Confounding in statistical mediation analysis : (Record no. 79022)

MARC details
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fixed length control field 02373nam a2200229Ia 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180926s2017 xx 000 0 und d
040 ## - CATALOGING SOURCE
Transcribing agency MANILA TYTANA COLLEGES LIBRARY
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Valente, Matthew J.
245 #0 - TITLE STATEMENT
Title Confounding in statistical mediation analysis :
Remainder of title what it is and how to address it /
Statement of responsibility, etc. Matthew J. Valente, William E. Pelham III, Heather Smyth, David P. MacKinnon
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Date of publication, distribution, etc. November 2017
336 ## - CONTENT TYPE
Content type term text
337 ## - MEDIA TYPE
Media type term unmediated
338 ## - CARRIER TYPE
Carrier type term volume
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Number of part/section of a work 64 : 6, page 659-671
Title Journal of Counseling Psychology
520 ## - SUMMARY, ETC.
Summary, etc. Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last 2 decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcomes framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator-outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses.
521 ## - TARGET AUDIENCE NOTE
Target audience note Psychology.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Causal inference.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Confounder adjustment.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mediation.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Articles
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Cataloger's initials, CIN (RLIN) 82480
First Date, FD (RLIN) 140843
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Date last seen Price effective from Koha item type
    Library of Congress Classification     Manila Tytana Colleges Library Manila Tytana Colleges Library REFERENCE SECTION 09/26/2018   09/26/2018 09/26/2018 Articles
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