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Home > ETD > Doctoral > 5868
A quasi-experimental study on the effects of small group learning on mathematical resilience in upper elementary students.
Joshua Adam Costelnock , Liberty University Follow
School of Education
Doctor of Philosophy in Education (PhD)
Janice Kooken
whole group instruction, small group learning, mathematical resilience, progressive classroom
Recommended citation.
Costelnock, Joshua Adam, "A Quasi-Experimental Study on the Effects of Small Group Learning on Mathematical Resilience in Upper Elementary Students" (2024). Doctoral Dissertations and Projects . 5868. https://digitalcommons.liberty.edu/doctoral/5868
The purpose of this quantitative, quasi-experimental study was to determine the effect of small group learning during the core mathematics block on 5th-grade students’ mathematical resilience, compared to a control group. Student collaboration and mathematical discourse decreased during the COVID-19 pandemic, leading to a drop in math proficiency levels in the United States. Approximately 80 5th-grade students from the southwest United States were divided into two sample groups of about 40 each. These groups were assessed using the Upper Elementary Mathematics Resilience Scale. One group primarily experienced teacher-centered whole group instruction, while the other group spent half of their daily core learning block in student-centered small group instruction. Differences between the two groups were analyzed using ANCOVA on the two measures of the Mathematical Resilience Scale: value and growth mindset. The ANCOVA tested for differences in the post-test, using the pre-test as the covariate. Data for the value subscale showed a statistically significant change between the groups, though the direction of the change was unexpected. Data for the growth subscale did not reach appropriate levels of significance. For future research, it is recommended that the scale be administered at the beginning of the school year instead of the end, and that the sample size be increased in both groups.
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HDS sediment is a type of solid waste produced when the high-concentration mud method (HDS) is adopted to treat acid wastewater from copper mines. It can rationally utilize sediment resources by using phytoremediation, which plays a role in the ecological restoration of mines.
To reveal the effect of different phytoremediation on the heavy metal, enrichment capacity and microbial diversity of the HDS sediments of copper mines, in this experiment, the HDS sediments of a copper mine without phytoremediation were selected as the control group, while the sediments of black locust ( Robinia pseudoacacia ), slash pine ( Pinus elliottii Engelmann ) and Chinese white poplar ( Populus tomentosa Carr. ) were used as test groups to analyze the physical and chemical properties, heavy metal pollution and bioaccumulation capacity of HDS sediments under three phytoremediation.
The results show that different phytoremediation can reduce the sediment's conductivity and adjust the sediment’s pH value to the range suitable for plant growth. The BCF Shoot and BTF values of Chinese white poplar to Cd and Zn and slash pine to Pb were both greater than 1.
As discovered from the bioconcentration coefficient and biotransport coefficient results, Chinese white poplar is a Cd-enriched and Zn-enriched plant, while slash pine is a Pb-enriched plant.
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This work was funded by the National Natural Science Foundation of China (Grants 51664016, 51664017), the Key R&D projects in Jiangxi Province (20212BBG73013), and Jiangxi Copper Company Limited Chengmenshan Copper Technology Projects (CTYJ2022006, CMS-23SCJS-07JS-01F).
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School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580, China
Zhuyu Zhao & Chuanliang Yan
School of Energy and Mechanical Engineering, Jiangxi University of Science and Technology, Nanchang, 330013, China
Zhuyu Zhao, Ruoyan Cai & Jinchun Xue
Zhejiang Shangfeng High-Tech Specialized Wind Industrial Co, LTD, Shaoxing, 311231, China
Key Laboratory of Environmental Geotechnical and Engineering Hazard Control of Jiangxi Province, Ganzhou, 341000, China
Jinchun Xue
State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao, 266580, China
Emergency Management Administration of Haojiang District, Shantou, 515071, China
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Zhuyu Zhao and Ruoyan Cai performed the data analyses and wrote the manuscript; Jinchun Xue contributed to the conception of the study; Li Tan and Chuanliang Yan helped perform the analysis with constructive discussions.
Correspondence to Jinchun Xue .
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Zhao, Z., Cai, R., Xue, J. et al. Experimental study on different phytoremediation of heavy metal pollution in HDS sediment of copper mines. Plant Soil (2024). https://doi.org/10.1007/s11104-024-06886-2
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Determining Cause and Effect
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A controlled experiment is a highly focused way of collecting data and is especially useful for determining patterns of cause and effect. This type of experiment is used in a wide variety of fields, including medical, psychological, and sociological research. Below, we’ll define what controlled experiments are and provide some examples.
To conduct a controlled experiment , two groups are needed: an experimental group and a control group . The experimental group is a group of individuals that are exposed to the factor being examined. The control group, on the other hand, is not exposed to the factor. It is imperative that all other external influences are held constant . That is, every other factor or influence in the situation needs to remain exactly the same between the experimental group and the control group. The only thing that is different between the two groups is the factor being researched.
For example, if you were studying the effects of taking naps on test performance, you could assign participants to two groups: participants in one group would be asked to take a nap before their test, and those in the other group would be asked to stay awake. You would want to ensure that everything else about the groups (the demeanor of the study staff, the environment of the testing room, etc.) would be equivalent for each group. Researchers can also develop more complex study designs with more than two groups. For example, they might compare test performance among participants who had a 2-hour nap, participants who had a 20-minute nap, and participants who didn’t nap.
In controlled experiments, researchers use random assignment (i.e. participants are randomly assigned to be in the experimental group or the control group) in order to minimize potential confounding variables in the study. For example, imagine a study of a new drug in which all of the female participants were assigned to the experimental group and all of the male participants were assigned to the control group. In this case, the researchers couldn’t be sure if the study results were due to the drug being effective or due to gender—in this case, gender would be a confounding variable.
Random assignment is done in order to ensure that participants are not assigned to experimental groups in a way that could bias the study results. A study that compares two groups but does not randomly assign participants to the groups is referred to as quasi-experimental, rather than a true experiment.
In a blind experiment, participants don’t know whether they are in the experimental or control group. For example, in a study of a new experimental drug, participants in the control group may be given a pill (known as a placebo ) that has no active ingredients but looks just like the experimental drug. In a double-blind study , neither the participants nor the experimenter knows which group the participant is in (instead, someone else on the research staff is responsible for keeping track of group assignments). Double-blind studies prevent the researcher from inadvertently introducing sources of bias into the data collected.
If you were interested in studying whether or not violent television programming causes aggressive behavior in children, you could conduct a controlled experiment to investigate. In such a study, the dependent variable would be the children’s behavior, while the independent variable would be exposure to violent programming. To conduct the experiment, you would expose an experimental group of children to a movie containing a lot of violence, such as martial arts or gun fighting. The control group, on the other hand, would watch a movie that contained no violence.
To test the aggressiveness of the children, you would take two measurements : one pre-test measurement made before the movies are shown, and one post-test measurement made after the movies are watched. Pre-test and post-test measurements should be taken of both the control group and the experimental group. You would then use statistical techniques to determine whether the experimental group showed a significantly greater increase in aggression, compared to participants in the control group.
Studies of this sort have been done many times and they usually find that children who watch a violent movie are more aggressive afterward than those who watch a movie containing no violence.
Controlled experiments have both strengths and weaknesses. Among the strengths is the fact that results can establish causation. That is, they can determine cause and effect between variables. In the above example, one could conclude that being exposed to representations of violence causes an increase in aggressive behavior. This kind of experiment can also zero-in on a single independent variable, since all other factors in the experiment are held constant.
On the downside, controlled experiments can be artificial. That is, they are done, for the most part, in a manufactured laboratory setting and therefore tend to eliminate many real-life effects. As a result, analysis of a controlled experiment must include judgments about how much the artificial setting has affected the results. Results from the example given might be different if, say, the children studied had a conversation about the violence they watched with a respected adult authority figure, like a parent or teacher, before their behavior was measured. Because of this, controlled experiments can sometimes have lower external validity (that is, their results might not generalize to real-world settings).
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Quantum materials — those with electronic properties that are governed by the principles of quantum mechanics, such as correlation and entanglement — can exhibit exotic behaviors under certain conditions, such as the ability to transmit electricity without resistance, known as superconductivity. However, in order to get the best performance out of these materials, they need to be properly tuned, in the same way that race cars require tuning as well. A team led by Mingda Li, an associate professor in MIT’s Department of Nuclear Science and Engineering (NSE), has demonstrated a new, ultra-precise way to tweak the characteristics of quantum materials, using a particular class of these materials, Weyl semimetals, as an example.
The new technique is not limited to Weyl semimetals. “We can use this method for any inorganic bulk material, and for thin films as well,” maintains NSE postdoc Manasi Mandal, one of two lead authors of an open-access paper — published recently in Applied Physics Reviews — that reported on the group’s findings.
The experiment described in the paper focused on a specific type of Weyl semimetal, a tantalum phosphide (TaP) crystal. Materials can be classified by their electrical properties: metals conduct electricity readily, whereas insulators impede the free flow of electrons. A semimetal lies somewhere in between. It can conduct electricity, but only in a narrow frequency band or channel. Weyl semimetals are part of a wider category of so-called topological materials that have certain distinctive features. For instance, they possess curious electronic structures — kinks or “singularities” called Weyl nodes, which are swirling patterns around a single point (configured in either a clockwise or counterclockwise direction) that resemble hair whorls or, more generally, vortices. The presence of Weyl nodes confers unusual, as well as useful, electrical properties. And a key advantage of topological materials is that their sought-after qualities can be preserved, or “topologically protected,” even when the material is disturbed.
“That’s a nice feature to have,” explains Abhijatmedhi Chotrattanapituk, a PhD student in MIT’s Department of Electrical Engineering and Computer Science and the other lead author of the paper. “When you try to fabricate this kind of material, you don’t have to be exact. You can tolerate some imperfections, some level of uncertainty, and the material will still behave as expected.”
Like water in a dam
The “tuning” that needs to happen relates primarily to the Fermi level, which is the highest energy level occupied by electrons in a given physical system or material. Mandal and Chotrattanapituk suggest the following analogy: Consider a dam that can be filled with varying levels of water. One can raise that level by adding water or lower it by removing water. In the same way, one can adjust the Fermi level of a given material simply by adding or subtracting electrons.
To fine-tune the Fermi level of the Weyl semimetal, Li’s team did something similar, but instead of adding actual electrons, they added negative hydrogen ions (each consisting of a proton and two electrons) to the sample. The process of introducing a foreign particle, or defect, into the TaP crystal — in this case by substituting a hydrogen ion for a tantalum atom — is called doping. And when optimal doping is achieved, the Fermi level will coincide with the energy level of the Weyl nodes. That’s when the material’s desired quantum properties will be most fully realized.
For Weyl semimetals, the Fermi level is especially sensitive to doping. Unless that level is set close to the Weyl nodes, the material’s properties can diverge significantly from the ideal. The reason for this extreme sensitivity owes to the peculiar geometry of the Weyl node. If one were to think of the Fermi level as the water level in a reservoir, the reservoir in a Weyl semimetal is not shaped like a cylinder; it’s shaped like an hourglass, and the Weyl node is located at the narrowest point, or neck, of that hourglass. Adding too much or too little water would miss the neck entirely, just as adding too many or too few electrons to the semimetal would miss the node altogether.
Fire up the hydrogen
To reach the necessary precision, the researchers utilized MIT’s two-stage “Tandem” ion accelerator — located at the Center for Science and Technology with Accelerators and Radiation (CSTAR) — and buffeted the TaP sample with high-energy ions coming out of the powerful (1.7 million volt) accelerator beam. Hydrogen ions were chosen for this purpose because they are the smallest negative ions available and thus alter the material less than a much larger dopant would. “The use of advanced accelerator techniques allows for greater precision than was ever before possible, setting the Fermi level to milli-electron volt [thousandths of an electron volt] accuracy,” says Kevin Woller, the principal research scientist who leads the CSTAR lab. “Additionally, high-energy beams allow for the doping of bulk crystals beyond the limitations of thin films only a few tens of nanometers thick.”
The procedure, in other words, involves bombarding the sample with hydrogen ions until a sufficient number of electrons are taken in to make the Fermi level just right. The question is: how long do you run the accelerator, and how do you know when enough is enough? The point being that you want to tune the material until the Fermi level is neither too low nor too high.
“The longer you run the machine, the higher the Fermi level gets,” Chotrattanapituk says. “The difficulty is that we cannot measure the Fermi level while the sample is in the accelerator chamber.” The normal way to handle that would be to irradiate the sample for a certain amount of time, take it out, measure it, and then put it back in if the Fermi level is not high enough. “That can be practically impossible,” Mandal adds.
To streamline the protocol, the team has devised a theoretical model that first predicts how many electrons are needed to increase the Fermi level to the preferred level and translates that to the number of negative hydrogen ions that must be added to the sample. The model can then tell them how long the sample ought to be kept in the accelerator chamber.
The good news, Chotrattanapituk says, is that their simple model agrees within a factor of 2 with trusted conventional models that are much more computationally intensive and may require access to a supercomputer. The group’s main contributions are two-fold, he notes: offering a new, accelerator-based technique for precision doping and providing a theoretical model that can guide the experiment, telling researchers how much hydrogen should be added to the sample depending on the energy of the ion beam, the exposure time, and the size and thickness of the sample.
Fine things to come with fine-tuning
This could pave the way to a major practical advance, Mandal notes, because their approach can potentially bring the Fermi level of a sample to the requisite value in a matter of minutes — a task that, by conventional methods, has sometimes taken weeks without ever reaching the required degree of milli-eV precision.
Li believes that an accurate and convenient method for fine-tuning the Fermi level could have broad applicability. “When it comes to quantum materials, the Fermi level is practically everything,” he says. “Many of the effects and behaviors that we seek only manifest themselves when the Fermi level is at the right location.” With a well-adjusted Fermi level, for example, one could raise the critical temperature at which materials become superconducting. Thermoelectric materials, which convert temperature differences into an electrical voltage, similarly become more efficient when the Fermi level is set just right. Precision tuning might also play a helpful role in quantum computing.
Thomas Zac Ward, a senior scientist at the Oak Ridge National Laboratory, offered a bullish assessment: “This work provides a new route for the experimental exploration of the critical, yet still poorly understand, behaviors of emerging materials. The ability to precisely control the Fermi level of a topological material is an important milestone that can help bring new quantum information and microelectronics device architectures to fruition.”
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A month ago, downballot democrats looked like dead meat. no longer..
Just a few weeks ago, Democrats were justifiably in a mortal panic about the 2024 election. Reeling from President Joe Biden’s career-ending debate fiasco against former President Donald Trump, they watched in horror as their elderly nominee’s already-grim polling further deteriorated and began to fear that his wake would take downballot Democrats to the bottom with him. And then, in one of the wildest and fastest political reversals in living memory, the president ended his reelection campaign, and Democrats rallied instantaneously around a suddenly ebullient Vice President Kamala Harris, who has been barnstorming with her running mate, Minnesota Gov. Tim Walz , in front of massive and amped-up crowds that are making her opponents sad and conspiratorial . And as unthinkable as this was in June, Democrats now have an increasingly plausible path to another governing trifecta.
The evidence is clear and growing by the day. In just over two weeks, Harris has not only erased Trump’s lead in polling averages but built one of her own. As of this writing, Harris has led 24 of the last 25 national polls (with one tie in the mix) in the FiveThirtyEight database and leads its national average by 2.8 points. The only firm still showing a Trump lead is Rasmussen, which FiveThirtyEight controversially excludes from its averages because it has become a strenuously partisan operation. Even with Rasmussen in the mix, Harris now leads in the RealClearPolitics averages of both the two-way contest with Trump and the five-person race that includes third-party contenders Robert F. Kennedy Jr., Jill Stein, and Cornel West.
The turnaround has also been manifest in the handful of battleground states that will decide the election. According to FiveThirtyEight averages, Harris has opened up small leads in Pennsylvania , Michigan , and Wisconsin —the so-called Blue Wall that Trump swept in 2016 in his unlikely path to the presidency. Even more ominously for the Trump campaign, the former president’s substantial leads in Arizona, Georgia , and Nevada have all but vanished. FiveThirtyEight averages now have Harris up by a hair in Arizona and tied in Georgia. Nate Silver has anointed Harris a narrow favorite on the basis of this data, giving her close to a 55 percent chance to win the Electoral College as of last Wednesday morning. Silver gave Biden just a 25.8 percent of winning reelection on July 19, so if his model is accurate, Harris has already more than doubled Democrats’ odds of holding the White House.
The downballot implications of this tectonic shift are also significant. Since Biden’s departure on July 21, Democratic Senate candidates in Nevada, Michigan, Wisconsin, Arizona, Ohio, and Pennsylvania have led all of the (admittedly quite limited) public polling , often substantially. While Republicans can still flip the Senate, even if they get swept in those races, by capturing seats in West Virginia and Montana and protecting all of their incumbents, it’s a much narrower path than it was a month ago. And with a floundering Trump facing an ascendant Harris, Republicans might want to check in on some races that Democrats had written off as unwinnable. A recent University of North Florida survey, for example, had Democrat Deborah Murcarsel-Powell within 4 points of incumbent Republican Rick Scott just two years after Sen. Marco Rubio drubbed his well-funded Democratic opponent by 16. Democrats also now lead narrowly in the FiveThirtyEight average of the “generic ballot” question, which asks voters whether they will vote for Democrats or Republicans for Congress.
It was the deterioration in downballot polling that really lit a fire under elected Democrats to push Biden from the race in July. Senate Democrats continued to run well ahead of Biden even at the height of Democrats’ public freakout last month, but it has been nearly 200 years since a party flipped a chamber of Congress while losing the presidency, and Democrats were spooked by apocalyptic internal polling that showed them slipping even in once safe states like Virginia and Minnesota. Now that dynamic is almost totally reversed. Are Democrats favored to win a trifecta at this moment? With that Senate map , almost certainly not, but rank-and-file volunteers can now work with renewed hope that a President Harris might actually get to govern rather than engage in two painful years of budget standoffs with a hostile Republican Congress.
Vice President Harris and her team deserve enormous credit for the smoothness of her campaign rollout and the way that they quietly worked party elites to shore up her support and head off the possibility of a contested convention. And Harris has clearly grown as a national candidate since her brief and uneven campaign in 2020. Those who thought that she would fall flat out of the gate and fail to change the basic dynamic of the campaign against Trump have been proved pretty conclusively wrong. And while her selection of Tim Walz over Pennsylvania Gov. Josh Shapiro as a running mate was a huge, calculated risk , the response among Democrats has been extremely positive. Both elected Democrats and rank-and-file supporters feel liberated from a doomed campaign, and their relief and enthusiasm are palpable .
But Harris has also had the enormous good fortune of facing a hopelessly inept and toxic Republican nominee whose party has clearly learned nothing from underperforming in four consecutive national elections. Just four years ago, incumbent Republican President Donald Trump was pink-slipped by more than 7 million votes in the midst of the once-in-a-lifetime exogenous shock of the COVID-19 pandemic . Kicking away the White House under what should have been slam-dunk rally-around-the-president circumstances was an extraordinary feat of political malpractice that the party didn’t even try to learn from. The GOP also became the first party in 130 years to lose the presidency and both chambers of Congress just four years after a change in party control of the White House. Instead of conducting an autopsy as any halfway competent political party would do, they claimed that the election was stolen from them and then swiftly executed a partywide capitulation to the sordid hallucinations of the preening narcissist who was responsible for the disaster in the first place.
In the weeks following Biden’s departure from the 2024 race, Republicans have proved conclusively that their problems go well beyond Donald Trump. Gifted with a once-in-a-generation opportunity to reverse the GOP’s two-decade-long slide with young voters and to make real inroads with Black voters, Republicans have reverted instead to the party’s losing 21 st century script of repellent, increasingly open racism and tone-deaf culture-warring that has thoroughly alienated a large and growing swath of the electorate. The presence of Joe Biden in the race, it turns out, was the only thing propping up the fortunes of a political movement that cannot seem to figure out a way to increase its appeal.
Even before Biden exited, Trump anointed the odious Sen. J.D. Vance over the strenuous objections of other party strategists only to realize in horror that the handpicked prince of MAGAstan has been running around giving unfathomably damaging quotes to right-wing podcasters and audiences for years, including some real gems about how Americans who don’t have kids are spiritually empty freeloaders who should pay higher taxes and an ongoing determination to code single, childless women as miserable cat ladies. Less than a month after his selection, Vance has become the least popular running mate in the history of the polling era.
Trump himself couldn’t keep it together for five minutes after Harris emerged as the likely nominee, calling her a “DEI hire” and speculating that Harris had decided to “ turn Black ” in his disastrous interview two weeks ago with the National Association of Black Journalists. He has whinged incessantly and embarrassingly about how much money he spent running against Biden and how unfair it all is, and almost immediately backed out of the scheduled general election debate on Sept. 10. His social media posts speculating about how Harris is using A.I. to create the impression of adoring crowds are pitiful and bizarre even by his standards. Instead of working together to push back on this calamitous “strategy,” or better, trying to force their 78-year-old albatross of a nominee from the race altogether, Republican elites have lined up to go on cable news and repeat or explain away his deranged talking points for public consumption and to defend the escalating series of baffling comments and decisions.
For the first time this cycle, it should be painfully obvious to Republicans willing to consider the evidence in front of them that nominating Donald Trump a third time was a monumental mistake. Even if he pulls out a W against Harris—and last week’s stock market slide should be a healthy reminder to Democrats that it is nowhere near time to start popping the Prosecco—Trump is likely to do so with small margins in Congress and a weak mandate from the American people. Poll after poll after poll showed former South Carolina Gov. Nikki Haley waxing Biden in a general election, and yet, unlike Democrats, Republicans just did not care.
They wanted Trump, and they got him. As a result, they might ultimately get a President Harris and another two years of a Democratic-controlled Congress.
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Learn about the difference between the control group and the experimental group in a scientific experiment, including positive and negative controls.
In research, the control group is the one not exposed to the variable of interest (the independent variable) and provides a baseline for comparison. The experimental group, on the other hand, is exposed to the independent variable. Comparing results between these groups helps determine if the independent variable has a significant effect on the outcome (the dependent variable).
Control groups in non-experimental research Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design. Control groups in quasi-experimental design While true experiments rely on random assignment to the treatment or control ...
In this lesson, discover what is an experimental group, compare the difference between an experimental group and a control group, and examine two examples of experimental groups. Updated: 11/21/2023
A control group in a scientific experiment is a group separated from the rest of the experiment, where the independent variable being tested cannot influence the results. This isolates the independent variable 's effects on the experiment and can help rule out alternative explanations of the experimental results.
A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect. There are different types of control groups. A controlled experiment has one more control group.
Control groups in experiments Control groups are essential to experimental design. When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups: The treatment group (also called the experimental group) receives the treatment whose effect the researcher is interested in. The control group receives either no treatment, a ...
Control group, the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term 'experiment' for study designs that include a control group.
A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments.
Control groups Controlled experiments require control groups. Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.
A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other. The experimental group receives some sort of treatment, and their results are compared against those of the control group ...
What is the control group? In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference - experimental manipulation.
Explore what an experimental group is in experimental design and get examples of how to tell this group and the control group apart.
The control group provides a baseline that lets us see if the treatment has an effect. Is there always one experimental group and one control group?
Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent ...
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not.
In a randomized and controlled psychology experiment, the researchers are examining the impact of an experimental condition on a group of participants (does the independent variable 'X' cause a change in the dependent variable 'Y'?). To determine cause and effect, there must be at least two groups to compare, the experimental group and the control group.
Discover the meaning of control group vs. experimental group, explore their differences, review some frequently asked questions, and highlight some examples.
The experimental group is where the key focus of the experiment resides, as it is subject to the variable or intervention being examined. The alterations made to this group are deliberate and strategic, aiming to explore the effects of specific changes or treatments. Comparing the outcomes from the experimental group with those of the control group allows researchers to deduce the impact of ...
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not.
A control group is a group in an experiment that does not receive the experimental treatment. The purpose of a control group is to provide a baseline against which to compare the experimental group results.
A good control group is identical to the experimental group in all way except for the difference in the experimental condition (except for the variable that is changing in the experiment) The effect or influence of independent variable on dependent variable is determined by comparing the experimental results with the control group.
The purpose of this quantitative, quasi-experimental study was to determine the effect of small group learning during the core mathematics block on 5th-grade students' mathematical resilience, compared to a control group. Student collaboration and mathematical discourse decreased during the COVID-19 pandemic, leading to a drop in math proficiency levels in the United States.
Question: In an experimental study, what is the purpose of a control group?A) To receive the experimental treatment being tested.B) To serve as a baseline for comparison by not receiving the experimental treatment.C) To eliminate the need for randomization in assigning subjects to groups.D) To ensure that all subjects receive some form of treatment.
Long March 4B launches experimental Yaogan-43 satellite group China successfully launched the first of a new group of Yaogan satellites Friday to test technologies for a low Earth orbit constellation.
Experimental group 1 was planted with black locality, experimental group 2 was planted with slash pine, and experimental group 3 was planted with Chinese white poplar. Before starting sample collection, the samplers such as garden shears and machetes were wiped with alcohol disinfection equipment.
A controlled experiment is a research study in which participants are randomly assigned to experimental and control groups. A controlled experiment allows researchers to determine cause and effect between variables. One drawback of controlled experiments is that they lack external validity (which means their results may not generalize to real ...
There is no real difference between Kamala Harris and Joe Biden on economic issues, and to the extent there is Harris's ideas are truly dangerous.
An MIT-led group has learned how to achieve precise control over the properties of Weyl semimetals and other exotic substances. The technique can be used any inorganic bulk material and for thin films. ... "This work provides a new route for the experimental exploration of the critical, yet still poorly understand, behaviors of emerging ...
Politics Kamala Harris Has Transformed the Race to Control Congress A month ago, downballot Democrats looked like dead meat. No longer.