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DTSTAMP:20201031T071455Z
SUMMARY:Interactions in Linear Regression Analysis
DESCRIPTION:Taught by Andrew Hayes, Ph.D. \nThe specification and in
terpretation of interactions is one of the more confusing and problema
tic areas of regression analysis. Two variables X and W interact in ex
plaining some outcome Y if the effect of X on Y depends on the value o
f W. Interaction is also called moderation. If X’s effect on Y depen
ds on W, then W is a moderator of the effect of X on Y.\nThe identific
ation and analysis of moderators is important in nearly all areas of s
cience. Is psychotherapy more effective in treating depression when co
mbined with an anti-depressive drug or when used by itself? Does a mar
keting campaign increase sales more among customers who are loyal to t
he brand or among those who are not? Does watching The Daily Show incr
ease knowledge of current political events more for people who are int
erested in politics or those who are not? These are all questions abou
t whether one variable’s effect is moderated by another.\nMany resea
rchers make fundamental errors in specifying and interpreting interact
ions. During their statistics training, most researchers are exposed t
o factorial analysis of variance, and it is in this context that conce
pt of interaction is often introduced. But ANOVA is just a special cas
e of linear regression with X and W as categorical variables. Research
ers familiar with ANOVA but not the more general analysis of interacti
on in linear regression often resort to undesirable practices when the
ir X or W (or both) is a continuum, such as categorizing the data prio
r to analysis.\nBy the end of this class, students will understand the
analysis of interaction in linear regression and be able to use it in
their own research. The course covers two-way interaction between con
tinuous and dichotomous variables, between two continuous variables, a
nd between multicategorical (i.e., more than two categories) and conti
nuous variables. Also included are methods for visualizing interaction
s, and methods of probing interactions such as the “pick-a-point”
approach (also called “simple slopes” or “spotlight” analysis)
and the Johnson-Neyman technique (also called a “floodlight” anal
ysis).\nWith the two-way case covered, the course shifts to models wit
h more than one moderator, including “moderated-moderation”, or th
ree-way interaction. The estimation and interpretation of models with
multiple moderators, including visualization and probing of three-way
interactions is the focus of this part of the course. Also covered is
the comparison of conditional effects (“simple slopes”) defined by
different values of two moderators.\nComputer applications will inclu
de the use of SPSS’s regression routine as well as SAS’s PROC REG
but will emphasize the PROCESS macro for SPSS and SAS developed by the
instructor that greatly simplifies the analysis, probing, and visuali
zation of interactions and that aids interpretation.\nThis is a hands-
on course with many opportunities for participants to practice the met
hods they learn.\n\nFor more information visit https://www.datascience
central.com/events/interactions-in-linear-regression-analysis-2
DTSTART;TZID=Pacific/Gambier:20160513T090000
DTEND;TZID=Pacific/Gambier:20160514T170000
CATEGORIES:training, seminar
LOCATION:Temple University Center City
WEBSITE:http://statisticalhorizons.com/seminars/public-seminars/intera
ctions-spring-2016
URL:http://statisticalhorizons.com/seminars/public-seminars/interactio
ns-spring-2016
CONTACT:610-642-1941
ORGANIZER;CN="Statistical Horizons LLC":https://www.datasciencecentral
.com/profile/MaryKiely
ATTACH;FMTTYPE="image/jpeg":
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=ACCEPTED;RSVP=TRUE;CN="Statisti
cal Horizons LLC":https://www.datasciencecentral.com/profile/MaryKiely
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