes and not calculated from IVs. This violates regression assumptions and produces questionable results.
Additionally, it complicates the interpretation. Because the DV is calculated from the IV, you know the regression analysis will find a relationship between them. But you have no idea if that relationship exists in the real world. This complication occurs because your results are based on forcing the DV to equal a function of the IVs and do not reflect real-world outcomes.
In short, DVs should be real-world outcomes that you measure! And be sure to keep your IVs and DV independent. Let the regression analysis estimate the regression equation from your data that contains measured DVs. Don’t use a function to force the DV to equal some function of the IVs because that’s the opposite direction of how regression works!
I hope that helps!
September 6, 2022 at 7:43 pm
Thank you for sharing.
March 3, 2022 at 1:59 am
Excellent explanation.
February 13, 2022 at 12:31 pm
Thanks a lot for creating this excellent blog. This is my go-to resource for Statistics.
I had been pondering over a question for sometime, it would be great if you could shed some light on this.
In linear and non-linear regression, should the distribution of independent and dependent variables be unskewed? When is there a need to transform the data (say, Box-Cox transformation), and do we transform the independent variables as well?
October 28, 2021 at 12:55 pm
If I use a independent variable (X) and it displays a low p-value <.05, why is it if I introduce another independent variable to regression the coefficient and p-value of Y that I used in first regression changes to look insignificant? The second variable that I introduced has a low p-value in regression.
October 29, 2021 at 11:22 pm
Keep in mind that the significance of each IV is calculated after accounting for the variance of all the other variables in the model, assuming you’re using the standard adjusted sums of squares rather than sequential sums of squares. The sums of squares (SS) is a measure of how much dependent variable variability that each IV accounts for. In the illustration below, I’ll assume you’re using the standard of adjusted SS.
So, let’s say that originally you have X1 in the model along with some other IVs. Your model estimates the significance of X1 after assessing the variability that the other IVs account for and finds that X1 is significant. Now, you add X2 to the model in addition to X1 and the other IVs. Now, when assessing X1, the model accounts for the variability of the IVs including the newly added X2. And apparently X2 explains a good portion of the variability. X1 is no longer able to account for that variability, which causes it to not be statistically significant.
In other words, X2 explains some of the variability that X1 previously explained. Because X1 no longer explains it, it is no longer significant.
Additionally, the significance of IVs is more likely to change when you add or remove IVs that are correlated. Correlated IVs is known as multicollinearity. Multicollinearity can be a problem when you have too much. Given the change in significance, I’d check your model for multicollinearity just to be safe! Click the link to read a post that wrote about that!
September 6, 2021 at 8:35 am
nice explanation
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Insights into the effects of tile size and tile overlap levels on semantic segmentation models trained for road surface area extraction from aerial orthophotography.
3. related work, 5. training method, 6.1. mean performance on the test set grouped by training scenario, 6.2. mean performance on the test data grouped by tile size, tile overlap and semantic segmentation model, 6.3. main and interaction effects with factorial anova, 7. qualitative evaluation, 7.1. general trends, 7.2. areas with higher error rates, 7.3. observed behavior in rural and urban scenes, 7.4. tile size with best predictions and other considerations, 8. discussion, 8.1. on the mean performance, 8.2. on the main and interaction effects, 8.3. on the qualitative evaluation, 8.4. on the uncertainty of the models, limitations of the study, and future directions, 9. conclusions, author contributions, data availability statement, conflicts of interest.
Experiment No. | Training Scenario ID | Iteration No. | Loss | IoU score | F1 score | Precision | Recall | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Train | Validation | Test | Train | Validation | Test | Train | Validation | Test | Train | Validation | Test | Train | Validation | Test | |||
1 | 1 | 1 | 0.4694 | 0.4801 | 0.4742 | 0.6050 | 0.5975 | 0.5867 | 0.7247 | 0.7177 | 0.7216 | 0.7437 | 0.7360 | 0.8118 | 0.7159 | 0.7110 | 0.6389 |
2 | 2 | 0.4637 | 0.4754 | 0.4758 | 0.6089 | 0.6005 | 0.5847 | 0.7283 | 0.7204 | 0.7196 | 0.7480 | 0.7392 | 0.8079 | 0.7169 | 0.7114 | 0.6380 | |
3 | 3 | 0.4693 | 0.4819 | 0.4718 | 0.6036 | 0.5946 | 0.5865 | 0.7233 | 0.7149 | 0.7221 | 0.7389 | 0.7298 | 0.8074 | 0.7198 | 0.7140 | 0.6437 | |
4 | 2 | 1 | 0.4581 | 0.4668 | 0.4619 | 0.6153 | 0.6099 | 0.5958 | 0.7334 | 0.7295 | 0.7302 | 0.7485 | 0.7447 | 0.8121 | 0.7291 | 0.7260 | 0.6539 |
5 | 2 | 0.4578 | 0.4679 | 0.4599 | 0.6150 | 0.6084 | 0.5974 | 0.7322 | 0.7271 | 0.7317 | 0.7441 | 0.7388 | 0.8123 | 0.7351 | 0.7314 | 0.6566 | |
6 | 3 | 0.4765 | 0.4837 | 0.4729 | 0.6013 | 0.5968 | 0.5861 | 0.7194 | 0.7162 | 0.7202 | 0.7347 | 0.7313 | 0.7998 | 0.7217 | 0.7201 | 0.6499 | |
7 | 3 | 1 | 0.4832 | 0.4899 | 0.4503 | 0.5726 | 0.5662 | 0.5860 | 0.6984 | 0.6923 | 0.7225 | 0.7084 | 0.7004 | 0.8021 | 0.7172 | 0.7109 | 0.6460 |
8 | 2 | 0.4779 | 0.4870 | 0.4559 | 0.5781 | 0.5698 | 0.5830 | 0.7030 | 0.6951 | 0.7192 | 0.7165 | 0.7102 | 0.8059 | 0.7116 | 0.7048 | 0.6355 | |
9 | 3 | 0.4729 | 0.4821 | 0.4604 | 0.5827 | 0.5741 | 0.5788 | 0.7077 | 0.6993 | 0.7163 | 0.7169 | 0.7094 | 0.7994 | 0.7198 | 0.7139 | 0.6372 | |
10 | 1 | 0.4898 | 0.4898 | 0.4540 | 0.5702 | 0.5702 | 0.5834 | 0.6986 | 0.6986 | 0.7208 | 0.7115 | 0.7115 | 0.8047 | 0.7032 | 0.7032 | 0.6384 | |
12 | 3 | 0.4806 | 0.4806 | 0.4436 | 0.5781 | 0.5781 | 0.5916 | 0.7029 | 0.7029 | 0.7279 | 0.7103 | 0.7103 | 0.8013 | 0.7223 | 0.7223 | 0.6577 | |
13 | 5 | 1 | 0.5493 | 0.5609 | 0.4504 | 0.5073 | 0.4983 | 0.5813 | 0.6433 | 0.6347 | 0.7209 | 0.6286 | 0.6162 | 0.7852 | 0.7250 | 0.7268 | 0.6548 |
14 | 2 | 0.5659 | 0.5767 | 0.4422 | 0.4960 | 0.4879 | 0.5889 | 0.6337 | 0.6260 | 0.7281 | 0.6076 | 0.5987 | 0.7819 | 0.7387 | 0.7405 | 0.6752 | |
15 | 3 | 0.5541 | 0.5634 | 0.4470 | 0.5044 | 0.4977 | 0.5827 | 0.6409 | 0.6340 | 0.7210 | 0.6213 | 0.6132 | 0.7652 | 0.7297 | 0.7285 | 0.6819 | |
16 | 1 | 0.5559 | 0.5448 | 0.4316 | 0.5019 | 0.5129 | 0.5971 | 0.6363 | 0.6480 | 0.7342 | 0.6140 | 0.6238 | 0.7930 | 0.7401 | 0.7528 | 0.6736 | |
18 | 3 | 0.5491 | 0.5362 | 0.4393 | 0.5112 | 0.5234 | 0.5910 | 0.6456 | 0.6584 | 0.7300 | 0.6304 | 0.6411 | 0.7997 | 0.7304 | 0.7457 | 0.6544 | |
19 | 7 | 1 | 0.4773 | 0.4842 | 0.4736 | 0.5980 | 0.5933 | 0.5855 | 0.7192 | 0.7145 | 0.7213 | 0.7415 | 0.7360 | 0.8096 | 0.7057 | 0.7031 | 0.6395 |
20 | 2 | 0.4777 | 0.4852 | 0.4815 | 0.5995 | 0.5944 | 0.5808 | 0.7193 | 0.7144 | 0.7170 | 0.7339 | 0.7281 | 0.8043 | 0.7183 | 0.7155 | 0.6383 | |
21 | 3 | 0.4869 | 0.4937 | 0.4694 | 0.5887 | 0.5840 | 0.5871 | 0.7095 | 0.7050 | 0.7220 | 0.7253 | 0.7204 | 0.8072 | 0.7090 | 0.7055 | 0.6430 | |
22 | 8 | 1 | 0.4770 | 0.4813 | 0.4510 | 0.5986 | 0.5961 | 0.6017 | 0.7179 | 0.7165 | 0.7354 | 0.7256 | 0.7240 | 0.8122 | 0.7304 | 0.7295 | 0.6633 |
23 | 2 | 0.4885 | 0.4917 | 0.4730 | 0.5890 | 0.5873 | 0.5833 | 0.7091 | 0.7085 | 0.7184 | 0.7215 | 0.7210 | 0.8034 | 0.7151 | 0.7148 | 0.6390 | |
24 | 3 | 0.4787 | 0.4823 | 0.4645 | 0.5974 | 0.5956 | 0.5907 | 0.7173 | 0.7164 | 0.7250 | 0.7292 | 0.7282 | 0.8102 | 0.7231 | 0.7224 | 0.6441 | |
25 | 9 | 1 | 0.4988 | 0.5035 | 0.4872 | 0.5606 | 0.5560 | 0.5568 | 0.6911 | 0.6868 | 0.6976 | 0.7082 | 0.7049 | 0.7988 | 0.6907 | 0.6858 | 0.5992 |
26 | 2 | 0.4933 | 0.4981 | 0.4522 | 0.5650 | 0.5601 | 0.5848 | 0.6918 | 0.6868 | 0.7210 | 0.6997 | 0.6956 | 0.8033 | 0.7119 | 0.7064 | 0.6411 | |
27 | 3 | 0.4943 | 0.4988 | 0.4457 | 0.5647 | 0.5602 | 0.5903 | 0.6914 | 0.6869 | 0.7262 | 0.6922 | 0.6892 | 0.8012 | 0.7234 | 0.7177 | 0.6552 | |
28 | 10 | 1 | 0.4930 | 0.4930 | 0.4550 | 0.5696 | 0.5696 | 0.5834 | 0.6960 | 0.6960 | 0.7194 | 0.7056 | 0.7056 | 0.8050 | 0.7129 | 0.7129 | 0.6345 |
29 | 2 | 0.4799 | 0.4799 | 0.4497 | 0.5798 | 0.5798 | 0.5879 | 0.7068 | 0.7068 | 0.7243 | 0.7206 | 0.7206 | 0.8121 | 0.7101 | 0.7101 | 0.6370 | |
30 | 3 | 0.4903 | 0.4903 | 0.4573 | 0.5700 | 0.5700 | 0.5802 | 0.6961 | 0.6961 | 0.7152 | 0.7027 | 0.7027 | 0.7942 | 0.7175 | 0.7175 | 0.6398 | |
31 | 11 | 1 | 0.5459 | 0.5575 | 0.4515 | 0.5124 | 0.5033 | 0.5802 | 0.6539 | 0.6460 | 0.7228 | 0.6322 | 0.6239 | 0.7892 | 0.7338 | 0.7315 | 0.6537 |
32 | 2 | 0.5485 | 0.5577 | 0.4430 | 0.5094 | 0.5024 | 0.5873 | 0.6495 | 0.6437 | 0.7287 | 0.6270 | 0.6213 | 0.7933 | 0.7346 | 0.7331 | 0.6599 | |
33 | 3 | 0.5548 | 0.5630 | 0.4453 | 0.5053 | 0.4994 | 0.5860 | 0.6458 | 0.6408 | 0.7271 | 0.6148 | 0.6084 | 0.7933 | 0.7506 | 0.7526 | 0.6582 | |
34 | 12 | 1 | 0.5368 | 0.5275 | 0.4501 | 0.5200 | 0.5286 | 0.5816 | 0.6589 | 0.6669 | 0.7232 | 0.6519 | 0.6578 | 0.8020 | 0.7158 | 0.7268 | 0.6386 |
35 | 2 | 0.5402 | 0.5257 | 0.4397 | 0.5208 | 0.5342 | 0.5920 | 0.6597 | 0.6734 | 0.7321 | 0.6467 | 0.6570 | 0.8036 | 0.7288 | 0.7456 | 0.6544 | |
36 | 3 | 0.5273 | 0.5184 | 0.4538 | 0.5292 | 0.5378 | 0.5787 | 0.6681 | 0.6766 | 0.7207 | 0.6549 | 0.6596 | 0.8007 | 0.7321 | 0.7469 | 0.6348 | |
37 | 13 | 1 | 0.4784 | 0.4850 | 0.4771 | 0.5954 | 0.5911 | 0.5801 | 0.7150 | 0.7107 | 0.7155 | 0.7258 | 0.7207 | 0.7999 | 0.7221 | 0.7195 | 0.6392 |
38 | 2 | 0.4836 | 0.4893 | 0.4798 | 0.5943 | 0.5906 | 0.5816 | 0.7153 | 0.7116 | 0.7173 | 0.7323 | 0.7278 | 0.8058 | 0.7120 | 0.7096 | 0.6365 | |
39 | 3 | 0.4822 | 0.4898 | 0.4737 | 0.5919 | 0.5866 | 0.5830 | 0.7134 | 0.7081 | 0.7197 | 0.7262 | 0.7199 | 0.8038 | 0.7170 | 0.7142 | 0.6412 | |
40 | 1 | 0.4752 | 0.4784 | 0.4558 | 0.5994 | 0.5977 | 0.5976 | 0.7191 | 0.7185 | 0.7316 | 0.7304 | 0.7296 | 0.8103 | 0.7249 | 0.7248 | 0.6592 | |
41 | 2 | 0.4758 | 0.4780 | 0.4737 | 0.6003 | 0.5993 | 0.5869 | 0.7200 | 0.7201 | 0.7211 | 0.7378 | 0.7377 | 0.8103 | 0.7148 | 0.7153 | 0.6383 | |
43 | 15 | 1 | 0.4760 | 0.4803 | 0.4650 | 0.5817 | 0.5773 | 0.5764 | 0.7078 | 0.7038 | 0.7129 | 0.7243 | 0.7217 | 0.7980 | 0.7087 | 0.7052 | 0.6333 |
44 | 2 | 0.4871 | 0.4906 | 0.4658 | 0.5737 | 0.5702 | 0.5767 | 0.7009 | 0.6974 | 0.7138 | 0.7214 | 0.7190 | 0.7994 | 0.6968 | 0.6940 | 0.6329 | |
45 | 3 | 0.4841 | 0.4865 | 0.4519 | 0.5732 | 0.5705 | 0.5852 | 0.7012 | 0.6987 | 0.7220 | 0.7042 | 0.7027 | 0.8013 | 0.7244 | 0.7220 | 0.6468 | |
46 | 16 | 1 | 0.4850 | 0.4850 | 0.4535 | 0.5731 | 0.5731 | 0.5829 | 0.6999 | 0.6999 | 0.7194 | 0.6994 | 0.6994 | 0.7952 | 0.7308 | 0.7308 | 0.6489 |
47 | 2 | 0.4801 | 0.4801 | 0.4453 | 0.5799 | 0.5799 | 0.5917 | 0.7075 | 0.7075 | 0.7276 | 0.7205 | 0.7205 | 0.8101 | 0.7140 | 0.7140 | 0.6463 | |
48 | 3 | 0.4829 | 0.4829 | 0.4495 | 0.5776 | 0.5776 | 0.5880 | 0.7048 | 0.7048 | 0.7236 | 0.7200 | 0.7200 | 0.8083 | 0.7079 | 0.7079 | 0.6408 | |
49 | 17 | 1 | 0.6981 | 0.7106 | 0.4658 | 0.3831 | 0.3740 | 0.5664 | 0.5086 | 0.4998 | 0.7042 | 0.4618 | 0.4514 | 0.7240 | 0.7432 | 0.7405 | 0.7115 |
50 | 2 | 0.6736 | 0.6876 | 0.4633 | 0.4002 | 0.3897 | 0.5659 | 0.5317 | 0.5217 | 0.7073 | 0.4825 | 0.4701 | 0.7333 | 0.7472 | 0.7473 | 0.6968 | |
51 | 3 | 0.6993 | 0.7142 | 0.4733 | 0.3811 | 0.3702 | 0.5597 | 0.5091 | 0.4984 | 0.7014 | 0.4608 | 0.4484 | 0.7014 | 0.7337 | 0.7338 | 0.7070 | |
52 | 18 | 1 | 0.6633 | 0.6566 | 0.4802 | 0.4128 | 0.4200 | 0.5548 | 0.5428 | 0.5514 | 0.6930 | 0.5078 | 0.5126 | 0.7224 | 0.7161 | 0.7313 | 0.6856 |
53 | 2 | 0.6807 | 0.6721 | 0.4840 | 0.3951 | 0.4053 | 0.5500 | 0.5229 | 0.5357 | 0.6901 | 0.4830 | 0.4913 | 0.7167 | 0.7235 | 0.7420 | 0.6869 | |
54 | 3 | 0.6678 | 0.6630 | 0.4786 | 0.4065 | 0.4105 | 0.5530 | 0.5399 | 0.5463 | 0.6930 | 0.5057 | 0.5071 | 0.7280 | 0.7003 | 0.7154 | 0.6714 |
Dependent Variable | Semantic Segmentation Model | Tile Resolution (pixels × pixels) | Tile Overlap (%) | Mean | Std. Error | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
IoU score | U-Net—Inception-ResNet-v2 | 256 | 0 | 0.5860 | 0.0036 | 0.5786 | 0.5933 |
12.5 | 0.5931 | 0.0036 | 0.5857 | 0.6005 | |||
512 | 0 | 0.5826 | 0.0036 | 0.5752 | 0.5900 | ||
12.5 | 0.5891 | 0.0036 | 0.5817 | 0.5965 | |||
1024 | 0 | 0.5843 | 0.0036 | 0.5769 | 0.5917 | ||
12.5 | 0.5943 | 0.0036 | 0.5869 | 0.6017 | |||
U-Net—SEResNeXt-50 | 256 | 0 | 0.5845 | 0.0036 | 0.5771 | 0.5918 | |
12.5 | 0.5919 | 0.0036 | 0.5845 | 0.5993 | |||
512 | 0 | 0.5773 | 0.0036 | 0.5699 | 0.5847 | ||
12.5 | 0.5838 | 0.0036 | 0.5765 | 0.5912 | |||
1024 | 0 | 0.5845 | 0.0036 | 0.5771 | 0.5919 | ||
12.5 | 0.5841 | 0.0036 | 0.5767 | 0.5915 | |||
LinkNet—EfficientNet-b5 | 256 | 0 | 0.5816 | 0.0036 | 0.5742 | 0.5889 | |
12.5 | 0.5948 | 0.0036 | 0.5874 | 0.6021 | |||
512 | 0 | 0.5794 | 0.0036 | 0.5721 | 0.5868 | ||
12.5 | 0.5875 | 0.0036 | 0.5802 | 0.5949 | |||
1024 | 0 | 0.5640 | 0.0036 | 0.5566 | 0.5714 | ||
12.5 | 0.5526 | 0.0036 | 0.5452 | 0.5600 | |||
F1 score | U-Net—Inception-ResNet-v2 | 256 | 0 | 0.7211 | 0.0033 | 0.7145 | 0.7277 |
12.5 | 0.7274 | 0.0033 | 0.7208 | 0.7340 | |||
512 | 0 | 0.7193 | 0.0033 | 0.7127 | 0.7259 | ||
12.5 | 0.7254 | 0.0033 | 0.7188 | 0.7320 | |||
1024 | 0 | 0.7233 | 0.0033 | 0.7167 | 0.7299 | ||
12.5 | 0.7326 | 0.0033 | 0.7260 | 0.7392 | |||
U-Net—SEResNeXt-50 | 256 | 0 | 0.7201 | 0.0033 | 0.7135 | 0.7267 | |
12.5 | 0.7263 | 0.0033 | 0.7197 | 0.7329 | |||
512 | 0 | 0.7149 | 0.0033 | 0.7083 | 0.7215 | ||
12.5 | 0.7196 | 0.0033 | 0.7130 | 0.7262 | |||
1024 | 0 | 0.7262 | 0.0033 | 0.7196 | 0.7328 | ||
12.5 | 0.7253 | 0.0033 | 0.7187 | 0.7319 | |||
LinkNet—EfficientNet-b5 | 256 | 0 | 0.7175 | 0.0033 | 0.7109 | 0.7241 | |
12.5 | 0.7289 | 0.0033 | 0.7223 | 0.7355 | |||
512 | 0 | 0.7162 | 0.0033 | 0.7096 | 0.7228 | ||
12.5 | 0.7235 | 0.0033 | 0.7169 | 0.7301 | |||
1024 | 0 | 0.7043 | 0.0033 | 0.6977 | 0.7109 | ||
12.5 | 0.6920 | 0.0033 | 0.6854 | 0.6986 | |||
Loss | U-Net—Inception-ResNet-v2 | 256 | 0 | 0.4739 | 0.0047 | 0.4645 | 0.4834 |
12.5 | 0.4649 | 0.0047 | 0.4555 | 0.4743 | |||
512 | 0 | 0.4555 | 0.0047 | 0.4461 | 0.4650 | ||
12.5 | 0.4480 | 0.0047 | 0.4386 | 0.4575 | |||
1024 | 0 | 0.4465 | 0.0047 | 0.4371 | 0.4560 | ||
12.5 | 0.4319 | 0.0047 | 0.4225 | 0.4413 | |||
U-Net—SEResNeXt-50 | 256 | 0 | 0.4748 | 0.0047 | 0.4654 | 0.4843 | |
12.5 | 0.4628 | 0.0047 | 0.4534 | 0.4723 | |||
512 | 0 | 0.4617 | 0.0047 | 0.4523 | 0.4711 | ||
12.5 | 0.4540 | 0.0047 | 0.4446 | 0.4634 | |||
1024 | 0 | 0.4466 | 0.0047 | 0.4372 | 0.4560 | ||
12.5 | 0.4479 | 0.0047 | 0.4384 | 0.4573 | |||
LinkNet—EfficientNet-b5 | 256 | 0 | 0.4769 | 0.0047 | 0.4674 | 0.4863 | |
12.5 | 0.4610 | 0.0047 | 0.4515 | 0.4704 | |||
512 | 0 | 0.4609 | 0.0047 | 0.4515 | 0.4703 | ||
12.5 | 0.4494 | 0.0047 | 0.4400 | 0.4589 | |||
1024 | 0 | 0.4675 | 0.0047 | 0.4580 | 0.4769 | ||
12.5 | 0.4809 | 0.0047 | 0.4715 | 0.4904 |
Click here to enlarge figure
Tile Size (Pixels) | Tile Overlap (%) | Set | No. Images | No. Pixels (per Class) | |
---|---|---|---|---|---|
Road | No Road (Background) | ||||
256 × 256 | 0% | Train | 237,919 | 672,864,947 | 14,919,394,637 |
Validation | 12,523 | 35,644,583 | 785,062,745 | ||
Percentage of data | 4.32% | 95.68% | |||
12.5% | Train | 312,092 | 902,912,960 | 19,550,348,352 | |
Validation | 16,426 | 47,987,916 | 1,028,506,420 | ||
Percentage of data | 4.42% | 95.58% | |||
Test set (novel area, no overlap) | 7708 | 18,158,800 | 486,992,688 | ||
Percentage of data | 3.59% | 96.41% | |||
512 × 512 | 0% | Train | 90,475 | 669,081,651 | 23,048,396,749 |
Validation | 4762 | 34,773,408 | 1,213,556,320 | ||
Percentage of data | 2.82% | 97.18% | |||
12.5% | Train | 118,078 | 901,745,879 | 30,051,693,353 | |
Validation | 6215 | 48,197,575 | 1,581,027,385 | ||
Percentage of data | 2.92% | 97.08% | |||
Test set (novel area, no overlap) | 3110 | 18,137,722 | 797,130,118 | ||
Percentage of data | 2.22% | 97.78% | |||
1024 × 1024 | 0% | Train | 27,705 | 661,863,036 | 27,188,935,044 |
Validation | 1457 | 35,975,504 | 1,491,799,728 | ||
Percentage of data | 2.38% | 97.62% | |||
12.5% | Train | 36,034 | 891,014,527 | 36,893,373,057 | |
Validation | 1897 | 47,973,497 | 1,941,175,175 | ||
Percentage of data | 2.36% | 97.64% | |||
Test set (novel area, no overlap) | 955 | 18,150,383 | 983,239,697 | ||
Percentage of data | 1.81% | 98.34% |
Training Scenario ID | Semantic Segmentation Model | Tile Size (Pixels) | Tile Overlap (%) |
---|---|---|---|
1 | U-Net—Inception-ResNet-v2 | 256 × 256 | 0 |
2 | 12.5 | ||
3 | U-Net—Inception-ResNet-v2 | 512 × 512 | 0 |
4 | 12.5 | ||
5 | U-Net—Inception-ResNet-v2 | 1024 × 1024 | 0 |
6 | 12.5 | ||
7 | U-Net—SEResNeXt-50 | 256 × 256 | 0 |
8 | 12.5 | ||
9 | U-Net—SEResNeXt-50 | 512 × 512 | 0 |
10 | 12.5 | ||
11 | U-Net—SEResNeXt-50 | 1024 × 1024 | 0 |
12 | 12.5 | ||
13 | LinkNet—EfficientNet-b5 | 256 × 256 | 0 |
14 | 12.5 | ||
15 | LinkNet—EfficientNet-b5 | 512 × 512 | 0 |
16 | 12.5 | ||
17 | LinkNet—EfficientNet-b5 | 1024 × 1024 | 0 |
18 | 12.5 |
Independent Variable | Category (Training Scenario ID) | Statistical MeasurE | Loss | IoU Score | F1 Score | Precision | Recall |
---|---|---|---|---|---|---|---|
Training Scenario ID (Road Segmentation) | 1 | Mean | 0.4739 | 0.5860 | 0.7211 | 0.8090 | 0.6402 |
Std. Deviation | 0.0020 | 0.0011 | 0.0013 | 0.0024 | 0.0031 | ||
2 | Mean | 0.4649 | 0.5931 | 0.7274 | 0.8081 | 0.6535 | |
Std. Deviation | 0.0070 | 0.0061 | 0.0063 | 0.0072 | 0.0034 | ||
3 | Mean | 0.4555 | 0.5826 | 0.7193 | 0.8025 | 0.6396 | |
Std. Deviation | 0.0051 | 0.0036 | 0.0031 | 0.0033 | 0.0056 | ||
4 | Mean | 0.4480 | 0.5891 | 0.7254 | 0.8059 | 0.6468 | |
Std. Deviation | 0.0054 | 0.0049 | 0.0040 | 0.0054 | 0.0099 | ||
5 | Mean | 0.4465 | 0.5843 | 0.7233 | 0.7774 | 0.6706 | |
Std. Deviation | 0.0041 | 0.0040 | 0.0041 | 0.0107 | 0.0141 | ||
6 | Mean | 0.5943 | 0.7986 | 0.6625 | |||
Std. Deviation | 0.0073 | 0.0031 | 0.0023 | 0.0051 | 0.0100 | ||
7 | Mean | 0.4748 | 0.5845 | 0.7201 | 0.8070 | 0.6403 | |
Std. Deviation | 0.0061 | 0.0033 | 0.0027 | 0.0027 | 0.0024 | ||
8 | Mean | 0.4628 | 0.5919 | 0.7263 | 0.8086 | 0.6488 | |
Std. Deviation | 0.0111 | 0.0093 | 0.0086 | 0.0046 | 0.0128 | ||
9 | Mean | 0.4617 | 0.5773 | 0.7149 | 0.8011 | 0.6318 | |
Std. Deviation | 0.0223 | 0.0180 | 0.0152 | 0.0023 | 0.0291 | ||
10 | Mean | 0.4540 | 0.5838 | 0.7196 | 0.8038 | 0.6371 | |
Std. Deviation | 0.0039 | 0.0039 | 0.0046 | 0.0090 | 0.0027 | ||
11 | Mean | 0.4466 | 0.5845 | 0.7262 | 0.7919 | 0.6573 | |
Std. Deviation | 0.0044 | 0.0038 | 0.0031 | 0.0024 | 0.0032 | ||
12 | Mean | 0.4479 | 0.5841 | 0.7253 | 0.8021 | 0.6426 | |
Std. Deviation | 0.0073 | 0.0070 | 0.0060 | 0.0015 | 0.0104 | ||
13 | Mean | 0.4769 | 0.5816 | 0.7175 | 0.8032 | 0.6390 | |
Std. Deviation | 0.0031 | 0.0015 | 0.0021 | 0.0030 | 0.0024 | ||
14 | Mean | 0.4610 | 0.7289 | 0.6538 | |||
Std. Deviation | 0.0111 | 0.0069 | 0.0069 | 0.0003 | 0.0136 | ||
15 | Mean | 0.4609 | 0.5794 | 0.7162 | 0.7996 | 0.6377 | |
Std. Deviation | 0.0078 | 0.0050 | 0.0050 | 0.0017 | 0.0079 | ||
16 | Mean | 0.4494 | 0.5875 | 0.7235 | 0.8045 | 0.6453 | |
Std. Deviation | 0.0041 | 0.0044 | 0.0041 | 0.0081 | 0.0041 | ||
17 | Mean | 0.4675 | 0.5640 | 0.7043 | 0.7196 | ||
Std. Deviation | 0.0052 | 0.0037 | 0.0030 | 0.0164 | 0.0075 | ||
18 | Mean | 0.4809 | 0.5526 | 0.6920 | 0.7224 | 0.6813 | |
Std. Deviation | 0.0028 | 0.0024 | 0.0017 | 0.0057 | 0.0086 | ||
Inferential Statistics | F-statistic | 7.678 | 8.257 | 8.464 | 54.498 | 9.196 | |
p-value | |||||||
η | 0.885 | 0.892 | 0.894 | 0.981 | 0.902 | ||
η | 0.784 | 0.796 | 0.800 | 0.963 | 0.813 | ||
Total (Descriptive Statistics) | Mean | 0.4592 | 0.5831 | 0.7202 | 0.7931 | 0.6518 | |
Std. Deviation | 0.0143 | 0.0115 | 0.0104 | 0.0273 | 0.0201 |
Independent Variable | Category | Statistical Measure | Loss | IoU Score | F1 Score | Precision | Recall |
---|---|---|---|---|---|---|---|
Tile Size (pixels × pixels) | 256 | Mean | 0.4691 | 0.6459 | |||
Std. Deviation | 0.0091 | 0.0068 | 0.0062 | 0.0040 | 0.0093 | ||
512 | Mean | 0.4549 | 0.5833 | 0.7199 | 0.8029 | 0.6397 | |
Std. Deviation | 0.0102 | 0.0082 | 0.0072 | 0.0053 | 0.0124 | ||
1024 | Mean | 0.5773 | 0.7173 | 0.7687 | |||
Std. Deviation | 0.0171 | 0.0151 | 0.0150 | 0.0363 | 0.0218 | ||
Inferential Statistics | F-statistic | 8.279 | 5.054 | 1.688 | 17.915 | 19.164 | |
p-value | 0.195 | ||||||
η | 0.495 | 0.407 | 0.249 | 0.642 | 0.655 | ||
η | 0.245 | 0.165 | 0.062 | 0.413 | 0.429 | ||
Tile Overlap (%) | 0 | Mean | 0.4627 | 0.5805 | 0.7181 | 0.7901 | 0.6513 |
Std. Deviation | 0.0133 | 0.0086 | 0.0078 | 0.0276 | 0.0245 | ||
12.5 | Mean | ||||||
Std. Deviation | 0.0146 | 0.0134 | 0.0123 | 0.0272 | 0.0147 | ||
Inferential Statistics | F-statistic | 3.445 | 2.904 | 2.290 | 0.618 | 0.042 | |
p-value | 0.069 | 0.094 | 0.136 | 0.435 | 0.838 | ||
η | 0.249 | 0.230 | 0.205 | 0.108 | |||
η | 0.062 | 0.053 | 0.042 | 0.012 | 0.001 | ||
Semantic Segmentation Model | U-Net—Inception-ResNet-v2 | Mean | 0.8003 | 0.6522 | |||
Std. Deviation | 0.0146 | 0.0057 | 0.0055 | 0.0123 | 0.0138 | ||
U-Net—SEResNeXt-50 | Mean | 0.4580 | 0.5844 | 0.7221 | 0.6430 | ||
Std. Deviation | 0.0137 | 0.0088 | 0.0080 | 0.0067 | 0.0144 | ||
LinkNet—EfficientNet-b5 | Mean | 0.4661 | 0.5767 | 0.7138 | 0.7766 | ||
Std. Deviation | 0.0121 | 0.0151 | 0.0131 | 0.0411 | 0.0264 | ||
Inferential Statistics | F-statistic | 4.021 | 5.554 | 6.768 | 5.889 | 3.736 | |
p-value | |||||||
η | 0.369 | 0.423 | 0.458 | 0.433 | 0.357 | ||
η | 0.136 | 0.179 | 0.210 | 0.188 | 0.128 |
ID | Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | p-Value |
---|---|---|---|---|---|---|---|
1 | Corrected Model | IoU score | 0.0056 | 17 | 0.0003 | 8.257 | |
F1 score | 0.0046 | 17 | 0.0003 | 8.464 | |||
Loss | 0.0085 | 17 | 0.0005 | 7.678 | |||
2 | Intercept | IoU score | 18.3588 | 1 | 18.3588 | 463,213.653 | |
F1 score | 28.0115 | 1 | 28.0115 | 879,920.640 | |||
Loss | 11.3857 | 1 | 11.3857 | 175,315.654 | |||
3 | Model | IoU score | 0.0013 | 2 | 0.0006 | 15.772 | |
F1 score | 0.0012 | 2 | 0.0006 | 18.865 | |||
Loss | 0.0015 | 2 | 0.0007 | 11.342 | |||
4 | Size | IoU score | 0.0012 | 2 | 0.0006 | 14.586 | |
F1 score | 0.0004 | 2 | 0.0002 | 5.583 | |||
Loss | 0.0027 | 2 | 0.0013 | 20.407 | |||
5 | Overlap | IoU score | 0.0004 | 1 | 0.0004 | 9.329 | |
F1 score | 0.0002 | 1 | 0.0002 | 7.587 | |||
Loss | 0.0007 | 1 | 0.0007 | 10.348 | |||
6 | Size * Overlap | IoU score | 0.0002 | 2 | 0.0001 | 3.036 | 0.060 |
F1 score | 0.0002 | 2 | 0.0001 | 3.372 | |||
Loss | 0.0004 | 2 | 0.0002 | 2.814 | 0.073 | ||
7 | Model * Size | IoU score | 0.0022 | 4 | 0.0005 | 13.658 | |
F1 score | 0.0022 | 4 | 0.0005 | 17.186 | |||
Loss | 0.0027 | 4 | 0.0007 | 10.276 | |||
8 | Model * Overlap | IoU score | 0.0001 | 2 | 2.5282 | 0.638 | 0.534 |
F1 score | 0.0001 | 2 | 3.1339 | 0.984 | 0.383 | ||
Loss | 0.0001 | 2 | 4.0069 | 0.617 | 0.545 | ||
9 | Model * Size * Overlap | IoU score | 0.0003 | 4 | 8.2644 | 2.085 | 0.103 |
F1 score | 0.0003 | 4 | 7.9184 | 2.487 | 0.061 | ||
Loss | 0.0006 | 4 | 0.0001 | 2.179 | 0.091 | ||
10 | Error | IoU score | 0.0014 | 36 | 3.9634 | ||
F1 score | 0.0011 | 36 | 3.1834 | ||||
Loss | 0.0023 | 36 | 6.4944 | ||||
11 | Total | IoU score | 18.3658 | 54 | |||
F1 score | 28.0172 | 54 | |||||
Loss | 11.3965 | 54 | |||||
12 | Corrected Total | IoU score | 0.0070 | 53 | |||
F1 score | 0.0057 | 53 | |||||
Loss | 0.0108 | 53 |
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Cira, C.-I.; Manso-Callejo, M.-Á.; Alcarria, R.; Iturrioz, T.; Arranz-Justel, J.-J. Insights into the Effects of Tile Size and Tile Overlap Levels on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography. Remote Sens. 2024 , 16 , 2954. https://doi.org/10.3390/rs16162954
Cira C-I, Manso-Callejo M-Á, Alcarria R, Iturrioz T, Arranz-Justel J-J. Insights into the Effects of Tile Size and Tile Overlap Levels on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography. Remote Sensing . 2024; 16(16):2954. https://doi.org/10.3390/rs16162954
Cira, Calimanut-Ionut, Miguel-Ángel Manso-Callejo, Ramon Alcarria, Teresa Iturrioz, and José-Juan Arranz-Justel. 2024. "Insights into the Effects of Tile Size and Tile Overlap Levels on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography" Remote Sensing 16, no. 16: 2954. https://doi.org/10.3390/rs16162954
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6. Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0, while the alternative hypothesis is H 1 or H a.
Choose a testable hypothesis with an independent variable that you have absolute control over. Independent and dependent variables. Define your variables in your hypothesis so your readers understand the big picture. You don't have to specifically say which ones are independent and dependent variables, but you definitely want to mention them all.
While the independent variable is the " cause ", the dependent variable is the " effect " - or rather, the affected variable. In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable. Keeping with the previous example, let's look at some dependent variables ...
Step 4: Refine your hypothesis. You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain: The relevant variables. The specific group being studied.
Directional Hypothesis: This type predicts the nature of the effect of the independent variable on the dependent variable. It specifies the direction of the expected relationship. ... How to Write a Good Hypothesis. Writing a good hypothesis is definitely a good skill to have in scientific research. But it is also one that you can definitely ...
If you need still more detail, visit the SAGE Research Methods Map . A good hypothesis will be written as a statement or question that specifies: The dependent variable (s): who or what you expect to be affected. The independent variable (s): who or what you predict will affect the dependent variable. What you predict the effect will be.
In this article, we will explore the key elements that make a good hypothesis and provide practical tips for developing one. Key Takeaways. A good hypothesis should be clear, precise, and testable. Understanding the role of independent, dependent, and control variables is crucial in hypothesis formulation.
A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.
Research hypothesis checklist. Once you've written a possible hypothesis, make sure it checks the following boxes: It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis. It must include a dependent and independent variable: At least one independent variable ( cause) and one dependent ...
The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.
Independent and Dependent Variables, Explained With Examples. Written by MasterClass. Last updated: Mar 21, 2022 • 4 min read. In experiments that test cause and effect, two types of variables come into play. One is an independent variable and the other is a dependent variable, and together they play an integral role in research design.
A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable.
Merriam Webster defines a hypothesis as "an assumption or concession made for the sake of argument.". In other words, a hypothesis is an educated guess. Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it's true or not.
Simple hypothesis: This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.; Complex hypothesis: This type suggests a relationship between three or more variables, such as two independent and dependent variables.; Null hypothesis: This hypothesis suggests no relationship exists between two or more variables.
Sandra says: "This hypothesis gives a clear indication of what is to be tested (the ability of ladybugs to curb an aphid infestation), is a manageable size for a single experiment, mentions the independent variable (ladybugs) and the dependent variable (number of aphids), and predicts the effect (exposure to ladybugs reduces the number of aphids)."
If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won't make sense. Independent variable causes an effect on the dependent variable. Example: How long you sleep (independent variable) affects ...
Here are several examples of independent and dependent variables in experiments: In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to know which brand of fertilizer is best for your plants.
In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome.
Selecting Levels of an Independent Variable Selecting a Dependent Variable Characteristics of a Good Dependent Variable ... Several very good electronic databases contain references to journal articles or books. Ask your instructor or a reference librarian for the databases available on your ... hypothesis. It is an educated guessregarding what ...
The hypothesis is often written using the words "IF" and "THEN." For example, "If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables. The hypothesis should relate back to your original question and must be testable.
A hypothesis has three essential parts: explanation, independent variable, and dependent variable. Some of the most common mistakes people make are leaving out the explanation or misidentifying ...
Regardless of the study type, if you see an estimated effect size, it is an independent variable. Identifying DVs. Dependent variables are the outcome. The IVs explain the variability or causes changes in the DV. Focus on the "depends" aspect. The value of the dependent variable depends on the IVs. If Y depends on X, then Y is the dependent ...
The dependent variable(s): Who or what you can vary or control. The independent variable(s): Who or what you predict will affect the dependent variable. What you predict the effect will be. A good hypothesis statement is written as, IF (the Dependent Variable) THEN (Independent Variable) is affected in a specific way. Assumptions Versus Hypothesis
The null hypothesis of the interaction effect asserts that the effect of one independent variable on the dependent variable remains consistent regardless of the level of another independent variable. Analyzing the interaction effect between two factors reveals whether the relationship between one factor and the dependent variable (performance ...