one shot case study experimental design

Child Care and Early Education Research Connections

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.

One-group posttest-only design representation

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.

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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12.2: Pre-experimental and quasi-experimental design

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Matthew DeCarlo
Radford University via Open Social Work Education

Learning Objectives

Identify and describe the various types of quasi-experimental designs
Distinguish true experimental designs from quasi-experimental and pre-experimental designs
Identify and describe the various types of quasi-experimental and pre-experimental designs

As we discussed in the previous section, time, funding, and ethics may limit a researcher’s ability to conduct a true experiment. For researchers in the medical sciences and social work, conducting a true experiment could require denying needed treatment to clients, which is a clear ethical violation. Even those whose research may not involve the administration of needed medications or treatments may be limited in their ability to conduct a classic experiment. When true experiments are not possible, researchers often use quasi-experimental designs.

Quasi-experimental designs are similar to true experiments, but they lack random assignment to experimental and control groups. The most basic of these quasi-experimental designs is the nonequivalent comparison groups design (Rubin & Babbie, 2017). [1] The nonequivalent comparison group design looks a lot like the classic experimental design, except it does not use random assignment. In many cases, these groups may already exist. For example, a researcher might conduct research at two different agency sites, one of which receives the intervention and the other does not. No one was assigned to treatment or comparison groups. Those groupings existed prior to the study. While this method is more convenient for real-world research, researchers cannot be sure that the groups are comparable. Perhaps the treatment group has a characteristic that is unique–for example, higher income or different diagnoses–that make the treatment more effective.

Quasi-experiments are particularly useful in social welfare policy research. Social welfare policy researchers like me often look for what are termed natural experiments , or situations in which comparable groups are created by differences that already occur in the real world. For example, Stratmann and Wille (2016) [2] were interested in the effects of a state healthcare policy called Certificate of Need on the quality of hospitals. They clearly cannot assign states to adopt one set of policies or another. Instead, researchers used hospital referral regions, or the areas from which hospitals draw their patients, that spanned across state lines. Because the hospitals were in the same referral region, researchers could be pretty sure that the client characteristics were pretty similar. In this way, they could classify patients in experimental and comparison groups without affecting policy or telling people where to live.

There are important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013). [3] Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications with using people on a wait list as a control group—most obviously, what happens when people on the wait list are accepted into the program while you’re still collecting data? Natural experiments aren’t a specific kind of experiment like quasi- or pre-experimental designs. Instead, they are more like a feature of the social world that allows researchers to use the logic of experimental design to investigate the connection between variables.

Matching is another approach in quasi-experimental design to assigning experimental and comparison groups. Researchers should think about what variables are important in their study, particularly demographic variables or attributes that might impact their dependent variable. Individual matching involves pairing participants with similar attributes. When this is done at the beginning of an experiment, the matched pair is split—with one participant going to the experimental group and the other to the control group. An ex post facto control group , in contrast, is when a researcher matches individuals after the intervention is administered to some participants. Finally, researchers may engage in aggregate matching , in which the comparison group is determined to be similar on important variables.

There are many different quasi-experimental designs in addition to the nonequivalent comparison group design described earlier. Describing all of them is beyond the scope of this textbook, but one more design is worth mentioning. The time series design uses multiple observations before and after an intervention. In some cases, experimental and comparison groups are used. In other cases where that is not feasible, a single experimental group is used. By using multiple observations before and after the intervention, the researcher can better understand the true value of the dependent variable in each participant before the intervention starts. Additionally, multiple observations afterwards allow the researcher to see whether the intervention had lasting effects on participants. Time series designs are similar to single-subjects designs, which we will discuss in Chapter 15.

When true experiments and quasi-experiments are not possible, researchers may turn to a pre-experimental design (Campbell & Stanley, 1963). [4] Pre-experimental designs are called such because they often happen before a true experiment is conducted. Researchers want to see if their interventions will have some effect on a small group of people before they seek funding and dedicate time to conduct a true experiment. Pre-experimental designs, thus, are usually conducted as a first step towards establishing the evidence for or against an intervention. However, this type of design comes with some unique disadvantages, which we’ll describe as we review the pre-experimental designs available.

If we wished to measure the impact of a natural disaster, such as Hurricane Katrina for example, we might conduct a pre-experiment by identifying an experimental group from a community that experienced the hurricane and a control group from a similar community that had not been hit by the hurricane. This study design, called a static group comparison , has the advantage of including a comparison group that did not experience the stimulus (in this case, the hurricane). Unfortunately, it is difficult to know those groups are truly comparable because the experimental and control groups were determined by factors other than random assignment. Additionally, the design would only allow for posttests, unless one were lucky enough to be gathering the data already before Katrina. As you might have guessed from our example, static group comparisons are useful in cases where a researcher cannot control or predict whether, when, or how the stimulus is administered, as in the case of natural disasters.

In cases where the administration of the stimulus is quite costly or otherwise not possible, a one- shot case study design might be used. In this instance, no pretest is administered, nor is a control group present. In our example of the study of the impact of Hurricane Katrina, a researcher using this design would test the impact of Katrina only among a community that was hit by the hurricane and would not seek a comparison group from a community that did not experience the hurricane. Researchers using this design must be extremely cautious about making claims regarding the effect of the stimulus, though the design could be useful for exploratory studies aimed at testing one’s measures or the feasibility of further study.

Finally, if a researcher is unlikely to be able to identify a sample large enough to split into control and experimental groups, or if she simply doesn’t have access to a control group, the researcher might use a one-group pre-/posttest design. In this instance, pre- and posttests are both taken, but there is no control group to which to compare the experimental group. We might be able to study of the impact of Hurricane Katrina using this design if we’d been collecting data on the impacted communities prior to the hurricane. We could then collect similar data after the hurricane. Applying this design involves a bit of serendipity and chance. Without having collected data from impacted communities prior to the hurricane, we would be unable to employ a one- group pre-/posttest design to study Hurricane Katrina’s impact.

As implied by the preceding examples where we considered studying the impact of Hurricane Katrina, experiments do not necessarily need to take place in the controlled setting of a lab. In fact, many applied researchers rely on experiments to assess the impact and effectiveness of various programs and policies. You might recall our discussion of arresting perpetrators of domestic violence in Chapter 6, which is an excellent example of an applied experiment. Researchers did not subject participants to conditions in a lab setting; instead, they applied their stimulus (in this case, arrest) to some subjects in the field and they also had a control group in the field that did not receive the stimulus (and therefore were not arrested).

Key Takeaways

Quasi-experimental designs do not use random assignment.
Comparison groups are often used in quasi-experiments.
Matching is a way of improving the comparability of experimental and comparison groups.
Quasi-experimental designs and pre-experimental designs are often used when experimental designs are impractical.
Quasi-experimental and pre-experimental designs may be easier to carry out, but they lack the rigor of true experiments.
Aggregate matching- when the comparison group is determined to be similar to the experimental group along important variables
Ex post facto control group- a control group created when a researcher matches individuals after the intervention is administered
Individual matching- pairing participants with similar attributes for the purpose of assignment to groups
Natural experiments- situations in which comparable groups are created by differences that already occur in the real world
Nonequivalent comparison group design- a quasi-experimental design similar to a classic experimental design but without random assignment
One-group pre-/posttest design- a pre-experimental design that applies an intervention to one group but also includes a pretest
One-shot case study- a pre-experimental design that applies an intervention to only one group without a pretest
Pre-experimental designs- a variation of experimental design that lacks the rigor of experiments and is often used before a true experiment is conducted
Quasi-experimental design- designs lack random assignment to experimental and control groups
Static group design- uses an experimental group and a comparison group, without random assignment and pretesting
Time series design- a quasi-experimental design that uses multiple observations before and after an intervention

Image attributions

cat and kitten matching avocado costumes on the couch looking at the camera by Your Best Digs CC-BY-2.0

Rubin, C. & Babbie, S. (2017). Research methods for social work (9th edition) . Boston, MA: Cengage. ↵
Stratmann, T. & Wille, D. (2016). Certificate-of-need laws and hospital quality . Mercatus Center at George Mason University, Arlington, VA. Retrieved from: https://www.mercatus.org/system/files/mercatus-stratmann-wille-con-hospital-quality-v1.pdf ↵
Baicker, K., Taubman, S. L., Allen, H. L., Bernstein, M., Gruber, J. H., Newhouse, J. P., ... & Finkelstein, A. N. (2013). The Oregon experiment—effects of Medicaid on clinical outcomes. New England Journal of Medicine , 368 (18), 1713-1722. ↵
Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research . Chicago, IL: Rand McNally. ↵

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8.2 Quasi-experimental and pre-experimental designs

Learning objectives.

Identify and describe the various types of quasi-experimental designs
Distinguish true experimental designs from quasi-experimental and pre-experimental designs
Identify and describe the various types of quasi-experimental and pre-experimental designs

Quasi-experimental designs

Quasi-experimental designs are similar to true experiments, but they lack random assignment to experimental and control groups. Quasi-experimental designs have a comparison group that is similar to a control group except assignment to the comparison group is not determined by random assignment. The most basic of these quasi-experimental designs is the nonequivalent comparison groups design (Rubin & Babbie, 2017). The nonequivalent comparison group design looks a lot like the classic experimental design, except it does not use random assignment. In many cases, these groups may already exist. For example, a researcher might conduct research at two different agency sites, one of which receives the intervention and the other does not. No one was assigned to treatment or comparison groups. Those groupings existed prior to the study. While this method is more convenient for real-world research, it is less likely that that the groups are comparable than if they had been determined by random assignment. Perhaps the treatment group has a characteristic that is unique–for example, higher income or different diagnoses–that make the treatment more effective.

Quasi-experiments are particularly useful in social welfare policy research. Social welfare policy researchers often look for what are termed natural experiments , or situations in which comparable groups are created by differences that already occur in the real world. Natural experiments are a feature of the social world that allows researchers to use the logic of experimental design to investigate the connection between variables. For example, Stratmann and Wille (2016) were interested in the effects of a state healthcare policy called Certificate of Need on the quality of hospitals. They clearly could not randomly assign states to adopt one set of policies or another. Instead, researchers used hospital referral regions, or the areas from which hospitals draw their patients, that spanned across state lines. Because the hospitals were in the same referral region, researchers could be pretty sure that the client characteristics were pretty similar. In this way, they could classify patients in experimental and comparison groups without dictating state policy or telling people where to live.

Matching is another approach in quasi-experimental design for assigning people to experimental and comparison groups. It begins with researchers thinking about what variables are important in their study, particularly demographic variables or attributes that might impact their dependent variable. Individual matching involves pairing participants with similar attributes. Then, the matched pair is split—with one participant going to the experimental group and the other to the comparison group. An ex post facto control group , in contrast, is when a researcher matches individuals after the intervention is administered to some participants. Finally, researchers may engage in aggregate matching , in which the comparison group is determined to be similar on important variables.

Time series design

Pre-experimental design

When true experiments and quasi-experiments are not possible, researchers may turn to a pre-experimental design (Campbell & Stanley, 1963). Pre-experimental designs are called such because they often happen as a pre-cursor to conducting a true experiment. Researchers want to see if their interventions will have some effect on a small group of people before they seek funding and dedicate time to conduct a true experiment. Pre-experimental designs, thus, are usually conducted as a first step towards establishing the evidence for or against an intervention. However, this type of design comes with some unique disadvantages, which we’ll describe below.

A commonly used type of pre-experiment is the one-group pretest post-test design . In this design, pre- and posttests are both administered, but there is no comparison group to which to compare the experimental group. Researchers may be able to make the claim that participants receiving the treatment experienced a change in the dependent variable, but they cannot begin to claim that the change was the result of the treatment without a comparison group. Imagine if the students in your research class completed a questionnaire about their level of stress at the beginning of the semester. Then your professor taught you mindfulness techniques throughout the semester. At the end of the semester, she administers the stress survey again. What if levels of stress went up? Could she conclude that the mindfulness techniques caused stress? Not without a comparison group! If there was a comparison group, she would be able to recognize that all students experienced higher stress at the end of the semester than the beginning of the semester, not just the students in her research class.

In cases where the administration of a pretest is cost prohibitive or otherwise not possible, a one- shot case study design might be used. In this instance, no pretest is administered, nor is a comparison group present. If we wished to measure the impact of a natural disaster, such as Hurricane Katrina for example, we might conduct a pre-experiment by identifying a community that was hit by the hurricane and then measuring the levels of stress in the community. Researchers using this design must be extremely cautious about making claims regarding the effect of the treatment or stimulus. They have no idea what the levels of stress in the community were before the hurricane hit nor can they compare the stress levels to a community that was not affected by the hurricane. Nonetheless, this design can be useful for exploratory studies aimed at testing a measures or the feasibility of further study.

In our example of the study of the impact of Hurricane Katrina, a researcher might choose to examine the effects of the hurricane by identifying a group from a community that experienced the hurricane and a comparison group from a similar community that had not been hit by the hurricane. This study design, called a static group comparison , has the advantage of including a comparison group that did not experience the stimulus (in this case, the hurricane). Unfortunately, the design only uses for post-tests, so it is not possible to know if the groups were comparable before the stimulus or intervention. As you might have guessed from our example, static group comparisons are useful in cases where a researcher cannot control or predict whether, when, or how the stimulus is administered, as in the case of natural disasters.

As implied by the preceding examples where we considered studying the impact of Hurricane Katrina, experiments, quasi-experiments, and pre-experiments do not necessarily need to take place in the controlled setting of a lab. In fact, many applied researchers rely on experiments to assess the impact and effectiveness of various programs and policies. You might recall our discussion of arresting perpetrators of domestic violence in Chapter 2, which is an excellent example of an applied experiment. Researchers did not subject participants to conditions in a lab setting; instead, they applied their stimulus (in this case, arrest) to some subjects in the field and they also had a control group in the field that did not receive the stimulus (and therefore were not arrested).

Key Takeaways

Quasi-experimental designs do not use random assignment.
Comparison groups are used in quasi-experiments.
Matching is a way of improving the comparability of experimental and comparison groups.
Quasi-experimental designs and pre-experimental designs are often used when experimental designs are impractical.
Quasi-experimental and pre-experimental designs may be easier to carry out, but they lack the rigor of true experiments.
Aggregate matching – when the comparison group is determined to be similar to the experimental group along important variables
Comparison group – a group in quasi-experimental design that does not receive the experimental treatment; it is similar to a control group except assignment to the comparison group is not determined by random assignment
Ex post facto control group – a control group created when a researcher matches individuals after the intervention is administered
Individual matching – pairing participants with similar attributes for the purpose of assignment to groups
Natural experiments – situations in which comparable groups are created by differences that already occur in the real world
Nonequivalent comparison group design – a quasi-experimental design similar to a classic experimental design but without random assignment
One-group pretest post-test design – a pre-experimental design that applies an intervention to one group but also includes a pretest
One-shot case study – a pre-experimental design that applies an intervention to only one group without a pretest
Pre-experimental designs – a variation of experimental design that lacks the rigor of experiments and is often used before a true experiment is conducted
Quasi-experimental design – designs lack random assignment to experimental and control groups
Static group design – uses an experimental group and a comparison group, without random assignment and pretesting
Time series design – a quasi-experimental design that uses multiple observations before and after an intervention

Image attributions

cat and kitten matching avocado costumes on the couch looking at the camera by Your Best Digs CC-BY-2.0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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 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

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

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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

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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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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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Peer-reviewed

Research Article

Time series experimental design under one-shot sampling: The importance of condition diversity

Roles Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Coordinated Science Laboratory and Department of Electrical and Computer Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois, United States of America

Roles Writing – review & editing

Affiliation Department of Biology, California State University, Northridge, Northridge, California, United States of America

Xiaohan Kang,
Bruce Hajek,
Faqiang Wu,
Yoshie Hanzawa

Published: October 31, 2019
https://doi.org/10.1371/journal.pone.0224577
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Many biological data sets are prepared using one-shot sampling, in which each individual organism is sampled at most once. Time series therefore do not follow trajectories of individuals over time. However, samples collected at different times from individuals grown under the same conditions share the same perturbations of the biological processes, and hence behave as surrogates for multiple samples from a single individual at different times. This implies the importance of growing individuals under multiple conditions if one-shot sampling is used. This paper models the condition effect explicitly by using condition-dependent nominal mRNA production amounts for each gene, it quantifies the performance of network structure estimators both analytically and numerically, and it illustrates the difficulty in network reconstruction under one-shot sampling when the condition effect is absent. A case study of an Arabidopsis circadian clock network model is also included.

Citation: Kang X, Hajek B, Wu F, Hanzawa Y (2019) Time series experimental design under one-shot sampling: The importance of condition diversity. PLoS ONE 14(10): e0224577. https://doi.org/10.1371/journal.pone.0224577

Editor: Steven M. Abel, University of Tennessee, UNITED STATES

Received: June 14, 2019; Accepted: October 16, 2019; Published: October 31, 2019

Copyright: © 2019 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The computer simulation code is available at https://github.com/Veggente/one-shot-sampling .

Funding: This work was supported by the Plant Genome Research Program from the National Science Foundation (NSF-IOS-PGRP-1823145) to B.H. and Y.H.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Time series data is important for studying biological processes in organisms because of the dynamic nature of the biological systems. Ideally it is desirable to use time series with multi-shot sampling , where each individual (such as a plant, animal, or microorganism) is sampled multiple times to produce the trajectory of the biological process, as in Fig 1 . Then the natural biological variations in different individuals can be leveraged for statistical inference, and thus inference can be made even if the samples are prepared under the same experimental condition.

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Each plant is observed four times.

https://doi.org/10.1371/journal.pone.0224577.g001

However, in many experiments multi-shot sampling is not possible. Due to stress response of the organisms and/or the large amount of cell tissue required for accurate measurements, the dynamics of the relevant biological process in an individual of the organism cannot be observed at multiple times without interference. For example, in an RNA-seq experiment an individual plant is often only sampled once in its entire life, leaving the dynamics unobserved at other times. See the Discussion section for a review of literature on this subject. We call the resulting time series data, as illustrated in Fig 2 , a time series with one-shot sampling . Because the time series with one-shot sampling do not follow the trajectories of the same individuals, they do not capture all the correlations in the biological processes. For example, the trajectory of observations on plants 1–2–3–4 and that on 1–6–7–4 in Fig 2 are statistically identical. The resulting partial observation renders some common models for the biological system dynamics inaccurate or even irrelevant.

Each plant is observed once.

https://doi.org/10.1371/journal.pone.0224577.g002

To address this problem, instead of getting multi-shot time series of single individuals, one can grow multiple individuals under each condition with a variety of conditions, and get one-shot time series of the single conditions. The one-shot samples from the same condition then become a surrogate for multi-shot samples for a single individual, as illustrated in Fig 3 . In essence, if we view the preparation condition of each sample as being random, then there should be a positive correlation among samples grown under the same condition. We call this correlation the condition variation effect . It is similar to the effect of biological variation of a single individual sampled at different times, if such sampling were possible.

https://doi.org/10.1371/journal.pone.0224577.g003

For each condition, the one-shot samples at different times are also complemented by biological replicates , which are samples from independent individuals taken at the same time used to reduce technical and/or biological variations. See the Discussion section for a review on how replicates are used for biological inference. With a budget over the number of samples, a balance must be kept between the number of conditions, the number of sampling times and the number of replicates.

To illustrate and quantify the effect of one-shot sampling in biological inference, we introduce a simple dynamic gene expression model with a condition variation effect. We consider a hypothesis testing setting and model the dynamics of the gene expression levels at different sampling times by a dynamic Bayesian network (DBN), where the randomness comes from nominal (or basal) biological and condition variations for each gene. The nominal condition-dependent variation of gene j is the same for all plants in that condition and the remaining variation is biological and is independent across the individuals in the condition. In contrast to GeneNetWeaver [ 1 ], where the effect of a condition is modeled by a random perturbation to the network coefficients, in our model the condition effect is characterized by correlation in the nominal variation terms of the dynamics. Note in both models samples from different individuals under the same condition are statistically independent given the randomness associated with the condition.

The contributions of this paper are threefold.

A composite hypothesis testing problem on the gene regulatory network is formulated and a gene expression dynamic model that explicitly captures the per-gene condition effect and the gene regulatory interactions is proposed.
The performance of gene regulatory network structure estimators is analyzed for both multi-shot and one-shot sampling, with focus on two algorithms. Furthermore, single-gene and multi-gene simulation results indicate that multiple-condition experiments can somewhat mitigate the shortcomings of one-shot sampling.
The difficulty of network reconstruction under one-shot sampling with no condition effect is illustrated. This difficulty connects network analysis and differential expression analysis, two common tasks in large-scale genomics studies, in the sense that the part of network involving non-differentially expressed genes may be harder to reconstruct.

The simulation code for generating the figures is available at [ 2 ].

Materials and methods

Stochastic model of time-series samples.

This section formulates the hypothesis testing problem of learning the structure of the gene regulatory network (GRN) from gene expression data with one-shot or multi-shot sampling. The GRN is characterized by an unknown adjacency matrix. Given the GRN, a dynamic Bayesian network model is used for the gene expression evolution with time. Two parameters σ co, j and σ bi, j are used for each gene j , with the former explicitly capturing the condition variation effect and the latter capturing the biological variation level.

Model for gene regulatory network topology.

Model for gene expression dynamics.

This section models the gene expression dynamics of individuals by a dynamic Bayesian networks with parameters σ co, j and σ bi, j as the condition variation level and biological variation level for gene j .

Model for sampling method.

In this section two sampling methods are described: one-shot sampling and multi-shot sampling. For simplicity, throughout this paper we consider a full factorial design with CRT samples obtained under C conditions, R replicates and T sampling times, denoted by Y = ( Y c , r , t ) ( c , r , t )∈[ C ]×[ R ]×[ T ] . In other words, instead of X we observe Y , a noisy and possibly partial observation of X . Here the triple index for each sample indicates the condition, replicate, and time. As we will see in the Discussion at the end of this section, for either sampling method, the biological variation level σ bi, j can be reduced by combining multiple individuals to form a single sample.

Multi-shot sampling.

One-shot sampling.

Discussion on sources of variance.

If the samples of the individuals under many different conditions are averaged and treated as a single sample, then effectively σ co, j , σ bi, j and σ te, j are reduced.

If material from multiple individuals grown under the same condition is combined into a composite sample before measuring, then effectively σ bi, j is reduced while σ co, j and σ te, j remain unchanged. Note for microorganisms a sample may consist of millions of individuals and the biological variation is practically eliminated ( σ bi, j ≈ 0).
If the samples from same individuals (technical replicates) are averaged and treated as a single sample, then effectively σ te, j is reduced while σ co, j and σ bi, j remain unchanged.

Note this sampling model with hierarchical driving and observational noises can also be used for single-cell RNA sequencing (scRNAseq) in addition to bulk RNA sequencing and microarray experiments. For scRNAseq, σ co, j can model the tissue-dependent variation (the global effect) and σ bi, j the per-cell variation (the local effect).

Performance evaluation of network structure estimators

This section studies the performance of network structure estimators with multi-shot and one-shot sampling data. First, general properties of the two sampling methods are obtained. Then two algorithms, the generalized likelihood-ratio test (GLRT) and the basic sparse linear regression (BSLR), are studied. The former is a standard decision rule for composite hypothesis testing problems and is shown to have some properties but is computationally infeasible for even a small number of genes. The latter is an algorithm based on linear regression, and is feasible for a moderate number of genes. Finally simulation results for a single-gene network with GLRT and for a multi-gene network with BSLR are shown.

General properties.

By ( 3 ), ( 4 ) and ( 5 ), the samples Y are jointly Gaussian with zero mean. The covariance of the random tensor Y is derived under the two sampling methods in the following.

If Σ bi = 0 and C , R and T are fixed, the joint distribution of the data is the same for both types of sampling. So the performance of the estimator would be the same for multi-shot and one-shot sampling.
If Σ bi = 0 and Σ te = 0 (no observation noise) and C , T are fixed, the joint distribution of the data is the same for both types of sampling (as noted in item 1) and any replicates beyond the first are identical to the first. So the performance of the estimator can be obtained even if all replicates beyond the first are discarded.
Under multi-shot sampling, when C , R , T are fixed with R = 1, the joint distribution of the data depends on Σ co and Σ bi only through their sum. So the performance of the estimator would be the same for all Σ co and Σ bi such that Σ co + Σ bi is the same.

Network reconstruction algorithms.

In this section we introduce GLRT and BSLR. GLRT is a standard choice in composite hypothesis testing setting. We observe some properties for GLRT under one-shot and multi-shot sampling. However, GLRT involves optimizing the likelihood over the entire parameter space, which grows exponentially with the square of the number of genes. Hence GLRT is hard to compute for multiple-gene network reconstruction. In contrast, BSLR is an intuitive algorithm based on linear regression, and will be shown in simulations to perform reasonably well for multi-gene scenarios.

Proposition 1 GLRT ( with the knowledge of Σ co , Σ bi and Σ te ) has the following properties .

Under one-shot sampling and Σ co = 0, the log likelihood of the data as a function of A ( i . e . the log likelihood function ) is invariant with respect to replacing A by − A . So , for the single-gene n = 1 case , the log likelihood function is an even function of A , and thus the GLRT will do no better than random guessing .

For 2 it suffices to notice in ( 6 ) the covariance is invariant with respect to changing A to − A . A proof of 1 is given below.

Proof of 1) We first prove it for the case of a single gene with constant T and a constant number of individuals, CR . To do that we need to look at the likelihood function closely.

In BSLR, replicates are averaged and the average gene expression levels at different times under different conditions are fitted in a linear regression model with best-subset sparse model selection, followed by a Granger causality test to eliminate the false discoveries. BSLR is similar to other two-stage linear regression–based network reconstruction algorithms, notably oCSE [ 4 ] and CaSPIAN [ 5 ]. Both oCSE and CaSPIAN use greedy algorithms in the first build-up stage, making them more suitable for large-scale problems. In contrast, BSLR uses best subset selection, which is conceptually simpler but computationally expensive for large n . For the tear-down stage both BSLR and CaSPIAN use the Granger causality test, while oCSE uses a permutation test.

Build-up stage.

A naive algorithm to solve the above optimization has a computational complexity of O ( n k +1 ) for fixed k as n → ∞. Faster near-optimal alternatives exist [ 6 ].

Tear-down stage.

Simulations on single-gene network reconstruction.

https://doi.org/10.1371/journal.pone.0224577.g004

The numerical simulations reflect the following observations implied by the analytical model.

Under one-shot sampling, when γ = 0, the GLRT is equivalent to random guessing.
The GLRT performs the same under one-shot and multi-shot sampling when γ = 1.
Under no observation noise, the performance for multi-shot sampling is the same for all γ < 1.

Some empirical observations are in order.

Multi-shot sampling outperforms one-shot sampling (unless γ = 1, where they have the same error probability).
For one-shot sampling, the performance improves as γ increases. Regarding the number of replicates R per condition, if γ = 0.2 (small condition effect), a medium number of replicates (2 to 5) outperforms the single replicate strategy. For larger γ , one replicate per condition is the best.
For multi-shot sampling, performance worsens as γ increases. One replicate per condition ( R = 1) is best.
Comparing Fig 4a–4d vs. Fig 4e–4h , we observe that the performance degrades with the addition of observation noise, though for moderate noise ( σ te = 1.0) the effect of observation noise on the sign error is not large. Also, the effect of the algorithm not knowing γ is not large.

Simulations on multi-gene network reconstruction.

This subsection studies the case when multiple genes interact through the GRN. The goal is to compare one-shot vs. multi-shot sampling for BSLR under a variety of scenarios, including different homogeneous γ values, varying number of replicates, varying observation noise level, and heterogeneous γ values.

The performance evaluation for multi-gene network reconstruction is trickier than the single-gene case because of the many degrees of freedom introduced by the number of genes. First, the network adjacency matrix A is now an n -by- n matrix. While some notion of “size” of A (like the spectral radius or the matrix norm) may be important, potentially every entry of A may affect the reconstruction result. So instead of fixing a ground truth A as in Fig 4 , we fix a prior distribution of A with split Gaussian prior described in S2 Appendix (note we assume the knowledge of no autoregulation), and choose A i.i.d. from the prior distribution with d max = 3. Second, because the prior of A can be subject to sparsity constraints and thus far from a uniform distribution, multiple loss functions that are more meaningful than the ternary error rate can be considered for performance. So we consider ternary FDR, ternary FNR and ternary FPR for the multi-gene case. In the simulations we have 20 genes and d max = 3 with in-degree uniformly distributed over {0, 1, …, d max }, so the average in-degree is 1.5. The number of sampling times is T = 6 and CR = 30.

Varying γ , R and σ te .

In this set of simulations we fix the observation noise level and vary the number of replicates R and the condition correlation coefficient γ . The performance of BSLR under one-shot and multi-shot sampling is shown in Fig 5 ( σ te = 0) and Fig 6 ( σ te = 1). Note BSLR does not apply to a single condition with 30 replicates due to the constraint that the degrees of freedom C ( T − 1) − k − 2 in the second stage must be at least 1.

https://doi.org/10.1371/journal.pone.0224577.g005

https://doi.org/10.1371/journal.pone.0224577.g006

For multi-shot sampling, in the noiseless case, we see all three losses are invariant with respect to different γ for fixed R , which is consistent with property 4 in the section on general properties because BSLR is an average-based scale-invariant algorithm (note CR is a constant so for different R the performance is different due to the change in C ). In the noisy case, the FDR and FNR slightly decrease as γ increases, which is an opposite trend compared with Fig 4f and 4h .

In summary, the main conclusions from Figs 5 and 6 are the following.

The performance of BSLR under multi-shot sampling is consistently better than that under one-shot sampling.
The performance of BSLR under one-shot sampling varies with γ , from random guessing performance at γ = 0 to the same performance as multi-shot sampling at γ = 1.
By comparing Figs 5 with 6 , we see the observation noise of σ te = 1 has only a small effect on the performance with the two sampling methods.

Reduced number of directly differentially expressed genes.

https://doi.org/10.1371/journal.pone.0224577.g007

We summarize the simulations performed in Table 1 . Note the last row is a summary of Table 2 in the Discussion section.

OS and MS stand for the losses of one-shot sampling and multi-shot sampling. RG stands for random guessing. * indicates mathematically proved results.

https://doi.org/10.1371/journal.pone.0224577.t001

The errors are estimated using Hoeffding’s inequality over 1000 simulations with significance level 0.05.

https://doi.org/10.1371/journal.pone.0224577.t002

Information limitation for reconstruction under one shot sampling without condition effect

In the previous section it is shown that both GLRT and BSLR are close to random guessing under one-shot sampling when σ co, j = 0 for all j . This leads us to the following question: is the network reconstruction with no condition effect ( σ co, j = 0 for all j ) information theoretically possible? In this section we examine this question under general estimator-independent settings. Note in this case the trajectories of all individuals are independent given A regardless of ( c k ) k ∈[ K ] .

As we have seen in Proposition 1 part 2, when Σ co = 0, the distribution of the observed data Y is invariant under adjacency matrix A or − A , implying any estimator will have a sign error probability no better than random guessing for the average or worst case over A and − A . Here, instead of sign error probability, we consider the estimation for A itself.

The extreme case with infinite number of samples available for network reconstruction is considered to give a lower bound on the accuracy for the finite data case. Note that with infinite number of samples a sufficient statistic for the estimation of the parameter A is the marginal distributions of X 1 ( t ); no information on the correlation of ( X 1 ( t )) t ∈[ T ] across time t can be obtained. A similar observation is made in [ 7 ] for sampling stochastic differential equations.

In summary, the recovery of the matrix A is generally not possible in the stationary case, and also not possible in the transient case at least when A is orthogonal. To reconstruct A , further constraints (like sparsity) may be required.

One-shot sampling in the literature

This section reviews the sampling procedures reported in several papers measuring gene expression levels in biological organisms with samples collected at different times to form time series data. In all cases, the sampling is one-shot, in the sense that a single plant or cell is only sampled at one time.

Microorganisms.

In the transcriptional network inference challenge from DREAM5 [ 8 ], three compendia of biological data sets were provided based on microorganisms ( E. coli , S. aureus , and S. cerevisiae ), and some of the data corresponded to different sampling times in a time series. Being based on microorganisms, the expression level measurements involved multiple individuals per sample, a form of one-shot sampling.

In [ 9 ], the plants are exposed to nitrate, which serves as a synchronizing event, and samples are taken from three to twenty minutes after the synchronizing event. The statement “… each replicate is independent of all microarrays preceding and following in time” suggests the experiments are based on one-shot sampling. In contrast, the state-space model with correlation between transcription factors in an earlier time and the regulated genes in a later time fits multi-shot sampling. [ 10 ] studied the gene expression difference between leaves at different developmental stages in rice. The 12th, 11th and 10th leaf blades were collected every 3 days for 15 days starting from the day of the emergence of the 12th leaves. While a single plant could provide multiple samples, namely three different leaves at a given sampling time, no plant was sampled at two different times. Thus, from the standpoint of producing time series data, the sampling in this paper was one-shot sampling. [ 11 ] devised the phenol-sodium dodecyl sulfate (SDS) method for isolating total RNA from Arabidopsis . It reports the relative level of mRNA of several genes for five time points ranging up to six hours after exposure to a synchronizing event, namely being sprayed by a hormone trans -zeatin. The samples were taken from the leaves of plants. It is not clear from the paper whether the samples were collected from different leaves of the same plant, or from leaves of different plants.

[ 12 ] likely used one-shot sampling for their −24, 60, 120, 168 hour time series data from mouse skeletal muscle C2C12 cells without specifying whether the samples are all taken from different individuals. [ 13 ] produced time series data by extracting cells from a human, seeding the cells on plates, and producing samples in triplicate, at a series of six times, for each of five conditions. Multiple cells are used for each sample with different sets of cells being used for different samples, so this is an example of one-shot sampling of in vitro experiment in the sense that each plate of cells is one individual. The use of (five) multiple conditions can serve as a surrogate for a single individual set of cells to gain the effect of multi-shot sampling. Similarly, the data sets produced by [ 14 ] involving the plating of HeLa S3 cells can be classified as one-shot samples because different samples are made from different sets of individual cells. Interestingly, the samples are prepared under one set of conditions, so the use of different conditions is not adopted as a surrogate for multi-shot sampling. However, a particular line of cells was selected (HeLa S3) for which cells can be highly synchronized. Also, the paper does not attempt to determine causal interactions.

The three in silico benchmark suites described in the GeneNetWeaver paper on performance profiling of network inference methods [ 1 ] consisted of steady state, and therefore one-shot, samples from dynamical models. However, the GeneNetWeaver software can be used to generate multi-shot time series data, and some of that was included in the network inference challenges, DREAM3, DREAM4, and DREAM5 [ 1 , 8 ].

On biological replicates

In many biological experiments, independent biological replicates are used to reduce the variation in the measurements and to consequently increase the power of the statistical tests. It turns out that both how to use biological replicates, and the power of biological replicates, depend on whether the sampling is one-shot or multi-shot. To focus on this issue we first summarize how replicates have traditionally been used for the more common problem of gene differential expression analysis, before turning to the use of replicates for recovery of gene regulatory networks.

The following summarizes the use of replicates for gene differential expression analysis. A recent survey [ 15 ] suggests a minimum of three replicates for RNA-seq experiments whenever sample availability allows. Briggs et al. [ 16 ] studies the effect of biological replication together with dye switching in microarray experiments and recommends biological replication when precision in the measurements is desired. Liu et al. [ 17 ] studies the tradeoff between biological replication and sequencing depth under a sequencing budget limit in RNA-seq differential expression (DE) analysis. It proposes a metric for cost effectiveness that suggests a sequencing depth of 10 million reads per library of human breast cells and 2–6 biological replicates for optimal RNA-seq DE design. Schurch et al. [ 18 ] studies the number of necessary biological replicates in RNA-seq differential expression experiments on S. cerevisiae quantitatively with various statistical tools and concludes with the usage of a minimum of six biological replicates.

For regulatory network reconstruction there is even less consensus on how replicates should be used. One straightforward way is to reduce the replicates into a single set of data by averaging either directly or after a random resampling of the original replicated data. In this case the mean of the replicates are used as a better estimate of the population than each single replicate, while higher moments of the empirical distribution of the replicates are practically ignored. Another way adopted in [ 9 ] is to account for all four potential transitions between two replicates in two adjacent sampling times in their machine learning algorithm due to the one-shot nature of the replicates. In the next section, we illustrate why replicates should be used differently for one-shot and multi-shot sampling, in the context of recovering a circadian clock network model.

A case study on Arabidopsis circadian clock network

As we have discussed earlier, the current expression datasets are prominently one-shot, making a direct comparison between one-shot and multi-shot sampling in real biological data difficult. The lack of a well-accepted ground truth of the gene regulatory network also makes performance evaluation hard, if not impossible. To test the applicability of the sampling models on real biological data, we generate expression data from a most-accepted Arabidopsis circadian clock model using stochastic differential equation (SDE) model similar to GeneNetWeaver with condition-dependent Brownian motions, and evaluate the performance of BSLR.

https://doi.org/10.1371/journal.pone.0224577.g008

In summary, we demonstrated a setting of the biologically plausible Arabidopsis circadian clock network with a single condition, where the BSLR performs similarly to a random guessing algorithm under one-shot sampling, and performs significantly better under multi-shot sampling. We also show that whether replicate averaging should be done or not varies with the sampling method.

Conclusions

One-shot sampling can miss a lot of potentially useful correlation information. Often gene expression data collected from plants is prepared under one-shot sampling. One factor that can partially mitigate the shortcomings of one-shot sampling is to prepare samples under a variety of conditions or perturbations. One-shot samples grown under the same condition can then be thought of as a surrogate for the multi-shot samples of an individual plant.

To clarify issues and take a step towards quantifying them, we proposed a gene expression dynamic model for gene regulatory network reconstruction that explicitly captures the condition variation effect. We show analytically and numerically the performance of two algorithms for single-gene and multi-gene settings. We also demonstrate the difficulty of network reconstruction without condition variation effect.

There is little agreement across the biology literature about how to model the impact of condition on the gene regulatory network. In some cases, it is not even clear that we are observing the same network structure as conditions vary. Nevertheless, our results suggest that the preparation of samples under different conditions can partially compensate for the shortcomings of one-shot sampling.

Supporting information

S1 appendix. joint estimation for single-gene autoregulation recovery..

The parameters A , γ , σ , and σ te are assumed unknown and jointly estimated in GLRT.

https://doi.org/10.1371/journal.pone.0224577.s001

S2 Appendix. Split Gaussian network prior.

The random network prior distribution used to generate the multi-gene network.

https://doi.org/10.1371/journal.pone.0224577.s002

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2. Kang X. One-shot sampling simulations; 2019. Available from: https://github.com/Veggente/one-shot-sampling .
3. Poor HV. An Introduction to Signal Detection and Estimation. Springer-Verlag New York; 1994.

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Types of Pre-Experimental Design. One-shot case study design; One-group pretest-posttest design; Static-group comparison; One-shot case study design. 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 ...
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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.
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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 ...
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One-shot case study- a pre-experimental design that applies an intervention to only one group without a pretest. Pre-experimental designs- a variation of experimental design that lacks the rigor of experiments and is often used before a true experiment is conducted. Quasi-experimental design- designs lack random assignment to experimental and ...
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When true experiments and quasi-experiments are not possible, researchers may turn to a pre-experimental design (Campbell & Stanley, 1963). Pre-experimental designs are called such because they often happen as a pre-cursor to conducting a true experiment. ... One-shot case study - a pre-experimental design that applies an intervention to only ...
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One-group pretest-posttest design; Static-group comparison; One-shot case study design. 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 ...
APA Dictionary of Psychology
one-shot case study. a research design in which a single group is observed on a single occasion after experiencing some event, treatment, or intervention. Because there is no control group against which to make comparisons, it is a weak design; any changes noted are merely presumed to have been caused by the event.
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This is a new series of videos I am starting on experimental research designs, and in this regard, this video is the first in the series of many.In this vide...
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this design. There are four types of pre-experimental designs which are: one-shot case study, one-group pretest-posttest design, posttest-only with nonequivalent groups, and posttest-only with nonequivalent groups design (Creswell, 2017). Referring to the case example of the designs, the pre-experimental method was
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The "One Shot" Case Study Revisited. 2. The "One Shot" Case Study Revisited. Gary King. 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.
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A. Pre-experimental designs: 1. One shot case study X O 2. One group pretest-posttest design O X O 7 . ... (Classic experimental design) R O X O R O O 2. Posttest only control group design ... Sampling and survey research 13 . NEXT WEEK: Single-case design 14 .
Chapter 5.2 Pre-Experimental Design
Pre-Experimental Design Pre-experimental designs are so named because they follow basic experimental steps but fail to include a control group. In other words, a single group is often studied but no comparison between an equivalent non-treatment group is made. Examples include the following: The One-Shot Case Study. In this arrangement, subjects are presented with some
Pre-experimental design: Definition, types & examples
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. ... One-shot case study design. This design practices the treatment of a single group. It only takes a single measurement after the ...
12.2 Pre-experimental and quasi-experimental design
One-shot case study- a pre-experimental design that applies an intervention to only one group without a pretest. Pre-experimental designs- a variation of experimental design that lacks the rigor of experiments and is often used before a true experiment is conducted. Quasi-experimental design- these designs lack random assignment to experimental ...
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Penelitian ini termasuk kedalam pre-experimental design dengan rancangan one-shot case study. Rancangan one-shot case study menurut Arikunto (2013: 124) ... Menurut Sugiyono (2017: 74) desain one-shot case study digambarkan seperti berikut: Tabel 3.1 One-Shot Case Study Treatment Observasi X O Sumber : Sugiono, 2017.
Good example one shot case study research?
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Types of Pre-Experimental Design

Validity of Results

Advantages and Disadvantages

Disadvantages

One-Group Posttest Only Design: An Introduction

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

2. Advantages related to the simplicity of this design:

Limitations of the one-group posttest-only design

2. Limitation due to maturation:

3. Limitation due to history:

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

What can we learn from this example?

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

Further reading

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

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Time series experimental design under one-shot sampling: The importance of condition diversity

Introduction

Materials and methods

Model for gene regulatory network topology.

Model for gene expression dynamics.

Model for sampling method.

Multi-shot sampling.

One-shot sampling.

Discussion on sources of variance.

Performance evaluation of network structure estimators

General properties.

Network reconstruction algorithms.

Build-up stage.

Tear-down stage.

Simulations on single-gene network reconstruction.

Simulations on multi-gene network reconstruction.

Varying γ , R and σ te .

Reduced number of directly differentially expressed genes.

Information limitation for reconstruction under one shot sampling without condition effect

One-shot sampling in the literature

Microorganisms.

On biological replicates

A case study on Arabidopsis circadian clock network

Conclusions

Supporting information

S2 Appendix. Split Gaussian network prior.