Podcast
Questions and Answers
Non-parametric analyses rely on many rules and assumptions.
Non-parametric analyses rely on many rules and assumptions.
False (B)
Parametric analyses are preferred when possible.
Parametric analyses are preferred when possible.
True (A)
Independence of observations refers to measurements influencing each other.
Independence of observations refers to measurements influencing each other.
False (B)
Which of the following is not a requirement for parametric analyses?
Which of the following is not a requirement for parametric analyses?
Factorial ANOVAs are generally robust to minor deviations from normality.
Factorial ANOVAs are generally robust to minor deviations from normality.
What is the appropriate alternative test for a one-way ANOVA if the assumptions are not met?
What is the appropriate alternative test for a one-way ANOVA if the assumptions are not met?
Which test should you use for comparing the mean difference scores of a repeated measures design when the assumptions of normality are not met?
Which test should you use for comparing the mean difference scores of a repeated measures design when the assumptions of normality are not met?
Non-parametric tests use the median to account for extreme values that can skew the mean.
Non-parametric tests use the median to account for extreme values that can skew the mean.
What test is used to check for homogeneity of variance?
What test is used to check for homogeneity of variance?
Which test is appropriate for comparing the medians of two independent groups?
Which test is appropriate for comparing the medians of two independent groups?
Which test is appropriate for comparing the medians of three or more independent groups?
Which test is appropriate for comparing the medians of three or more independent groups?
Which test is appropriate for comparing the medians of two related groups, such as pre-test and post-test scores?
Which test is appropriate for comparing the medians of two related groups, such as pre-test and post-test scores?
Which test is appropriate for comparing the medians of three or more related groups, such as measuring performance at multiple time points?
Which test is appropriate for comparing the medians of three or more related groups, such as measuring performance at multiple time points?
Flashcards
Non-parametric Analyses
Non-parametric Analyses
Statistical tests that don't require strict assumptions about the data distribution, making them suitable for non-normal data or when the data doesn't fit a specific distribution.
Parametric Analyses
Parametric Analyses
Statistical tests relying on specific assumptions regarding the data's distribution, often requiring normally distributed data.
Independence of Observations
Independence of Observations
The assumption that each data point is independent of the others. No influence of one measurement on another.
Interval or Ratio Data
Interval or Ratio Data
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Categorical or Ordinal Data
Categorical or Ordinal Data
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ANOVA Model Robustness
ANOVA Model Robustness
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Alternatives for One-Way ANOVAs and t-tests
Alternatives for One-Way ANOVAs and t-tests
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t-test Normality Assumption
t-test Normality Assumption
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Independent Variable
Independent Variable
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Dependent Variable
Dependent Variable
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ANOVA for Multi-Group Comparisons
ANOVA for Multi-Group Comparisons
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Evaluating ANOVA Model Assumptions
Evaluating ANOVA Model Assumptions
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Non-Parametric Alternatives for Non-Normal Data
Non-Parametric Alternatives for Non-Normal Data
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Median as a Central Tendency
Median as a Central Tendency
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Homogeneity of Variance
Homogeneity of Variance
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Levene's Test
Levene's Test
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Kruskal-Wallis Test
Kruskal-Wallis Test
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Wilcoxon Signed-Rank Test
Wilcoxon Signed-Rank Test
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Mann-Whitney U Test
Mann-Whitney U Test
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Study Notes
Non-Parametric Approaches
- Non-parametric analyses do not rely on many assumptions, making them less complex
- They are less useful for intricate study designs.
- Parametric analyses are generally preferred when possible
- Parametric analyses assume independence of observations (measurements do not influence each other).
- Parametric analyses require interval or ratio level data, which are measured on a scale with meaningful intervals.
- Non-parametric analyses are useful when data is categorical or ordinal, using ranks instead of actual scores.
Choosing the Right Test
- Factorial ANOVAs are generally robust to deviations from normality.
- For one-way ANOVAs and t-tests, alternative non-parametric tests can be used to handle skewed data.
- The t-test looks at normally distributed independent data. Repeated measures t-tests consider the differences.
- Example of research questions that might use non-parametric approaches: How many hours do students spend revising for psychology, geography or biology exams.
Non-Parametric Tests for Specific Designs
- Kruskal-Wallis, Wilcoxon, Mann-Whitney are non-parametric tests for various designs, as shown in the flow chart.
- These tests are utilized when the assumptions of parametric tests are not met.
- The chart guides researchers to choose the appropriate tests based on the design (e.g., 2 conditions vs. 3+ conditions; independent or repeated measures).
- The appropriate tests are recommended after calculating the median and using appropriate post-hoc analysis tests (Mann-Whitney for Kruskal-Wallis, Wilcoxon Signed-Rank).
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Description
This quiz covers non-parametric approaches in statistical analysis, highlighting their assumptions and comparison to parametric methods. It explores when to choose non-parametric tests over traditional tests like ANOVA and t-tests, especially in handling different data types. Enhance your understanding of statistical methodologies with practical examples.
