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.

Increasing numbers of comparative survey datasets span multiple waves. Moving beyond purely cross-sectional analyses, multilevel longitudinal analyses of such datasets should generate substantively important insights into the political, social and economic correlates of many individual-level outcomes of interest (attitudes, behaviors, etc.). This article describes two simple techniques for extracting such insights, which allow change over time in y to be a function of change over time in x and/or of a time-invariant x. The article presents results from simulation studies that assess the techniques in the presence of complications that are likely to arise with real-world data, and concludes with applications to the issues of generalized social trust and postmaterialist values, using data from World/European Values Surveys.

Around the world, most people are aware of the problem of climate change, believe it is anthropogenic, and feel concerned about its potential consequences. What they think should be done about the problem, however, is less clear. Particularly due to widespread support among policy experts for putting a price on greenhouse gas emissions, more studies have investigated public attitudes towards carbon taxes than any other type of policy. Such studies have found substantial public opposition to carbon taxes, largely due to political distrust, though also some evidence that careful design and messaging can mitigate people’s skepticism. Surprisingly few studies have investigated attitudes towards other climate policies, and there is an urgent need for more research about what—given their beliefs about the nature and severity of the problem—people would like to see their governments doing. This is especially the case for residents of lower-income and/or non-Western nations.

This article presents results from survey experiments investigating conditions under which Britons are willing to pay taxes on polluting activities. People are no more willing if revenues are hypothecated for spending on environmental protection, while making such taxes more relevant to people – by naming petrol and electricity as products to which they will apply – has a modestly negative effect. Public willingness increases sharply if people are told that new environmental taxes would be offset by cuts to other taxes, but political distrust appears to undermine much of this effect. Previous studies have argued that political trust shapes public opinion with respect to environmental and many other policies. But this article provides the first experimental evidence suggesting that the relationship is causal, at least for one specific facet: cynicism about public officials’ honesty and integrity. The results suggest a need to make confidence in the trustworthiness of public officials and their promises more central to conceptualizations of political trust.