Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure,illustrated by studies of British voting behaviour

Quality & Quantity - Many ecological- and individual-level analyses of voting behaviour use multiple regressions with a considerable number of independent variables but few discussions of their...     

Trust in online recommendations: an evolutionary psychology perspective

Electronic word-of-mouth (eWOM) is playing an increasingly important role in influencing consumer behavior, and it represents another opportunity for marketers to build support for their brands. This study explores how the gender of the reader influences how eWOM is perceived. An experiment grounded in evolutionary psychology examines how males and females differ in their perceived trust of eWOM of positive, negative, or mixed valence. The results indicate that females place greater trust than males in eWOM messages. But both genders find eWOM of mixed valence more trustworthy than entirely positive or negative eWOM. A follow-up phenomenological study of male and female consumers who had experienced eWOM shed more light on the experimental findings and offers additional support for their evolutionary basis. Theoretical and practical implications are discussed as well as recommendations for further research.     

Fixed and random effects models: making an informed choice

This paper assesses the options available to researchers analysing multilevel (including longitudinal) data, with the aim of supporting good methodological decision-making. Given the confusion in the literature about the key properties of fixed and random effects (FE and RE) models, we present these models’ capabilities and limitations. We also discuss the within-between RE model, sometimes misleadingly labelled a ‘hybrid’ model, showing that it is the most general of the three, with all the strengths of the other two. As such, and because it allows for important extensions—notably random slopes—we argue it should be used (as a starting point at least) in all multilevel analyses. We develop the argument through simulations, evaluating how these models cope with some likely mis-specifications. These simulations reveal that (1) failing to include random slopes can generate anti-conservative standard errors, and (2) assuming random intercepts are Normally distributed, when they are not, introduces only modest biases. These results strengthen the case for the use of, and need for, these models.

     

Current practice in the modelling of age,period and cohort effects with panel data: a commentary on Tawfik et al. (2012), Clarke et al. (2009), and McCulloch (2012)

This comment assesses how age, period and cohort (APC) effects are modelled with panel data in the social sciences. It considers variations on a 2-level multilevel model which has been used to show apparent evidence for simultaneous APC effects. We show that such an interpretation is often misleading, and that the formulation and interpretation of these models requires a better understanding of APC effects and the exact collinearity present between them. This interpretation must draw on theory to justify the claims that are made. By comparing two papers which over-interpret such a model, and another that in our view interprets it appropriately, we outline best practice for researchers aiming to use panel datasets to find APC effects, with an understanding that it is impossible for any statistical model to find and separate all three effects.     

The Mistreated Model: Some Technical Comments on Porojan's Paper on 'Trade Flows and Spatial Effects'

The paper by Porojan on 'Trade flows and spatial effects' in a recent issue of this journal is criticized on three methodological grounds: it includes two of the gravity model independent variables as relative rather than absolute numbers; the dummy variables included are not binary but interval; and the 'sample' of countries creates a non-normal distribution of inter-country distances, undoubtedly creating heteroscedasticity problems. Because of these three problems, the model is mis-specified, and the results thereby obtained are unreliable.     

The hierarchical age–period–cohort model: Why does it find the results that it finds?

It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure—that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439–1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125–128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.