Example: Factorial design applied in optimisation technique.
To meet the ethical considerations, you need to ensure that.
Collect the data by using suitable data collection according to your experiment’s requirement, such as observations, case studies , surveys , interviews , questionnaires, etc. Analyse the obtained information.
Write the report of your research. Present, conclude, and explain the outcomes of your study .
What is the first step in conducting an experimental research.
The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.
A dependent variable is one that completely depends on another variable, mostly the independent one.
Level of measurement in statistics is a classification that describes the values assigned to different variables and the relationship of these variables with each other.
Interval data is a type of discrete data that can be calculated along a scale where every point is placed at an equal interval from another, just as the name explains itself.
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Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc.
It is a collection of research designs which use manipulation and controlled testing to understand causal processes. Generally, one or more variables are manipulated to determine their effect on a dependent variable.
The experimental method is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and measures any change in other variables.
Experimental Research is often used where:
(Reference: en.wikipedia.org)
The word experimental research has a range of definitions. In the strict sense, experimental research is what we call a true experiment .
This is an experiment where the researcher manipulates one variable, and control/randomizes the rest of the variables. It has a control group , the subjects have been randomly assigned between the groups, and the researcher only tests one effect at a time. It is also important to know what variable(s) you want to test and measure.
A very wide definition of experimental research, or a quasi experiment , is research where the scientist actively influences something to observe the consequences. Most experiments tend to fall in between the strict and the wide definition.
A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define experiments more narrowly than social sciences, such as sociology and psychology, which conduct experiments closer to the wider definition.
Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to explain some kind of causation . Experimental research is important to society - it helps us to improve our everyday lives.
After deciding the topic of interest, the researcher tries to define the research problem . This helps the researcher to focus on a more narrow research area to be able to study it appropriately. Defining the research problem helps you to formulate a research hypothesis , which is tested against the null hypothesis .
The research problem is often operationalizationed , to define how to measure the research problem. The results will depend on the exact measurements that the researcher chooses and may be operationalized differently in another study to test the main conclusions of the study.
An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the contrary evidence. A poor ad hoc analysis may be seen as the researcher's inability to accept that his/her hypothesis is wrong, while a great ad hoc analysis may lead to more testing and possibly a significant discovery.
There are various aspects to remember when constructing an experiment. Planning ahead ensures that the experiment is carried out properly and that the results reflect the real world, in the best possible way.
Sampling groups correctly is especially important when we have more than one condition in the experiment. One sample group often serves as a control group , whilst others are tested under the experimental conditions.
Deciding the sample groups can be done in using many different sampling techniques. Population sampling may chosen by a number of methods, such as randomization , "quasi-randomization" and pairing.
Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample size to minimize chances of random errors .
Here are some common sampling techniques :
The research design is chosen based on a range of factors. Important factors when choosing the design are feasibility, time, cost, ethics, measurement problems and what you would like to test. The design of the experiment is critical for the validity of the results.
It may be wise to first conduct a pilot-study or two before you do the real experiment. This ensures that the experiment measures what it should, and that everything is set up right.
Minor errors, which could potentially destroy the experiment, are often found during this process. With a pilot study, you can get information about errors and problems, and improve the design, before putting a lot of effort into the real experiment.
If the experiments involve humans, a common strategy is to first have a pilot study with someone involved in the research, but not too closely, and then arrange a pilot with a person who resembles the subject(s) . Those two different pilots are likely to give the researcher good information about any problems in the experiment.
An experiment is typically carried out by manipulating a variable, called the independent variable , affecting the experimental group. The effect that the researcher is interested in, the dependent variable(s) , is measured.
Identifying and controlling non-experimental factors which the researcher does not want to influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling variables , if possible, or randomizing variables to minimize effects that can be traced back to third variables . Researchers only want to measure the effect of the independent variable(s) when conducting an experiment , allowing them to conclude that this was the reason for the effect.
In quantitative research , the amount of data measured can be enormous. Data not prepared to be analyzed is called "raw data". The raw data is often summarized as something called "output data", which typically consists of one line per subject (or item). A cell of the output data is, for example, an average of an effect in many trials for a subject. The output data is used for statistical analysis, e.g. significance tests, to see if there really is an effect.
The aim of an analysis is to draw a conclusion , together with other observations. The researcher might generalize the results to a wider phenomenon, if there is no indication of confounding variables "polluting" the results.
If the researcher suspects that the effect stems from a different variable than the independent variable, further investigation is needed to gauge the validity of the results. An experiment is often conducted because the scientist wants to know if the independent variable is having any effect upon the dependent variable. Variables correlating are not proof that there is causation .
Experiments are more often of quantitative nature than qualitative nature, although it happens.
This website contains many examples of experiments. Some are not true experiments , but involve some kind of manipulation to investigate a phenomenon. Others fulfill most or all criteria of true experiments.
Here are some examples of scientific experiments:
Oskar Blakstad (Jul 10, 2008). Experimental Research. Retrieved Jun 14, 2024 from Explorable.com: https://explorable.com/experimental-research
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Experimental research—often considered to be the ‘gold standard’ in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.
Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.
Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.
Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.
Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .
Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.
Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.
History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.
Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.
Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.
Not conducting a pretest can help avoid this threat.
Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.
Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.
Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.
Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.
Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.
Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.
The treatment effect is measured simply as the difference in the posttest scores between the two groups:
The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.
Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:
Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.
Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).
In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.
Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.
Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.
Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.
Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.
Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.
In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.
Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.
Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.
Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.
Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.
An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.
Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.
The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.
In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.
Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…
Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.
Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.
Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.
Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.
Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.
The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:
Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.
The way a researcher assigns subjects to different groups determines the types of experimental research design .
In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:
This design is practical but lacks in certain areas of true experimental criteria.
This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:
True experimental research design should have a variable to manipulate, a control group and random distribution.
With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:
Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:
Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences
Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :
Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.
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Design and experimental results of an aiot-enabled, energy-efficient ceiling fan system.
2. materials and method, 2.1. system description, 2.2. methodology, 2.3. hardware, 2.4. firmware, 2.5. software.
2.7. experiment design, 2.8. machine learning, 2.9. system usability scale (sus), 3.1. machine learning model, 3.2. temperature and humidity during fan operation, 3.3. energy efficiency, 3.4. saved energy, 3.5. sus results, 4. limitations and future work, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.
RPM | Speed Level | BLDC Fan Energy Consumption (Watt Hour) | Conventional AC Ceiling Fan (Watt Hour) |
---|---|---|---|
178 | 1 | 10 | 24 |
212 | 2 | 15 | 34 |
240 | 3 | 20 | 55 |
265 | 4 | 27 | 64 |
327 | 5 | 48 | 96 |
# | Items of System Usability Scale | Mean Score | Ideal Score | Absolute Difference |
---|---|---|---|---|
1 | I think that I would like to use the smart fan frequently. | 4.06 | 5 | 0.93 |
2 | The smart fan system is unnecessarily complex. | 2.56 | 1 | 1.56 |
3 | The smart fan system was easy to use. | 4.06 | 5 | 0.93 |
4 | I think that I would need the support of a technical person to be able to use this smart fan. | 2.43 | 1 | 1.43 |
5 | I found the various functions in this smart fan were well integrated. | 4.12 | 5 | 0.87 |
6 | I thought there was too much inconsistency in this smart fan. | 2.75 | 1 | 1.75 |
7 | I would imagine that most people would learn to use this smart fan very quickly. | 3.87 | 5 | 1.12 |
8 | I found this smart fan very cumbersome (awkward) to use. | 2.31 | 1 | 1.31 |
9 | I felt very confident using the smart fan system. | 4.18 | 5 | 0.81 |
10 | I needed to learn a lot of things before I could get going with this smart fan. | 2.43 | 1 | 1.43 |
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Khan, H.R.; Ahmed, W.; Masud, W.; Alam, U.; Arshad, K.; Assaleh, K.; Qazi, S.A. Design and Experimental Results of an AIoT-Enabled, Energy-Efficient Ceiling Fan System. Sustainability 2024 , 16 , 5047. https://doi.org/10.3390/su16125047
Khan HR, Ahmed W, Masud W, Alam U, Arshad K, Assaleh K, Qazi SA. Design and Experimental Results of an AIoT-Enabled, Energy-Efficient Ceiling Fan System. Sustainability . 2024; 16(12):5047. https://doi.org/10.3390/su16125047
Khan, Hashim Raza, Wajahat Ahmed, Wasiq Masud, Urooj Alam, Kamran Arshad, Khaled Assaleh, and Saad Ahmed Qazi. 2024. "Design and Experimental Results of an AIoT-Enabled, Energy-Efficient Ceiling Fan System" Sustainability 16, no. 12: 5047. https://doi.org/10.3390/su16125047
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Empirical evidence is information that is acquired by observation or experimentation.
Types of empirical research, identifying empirical evidence, empirical law vs. scientific law, empirical, anecdotal and logical evidence, additional resources and reading, bibliography.
Empirical evidence is information acquired by observation or experimentation. Scientists record and analyze this data. The process is a central part of the scientific method , leading to the proving or disproving of a hypothesis and our better understanding of the world as a result.
Empirical evidence might be obtained through experiments that seek to provide a measurable or observable reaction, trials that repeat an experiment to test its efficacy (such as a drug trial, for instance) or other forms of data gathering against which a hypothesis can be tested and reliably measured.
"If a statement is about something that is itself observable, then the empirical testing can be direct. We just have a look to see if it is true. For example, the statement, 'The litmus paper is pink', is subject to direct empirical testing," wrote Peter Kosso in " A Summary of Scientific Method " (Springer, 2011).
"Science is most interesting and most useful to us when it is describing the unobservable things like atoms , germs , black holes , gravity , the process of evolution as it happened in the past, and so on," wrote Kosso. Scientific theories , meaning theories about nature that are unobservable, cannot be proven by direct empirical testing, but they can be tested indirectly, according to Kosso. "The nature of this indirect evidence, and the logical relation between evidence and theory, are the crux of scientific method," wrote Kosso.
The scientific method begins with scientists forming questions, or hypotheses , and then acquiring the knowledge through observations and experiments to either support or disprove a specific theory. "Empirical" means "based on observation or experience," according to the Merriam-Webster Dictionary . Empirical research is the process of finding empirical evidence. Empirical data is the information that comes from the research.
Before any pieces of empirical data are collected, scientists carefully design their research methods to ensure the accuracy, quality and integrity of the data. If there are flaws in the way that empirical data is collected, the research will not be considered valid.
The scientific method often involves lab experiments that are repeated over and over, and these experiments result in quantitative data in the form of numbers and statistics. However, that is not the only process used for gathering information to support or refute a theory.
This methodology mostly applies to the natural sciences. "The role of empirical experimentation and observation is negligible in mathematics compared to natural sciences such as psychology, biology or physics," wrote Mark Chang, an adjunct professor at Boston University, in " Principles of Scientific Methods " (Chapman and Hall, 2017).
"Empirical evidence includes measurements or data collected through direct observation or experimentation," said Jaime Tanner, a professor of biology at Marlboro College in Vermont. There are two research methods used to gather empirical measurements and data: qualitative and quantitative.
Qualitative research, often used in the social sciences, examines the reasons behind human behavior, according to the National Center for Biotechnology Information (NCBI) . It involves data that can be found using the human senses . This type of research is often done in the beginning of an experiment. "When combined with quantitative measures, qualitative study can give a better understanding of health related issues," wrote Dr. Sanjay Kalra for NCBI.
Quantitative research involves methods that are used to collect numerical data and analyze it using statistical methods, ."Quantitative research methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques," according to the LeTourneau University . This type of research is often used at the end of an experiment to refine and test the previous research.
Identifying empirical evidence in another researcher's experiments can sometimes be difficult. According to the Pennsylvania State University Libraries , there are some things one can look for when determining if evidence is empirical:
The objective of science is that all empirical data that has been gathered through observation, experience and experimentation is without bias. The strength of any scientific research depends on the ability to gather and analyze empirical data in the most unbiased and controlled fashion possible.
However, in the 1960s, scientific historian and philosopher Thomas Kuhn promoted the idea that scientists can be influenced by prior beliefs and experiences, according to the Center for the Study of Language and Information .
— Amazing Black scientists
— Marie Curie: Facts and biography
— What is multiverse theory?
"Missing observations or incomplete data can also cause bias in data analysis, especially when the missing mechanism is not random," wrote Chang.
Because scientists are human and prone to error, empirical data is often gathered by multiple scientists who independently replicate experiments. This also guards against scientists who unconsciously, or in rare cases consciously, veer from the prescribed research parameters, which could skew the results.
The recording of empirical data is also crucial to the scientific method, as science can only be advanced if data is shared and analyzed. Peer review of empirical data is essential to protect against bad science, according to the University of California .
Empirical laws and scientific laws are often the same thing. "Laws are descriptions — often mathematical descriptions — of natural phenomenon," Peter Coppinger, associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology, told Live Science.
Empirical laws are scientific laws that can be proven or disproved using observations or experiments, according to the Merriam-Webster Dictionary . So, as long as a scientific law can be tested using experiments or observations, it is considered an empirical law.
Empirical, anecdotal and logical evidence should not be confused. They are separate types of evidence that can be used to try to prove or disprove and idea or claim.
Logical evidence is used proven or disprove an idea using logic. Deductive reasoning may be used to come to a conclusion to provide logical evidence. For example, "All men are mortal. Harold is a man. Therefore, Harold is mortal."
Anecdotal evidence consists of stories that have been experienced by a person that are told to prove or disprove a point. For example, many people have told stories about their alien abductions to prove that aliens exist. Often, a person's anecdotal evidence cannot be proven or disproven.
There are some things in nature that science is still working to build evidence for, such as the hunt to explain consciousness .
Meanwhile, in other scientific fields, efforts are still being made to improve research methods, such as the plan by some psychologists to fix the science of psychology .
" A Summary of Scientific Method " by Peter Kosso (Springer, 2011)
"Empirical" Merriam-Webster Dictionary
" Principles of Scientific Methods " by Mark Chang (Chapman and Hall, 2017)
"Qualitative research" by Dr. Sanjay Kalra National Center for Biotechnology Information (NCBI)
"Quantitative Research and Analysis: Quantitative Methods Overview" LeTourneau University
"Empirical Research in the Social Sciences and Education" Pennsylvania State University Libraries
"Thomas Kuhn" Center for the Study of Language and Information
"Misconceptions about science" University of California
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COVID-19 vaccines help our bodies develop immunity to the virus that causes COVID-19 without us having to get the illness.
Different types of vaccines work in different ways to offer protection. But with all types of vaccines, the body is left with a supply of “memory” T-lymphocytes as well as B-lymphocytes that will remember how to fight that virus in the future.
It typically takes a few weeks after vaccination for the body to produce T-lymphocytes and B-lymphocytes.
Sometimes after vaccination, the process of building immunity can cause symptoms, such as fever. These symptoms are normal signs the body is building immunity.
There are different types of vaccines.
None of these vaccines can give you COVID-19.
They do not affect or interact with our DNA.
To trigger an immune response, many vaccines put a weakened or inactivated germ into our bodies. Not mRNA vaccines. Instead, mRNA vaccines use mRNA created in a laboratory to teach our cells how to make a protein—or even just a piece of a protein—that triggers an immune response inside our bodies. This immune response, which produces antibodies, is what helps protect us from getting sick from that germ in the future.
Researchers have been studying and working with mRNA vaccines for decades .
PDF infographic explaining how mRNA COVID-19 vaccines work.
Protein subunit vaccines contain pieces (proteins) of the virus that causes COVID-19. These virus pieces are the spike protein. The vaccine also contains another ingredient called an adjuvant that helps the immune system respond to that spike protein in the future. Once the immune system knows how to respond to the spike protein, the immune system will be able to respond quickly to the actual virus spike protein and protect you against COVID-19.
Protein subunit vaccines have been used for years.
PDF infographic explaining how Protein Subunit COVID-19 vaccines work.
While COVID-19 vaccines were developed rapidly, all steps have been taken to ensure their safety and effectiveness. Bringing a new vaccine to the public involves many steps including:
As vaccines are distributed outside of clinical trials, monitoring systems are used to make sure that COVID-19 vaccines are safe.
New vaccines are first developed in laboratories. Scientists have been working for many years to develop vaccines against coronaviruses, such as those that cause severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). SARS-CoV-2, the virus that causes COVID-19, is related to these other coronaviruses. The knowledge that was gained through past research on coronavirus vaccines helped speed up the initial development of the current COVID-19 vaccines.
After initial laboratory development, vaccines go through three phases of clinical trials to make sure they are safe and effective. No trial phases have been skipped.
The clinical trials for COVID-19 vaccines have involved tens of thousands of volunteers of different ages, races, and ethnicities.
Clinical trials for vaccines compare outcomes (such as how many people get sick) between people who are vaccinated and people who are not. Results from these trials have shown that COVID-19 vaccines are safe and effective , especially against severe illness, hospitalization, and death.
Before vaccines are made available to people in real-world settings, FDA assesses the findings from clinical trials. Initially, they determined that COVID-19 vaccines met FDA’s safety and effectiveness standards and granted those vaccines Emergency Use Authorizations (EUAs) . The EUAs allowed the vaccines to be quickly distributed for use while maintaining the same high safety standards required for all vaccines. Learn more in this video about EUAs .
FDA has granted full approval for some COVID-19 vaccines. Before granting approval, FDA reviewed evidence that built on the data and information submitted to support the EUA. This included:
These vaccines were found to meet the high standards for safety, effectiveness, and manufacturing quality FDA requires of an approved product. Learn more about the process for FDA approval .
When FDA authorizes or approves a COVID-19 vaccine, ACIP reviews all available data about that vaccine to determine whether to recommend it and who should receive it. These vaccine recommendations then go through an approval process that involves both ACIP and CDC.
Watch Video: Understanding ACIP and How Vaccine Recommendations are Made [00:05:02]
Hundreds of millions of people in the United States have received COVID-19 vaccines under the most intense safety monitoring in U.S. history.
Several monitoring systems continue to track outcomes from COVID-19 vaccines to ensure their safety. Some people have no side effects. Many people have reported common side effects after COVID-19 vaccination , like pain or swelling at the injection site, a headache, chills, or fever. These reactions are common and are normal signs that your body is building protection.
Reports of serious adverse events after vaccination are rare .
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Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design: Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new ...
Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set.
Three types of experimental designs are commonly used: 1. Independent Measures. Independent measures design, also known as between-groups, is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.
Step 1: Define your variables. You should begin with a specific research question. We will work with two research question examples, one from health sciences and one from ecology: Example question 1: Phone use and sleep. You want to know how phone use before bedtime affects sleep patterns.
Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause ...
An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions. An experiment is a data collection ...
Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. Some uses of experimental research design are highlighted below.
Quantitative research designs can be divided into two main categories: Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables. Experimental and quasi-experimental designs are used to test causal relationships. Qualitative research designs
Experimental research serves as a fundamental scientific method aimed at unraveling. cause-and-effect relationships between variables across various disciplines. This. paper delineates the key ...
A good experimental design requires a strong understanding of the system you are studying. There are five key steps in designing an experiment: Consider your variables and how they are related. Write a specific, testable hypothesis. Design experimental treatments to manipulate your independent variable.
Abstract. Experimental research design is centrally concerned with constructing research that is high in causal (internal) validity. Randomized experimental designs provide the highest levels of causal validity. Quasi-experimental designs have a number of potential threats to their causal validity. Yet, new quasi-experimental designs adopted ...
Types of Experimental Designs. Experimental design is an umbrella term for a research method that is designed to test hypotheses related to causality under controlled conditions. Table 14.1 describes the three major types of experimental design (pre-experimental, quasi-experimental, and true experimental) and presents subtypes for each.
The classic experimental design definition is: "The methods used to collect data in experimental studies.". There are three primary types of experimental design: The way you classify research subjects based on conditions or groups determines the type of research design you should use. 01. Pre-Experimental Design.
Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping ...
Experimental design in macro-level research. You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals. For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational ...
Experimental design as a subset of scientific investigation is a popular and widely used research approach. The essence of experimental design and perhaps the most important reason researchers choose to design and conduct experiments is the precision with which one can analyze the relationship between and among variables and to make that ...
Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and posttest. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the posttest. Table 12.1 illustrates the features of each of the four groups in the Solomon four-group design.
The simplest type of experimental design is called a pre-experimental research design, and it has many different manifestations. Using a pre-experiment, some factor or treatment that is expected to cause change is implemented for a group or multiple groups of research subjects, and the subjects are observed over a period of time.
Collect the data by using suitable data collection according to your experiment's requirement, such as observations, case studies , surveys , interviews, questionnaires, etc. Analyse the obtained information. Step 8. Present and Conclude the Findings of the Study. Write the report of your research.
Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc. It is a collection of research designs which use manipulation and controlled testing to understand causal processes. Generally, one or more variables are manipulated to determine their effect on a dependent variable.
10 Experimental research. 10. Experimental research. Experimental research—often considered to be the 'gold standard' in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different ...
The three main types of experimental research design are: 1. Pre-experimental research. A pre-experimental research study is an observational approach to performing an experiment. It's the most basic style of experimental research. Free experimental research can occur in one of these design structures: One-shot case study research design: In ...
Here are some examples of experimental research: This research method can be used to evaluate employees' skills. Organizations ask candidates to take tests before filling a post. It is used to screen qualified candidates from a pool of applicants. This allows organizations to identify skills at the time of employment.
The Wemos D1 Mini development board built on a 32-bit ESP8266 microcontroller was used in this research. The Wemos D1 Mini is a Wi-Fi (System on Chip) SoC that is powered at 3.3 Volts through the microcontroller and runs at 80 MHz. ... Wajahat Ahmed, Wasiq Masud, Urooj Alam, Kamran Arshad, Khaled Assaleh, and Saad Ahmed Qazi. 2024. "Design and ...
Bibliography. Empirical evidence is information acquired by observation or experimentation. Scientists record and analyze this data. The process is a central part of the scientific method, leading ...
The experimental design of the bracket may present a reduced mesh contact area in the base, which can negatively influence bond strength. Additionally, the hole created through the bracket body may result in diminished stability and structural rigidity of the device, leading to distortion of the bracket's spatial geometry that could ...
An Experimental Design The F-16XL was one of two entries in the Air Force's 1981 Enhanced Tactical Fighter (ETF) competition to craft a replacement for the F-111 Aardvark. The F-16XL lost the ...
Research for protein subunit technology. Protein subunit vaccines have been used for years. More than 30 years ago, a hepatitis B vaccine became the first protein subunit vaccine to be approved for use in people in the United States. Another example of other protein subunit vaccines used today include whooping cough vaccines.