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Questions and Answers
What characterizes a Type I error in hypothesis testing?
What characterizes a Type I error in hypothesis testing?
- Detecting an effect that does not exist (correct)
- Rejecting the null hypothesis when it is true (correct)
- Failing to notice an effect that actually exists
- Failing to reject the null hypothesis when it is false
Which distinguishes between random selection and convenience sampling?
Which distinguishes between random selection and convenience sampling?
- Random selection involves every individual having an equal chance of being chosen. (correct)
- Random selection is more accurate than convenience sampling.
- Random selection offers a more biased result compared to convenience sampling.
- Random selection does not require sampling from an identified population.
In a factorial design, which aspect does NOT typically describe its structure?
In a factorial design, which aspect does NOT typically describe its structure?
- Factors refer to the independent variables in the study.
- Each cell in a factorial design must contain identical outcomes. (correct)
- Cells refer to the combinations of the factor levels.
- Levels represent the different conditions for each factor.
Which of the following designs would be classified as a true experiment?
Which of the following designs would be classified as a true experiment?
What is a common feature of both quasi-experiments and correlational designs?
What is a common feature of both quasi-experiments and correlational designs?
What does a mixed or 'split-plot' design in factorial experiments refer to?
What does a mixed or 'split-plot' design in factorial experiments refer to?
Flashcards
Type I Error
Type I Error
A false positive, where an effect is detected in the experiment but there is actually no real effect.
Type II Error
Type II Error
A false negative, where an effect in the experiment is not detected, even though it was really there.
Random Selection
Random Selection
A method where every individual in the population has an equal chance of being included in the sample.
Random Assignment
Random Assignment
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Factorial Design
Factorial Design
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Main Effect
Main Effect
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Study Notes
Type I and Type II Errors
- Type I error (α): A false positive; it looks like there is an effect, but there isn't.
- Type II error (β): A false negative; it looks like there is no effect, but there actually is.
Sampling Methods
- Random selection: Choosing participants randomly from a population.
- Convenience sampling: Selecting participants based on ease of access.
Factorial Designs
- Factorial designs involve multiple independent variables (factors).
- Within-subjects design: Participants are measured under all levels of all factors.
- Between-groups design: Different groups of participants are measured for each factor level.
- Mixed (split-plot) design: A combination of within- and between-subjects designs.
- Analyze for effects of individual factors and interactions between factors.
- Analyze how many independent variables, how many levels within each independent variable, and the total number of data points.
Research Designs
- True experiments: Manipulate an independent variable and measure the effect on a dependent variable, ideally using random assignment.
- Quasi-experiments: Similar to true experiments but without random assignment.
- Correlational studies: Explore relationships between variables without manipulating any variable.
- Observational studies: Observe and record data without manipulating any variable.
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