MPA analysis
Heather Perkins
2025-11-03
1 Load Libraries
2 Load Data
2.1 Missing Data
2.2 Attention Checks
3 Measure Checking
3.1 Source Credibility
3.1.1 EFA
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Q27_1* 1 184 3.92 1.18 4 3.95 1.48 1 6 5 -0.23 -0.26 0.09
## Q27_2* 2 184 3.94 1.44 4 3.95 1.48 1 7 6 -0.05 -0.41 0.11
## Q27_3* 3 184 3.87 1.36 4 3.86 1.48 1 7 6 -0.02 -0.22 0.10
## Q27_4* 4 184 4.64 1.43 5 4.66 1.48 1 7 6 -0.25 -0.55 0.11
## Q27_5* 5 184 5.01 1.36 5 5.07 1.48 1 7 6 -0.37 -0.26 0.10
## Q27_6* 6 184 3.98 1.21 4 3.99 1.48 1 6 5 -0.12 -0.25 0.09
## Q94_1* 7 184 4.70 1.37 5 4.71 1.48 1 7 6 -0.19 -0.57 0.10
## Q94_2* 8 184 4.41 1.50 4 4.43 1.48 1 7 6 -0.10 -0.57 0.11
## Q94_3* 9 184 4.59 1.32 5 4.63 1.48 1 7 6 -0.28 -0.16 0.10
##
## Call:
## factanal(x = d, factors = 1, rotation = "promax")
##
## Uniquenesses:
## Unintelligent : Intelligent Untrained : Trained
## 0.287 0.353
## Inexpert : Expert Uninformed : Informed
## 0.392 0.226
## Incompetent : Competent Stupid : Bright
## 0.280 0.283
## Dishonest : Honest Untrustworthy : Trustworthy
## 0.224 0.205
## Dishonorable : Honorable
## 0.269
##
## Loadings:
## [1] 0.844 0.804 0.780 0.880 0.849 0.847 0.881 0.892 0.855
##
## Factor1
## SS loadings 6.48
## Proportion Var 0.72
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 298.38 on 27 degrees of freedom.
## The p-value is 1.52e-47##
## Call:
## factanal(x = d, factors = 2, rotation = "promax")
##
## Uniquenesses:
## Unintelligent : Intelligent Untrained : Trained
## 0.217 0.372
## Inexpert : Expert Uninformed : Informed
## 0.408 0.200
## Incompetent : Competent Stupid : Bright
## 0.220 0.202
## Dishonest : Honest Untrustworthy : Trustworthy
## 0.155 0.055
## Dishonorable : Honorable
## 0.234
##
## Loadings:
## Factor1 Factor2
## Unintelligent : Intelligent 0.916
## Uninformed : Informed 0.753
## Incompetent : Competent 0.859
## Stupid : Bright 0.917
## Dishonest : Honest 0.835
## Untrustworthy : Trustworthy 1.019
## Dishonorable : Honorable 0.731
## Untrained : Trained 0.465
## Inexpert : Expert 0.497
##
## Factor1 Factor2
## SS loadings 3.489 2.526
## Proportion Var 0.388 0.281
## Cumulative Var 0.388 0.668
##
## Factor Correlations:
## Factor1 Factor2
## Factor1 1.000 0.829
## Factor2 0.829 1.000
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 156.68 on 19 degrees of freedom.
## The p-value is 1.12e-23
##
## Call:
## factanal(x = d, factors = 1, rotation = "promax")
##
## Uniquenesses:
## Unintelligent : Intelligent Uninformed : Informed
## 0.305 0.241
## Incompetent : Competent Stupid : Bright
## 0.271 0.268
## Dishonest : Honest Untrustworthy : Trustworthy
## 0.205 0.204
## Dishonorable : Honorable
## 0.251
##
## Loadings:
## [1] 0.834 0.871 0.854 0.855 0.892 0.892 0.866
##
## Factor1
## SS loadings 5.256
## Proportion Var 0.751
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 167.68 on 14 degrees of freedom.
## The p-value is 2.02e-28##
## Call:
## factanal(x = d, factors = 2, rotation = "promax")
##
## Uniquenesses:
## Unintelligent : Intelligent Uninformed : Informed
## 0.245 0.211
## Incompetent : Competent Stupid : Bright
## 0.202 0.172
## Dishonest : Honest Untrustworthy : Trustworthy
## 0.130 0.086
## Dishonorable : Honorable
## 0.218
##
## Loadings:
## Factor1 Factor2
## Unintelligent : Intelligent 0.823
## Uninformed : Informed 0.729
## Incompetent : Competent 0.853
## Stupid : Bright 0.918
## Dishonest : Honest 0.849
## Untrustworthy : Trustworthy 0.933
## Dishonorable : Honorable 0.730
##
## Factor1 Factor2
## SS loadings 2.826 2.166
## Proportion Var 0.404 0.309
## Cumulative Var 0.404 0.713
##
## Factor Correlations:
## Factor1 Factor2
## Factor1 1.000 0.795
## Factor2 0.795 1.000
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 22.81 on 8 degrees of freedom.
## The p-value is 0.003623.1.2 Plots
3.1.2.1 Density Plots (by sample)
3.1.2.2 Density Plots (by condition)
3.1.2.3 Density Plots (by sample*condition)
3.1.3 Analysis
3.1.3.1 Regression
Trustworthiness of sources for all social media posts as DV, Adj. R^2 = .69, F(2,181) = 202.20, p < .001 Competence of source was stronger predictor (beta = .61, p < .001) than expertise (beta = .27, p < .001)
## source_comp source_exp
## 2.257774 2.257774## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_trust ~ source_comp + source_exp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.71857 -0.31434 0.04788 0.33371 1.55647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.220e-16 4.122e-02 0.000 1
## source_comp 6.075e-01 6.210e-02 9.783 < 2e-16 ***
## source_exp 2.728e-01 6.210e-02 4.392 1.9e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5591 on 181 degrees of freedom
## Multiple R-squared: 0.6908, Adjusted R-squared: 0.6874
## F-statistic: 202.2 on 2 and 181 DF, p-value: < 2.2e-163.1.3.2 Plots (differences in source ratings by condition*sample)
3.2 Third Person Effect
3.2.1 Item Analysis
## vars n mean sd median trimmed mad min max range skew kurtosis
## Q28_1* 1 184 8.01 2.89 8 8.18 2.97 1 12 11 -0.46 -0.74
## Q28_2* 2 184 7.41 3.00 8 7.57 2.97 1 12 11 -0.40 -0.79
## Q28_3* 3 184 8.30 3.06 9 8.59 2.97 1 12 11 -0.68 -0.64
## Q28_4 4 184 10.68 0.47 11 10.72 0.00 10 11 1 -0.76 -1.43
## Q28_5* 5 184 7.93 2.68 9 8.28 1.48 1 11 10 -1.04 0.11
## se
## Q28_1* 0.21
## Q28_2* 0.22
## Q28_3* 0.23
## Q28_4 0.03
## Q28_5* 0.20## Call:corr.test(x = d)
## Correlation matrix
## Q28_1 Q28_2 Q28_3 Q28_5
## Q28_1 1.00 0.68 0.79 0.49
## Q28_2 0.68 1.00 0.66 0.50
## Q28_3 0.79 0.66 1.00 0.54
## Q28_5 0.49 0.50 0.54 1.00
## Sample Size
## [1] 184
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## Q28_1 Q28_2 Q28_3 Q28_5
## Q28_1 0 0 0 0
## Q28_2 0 0 0 0
## Q28_3 0 0 0 0
## Q28_5 0 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option3.2.2 Difference Plots
3.2.3 Difference Heatmaps
3.2.4 TPE by Sample/Condition Plots
3.2.5 TPE by Sample/Condition ANOVA
## Anova Table (Type 3 tests)
##
## Response: tpe_othr
## Effect df MSE F ges p.value
## 1 condition 1, 179 3.76 0.18 .001 .669
## 2 samp 1, 179 3.76 32.58 *** .154 <.001
## 3 condition:samp 1, 179 3.76 0.01 <.001 .934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Anova Table (Type 3 tests)
##
## Response: tpe_self
## Effect df MSE F ges p.value
## 1 condition 1, 179 5.33 0.34 .002 .563
## 2 samp 1, 179 5.33 31.54 *** .150 <.001
## 3 condition:samp 1, 179 5.33 1.16 .006 .282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Anova Table (Type 3 tests)
##
## Response: tpe_diff
## Effect df MSE F ges p.value
## 1 condition 1, 179 1.32 0.01 <.001 .909
## 2 samp 1, 179 1.32 4.97 * .027 .027
## 3 condition:samp 1, 179 1.32 1.50 .008 .222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## samp = Prolific:
## condition emmean SE df lower.CL upper.CL
## a 0.443 0.147 179 0.152 0.733
## m 0.688 0.144 179 0.404 0.971
##
## samp = SONA:
## condition emmean SE df lower.CL upper.CL
## a 1.074 0.221 179 0.638 1.510
## m 0.871 0.206 179 0.464 1.278
##
## Confidence level used: 0.953.3 SciPop
3.3.1 Factor Analysis
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Q29_1 1 183 2.86 0.66 3 2.86 0.00 1 4 3 -0.51 0.67 0.05
## Q29_2 2 183 2.96 0.67 3 2.97 0.00 1 4 3 -0.28 0.11 0.05
## Q29_3 3 183 1.77 0.80 2 1.68 1.48 1 4 3 0.75 -0.15 0.06
## Q29_4 4 183 2.03 0.95 2 1.93 1.48 1 4 3 0.53 -0.73 0.07
## Q29_5 5 183 2.27 0.94 2 2.22 1.48 1 4 3 0.14 -0.97 0.07
## Q29_6 6 183 2.29 0.96 2 2.24 1.48 1 4 3 0.14 -1.00 0.07
## Q29_7 7 183 2.09 0.91 2 2.03 1.48 1 4 3 0.34 -0.85 0.07
## Q29_8 8 183 1.87 0.88 2 1.79 1.48 1 4 3 0.63 -0.61 0.07## lavaan 0.6.16 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 22
##
## Number of observations 183
##
## Model Test User Model:
##
## Test statistic 25.043
## Degrees of freedom 14
## P-value (Chi-square) 0.034
##
## Model Test Baseline Model:
##
## Test statistic 483.527
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.976
## Tucker-Lewis Index (TLI) 0.952
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1587.330
## Loglikelihood unrestricted model (H1) -1574.809
##
## Akaike (AIC) 3218.660
## Bayesian (BIC) 3289.269
## Sample-size adjusted Bayesian (SABIC) 3219.591
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.066
## 90 Percent confidence interval - lower 0.018
## 90 Percent confidence interval - upper 0.107
## P-value H_0: RMSEA <= 0.050 0.241
## P-value H_0: RMSEA >= 0.080 0.313
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.038
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ppl =~
## Q29_1 0.538 0.230 2.341 0.019 0.538 0.811
## Q29_2 0.243 0.113 2.154 0.031 0.243 0.363
## eli =~
## Q29_3 0.641 0.059 10.833 0.000 0.641 0.803
## Q29_4 0.701 0.070 10.020 0.000 0.701 0.743
## dec =~
## Q29_5 0.805 0.068 11.891 0.000 0.805 0.855
## Q29_6 0.798 0.069 11.581 0.000 0.798 0.834
## tru =~
## Q29_7 0.744 0.063 11.727 0.000 0.744 0.818
## Q29_8 0.691 0.062 11.190 0.000 0.691 0.784
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ppl ~~
## eli -0.061 0.106 -0.573 0.567 -0.061 -0.061
## dec 0.148 0.111 1.339 0.181 0.148 0.148
## tru 0.152 0.114 1.330 0.184 0.152 0.152
## eli ~~
## dec 0.526 0.075 7.058 0.000 0.526 0.526
## tru 0.723 0.062 11.568 0.000 0.723 0.723
## dec ~~
## tru 0.624 0.065 9.624 0.000 0.624 0.624
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q29_1 0.150 0.244 0.615 0.538 0.150 0.342
## .Q29_2 0.387 0.064 6.057 0.000 0.387 0.868
## .Q29_3 0.225 0.049 4.571 0.000 0.225 0.355
## .Q29_4 0.399 0.067 5.996 0.000 0.399 0.448
## .Q29_5 0.239 0.067 3.556 0.000 0.239 0.270
## .Q29_6 0.280 0.068 4.115 0.000 0.280 0.305
## .Q29_7 0.274 0.055 4.937 0.000 0.274 0.331
## .Q29_8 0.299 0.052 5.797 0.000 0.299 0.385
## ppl 1.000 1.000 1.000
## eli 1.000 1.000 1.000
## dec 1.000 1.000 1.000
## tru 1.000 1.000 1.0003.3.2 Descriptives
## vars n mean sd median trimmed mad min max range skew kurtosis
## scipop 1 183 0 1 -0.03 -0.05 1.06 -2.18 2.59 4.78 0.38 -0.42
## scipop_ppl 2 183 0 1 0.17 0.01 1.38 -2.62 2.04 4.66 -0.11 0.11
## scipop_eli 3 183 0 1 0.13 -0.09 0.95 -1.15 2.69 3.84 0.55 -0.64
## scipop_dec 4 183 0 1 0.25 -0.03 0.84 -1.45 1.95 3.41 0.09 -0.89
## scipop_tru 5 183 0 1 0.02 -0.07 0.91 -1.21 2.48 3.69 0.39 -0.85
## se
## scipop 0.07
## scipop_ppl 0.07
## scipop_eli 0.07
## scipop_dec 0.07
## scipop_tru 0.073.3.3 Item Histograms
3.3.4 Composite Histograms
3.4 Misinformation Flagging
4 Analysis
4.1 Correlation Matrix
4.2 Regressions
4.2.1 TPE Diff
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_exp ~ tpe_diff + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.51356 -0.57658 0.03629 0.58273 2.56885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36361 0.08725 4.168 4.77e-05 ***
## tpe_diff -0.21394 0.05841 -3.663 0.000328 ***
## sampSONA -0.64174 0.14520 -4.420 1.70e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9022 on 180 degrees of freedom
## Multiple R-squared: 0.1786, Adjusted R-squared: 0.1694
## F-statistic: 19.56 on 2 and 180 DF, p-value: 2.049e-08
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_comp ~ tpe_diff + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.77001 -0.49273 -0.09052 0.62854 2.12931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37723 0.08609 4.382 1.99e-05 ***
## tpe_diff -0.19180 0.05763 -3.328 0.00106 **
## sampSONA -0.72836 0.14327 -5.084 9.23e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8902 on 180 degrees of freedom
## Multiple R-squared: 0.1944, Adjusted R-squared: 0.1855
## F-statistic: 21.72 on 2 and 180 DF, p-value: 3.544e-09
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_trust ~ tpe_diff + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.82426 -0.58155 0.01088 0.60330 2.44979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31684 0.08947 3.541 0.000507 ***
## tpe_diff -0.16132 0.05989 -2.694 0.007736 **
## sampSONA -0.60877 0.14889 -4.089 6.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9251 on 180 degrees of freedom
## Multiple R-squared: 0.1355, Adjusted R-squared: 0.1259
## F-statistic: 14.11 on 2 and 180 DF, p-value: 2.034e-06
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_cred ~ tpe_diff + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.35183 -0.53099 -0.00375 0.56146 2.27510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35256 0.07858 4.487 1.29e-05 ***
## tpe_diff -0.18902 0.05260 -3.593 0.000421 ***
## sampSONA -0.65962 0.13078 -5.044 1.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8126 on 180 degrees of freedom
## Multiple R-squared: 0.2011, Adjusted R-squared: 0.1922
## F-statistic: 22.65 on 2 and 180 DF, p-value: 1.678e-09
4.2.2 SciPop
## scipop_ppl scipop_eli scipop_dec scipop_tru samp
## 1.043590 1.537552 1.397721 1.699335 1.042054## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_exp ~ scipop_ppl + scipop_eli + scipop_dec +
## scipop_tru + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2978 -0.4693 -0.0358 0.4747 3.0640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20869 0.07855 2.657 0.00861 **
## scipop_ppl 0.19746 0.06606 2.989 0.00320 **
## scipop_eli 0.11956 0.08019 1.491 0.13775
## scipop_dec 0.13885 0.07646 1.816 0.07104 .
## scipop_tru 0.10195 0.08430 1.209 0.22812
## sampSONA -0.62139 0.14149 -4.392 1.93e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8724 on 177 degrees of freedom
## Multiple R-squared: 0.2446, Adjusted R-squared: 0.2233
## F-statistic: 11.46 on 5 and 177 DF, p-value: 1.343e-09## scipop_ppl scipop_eli scipop_dec scipop_tru samp
## 1.043590 1.537552 1.397721 1.699335 1.042054## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_comp ~ scipop_ppl + scipop_eli + scipop_dec +
## scipop_tru + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.82661 -0.60863 -0.05182 0.65160 2.61540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24153 0.08008 3.016 0.00294 **
## scipop_ppl 0.18121 0.06735 2.691 0.00782 **
## scipop_eli -0.07939 0.08175 -0.971 0.33281
## scipop_dec 0.13561 0.07794 1.740 0.08362 .
## scipop_tru 0.06478 0.08594 0.754 0.45203
## sampSONA -0.72016 0.14425 -4.992 1.42e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8894 on 177 degrees of freedom
## Multiple R-squared: 0.2092, Adjusted R-squared: 0.1869
## F-statistic: 9.365 on 5 and 177 DF, p-value: 6.204e-08## scipop_ppl scipop_eli scipop_dec scipop_tru samp
## 1.043590 1.537552 1.397721 1.699335 1.042054## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_trust ~ scipop_ppl + scipop_eli + scipop_dec +
## scipop_tru + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90523 -0.59975 -0.04114 0.62963 2.60365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.201736 0.082012 2.460 0.01486 *
## scipop_ppl 0.182259 0.068974 2.642 0.00897 **
## scipop_eli -0.010619 0.083721 -0.127 0.89922
## scipop_dec 0.193727 0.079824 2.427 0.01623 *
## scipop_tru -0.001031 0.088016 -0.012 0.99067
## sampSONA -0.598842 0.147727 -4.054 7.54e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9109 on 177 degrees of freedom
## Multiple R-squared: 0.1759, Adjusted R-squared: 0.1526
## F-statistic: 7.556 on 5 and 177 DF, p-value: 1.869e-06## scipop_ppl scipop_eli scipop_dec scipop_tru samp
## 1.043590 1.537552 1.397721 1.699335 1.042054## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = source_cred ~ scipop_ppl + scipop_eli + scipop_dec +
## scipop_tru + samp, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.05580 -0.51487 -0.02397 0.48632 2.76101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21732 0.07205 3.016 0.00294 **
## scipop_ppl 0.18698 0.06060 3.086 0.00236 **
## scipop_eli 0.00985 0.07355 0.134 0.89362
## scipop_dec 0.15607 0.07013 2.225 0.02732 *
## scipop_tru 0.05523 0.07733 0.714 0.47599
## sampSONA -0.64680 0.12978 -4.984 1.48e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8002 on 177 degrees of freedom
## Multiple R-squared: 0.238, Adjusted R-squared: 0.2165
## F-statistic: 11.06 on 5 and 177 DF, p-value: 2.787e-09
4.2.3 Misinformation Acceptance
## source_cred samp acc_rating
## 1.329312 1.221920 1.286172## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = mis_rating ~ source_cred + samp + acc_rating, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.58254 -0.29816 0.01652 0.33839 1.26046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92336 0.18089 5.105 8.42e-07 ***
## source_cred 0.11982 0.04791 2.501 0.0133 *
## sampSONA -0.12199 0.08900 -1.371 0.1722
## acc_rating 0.56761 0.06470 8.773 1.34e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5068 on 179 degrees of freedom
## Multiple R-squared: 0.4525, Adjusted R-squared: 0.4433
## F-statistic: 49.31 on 3 and 179 DF, p-value: < 2.2e-16
## source_cred samp acc_rating source_cred:samp
## 2.101822 1.306042 1.308811 1.928752## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## Call:
## lm(formula = mis_rating ~ source_cred * samp + acc_rating, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.64602 -0.32475 0.00682 0.36482 1.25232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.96463 0.17799 5.420 1.92e-07 ***
## source_cred 0.22184 0.05908 3.755 0.000234 ***
## sampSONA -0.18722 0.09024 -2.075 0.039445 *
## acc_rating 0.54364 0.06400 8.494 7.74e-15 ***
## source_cred:sampSONA -0.26292 0.09230 -2.849 0.004910 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.497 on 178 degrees of freedom
## Multiple R-squared: 0.4764, Adjusted R-squared: 0.4646
## F-statistic: 40.48 on 4 and 178 DF, p-value: < 2.2e-16## Analysis of Variance Table
##
## Model 1: mis_rating ~ source_cred + samp + acc_rating
## Model 2: mis_rating ~ source_cred * samp + acc_rating
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 45.971
## 2 178 43.966 1 2.0042 8.1141 0.00491 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
