Quasi-experimental Research Design

<p>Replication (D)</p> Signup and view all the answers

<p>External events occurring during the study (B)</p> Signup and view all the answers

<p>Selection Bias (C)</p> Signup and view all the answers

<p>Maturation (C)</p> Signup and view all the answers

<p>Quasi-experimental methods (C)</p> Signup and view all the answers

<p>Provides rich data to understand the effect of interventions over time (C)</p> Signup and view all the answers

<p>Results may be affected by external events coinciding with the intervention (A)</p> Signup and view all the answers

<p>X (C)</p> Signup and view all the answers

<p>Pre-existing differences could impact outcomes (A)</p> Signup and view all the answers

<p>Gradual or abrupt changes following an intervention (C)</p> Signup and view all the answers

<p>The intervention occurs at a specific interruption point in time (B)</p> Signup and view all the answers

<p>High external validity (B)</p> Signup and view all the answers

<p>Identifies causal relationships effectively (A)</p> Signup and view all the answers

<p>Difficulties in isolating treatment effects (A)</p> Signup and view all the answers

<p>They can significantly influence the observed trends (C)</p> Signup and view all the answers

<p>Strengthen causal inference by integrating multiple designs (A)</p> Signup and view all the answers

<p>Validating findings through different methods (A)</p> Signup and view all the answers

<p>Comparison of test scores before and after a tutoring program (C)</p> Signup and view all the answers

<p>Interruption time series (A)</p> Signup and view all the answers

<p>Complexity in execution and analysis (D)</p> Signup and view all the answers

<p>Controlling for all external variables (C)</p> Signup and view all the answers

<p>There is no baseline comparison. (B)</p> Signup and view all the answers

<p>One Group Post Test Only Design (A)</p> Signup and view all the answers

<p>To establish a baseline for comparison. (A)</p> Signup and view all the answers

<p>One Group Design (D)</p> Signup and view all the answers

<p>No understanding of initial fitness levels. (C)</p> Signup and view all the answers

<p>When assessing the effectiveness of a single workout program. (D)</p> Signup and view all the answers

<p>Measuring the outcome after the intervention. (C)</p> Signup and view all the answers

<p>It assigns treatment based on a specific cutoff or threshold. (D)</p> Signup and view all the answers

<p>To interpret results and determine the effectiveness of the intervention. (A)</p> Signup and view all the answers

<p>It takes advantage of naturally occurring events for treatment assignment. (D)</p> Signup and view all the answers

<p>It can be easily manipulated for biased results. (C)</p> Signup and view all the answers

<p>It lacks randomization when comparing two groups. (A)</p> Signup and view all the answers

<p>It focuses on trends after an intervention is implemented. (B)</p> Signup and view all the answers

<p>Causal evidence primarily for those near the cutoff. (D)</p> Signup and view all the answers

<p>Research observing the impact of a tax policy change on local businesses. (A)</p> Signup and view all the answers

<p>Manipulation of cutoff scores for potential gain. (C)</p> Signup and view all the answers

<p>They will regress towards the average. (B)</p> Signup and view all the answers

<p>It may lead to biased results if dropouts differ from remaining participants. (A)</p> Signup and view all the answers

<p>Changes in participants' behavior to compete with the intervention group. (A)</p> Signup and view all the answers

<p>Potential influence of confounding variables. (B)</p> Signup and view all the answers

<p>They are generally less expensive and require fewer resources. (C)</p> Signup and view all the answers

<p>The spread of intervention effects to the control group. (B)</p> Signup and view all the answers

<p>It may inadvertently impact participants' responses or outcomes. (A)</p> Signup and view all the answers

<p>They enable researchers to combine with strict experimental methods. (C)</p> Signup and view all the answers

A quasi-experimental design where a single group receives an intervention, and their outcome is measured after the intervention. There is no control group for comparison.

A quasi-experimental design where a single group is measured before and after receiving an intervention. There is no control group for comparison.

A type of quasi-experimental design susceptible to threats to internal validity, meaning it's difficult to determine if the intervention caused the observed outcome.

A quasi-experimental design where two groups are compared, one receiving an intervention and the other serving as a control group. Both groups are measured before and after the intervention.

The process of analyzing data to determine if the intervention had a significant effect on the outcome.

The phase of a study where the intervention is implemented, allowing participants to experience the treatment.

The stage of a study where the outcome variable is measured in both the treatment and control groups after the intervention has taken place.

A measurement taken before the intervention is implemented to establish a baseline for comparison with post-intervention results.

A type of non-equivalent control group design where observations are made over time to understand how an intervention impacts a specific group.

A time series design where a specific intervention is introduced, causing a clear shift in the data patterns.

A potential issue in non-equivalent control group designs where the characteristics of the groups differ significantly before the treatment is applied, making it difficult to isolate the true effect of the intervention.

Variables that are not being directly studied but can influence the results of a non-equivalent control group design.

Repeated measurements of the outcome variable over time before and after the intervention in a time series design.

An event or treatment that is implemented to see its impact in a time series design.

Used to represent the different observations in a time series design with the treatment marked as "X".

A quasi-experimental design that assigns treatment based on a cutoff or threshold. Compares individuals just above and below the cutoff to estimate the causal effects. Key Features:

Strict assignment (above the cutoff = treatment, below = no treatment).

The design compares participants just above and below the cutoff to isolate the treatment effect.

Occurs when researchers take advantage of naturally occurring events or circumstances that mimic random assignment, without direct control by the researcher.

Researchers study real-world interventions or events where the treatment is assigned naturally (e.g., policy changes, natural disasters).

Research design that takes advantage of naturally occurring events or changes in the real world to study causal relationships.

A specific type of quasi-experimental design that uses data collected before and after a policy or intervention is implemented to assess the impact.

A quasi-experimental design that combines multiple techniques to strengthen the causal inference and address threats to validity.

The degree to which the findings of a study can be generalized to other populations, settings, and times.

The confidence that the observed effect was truly caused by the treatment and not by other factors.

Combining multiple designs strengthens the study by controlling for more confounders and providing a more robust causal inference.

Factors that can threaten the ability to establish a clear cause-and-effect relationship. These factors can make it difficult to determine if the intervention truly caused the observed effects.

Changes in participants over time, like aging or fatigue, potentially affecting the results.

External events that occur during the study, unrelated to the intervention, potentially influencing outcomes.

Repeated testing or exposure to the same test can influence results, potentially making it seem like there is an effect when there is not.

The tendency for groups to be more different in the pretest than in the posttest, potentially masking the true effects of the intervention.

A threat to internal validity in research where changes in the measurement tools, techniques, or the observers over time can affect the results of the study, making it difficult to isolate the true effect of the intervention.

A threat to internal validity in research where participants in the control group become aware of the intervention and deliberately change their behavior to match the intervention group, impacting the results.

A threat to internal validity in research where the researcher's expectations or actions unconsciously influence the participants' behavior, potentially skewing the results.

A threat to internal validity where participants leave the research study over time. These participants may be different from those who remain, potentially affecting the results and making it difficult to generalize findings.

A threat to internal validity in research where the intervention's effects might inadvertently 'spill over' to the control group, because participants in different groups interact, making it difficult to isolate the true effect of the intervention.

A threat to internal validity in research where the researcher, trying to be fair, provides extra resources or attention to the control group to compensate for perceived inequalities. This can affect the study's results because the control group may become more similar to the intervention group.

Advantages of quasi-experiments include the ability to study real-world problems, less expense, and the potential for better external validity, since they often involve larger, more diverse populations.