- mturk - 499 subjects - 24 covid headlines (12 false, 12 true) - cluster subject and headline - [prereg 1.5 mturk covid headlines](https://osf.io/8cnyx) # Basic models Finally ideology-veracity interaction! ```r > sum2(m0c) term res 1: (Intercept) b = 0.30 [0.26, 0.34], p < .001 2: demrep_c b = 0.10 [0.07, 0.14], p < .001 3: veracity b = 0.22 [0.16, 0.29], p < .001 4: demrep_c:veracity b = -0.18 [-0.22, -0.13], p < .001 # significant interaction > sum2(m0c) term res 1: (Intercept) b = 0.30 [0.26, 0.34], p < .001 2: ideology b = 0.14 [0.11, 0.17], p < .001 3: veracity b = 0.22 [0.16, 0.29], p < .001 4: ideology:veracity b = -0.19 [-0.23, -0.15], p < .001 # significant interaction ``` # Model 1 (only false headlines) ```r > sum2(m1_1c) term res 1: (Intercept) b = -0.94 [-1.17, -0.71], p < .001 2: ideology b = 0.72 [0.55, 0.88], p < .001 3: bfi_c b = -0.13 [-0.28, 0.02], p = .083 4: ideology:bfi_c b = -0.04 [-0.17, 0.10], p = .603 # BF = 67.4514 ``` # Model 2 (all headlines; key test 1) ```r > sum2(m2_1c) term res 1: (Intercept) b = -0.36 [-0.61, -0.11], p = .005 2: ideology b = 0.21 [0.01, 0.41], p = .040 3: bfi_c b = -0.16 [-0.26, -0.07], p < .001 4: ideology:bfi_c b = -0.03 [-0.11, 0.05], p = .422 # BF01 = 79.07 ``` # Model 3 (all headlines) ```r > sum2(m3_1c) term res 1: (Intercept) b = -0.94 [-1.16, -0.71], p < .001 2: ideology b = 0.72 [0.56, 0.88], p < .001 3: bfi_c b = -0.13 [-0.28, 0.02], p = .082 4: veracity b = 1.03 [0.73, 1.33], p < .001 5: ideology:bfi_c b = -0.04 [-0.17, 0.10], p = .602 6: ideology:veracity b = -0.89 [-1.07, -0.71], p < .001 7: bfi_c:veracity b = -0.08 [-0.23, 0.07], p = .314 8: ideology:bfi_c:veracity b = 0.01 [-0.12, 0.14], p = .886 # BF01 = 108.1 ``` # Model 4 (only false headlines) - negative `ideology:attention_score` interaction (note veracity is coded 0/1) - positive `ideology:ctsq_aot` interaction ```r > sum2(m4_1c) term res 1: (Intercept) b = -0.99 [-1.25, -0.73], p < .001 2: ideology b = 0.68 [0.48, 0.89], p < .001 3: bfi_c b = -0.03 [-0.22, 0.16], p = .743 4: bfi_e b = 0.14 [-0.04, 0.33], p = .119 5: bfi_a b = -0.21 [-0.37, -0.04], p = .015 6: bfi_n b = 0.08 [-0.13, 0.29], p = .477 7: bfi_o b = 0.02 [-0.14, 0.19], p = .781 8: age b = -0.15 [-0.33, 0.03], p = .112 9: gender b = -0.04 [-0.20, 0.11], p = .590 10: edu b = -0.06 [-0.21, 0.09], p = .413 11: attention_score b = -0.12 [-0.29, 0.05], p = .169 12: ctsq_aot b = -0.71 [-0.90, -0.52], p < .001 ## 13: ideology:bfi_c b = -0.05 [-0.21, 0.10], p = .502 # BF01 = 60.84 14: ideology:bfi_e b = -0.16 [-0.34, 0.02], p = .074 15: ideology:bfi_a b = 0.12 [-0.04, 0.28], p = .130 16: ideology:bfi_n b = -0.07 [-0.25, 0.11], p = .452 17: ideology:bfi_o b = 0.15 [0.146, 0.29], p = .048 18: ideology:age b = 0.01 [-0.16, 0.17], p = .942 19: ideology:gender b = 0.07 [-0.08, 0.22], p = .365 20: ideology:edu b = -0.10 [-0.25, 0.04], p = .164 21: ideology:attention_score b = -0.22 [-0.41, -0.03], p = .025 ### 22: ideology:ctsq_aot b = 0.19 [0.01, 0.36], p = .039 ### term res ``` # Model 5 (all headlines; key test 2) - negative `ideology:attention_score` interaction (note veracity is coded 0/1) - negative `veracity:ideology:ctsq_aot` interaction ```r > sum2(m5_1c) term res 1: (Intercept) b = -0.99 [-1.24, -0.73], p < .001 2: veracity b = 1.09 [0.77, 1.41], p < .001 3: ideology b = 0.68 [0.48, 0.89], p < .001 4: bfi_c b = -0.03 [-0.22, 0.16], p = .742 5: bfi_e b = 0.14 [-0.04, 0.33], p = .118 6: bfi_a b = -0.21 [-0.37, -0.04], p = .015 7: bfi_n b = 0.08 [-0.13, 0.29], p = .476 8: bfi_o b = 0.02 [-0.14, 0.19], p = .781 9: age b = -0.15 [-0.33, 0.03], p = .110 10: gender b = -0.04 [-0.20, 0.11], p = .589 11: edu b = -0.06 [-0.21, 0.09], p = .412 12: attention_score b = -0.12 [-0.29, 0.05], p = .168 13: ctsq_aot b = -0.71 [-0.90, -0.52], p < .001 14: veracity:ideology b = -0.89 [-1.11, -0.67], p < .001 15: veracity:bfi_c b = -0.12 [-0.32, 0.07], p = .216 16: veracity:bfi_e b = -0.03 [-0.20, 0.14], p = .710 17: veracity:bfi_a b = 0.29 [0.12, 0.45], p < .001 18: veracity:bfi_n b = 0.18 [-0.03, 0.38], p = .091 19: veracity:bfi_o b = -0.06 [-0.22, 0.10], p = .483 20: veracity:age b = 0.32 [0.15, 0.49], p < .001 21: veracity:gender b = 0.10 [-0.06, 0.25], p = .225 22: veracity:edu b = 0.15 [0.01, 0.28], p = .035 23: veracity:attention_score b = 0.09 [-0.04, 0.22], p = .176 24: veracity:ctsq_aot b = 0.61 [0.42, 0.81], p < .001 ## 25: ideology:bfi_c b = -0.05 [-0.21, 0.10], p = .501 26: ideology:bfi_e b = -0.16 [-0.34, 0.02], p = .073 27: ideology:bfi_a b = 0.12 [-0.03, 0.28], p = .128 28: ideology:bfi_n b = -0.07 [-0.25, 0.11], p = .451 29: ideology:bfi_o b = 0.15 [0.146, 0.29], p = .047 30: ideology:age b = 0.01 [-0.16, 0.17], p = .942 31: ideology:gender b = 0.07 [-0.08, 0.22], p = .363 32: ideology:edu b = -0.10 [-0.25, 0.04], p = .163 33: ideology:attention_score b = -0.22 [-0.41, -0.03], p = .024 ## 34: ideology:ctsq_aot b = 0.19 [0.01, 0.36], p = .038 35: veracity:ideology:bfi_c b = 0.05 [-0.11, 0.21], p = .537 # BF01 = 89.05 36: veracity:ideology:bfi_e b = 0.21 [0.04, 0.39], p = .017 37: veracity:ideology:bfi_a b = -0.18 [-0.34, -0.01], p = .036 38: veracity:ideology:bfi_n b = 0.07 [-0.10, 0.24], p = .426 39: veracity:ideology:bfi_o b = -0.09 [-0.25, 0.07], p = .257 40: veracity:ideology:age b = -0.06 [-0.22, 0.10], p = .452 41: veracity:ideology:gender b = 0.07 [-0.07, 0.22], p = .320 42: veracity:ideology:edu b = 0.03 [-0.09, 0.16], p = .636 43: veracity:ideology:attention_score b = 0.16 [-0.01, 0.33], p = .068 44: veracity:ideology:ctsq_aot b = -0.17 [-0.33, -0.01], p = .034 ## term res ``` ![[Pasted image 20220128212755.png|900]] # Item analysis (all studies) ![[item_demrep-bfi 1.png|1200]]