Quasi-experiments are experiments in which the independent variables are chosen rather than varied or manipulated by the experimenter. In a quasi-experiment, the researcher does not have control over the assignment of participants to different conditions or groups. Instead, the independent variables occur naturally or are pre-existing characteristics of the participants.

Field experiments
Field experiments are conducted in real-world settings rather than in a controlled laboratory environment. In these experiments, the researcher manipulates the independent variable and measures the effect on the dependent variable. The independent variable is deliberately varied or manipulated by the experimenter.

Positive experiments
There is no specific term or concept known as "positive experiments" in the context of experimental design. It is important to use precise terminology to avoid confusion and ensure clear communication.

Negative experiments
Similar to "positive experiments," there is no specific term or concept known as "negative experiments" in the context of experimental design. It is important to use precise terminology to avoid confusion and ensure clear communication.

Quasi-experiments
Quasi-experiments are a type of research design that lacks full control over the independent variable. In these experiments, the researcher is unable to randomly assign participants to different conditions or groups. The independent variable is chosen instead of varied or manipulated.

Quasi-experiments are often used when it is not feasible or ethical to manipulate the independent variable directly. For example, if researchers want to study the effect of smoking on lung cancer, they cannot randomly assign individuals to smoke or not smoke. Instead, they can compare individuals who are already smokers to individuals who are non-smokers and examine the differences in lung cancer rates.

Quasi-experiments have some limitations compared to true experiments with random assignment. The lack of control over the independent variable can introduce confounding variables and make it more challenging to establish causal relationships. However, when conducted carefully and with appropriate statistical analysis, quasi-experiments can still provide valuable insights and contribute to scientific knowledge.