Performing Contrast Analysis in Factorial Designs
From NHST to Confidence Intervals and Beyond
Bibliographic Data
| ID | 20282249 |
|---|---|
| Authors | Stefan Wiens (0000-0003-4531-4313, Stockholm University, corresponding author), Monica E Nilsson (0000-0002-2081-7144, Stockholm University), Mats E Nilsson (Stockholm University) |
| Year | 2017 |
| Volume | 77 |
| Issue | 4 |
| Pages | 690-715 |
| Publication date | 2017-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164416668950 |
| PMID | 29805179 |
| OpenAlex | W2528813378 |
| Language | EN |
| Citations received | 4 |
| References cited | 91 |
Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful because it can be used to test specific questions of central interest in studies with factorial designs. It weighs several means and combines them into one or two sets that can be tested with t tests. The effect size produced by a contrast analysis is simply the difference between means. The CI of the effect size informs directly about direction, hypothesis exclusion, and the relevance of the effects of interest. However, any interpretation in terms of precision or likelihood requires the use of likelihood intervals or credible intervals (Bayesian). These various intervals and even a Bayesian t test can be obtained easily with free software. This tutorial reviews these methods to guide researchers in answering the following questions: When I analyze mean differences in factorial designs, where can I find the effects of central interest, and what can I learn about their effect sizes
Bayesian probability · Confidence interval · Contrast (vision) · Econometrics · Factorial · Factorial analysis · Factorial experiment · Fractional factorial design · Interpretation (philosophy) · Relevance (law) · Statistical hypothesis testing · Statistics · Test (biology) · Artificial Intelligence · Computer Science · Mathematics · Meta-analysis and systematic reviews · Optimal Experimental Design Methods · Psychology · Statistical Methods in Clinical Trials
Timing matters! Explaining between study phases enhances students’ learning
Is auditory awareness negativity confounded by performance
Auditory awareness negativity is an electrophysiological correlate of awareness in an auditory threshold task
Who brings you up when you're feeling down? Distinct implications of dispositional empathy versus situationally-prompted empathic mindsets for targets' affective experience in face-to-face interpersonal interaction
An Introduction to the Bootstrap
A practical solution to the pervasive problems ofp values
How Bayes factors change scientific practice
Statistical Evidence in Experimental Psychology
Using confidence intervals in within-subject designs
Bayesian Estimation and Inference
Bayesian hypothesis testing for psychologists
Scientific method
The ASA Statement on p -Values
Comfortably Numb
Effect size, confidence interval and statistical significance
Effect size estimates
Bayesian t tests for accepting and rejecting the null hypothesis
The fallacy of placing confidence in confidence intervals
Improving the Dependability of Research in Personality and Social Psychology
Some Practical Guidelines for Effective Sample Size Determination
A Gentle Introduction to Bayesian Analysis
Bayesian Versus Orthodox Statistics
Bayes Factors
The New Statistics
Alphabet Soup
Confidence Intervals for Effect Sizes in Analysis of Variance
Confidence Intervals Make a Difference
Practical Significance
Inference by Eye
Statistical methods in psychology journals
Why psychologists must change the way they analyze their data
Publication Manual of the American Psychological Association
Mindless statistics
Using Bayes to get the most out of non-significant results
Standardized or simple effect size
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,57 |
| Citation span | 2019 - 2020 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |