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SPSS and SAS procedures for estimating indirect effects in simple mediation models
Open accessAbstract
Researchers often conduct mediation analysis in order to indirectly assess the effect of a proposed cause on some outcome through a proposed mediator. The utility of mediation analysis stems from its ability to go beyond the merely descriptive to a more functional understanding of the relationships among variables. A necessary component of mediation is a statistically and practically significant indirect effect. Although mediation hypotheses are frequently explored in psychological research, formal significance tests of indirect effects are rarely conducted. After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approach advocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature. Electronic copies of these macros may be downloaded from the Psychonomic Society's Web archive at www.psychonomic.org/archive/.
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“one can argue that our study results demonstrate mediation only according to the original Baron and Kenny approach, but not according to more recent discussions [74,75], which would require an independent interaction term to be significant.”
From Exploring emotional well-being, spiritual, religious and personal beliefs and telomere length in chronic pain patients—A pilot study with cross-sectional design (2024) · cites this paper as [74]
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“The multiple mediation analysis used 1,000 bootstrap samples and 95 % bias-corrected confidence intervals (CI) around the standardised estimate of the effect as recommend by Preacher and Hayes (2004).”
From The role of experiential avoidance and cognitive fusion in the development of anxiety symptoms among family carers of people with dementia (2024) · cites this paper as Preacher and Hayes (2004)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Mediation was deemed as significant if the 95% bias corrected and accelerated (BCa) bootstrap confidence intervals (CI) for the indirect effects based on 20000 bootstrapped samples did not include zero (see Preacher & Hayes, 2004).”
From The relationship between low levels of mindfulness skills and pathological worry: The mediating role of psychological inflexibility. [La relación entre habilidades de mindfulness y preocupación patológica: El rol mediador de la inflexibilidad psicológica] (2014) · cites this paper as Preacher & Hayes, 2004
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“Bootstrapping is a nonparametric procedure that acknowledges this fact [61, 62].”
From Acceptance as a Mediator for Change in Acceptance and Commitment Therapy for Persons with Chronic Pain? (2016) · cites this paper as [61]
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Metadata
- Authors
- Preacher, K. J., & Hayes, A. F.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- The Ohio State University (US)
- Corresponding author
- Preacher, Kristopher J.
- Venue
- Behavior Research Methods, Instruments, & Computers
- Published
- 2004-11
- Volume
- 36
- Issue
- 4
- Pages
- 717-731
- ISSN
- 0743-3808 (ISSN-L)
- 1532-5970
- OpenAlex
- W1995031937
- Semantic Scholar
- 8432aa15730df7432e1ca82ce48f0a90da2a86ec
- Cited by
- 17,383
- References
- 41
- Citations by year
- 2026: 642 · 2025: 893 · 2024: 965 · 2023: 974 · 2022: 1187 · 2021: 1157 · 2020: 1100 · 2019: 1104 · 2018: 1149 · 2017: 1053 · 2016: 1089 · 2015: 1089 · 2014: 1063 · 2013: 988 · 2012: 840
- FWCI
- 84.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods, Instruments, & Computers (publishedVersion)
- PubMed (publishedVersion)
- KU ScholarWorks (University of Kansas) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- mediation analysis, indirect effects, bootstrap confidence intervals, SAS macro
- MeSH terms
- Humans, Models, Psychological, Psychology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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