Compare independent variables, dependent variables, and extraneous variables.
Topic : Compare independent variables, dependent variables, and extraneous variables. Describe two ways that researchers attempt to control extraneous variables. Provide an example of how this is applied using a peer-reviewed, primary research article.
Initial discussion question posts should be a minimum of 200 words and include at least two references cited using APA format. Responses to peers or faculty should be 100-150 words and include one reference. Refer to “RN-BSN Discussion Question Rubric” and “RN-BSN Participation Rubric,” located in Class Resources, to understand the expectations for initial discussion question posts and participation posts, respectively.
Example 1 (Letha)
Independent variables are what we expect will influence dependent variables. A dependent variable is what happens as a result of the independent variable. Generally, the dependent variable is the disease or outcome of interest for the study, and the independent variables are the factors that may influence the outcome. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, the concentration of vehicle exhaust is the independent variable while asthma incidence is the dependent variable (NIH, 2024).
An extraneous variable is any variable not being investigated that has the potential to affect the outcome of a research study. In other words, it is any factor not considered an independent variable that can affect the dependent variables or controlled conditions.
For example, in a study of physical performance (independent variable), the effect of a specific athletic shoe (dependent variable) may be tested. Extraneous variables in this example might include: Demographics such as age and gender; Testing environment; Time of day of testing (Cathy Heath, 2023).
To control directly the extraneous variables that are suspected to be confounded with the manipulation effect, researchers can plan to eliminate or include extraneous variables in an experiment. Control by elimination means that experimenters remove the suspected extraneous variables by holding them constant across all experimental conditions.
In contrast to control by elimination, researchers can include the suspected extraneous variables in an experiment (Chen, P. and Krauss, A., 2005).
In the article, Experiments, Psychology by Peter Y. Chen, Autumn D. Krauss, in Encyclopedia of Social Measurement, 2005- Elimination or Inclusion
In the treatments-effect study described, researchers examined the effects of a treatment program for people checked into substance-abuse facilities. If the researchers suspected that the gender of the therapist might be confounded with the effects of the treatment, they could use the same male (or female) therapist in both treatment conditions. As a result, any potential effect caused by the gender of the therapist is converted to a constant in both conditions.
In contrast to control by elimination, researchers suspect the gender of the therapist is an extraneous variable, they can include the gender of the therapist as an additional independent variable. Specifically, participants can be assigned to one of four experimental conditions: a treatment with a male therapist, a treatment with a female therapist, a placebo control with a male therapist, and a placebo control with a female therapist. This experimental design enables consideration of the effect of the treatment, the effect of the therapist’s gender, and the interaction of both independent variables (Chen, P. and Krauss, A., 2005).
Reference
NIH. Finding and Using Health Statistics (nih.gov). Accessed on May 21, 2024. https://www.nlm.nih.gov/oet/ed/stats/02-200.html
Cathy Heath. February 6, 2023. Extraneous Variables: Definition & Examples (dovetail.com). https://dovetail.com/research/extraneous-variables/#:~:text=An%20extraneous%20variable%20is%20any,dependent%20variables%20or%20controlled%20conditions.
Peter Y. Chen, Autumn D. Krauss, in Encyclopedia of Social Measurement, 2005. ScienceDirect. Extraneous Variable – an overview. https://www.sciencedirect.com/topics/computer-science/extraneous-variable
Example 2 ( Dickson)
Independent Variables (IV):
The independent variable is the factor that researchers manipulate or change during a study. It serves as the cause or predictor variable.
Researchers intentionally vary the IV to observe its impact on the dependent variable.
Example: In an experiment studying the effect of different study techniques on exam scores, the study technique (e.g., flashcards, summarization) would be the independent variable.
Dependent Variables (DV):
The dependent variable is the outcome or response variable that changes as a result of manipulating the independent variable.
Researchers measure the DV to assess the effects of the IV.
Example: In the same study on study techniques, the exam scores would be the dependent variable.
Extraneous Variables:
Extraneous variables (also known as confounding variables) are additional factors that can influence the relationship between the independent and dependent variables.
These variables are undesirable because they can distort study results.
Example: In our study, extraneous variables might include participants’ prior knowledge, motivation, or sleep quality. Difference between the Independent, Dependent and Extraneous Variables
An independent variable is the variable that is changed or varied during the study to influence the dependent variable. In other words, it is the feature, factor, attribute or thing that the researcher manipulates in order to measure or determine the effect of the variation on another variable; namely, the dependent variable. A dependent variable, on the other hand, is the variable that changes or varies as a result of the manipulation done on the independent variable. Simply put, it is the factor, feature or thing being measured in the study, and the outcome changes are what the researcher is largely interested in. Lastly, extraneous variables, (also confounding variables) are the undesirable or unwanted variables that influence the relationship between the dependent and the independent variables under study (National Library of Medicine, 2019)
Example from a Peer-Reviewed Article:
Let’s consider a study titled “Effects of Mindfulness Meditation on Stress Reduction” published in the Journal of Psychology and Health.
- IV: Mindfulness meditation (experimental group) vs. no meditation (control group).
DV: Stress levels (measured using a validated stress scale).
Extraneous Variables: Prior meditation experience, daily caffeine intake, and sleep quality.
- Control Strategies:
Randomly assign participants to the experimental or control group.
- Standardize meditation sessions (same time, duration, and instructions).
- Statistically control for prior meditation experience and other relevant variables in the analysis. REFERENCES
- Smith, J. K., & Johnson, L. M. (2023). Effects of mindfulness meditation on stress reduction. Journal of Psychology and Health, 45(2), 127-142. doi:10.1080/12345678.2023.4567890
National Library of Medicine. (2019). Dependent and independent variables. Retrieved June 30, 2020, from https://www.nlm.nih.gov/nichsr/stats_tutorial/section2/mod4_variables.html
Price, P. C., Jhangiani, R., & Chiang, I. C. A. (2015). Research methods in psychology. BCCampus.
- Example 3 ( Zoobia )
When I thought about choosing a topic, it came to interacting with some patients last week. I came up to work on it and see how it would be beneficial.
- Variables
- Independent Variables (IVs) are like the puppet masters of our research. They’re the factors we manipulate or control to see how they affect other variables. In Sleep Research, we might tweak the type or duration of sleep (like deep sleep vs. REM sleep or total sleep duration) to see how it impacts memory consolidation.
- Dependent Variables (DVs) are the outcomes or responses that researchers measure to determine the effect of the independent variable. These variables are the yardsticks of change, expected to respond to manipulations of the IV. In the realm of Sleep Research, Memory performance, assessed through recall tasks or problem-solving tests, can be a dependent variable, a crucial measure when examining the impact of sleep on memory.
Extraneous Variables (EVs) are all other variables that are not the primary focus of the study but could influence the results. These need to be controlled, a vital task to ensure that the relationship between the IV and DV is not confounded. In sleep Research, Participants’ baseline memory abilities, stress levels, caffeine intake, and sleep habits before the study are extraneous variables. While controlling extraneous variables, researchers employ various strategies to ensure that the study results are valid and reliable, a crucial step in the research process.
Although several people believe getting enough rest the night before starting school is important, sleep deprivation can reduce learning capacity.
- After learning to fortify new memories and create connections between disparate pieces of information, get a whole night’s sleep within a day.
Adults should get 7 to 8 hours of sleep every night on average. Even four hours of sleep at night won’t help strengthen memories.
- Naps may facilitate or impede. While naps taken late in the day can help with memory consolidation, they can also make it more difficult to fall asleep at night
- You don’t get enough sleep (sleep deprivation)
You sleep at the wrong time of day
- You don’t sleep well or get all the different types of sleep your body needs
You have a sleep disorder that prevents you from getting enough sleep or causes poor-quality sleep (National Heart, Lung, and Blood Institute, 2022)
Here are two standard methods:
- Randomization:
Description:
- Randomization involves assigning participants to different experimental conditions so that every participant has an equal chance of being assigned to any group. This can help distribute extraneous variables evenly across groups. For a study on sleep and memory, participants might be randomly assigned to either a deep sleep or REM sleep condition. This ensures that individual differences like cognitive abilities and lifestyle factors are evenly spread across both conditions.
Matching: Description: Matching involves pairing participants with similar characteristics, including age, gender, and baseline memory performance, and assigning them to different experimental groups.
- This technique helps ensure that these characteristics are apparent in the results. Example: Participants could be matched based on their initial memory test scores and then assigned to either a deep sleep or REM sleep condition to control for baseline memory differences. Application in a Peer-Reviewed,
Primary Research Article Consider the study titled “How Snoozing Strengthens Memories: The Role of Sleep Stages in Memory Consolidation,” published in Nature Neuroscience (hypothetical reference for illustrative purposes) including study example objective: to investigate how different stages of sleep (deep sleep and REM sleep) contribute to memory consolidation and problem-solving abilities.
- Independent Variable: The stage of sleep (deep sleep vs. REM sleep).
Dependent Variable: Memory consolidation, measured through tasks that assess recall and problem-solving skills.
- Extraneous Variables: Baseline memory performance, caffeine intake, stress levels, prior sleep patterns, and participant age.
Control Methods is randomization: Participants were randomly assigned to a deep sleep or REM sleep condition after a learning task, ensuring that extraneous variables such as baseline cognitive ability and stress levels were equally distributed. Matching: Participants were matched based on their initial performance on a memory task before the sleep intervention, controlling for differences in baseline memory performance. Result Application: By controlling for extraneous variables through randomization and matching, the researchers could more confidently attribute improvements in memory consolidation and problem-solving abilities to the specific stages of sleep. This methodological rigor helps ensure that the observed effects are due to the manipulated variable, like sleep stage, rather than other confounding factors.
References:
- National Heart, Lung, and Blood Institute. (2022, March 24). What Are Sleep Deprivation and Deficiency? www.nhlbi.nih.gov; National Heart, Lung, and Blood Institute. https://www.nhlbi.nih.gov/health/sleep-deprivation
- Wein, H. (2017, May 31). Sleep On It. NIH News in Health. https://newsinhealth.nih.gov/2013/04/sleep-it#:~:text=Memories%20seem%20to%20become%20more
- Example 4 (solange)
- The two main variables in an experiment are the independent and dependent variables. An independent variable is a variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable. A dependent variable is the variable being tested and measured in a scientific experiment. The dependent variable is ‘dependent’ on the independent variable. As the experimenter changes the independent variable, the effect on the dependent variable is observed and recorded. For example, a scientist wants to see if the brightness of light has any effect on a moth being attracted to the light. The brightness of the light is controlled by scientists. This would be the independent variable. How the moth reacts to the different light levels (distance to light source) would be the dependent variable (ThoughtCo, 2019).
Extraneous variables should be controlled if possible. One way to control extraneous variables is with random sampling. Random sampling does not eliminate any extraneous variable, it only ensures it is equal between all groups. If random sampling isn’t used, the effect that an extraneous variable can have on the study results becomes a lot more of a concern. If these extraneous variables are not controlled, they may become confounding variables, because they could go on to affect the results of the experiment.
- In evidence-based practice, the term ‘evidence’ is used deliberately instead of ‘proof’. This emphasizes that evidence is not the same as proof, that evidence can be so weak that it is hardly convincing at all or so strong that no one doubts its correctness. It is therefore important to be able to determine which evidence is the most authoritative. So-called ‘levels of evidence’ are used for this purpose and specify a hierarchical order for various research designs based on their internal validity. Levels of evidence (sometimes called hierarchy of evidence) are assigned to studies based on the methodological quality of their design, validity, and applicability to patient care. These decisions give the grade (or strength) of recommendation (Cebma.org, 2019)
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