Statistical mediation analysis with a multicategorical independent variable
Bibliographic Data
| ID | 23313425 |
|---|---|
| Authors | Andrew F Hayes (0000-0002-8329-1696, Department of Psychology The Ohio State University Columbus Ohio USA, corresponding author), Kristopher J Preacher (0000-0003-4099-3636, Department of Psychology and Human Development Vanderbilt University Nashville Tennessee USA) |
| Year | 2014 |
| Volume | 67 |
| Issue | 3 |
| Pages | 451-470 |
| Publication date | 2014-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Mathematical and Statistical Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1102 • E-ISSN: 2044-8317 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bmsp.12028 |
| PMID | 24188158 |
| OpenAlex | W1984426948 |
| Language | EN |
| Citations received | 734 |
| References cited | 64 |
Virtually all discussions and applications of statistical mediation analysis have been based on the condition that the independent variable is dichotomous or continuous, even though investigators frequently are interested in testing mediation hypotheses involving a multicategorical independent variable (such as two or more experimental conditions relative to a control group). We provide a tutorial illustrating an approach to estimation of and inference about direct, indirect, and total effects in statistical mediation analysis with a multicategorical independent variable. The approach is mathematically equivalent to analysis of (co)variance and reproduces the observed and adjusted group means while also generating effects having simple interpretations. Supplementary material available online includes extensions to this approach and Mplus, SPSS , and SAS code that implements it.
Analysis of variance · Econometrics · Inference · Mediation · Simple (philosophy) · Statistical analysis · Statistical hypothesis testing · Statistical inference · Statistics · Variable (mathematics) · Variables · Variance (accounting) · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mathematics
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| Unique citing works | 734 |
|---|---|
| Citations per year | 56,46 |
| Citation span | 2013 - 2026 (14) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 645 |