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Comment on "Why and When 'Flawed' Social Network Analyses Still Yield Valid Tests of no Contagion"

Shalizi, Cosma Rohilla - In: Statistics, Politics, and Policy 3 (2012) 1

contagion from purely observational data. It is especially helpful as a corrective to some of the more extreme statements of …

Persistent link: https://www.econbiz.de/10014621097

Students’ dependence on smartphones and its effect on purchasing behavior

Arif, Imtiaz; Aslam, Wajeeha; Ali, Muhammad - In: South Asian Journal of Global Business Research 5 (2016) 2, pp. 285-302

Purpose – The purpose of this paper is to explore how social need, social influence and convenience affect dependence … and convenience significantly affect students’ dependence on their smartphones. A significant relationship also existed … between students’ dependence on smartphones and their purchasing behavior. Originality/value – The availability of 3G/4G …

Persistent link: https://www.econbiz.de/10015005850

The impact of culture and social interaction on weblog design: a Malaysian case

Ali, Maimunah; Lee, Habin - In: Journal of Enterprise Information Management 24 (2011) 5, pp. 406-423

Purpose – This paper aims to investigate any cultural impacts on end‐users' web page design in weblogs. Design/methodology/approach – The design preferences of 201 weblogs in Malaysia have been collected using content analysis method and the existence of Malaysian cultural traits on the...

Persistent link: https://www.econbiz.de/10014859837

Social influence in group recommender systems

Alina Christensen, Ingrid; Schiaffino, Silvia - In: Online Information Review 38 (2014) 4, pp. 524-542

Purpose – The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social...

Persistent link: https://www.econbiz.de/10014966944

Confounding and Effect Modification: Distribution and Measure

VanderWeele, Tyler J. - In: Epidemiologic Methods 1 (2012) 1, pp. 55-82

Abstract The paper considers the properties of and relations between confounding and effect modification from the … of confounding depends on how the exposure was assigned; the presence of effect modification does not. The possibility of … confounding without effect modification and vice versa is discussed both with respect to distribution and measure. Discussion is …

Persistent link: https://www.econbiz.de/10014590578

A Tutorial on Interaction

VanderWeele, Tyler J.; Knol, Mirjam J. - In: Epidemiologic Methods 3 (2014) 1, pp. 33-72

, different mechanistic forms of interaction, when interaction is robust to unmeasured confounding, interaction for continuous …

Persistent link: https://www.econbiz.de/10014590592

Doubly Robust Estimation with the R Package drgee

Zetterqvist, Johan; Sjölander, Arvid - In: Epidemiologic Methods 4 (2015) 1, pp. 69-86

Abstract A common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in...

Persistent link: https://www.econbiz.de/10014590607

Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models

Petersen, Maya; Schwab, Joshua; Gruber, Susan; Blaser, Nello - In: Journal of Causal Inference 2 (2014) 2, pp. 147-185

Abstract This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate...

Persistent link: https://www.econbiz.de/10014610797

Confounding Equivalence in Causal Inference

Pearl, Judea; Paz, Azaria - In: Journal of Causal Inference 2 (2014) 1, pp. 75-93

Abstract The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the...

Persistent link: https://www.econbiz.de/10014610801

To Adjust or Not to Adjust? Sensitivity Analysis of M -Bias and Butterfly-Bias

Ding, Peng; Miratrix, Luke W. - In: Journal of Causal Inference 3 (2015) 1, pp. 41-57

relatively small compared to confounding bias, suggesting that it is generally not a serious concern in many applied settings … -Structure tend to increase confounding bias more rapidly than M -Bias, suggesting that choosing to condition on any given …

Persistent link: https://www.econbiz.de/10014610802