* Encoding: UTF-8.
*this document contains all syntax for reproducing analyses in Study 2 from Gainsburg & Earl, 2018*

*Statistics about our sample*

*filtering out those who said English was not first language, those who did not make it to random asssignment,
 those who did not consent, and those in the unused condition."
 
COMPUTE filter_sample=(IncludeEnglish=1 & IncludeConsent=1&IncludeRandom=1&Cond>0).
VARIABLE LABELS filter_sample 'people in sample of interest'.
VALUE LABELS filter_sample 0 'Not Selected' 1 'Selected'.
execute.
FILTER BY filter_sample.
EXECUTE.

*seeing how many people were excluded due to not making a choice*
*There were 289 participants in this subgroup. 13 did not makie a choice, with 276 remaining*

Frequencies IncludeChoice.

*Creating a new filter for people who were in samplee of interest, spoke english, and made it to 
question about their definition*

USE ALL.
FILTER BY filter_include.
EXECUTE.

*Demographics:

*White Ps: 200.

if (race_3=1) White=1.
if (race_1=1) White=0.
if (race_2=1) White=0.
if (race_4=1) White=0.
if (race_5=1) White=0.
if (race_6=1) White=0.
if (race_7=1) White=0.
if (race_8=1) White=0.
if (race_9=1) White=0.
variable labels white 'Are they white or nonwhite'.
execute.
Frequencies White.

*Black Ps:25*

if (race_1=1) black=1.
if (race_3=1) Black =0.
if (race_2=1) black=0.
if (race_4=1) black=0.
if (race_5=1) black=0.
if (race_6=1) black=0.
if (race_7=1) black=0.
if (race_8=1) black=0.
if (race_9=1) black=0..
variable labels black 'Are they black or nonblack'.
execute.
Frequencies Black.

*Hispanic/Latino Ps: 8*

if (race_5=1) latino =1.
if (race_2=1) latino=0.
if (race_4=1 )latino=0.
if (race_3=1 )latino=0.
if (race_6=1) latino=0.
if (race_7=1) latino=0.
if (race_8=1) latino=0.
if (race_9=1) latino=0.
if (race_1=1) latino=0..
variable labels latino 'Are they latino'.
execute.
frequencies latino.

*Asian-american Ps: 18*

if (race_4=1) asian=1.
if (race_7=1) asian=0.
if (race_6=1) asian=0.
if (race_8=1) asian=0.
if (race_3=1) asian =0.
if (race_1=1) asian=0.
if (race_2=1) asian=0.
if (race_5=1) asian=0.
if (race_9=1) asian=0.
variable labels asian 'Are they asian or nonasian'.
execute.
Frequencies Asian.

*multi-racial or other: 14*
*missing: 11 here and in all others*

compute coded_race = sum(white,asian,latino,black).
variable labels coded_race 'People who are 1 are one of the races we coded; 0 are mixed or other'.
execute.
Frequencies coded_race.

*Gender
Male: 129
Female 133
Other: 2
Missing: 12*

frequencies gen.

*Education. All matches original paper*.

frequencies edu.

*RESULTS 8.2*

*8.2.1. Anticipated negative affecy*
*Paired sample t-test and printing CIs for the two conditions with repeated measures ANOVA*
*Effect size for difference between two videos: (m1 - m2) / sqrt ( s1^2 + s2^2 - (2r)) from this
wesbite: https://memory.psych.mun.ca/models/stats/effect_size.shtml
*here, m1=10.51, m2=6.26, s1=13.29, s2=10.09, r=.56. using this forumla, d =.38*

T-TEST PAIRS=AntComp_TW WITH AntComp_NoTW (PAIRED)
  /CRITERIA=CI(.9500)
  /MISSING=ANALYSIS.

GLM AntComp_TW AntComp_NoTW
  /WSFACTOR=factor1 2 Polynomial 
  /METHOD=SSTYPE(3)
  /EMMEANS=TABLES(OVERALL) 
  /EMMEANS=TABLES(factor1) 
  /CRITERIA=ALPHA(.05)
  /WSDESIGN=factor1.

CORRELATIONS
  /VARIABLES=AntComp_TW AntComp_NoTW
  /PRINT=TWOTAIL NOSIG
  /MISSING=PAIRWISE.

*Moderation by z-scored beliefs*

GLM AntComp_TW AntComp_NoTW WITH ZCodPro
  /WSFACTOR=factor1 2 Polynomial 
  /METHOD=SSTYPE(3)
  /EMMEANS=TABLES(OVERALL) WITH(ZCodPro=MEAN)
  /EMMEANS=TABLES(factor1) WITH(ZCodPro=-1) compare (factor1)
  /EMMEANS=TABLES(factor1) WITH(ZCodPro=MEAN)
  /EMMEANS=TABLES(factor1) WITH(ZCodPro=1) compare (factor1)
  /PRINT=DESCRIPTIVE ETASQ 
  /CRITERIA=ALPHA(.05)
  /WSDESIGN=factor1 
  /DESIGN=ZCodPro.

*8.2.2. Selective exposure
*binomial test*

NPAR TESTS
  /BINOMIAL (0.50)=xChoice_TW
  /STATISTICS DESCRIPTIVES
  /MISSING ANALYSIS.

*Beliefs predicting avoidance in logistic regression*

LOGISTIC REGRESSION VARIABLES xChoice_TW
  /METHOD=ENTER CodPro 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*affect predicting avoidance in logistic regression*

LOGISTIC REGRESSION VARIABLES xChoice_TW
  /METHOD=ENTER xAntCompDifTW 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).
  
  .