Understanding Quasi Experiments

A research design that resembles a true experiment but lacks full experimental control, especially over the independent variable (IV). Participants are not randomly assigned to groups.

The ability to confidently conclude that the independent variable (IV) caused the observed effect on the dependent variable (DV).

A threat to internal validity in quasi-experiments where the groups being compared may differ systematically before the treatment, leading to biased results.

A threat to internal validity occurs when some uncontrolled factor systematically changes along with the independent variable, making it impossible to determine which factor is responsible for the observed effect.

Where changes observed in a study could be due to natural development or maturation over time rather than the experimental treatment.

A threat to internal validity occurs when an outside event happens for everyone in a study at the same time as the treatment, making it impossible to tell if the event or the treatment caused the observed change.

A quasi-experimental design involving two or more groups where participants are not randomly assigned. Participants are measured repeatedly on a dependent variable before, during, and after a specific event or treatment, which is varied in timing or presence among the groups.

A phenomenon where an extreme outcome, due to a combination of random factors, becomes less extreme over time.

A threat to internal validity where repeated testing can influence participants' responses, making it unclear whether changes are due to the intervention or simply the repeated exposure to testing.

A threat to internal validity when the measuring tool changes over time, affecting the accuracy of results.

When researchers' expectations influence how they interpret data, potentially biasing the results.

Participants modify their behavior based on their understanding of the study's goals, jeopardizing the validity of the results.

A phenomenon where participants improve because they believe they are receiving an effective treatment, even if the treatment is fake.

A research design that focuses on studying a single individual or a small group in great detail.

A research design that involves studying a specific individual or group with unique characteristics.

A period of time in a small-N study where the researcher observes the behavior of interest before any intervention is introduced.

A small-N design where an intervention is introduced to different participants at staggered times.

A type of small-N design that involves introducing and then removing an intervention to see if the behavior reverts back to baseline.

A technique used to strengthen the validity of a case study by comparing its results to findings from other research methods.

Using reinforcement principles to improve a client's behavior.

The practice of removing an effective treatment to see if the behavior returns to its previous state.

A statistical technique to combine results from multiple similar studies.

A problem in meta-analysis where studies with non-significant findings are less likely to be published, leading to an overestimate of the effect size.

The practice of reporting only statistically significant findings while ignoring non-significant results.

This occurs when a researcher presents only strong findings from an experiment, neglecting to mention weaker or non-significant results.

Publishing only studies with positive results can create a misleading impression of a theory's strength.

Combining results from single-participant studies with animal studies or other groups to increase the generalizability of findings.

Researchers clearly identify the population they want to apply their findings to. This doesn't mean generalizing to everyone.

Even when a causal statement applies only to one individual, it can still be valuable if the researcher is not interested in generalizability.

Multiple observers are used to assess behavior, reducing bias and ensuring consistency in observations.

Direct replication involves repeating an original study as closely as possible to see if the results are consistent.

Researchers explore the same research question but use different methods and procedures.

Researchers replicate their original experiment while adding new variables to answer additional questions.

When a study fails to be replicated, it can be due to problems with the replication study itself, despite attempts to be as close as possible to the original.

Even direct replications have small variations in samples, materials, or geographic location, which might contribute to differences in findings.

A collection of studies that examine similar variables by different researchers.