Understanding Key Concepts in Psychology

What are confounding variables?

• Confounding variables are the same as dependent variables in research.

• Confounding variables are factors that can distort the perceived relationship between the independent and dependent variables in a study.

• Confounding variables are factors that have no impact on the study's results.

• Confounding variables are always the independent variables in a study.

Answer explanation

Confounding variables can obscure the true relationship between independent and dependent variables, making it difficult to determine causation. The correct choice accurately describes their role in research.

Why is it important to control for confounding variables?

• To simplify the research process.

• It is important to control for confounding variables to ensure accurate and valid conclusions in research.

• To make the results more appealing to the audience.

• To increase the sample size needed for the study.

Answer explanation

It is important to control for confounding variables to ensure accurate and valid conclusions in research. This helps isolate the effect of the independent variable on the dependent variable, leading to more reliable results.

Give an example of a confounding variable.

• Diet is an example of a confounding variable.

• Weather conditions are an example of a confounding variable.

• Exercise is an example of a confounding variable.

• Sleep patterns are an example of a confounding variable.

Answer explanation

Diet can influence both health outcomes and behaviors, making it a confounding variable. It can affect the relationship between other variables, such as exercise and health, thus obscuring true effects.

What does representativeness mean in sampling?

• Representativeness refers to the size of the sample being large enough.

• Representativeness means selecting a sample randomly without any criteria.

• Representativeness indicates that the sample is taken from a single demographic group.

• Representativeness means that a sample accurately reflects the characteristics of the population.

Answer explanation

Representativeness means that a sample accurately reflects the characteristics of the population, ensuring that the findings can be generalized. This is crucial for valid research outcomes.

What is random sampling?

• Random sampling is a method where individuals are chosen based on their characteristics.

• Random sampling is a method of selecting a subset of individuals from a population where each individual has an equal chance of being chosen.

• Random sampling is a technique used to gather data from a single individual.

• Random sampling ensures that only the most qualified individuals are selected.

Answer explanation

The correct choice defines random sampling as selecting individuals from a population where each has an equal chance of being chosen, ensuring unbiased representation, unlike the other options which misrepresent the concept.

How does stratified sampling work?

• Stratified sampling is a method that only focuses on the largest subgroup within a population.

• Stratified sampling works by dividing a population into strata and sampling from each stratum.

• Stratified sampling involves selecting a single random sample from the entire population.

• Stratified sampling requires that all individuals in the population are sampled equally.

Answer explanation

Stratified sampling involves dividing a population into distinct subgroups, or strata, and then sampling from each stratum. This ensures that all subgroups are represented, making it a more accurate method than random sampling alone.

What is opportunity sampling?

• Opportunity sampling requires participants to be pre-screened for specific criteria.

• Opportunity sampling is based on demographic quotas.

• Opportunity sampling is a method of selecting participants based on their availability and willingness to participate.

• Opportunity sampling involves random selection of participants.

Answer explanation

Opportunity sampling selects participants based on their availability and willingness to participate, making it a convenient method for researchers. The other options describe different sampling techniques.

What is self-selected sampling?

• Self-selected sampling is a technique where participants are paid to join a study.

• Self-selected sampling is a method where participants are randomly chosen.

• Self-selected sampling is a method where participants volunteer to be part of a study.

• Self-selected sampling involves researchers selecting participants based on specific criteria.

Answer explanation

Self-selected sampling occurs when participants choose to volunteer for a study, making it distinct from methods that involve random selection or criteria-based selection.

Why is random sampling important for generalization?

• Random sampling is only necessary for small populations.

• Random sampling guarantees accurate results without bias.

• Random sampling is irrelevant to the research process.

• Random sampling is important for generalization because it creates a representative sample that reduces bias.

Answer explanation

Random sampling is crucial for generalization as it ensures the sample reflects the larger population, minimizing bias and allowing for more accurate conclusions about the entire group.

What is the main goal of using stratified sampling?

• To increase the sample size significantly.

• To focus solely on the largest subgroup.

• To ensure representation of specific subgroups in the sample.

• To eliminate all biases in sampling.

Answer explanation

The main goal of stratified sampling is to ensure representation of specific subgroups in the sample. This method allows researchers to capture the diversity within a population, leading to more accurate and reliable results.