what is controlled variable in research

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

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
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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• What Are Control Variables | Definition & Examples

What Are Control Variables? | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 16 June 2023.

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 experiment), or they may be controlled indirectly through methods like randomisation or statistical control (e.g., to account for participant characteristics like age in statistical tests).

Table of contents

Why do control variables matter, how do you control a variable, control variable vs control group, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest.

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

• Marital status

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There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook have more screen time during the day, and this might influence how alert they are in your study.

Standardised procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables, you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

• To control for diet, fresh and frozen meals are delivered to participants three times a day.
• To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
• To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other variables.

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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What is a control variable in science?

What is a Control Variable in Science?

As we delve into the world of science, we often encounter various concepts that enable us to conduct experiments and make observations. One crucial element in scientific research is the control variable , which plays a vital role in ensuring the accuracy and reliability of our findings. But what exactly is a control variable, and how does it impact our understanding of the scientific method? In this article, we’ll explore the concept of a control variable, its importance, and the role it plays in scientific research.

What is a Control Variable?

A control variable, also known as a constant or a controlled factor, is a variable that is intentionally kept constant or identical throughout an experiment to help ensure that the results are accurate and reliable. In other words, a control variable is a variable that is not being studied, but is kept the same for all experimental conditions. This is done to minimize external factors that could affect the outcome of the experiment and to isolate the effect of the variable being studied.

Why is a Control Variable Important?

A control variable is essential in scientific research for several reasons:

• Reduces Errors : By keeping a control variable constant, researchers can minimize the impact of external factors that could affect the outcome of the experiment, thus eliminating the risk of bias and increasing the accuracy of the results.
• Facilitates Comparison : A control variable allows researchers to compare the effects of different treatments or variables, making it possible to draw meaningful conclusions.
• Provides a Baseline : A control variable serves as a baseline measurement, allowing researchers to against which to compare the results.
• Enhances Statistical Analysis : A control variable can aid in statistical analysis by reducing the required sample size and increasing the sensitivity of the results.
• Improves Reproducibility : By controlling for external factors, researchers can increase the reproducibility of the experiment, making it easier to replicate the results in other studies.

Types of Control Variables

There are several types of control variables, including:

• Independent variables : These are the variables being studied and manipulated to observe their effect on the dependent variable.
• Dependent variables : These are the variables being measured or observed in response to changes in the independent variable.
• Confounding variables : These are variables that could affect the results of the experiment and need to be controlled for.
• Noise variables : These are variables that arise from experimental errors and need to be minimized.
• Confounding-by-design variables : These are variables that are intentionally introduced to test their effect on the results.

Examples of Control Variables

• In a study on the effect of exercise on weight loss, the control variable would be the diet and daily routine of the participants, held constant to ensure that any changes observed are due to the effect of exercise and not other factors.
• In a study on the effect of different medications on blood pressure, the control variable would be the dosing regimen, held constant to ensure that any differences observed are due to the medication being tested and not other factors.

A control variable is a crucial component of the scientific method, allowing researchers to isolate the effect of a variable and ensure accurate and reliable results. By understanding the importance and role of control variables, scientists can design experiments that are more effective, efficient, and informative. Whether you’re a scientist, student, or simply a curious individual, understanding the concept of control variables can help you approach research with a clearer understanding of the variables at play.

Key Takeaways:

• A control variable is a variable that is intentionally kept constant or identical throughout an experiment to minimize external factors and isolate the effect of the variable being studied.
• Control variables are essential for reducing errors, facilitating comparison, providing a baseline, enhancing statistical analysis, and improving reproducibility.
• There are different types of control variables, including independent, dependent, confounding, noise, and confounding-by-design variables.
• Control variables are used in various scientific studies, including experiments on the effect of exercise on weight loss, the effect of different medications on blood pressure, and many others.

Additional Resources:

• [1] "The Environment of the Experiment" by Robert H. Rohwer, Journal of Chemistry Education, 2015
• [2] "Control Variables and Experimental Design" by J. Smucker, Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
• [3] "The Concept of Control in Scientific Research" by M. W. E. Welton, Science, 2012
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A Practical Guide to Choosing the Right Control Variables for Your Research

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.

Importance of Selecting the Right Control Variables

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.

How Control Variables Enhance Research Validity

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.

Understanding Control Variables

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.

How Control Variables Differ from Independent and Dependent Variables

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.

Examples of Control Variables

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.

Criteria for Selecting Control Variables

The following are the criteria for selecting the right control variables.

Relevance to the Research Question

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.

Potential Confounding Factors

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.

Feasibility and Practicality

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.

Balance Between Inclusivity and Specificity

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.

Common Pitfalls in Control Variable Selection

Here are some common pitfalls in control variable selection.

Overlooking Relevant Variables

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.

Including Unnecessary Variables

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.

Confusing Control Variables with Mediators or Moderators

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.

Strategies for Identifying Control Variables

You can identify control variables with the help of the following strategies.

Literature Review and Prior Research

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.

Preliminary Data Analysis

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.

Expert Consultation and Peer Feedback

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.

Documentation and Transparency

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.

Case Studies

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.

Analysis of Studies with Inadequate Control Variable Selection

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.

Lessons Learned from Real-World Examples

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.

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Practical Tips for Implementing Control Variables

With the help of these tips, you can implement control variables. 

Tip 1: Monitoring and Adjusting Control Variables During the Research Process

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.

Tip 2: Using Statistical Techniques to Assess the Impact of Control Variables

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.

Tip 3: Considerations for Longitudinal or Experimental Studies

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.

Frequently Asked Questions

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.

What are 3 controlled variables?

• Temperature: Ensuring a constant temperature in an experiment to isolate the effects of other variables.
• Time: Controlling the duration of an experiment to prevent time-related influences on the dependent variable.
• Light: Standardizing light conditions to eliminate its impact on experimental outcomes.

What are system control variables?

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.

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In research, variables are elements that can be manipulated, measured, or controlled. Understanding them is crucial for any study. Here's a simple breakdown of two fundamental types of variables:

• For example, if you're studying the effects of sunlight on plant growth, the amount of sunlight would be your independent variable.
• Continuing with the plant growth example, the height of the plant would be the dependent variable, as it could change based on the amount of sunlight received.

In a research statement, the independent variable typically comes before the dependent variable.

• For example, you might say: "This study examines how the amount of sunlight ( independent variable ) affects plant growth ( dependent variable )." This helps clarify the cause-and-effect relationship you're investigating.

Here are some examples of independent and dependent variables in research related to Speech-Language Pathology (SLP):

Example 1: Stuttering Therapy

• Independent Variable: Type of therapy used, e.g., [FLUENCY SHAPING OR STUTTERING MODIFICATION] )
• Dependent Variable:  Frequency of stuttering events during conversation, e.g.,  [FREQUENCY OR RATE AND STUTTERING EVENTS]

Example 2: Aphasia Rehabilitation

• Independent Variable: Types of rehabilitation exercises (e.g., word retrieval exercises, picture naming)
• Dependent Variable: Improvement in language skills, measured using a standardized language assessment

Develop your Keyword Search Around your main topic, then the Independent and Dependent Variables:

For example:

1. Main topic:

2. Independent Variable(s):

("picture naming" OR "semantic mapping")

3. POSSIBLEY HELPFUL: Include terms to describe your Dependent Variable:

("Improvement in language skills" OR "language assessment" OR "language skills measurement" OR "standardized language assessment")

4. Put it all together:

​​​​​​​(Aphasia) AND (picture naming" OR semantic mapping) AND ("Improvement in language skills" OR "language assessment" OR "language skills measurement" OR "standardized language assessment")

Example 3: Early Language Development

• Independent Variable: Age of exposure to a second language (e.g., before age 3, between ages 4-6, etc.)
• Dependent Variable: Proficiency in second language skills, such as vocabulary size or grammatical accuracy

Example 4: Hearing Aids and Speech Comprehension

• Independent Variable: Use of hearing aids (e.g., with or without hearing aids)
• Dependent Variable: Speech comprehension scores in noisy environments

Example 5: Speech Sound Disorders in Children

• Independent Variable: Intervention approach (e.g., traditional articulation therapy, phonological process therapy)
• Dependent Variable: Number of speech sound errors made in conversation

Example 6: Telepractice vs. In-Person Therapy

• Independent Variable: Mode of therapy delivery (e.g., telepractice or in-person)
• Dependent Variable: Client satisfaction rates or treatment outcomes
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