7.3

Understanding Quasi-Experimental Research

Definition and Characteristics of Quasi-Experiments

• The term 'quasi' means 'resembling', indicating that quasi-experiments mimic true experimental designs but lack certain criteria, particularly manipulation and control.

• Quasi-experiments utilize quasi-independent variables, which are non-manipulated variables that differentiate groups (e.g., gender, age).

• Unlike true experiments, quasi-experiments do not allow for random assignment, leading to potential biases in group comparisons.

The Quasi Problem

• Researchers often fail to specify that their independent variable is quasi-independent, leading to confusion in interpreting results.

• The abbreviation 'IV' is used for both independent and quasi-independent variables, which can mislead readers about the nature of the study.

• Understanding the design and variables is crucial for readers to discern whether a study is truly experimental or quasi-experimental.

Forms of Quasi-Experimental Research

Nonequivalent Groups Design

• This design compares different groups of participants formed under conditions that do not allow for random assignment.

• It is also known as a between-subjects nonexperimental design, focusing on differences between groups (e.g., males vs. females in aggression studies).

• The lack of control over group assignment introduces assignment bias, making it difficult to establish cause-and-effect relationships.

Pretest-Posttest Design

• This design measures the dependent variable before and after a treatment, allowing researchers to assess changes over time.

• All participants typically receive the treatment, which is a non-manipulated variable, making it a within-subjects nonexperimental design.

• Validity threats include history, instrumentation, testing effects, maturation, and regression, which can confound results.

Examples and Applications of Quasi-Experimental Designs

Example of Nonequivalent Groups Design

• A study comparing aggressive behavior in males and females shows higher aggression in males, but causation cannot be established due to the design's limitations.

• The results highlight the importance of understanding the context and characteristics of the groups being compared.

Example of Pretest-Posttest Design

• A political consultant evaluates a new advertisement's effectiveness by polling voters before and after exposure, but external factors may influence results.

• This design illustrates the challenges of attributing changes solely to the treatment without considering other variables.

Advanced Quasi-Experimental Designs

Interrupted Time-Series Design

• This design involves multiple measurements taken over time, allowing researchers to assess the impact of a treatment or event.

• It minimizes threats to internal validity by providing a clearer picture of changes over time, as seen in studies measuring blood pressure before and after a treatment.

• External events can confound results, emphasizing the need for careful interpretation of data.

Combination Designs

• These designs integrate elements of both nonequivalent groups and pretest-posttest designs, allowing for a more comprehensive analysis.

• They help eliminate order effect confounds and reduce participant numbers while addressing individual differences.

• An example includes a study on memory retrieval influenced by mood, demonstrating the interaction between within-subjects and between-subjects factors.