An Introduction and Illustration of Bayesian Modeling in Couple and Family Therapy Research.
Bayesian modeling is becoming increasing popular as a method for data analyses in the social sciences and can move couple, marriage, and family therapy (C/MFT) research forward. Bayesian modeling helps researchers better understand the uncertainty of findings and incorporate previous research into a...
| Publicado en: | Journal of Marital & Family Therapy Vol. 46; no. 4; pp. 620 - 638 |
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| Autores principales: | , |
| Formato: | journal article |
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Wiley-Blackwell
Oct2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=146808760&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 146808760 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0194472X JMF jtl: Journal of Marital & Family Therapy issn: 0194472X maglogo: Y pubinfo: dt: Oct2020 vid: 46 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 146808760 10.1111/jmft.12461 ppf: 620 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 431KB tig: atl: An Introduction and Illustration of Bayesian Modeling in Couple and Family Therapy Research. aug: au: Johnson, Lee N. Baldwin, Scott A. affil: Brigham Young University su: Family psychotherapy Couples therapy Family research Psychology Interpersonal relations Data structures Data analysis Experimental design Mathematical models Statistical models Probability theory sug: subj: Family psychotherapy Couples therapy Family research Psychology Interpersonal relations Data structures Data analysis Experimental design Mathematical models Statistical models Probability theory ab: Bayesian modeling is becoming increasing popular as a method for data analyses in the social sciences and can move couple, marriage, and family therapy (C/MFT) research forward. Bayesian modeling helps researchers better understand the uncertainty of findings and incorporate previous research into analyses. Other benefits of Bayesian modeling are the straightforward interpretation of findings, high-quality inferences even with small samples (in combination with an informative prior), and the ability to work with complex data structures (observations nested in relationships and time points) which are common in C/MFT research. This article introduces the benefits of Bayesian modeling and provides an example of an Actor-Partner Interdependence Model using R. Information on how to conduct the same analyses using Stata and MPlus is provided in the Supplemental Information. pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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