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Quizzes > Science

Psychology Research Methods Quiz: Test Design, Data, and Ethics

Quick, free research methods quiz to test your knowledge. Instant results.

Editorial: Review CompletedCreated By: Laurie CluffUpdated Aug 27, 2025
Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art depicting elements related to Psychology Research Methods Quiz

This quiz helps you check your grasp of psychology research methods, from study design and sampling to data analysis and ethics. Use it to review before a test or warm up for class. For more practice, take the scientific method practice quiz or build general knowledge with a psychology quiz.

In an experiment on memory, what is the operational definition of recall?
The number of words a participant correctly writes from a studied list within 60 seconds (a specific measurable procedure)
How well a participant thinks they remember the words (a subjective feeling)
The importance of the words to the participant (personal relevance)
The general concept of memory ability (a broad construct)
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Which variable is deliberately manipulated by the researcher in a true experiment?
Dependent variable (the measured outcome)
Independent variable (the factor the researcher changes to test its effect)
Control variable (held constant)
Confounding variable (an unwanted influence)
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Random assignment ensures that each participant has an equal chance of being placed in any experimental condition.
False
True
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What is the primary purpose of a control group in experimental research?
To provide a baseline for comparison with the experimental group (isolates the effect of the manipulation)
To guarantee external validity
To increase the sample size without affecting design
To ensure participants are blind to hypotheses
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In a double-blind study, neither participants nor data collectors know which condition participants are in.
False
True
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The placebo effect refers to which phenomenon?
Bias introduced by the researcher during analysis
Random fluctuations around the mean
Improvement due to expectations rather than the active treatment (expectancy-driven change)
Worsening due to side effects of the active drug
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Random selection from the population increases internal validity.
False
True
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A confounding variable is best described as which of the following?
Any variable measured as an outcome
A variable that varies with the independent variable and can explain away the effect (threatens causal inference)
A nuisance variable that has no relation to the outcome
A variable that is statistically controlled after data collection
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Reliability is necessary but not sufficient for validity.
False
True
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Which threat primarily compromises the ability to infer causality in an experiment?
Low internal validity (alternative explanations remain)
Low external validity (limited generalizability)
High statistical power
High reliability
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Test-retest reliability assesses the stability of scores over time when the construct is assumed to be stable.
True
False
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Ecological validity is the same as external validity.
True
False
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A Likert-type item typically uses semantic differential scales anchored by opposing adjectives.
False
True
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Increasing statistical power is best achieved by reducing alpha from .05 to .01.
False
True
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A p-value is the probability of the null hypothesis being true given the observed data.
False
True
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Which interpretation of a 95 percent confidence interval for a mean difference is most appropriate?
The null hypothesis is false with 95 percent probability
If we repeated the study many times, about 95 percent of such intervals would contain the true mean difference (long-run frequency)
Exactly 95 percent of individuals show the mean difference
There is a 95 percent chance the true mean difference equals the observed mean difference
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Counterfactual reasoning in causal inference considers what would have happened to the same unit under a different treatment condition.
False
True
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Missing completely at random (MCAR) means the probability of missingness is unrelated to observed or unobserved data.
True
False
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Balance in block randomization is achieved by assigning participants in fixed-size blocks to conditions in random order.
True
False
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Manipulating multiple dependent variables in one study always increases internal validity.
True
False
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Learning Outcomes

  1. Analyse research designs and choose appropriate methodologies
  2. Evaluate sampling techniques and assess their impact on validity
  3. Demonstrate understanding of ethical considerations in studies
  4. Apply statistical reasoning to interpret experimental data
  5. Identify key variables and operational definitions in experiments

Cheat Sheet

  1. Understand the scientific method - Dive into the step-by-step process scientists use to ask questions, form hypotheses, run experiments, and draw conclusions. It's like solving a mystery with repeatable clues!
  2. Differentiate between research methods - Get to know descriptive techniques like surveys and case studies, and compare them to experimental setups that test cause-and-effect. Knowing when to observe vs. manipulate variables is key to solid findings.
  3. Grasp ethical considerations - Research isn't just about data; it's about people. Learn why informed consent, confidentiality, and minimizing harm are non-negotiable.
  4. Master sampling techniques - Sampling decides how well your study represents the real world. Explore random, stratified, and convenience sampling to avoid skewed results.
  5. Identify variables and operational definitions - Clearly define what you measure and how you measure it so others can replicate your study. Precision here turns vague ideas into concrete data points.
  6. Apply statistical reasoning - Use descriptive stats to summarize your data and inferential stats to make predictions about a larger group. Statistics turn raw numbers into compelling stories.
  7. Recognize the importance of reliability and validity - Reliable tools give consistent results; valid tools measure exactly what they're supposed to. Balancing both ensures your findings stand up to scrutiny.
  8. Understand the role of control groups - Control groups act as your "what if nothing happened" benchmark, helping you isolate the true effect of your variables. They're the silent heroes of solid experiments!
  9. Be aware of potential biases - Spot errors like sampling bias or experimenter bias before they sneak into your results. A sharp eye on bias keeps your research honest and reliable.
  10. Interpret data accurately - Crunch the numbers with the right statistical methods to draw meaningful conclusions. Accurate interpretation bridges the gap between raw results and real-world insights.
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