Erratum

This article refers to:

Age for learning, age for teaching: the role of inter-generational, intra-household learning in Internet use by older adults in Latin America

Cáceres, R. B., & Chaparro, A.C. (2017). Age for learning, age for teaching: the role of intergenerational, intra-household learning in Internet use by older adults in Latin America. Information, Communication & Society.

https://doi.org/10.1080/1369118X.2017.1371785

When the above article was first published online, the columns of Table 1, column headers of Tables 2, 3, A1, A3 and A4 were incorrectly moved and the minus sign (−) has been incorrectly deleted in Tables 2, 3, A1 and A5.

The correct tables can be found below:

Table 1. Regression variables characterization and summary statistics.

Type of variable	TAM operationalization	Indicator	Average	Std. Dev.	Median	Min	Max
Dependent Variable	Use Internet	Used Internet at least once^d	0.38	0.49	0	0	1
	Days of Internet use last month	Number of days of internet use	7.45	11.42	0	0	30
Hypothesis variables	Children presence	Number of children in household	0.23	0.63	0	0	4
	Spouse presence	Married or cohabiting^d	0.55	0.50	1	0	1
Explanatory Variables	Socioeconomic status	Years of education	9.52	4.22	11	0	18
		Total average monthly spending	1202.9	1077.1	976.8	106.5	15577.4
	Perceived usefulness	Agrees: Internet is a need^d	0.62	0.49	1	0	1
	Perceived enjoyment	Agrees: Internet to be integrated^d	0.85	0.36	1	0	1
	Internet Stress	Agrees: Internet is a waste of time^d	0.23	0.42	0	0	1
	Subjective Norm	Learned to use with family or friend^d	0.17	0.38	0	0	1
	Perceived Ease of Use	Has a job^d	0.40	0.49	0	0	1
Control Variables	Geographic Location (country)	Argentina^d	0.48	0.50	0	0	1
		Peru^d	0.26	0.44	0	0	1
	Age	Age in years	65.55	4.24	65	60	91
	Gender	Male^d	0.47	0.50	0	0	1
	Devices	Devices per capita in the household	1.02	0.65	1	0	4
		Has own device^d	0.83	0.38	1	0	1

Note: The superscript d indicates the dummy variables.Source: Survey about Internet Use: Platforms and Open Data – 2014. Compiled by authors.

Table 2. Hurdle regression.

Variables	(1)	(2)
	Probit	TNB2 REG
Number of children	0.347***	0.0001
	(0.120)	(0.052)
Presence of spouse	0.132	0.240***
	(0.175)	(0.085)
Years of education	0.107***	0.012
	(0.024)	(0.014)
Total average monthly spending	0.00007	0.00002
	(0.0001)	(0.00002)
Agree: Internet is a need	0.762***	0.256**
	(0.195)	(0.130)
Agree: Internet to be integrated	−0.375	0.307
	(0.235)	(0.199)
Agree: Internet is a waste of time	−0.539**	0.258**
	(0.228)	(0.117)
Has job	0.693***	0.045
	(0.177)	(0.085)
Country: Argentina	0.894***	0.181
	(0.242)	(0.138)
Country: Peru	1.178***	0.106
	(0.261)	(0.135)
Age	−0.225	−0.025
	(0.336)	(0.127)
Age squared	0.002	0.0002
	(0.003)	(0.001)
Gender: male	−0.173	−0.145
	(0.178)	(0.102)
Devices per capita in household	1.038***	0.041
	(0.188)	(0.057)
Has own device	0.863**	0.768
	(0.413)	(0.497)
Learned to use from family - friends		−0.060
		(0.076)
Constant	1.951	2.283
	(11.360)	(4.440)
Observations	403	148
P-value (Waldchi^2)	0.00	0.00
Pseudo R^2	0.427	0.029
Count R^2	0.791

Notes: Robust standard errors in parentheses.

* p < 0.1.

** p < .05.

*** p < .01.

Table 3. Marginal effects, Hurdle regression.

Variables	(1)	(2)
	AME Probit	AME TNB2 REG
Number of children	0.073***	0.002
	(0.025)	(1.063)
Presence of spouse	0.028	4.766***
	(0.037)	(1.616)
Years of education	0.023***	0.238
	(0.005)	(0.292)
Total average monthly spending	0.00002	0.0003
	(0.00002)	(0.0003)
Agree: Internet is a necessity	0.163***	4.799**
	(0.041)	(2.245)
Agree: Internet to be integrated	−0.079	5.515*
	(0.050)	(3.121)
Agree: Internet is a waste of time	−0.114**	5.830**
	(0.048)	(2.911)
Has job	0.152***	0.900
	(0.040)	(1.705)
Country: Argentina	0.180***	3.697
	(0.044)	(2.863)
Country: Peru	0.265***	2.186
	(0.056)	(2.827)
Age	0.005	−0.090
	(0.005)	(0.262)
Gender: male	−0.036	−2.940
	(0.037)	(2.076)
Devices per capita in household	0.218***	0.825
	(0.035)	(1.150)
Has own device	0.171**	10.943**
	(0.071)	(4.767)
Learned to use from family – friend		−1.212
		(1.541)
Observations	403	148

Notes: Robust standard errors in parentheses. dy/dx for dichotomous variables calculated as the discrete change.

* p < .1.

** p < .05.

*** p < .01.

Table A1. Goodness of fit statistics – discrete choice models.

Goodness of fit	Discrete choice models
	Probit	Logit
Log verosim (null)	−264.9618	−264.9618
Log verosim (model)	−151.7035	−152.1946
AIC	335.407	336.3893
BIC	399.39	400.3723
Pseudo R^2	0.4275	0.4256

Note: Compiled by authors.

Table A3. Goodness of fit statistics – count models

Goodness of fit	Count models
	Hurdle Poisson	Hurdle NB
AIC	1510.341	1284.309
BIC	1658.302	1436.269
Squared correlation^a	0.421	0.422

Note: Compiled by authors.

^acorr(y,ŷ).

Table A4. Prediction table – Probit

y			Accuracy Rate
	0	1^a
0	231	24	91%
1	62	96	61%
Total	293	120	79%

Note: Compiled by authors.

.

Table A5. Correlations matrix – regression’s variables.

days_inter	1
Child	0.039	1
Spouse	0.094	−0.035	1
Yearseduc	0.366*	−0.152	0.118	1
Expendit_pm	0.172*	0.130	0.053	0.137	1
inter_need	0.298*	0.078	0.097	0.159	0.190*	1
inter_integ	0.135	0.053	−0.022	0.063	0.0889	0.299*	1
inter_waster	−0.188*	0.103	0.054	−0.204*	−0.044	−0.103	−0.074	1
Learn_fam_fri	0.501*	0.054	0.010	0.178*	0.096	0.159	0.053	−0.123	1
Job	0.250*	−0.004	0.064	0.078	0.065	0.095	0.115	−0.01	0.168*	1
Country_arg	0.007	−0.182*	−0.086	−0.166*	0.073	−0.203*	−0.016	−0.067	−0.028	−0.010	1
Country_per	0.1660*	0.148	0.033	0.092	−0.128	0.031	−0.022	−0.160	0.170*	0.107	−0.570*	1
Age	−0.0585	−0.0549	0.0013	−0.054	−0.059	0.020	0.026	−0.003	−0.043	−0.231*	−0.027	0.155	1
Male	0.0073	0.023	0.222*	0.063	0.037	0.025	0.005	−0.002	0.055	0.220*	−0.189*	0.202*	0.127	1
Device_pc	0.4729*	−0.092	−0.030	0.410*	0.165*	0.224*	0.156	−0.144	0.340*	0.152	−0.027	0.006	−0.091	0.001	1
Device_own	0.2942*	0.029	0.048	0.286*	0.094	0.232*	0.050	−0.058	0.193*	0.164*	−0.221*	0.112	−0.083	0.053	0.499*	1

Note: Values adjusted for the Sidak correction. Symmetrical matrix. Compiled by authors.

*Correlation significant at 10% or less.

Taylor & Francis apologizes for these errors.