quantitive research - experimental & non-experimental design

Question 1

Why is research design important for critical evaluation?

Answer 1

Research design determines whether the study can answer its questions without bias (internal validity), whether the results can be generalized to other contexts (external validity), and whether findings can be applied to real-life situations (ecological validity).

Question 2

What are the key aspects to evaluate in research?

Answer 2

When evaluating research, consider:

• Internal validity: Is the research free from bias?
• External validity: Can the findings be generalized to other populations or settings?
• Ecological validity: Can the findings be applied to real-world scenarios?

Question 3

What are the steps in the quantitative research process?

Answer 3

1. Generate a research question
2. Develop a hypothesis
3. Design a study
4. Obtain ethics approval
5. Collect data
6. Analyze data
7. Report results

Question 4

What is statistics anxiety?

Answer 4

Statistics anxiety is defined as a negative emotional state caused by encountering statistics. Around 15% of students worldwide report high levels of anxiety, which can impact learning and performance, although its effects are complex.

Question 5

How does statistics anxiety affect academic performance?

Answer 5

Studies show that statistics anxiety can negatively affect grades, but recent research (e.g., Trassi et al., 2022) suggests the relationship is influenced by individual factors such as cognitive disruption and coping strategies.

Question 6

What is the goal of participant sampling in research?

Answer 6

The goal is to select a sample that can represent a larger population (e.g., students, smokers, or athletes) so findings can be generalized. In practice, it’s rare to research the entire population.

Question 7

What is probability sampling?

Answer 7

Probability sampling involves selecting individuals with a known probability, ensuring a representative sample. It supports high external validity and allows for generalization to the broader population.

Question 8

What is non-probability sampling?

Answer 8

Non-probability sampling lacks known probabilities for selection, which can result in a non-representative sample. This can lead to lower external validity and generalizability.

Question 9

What is random sampling?

Answer 9

Random sampling gives every individual in the population an equal chance of being selected. While ideal for representing a population, it’s rare in psychology due to practical constraints and research focus on testing theories rather than generalizing.

Question 10

What is convenience sampling?

Answer 10

Convenience sampling involves selecting participants based on their availability. It is commonly used in psychological research, particularly with psychology student samples, but has low external validity due to potential bias.

Question 11

What is sampling bias?

Answer 11

Sampling bias occurs when certain groups in the population are more likely to be selected than others, which undermines external validity and limits generalizability. It’s more common in non-probability sampling methods.

Question 12

What are experimental methods used for?

Answer 12

Experimental methods test causal hypotheses by manipulating an independent variable (predictor) and measuring the effect on a dependent variable (outcome), allowing researchers to infer cause and effect relationships.

Question 13

What is operationalization in experiments?

Answer 13

Operationalization involves defining variables in measurable terms. The independent variable (predictor) is manipulated, and the dependent variable (outcome) is measured, with specific conditions set for each.

Question 14

What is an example of operationalizing a predictor in an experiment?

Answer 14

In a study on exam duration and statistics anxiety:

• Independent variable (predictor): Exam duration
• Conditions: 30-minute vs. 2-hour exam
• Dependent variable (outcome): Level of statistics anxiety measured by self-report scales or physiological responses.

Question 15

How do researchers measure outcomes in experiments?

Answer 15

Outcomes (dependent variables) are measured through tasks, behavioral observations, or self-reports (e.g., anxiety scales, reaction times). The researcher does not manipulate the outcome but observes its changes based on the independent variable.

Question 16

What is the difference between between- and within-participant designs?

Answer 16

• Between-participant designs: Different participants are assigned to different conditions. This avoids order effects but may introduce individual differences.
• Within-participant designs: The same participants experience all conditions, which eliminates individual differences but may introduce order effects.

Question 17

What is random allocation in experiments?

Answer 17

Random allocation ensures that participants have an equal chance of being assigned to any experimental condition, helping to control for individual differences and improve the validity of comparisons between groups.

Question 18

What are order effects in within-participant designs?

Answer 18

Order effects occur when the sequence of conditions influences participant behavior, e.g., practice effects (better performance in later conditions) or fatigue effects (worse performance due to exhaustion). These can be controlled by counterbalancing.

Question 19

What is counterbalancing in experimental design?

Answer 19

Counterbalancing is used in within-participant designs to control for order effects by varying the order of conditions for different participants. This ensures that each condition is tested equally across participants.

Question 20

What are design confounds in experiments?

Answer 20

Design confounds are variables other than the manipulated predictor that could affect the outcome, such as inconsistencies in testing conditions (e.g., different exam questions or test days), leading to invalid conclusions.

Question 21

How do confounds impact experimental validity?

Answer 21

Confounds can distort the cause-and-effect relationship by introducing alternative explanations for results. Controlling for confounds is essential for drawing valid conclusions about the effects of the predictor variable.

Question 22

What are demand characteristics in experiments?

Answer 22

Demand characteristics are cues that influence participants’ behavior based on their interpretation of the study’s purpose. These can lead to biased results if participants alter their behavior to meet perceived expectations.

Question 23

What are experimenter effects?

Answer 23

Experimenter effects occur when the researcher’s behavior unintentionally influences the participants’ responses, potentially introducing bias. This can be controlled using single-blind or double-blind procedures.

Question 24

What is the difference between a true experiment and a non-experimental method?

Answer 24

A true experiment involves random allocation and manipulation of independent variables to infer causal relationships. Non-experimental methods, such as correlational studies or quasi-experiments, lack these features and can’t establish cause-and-effect conclusions.

Question 25

What is a quasi-experiment?

Answer 25

A quasi-experiment lacks random allocation but still involves a predictor and outcome. It is used when random assignment isn’t feasible (e.g., in natural settings) but can still suggest causal relationships based on pre-post designs.

Question 26

What is a natural experiment?

Answer 26

A natural experiment takes advantage of naturally occurring conditions to study their effects, such as differences between groups (e.g., high vs. low anxiety students). It has high ecological validity but cannot establish causality due to lack of control over variables.

Question 27

What is correlational research?

Answer 27

Correlational research measures the relationship between two or more variables without manipulation. It is useful for identifying patterns but does not establish causality, as correlation does not imply causation.

Question 28

What makes a perfect research design?

Answer 28

A perfect design includes random sampling, careful consideration of confounds, and thoughtful operationalization. However, no research design is perfect, and compromises are often made, with limitations clearly acknowledged.

Question 29

Why are limitations important in research design?

Answer 29

Acknowledging limitations, such as using non-experimental methods, helps researchers avoid over-generalizing findings and supports transparent, honest evaluation of the research’s validity and applicability.