Correction

This article refers to:

Click to Count: The Effects of Nonpartisan Cue Endorsements in Selecting Political Information

Article title: Click to Count: The Effects of Nonpartisan Cue Endorsements in Selecting Political Information

Authors: Brian Calfano and Jelena Vićić

Journal: Journal of Political Marketing

DOI: https://doi.org/10.1080/15377857.2021.1884166

When the article was first published online, Table 3 was published with a typo. It has been corrected and republished online.

The corrected version of Table 3 is as follows:

Table 3. Probability of users clicking to non-partisan information video (2018 survey experiments).

	“One click” to treatment “Two clicks” to treatment
Treatment only models	Coefficient/Robust SE	Predicted Probability	Coefficient/Robust SE	Predicted Probability
Treatment	1.09/.160**	.23	.971/.170**	.20
Baseline		.68		.66
Constant	−1.27/.120**		−1.28/.125**
Covariate models
Treatment	1.13/.162**	.23	1.07/.180**	.22
Female	−.276/.161	.06	−.298/.181	.06
African American	.257/.212	.05	.226/.242	.05
Latino	−.022/.231	.01	−.142/.265	.03
Age	−.026/.059	.01	−.050/.070	.01
Political Interest	−.164/.128	.03	−.204/.138	.04
Republican	−.081/.220	.02	.033/.279	.01
Democrat	−.024/.210	.01	.130/.253	.03
LWV Impartial	.384/.164*	.08	.560/.200*	.11
Constant	−1.06/.357**		−1.18/.460**
Baseline		.49		.70
Wald Chi^2/Prob	58.01/.000		42.3/.000
Log-Likelihood	−450.51		−369.6
N	759		633

Unstandardized coefficients from binary logit models with robust standard errors. “Treatment” variables are binary measures of whether subjects were randomly assigned for exposure to the LWV treatment image (= 1) versus the control.

** = p < .01 (two-tailed) * = p < .05 (two-tailed).