Experiments and Quasi-Experiments

Table Of Content

• Quasi-Experimental Research Design – Types, Methods
• What Are the Different Quasi-experimental Study Designs?
• Explanatory Research – Types, Methods, Guide
• Quasi-experimental Designs That Use Control Groups and Pretests
• Interrupted Time Series Design
• How Survey Software is Revolutionizing Data Collection in Market Research
• Differences between quasi-experiments and true experiments

If there had been only one measurement of absences before the treatment at Week 7 and one afterward at Week 8, then it would have looked as though the treatment were responsible for the reduction. The multiple measurements both before and after the treatment suggest that the reduction between Weeks 7 and 8 is nothing more than normal week-to-week variation. A nonequivalent groups design, then, is a between-subjects design in which participants have not been randomly assigned to conditions. With this study design, the researcher administers an intervention at a later time to a group that initially served as a nonintervention control.

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Quasi-Experimental Research Design – Types, Methods

Stepped wedge designs (SWDs) involve a sequential roll-out of an intervention to participants (individuals or clusters) over several distinct time periods (5, 7, 22, 24, 29, 30, 38). SWDs can include cohort designs (with the same individuals in each cluster in the pre and post intervention steps), and repeated cross-sectional designs (with different individuals in each cluster in the pre and post intervention steps) (7). In the SWD, there is a unidirectional, sequential roll- out of an intervention to clusters (or individuals) that occurs over different time periods. Initially all clusters (or individuals) are unexposed to the intervention, and then at regular intervals, selected clusters cross over (or ‘step’) into a time period where they receive the intervention [Figure 3 here]. All clusters receive the intervention by the last time interval (although not all individuals within clusters necessarily receive the intervention). Data is collected on all clusters such that they each contribute data during both control and intervention time periods.

What Are the Different Quasi-experimental Study Designs?

Thus, if feasible from a design and implementation point of view, investigators should aim to design studies that fall in to the higher rated categories. Shadish et al.4 discuss 17 possible designs, with seven designs falling into category A, three designs in category B, and six designs in category C, and one major design in category D. Thus, for simplicity, we have summarized the 11 study designs most relevant to medical informatics research in ▶. These designs are frequently used when it is not logistically feasible or ethical to conduct a randomized controlled trial. As one example of a quasi-experimental study, a hospital introduces a new order-entry system and wishes to study the impact of this intervention on the number of medication-related adverse events before and after the intervention. As another example, an informatics technology group is introducing a pharmacy order-entry system aimed at decreasing pharmacy costs.

Explanatory Research – Types, Methods, Guide

All original scientific manuscripts published between January 2000 and December 2003 in the Journal of the American Medical Informatics Association (JAMIA) and the International Journal of Medical Informatics (IJMI) were reviewed. Other authors (ADH, JCM, JF) then independently reviewed all the studies identified as quasi-experimental. The three authors then convened as a group to resolve any disagreements in study classification, application domain, and acknowledgment of limitations. Suppose, for example, a group of researchers was interested in the causes of maternal employment. They might hypothesize that the provision of government-subsidized child care would promote such employment. They could then design an experiment in which some subjects would be provided the option of government-funded child care subsidies and others would not.

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Quasi-experimental Designs That Use Control Groups and Pretests

As done in the study by Bailet et al (3), the investigators refined intervention, based on year 1 data, and then applied in years 2–3, at this later time collecting additional data on training and measurement fidelity. This phasing aspect of implementation generates a tension between protocolizing interventions and adapting them as they go along. When this is the case, additional designs for the intervention roll-out, such as adaptive or hybrid designs can also be considered. An interrupted time series (ITS) design involves collection of outcome data at multiple time points before and after an intervention is introduced at a given point in time at one or more sites (6, 13).

The pre-intervention outcome data is used to establish an underlying trend that is assumed to continue unchanged in the absence of the intervention under study (i.e., the counterfactual scenario). Any change in outcome level or trend from the counter-factual scenario in the post-intervention period is then attributed to the impact of the intervention. Individual-level DID analyses use participant-level panel data (i.e., information collected in a consistent manner over time for a defined cohort of individuals). The Familias en Accion program in Colombia was evaluated using a DID analysis, where eligible and ineligible administrative clusters were matched initially using propensity scores. The effect of the intervention was estimated as the difference between groups of clusters that were or were not eligible for the intervention, taking into account the propensity scores on which they were matched [25]. DID analysis is only a credible method when we expect unobservable factors which determine outcomes to affect both groups equally over time (the “common trends” assumption).

Differences Between Quasi-Experiments And True Experiments

Because productivity increased rather quickly after the shortening of the work shifts, and because it remained elevated for many months afterward, the researcher concluded that the shortening of the shifts caused the increase in productivity. Notice that the interrupted time-series design is like a pretest-posttest design in that it includes measurements of the dependent variable both before and after the treatment. It is unlike the pretest-posttest design, however, in that it includes multiple pretest and posttest measurements.

How Survey Software is Revolutionizing Data Collection in Market Research

They found that overall psychotherapy was quite effective, with about 80% of treatment participants improving more than the average control participant. Subsequent research has focused more on the conditions under which different types of psychotherapy are more or less effective. 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.

Unlike true experiments, quasi-experiment lack random assignment of participants to groups, making them more practical and ethical in certain situations. In this article, we will delve into the concept, applications, and advantages of quasi-experiments, shedding light on their relevance and significance in the scientific realm. In a pretest-posttest design, the dependent variable is measured once before the treatment is implemented and once after it is implemented.

As we continue to explore the boundaries of research methodology, platforms like Voxco provide essential tools and support for conducting and analyzing, driving advancements in knowledge and understanding. There is a new article in the field of hospital epidemiology which also highlights various features of what it terms as quasi-experimental designs [56]. There is some overlap with our checklist, but the list described also includes several study attributes intended to reduce the risk of bias, for example, blinding. By contrast, we consider that an assessment of the risk of bias in a study is essential and needs to be carried out as a separate task. The table also sets out our responses for the range of study designs as described in Box 1, Box 2. The response “possibly” (P) is prevalent in the table, even given the descriptions in these boxes.

However, this type of analysis alone does not satisfy the criterion of enabling adjustment for unobservable sources of confounding because it cannot rule out confounding of health outcomes data by unmeasured confounding factors, even when participants are well characterized at baseline. Internal validity is defined as the degree to which observed changes in outcomes can be correctly inferred to be caused by an exposure or an intervention. 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.

With QuestionPro, researchers can design surveys to collect data, analyze results, and gain valuable insights for their quasi-experimental research. Frameworks can be helpful to enhances interpretability of many kinds of studies, including QEDs and can help ensure that information on essential implementation strategies are included in the results (44). External validity can be improved when the intervention is applied to entire communities, as with some of the community-randomized studies described in Table 2 (12, 21). In these cases, the results are closer to the conditions that would apply if the interventions were conducted ‘at scale’, with a large proportion of a population receiving the intervention.