Table Of Content
- Qualitative Research Methods
- Random Sampling
- Part 1: “quasi-experimental” studies considered by health system researchers and health economists
- What Are the Different Quasi-experimental Study Designs?
- Regression discontinuity
- Case Study – Methods, Examples and Guide
- External Validity
- Causal Evidence Rating
Quasi-experimental designs can also help inform policy and practice by providing valuable insights into the causal relationships between variables. The use of a comparison group helps prevent certain threats to validity including the ability to statistically adjust for confounding variables. Because in this study design, the two groups may not be equivalent (assignment to the groups is not by randomization), confounding may exist. For example, suppose that a pharmacy order-entry intervention was instituted in the medical intensive care unit (MICU) and not the surgical intensive care unit (SICU). O1 would be pharmacy costs in the MICU after the intervention and O2 would be pharmacy costs in the SICU after the intervention.
Qualitative Research Methods
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.
Random Sampling
In addition to including a control group, several analysis phase strategies can be employed to strengthen causal inference including adjustment for time varying confounders and accounting for auto correlation. The directionality problem is avoided in quasi-experimental research since the regression analysis is altered before the multiple regression is assessed. However, because individuals are not randomized at random, there are likely to be additional disparities across conditions in quasi-experimental research. Researchers assessed the program's effectiveness by assigning the selected subjects to a randomly assigned treatment group, while those that didn't win the lottery were considered the control group. This study didn't use specific aspects to determine which start-up companies should participate. Therefore, the results may seem straightforward, but several aspects may determine the growth of a specific company, apart from the variables used by the researchers.
Part 1: “quasi-experimental” studies considered by health system researchers and health economists
In Cambodia, the evaluators used DDD analysis to evaluate the Cambodia Education Sector Support Project, overcoming the observed lack of common trends in preprogram outcomes between beneficiaries and nonbeneficiaries [26]. Specific objectives were (1) to include a question to capture information about clustering; and (2) to extend the checklist to include study designs often used by health systems researchers and econometricians in a way that deals with the design/analysis challenge. We intended that the revised checklist should be able to resolve the differences in opinion about the extent to causality can be inferred from nonrandomized studies with different design features, improving communication between different health research communities. We did not intend that the checklist should be used as a tool to assess risk of bias, which can vary across studies with the same design features.
What Are the Different Quasi-experimental Study Designs?
In the study by Zombré et al (52) on health care access in Burkina Faso, the authors examined clinic density characteristics to determine its impact on sustainability. While the basic ITS design has important strengths, the key threat to internal validity is the possibility that factors other than the intervention are affecting the observed changes in outcome level or trend. Changes over time in factors such as the quality of care, data collection and recording, and population characteristics may not be fully accounted for by the pre-intervention trend. Similarly, the pre-intervention time period, particularly when short, may not capture seasonal changes in an outcome. But at the same time there is a control group that is given a pretest, does not receive the treatment, and then is given a posttest. The question, then, is not simply whether participants who receive the treatment improve but whether they improve more than participants who do not receive the treatment.
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In this design, all subjects receive the new PDA-based order entry system and the old computer terminal-based order entry system. The counterbalanced design is a within-participants design, where the order of the intervention is varied (e.g., one group is given software A followed by software B and another group is given software B followed by software A). The counterbalanced design is typically used when the available sample size is small, thus preventing the use of randomization. This design also allows investigators to study the potential effect of ordering of the informatics intervention. In the four-year period of JAMIA publications that the authors reviewed, 25 quasi-experimental studies among 22 articles were published.
Case Study – Methods, Examples and Guide
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.
External Validity
Commonly used in medical informatics (a field that uses digital information to ensure better patient care), researchers generally use this design to evaluate the effectiveness of a treatment – perhaps a type of antibiotic or psychotherapy, or an educational or policy intervention. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, differences between groups on both observed and unobserved characteristics would be due to chance, rather than to a systematic factor related to treatment (e.g., illness severity). The study used a nonexperimental design to assess the impact of a workplace financial education program. Two companies offered a financial education program to all employees for a one-time reimbursed fee of $150. The program group consisted of 46 employees, 10 from Company A and 36 from Company B. Program participants attended classes once a week over a 10-week period.
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Quasi-experimental research designs play a vital role in scientific inquiry by allowing researchers to investigate cause-and-effect relationships in real-world settings. These designs offer practical and ethical alternatives to true experiments, making them valuable tools in various fields of study. With their versatility and applicability, quasi-experimental designs continue to contribute to our understanding of complex phenomena. Traditionally, experimental researchers have used convenience sampling to select study participants. However, as research methods have become more rigorous, and the problems with generalizing from a convenience sample to the larger population have become more apparent, experimental researchers are increasingly turning to random sampling. In experimental policy research studies, participants are often randomly selected from program administrative databases and randomly assigned to the control or treatment groups.
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. The purpose of quasi-experimental design is to investigate the causal relationship between two or more variables when it is not feasible or ethical to conduct a randomized controlled trial (RCT). Quasi-experimental designs attempt to emulate the randomized control trial by mimicking the control group and the intervention group as much as possible. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design.
Here, X is the intervention and O is the outcome variable (this notation is continued throughout the article). In this study design, an intervention (X) is implemented and a posttest observation (O1) is taken. For example, X could be the introduction of a pharmacy order-entry intervention and O1 could be the pharmacy costs following the intervention. This design is the weakest of the quasi-experimental designs that are discussed in this article. Without any pretest observations or a control group, there are multiple threats to internal validity.
Detailed reviews have been published of variations on the basic ITS design that can be used to enhance causal inference. In particular, the addition of a control group can be particularly useful for assessing for the presence of seasonal trends and other potential time-varying confounders (52). Zombre et al (52) maintained a large number of control number of sites during the extended study period and were able to look at variations in seasonal trends as well as clinic-level characteristics, such as workforce density and sustainability.
