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2015 - International Communication Association 65th Annual Conference Pages: unavailable || Words: 1195 words || 
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1. Lull, Robert. "Conditional Process Analysis With Multicategorical Independent Variables and Multicategorical Moderator Variables" Paper presented at the annual meeting of the International Communication Association 65th Annual Conference, Caribe Hilton, San Juan, Puerto Rico, May 21, 2015 Online <APPLICATION/PDF>. 2018-11-21 <http://citation.allacademic.com/meta/p986160_index.html>
Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Recent technological advancements make it easier for researchers to test complex statistical models. Moderated mediation is one such model, in which a process (mediation) is contingent on another variable (moderation). This tutorial describes proper parameterization of specific types of moderated mediation models: models in which multicategorical variables are used as either independent variables or moderators. This procedure is especially useful for scholars whose research often necessitates multicategorical designs, such as political communication scholars (e.g., conservative, liberal, or independent respondents) and media violence scholars (e.g., participants exposed to violent media, prosocial media, or neutral media). It allows them to test moderated mediation within such designs without altering the nature of their datasets. In other words, previously popular practices such as collapsing conditions and excluding conditions are no longer necessary with the implementation of this procedure.

2017 - American Society of Criminology Words: 143 words || 
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2. Buccine-Schraeder, Henri., Caplan, Joel. and Boxer, Paul. "Comparing Composite Dependent Variables to Individual Dependent Variables using Risk Terrain Modeling" Paper presented at the annual meeting of the American Society of Criminology, Philadelphia Marriott Downtown, Philadelphia, PA, Nov 14, 2017 <Not Available>. 2018-11-21 <http://citation.allacademic.com/meta/p1291046_index.html>
Publication Type: Poster
Review Method: Peer Reviewed
Abstract: For the purposes of crime policy and reporting, various types of violent crime are often aggregated, most prominently in the context of the USDOJ's Uniform Crime Reporting system. The violent crime index takes for granted that there is coherence in a given jurisdiction among the discrete crime types that comprise it -- assault, homicide, rape, and robbery, yet studies of crime forecasting techniques and theories suggest that more precise aggregates might be warranted. This poster will look at the validity of the construct of aggravated violence in a medium-sized Northeast city when using risk terrain modeling. Aggravated violence is defined for these purposes as assault, robbery, and aggravated assault. Risk terrain modeling’s predictive validity of aggravated violence will be compared to the individual crime types’ predictive validity to determine whether an aggregate dependent variable is an appropriate approach when running risk terrain models.

2018 - 89th Annual SPSA Conference Words: 160 words || 
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3. Wang, Erik. and Yamauchi, Soichiro. "Mitigating Omitted Variable Bias via Lagged Dependent Variables in Time‐Series Cross‐Section Data" Paper presented at the annual meeting of the 89th Annual SPSA Conference, Hyatt Regency, New Orleans, LA, Jan 04, 2018 <Not Available>. 2018-11-21 <http://citation.allacademic.com/meta/p1328497_index.html>
Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Omitted variable bias is a primary challenge facing observational studies. With time-series cross-section data, researchers are able to eliminate unit-specific time -invariant or time-specific unit-invariant unobserved confounders. However, time-varying unobservables at the unit level often remain and have to be assumed away when carrying out estimation and inference. We propose a simple strategy to possibly reduce omitted variable bias of this sort - by including lagged dependent variables in model estimation. This strategy exploits the fact that time-varying confounders in the real world are often serially correlated. We formally characterize this strategy and evaluate it with a wide range of data generating processess. Our simulations suggest that, by conditioning on past outcomes, researchers can meaningfully reduce confounding so long as the unobservables take on reasonably autocorrelated patterns. The strategy also works across different functional forms. In addition, we document that the gain in omitted variable bias reduction may even outweigh the loss in Nickell bias induced by unit fixed effects.

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