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Ethical concerns often arise in research when randomizing participants to different groups could potentially deny individuals access to beneficial treatments or interventions. In such cases, quasi-experimental designs provide an ethical alternative, allowing researchers to study the impact of interventions without depriving anyone of potential benefits. One potential threat to internal validity in experiments occurs when participants either drop out of the study or refuse to participate in the study. If particular types of individuals drop out or refuse to participate more often than individuals with other characteristics, this is called differential attrition. For example, suppose an experiment was conducted to assess the effects of a new reading curriculum.
Experiments and Quasi-Experiments
Of these 25, 15 studies were of category A, five studies were of category B, two studies were of category C, and no studies were of category D. Although there were no studies of category D (interrupted time-series analyses), three of the studies classified as category A had data collected that could have been analyzed as an interrupted time-series analysis. Nine of the 25 studies (36%) mentioned at least one of the potential limitations of the quasi-experimental study design. In the four-year period of IJMI publications reviewed by the authors, nine quasi-experimental studies among eight manuscripts were published. Of these nine, five studies were of category A, one of category B, one of category C, and two of category D. Two of the nine studies (22%) mentioned at least one of the potential limitations of the quasi-experimental study design.
Research Methods in Psychology
Researchers left out certain variables that would play a crucial role in determining the growth of each city. They used pre-existing groups of people based on research conducted in each city, rather than random groups. A quasi-experimental design allows researchers to take advantage of previously collected data and use it in their study. This method is used to compare the outcomes of participants who fall on either side of a predetermined cutoff point. This method can help researchers determine whether an intervention had a significant impact on the target population. This method is used to examine the impact of an intervention or treatment over time by comparing data collected before and after the intervention or treatment.
Quasi-experimental Designs That Use Control Groups and Pretests
For example, O1 could be pharmacy costs prior to the intervention, X could be the introduction of a pharmacy order-entry system, and O2 could be the pharmacy costs following the intervention. Including a pretest provides some information about what the pharmacy costs would have been had the intervention not occurred. In medical informatics, what often triggers the development and implementation of an intervention is a rise in the rate above the mean or norm. For example, increasing pharmacy costs and adverse events may prompt hospital informatics personnel to design and implement pharmacy order-entry systems. However, often informatics personnel and hospital administrators cannot wait passively for this decline to occur. Therefore, hospital personnel often implement one or more interventions, and if a decline in the rate occurs, they may mistakenly conclude that the decline is causally related to the intervention.
Instead, researchers can select two comparable classes, one receiving the math app intervention and the other continuing with traditional teaching methods. By comparing the performance of the two groups, researchers can draw conclusions about the app’s effectiveness. This design involves measuring the dependent variable multiple times before and after the introduction of an intervention or treatment. By comparing the trends in the dependent variable, researchers can infer the impact of the intervention. This design involves selecting pre-existing groups that differ in some key characteristics and comparing their responses to the independent variable. Although the researcher does not randomly assign the groups, they can still examine the effects of the independent variable.
Discontinuity in regression
It has been observed that it is more difficult to conduct a good quasi-experiment than to conduct a good randomized trial (43). Although QEDs are increasingly used, it is important to note that randomized designs are still preferred over quasi-experiments except where randomization is not possible. In this paper we present three important QEDs and variants nested within them that can increase internal validity while also improving external validity considerations, and present case studies employing these techniques. One of the strengths of QEDs is that they are often employed to examine intervention effects in real world settings and often, for more diverse populations and settings. Consequently, if there is adequate examination of characteristics of participants and setting-related factors it can be possible to interpret findings among critical groups for which there may be no existing evidence of an intervention effect for. For example in the Campus Watch intervention (16), the investigator over-sampled the Maori indigenous population in order to be able to stratify the results and investigate whether the program was effective for this under-studied group.
What are the characteristics of quasi-experimental designs?
In addition, ITS designs can increase power by making full use of longitudinal data instead of collapsing all data to single pre- and post-intervention time points. The use of longitudinal data can also be helpful for assessing whether intervention effects are short-lived or sustained over time. In order to enhance the causal inference for pre-post designs with non-equivalent control groups, the best strategies improve the comparability of the control group with regards to potential covariates related to the outcome of interest but are not under investigation. One strategy involves creating a cohort, and then using targeted sampling to inform matching of individuals within the cohort. Matching can be based on demographic and other important factors (e.g. measures of health care access or time-period). Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows data from a hypothetical interrupted time-series study.
Types of Quasi-Experimental Designs
The answers to this question will be less important if the researchers of the original study used a method to control for any confounding, that is, used a credible quasi-experimental design. Clusters exist when observations are nested within higher level organizational units or structures for implementing an intervention or data collected; typically, observations within clusters will be more similar with respect to outcomes of interest than observations between clusters. Clustering is a natural consequence of many methods of nonrandomized assignment/designation because of the way in which many interventions are implemented. Analyses of clustered data that do not take clustering into account will tend to overestimate the precision of effect estimates. Some of the study designs described in parts 1 and 2 may seem similar, for example, DID and CBA, although they are labeled differently.
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Unfortunately, one often cannot conclude this with a high degree of certainty because there may be other explanations for why the posttest scores are better. Perhaps an antidrug program aired on television and many of the students watched it, or perhaps a celebrity died of a drug overdose and many of the students heard about it. Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a yearlong program, participants might become less impulsive or better reasoners and this might be responsible for the change. 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.
In the most controlled situations within this design, the investigators might include elements of randomization or matching for individuals in the intervention or comparison site, to attempt to balance the covariate distribution. Implicit in this approach is the assumption that the greater the similarity between groups, the smaller the likelihood that confounding will threaten inferences of causality of effect for the intervention (33, 47). Thus, it is important to select this group or multiple groups with as much specificity as possible. This design involves studying the effects of an intervention or event that occurs naturally, without the researcher’s intervention. For example, a researcher might study the effects of a new law or policy that affects certain groups of people.
However, in quasi-experiments, this random assignment is often not possible or ethically permissible, leading to the adoption of alternative strategies. By contrast with the above examples, a conventional cohort study design was used to evaluate Tekoporã in Paraguay, relying on PSM and propensity weighted regression analysis of beneficiaries and nonbeneficiaries at entry into the cohort to control for confounding [27]. Similarly, for Bolsa Familia in Brazil evaluators applied PSM to cross-sectional (census) data [28]. Variables used to match observations in treatment and comparison should not be determined by program participation and are therefore best collected at baseline.
This type of design does not completely eliminate the possibility of confounding variables, however. Something could occur at one of the schools but not the other (e.g., a student drug overdose), so students at the first school would be affected by it while students at the other school would not. Ethical considerations typically will not allow random withholding of an intervention with known efficacy.
