연구 설계 및 통계 해석
도입됨| English | 의미 |
|---|---|
| random assignment/ˈrændəm əˈsaɪnmənt/ | 무작위 배정 |
| interaction effect/ˌɪntəˈrækʃn ɪˈfekt/ | 상호작용 효과 |
A decision before an answer
- Random assignment and random sampling sound alike but licence completely different conclusions.
- Your goal: Classify designs by assignment and measurement structure.
Classify the design
- A true experiment manipulates an independent variable with random assignment of participants to conditions; between-subjects designs give different people to each condition, within-subjects designs measure the same people in every condition and need counterbalancing. A quasi-experiment lacks full assignment, and a correlational study does not manipulate an independent variable. Correlation alone does not establish causation or temporal order.
- Random assignment supports internal validity (causal inference); random sampling supports external validity (generalisability). They answer different questions.
Each participant learns a list using exactly one of three rehearsal strategies. The design is:
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Different participants serve in each condition, with the strategy manipulated between them.
Match the test
- Match analysis to design, measurement scale and assumptions. Two independent quantitative groups may suit an independent-samples t-test; paired data require a paired analysis.
- Factorial designs often use analysis of variance; categorical counts may suit a chi-square test when expected-count assumptions hold. Correlation or regression examines specified associations; neither alone establishes causation.
Counts of students choosing each of three majors, broken down by gender, are best analysed with:
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The data are categorical counts in a 2×3 table, df=(2−1)(3−1)=2.
Read the effects
- A main effect averages one factor across the other factor. An interaction means one effect depends on the other factor.
- Nonparallel sample means suggest an interaction pattern, but variability and the design are needed for population inference. Interactions can exist when averaged main effects cancel; lack of significance is not proof of no effect.
Fictional mean scores are: task A, novices 4 and experts 8; task B, novices 8 and experts 4. Compute marginal means and describe the pattern. Novice marginal mean=(4+8)/2=6; expert marginal mean=(8+4)/2=6. Task A marginal mean=(4+8)/2=6; task B marginal mean=(8+4)/2=6. The task contrast reverses by expertise, an interaction pattern despite equal marginal means. Means alone do not establish a population interaction; variability, design and inference are needed.
A 2×3 contingency table has ____ degrees of freedom.
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(rows−1)(columns−1)=(2−1)(3−1)=2.
Weigh the evidence
- Mean age 26 with most students 25 or younger is compatible with right skew, but does not uniquely determine distribution shape. Statistical significance is not effect size.
- A p-value is a probability of results at least as extreme under the specified null model and assumptions, not the probability the null is true. Type I and Type II errors depend on design and decision thresholds.
Drawing a causal conclusion from a correlational design, or reporting a significant interaction as if it were a main effect of one factor.
다음 중 이 사례에 부합하는 답안은 무엇입니까?
할당 및 측정 구조에 따라 연구 설계를 분류함
Random sampling from a population is what licenses a causal conclusion.
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Random assignment licenses causal inference; random sampling supports generalisability.
Keep the distinctions
- interaction effect 交互作用 — The dependence of one factor's effect on the level of another factor.
- random assignment 随机分配 — Allocating participants to conditions by chance to support causal inference.
- Classify designs by assignment and measurement structure.
- Choose the statistical test that matches design and data type.
- Interpret main effects, interactions and distributional evidence.
Match each term with its precise meaning in this lesson.
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Keep the distinctions stated in the teaching example.
Put this lesson’s reasoning or event sequence in order.
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The order follows the stated process; check each stage before the next.