Chapter 13: Quasi-Experiments and Small-N Designs

Balancing Priorities in Quasi-Experiments

- Real-World Opportunities - External Validity - Ethics - Construct Validity and Statistical Validity in Quasi-Experiments

Human Subjectivity Threats to Internal Validity (3)

-Observer Bias -Demand Characteristics -Placebo Effect

Internal Validity in Quasi-Experiments

-Selection Effects -Design Confounds -Maturation Threat -History Threat -Regression to the Mean -Testing and Instrumentation Threats -Observer Bias, Demand Characteristics, and Placebo Effect (Human Subjectivity)

Nonequivalent Control Group Design

A quasi-experimental study that has at least one treatment group and one comparison group, but participants have not been randomly assigned to the two groups (e.g. Head Start group; family income, etc.)

Interrupted Time-Series Design

A quasi-experimental study that measures participants repeatedly on a dependent variable (e.g. parole decision making) before, during, and after the "interruption" caused by some event (a food break).

Reversal Design

A researcher observes a problem behavior both with and without treatment, but takes the treatment away for a while (the reversal period) to see whether the problem returns (reverses). By discontinuing a treatment that seems to be working, the researcher can test for internal validity and make a causal statement: if the treatment was really working, the behavior should worsen again when the treatment is discontinued.

Stable-Baseline Design

A study in which a researcher observes behavior for an extended baseline period before beginning a treatment or other intervention; if the behavior during the baseline is stable, the researcher is more certain of the treatment's effectiveness.

Waitlist Design

All the participants plan to receive treatment, but are assigned to do so at different times

Nonequivalent Control Group Interrupted Time-Series Design

Combines two of the previous designs (the nonequivalent control group design and the interrupted time series design) TV example idk

Attrition

In designs with pretests and posttests, this occurs when people drop out of a study over time

Small-N Design

Instead of gathering a little information from a larger sample, scientists obtain a lot of information from just a few cases.

Differences Between Large-N and Small-N Designs

Large-N: Participants are grouped, and data is represented as group averages. Small-N: Each participant is treated as a separate experiment, and small-n designs are almost always repeated-measures designs. Individuals' datas are presented.

Maturation Threats

Occur when, in an experimental or quasi-experimental design with a pretest and a posttest, a treatment group shows an improvement over time, but it is not clear whether the improvement was caused by the treatment or whether the group would have improved spontaneously, even without treatment

History Threat

Occurs when an external, historical event happens for everyone in a study at the same time as the treatment variable. With this threat, it is unclear whether the outcome is caused by the treatment or by the common, external event or factor.

Regression to the Mean

Occurs when an extreme finding is caused by a combination of random factors that are unlikely to happen in the same combination again, so the extreme finding gets less extreme over time.

Nonequivalent Control Group Pretest / Posttest Design

Participants are not randomly assigned to groups, and were tested both before and after some intervention

Repeated-Measures Quasi-Experiments

Participants experience all levels of an independent variable. But as opposed to a true repeated-measures experiment, the researcher takes advantage of an already-scheduled event, a new policy or regulation, or a chance occurrence to manipulate the independent variable.

Demand Characteristics

Participants guess what the study is about and change their behavior in the expected direction Fix: Think about whether the participants were able to detect the study's goals and responded accordingly.

Selection Effects

Relevant only for independent-groups designs, not for repeated-measures designs. A selection threat to internal validity applies when the groups at the various levels of an independent variable contain different types of participants.In such cases, it is not clear whether it was the independent variable or the different types of participants in each group that led to a difference in the dependent variable between groups.

Quasi-Experiment

Researchers do not have full experimental control

Multiple-Baseline Design

Researchers stagger their introduction of an intervention across a variety of contexts, times, and situations.

Single-N Design

Restricting a study to only one animal or one person

Observer Bias

The experimenters' expectations influence their interpretation of the results Fix: Ask who measured the behaviors. Was the design blind (masked) or double-blind?

Placebo Effects

When participants improve, but only because they believe they are receiving an effective treatment Fix: Ask whether the design of a study included a comparison group that received an inert, or placebo, treatment.