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 .
This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...
Very informative, concise and helpful. Thank you
Helping information.Thanks
practical and well-demonstrated
Very helpful and insightful
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
Published by Nicolas at March 17th, 2024 , Revised On March 12, 2024
Research is a dynamic process, where scientists strive to unravel the mysteries of the world through systematic inquiry. In this pursuit, control variables play a crucial role in shaping the reliability and validity of research findings. This blog serves as a practical guide to aid researchers in the thoughtful selection of control variables.
Table of Contents
“Control variables, often referred to as covariates, are elements in a study that are intentionally held constant or systematically manipulated to examine their impact on the relationship between independent and dependent variables. These variables act as safeguards against confounding factors, ensuring that the observed effects can be attributed more accurately to the independent variable under investigation.
The choice of control variables is not arbitrary; it demands careful consideration and a deep understanding of the research context. The significance of selecting the right control variables cannot be overstated, as these elements serve as the bedrock for establishing the internal validity of a study.
Internal validity refers to the accuracy of causal inferences within an experiment – the extent to which changes in the dependent variable can be confidently attributed to manipulating the independent variable.
By meticulously selecting control variables, researchers can minimize the risk of alternative explanations, ensuring that observed effects are more likely to reflect true causal relationships.
Research validity is a multifaceted concept that encompasses various dimensions, including internal, external, construct, and statistical validity. Control variables primarily enhance internal validity by minimizing the influence of extraneous variables that could introduce bias or confound the results.
Researchers create a more controlled and precise experimental environment by strategically incorporating control variables. This, in turn, allows for a clearer understanding of the relationship between the independent and dependent variables, bolstering the overall validity of the research findings.
In essence, control variables act as gatekeepers, fortifying the integrity of the research process and paving the way for more robust and trustworthy scientific conclusions.
Control variables, also known as covariates, are integral components of experimental design and statistical analysis in research. Their primary purpose is to add precision to investigations by accounting for potential confounding factors that might otherwise distort the interpretation of results.
For instance, imagine a study examining the impact of a new drug on patients’ recovery time after surgery. The type of anesthesia used, the patient’s age, and pre-existing health conditions are all factors that could influence the recovery time.
By identifying and controlling for these variables, researchers can more confidently attribute any observed changes in recovery time to the specific effects of the drug being studied.
To grasp the role of control variables, it is essential to differentiate them from independent and dependent variables. The researcher manipulates or selects independent variables to observe their effect on the dependent variable.
On the other hand, dependent variables are the outcomes or responses measured in the experiment, dependent on the changes in the independent variable.
Control variables, however, are not the variables of primary interest. Instead, they are chosen to minimize the influence of extraneous variables that might interfere with the relationship between the independent and dependent variables. While independent and dependent variables are central to the research question, control variables act as safeguards to ensure the integrity and validity of the study.
Control variables are versatile and their selection depends on the specifics of each study.
In social science research, control variables may include demographic factors like age, gender, and socioeconomic status.
In experimental studies in the physical sciences, factors such as temperature, humidity, or pressure might be controlled to isolate the effects of the manipulated variables.
Consider a psychological study exploring the impact of a new therapy on reducing anxiety levels. Control variables in this scenario could include the participants’ previous experiences with therapy, baseline anxiety levels, or even the time of day the therapy sessions are conducted.
These variables, when controlled, allow the researcher to attribute any observed changes in anxiety levels more confidently to the therapeutic intervention.
The following are the criteria for selecting the right control variables.
One of the foremost considerations when selecting control variables is their relevance to the research question or thesis statement . The chosen control variables should have a logical and theoretical connection to the study, aligning with the overarching objectives.
Researchers must carefully evaluate whether the control variables are likely to influence the relationship between the independent and dependent variables. A judicious selection based on relevance ensures that the controlled factors contribute meaningfully to the study’s internal validity.
Control variables act as a shield against confounding factors—variables that might distort the observed relationship between the independent and dependent variables. Identifying potential confounding factors requires an understanding of the subject and a thorough literature review.
Researchers must anticipate variables that could muddy the waters and strategically incorporate them as control variables to isolate the effects of the independent variable accurately.
While researchers aim for inclusivity in control variable selection, practical considerations cannot be ignored. Feasibility and practicality play a pivotal role in the decision-making process.
Researchers must assess whether the chosen control variables are measurable, obtainable, and manageable within the constraints of the study. Pragmatic decisions ensure that the research remains feasible without compromising the overall quality and validity.
Achieving a delicate balance between inclusivity and specificity is crucial in control variable selection. Including too few control variables may leave the study vulnerable to lurking confounders, while an overly exhaustive list may complicate the analysis and risk diluting the primary focus.
Researchers must strike a balance, aiming for inclusivity without sacrificing the specificity necessary to draw meaningful and precise conclusions from the data.
Here are some common pitfalls in control variable selection.
One common pitfall in control variable selection is overlooking variables that could significantly impact the study’s outcomes. Researchers may inadvertently omit relevant factors that, when unaccounted for, introduce bias or confound the results.
Rigorous literature reviews and a comprehensive understanding of the research domain are crucial in avoiding this oversight.
Conversely, the inclusion of unnecessary variables poses another challenge. Researchers may be tempted to incorporate a multitude of control variables without clear theoretical or empirical justification.
This not only complicates the study unnecessarily but can also lead to overfitting models, reducing the generalizability of findings. Prudent selection is key to avoiding this pitfall.
Control variables should not be confused with mediators or moderators . Mediators explain how an independent variable affects a dependent variable, while moderators influence the strength or direction of the relationship between the independent and dependent variables.
Confusing these concepts can lead to misinterpretation of results and compromise the overall integrity of the study. Researchers must delineate between control variables, mediators, and moderators to ensure accurate analyses.
You can identify control variables with the help of the following strategies.
A robust literature review is a cornerstone for identifying relevant control variables. Existing research provides valuable insights into potential factors that could confound or influence the relationships under investigation.
By examining similar studies and drawing on the collective knowledge within the field, researchers can identify common control variables used by peers and gain a better understanding of the variables that warrant consideration in their own work.
Conducting preliminary data analysis can unearth patterns and relationships that may guide the selection of control variables. Exploratory data analysis allows researchers to identify potential confounding factors by examining correlations, patterns, and outliers.
By scrutinizing the data before formal analysis, researchers can make informed decisions about which variables to control for, refine their study design, and ensure a more robust research paper approach.
Seeking input from experts in the field and obtaining peer feedback can provide valuable perspectives on control variable selection. Collaborating with colleagues who have expertise in the subject or statistical methods can offer fresh insights and help researchers consider variables they might have overlooked.
Peer review processes also serve as a checkpoint, allowing external experts to assess the validity and appropriateness of chosen control variables.
Thorough documentation of control variable choices is essential for the transparency and replicability of research. Researchers should meticulously record the rationale behind each control variable selection, detailing the theoretical or empirical basis for inclusion.
This documentation serves as a critical reference point for both internal and external stakeholders, aiding in the understanding and evaluation of the study’s design and validity.
Here are some case studies to help you better understand control variables.
Examining real-world examples of well-selected control variables can provide valuable insights into effective research practices. In a study investigating the impact of a nutritional intervention on weight loss, well-chosen control variables might include participants’ baseline body mass index (BMI), exercise habits, and pre-existing medical conditions.
These control variables help ensure that observed changes in weight can be confidently attributed to the nutritional intervention, minimizing the influence of extraneous factors.
In another example, a social science study exploring the effects of a community development program may appropriately control for demographic factors such as income, education level, and employment status. By doing so, the researchers can isolate the specific impact of the intervention on community outcomes without the interference of socioeconomic disparities.
Conversely, inadequate control variable selection can compromise the validity of study findings. For instance, a study examining the effectiveness of a new teaching method in improving student performance may fall short if it fails to control for factors like students’ prior academic achievement, socio-economic background, or teacher-student ratios.
In such cases, the observed improvements in student performance may be confounded by these uncontrolled variables, making it challenging to attribute the effects solely to the teaching method.
Similarly, a health-related study investigating the impact of a wellness program may encounter issues if it neglects to control for participants’ pre-existing health conditions or lifestyle factors. Without proper controls, the study risks drawing inaccurate conclusions about the program’s effectiveness.
Analyzing case studies with both effective and inadequate control variable selection provides valuable lessons for researchers. It underscores the importance of understanding the research context and the critical role that control variables play in ensuring the internal validity of a study.
Researchers can learn to anticipate potential confounding factors, appreciate the complexity of real-world scenarios, and recognize the significance of meticulous control variable selection in generating trustworthy research outcomes.
With the help of these tips, you can implement control variables.
The research process is dynamic, and unforeseen variables may emerge. Researchers should adopt a proactive approach to monitor and adjust control variables as necessary throughout the study.
Regularly assessing the relevance and impact of control variables allows researchers to adapt to changing circumstances, ensuring that the study remains robust and that unexpected confounding factors are addressed promptly.
Statistical techniques can aid researchers in assessing the impact of control variables on study outcomes. Regression analysis, for example, allows researchers to examine how changes in the independent variable relate to changes in the dependent variable while holding control variables constant.
This analysis helps quantify the contribution of each variable and ensures that control variables are appropriately considered in the interpretation of results.
Longitudinal or experimental studies present unique challenges in control variable selection. In longitudinal studies, where data is collected over an extended period, researchers must carefully choose control variables that account for changes over time.
In experimental studies, the manipulation of variables introduces complexities that require strategic control variable selection. Researchers should be attuned to their study design, ensuring that control variables are relevant and measurable, and effectively mitigate potential confounding factors specific to their experimental or longitudinal context.
What are the examples of variable control.
Examples of variable control include maintaining consistent temperature in a scientific experiment, controlling for participants’ age and gender in social research, or standardizing testing conditions to isolate the impact of an independent variable on a dependent variable.
System control variables are parameters or factors intentionally regulated or kept constant in a system to observe the impact of independent variables. By controlling these elements, researchers can isolate and assess the effects of specific variables on the system’s behaviour or outcomes.
If you are looking for research paper format, then this is your go-to guide, with proper guidelines, from title page to the appendices.
Find out if you need permission to publish your dissertation in canada. Understand copyright, university rules, and third-party content.
Are you in need of captivating and achievable research topics within the field of biology? Your quest for the best […]
Ready to place an order?
Learning resources, company details.
Automated page speed optimizations for fast site performance
Reviewed by: BD Editors
A controlled variable is a commonly used term in the field of scientific research, where finding evidence to support a theory is rarely straightforward. In the case of the natural sciences, some research features are constant, but the majority of these have inconsistencies. These inconsistencies are known as variables.
For an experiment to give statistically useful results , every aspect of the study subject and the environment must be the same, or as similar as possible . Studies are made up of independent variables (the effects of a specific change the researcher wishes to observe), the dependent variable (the measurement of this change), and the controlled variable. A controlled variable creates a similar environment across the board, so that the change that is being studies is not influenced by multiple, uncontrolled factors.
If seedlings are being tested for their rates of growth at two different light levels, the results of the independent variable (light levels) and the dependent variable (millimeters of growth) will be much more accurate if the seedlings are exactly the same. This does not only refer to their genetic make-up (the size of the seed, the parent plants, the species), but also external variables such as temperature, moisture levels, soil mineral content, air quality, position, and many others.
By using genetically cloned seeds in a carefully prepared growth medium placed inside a closed and highly controlled environment, and by following exact schedules for sowing and measuring times – as is the case in the image above – this study may then come to the conclusion that any changes in growth are due to light levels, rather than to other changes. Controlled variables should make study subjects and their environment as similar as possible. The perfect experiment controls all variables except the dependent variable – the result.
In science, and in basic and applied research, variables are innumerable . From the simplest of elements to the most complex organisms, any number of differences can change the results of a line of study. The conclusions of an experiment carried out in one facility can differ to that of another, even when the same methods are applied . Living organisms are often too complicated to be expected to react in exactly the same way, whether this refers to the research subject, or to the researcher.
Controlled variable examples in non-living materials are easier to implement than in research on living organisms. Research that looks at the reaction of one non-living material to another has the potential to implement near-perfect experimental controls. One example of a study on non-living materials could be the testing of two different smoothing processes on four different brands of dental cement. Testing can be carried out ‘ in vitro’ , meaning outside of a living organism, and thereby removing countless potential variables.
Controlled variables of this experiment would include application method and materials, light-curing intensities on the cement, specimen storage (temperature and duration), the length of time of the polishing process, the settings of the electron microscope, and the rotation speed of the polishing device. The addition of a control group would be a subdivision of the controlled variable. A control group is a group that undergoes the same preparation and is kept in the same environment as the tested samples, but is not exposed to the independent variable . In the above example, the control group features cement left unpolished. This removes the possibility of natural processes such as oxidation or air humidity that might affect the smoothness of untreated cement being counted towards the effect of the mechanical smoothing processes.
What this experiment would find difficult to control would be how identical each cement sample would be, as manufactured samples can differ. The distribution of ingredients in manufactured compounds can not be considered to be identical unless stringent tests are carried out before research commences.
Controlled variable examples for living organisms are much more complex than in the majority of research upon non-living materials. In more complex and naturally produced living organisms, variables are predominantly uncontrolled. This is the primary reason why very simple organisms like fruit flies, or very similar organisms such as genetically cloned mice and rats, are used in testing environments. Once statistically relevant results are available in non-human models, human testing is initiated on groups that are as non-diverse as possible. The graphic below shows the steps all FDA-approved medications must go through. From the pre-clinical stage to stage III of the clinical trial, the possibilities for implementing controlled variables drastically diminish.
Advertisements for research subjects often ask for people of a certain age group, gender, or body mass index. They also refer to medical, behavioral or lifestyle variables such as no cigarette or alcohol consumption, no medication use, no co-morbidities, and medium to high levels of exercise . By implementing controlled variables early on, a researcher can create a group where results are more generic .
If a potential study subject fits this initial brief they are usually invited for further analysis. In the case of a weight-loss drug, for example, this might include insulin resistance and glucose testing, endocrine function , blood count, heart and lung function tests, and medical and familial history taking. Examples of controlled variables at this phase might be candidates without any family history of diabetes, or those who pass a specific psychological test.
Initial trials are able to study the effects of treatment in a very generic and similar population. The importance of psychological variables – something less influential in animal models – is never underestimated. However, once a drug, a chemical, or a therapy must be tested on the population for which it is designed, controlled variables become more difficult to achieve. In the case of the weight-loss drug example, a morbidly obese adult with a binge-eating disorder, sedentary lifestyle, anxiety and diabetes may not respond in the same way as a slightly overweight adult who gained weight after breaking a leg and has no comorbidities. But to which variables can a difference in response be attributed?
In all types of research, limiting other factors which may change either the action of the independent variable, or the result of the dependent variable is essential to obtain the best quality data. As organisms become more complex, the ability to limit these factors progressively decreases. By implementing as many controlled variables as possible, scientific evidence becomes more accurate and is a more solid and trustworthy foundation for the next generation of researchers.
In experimental processes, variables can influence the final results. Researchers must attempt to limit these variables to the specific changes they are studying. A variable represents anything that undergoes change . Variables may be temperature fluctuations, comorbidities, behaviors, environments, diet, air quality, stress levels, metabolism, or allergies. Even seasonal or global events may have an effect upon the final results.
For research purposes, variables are categorized into three groups. The first is the independent (or manipulated) variable – the change that is consciously made in order to study a particular action or reaction, or change that is independent of our control, namely time and the ageing process.
The second variable is the dependent (or responding) variable, which the researcher measures in order to come to the final result. For example, a study may look at the effect a serving of blueberries has to the results of a color-coded memory test. The independent variable is the dietary change (blueberries). The dependent variable is the memory test used to measure whether blueberries affect the memory. It is easy to envision how potential variables can limit the accuracy of the researcher’s findings. Did the subject get a good night’s sleep? Did the subject feel unwell at the time? Did the subject understand the concept of the game? Is the subject color-blind? To limit these variables, this study requires a third type of variable – the controlled variable.
1. Which of the following is a definition of a dependent variable?
2. Which of these is not a controlled variable?
3. The further along the research route a clinical drug trial, the less the controlled variables.
4. Which of the following provides the most controlled variables?
5. A control group:
Enter your email to receive results:
Subscribe to our newsletter, privacy policy, terms of service, scholarship, latest posts, white blood cell, t cell immunity, satellite cells, embryonic stem cells, popular topics, endocrine system, natural selection, digestive system, hydrochloric acid, pituitary gland, hermaphrodite.
The knowledge of control variables is extremely important for the students and academic professionals which will give us critical insight into how it improves our research outcome. So, first, we will start with the control variables’ definitions and examples associated with them.
A control variable is an experimental element which is constant or limited throughout the course of the research investigation. More often, the control variables may not have a direct interest in the aim and objectives of the study, but it tends to have a significant influence on the resulting outcome of the research.
Not just the control variables definition , but we will put forth the examples of it for better clarity.
For example, to evaluate the effect of soil quality on plant growth, the temperature, light and water are held constant during an experiment, which is referred to as the controlled variables.
Similarly, to investigate the relationship between happiness and income, we measure the control variables of age, health and marital status.
Let us provide some more examples for better understanding!!
Medicine can reduce illness | Health Age |
Supplements can improve the memory recall | Time of medication Sleep amount Familiarity with recall |
Effect of temperature and kiln time on clay pot quality | Clay type Level of ambient humidity Clay moisture |
In research studies and experiments, the aim is to understand the impact of an independent variable on a dependent variable. The control variables ensure to keep the experimental results are fair and unskewed devoid of any experimental manipulation.
The above examples indicate that control variables ensure the results obtained are solely dependent on the experimental evaluation. The variables independent or dependent, are not the primary focus of any research, rather keeping their values constant throughout helps in the establishment of true correlations between the dependent and independent variables. Now don’t get confused between the control variables and control groups as they strike a stark distinction.
In the research methodology , the use of control variables must be identified with recorded values to evaluate the results with precision. Moreover, the implication of control variables increases the internal validity of your research study which is otherwise a pretty difficult task to attain. To be specific, internal validity improves the degree of confidence in the differences you observe in the findings and attain the correct conclusions.
Very simple!! If researchers do not have control variables planned in the research methodology , it will become difficult to figure out or prove their exact impact on the results. It is crucial to find out whether the results of the research are an effect of the independent variable to justify experimental errors. Moreover, controlled variations are important because even the slightest variations in the research findings could have a significant influence on the results. Another major advantage of control variables points out the convenience of reproducing any research study while creating a strong relationship between the dependent and independent variables.
Taking over the examples set above, while we try to determine the effect of soil quality on plant growth, the independent variable refers to the soil quality whereas the dependent variable indicates the rate of plant growth. Hence, if we do not have control over the soil quality, we may end up with skewed results which may distort the actual outcome of the study.
You can make use of several approaches to control the variables in a research study. In some scenarios, variables can be controlled directly or by using standardized procedures which will be discussed further.
Nevertheless, the direct approach and random control of the variables are effective in equalizing the experimental groups, however, it may not be feasible always. So we apply the statistical approaches for better clarity in the process.
We know that experimentation, controlling variables, and recording outcomes are not as simple as it seems. In reality, every research study may have several different factors and variables that can have a noteworthy influence on the results. Don’t stress, coz we are here to help you out!! We help you plan the appropriate research methodology and approaches to keep the control variables constant so that the comparison can take place following an unbiased pattern.
So, if you come up with a query on experimentation and research methodology , we will assign an expert who can guide you thoroughly right from scratch on control variable definition , followed by simple examples, applications, benefits and challenges, and approaches inclusive of all the related content.
Don’t rush while placing the order!! Rather, I would suggest you take a ride of our official website and get a clear idea about our quality of content, check out the testimonies and client reviews and then decide. We are a one-stop solution for all your research requirements. Our services not only focus on providing you with a successful academic journey but also give immense effort to improve your understanding and knowledge.
So what are you waiting for? Connect with us for better results.
Your email address will not be published. Required fields are marked *
Control Variable is a type of variable used to verify the accuracy of any experiment, as the control variable is an essential part of experimental design. Control Variables are used extensively in the field of research where experiments are conducted to compare the new approach to the standard baseline.
In this article, we will discuss the concept of the Control Variable in sufficient detail including its definition, and examples as well as its differences with dependent and independent variables.
Table of Content
Examples of control variables, independent, dependent, and control variables, importance of control variables.
A control variable, also known as a constant variable, is a variable that does not change during the investigation in scientific experiments. Its function is to serve as a reliable benchmark, assisting researchers in separating the impacts of the independent variable and guaranteeing that changes in the dependent variable that are noticed are due to deliberate manipulation and not extraneous influences.
A control variable in an experiment is a variable that is kept constant so as not to affect the result.
To confirm that any observed effects are most likely the result of the manipulated variables rather than outside influences, it is used to isolate and evaluate the impact of the independent variable(s) on the dependent variable.
Imagine conducting an experiment to determine how different musical genres affect focus. As control variables, the music’s volume, the lighting in the space, and the temperature of the room would enable researchers to concentrate only on how different types of music affect concentration without being distracted by other variables.
Let us dissect these variables’ functions to better understand the differences between them:
The key difference between Independent, Dependent, and Control Variables are listed in the following table:
Variable Type | Definition | Role in Experiment | Example |
---|---|---|---|
Independent | The variable that is manipulated or changed by the experimenter. | It is the presumed cause or input that is tested to see its effect on the dependent variable. | In a study examining the effect of different doses of a drug on blood pressure, the independent variable is the drug dosage. |
Dependent | The variable that is measured or observed. | It is the presumed effect or outcome that is influenced by the independent variable. | In the drug dosage study, the dependent variable is the blood pressure of the participants. |
Control | Variables that are kept constant or controlled to eliminate their potential influence on the dependent variable. | They help ensure that any observed effects are due to the manipulation of the independent variable and not other factors. | In the drug dosage study, factors like age, gender, and diet may be controlled to isolate the impact of the drug dosage on blood pressure. |
Control Variable are important because it:
1. what is the meaning of control variable.
A factor intentionally kept constant in an experiment to isolate the effect of the independent variable.
In a plant growth experiment, if researchers are testing the effect of different fertilizers on plant height, the amount of sunlight, water, and temperature should be kept constant (controlled) to make them control variables.
A control variable in an experiment is a factor that is intentionally kept constant and unchanged throughout the study.
In experiments, control variables are kept constant to isolate the effect of the independent variable on the dependent variable.
Control variables are used in research experiments to eliminate or minimize the impact of extraneous factors that could affect the dependent variable.
Control Variable is also knonw as Extraneous variable.
The key difference is their role in an experiment. The independent variable is manipulated to observe its effect, while the control variable is kept constant to eliminate potential confounding factors.
Similar reads.
A controlled variable is one which the researcher holds constant (controls) during an experiment. It is also known as a constant variable or simply as a "control." The control variable is not part of an experiment itself—it is neither the independent nor dependent variable —but it is important because it can have an effect on the results. It is not the same as a control group.
Any given experiment has numerous control variables, and it's important for a scientist to try to hold all variables constant except for the independent variable. If a control variable changes during an experiment, it may invalidate the correlation between the dependent and independent variables. When possible, control variables should be identified, measured, and recorded.
Temperature is a common type of controlled variable . If a temperature is held constant during an experiment, it is controlled.
Other examples of controlled variables could be an amount of light, using the same type of glassware, constant humidity, or duration of an experiment.
Although control variables may not be measured (though they are often recorded), they can have a significant effect on the outcome of an experiment. Lack of awareness of control variables can lead to faulty results or what are called "confounding variables." Additionally, noting control variables makes it easier to reproduce an experiment and establish the relationship between the independent and dependent variables .
For example, say you are trying to determine whether a particular fertilizer has an effect on plant growth. The independent variable is the presence or absence of the fertilizer, while the dependent variable is the height of the plant or rate of growth. If you don't control the amount of light (e.g., you perform part of the experiment in the summer and part during the winter), you may skew your results.
Human behavior is usually too complicated to be studied with only two variables. Often we will want to consider sets of three or more variables (called multivariate analysis ). We will want to consider three or more variables when we have discovered a relationship between two variables and want to find out 1) if this relationship might be due to some other factor, 2) how or why these variables are related, or 3) if the relationship is the same for different types of individuals. In each situation, we identify a third variable that we want to consider. This is called the control or the test variable . (Although it is possible to use several control variables simultaneously, we will limit ourselves to one control variable at a time.) To introduce a third variable, we identify the control variable and separate the cases in our sample by the categories of the control variable. For example, if the control variable is age divided into these two categories--younger and older, we would separate the cases into two groups. One group would consist of individuals who are younger and the other group would be those who are older. We would then obtain the crosstabulation of the independent and dependent variables for each of these age groups. Since there are two categories in this control variable, we obtain two partial tables , each containing part of the original sample. (If there were three categories in our control variable, for example, young, middle aged, and old, we would have three partial tables.) The process of using a control variable in the analysis is called elaboration and was developed at Columbia University by Paul Lazarsfeld and his associates. There are several different types of outcomes to the elaboration process. We will discuss each briefly. Table 2.3 showed that females were more likely than males to say they were willing to vote for a woman. Let's introduce a control variable and see what happens. In this example we are going to use age as the control variable. Table 3.1 is the three-variable table with voting preference as the dependent variable, sex as the independent variable, and age as the control variable. When we look at the older respondents (the left-hand partial table), we discover that this partial table is very similar to the original two-variable table (Table 2.3). The same is true for the younger respondents (the right-hand partial table). Each partial table is very similar to the original two-variable table. This is often referred to as replication because the partial tables repeat the original two-variable table (see Babbie 1997: 393-396). It is not necessary that they be identical; just that each partial table be basically the same as the original two-variable table. Our conclusion is that age is not affecting the relationship between sex and voting preference. In other words, the difference between males and females in voting preference is not due to age. Table 3.1 -- Voting Preference by Sex Controlling for Age Older Younger Male % Female % Total % Male % Female % Total % Voting Preference Willing to Vote for a Woman 43.8 56.1 49.0 44.2 55.8 52.9 Not Willing to Vote for a Woman 56.2 43.9 51.0 55.8 44.2 100.0 100.0 100.0 100.0 100.0 100.0 (240) (180) (420) (120) (360) (480) Since this is a hypothetical example, imagine a different outcome. Suppose we introduce age as a control variable and instead of getting Table 2.1, we get Table 3.2. How do these two tables differ? In Table 3.2, the percentage difference between males and females has disappeared in both of the partial tables. This is called explanation because the control variable, age, has explained away the original relationship between sex and voting preference. (We often say that the relationship between the two variables is spurious , not genuine.) When age is held constant, the difference between males and females disappears. The difference in the relationship does not have to disappear entirely, only be reduced substantially in each of the partial tables. This can only occur when there is a relationship between the control variable (age) and each of the other two variables (sex and voting preference). Next, we are interested in how or why the two variables are related. Suppose females are more likely than males to vote for a woman and that this difference cannot be explained away by age or by any other variable we have considered. We need to think about why there might be such a difference in the preferences of males and females. Perhaps females are more often liberal Table 3.2 -- Voting Preference by Sex Controlling for Age Older Younger Male % Female % Total % Male % Female % Total % Voting Preference Willing to Vote for a Woman 32.9 33.9 33.3 65.8 66.9 66.7 Not Willing to Vote for a Woman 67.1 66.1 66.7 34.2 33.1 33.3 100.0 100.0 100.0 100.0 100.0 100.0 (240) (180) (420) (120) (360) (480) than males, and liberals are more likely to say they would vote for a woman. So we introduce liberalism/conservatism as a control variable in our analysis. If females are more likely to support a woman because they are more liberal, then the difference between the preferences of men and women should disappear or be substantially reduced when liberalism/conservatism is held constant. This process is called interpretation because we are interpreting how one variable is related to another variable. Table 3.3 shows what we would expect to find if females supported the woman because they were more liberal. Notice that in both partial tables, the differences in the percentages between men and women has disappeared. (It is not necessary that it disappears entirely, but only that it is substantially reduced in each of the partial tables.) Table 3.3 -- Voting Preference by Sex Controlling for Liberalism/Conservatism Older Younger Male % Female % Total % Male % Female % Total % Voting Preference Willing to Vote for a Woman 32.9 33.9 33.3 65.8 66.9 66.7 Not Willing to Vote for a Woman 67.1 66.1 66.7 34.2 33.1 33.3 100.0 100.0 100.0 100.0 100.0 100.0 (240) (180) (420) (120) (360) (480) Finally, let's focus on the third of the situations outlined at the beginning of this section--whether the relationship is the same for different types of individuals. Perhaps the relationship between sex and voter preference varies with other characteristics of the individuals. Maybe among whites, females are more likely to prefer women candidates than the males are, but among blacks, there is little difference between males and females in terms of voter preference. This is the outcome shown in Table 3.4. This process is called specification because it specifies the conditions under which the relationship between sex and voter preference varies. In the earlier section on bivariate analysis, we discussed the use of chi square. Remember that chi square is a test of independence used to determine if there is a relationship between two variables. Chi square is used in multivariate analysis the same way it is in bivariate analysis. There will be a separate value of chi square for each partial table in the multivariate analysis. You should keep a number of warnings in mind. Chi square assumes that the expected frequencies for each cell are five or larger. As long as 80% of these expected frequencies are five or larger and no single expected frequency is very small, we don't have to worry. However, the expected frequencies often drop below five when the number of cases in a column or row gets too small. If this should occur, you will have to either recode (i.e., combine columns or rows) or eliminate a column or row from the table. Table 3.4 -- Voting Preference by Sex Controlling for Race White African American Male % Female % Total % Male % Female % Total % Voting Preference Willing to Vote for a Woman 42.9 56.5 51.2 50.0 50.0 50.0 Not Willing to vote for a Woman 57.1 43.5 48.8 50.0 50.0 50.0 100.00 100.00 100.00 100.00 100.00 100.00 (310) (490) (800) (50) (50) (100) Another point to keep in mind is that chi square is affected by the number of cases in the table. With a lot of cases it is easy to reject the null hypothesis of no relationship. With a few cases, it can be quite hard to reject the null hypothesis. Also, consider the percentages within the table. Look for patterns. Do not rely on any single piece of information. Look at the whole picture. We have concentrated on crosstabulation and chi square. There are other types of statistical analysis such as regression and log-linear analysis. When you have mastered these techniques, look at some other types of analysis. REFERENCES AND SUGGESTED READING Methods of Social Research Riley, Matilda White. 1963. Sociological Research I: A Case Approach . New York: Harcourt, Brace and World. Survey Research and Sampling Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA: Wadsworth. Babbie, Earl R. 1997. The Practice of Social Research (8 th Ed.). Belmont, CA: Wadsworth. Statistical Analysis K noke, David, and George W. Bohrnstedt. 1991. Basic Social Statistics . Itesche, IL: Peacock. Riley, Matilda White. 1963. Sociological Research II Exercises and Manual . New York: Harcourt, Brace & World. Norusis, Marija J. 1997. SPSS 7.5 Guide to Data Analysis . Upper Saddle River, New Jersey: Prentice Hall. Elaboration and Causal Analysis Hirschi, Travis and Hanan C. Selvin. 1967. Delinquency Research--An Appraisal of Analytic Methods . New York: Free Press. Rosenberg, Morris. 1968. The Logic of Survey Analysis . New York: Basic Books. Data Sources The Field Institute. 1985. California Field Poll Study, July, 1985 . Machine-readable codebook. The Field Institute. 1991. California Field Poll Study, September, 1991 . Machine-readable codebook. The Field Institute. 1995. California Field Poll Study, February, 1995. Machine-readable codebook.
Document Viewers
The Plagiarism Checker Online For Your Academic Work
Start Plagiarism Check
Editing & Proofreading for Your Research Paper
Get it proofread now
Online Printing & Binding with Free Express Delivery
Configure binding now
Plagiarism Check within 10min
Printing & Binding with 3D Live Preview
How do you like this article cancel reply.
Save my name, email, and website in this browser for the next time I comment.
Control variables play a pivotal role in scientific methodologies , ensuring the integrity of research outcomes. These are specific variables that researchers keep constant throughout an experiment to minimize external influence and ensure that the results accurately reflect the relationship between independent vs. dependent variables .
Inhaltsverzeichnis
Control variables are essential empirical tools. They help us create replicable, verifiable data (i.e. statistics) from direct experimentation, observation, and sampling by setting hard limits. They also allow us to bypass ‘false’ causatives in observational studies.
Variables may be expressed as a qualitative or quantitative statement and may be limited (i.e. within a set range) or total. Integers (i.e. whole numbers) are usual for statistical controls. A single study may use many different control variables.
Control variables are crucial, as they greatly enhance an experiment’s internal validity. When you’re assessing a study’s statistical power, internal validity plays an important role.
Why? Quality, empirical statistics must reflect ‘control’ conditions relevant to that topic (i.e. model reality) to their absolute best. Improving internal validity means a study becomes more reliable, repeatable, broadly applicable, and likely to survive intense peer review.
Experimental control values let us isolate the dependent and independent variables in a closed environment. They’re the ‘lab’ variety.
In research, to see if adding a different mineral to soil stimulates houseplant growth, set control variables must detail how much sunlight, water, and air each plant should receive. As these factors are recognized as growth causatives (i.e. positive and negative confounders), quantitive nominal amounts may be required.
To do so, the scientists may read past papers, search through data about chronic conditions and rainfall, and examine similar nearby plants in the wild. Afterwards, they may agree that the plants should all receive 8.0 hours of daily light and 500 ml of daily water in a closed, single-fan-circulated shed. As a result, they may reach a consensus on their chosen brand of compost, pot diameter and volume, and the species and variety of plant seed.
Due to control variables, the experiment’s active and passive constants and constraints may now be ready and the effects of the independent variable (i.e. mineral type) on the dependent variable (i.e. plant growth rate) may now be safely observable.
Non-experimental control variables are similar. However, they’re tailored much more towards validating observations of natural phenomena, particularly human behavior.
Non-experimental variables are helpful when potential confounding causative factors (i.e. income, age, gender) may not be removed entirely from samples for ethical, legal, or practical reasons. Instead, they monitor or neutralize data on known causatives.
Suppose a freak lightning strike incinerates the carefully planned plant lab. The paper is due for submission in two weeks. There’s no money left. How will the researchers create useable statistics now?
One generous scientist suggests they may borrow their garden and spare (uniform) compost bags as a ‘real’ test bed. However, a few non-experimental design changes are needed first so that this new, natural approach works.
It’s impossible to control the rainfall and sunlight each day outdoors. However, they may be tracked instead by converting the set of experimental variables into monitored, non-experimental categories. Statistical reasoning (i.e. mean value calculation) allows the scientists to work out expected ‘real’ growth ranges for the plants they study, limiting bias.
Three advanced techniques that use control variables help remove bias from sample sets – if applied correctly. Here’s how they work.
If you have a set prone to outliers or clusters with wildly different behavior, you may want to make sure ‘lucky’ or ‘unlucky’ samples don’t skew your findings.
Researchers may use variables to ‘scramble’ set populations containing biased sub-sets, offsetting sampling bias. Random assignment ensures that sub-samples, with a balanced demographic ratio, occur. Pure luck determines which sample points are selected.
Here’s another example.
The unfortunate scientists double-check their equipment to find they have accidentally purchased three different varieties of tomato seed packet plant – ‘Medium-Mato’, ‘Mini-Mato’, and ‘Mega-Mato’ (x 100). A conundrum!
Luckily, the scientists also find past papers indicating these three varieties map comfortably onto a standard natural distribution. The researchers quickly pour the seeds into a container and shake it thoroughly for ten minutes before selecting exactly 100 samples (c.33%). Here, randomization guards against confounding influence.
Modern statistical studies may use a digital database to calculate random samples rather than a simple glass jar. Demographic weighting (i.e. stratification) might also be applied to better model divided random populations.
Do you have a repetitive daily routine that governs your time? Experiments often do. The exact time and way something must be delivered may have surprising, unforeseen effects on a subject.
It’s therefore critical that all manual test procedures remain uniform. We may set variables as timed and listed instructions to ensure this happens. They may also exclude any eccentric behavior that might skew the tests.
Let’s look at our final practical example.
Forward-thinking researchers set an additional control rule that outside plants should be watered at 10 AM and 5 PM if it hasn’t rained for two consecutive days. They also specify that no researchers should substitute cups of tea for rain-trough water as an exclusionary control.
If everything else fails? You can apply statistical control methods to limit bias via final analysis.
Sometimes, removing all traces of extraneous influence is impossible. By applying modeling, weighting, and averaging based on what’s known about the factors you’re trying to account for, a more realistic statistical picture may emerge.
Applying multiple linear regression may help. By using averaged predictors as hypotheticals to weight and limit your values, trends and correlations may be better isolated.
Control variables shouldn’t be confused with control groups ! Control groups are governed by control variables, allowing the creation of a ‘neutral’ sub-sample.
set a distinct rule or base value for a variable causative factor. | are single-study groups that create a frame of reference for other samples. |
remain completely consistent across time. | are controlled by variables. |
can affect sets or a singular value. | may change over time. |
aren't created from a sampled population. | don't directly affect statistical results - outside of analysis. |
are always created from a population of samples. |
A scientific safeguard that details a factor in a study that should (ideally) be kept the same. Control variables can also set out what factors should be accounted for and excluded from causative arguments.
Control variables add immense statistical power and validity. They’re an easy-to-use, effective way to guard against confounding factors that might warp our understanding of a complex topic.
A good set of variables may create an unchanging, dependable ‘test chamber’. Within, researchers may modify an independent variable to see how it affects a dependent one without risk.
Because of the positive experience, I recommend this printing service not just...
We use cookies on our website. Some of them are essential, while others help us to improve this website and your experience.
Individual Privacy Preferences
Cookie Details Privacy Policy Imprint
Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.
Accept all Save
Essential cookies enable basic functions and are necessary for the proper function of the website.
Show Cookie Information Hide Cookie Information
Name | |
---|---|
Anbieter | Eigentümer dieser Website, |
Zweck | Speichert die Einstellungen der Besucher, die in der Cookie Box von Borlabs Cookie ausgewählt wurden. |
Cookie Name | borlabs-cookie |
Cookie Laufzeit | 1 Jahr |
Name | |
---|---|
Anbieter | Bachelorprint |
Zweck | Erkennt das Herkunftsland und leitet zur entsprechenden Sprachversion um. |
Datenschutzerklärung | |
Host(s) | ip-api.com |
Cookie Name | georedirect |
Cookie Laufzeit | 1 Jahr |
Statistics cookies collect information anonymously. This information helps us to understand how our visitors use our website.
Akzeptieren | |
---|---|
Name | |
Anbieter | Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland |
Zweck | Cookie von Google zur Steuerung der erweiterten Script- und Ereignisbehandlung. |
Datenschutzerklärung | |
Cookie Name | _ga,_gat,_gid |
Cookie Laufzeit | 2 Jahre |
Content from video platforms and social media platforms is blocked by default. If External Media cookies are accepted, access to those contents no longer requires manual consent.
Akzeptieren | |
---|---|
Name | |
Anbieter | Meta Platforms Ireland Limited, 4 Grand Canal Square, Dublin 2, Ireland |
Zweck | Wird verwendet, um Facebook-Inhalte zu entsperren. |
Datenschutzerklärung | |
Host(s) | .facebook.com |
Akzeptieren | |
---|---|
Name | |
Anbieter | Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland |
Zweck | Wird zum Entsperren von Google Maps-Inhalten verwendet. |
Datenschutzerklärung | |
Host(s) | .google.com |
Cookie Name | NID |
Cookie Laufzeit | 6 Monate |
Akzeptieren | |
---|---|
Name | |
Anbieter | Meta Platforms Ireland Limited, 4 Grand Canal Square, Dublin 2, Ireland |
Zweck | Wird verwendet, um Instagram-Inhalte zu entsperren. |
Datenschutzerklärung | |
Host(s) | .instagram.com |
Cookie Name | pigeon_state |
Cookie Laufzeit | Sitzung |
Akzeptieren | |
---|---|
Name | |
Anbieter | Openstreetmap Foundation, St John’s Innovation Centre, Cowley Road, Cambridge CB4 0WS, United Kingdom |
Zweck | Wird verwendet, um OpenStreetMap-Inhalte zu entsperren. |
Datenschutzerklärung | |
Host(s) | .openstreetmap.org |
Cookie Name | _osm_location, _osm_session, _osm_totp_token, _osm_welcome, _pk_id., _pk_ref., _pk_ses., qos_token |
Cookie Laufzeit | 1-10 Jahre |
Akzeptieren | |
---|---|
Name | |
Anbieter | Twitter International Company, One Cumberland Place, Fenian Street, Dublin 2, D02 AX07, Ireland |
Zweck | Wird verwendet, um Twitter-Inhalte zu entsperren. |
Datenschutzerklärung | |
Host(s) | .twimg.com, .twitter.com |
Cookie Name | __widgetsettings, local_storage_support_test |
Cookie Laufzeit | Unbegrenzt |
Akzeptieren | |
---|---|
Name | |
Anbieter | Vimeo Inc., 555 West 18th Street, New York, New York 10011, USA |
Zweck | Wird verwendet, um Vimeo-Inhalte zu entsperren. |
Datenschutzerklärung | |
Host(s) | player.vimeo.com |
Cookie Name | vuid |
Cookie Laufzeit | 2 Jahre |
Akzeptieren | |
---|---|
Name | |
Anbieter | Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland |
Zweck | Wird verwendet, um YouTube-Inhalte zu entsperren. |
Datenschutzerklärung | |
Host(s) | google.com |
Cookie Name | NID |
Cookie Laufzeit | 6 Monate |
Privacy Policy Imprint
Cite this chapter.
2305 Accesses
3 Citations
Schjoedt and Sangboon hold a positivist ideology. In this chapter they discuss an important aspect of the unit of analysis strategy in research designs: How does one account for or control factors that the researcher is aware of in the model but are beyond the focus of a within-groups or between-groups comparison? In other words, control factors are confounding, moderating, or mediating variables. The reason it is important to identify and control (or account for) these factors is so that the researcher can generalize to other populations, that is, by identifying the confounding factors that are present but are beyond the unit of analysis interest. When participants are samples for a between-group unit of analysis comparison, individual attributes in each participant often differ. Designing control variables is one approach among others to address this.
This is a preview of subscription content, log in via an institution to check access.
Subscribe and save.
Tax calculation will be finalised at checkout
Purchases are for personal use only
Institutional subscriptions
Unable to display preview. Download preview PDF.
Atinc, G., Simmering, M. J., & Kroll, M. J. (2012). Control variable use and reporting in macro and micromanagement research. Organizational Research Methods , 15 (1), 57–74.
Article Google Scholar
Austin, J. T., Scherbaum, C. A., & Mahlman, R. A. (2002). History of research methods in industrial and organizational psychology. In S. G. Rogelberg (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 3–33). Malden, MA: Blackwell Publishers.
Google Scholar
Becker, T. E. (2005). Potential problems in the statistical control of variables in organizational research: A qualitative analysis with recommendations. Organizational R esearch Methods , 8 (1), 274–289.
Breaugh, J. A. (2006). Rethinking the control of nuisance variables in theory testing. Journal of Business and Psychology , 20 (1), 429–443.
Breaugh, J. A. (2008). Important considerations in using statistical procedures to control for nuisance variables in non-experimental studies. Human Resource Management Review , 18 (3), 282–293.
Carlson, K. D. & Wu, J. (2012). The illusion of statistical control: Control variable practice in management research. Organizational Research Methods , 15 (2), 413–435.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavior sciences . Mahwah, NJ: Lawrence Erlbaum.
D’Andrade, R., & Dart, J. (1990). The interpretation of R versus R2 or why percent of variance accounted for is a poor measure of effect size. Journal of Quantitative Anthropology , 2 (1), 47–59.
Doty, D. H., & Glick, W. H. (1998). Common methods bias: Does common methods variance really bias results; An investigation of prevalence and effect. Organizational Research Methods , 1 (2), 374–406.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis . Thousand Oaks, CA: Sage.
Ketchen, D., Boyd, B., & Bergh, D. (2008). Research methodology in strategic management: Past accomplishments and future challenges. Organizational Research Methods , 11 (3), 643–658.
MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science , 1 (2), 173–181.
Meehl, P. E. (1970). Nuisance variables and the ex post facto design. In M. Radner & S. Winokur (Eds.), Analyses of theories and methods of physics and psychology (pp. 373–402). Minneapolis: University of Minnesota Press.
Meehl, P. E. (1971). High school yearbooks: A reply to Schwarz. Journal of Abnormal Psychology , 77 (4), 143–148.
Newcombe, N. S. (2003). Some controls control too much. Child Development , 74 (1), 1050–1052.
Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach . Hillsdale, NJ: Lawrence Erlbaum.
Podsakoff, P. M., MacKenzie, S. G., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology , 88 (3), 879–903.
Schjoedt, L. (2013). The influence of work-and-family conflict on male entrepreneurs’ life satisfaction: A comparison of entrepreneurs and non-entrepreneurs. Journal of Small Business & Entrepreneurship , 26 (1), 45–64.
Schjoedt, L. & Bird, B. (2014). Control variables: Use, misuse, and recommended use. In A. Carsrud & M. E. Brännback (Eds.), Handbook of research methods and applications in entrepreneurship and small business (pp. 136–155). Northampton, MA: Edward Elgar.
Schwab, D. B. (2005). Research methods for organizational studies . Mahwah, NJ: Lawrence Erlbaum Associates.
Schwab, D. P. (1980). Construct validity in organizational behavior. Research in Organizational Behavior , 2 (1), 3–43.
Spector, P. E. (2006). Method variance in organization research: Truth or urban legend? Organizational Research Methods , 9 (1), 221–232.
Spector, P. A., & Brannick, M. T. (2011). Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods , 14 (2), 287–305.
Spector, P. E., Zapf, D., Chen, P. Y., & Frese, M. (2000). Why negative affectivity should not be controlled in job stress research: Don’t throw the baby out with the bath water. Journal of Organizational Behavior , 21 (3), 79–95.
Stone-Romero, E. F. (2007). Non-experimental designs. In S. Rogelberg (Ed.), The encyclo-pedia of industrial and organizational psychology (pp. 519–522). Beverly Hills, CA: Sage Publishing.
Download references
You can also search for this author in PubMed Google Scholar
Editors and affiliations.
State University of New York, Plattsburgh, Queensbury campus, New York, USA
Kenneth D. Strang ( Senior scholar and professor ) ( Senior scholar and professor )
© 2015 Kenneth D. Strang
Schjoedt, L., Sangboon, K. (2015). Control Variables: Problematic Issues and Best Practices. In: Strang, K.D. (eds) The Palgrave Handbook of Research Design in Business and Management. Palgrave Macmillan, New York. https://doi.org/10.1057/9781137484956_15
DOI : https://doi.org/10.1057/9781137484956_15
Publisher Name : Palgrave Macmillan, New York
Print ISBN : 978-1-349-47906-1
Online ISBN : 978-1-137-48495-6
eBook Packages : Palgrave Business & Management Collection Business and Management (R0)
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Policies and ethics
Let's say the service has a row finder, "FindByBusinessUnit" , with a variable, "BusinessUnit" . Instead of setting the variable's default value to a static value—like "Manufacturing" —you may want to write the user's business unit to a workbook parameter and use an expression that references this parameter.
When a business user downloads data, they are prompted to provide the business unit for the row finder, which then returns expense reports matching this value.
If you set the default value dynamically based on where the workbook is retrieved from, you would need to embed the value as a workbook parameter while it is being retrieved from the web application. Workbook parameters are name-value pairs that are embedded in your workbook programmatically, typically by a web developer. For help writing values to workbook parameters, see Embedding Workbook Parameters in a Workbook .
To use a workbook parameter value as the default value, enter an expression like this: { Workbook.Parameters[' <para_name> '].Value } in the Default Value field of the Row Finder Variable Editor. See Workbook Parameters in Expressions .
In this example, the expression references a workbook parameter, BusUnit which stores the name of the business unit.
Oracle Visual Builder Add-in for Excel does not validate expressions in the Row Finder Variable Editor. You should test your expression before distributing the workbook.
Keep in mind the following when testing:
There are no conversions for different cultures within the expression.
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
Research and simulation analysis of fuzzy intelligent control system algorithm for a servo precision press.
2. principle of servo precision press and transmission mechanism, 3. design of fuzzy pid controller for servo precision press, 3.1. determining the structure of the fuzzy controller, 3.2. fuzzification of input and output quantities, 3.3. determination of domain and related parameters, 3.4. principles of parameter adjustment and establishment of fuzzy rules, 3.5. fuzzy inference rules, 4. experimental study on the position of the slider in servo precision press, 4.1. implementation plan, 4.2. step response experiment, 4.3. sinusoidal tracking experiment, 5. servo high-precision press closed-loop control system design, 5.1. servo motor transfer function block diagram, 5.2. current loop block diagram, 5.3. speed loop block diagram, 5.4. position ring block diagram, 6. control system simulation analysis based on simulink, 6.1. dynamics simulation analysis of each stamping stage, 6.2. dynamic simulation analysis with load, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Click here to enlarge figure
Performance Indicators | Linear PID | Fuzzy PID |
---|---|---|
Settling Time (s) | 0.61 | 0.22 |
Overshoot (%) | 4.3 | 1.6 |
Steady-State Error (%) | 0.4 | 0.3 |
Parameter Name | Value | Parameter Name | Value |
---|---|---|---|
Rated torque | 6000 Nm | Back electromotive force constant | 1368 V/1000 rpm |
Rated current | 275 A | Stator phase resistance | 0.01094 Ω |
rated power | 157.1 kW | Direct axis inductance | 1.389 mH |
Rated speed | 250 r/min | Quadrature axis inductance | 1.389 mH |
Inverter output voltage | 380 V | Motor moment inertia | 7.78 kg·m |
Peak torque | 15,000 Nm | Passive mechanism inertia | 19.372 kg·m |
Current at maximum torque | 530 A | Connecting rod coefficient | 0.7143 |
Rated torque constant | 21.8 Nm/A | Gear transmission ratio | 71/14 |
Workpiece One | Workpiece Two | |
---|---|---|
) | 40 × 60 × 1.5 | 20 × 70 × 2.5 |
) | 70 | 200 |
) | 1100 | 500 |
) | 0.0098 | 0.0045 |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
He, Y.; Luo, X.; Wang, X. Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press. Appl. Sci. 2024 , 14 , 6592. https://doi.org/10.3390/app14156592
He Y, Luo X, Wang X. Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press. Applied Sciences . 2024; 14(15):6592. https://doi.org/10.3390/app14156592
He, Yanzhong, Xiang Luo, and Xingsong Wang. 2024. "Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press" Applied Sciences 14, no. 15: 6592. https://doi.org/10.3390/app14156592
Article access statistics, further information, mdpi initiatives, follow mdpi.
Subscribe to receive issue release notifications and newsletters from MDPI journals
This paper studies how to improve the power output and the stability of the power generation system under the influence of different wind speed. This paper proposes a power control strategy based on PID controller using the optimal tip speed ratio method to optimize the control parameters and combine the variable pitch control method to realize the effective power control of 1.5MW direct drive permanent magnet synchronous wind turbine. The simulation results prove that the controller can improve the dynamic performance of the 1.5MW direct drive permanent magnet synchronous wind turbine power control system, improve the reliability of the unit operation, especially the capture efficiency of the wind energy of the power generation system, so as to improve the power generation.
It’s a strong predictor for how satisfied they are with their work — and their lives.
To better understand the role that control over one’s time plays in job and life satisfaction, the authors analyzed survey data from a nationally representative sample. They found: 1) People who had greater control over their time had the highest job satisfaction and overall satisfaction with their lives, 2) Those who felt a sense of time scarcity had less satisfaction with their jobs and were less satisfied with their lives, 3) The number of hours people worked was not related to how satisfied people were with their jobs, and 4) For those who had more control over their time, feeling time scarcity did not undermine their job satisfaction as much as it did for those who had less control over their time. Employers should therefore create and tailor flexible work policies to meet diverse employee needs, fostering satisfaction and retention.
Workers — particularly those considered “ knowledge workers ” who are able to do most if not all of their work with a laptop and an internet connection — have been fighting for the right to maintain control over their time for years. While working from home in 2020 and 2021, they demonstrated to their bosses that they are able to maintain, or in some cases even increase , their productivity while working flexibly. Their bosses, on the other hand, have been pulling them in the opposite direction; executives and managers have been fighting to get workers back into the offices that companies are paying a lot of money to lease. This struggle has affected workers and companies alike. Workers quit en masse during a period that became known as “ the Great Resignation ,” and employers who instituted return-to-office mandates have struggled to hire and retain top talent . Now, especially with Gen Z making up an increasing share of the working population and the conversations around hybrid work and returning to the office stagnating, demands for increased flexibility in work arrangements are still top of mind for many employees and job seekers.
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Methodology
Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.
In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .
If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .
You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.
You can usually identify the type of variable by asking two questions:
Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.
Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:
A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.
When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .
Type of variable | What does the data represent? | Examples |
---|---|---|
Discrete variables (aka integer variables) | Counts of individual items or values. | |
Continuous variables (aka ratio variables) | Measurements of continuous or non-finite values. |
Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.
There are three types of categorical variables: binary , nominal , and ordinal variables .
Type of variable | What does the data represent? | Examples |
---|---|---|
Binary variables (aka dichotomous variables) | Yes or no outcomes. | |
Nominal variables | Groups with no rank or order between them. | |
Ordinal variables | Groups that are ranked in a specific order. | * |
*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.
To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.
To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .
Professional editors proofread and edit your paper by focusing on:
See an example
Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.
You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.
You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.
Type of variable | Definition | Example (salt tolerance experiment) |
---|---|---|
Independent variables (aka treatment variables) | Variables you manipulate in order to affect the outcome of an experiment. | The amount of salt added to each plant’s water. |
Dependent variables (aka ) | Variables that represent the outcome of the experiment. | Any measurement of plant health and growth: in this case, plant height and wilting. |
Control variables | Variables that are held constant throughout the experiment. | The temperature and light in the room the plants are kept in, and the volume of water given to each plant. |
In this experiment, we have one independent and three dependent variables.
The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.
When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).
However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .
Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .
But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.
Type of variable | Definition | Example (salt tolerance experiment) |
---|---|---|
A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven’t controlled it in your experiment. Be careful with these, because confounding variables run a high risk of introducing a variety of to your work, particularly . | Pot size and soil type might affect plant survival as much or more than salt additions. In an experiment you would control these potential confounders by holding them constant. | |
Latent variables | A variable that can’t be directly measured, but that you represent via a proxy. | Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment. |
Composite variables | A variable that is made by combining multiple variables in an experiment. These variables are created when you analyze data, not when you measure it. | The three plant health variables could be combined into a single plant-health score to make it easier to present your findings. |
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
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
Discrete and continuous variables are two types of quantitative variables :
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.
Bevans, R. (2023, June 21). Types of Variables in Research & Statistics | Examples. Scribbr. Retrieved July 30, 2024, from https://www.scribbr.com/methodology/types-of-variables/
Other students also liked, independent vs. dependent variables | definition & examples, confounding variables | definition, examples & controls, control variables | what are they & why do they matter, "i thought ai proofreading was useless but..".
I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”
IMAGES
VIDEO
COMMENTS
A control variable is anything that is held constant or limited in a research study. It's a variable that is not of interest to the study's objectives, but is controlled because it could influence the outcomes. Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an ...
Here are some concrete examples to better understand the role of control variables: 1. Participant Age. When studying the effect of a new teaching method on students' mathematical abilities, the age of the participants (all students studied are in the 8th grade) remains a control variable. 2. Participant Gender.
A control variable is anything that is held constant or limited in a research study. It's a variable that is not of interest to the study's aims but is controlled because it could influence the outcomes. Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an ...
Control Variable Examples. Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include: Duration of the experiment. Size and composition of containers. Temperature.
Examples of Control Variables. Here are some examples of control variables in Scientific Experiments and Research: Environmental Control Variables Example: Suppose you are conducting an experiment to study the effect of light on plant growth. You would want to control environmental factors such as temperature, humidity, and soil nutrients.
Control variables, also known as controlled variables, are properties that researchers hold constant for all observations in an experiment. ... In your explanation of control variables you use the example of a research study of plant fertilizers and their growth, wanting to control for moisture, sunshine and temperature. You state ...
These factors are then considered control variables. Some examples of variables that you may need to control include: Temperature; Time of day; Lighting; Stress; Noise or distractions; 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 ...
Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023. In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don't influence the dependent variable.
The main reason we want to include control variables is that the control variables are having an effect on the dependent variable we are studying. Since control variables are not the independent variables in our research, they could potentially confound the results of the study if left unattended. In other words, they can impose threats to the ...
A control variable is an experimental condition or element that is kept the same throughout the experiment, and it is not of primary concern in the experiment, nor will it influence the outcome of the experiment. [2] Any unexpected (e.g.: uncontrolled) change in a control variable during an experiment would invalidate the correlation of ...
In a scientific study, a control group is used to establish causality by isolating the effect of an independent variable. Here, researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups. Using a control group means that any change in the dependent ...
The choice of control variables is not arbitrary; it demands careful consideration and a deep understanding of the research context. The significance of selecting the right control variables cannot be overstated, as these elements serve as the bedrock for establishing the internal validity of a study. Internal validity refers to the accuracy of ...
Definition. A controlled variable is a commonly used term in the field of scientific research, where finding evidence to support a theory is rarely straightforward. In the case of the natural sciences, some research features are constant, but the majority of these have inconsistencies. These inconsistencies are known as variables.
The knowledge of control variables is extremely important for the students and academic professionals which will give us critical insight into how it improves our research outcome. So, first, we will start with the control variables' definitions and examples associated with them. A control variable is an experimental element which is constant or limited throughout
Control Variable is a variable used in experimental design and various branches of science to test hypotheses. In this article, we will explore the concept of control variables, including their role in scientific research, and provide real-world examples.
1. Introduction. Control variables (CVs) constitute a central element of the research design of any empirical study. Confounding variables are likely to covary with the hypothesized focal independent variables thus limiting both the elucidation of causal inference as well as the explanatory power of the model (Pehazur & Schmelkin, 1991; Stone-Romero, 2009).
A controlled variable is one which the researcher holds constant (controls) during an experiment. It is also known as a constant variable or simply as a "control." The control variable is not part of an experiment itself—it is neither the independent nor dependent variable —but it is important because it can have an effect on the results.
For example, if the control variable is age divided into these two categories--younger and older, we would separate the cases into two groups. ... Sociological Research I: A Case Approach. New York: Harcourt, Brace and World. Survey Research and Sampling. Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA:
Control variables let us control an experiment's conditions via set criteria by improving statistical repeatability, validity, and applicability. Control variables protect the independent and dependent variables of the study from undue bias (e.g. confounding causative variables). How and which variables are selected depends on the topic and ...
In S. G. Rogelberg (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 3-33). Malden, MA: Blackwell Publishers. Google Scholar Becker, T. E. (2005). Potential problems in the statistical control of variables in organizational research: A qualitative analysis with recommendations.
Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research. Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group.As a result, the characteristics of the participants who drop out differ from the characteristics of those who ...
To use a workbook parameter value as the default value, enter an expression like this: { Workbook.Parameters[' <para_name> '].Value } in the Default Value field of the Row Finder Variable Editor. See Workbook Parameters in Expressions. In this example, the expression references a workbook parameter, BusUnit which stores the name of the business ...
With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become ...
This paper studies how to improve the power output and the stability of the power generation system under the influence of different wind speed. This paper proposes a power control strategy based on PID controller using the optimal tip speed ratio method to optimize the control parameters and combine the variable pitch control method to realize the effective power control of 1.5MW direct drive ...
To better understand the role that control over one's time plays in job and life satisfaction, the authors analyzed survey data from a nationally representative sample. They found: 1) People who ...
We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video.
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.
CODEBOOK FOR SAMPLE VARIABLES TO BE USED IN SURVEY PROGRAMMING LOGIC: X_FORM 1 Form 1 2 Form 2 Note: Flag to randomly assign panelists to one of two forms (Form 1, Form 2) and weight within form ATP source: Randomly assigned for each survey. X_CITIZEN 1 Yes 2 No 99 Refused Note: Flag to identify citizens filtered on VOTEGEN24.
In contrast, the variable speed pump control system (VSPCS) realizes the precise servo of steering system through direct volume control, which basically eliminates the energy dissipation at the valve port. However, the VSPCS lacks sufficient system stiffness due to low back pressure, making it difficult to achieve precise steering.
Example (salt tolerance experiment) Independent variables (aka treatment variables) Variables you manipulate in order to affect the outcome of an experiment. The amount of salt added to each plant's water. Dependent variables (aka response variables) Variables that represent the outcome of the experiment.