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Pre-experimental designs.

Pre-experiments are the simplest form of research design. In a pre-experiment either a single group or multiple groups are observed subsequent to some agent or treatment presumed to cause change.

Types of Pre-Experimental Design

One-shot case study design, one-group pretest-posttest design, static-group comparison.

A single group is studied at a single point in time after some treatment that is presumed to have caused change. The carefully studied single instance is compared to general expectations of what the case would have looked like had the treatment not occurred and to other events casually observed. No control or comparison group is employed.

A single case is observed at two time points, one before the treatment and one after the treatment. Changes in the outcome of interest are presumed to be the result of the intervention or treatment. No control or comparison group is employed.

A group that has experienced some treatment is compared with one that has not. Observed differences between the two groups are assumed to be a result of the treatment.

Validity of Results

An important drawback of pre-experimental designs is that they are subject to numerous threats to their  validity . Consequently, it is often difficult or impossible to dismiss rival hypotheses or explanations. Therefore, researchers must exercise extreme caution in interpreting and generalizing the results from pre-experimental studies.

One reason that it is often difficult to assess the validity of studies that employ a pre-experimental design is that they often do not include any control or comparison group. Without something to compare it to, it is difficult to assess the significance of an observed change in the case. The change could be the result of historical changes unrelated to the treatment, the maturation of the subject, or an artifact of the testing.

Even when pre-experimental designs identify a comparison group, it is still difficult to dismiss rival hypotheses for the observed change. This is because there is no formal way to determine whether the two groups would have been the same if it had not been for the treatment. If the treatment group and the comparison group differ after the treatment, this might be a reflection of differences in the initial recruitment to the groups or differential mortality in the experiment.

Advantages and Disadvantages

As exploratory approaches, pre-experiments can be a cost-effective way to discern whether a potential explanation is worthy of further investigation.

Disadvantages

Pre-experiments offer few advantages since it is often difficult or impossible to rule out alternative explanations. The nearly insurmountable threats to their validity are clearly the most important disadvantage of pre-experimental research designs.

One-Group Posttest Only Design: An Introduction

The one-group posttest-only design (a.k.a. one-shot case study ) is a type of quasi-experiment in which the outcome of interest is measured only once after exposing a non-random group of participants to a certain intervention.

The objective is to evaluate the effect of that intervention which can be:

• A training program
• A policy change
• A medical treatment, etc.

As in other quasi-experiments, the group of participants who receive the intervention is selected in a non-random way (for example according to their choosing or that of the researcher).

The one-group posttest-only design is especially characterized by having:

• No control group
• No measurements before the intervention

It is the simplest and weakest of the quasi-experimental designs in terms of level of evidence as the measured outcome cannot be compared to a measurement before the intervention nor to a control group.

So the outcome will be compared to what we assume will happen if the intervention was not implemented. This is generally based on expert knowledge and speculation.

Next we will discuss cases where this design can be useful and its limitations in the study of a causal relationship between the intervention and the outcome.

Advantages and Limitations of the one-group posttest-only design

Advantages of the one-group posttest-only design, 1. advantages related to the non-random selection of participants:.

• Ethical considerations: Random selection of participants is considered unethical when the intervention is believed to be harmful (for example exposing people to smoking or dangerous chemicals) or on the contrary when it is believed to be so beneficial that it would be malevolent not to offer it to all participants (for example a groundbreaking treatment or medical operation).
• Difficulty to adequately randomize subjects and locations: In some cases where the intervention acts on a group of people at a given location, it becomes infeasible to adequately randomize subjects (ex. an intervention that reduces pollution in a given area).

2. Advantages related to the simplicity of this design:

• Feasible with fewer resources than most designs: The one-group posttest-only design is especially useful when the intervention must be quickly introduced and we do not have enough time to take pre-intervention measurements. Other designs may also require a larger sample size or a higher cost to account for the follow-up of a control group.
• No temporality issue: Since the outcome is measured after the intervention, we can be certain that it occurred after it, which is important for inferring a causal relationship between the two.

Limitations of the one-group posttest-only design

1. selection bias:.

Because participants were not chosen at random, it is certainly possible that those who volunteered are not representative of the population of interest on which we intend to draw our conclusions.

2. Limitation due to maturation:

Because we don’t have a control group nor a pre-intervention measurement of the variable of interest, the post-intervention measurement will be compared to what we believe or assume would happen was there no intervention at all.

The problem is when the outcome of interest has a natural fluctuation pattern (maturation effect) that we don’t know about.

So since certain factors are essentially hard to predict and since 1 measurement is certainly not enough to understand the natural pattern of an outcome, therefore with the one-group posttest-only design, we can hardly infer any causal relationship between intervention and outcome.

3. Limitation due to history:

The idea here is that we may have a historical event, which may also influence the outcome, occurring at the same time as the intervention.

The problem is that this event can now be an alternative explanation of the observed outcome. The only way out of this is if the effect of this event on the outcome is well-known and documented in order to account for it in our data analysis.

This is why most of the time we prefer other designs that include a control group (made of people who were exposed to the historical event but not to the intervention) as it provides us with a reference to compare to.

Example of a study that used the one-group posttest-only design

In 2018, Tsai et al. designed an exercise program for older adults based on traditional Chinese medicine ideas, and wanted to test its feasibility, safety and helpfulness.

So they conducted a one-group posttest-only study as a pilot test with 31 older adult volunteers. Then they evaluated these participants (using open-ended questions) after receiving the intervention (the exercise program).

The study concluded that the program was safe, helpful and suitable for older adults.

What can we learn from this example?

1. work within the design limitations:.

Notice that the outcome measured was the feasibility of the program and not its health effects on older adults.

The purpose of the study was to design an exercise program based on the participants’ feedback. So a pilot one-group posttest-only study was enough to do so.

For studying the health effects of this program on older adults a randomized controlled trial will certainly be necessary.

2. Be careful with generalization when working with non-randomly selected participants:

For instance, participants who volunteered to be in this study were all physically active older adults who exercise regularly.

Therefore, the study results may not generalize to all the elderly population.

• Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference . 2nd Edition. Cengage Learning; 2001.
• Campbell DT, Stanley J. Experimental and Quasi-Experimental Designs for Research . 1st Edition. Cengage Learning; 1963.

Further reading

• Understand Quasi-Experimental Design Through an Example
• Experimental vs Quasi-Experimental Design
• Static-Group Comparison Design
• One-Group Pretest-Posttest Design

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Pre-Experimental Designs

Pre-experiments are the simplest form of research design. In a pre-experiment either a single group or multiple groups are observed subsequent to some agent or treatment presumed to cause change.

Types of Pre-Experimental Design

One-shot case study design, one-group pretest-posttest design, static-group comparison.

A single group is studied at a single point in time after some treatment that is presumed to have caused change. The carefully studied single instance is compared to general expectations of what the case would have looked like had the treatment not occurred and to other events casually observed. No control or comparison group is employed.

A single case is observed at two time points, one before the treatment and one after the treatment. Changes in the outcome of interest are presumed to be the result of the intervention or treatment. No control or comparison group is employed.

A group that has experienced some treatment is compared with one that has not. Observed differences between the two groups are assumed to be a result of the treatment.

Validity of Results

An important drawback of pre-experimental designs is that they are subject to numerous threats to their validity . Consequently, it is often difficult or impossible to dismiss rival hypotheses or explanations. Therefore, researchers must exercise extreme caution in interpreting and generalizing the results from pre-experimental studies.

One reason that it is often difficult to assess the validity of studies that employ a pre-experimental design is that they often do not include any control or comparison group. Without something to compare it to, it is difficult to assess the significance of an observed change in the case. The change could be the result of historical changes unrelated to the treatment, the maturation of the subject, or an artifact of the testing.

Even when pre-experimental designs identify a comparison group, it is still difficult to dismiss rival hypotheses for the observed change. This is because there is no formal way to determine whether the two groups would have been the same if it had not been for the treatment. If the treatment group and the comparison group differ after the treatment, this might be a reflection of differences in the initial recruitment to the groups or differential mortality in the experiment.

Advantages and Disadvantages

As exploratory approaches, pre-experiments can be a cost-effective way to discern whether a potential explanation is worthy of further investigation.

Disadvantages

Pre-experiments offer few advantages since it is often difficult or impossible to rule out alternative explanations. The nearly insurmountable threats to their validity are clearly the most important disadvantage of pre-experimental research designs.

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Ip S, Paulus JK, Balk EM, et al. Role of Single Group Studies in Agency for Healthcare Research and Quality Comparative Effectiveness Reviews [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Jan.

Role of Single Group Studies in Agency for Healthcare Research and Quality Comparative Effectiveness Reviews [Internet].

Evidence from randomized controlled trials is often unavailable or insufficient to answer all questions posed in a comparative effectiveness review (CER). Thus, following a best-available-evidence approach, 1 systematic reviewers often use observational studies including a comparison group to examine the comparative effectiveness and safety of alternative therapeutic strategies. However, there are many instances where even observational studies with a comparison group are unavailable. Therefore, single group studies—those that evaluate a single intervention given to all subjects included in the study design—are often part of the evidence available to systematic reviewers conducting CERs.

We define a single group study as a study that consists of only a single group of subjects included in the study design, in which all subjects received a single intervention and the outcomes are assessed over time (i.e., not a cross-sectional study). These studies may be prospective or retrospective cohort studies. A number of study types would be included in this category, including investigations described as “single arm studies,” case series, registries, “before-after designs,” and time series studies. A classification scheme developed by Campbell and Stanley describes two single group studies consistent with our definition: the “one-shot case study” and the “one-group pretest–post-test design.” 2 In the one-shot case study, a single group is studied only once after a treatment is applied. In the one-group pretest–post-test design, a pretest evaluation is followed by a treatment and then a post-test. For the rest of this paper, we will use the simplified term “single group study” when describing these designs in general.

Single group studies are often conducted in the setting of strong therapy preferences (e.g., hyperbaric oxygen therapy for arterial gas embolism 3 ). This is especially true for transplantation studies of vital organs in the setting of rapid and fatal disease progression. For example, in patients with end-stage liver disease, the natural history of disease is so well known that it would be difficult to carry out a trial with an untransplanted study arm. Also, a field of clinical inquiry that is relatively new may not be sufficiently mature to rationalize a comparative hypothesis. For example, novel procedures or drugs are often initially evaluated in single group studies that are used to inform the design of a subsequent study with an internal comparison group.

Single group study designs are commonly used to monitor adverse events that may become evident only with long-term followup of large numbers of treated patients, which is not practical or efficient with other study designs. For example, phase 4 studies to monitor postmarketing adverse events and evaluations of therapies often include a single group of patients managed with the same treatment strategy and followed over time. Open-label extensions of clinical trials present another type of clinical investigation that often lacks an internal, concurrent comparison group. Although they are designed to follow patients for an extended period of time, they also usually evaluate a more highly selected population of patients who completed the randomized trial, tolerated the medication, and agreed to participate in the extension. Expanded access programs (or “compassionate use”) allow the use of an investigational drug outside of a clinical trial to treat a patient with a serious or immediately life-threatening disease or condition lacking satisfactory alternative treatment options. These investigations commonly describe the experience of a single group of patients without a comparison group (for example, see Janne 2004 4 ). Finally, registries of patients who have been exposed to a single drug or device may also be assembled for monitoring long-term sequelae without an internal comparison group. An example includes the coordinated effort to study newly introduced devices through the Interagency Registry for Mechanically Assisted Circulatory Support, established to capture detailed clinical data on all patients receiving implantable ventricular assist pumps in the United States. 5

Since single group studies do not include a direct, concurrent comparison group, their role in informing comparative effectiveness questions is not straightforward. Observational study designs in general suffer from a potential lack of exchangeability of exposed and unexposed subjects. In other words, the outcome in the untreated group may differ from what would have occurred in the treated in the absence of treatment (the “counterfactual outcome”). The absence of a direct, concurrent untreated comparator in single group studies presents an added challenge to identifying a proxy for the counterfactual, or an answer to the question: “What would have been the treated person's experience if there had been no treatment?” Extrapolations based on the expected outcomes in the “missing” untreated arm are required for inference about treatment effects. In fact, explicit and implicit comparisons are frequently made in single group studies even in the absence of a direct, concurrent comparator. The appropriate interpretation of these implicit and explicit comparisons and their potential utility in CERs must include consideration of the key assumptions underlying each single group design.

The ability of observational studies to answer questions about the benefits or intended effects of pharmacotherapeutic agents, devices, or procedural interventions has been a matter of debate. 6 Guidance has been developed for systematic reviewers for decisionmaking on the inclusion of observational studies in general in CERs. 6 However, to the best of our knowledge, the use of single group observational studies in CERs has not been specifically addressed in this methods guide or elsewhere. While the value of using single group studies to identify and quantify the occurrence of harms a of interventions is well recognized, the role of these studies in evaluating comparative effectiveness and safety is not well developed. Given that single group studies may comprise a substantial portion of the evidence base for a given clinical question, and in light of the challenges in their interpretation and relevance to questions that are comparative in nature, it is important to clarify whether they are useful in informing comparative effectiveness assessments, and if so, to clarify the assumptions required to support their use.

In order to illuminate the use of single group studies in CERs, we conducted an empirical review of current practices in using single group studies in CERs conducted by Evidence-based Practice Centers (EPCs) for the Agency for Healthcare Research and Quality (AHRQ). The summary findings should serve as an impetus for future work in reaching a consensus across EPCs as to when and how single group studies should be used in CERs specifically and systematic reviews in general. In addition to the empirical review, we also provided a narrative review section describing the common single group study designs and the key considerations and assumptions required for their interpretation to help guide comparative effectiveness reviewers who encounter this type of evidence.

Includes adverse events of interventions as well as other harmful events that may be indirectly related to the intervention.

• Cite this Page Ip S, Paulus JK, Balk EM, et al. Role of Single Group Studies in Agency for Healthcare Research and Quality Comparative Effectiveness Reviews [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Jan. Background.
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Pre-Experimental Design

Pre-experimental design refers to the simplest form of research design often used in the field of psychology, sociology, education, and other social sciences. These designs are called “pre-experimental” because they precede true experimental design in terms of complexity and rigor.

In pre-experimental designs, researchers observe or measure subjects without manipulating variables or controlling conditions. Often, these designs lack certain elements of a true experiment, such as random assignment, control groups, or pretest measurements, making it difficult to determine causality.

Three common types of pre-experimental designs include the one-shot case study, the one-group pretest-posttest design, and the static-group comparison. These designs offer a starting point for researchers but are typically seen as less reliable than more controlled experimental designs due to the lack of randomization and the potential for confounding variables.

Characteristics of Pre-Experimental Design

Pre-experimental designs are characterized by their simplicity and ease of execution. They are typically used when resources are limited, or when the research question does not require a high degree of control or precision. Key characteristics of these designs include the use of a single group, the lack of a control group, and the absence of random assignment.

Single Group

In a pre-experimental design, there is typically only one group of subjects, and this group is measured or observed both before and after an intervention or treatment.

Lack of Control Group

Pre-experimental designs often lack a control group for comparison. As a result, it’s difficult to determine whether observed changes are the result of the intervention or due to extraneous factors.

Absence of Random Assignment

Another characteristic of pre-experimental design is the absence of random assignment. Subjects are not randomly assigned to groups, which can lead to selection bias and limits the generalizability of the findings.

There are several types of pre-experimental designs, including the one-shot case study, the one-group pretest-posttest design, and the static-group comparison.

One-Shot Case Study

In a one-shot case study, a single group or case is studied at a single point in time after some intervention or treatment that is presumed to cause change.

One-Group Pretest-Posttest Design

In the one-group pretest-posttest design, a single group is observed at two time points, one before the treatment and one after the treatment.

Static-Group Comparison

In a static-group comparison, there are two groups that are not created through random assignment. One group receives the treatment and the other does not, and the outcomes are compared.

Limitations

While pre-experimental designs offer advantages in terms of simplicity and convenience, they also come with notable limitations. The lack of a control group and the absence of random assignment limits the ability to establish causality. There is also a risk of selection bias, and the findings may not be generalizable to other populations or settings.

Despite these limitations, pre-experimental designs can serve as valuable starting points in exploratory research, laying the groundwork for more rigorous experimental designs in the future.

In conclusion, pre-experimental design, while limited in its ability to provide strong evidence of causality, plays a crucial role in exploratory research. It presents a simplified and cost-effective approach to experimentation that is especially useful when resources are limited or when the goal is to explore a new area of study. However, the inherent limitations of pre-experimental designs necessitate caution in interpreting their results. Consequently, they are often used as stepping stones towards more rigorous research designs. As such, understanding pre-experimental designs is a fundamental part of the researcher’s toolkit, paving the way for more comprehensive and controlled investigations.

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Neag School of Education

Educational Research Basics by Del Siegle

Experimental research designs.

R=Random Assignment X= Treatment O=Observation (Assessment)

R = Random Assignment X = Treatment O = Observation (test or measurement of some type)

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them. 

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

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.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive. 
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure. 

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Conclusion  

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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Pre-experimental Design: Definition, Types & Examples

• October 1, 2021

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Experimental research is conducted to analyze and understand the effect of a program or a treatment. There are three types of experimental research designs – pre-experimental designs, true experimental designs, and quasi-experimental designs . 

In this blog, we will be talking about pre-experimental designs. Let’s first explain pre-experimental research. 

What is Pre-experimental Research?

As the name suggests, pre- experimental research happens even before the true experiment starts. This is done to determine the researchers’ intervention on a group of people. This will help them tell if the investment of cost and time for conducting a true experiment is worth a while. Hence, pre-experimental research is a preliminary step to justify the presence of the researcher’s intervention. 

The pre-experimental approach helps give some sort of guarantee that the experiment can be a full-scale successful study. 

What is Pre-experimental Design?

The pre-experimental design includes one or more than one experimental groups to be observed against certain treatments. It is the simplest form of research design that follows the basic steps in experiments. 

The pre-experimental design does not have a comparison group. This means that while a researcher can claim that participants who received certain treatment have experienced a change, they cannot conclude that the change was caused by the treatment itself. 

The research design can still be useful for exploratory research to test the feasibility for further study. 

Let us understand how pre-experimental design is different from the true and quasi-experiments:

The above table tells us pretty much about the working of the pre-experimental designs. So we can say that it is actually to test treatment, and check whether it has the potential to cause a change or not. For the same reasons, it is advised to perform pre-experiments to define the potential of a true experiment.

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Types of Pre-experimental Designs

Assuming now you have a better understanding of what the whole pre-experimental design concept is, it is time to move forward and look at its types and their working:

One-shot case study design

• This design practices the treatment of a single group.
• It only takes a single measurement after the experiment.
• A one-shot case study design only analyses post-test results.

The one-shot case study compares the post-test results to the expected results. It makes clear what the result is and how the case would have looked if the treatment wasn’t done. 

A team leader wants to implement a new soft skills program in the firm. The employees can be measured at the end of the first month to see the improvement in their soft skills. The team leader will know the impact of the program on the employees.

One-group pretest-posttest design

• Like the previous one, this design also works on just one experimental group.
• But this one takes two measures into account. 
• A pre-test and a post-test are conducted. 

As the name suggests, it includes one group and conducts pre-test and post-test on it. The pre-test will tell how the group was before they were put under treatment. Whereas post-test determines the changes in the group after the treatment. 

This sounds like a true experiment , but being a pre-experiment design, it does not have any control group. 

Following the previous example, the team leader here will conduct two tests. One before the soft skill program implementation to know the level of employees before they were put through the training. And a post-test to know their status after the training.

Now that he has a frame of reference, he knows exactly how the program helped the employees. 

Static-group comparison

• This compares two experimental groups.
• One group is exposed to the treatment.
• The other group is not exposed to the treatment.
• The difference between the two groups is the result of the experiment.

As the name suggests, it has two groups, which means it involves a control group too. 

In static-group comparison design, the two groups are observed as one goes through the treatment while the other does not. They are then compared to each other to determine the outcome of the treatment.

The team lead decides one group of employees to get the soft skills training while the other group remains as a control group and is not exposed to any program. He then compares both the groups and finds out the treatment group has evolved in their soft skills more than the control group. 

Due to such working, static-group comparison design is generally perceived as a quasi-experimental design too. 

Characteristics of Pre-experimental Designs

In this section, let us point down the characteristics of pre-experimental design:

• Generally uses only one group for treatment which makes observation simple and easy.
• Validates the experiment in the preliminary phase itself. 
• Pre-experimental design tells the researchers how their intervention will affect the whole study. 
• As they are conducted in the beginning, pre-experimental designs give evidence for or against their intervention.
• It does not involve the randomization of the participants. 
• It generally does not involve the control group, but in some cases where there is a need for studying the control group against the treatment group, static-group comparison comes into the picture. 
• The pre-experimental design gives an idea about how the treatment is going to work in case of actual true experiments.  

Validity of results in Pre-experimental Designs

Validity means a level to which data or results reflect the accuracy of reality. And in the case of pre-experimental research design, it is a tough catch. The reason being testing a hypothesis or dissolving a problem can be quite a difficult task, let’s say close to impossible. This being said, researchers find it challenging to generalize the results they got from the pre-experimental design, over the actual experiment. 

As pre-experimental design generally does not have any comparison groups to compete for the results with, that makes it pretty obvious for the researchers to go through the trouble of believing its results. Without comparison, it is hard to tell how significant or valid the result is. Because there is a chance that the result occurred due to some uncalled changes in the treatment, maturation of the group, or is it just sheer chance. 

Let’s say all the above parameters work just in favor of your experiment, you even have a control group to compare it with, but that still leaves us with one problem. And that is what “kind” of groups we get for the true experiments. It is possible that the subjects in your pre-experimental design were a lot different from the subjects you have for the true experiment. If this is the case, even if your treatment is constant, there is still going to be a change in your results. 

Advantages of Pre-experimental Designs

• Cost-effective due to its easy process. 
• Very simple to conduct.
• Efficient to conduct in the natural environment. 
• It is also suitable for beginners. 
• Involves less human intervention. 
• Determines how your treatment is going to affect the true experiment. 

Disadvantages of Pre-experimental Designs

• It is a weak design to determine causal relationships between variables. 
• Does not have any control over the research. 
• Possess a high threat to internal validity. 
• Researchers find it tough to examine the results’ integrity. 
• The absence of a control group makes the results less reliable. 

This sums up the basics of pre-experimental design and how it differs from other experimental research designs. Curious to learn how you can use survey software to conduct your experimental research, book a meeting with us . 

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Pre-experimental design is a research method that happens before the true experiment and determines how the researcher’s intervention will affect the experiment.

An example of a pre-experimental design would be a gym trainer implementing a new training schedule for a trainee.

Characteristics of pre-experimental design include its ability to determine the significance of treatment even before the true experiment is performed.

Researchers want to know how their intervention is going to affect the experiment. So even before the true experiment starts, they carry out a pre-experimental research design to determine the possible results of the true experiment.

The pre-experimental design deals with the treatment’s effect on the experiment and is carried out even before the true experiment takes place. While a true experiment is an actual experiment, it is important to conduct its pre-experiment first to see how the intervention is going to affect the experiment.

The true experimental design carries out the pre-test and post-test on both the treatment group as well as a control group. whereas in pre-experimental design, control group and pre-test are options. it does not always have the presence of those two and helps the researcher determine how the real experiment is going to happen.

The main difference between a pre-experimental design and a quasi-experimental design is that pre-experimental design does not use control groups and quasi-experimental design does. Quasi always makes use of the pre-test post-test model of result comparison while pre-experimental design mostly doesn’t.

Non-experimental research methods majorly fall into three categories namely: Cross-sectional research, correlational research and observational research.

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2. The “One Shot” Case Study Revisited

Method. Thousand Oaks, CA: Pine Forge Press.———.(2000). Fuzzy Set Social Science. Chicago, IL: University of Chicago Press. Ragin, Charles C. and Howard Becker, eds.(1992). What Is a Case? Exploring the Foundations of Social Inquiry. Cambridge, UK: Cambridge University Press. Savolainen, Jukka.(1994).“The Rationality of Drawing Big Conclusions Based on Small Samples: In Defense of Mill's Methods.” Social Forces 72 (2), 1217–1224. Stinchcombe, Arthur.(1968). Constructing Social Theories.

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A reflection on the implementation of a waterfront greenway from a social–ecological perspective: a case study of huangyan-taizhou in china.

1. Introduction

• How are forms of land use and ecosystem services—in particular, cultural services (e.g., recreation)—impacted by the process of urbanisation?
• What forms of everyday activities are performed in different parts of the greenway, specifically in urban and rural areas as well as the urban–rural interface?
• For more sustainable planning and improved decision-making, how can the social–ecological perspective support a better understanding of human–nature relationships?

2. Case Selection and Methodology

2.1. case selection, 2.2. methodical frame-scheme for trans-scaled research, 2.3. methods and data collection, 3. an understanding of greenways from social and ecological interactions, 3.1. the social–ecological system, 3.2. the perspective of ecosystem services, 3.3. summary, 4. analysis of ecosystem services and yrg in huangyan-taizhou, 4.1. dynamics of land use and ecosystem services in huangyan, 4.2. yrg in the local plans for green space, 4.3. three sections of the yrg in huangyan-taizhou, 6. findings and discussion.

• While the macro analysis of the land cover showed decreasing extents of water, forest, and grassland, the regional greenways by the river improved the living environment and enriched everyday leisure. In the field investigation, we obtained positive statements from local people regarding the ecosystem restructuring: “I don’t know if the ecosystem has improved, but I feel the green space has increased. I often take my granddaughter to play at the riverbank. A place for playing is much closer than before” (in-depth interview, October 2019).
• At the macro scale, the greenway efficiently connects local ecological resources and provides forms of recreation for residents. At the micro scale, these recreations were not always ‘designed’, but rather the spaces were reshaped by everyday activities. For example, farmed areas in the urban–rural interface and rural sections of the greenway were initiated by local residents. According to the interviews conducted in 2023 and 2024, people stated that retaining these areas as grassland would be a waste of the riverbank.
• Different patterns of activities could be identified. The spatial distribution of activities can be divided into three patterns: strip distribution, concentrated distribution, and scattered distribution. For instance, walking, fishing, and sightseeing are mainly distributed in strips along the river, while social activities such as dancing are concentrated in the main square. However, the distribution of parent–child interactions, picking herbs, etc. is scattered.
• The distance between activities and water bodies also showed certain patterns. Activities closer to the water body showing a strip distribution can reflect the supply functions of the ecosystem such as fishing and washing, which are generally found along the river activity zone in a strip up to 5 m from the water body. At the same time, larger crowds are normally found within 150 m of the greenway, combined with other functions such as commercial usage.
• People living in urban areas use the greenway for recreation much more frequently than those living in the urban–rural interface and rural areas. Similarly, people in urban areas prefer cycling in the natural environment of the greenways whilst rural residents prefer to ride electronic scooters by the river, an activity that has become restricted in the greenway.

7. Conclusions

Author contributions, data availability statement, acknowledgments, conflicts of interest.

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Huang, H.; Qi, J.; Xiao, S.; Wende, W.; Xin, Y. A Reflection on the Implementation of a Waterfront Greenway from a Social–Ecological Perspective: A Case Study of Huangyan-Taizhou in China. Land 2024 , 13 , 989. https://doi.org/10.3390/land13070989

Huang H, Qi J, Xiao S, Wende W, Xin Y. A Reflection on the Implementation of a Waterfront Greenway from a Social–Ecological Perspective: A Case Study of Huangyan-Taizhou in China. Land . 2024; 13(7):989. https://doi.org/10.3390/land13070989

Huang, Huang, Jiaxin Qi, Suili Xiao, Wolfgang Wende, and Yingdu Xin. 2024. "A Reflection on the Implementation of a Waterfront Greenway from a Social–Ecological Perspective: A Case Study of Huangyan-Taizhou in China" Land 13, no. 7: 989. https://doi.org/10.3390/land13070989

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