Quasi-Experiments Overview

Study Notes

Quasi-Experiments

Quasi-experiments aim to explore cause-and-effect relationships, but lack the control of true experiments.
They are a suitable alternative when true experiments are not feasible or ethical.

Reasons for Choosing Quasi-Experiments Over True Experiments

Practical Considerations: True experiments may be impractical due to ethical issues, complex situations, or resource limitations.
Ethical Considerations: Some manipulations in true experiments are ethically problematic.
Natural Settings: Quasi-experiments allow for studying real-world events, leading to more ecologically valid findings.
Existing Groups: Quasi-experiments are useful when studying pre-existing groups that can't be randomly assigned as in a true experiment.

Types of Quasi-Experimental Designs

Pre-Experimental Design

One group without a control group.
Lacks a crucial comparison group to determine the relationship between variables.

Nonequivalent Groups Design

Involves comparing two groups (one experimental and one control), but participants are NOT randomly assigned.
Potential for unmeasured differences between groups, leading to confounding variables.

Time-Series Design

A single group is measured repeatedly over time to understand a possible relationship between the measured variable and the treatment.

Multiple Time-Series Design

Involves multiple groups measured repeatedly over time, enabling comparisons like in a Nonequivalent Groups design.
Increased group numbers enhances the validity of results when assessing the treatment's effect.

Potential Confounding Variables and Concerns in Quasi-Experiments

Selection Bias: Initial imbalances between comparison groups due to non-random assignment.
History: External events occurring during the study which can affect the outcome.
Maturation: Natural changes in participants over time, particularly relevant in longitudinal studies.
Testing: Repeated testing can influence responses.
Instrumentation: Changes in measuring tools or procedures, especially in time-series studies.
Mortality: Participants dropping out in a systematic way can introduce bias.

Difference Between Confounding in Quasi & True Experiments

True experiments minimize confounding variables using random assignment, allowing for more reliable conclusions.
Quasi-experiments lack random assignment, meaning pre-existing differences may introduce confounding variables.

Observational Studies & Field Experiments

Observational studies differ from casual "people-watching" by employing systematic methods for data collection.
Observational studies and field experiments are used when true experiments are impractical or undesirable.
Naturalistic observation: Observing subjects in their natural environment, without any manipulation.
Participant observation: Researcher interacts with the subjects; this can be disguised or undisguised.
Field experiments: Researcher manipulates variables in a natural setting.
Lab experiments: Researcher manipulates variables in a controlled setting.
Tradeoffs:
- Natural setting enhances ecological validity (naturalistic observation)
- Controlled setting (lab experiments) enhances internal validity.
- Field experiments balance nature and control.
- Disguised observation minimizes participant reactivity but has ethical considerations.
- Undisguised observation can introduce reactivity.
Minimizing Experimenter Effects: Standardize procedures, use unobtrusive measures, and have multiple observers.

Confounding in Observational/Field Research

Observer bias, participant reactivity, and other unmeasured variables can be confounding factors.

Types of Observations

Narrative records: detailed descriptions of observations.
Recoding: categorize and classify observations.
Data reduction: Summarize and simplify data.
Checklists: Structures and standardizes observations.
Sampling methods: Choosing participants and time periods for observation.

Nomothetic vs. Idiographic Approaches

Nomothetic approach: Aims to identify general laws that apply to a broad range of individuals.
Idiographic approach: Focuses on the unique characteristics of a single individual.

Single-Subject Experimental Designs vs. Case Studies

Single-subject designs: Apply experimental techniques with a single participant to investigate a treatment's effectiveness.
Case studies: Explore a limited number of individuals to understand deeply.

Characteristics of a Good Baseline & Baselines

Baselines are crucial in single-subject designs as they establish a reference point for evaluating treatment effects.
- Sufficient Duration: Should be long enough
- Stability: The baseline data should show some level of consistent behavior.
- Predictability: The trend should be discernable.

Types of Single-Subject Experimental Designs

Withdrawal design: Introduce and remove a treatment to observe its impact.
Reversal design: Treatment is introduced, and if effective, removed, and possibly reintroduced.
Alternating-treatments design: Several conditions are tested in alternation.
Multiple-baselines design: Different behaviors or participants are measured over time.
Changing-criterion design: The treatment's criteria are progressively increased over time.

Threats to Internal Validity in Single-Subject Designs

History, maturation, testing, instrumentation, regression towards the mean, selection bias, and observer bias.

Description

Explore the fundamentals of quasi-experiments, including their significance in research and the distinction from true experiments. Learn why researchers opt for quasi-experimental designs, focusing on practicality, ethics, and real-world settings. Discover the various types of quasi-experimental designs and their applications.

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Questions and Answers

Which of the following is a characteristic of a quasi-experiment?

What is one reason a researcher might choose a quasi-experiment over a true experiment?

Which quasi-experimental design involves conducting observations before and after a treatment without random assignment?

What is a common potential confound in quasi-experimental designs that is less of a concern in true experiments?

Which type of quasi-experimental design is characterized by comparing two groups that are not randomly assigned but are used to evaluate an intervention at different times?

Which of the following best describes a withdrawal design in single-subject experimental research?

In the context of observational studies, when should a researcher opt for participant observation over naturalistic observation?

What is a significant challenge unique to quasi-experimental designs compared to true experiments?

Which of the following observational methods would be most appropriate for obtaining subjective experiences without interference?

What characteristic distinguishes the alternating-treatments design from the changing-criterion design in single-subject experimental research?