What Is A Quasi Experiment In Psychology?

What is a quasi-experiment? A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Contents

1 What is quasi-experiment in psychology with examples?
2 What is quasi-experiment in simple terms?
3 Why would you do a quasi-experiment?
4 What is a weakness of a quasi-experiment?
5 What is the opposite of quasi-experiment?

What is quasi-experiment in psychology with examples?

In a quasi-experiment, the independent variable can not be randomly assigned because it is an innate difference of the participants themselves. A memory task with a group of clinically depressed participants compared to a control group of non-depressed participants is a common example in psychology.

What is quasi-experiment in simple terms?

What Is a Quasi-experiment? – Quasi-experiments are studies that aim to evaluate interventions but that do not use randomization. Similar to randomized trials, quasi-experiments aim to demonstrate causality between an intervention and an outcome. Quasi-experimental studies can use both preintervention and postintervention measurements as well as nonrandomly selected control groups.

Using this basic definition, it is evident that many published studies in medical informatics utilize the quasi-experimental design.
Although the randomized controlled trial is generally considered to have the highest level of credibility with regard to assessing causality, in medical informatics, researchers often choose not to randomize the intervention for one or more reasons: (1) ethical considerations, (2) difficulty of randomizing subjects, (3) difficulty to randomize by locations (e.g., by wards), (4) small available sample size.

Each of these reasons is discussed below. Ethical considerations typically will not allow random withholding of an intervention with known efficacy. Thus, if the efficacy of an intervention has not been established, a randomized controlled trial is the design of choice to determine efficacy.

But if the intervention under study incorporates an accepted, well-established therapeutic intervention, or if the intervention has either questionable efficacy or safety based on previously conducted studies, then the ethical issues of randomizing patients are sometimes raised. In the area of medical informatics, it is often believed prior to an implementation that an informatics intervention will likely be beneficial and thus medical informaticians and hospital administrators are often reluctant to randomize medical informatics interventions.

In addition, there is often pressure to implement the intervention quickly because of its believed efficacy, thus not allowing researchers sufficient time to plan a randomized trial. For medical informatics interventions, it is often difficult to randomize the intervention to individual patients or to individual informatics users.

So while this randomization is technically possible, it is underused and thus compromises the eventual strength of concluding that an informatics intervention resulted in an outcome.
For example, randomly allowing only half of medical residents to use pharmacy order-entry software at a tertiary care hospital is a scenario that hospital administrators and informatics users may not agree to for numerous reasons.

Similarly, informatics interventions often cannot be randomized to individual locations. Using the pharmacy order-entry system example, it may be difficult to randomize use of the system to only certain locations in a hospital or portions of certain locations.

For example, if the pharmacy order-entry system involves an educational component, then people may apply the knowledge learned to nonintervention wards, thereby potentially masking the true effect of the intervention.
When a design using randomized locations is employed successfully, the locations may be different in other respects (confounding variables), and this further complicates the analysis and interpretation.

In situations where it is known that only a small sample size will be available to test the efficacy of an intervention, randomization may not be a viable option. Randomization is beneficial because on average it tends to evenly distribute both known and unknown confounding variables between the intervention and control group.

What are quasi-experimental method examples?

Common examples of quasi-experimental methods include difference-in-differences, regression discontinuity design, instrumental variables and matching.

What are natural and quasi-experiments in psychology?

Ethics – A true experiment would, for example, randomly assign children to a scholarship, in order to control for all other variables. Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition.

As an example, suppose we divide households into two categories: Households in which the parents spank their children, and households in which the parents do not spank their children.
We can run a linear regression to determine if there is a positive correlation between parents’ spanking and their children’s aggressive behavior.

However, to simply randomize parents to spanking or not spanking categories may not be practical or ethical, because some parents may believe it is morally wrong to spank their children and refuse to participate. Some authors distinguish between a natural experiment and a “quasi-experiment”.

The difference is that in a quasi-experiment, the criterion for assignment is selected by the researcher; while in a natural experiment, the assignment occurs ‘naturally,’ without the researcher’s intervention.
Quasi-experiments have outcome measures, treatments, and experimental units, but do not use random assignment,

Quasi-experiments are often the design that most people choose over true experiments. It is usually easily conducted as opposed to true experiments, because they bring in features from both experimental and non-experimental designs. Measured variables as well as manipulated variables can be brought in.

What is the difference between an experiment and a quasi-experiment?

When to Use Quasi-Experimental Design? – All that studying but shouldn’t you know when to perfectly use quasi-experiments? Well, now as we are to the end of the matter, let us discuss when to use quasi-experiments and for what reasons. Remember when we discussed the “willingness” of obese people to participate in the experiment? That is when ethics start to matter.

You cannot go on putting random participants under treatments as you do with true experiments.
Especially when it directly affects the participants’ lives.
One of the best examples is Oregon Health Study where health insurance is given to certain people while others were restricted from it.
True experiments, despite having higher internal validity, can be expensive.

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Also, it requires enough participants so that the true experiment can be justified. Unlike that, in quasi-experiment, you can use the already gathered data. The data is collected and paid by some strong entity, say the government, and you use that to study your questions.

Well, that concludes our guide. If you’re looking for extensive research tools, Voxco offers a complete market research tool kit that includes market research trends, a guide to online surveys, an agile market research guide, and 5 market research templates. Let us say you want to study the effect of eating cheese on bad breath.

So you make the people with not so bad breath take the treatment and the other half with bad breath to be in the control group. After taking the post-test you discover that the participants in the treatment group start to have bad breath. The quasi-experimental are used to evaluate interventions without using randomization.

It also interprets the problems using pre-intervention and post-intervention measurements along with non-random assignments.
A true experiment uses random assignment of the participants while quasi-experiments does not.
This allows its wide use in ethical problems.
Quasi-experiments allots the participants based on a study, unlike true experiments where they have an equal chance of getting into any of the groups.

Quasi-experiment also makes use of the pre-test as well as post-test measurements which opens a door to before-after comparisons. The quasi-experimental design does not randomly assign groups to the participants, rather it studies their nature and then treats them accordingly.

It studies the participants before and after the program known as pre-test and post-test which helps get an idea about the progress of the groups. Quasi-experiments also are ethical, due to their non-randomization characteristic. Quasi design or quasi-experimental design mostly resembles the true experimental design, just minus the key component.

That is a random assignment. Two prime quasi-experimental methods include:

Nonequivalent Groups Design
Regression-Discontinuity Design

Some other, rather equally important Quasi Designs are:

The Proxy Pretest Design
The Separate Pre-Post Samples Design
The Double Pretest Design
The Switching Replications Design
The Nonequivalent Dependent Variables (NEDV) Design
The Regression Point Displacement (RPD) Design

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What are the characteristics of a quasi-experiment?

Quasi-Experimental Design is a unique research methodology because it is characterized by what is lacks. For example, Abraham & MacDonald (2011) state: ” Quasi-experimental research is similar to experimental research in that there is manipulation of an independent variable.

It differs from experimental research because either there is no control group, no random selection, no random assignment, and/or no active manipulation.
This type of research is often performed in cases where a control group cannot be created or random selection cannot be performed.
This is often the case in certain medical and psychological studies.

For more information on quasi-experimental design, review the resources below:

Why is it called quasi-experimental?

Nonequivalent Groups Design – Recall that when participants in a between-subjects experiment are randomly assigned to conditions, the resulting groups are likely to be quite similar. In fact, researchers consider them to be equivalent. When participants are not randomly assigned to conditions, however, the resulting groups are likely to be dissimilar in some ways.

For this reason, researchers consider them to be nonequivalent.
A nonequivalent groups design, then, is a between-subjects design in which participants have not been randomly assigned to conditions.
Imagine, for example, a researcher who wants to evaluate a new method of teaching fractions to third graders.

One way would be to conduct a study with a treatment group consisting of one class of third-grade students and a control group consisting of another class of third-grade students. This design would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them.

For example, the parents of higher achieving or more motivated students might have been more likely to request that their children be assigned to Ms. Williams’s class. Or the principal might have assigned the “troublemakers” to Mr. Jones’s class because he is a stronger disciplinarian. Of course, the teachers’ styles, and even the classroom environments, might be very different and might cause different levels of achievement or motivation among the students.

If at the end of the study there was a difference in the two classes’ knowledge of fractions, it might have been caused by the difference between the teaching methods—but it might have been caused by any of these confounding variables. Of course, researchers using a nonequivalent groups design can take steps to ensure that their groups are as similar as possible.

In the present example, the researcher could try to select two classes at the same school, where the students in the two classes have similar scores on a standardized math test and the teachers are the same sex, are close in age, and have similar teaching styles. Taking such steps would increase the internal validity of the study because it would eliminate some of the most important confounding variables.

But without true random assignment of the students to conditions, there remains the possibility of other important confounding variables that the researcher was not able to control.

What are the 3 types of quasi-experimental research explain?

Key Takeaways –

Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Among the important types are nonequivalent groups designs, pretest-posttest, and interrupted time-series designs.
Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable. It does not eliminate the problem of confounding variables, however, because it does not involve random assignment to conditions. For these reasons, quasi-experimental research is generally higher in internal validity than correlational studies but lower than true experiments.

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Why would you do a quasi-experiment?

Pros – Benefits of quasi-experiments include:

they can mimic an experiment and provide a high level of evidence without randomisation there are several designs to choose from that you can adapt depending on your context they can be used when there are practical or ethical reasons why participants can’t be randomised

What is a weakness of a quasi-experiment?

Table 1 – Advantages, disadvantages, and important pitfalls in using quasi-experimental designs in healthcare epidemiology research.


Advantages	Notes
Less expensive and time consuming than RCTs or Cluster Randomized Trials	Do not need to randomize groups
Pragmatic	Include patients that are often excluded in RCTs, tests effectiveness more than efficacy, may have good external validity
Can retrospectively analyze policy changes	Even if policy implementation is out of your control
Meets some requirements of causality	Quasi-experimental studies meet some requirements for causality including temporality, strength of association and dose response 2
Designs can be strengthened with control groups, multiple measures over time and cross-overs	Not gold standard to establish causation but can be next level below RCT if well-designed
Disadvantages	Notes
Retrospective data is often incomplete or difficult to obtain	Need processes to assess availability, accuracy and completeness during baseline phase before implementation
Not randomized	Nonrandomized designs tend to overestimate effect size 3 Does not meet all requirements to determine causality Lack of internal validity
Potential pitfalls	Notes
Selection Bias	When group receiving the intervention differs from the baseline group.2
Maturation Bias	Maturation bias can occur when natural changes over the passage of time may influence the study outcome.1 Examples include seasonality, fatigue, aging, maturity or boredom.2
Hawthorne Effect	Could bias quasi-experimental studies in which baseline rates are collected retrospectively and intervention rates are collected prospectively, because the intervention group could be more likely to improve when they are aware of being observed.3
Historical Bias	Historical bias is a threat when other events occur during the study period that may have an effect on the outcome.2
Regression to the Mean	Regression to the mean is a statistical phenomenon in which extreme measures tend to naturally revert back to normal.2
Instrumentation Bias	Instrumentation bias occurs when a measuring instrument changes over time (e.g. improved sensitivity of laboratory tests) or when data are collected differently before and after an intervention.2
Ascertainment Bias	Systematic error or deviation in the identification or measurement of outcomes.
Reporting Bias	Reporting bias is especially prevalent in retrospective quasi-experimental studies, in which researchers only publish quasi-experimental studies with positive findings and do not publish null or negative findings.
Need advanced statistical analysis when using more complex designs	With time series designs, should use interrupted time series analysis, not just single measurements before and after a response to an outbreak. Should account for intracluster correlation in power calculations

The greatest advantages of quasi-experimental studies are that they are less expensive and require fewer resources compared with individual randomized controlled trials (RCTs) or cluster randomized trials. Quasi-experimental studies are appropriate when randomization is deemed unethical (e.g., effectiveness of hand hygiene studies).1 Quasi-experimental studies are often performed at a population-level not an individual-level, and thus they can include patients who are often excluded from RCTs, such as those too ill to give informed consent or urgent surgery patients, with IRB approval as appropriate.5 Quasi-experimental studies are also pragmatic because they evaluate the real-world effectiveness of an intervention implemented by hospital staff, rather than efficacy of an intervention implemented by research staff under research conditions.5 Therefore, quasi-experimental studies may also be more generalizable and have better external validity than RCTs.

What is the opposite of quasi-experiment?

Non-Experiment : the researcher cannot control, manipulate or alter the predictor variable or subjects, but instead, relies on interpretation, observation or interactions to come to a conclusion.

Is a quasi-experiment a case study?

In this essay, the author –

Explains the purpose of this section is to compare and contrast case study and quasi-experiment research designs. they will outline how they differ in their general purpose and goals, which dictates their differences in approaching sampling concerns, data collection methods, and data analysis techniques.Explains that case studies are qualitative descriptive studies, whereas quasi-experiments are quantitative studies. winsor does not manipulate her subject or environment, but examines the types of documents phillips utilizes and produces.Explains that in both cases and quasi-experiments, subjects are purposely selected, and there is no randomization of subjects. in case studies, emphasis is placed on obtaining a representative sample.Compares limaye’s brevity vs. clarity study with spyridakis’ measuring the translatability of simplified english in procedural documents study.Analyzes how the experimental nature of quasi-experiments requires it researchers to focus on treatment centered, cause and effect forms of data collection.Explains the differences between quantitative descriptive studies and quasi-experiments in their purpose and goals, sampling concerns, types of data collection, and data analyses.Explains that subject selection is directly affected by the number of variables present within a study. limaye’s use of liberal arts undergraduates presents itself as sampling issue because they are not relevant to what is being measured.

Is quasi-experimental the same as non experimental?

In quasi-experimental designs, the experimenter can still manipulate the value of the independent variable, even though the groups to be compared are already established. In nonexperimental designs, the groups already exist and the experimenter cannot or does not attempt to manipulate an independent variable.

What type of evidence is quasi-experimental?

QUASI-EXPERIMENTAL STUDIES IN SYSTEMATIC REVIEWS – Quasi-experimental studies offer certain advantages over experimental methods and should be considered for inclusion in systematic reviews of HPSR ( 4 ). Studies using QE methods often produce evidence under real-world scenarios that are not controlled by the researcher, whereas experiments are usually implemented under researcher control, a factor that may introduce external validity concerns.

In addition, QE studies based on secondary analyses of administrative data usually have significantly lower costs than would be incurred for similar experimental studies.
Finally, policy questions, which may be difficult to investigate experimentally because of feasibility, political or ethical constraints, can often be addressed using a QE design.

Like experimental studies and studies with other designs, QE studies can produce valuable information on contextual factors and causal mechanisms that might be synthesized in quantitative or qualitative systematic reviews ( 10 ). The advantages of QE studies in estimating causal impacts are realized only when the relevant methodologies are employed appropriately, resulting in high internal validity.

Perhaps because of concerns about study quality – or about reviewers’ inability to accurately assess study quality consistently – QE evidence has been screened out of most systematic reviews of HPSR, on the basis of study design criteria ( 11 ). This omission can lead to key pieces of evidence being excluded from a review, resulting in an incomplete picture of the body of evidence on an important policy question.

In some instances, research questions that are not amenable to experimentation are missed entirely by the systematic review literature, despite the existence of relevant QE evidence. For example, a recent overview of systematic reviews found that no systematic review existed on the impact of decentralized governance on health outcomes ( 12 ), a policy that is difficult to test experimentally but for which several QE studies exist ( 13 – 16 ).

When relevant QE studies on a review topic exist alongside studies with other designs, authors of systematic reviews face important decisions on how to handle the different forms of evidence.
A recent special issue of the Journal of Clinical Epidemiology (JCE) describes the main considerations ( 3 – 7, 17 – 24 ).

First, authors must decide which (if any) QE study designs to include in their review. Whereas the Cochrane Collaboration’s Effective Practice and Organisation of Care (EPOC) Group recommends including two QE designs – interrupted time series (ITS) analyses and controlled before-and-after (CBA) studies – the authors of the JCE series identify an expanded list that also includes instrumental variable analyses, regression discontinuity designs and fixed-effects analyses of panel data ( 6, 19 ).

This expanded list is consistent with the recommendations of the Campbell Collaboration’s International Development Coordinating Group ( 25 ). Second, authors must establish a robust search strategy for identifying relevant QE studies. This task is complicated by the fact that indexing based on study design is imprecise in most evidence databases, and using study design search criteria is usually not recommended ( 22 ).

Third, authors must assess the quality of identified QE studies to determine potential risk of bias. Although relevant tools for this task exist, more work is needed to develop standard guidelines for assessing risk of bias in QE studies ( 20, 26, 27 ).

In particular, the ROBINS-I (Risk Of Bias In Non-randomized Studies – of Interventions) tool has been developed to assess risk of bias in nonrandomized studies, but it does not yet include guidelines for the full breadth of QE designs ( 26 ).
Finally, in cases where meta-analysis is being considered, authors must decide whether and how to include effect estimates from QE studies.

The authors of the JCE series consider the challenges associated with including QE evidence in meta-analyses, and argue that doing so is usually warranted, but they also caution that a careful modelling approach that accounts for potential risk of bias is necessary ( 7, 22 ).

Is a quasi-experiment descriptive?

First are experimental designs with an intervention, control group, and randomization of participants into groups. Next are quasi-experimental designs with an intervention but no randomization. Descriptive designs do not have an intervention or treatment and are considered nonexperimental.

What is the process of quasi-experiments?

What are quasi-experimental research designs? – Quasi-experimental research designs are a type of research design that is similar to experimental designs but doesn’t give full control over the independent variable(s) like true experimental designs do.

In a quasi-experimental design, the researcher changes or watches an independent variable, but the participants are not put into groups at random. Instead, people are put into groups based on things they already have in common, like their age, gender, or how many times they have seen a certain stimulus.

Because the assignments are not random, it is harder to draw conclusions about cause and effect than in a real experiment. However, quasi-experimental designs are still useful when randomization is not possible or ethical. The true experimental design may be impossible to accomplish or just too expensive, especially for researchers with few resources.

What is quasi-experimental research in biology?

A quasi-experiment is defined as a study in which the treatments cannot be applied at random to the experimental units. Many field experiments are quasi-experiments.

Sabrina Sarro