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The Impact of Contradicting Media Messages on Political Perceptions: The Case of a Partisan Dispute in Korea over Lifting Ban on U.S. Beef Imports
Unformatted Document Text:  THE IMPACT OF CONFLICTING FRAMES 22 Table 1. Multiple Regression Analysis for Predicting Political Evaluations Presidential Approval Trust in Government Variables RQ1 (N = 293) H1a (N = 293) H2a (N = 291) H3a (N = 291) RQ1 (N = 282) H1b (N = 282) H2b(N = 280) H3b(N = 282) Controls Ideology .184 *** .126 .159 ** .152 ** .126 ** .184 .105 * .098 * Age .091 .100 .074 .086 .075 .077 .059 .071 Education –.013 –.022 –.006 –.014 .077 .072 .085 .073 Income .059 .050 .068 .061 .110 * .105 * .124 * .113 * Gender Beef evaluation Predictors TV frames –.018 .556 *** .063 –.010 .557 *** –.078 –.016 .441 *** .055 –.014 .436 *** .067 –.001 .619 *** .027 .004 .618 *** .011 –.003 .530 *** .017 .002 .523 *** .040 Newspaper frames Online frames Ideology × TV Ideology × NP Ideology × Online Responsibility TV×Responsibility NP×Responsibility Online×Responsib. .017 –.060 .264 * –.153 .231 –.377 * .152 .003 .007 –.251 *** –.010 .008 –.273 *** –.045 .089–.057 –.024 –.075 .111 –.109 .029 –.205 .058 –.032–.022 –.199 *** –.054–.023 –.232 *** –.102 * .140 * –.074 R 2 .478 .488 .519 .524 .541 .545 .568 .582 Adjusted R 2 .462 .466 .502 .502 .526 .525 .552 .561 Notes. a. Cell entries are standardized regression coefficients. b. Missing values were deleted on a listwise basis. *p < .05, **p < .01, ***p < .001

Authors: Lee, ByungGu.
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THE IMPACT OF CONFLICTING FRAMES   
      22
Table 1. 
Multiple Regression Analysis for Predicting Political Evaluations
Presidential Approval
Trust in Government
Variables
RQ1
(= 293)
H1a
(= 293)
H2a
(= 291)
H3a
(= 291)
RQ1
(= 282)
H1b
(= 282)
     H2b
(= 280)
    H3b
(= 282)
Controls
   Ideology
     
     .184
***
     
     .126
  
  .159
**
 
  .152
**
  
     .126
**
   
     .184
  .105
*
  .098
*
   Age
     .091
     .100
  .074
  .086
     .075
     .077
  .059
  .071
   Education
   –.013
   –.022
–.006
–.014
     .077
     .072
  .085
  .073
   Income
     .059
     .050
  .068
  .061
     .110
*
     .105
*
  .124
*
  .113
*
   Gender
   Beef evaluation 
Predictors
  TV frames
   –.018
     .556
***
  
  
     .063
   –.010
     .557
***
   
   –.078
–.016
  .441
***
  .055
–.014
  .436
***
  .067
   –.001
     .619
***
  
     .027
     .004
     .618
***
  
 
     .011
–.003
  .530
***
  .017
  .002
  .523
***
  .040
   Newspaper frames
   Online frames
   Ideology × TV 
   Ideology × NP 
   Ideology × Online
   Responsibility
   TV×Responsibility
   NP×Responsibility
   Online×Responsib.
     .017
   –.060
      
     .264
*
   –.153
     .231
   –.377
*
     .152
  .003
  .007
     
–.251
***
–.010
  .008
–.273
***
–.045
  .089
–.057
   –.024
   –.075
     .111
   –.109
     .029
   –.205
     .058
–.032
–.022
–.199
***
–.054
–.023
–.232
***
–.102
*
  .140
*
–.074
R
2
     .478
     .488
  .519
  .524
     .541
     .545
  .568
  .582
Adjusted R
2
     .462
     .466
  .502
  .502
     .526
     .525
  .552
  .561
Notes. a. Cell entries are standardized regression coefficients. 
b. Missing values were deleted on a listwise basis. *p < .05, **< .01, ***p < .001


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