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This article examines the development of methods of political analysis concerned with ideas, beliefs and meanings and argues that these need to be supplemented by an approach attuned to the specific nature of political action. It argues that since politics involves the contest of ideas, beliefs and meanings, analysis should focus on arguments. Considering methods for the study of political arguments the article argues for a re-examination of the rhetorical tradition and the development of a Rhetorical Political Analysis (RPA). It then outlines the sorts of things this would examine, the questions it would ask and the ways in which it might go about answering them.

AbstractData envelopment analysis is a linear programming–based method that has clear advantages over competing approaches. However, its own limitations should not be overlooked.

This document is sharing the methodological perspective on ethnography and data analysis of the TRANSGANG project. The aim is to provide to the local research teams a guide about ethnographic perspectives, tools and data analyses strategy, to be able to apply using NVivo software, and some references to ensure these perspectives. The objective of the TRANSGANG project is a comparative investigation based on one stage of local analysis and two stages of secondary analysis of the data collected according to the methodological approach. These three-level analyses will combine the results in a unique and transnational picture of street youth groups without losing the cultural particularities of the different places. The central and contrast cases involve a combination of qualitative techniques (our tools) such as narrative interviews, focus groups, life stories and participant observation, but it is necessary to work with similar analysis categories in each country. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 742705

Since WWII, the field of survey research has experienced substantial growth in methods for analysis of survey data. The basic general multivariate paradigm was established by Paul Felix Lazarsfeld; subsequently regression methods were introduced, including path analysis, structural equation models, & partial correlations. A causal approach to measurement error has been developed that relies on linear structural relations. Categoric methods include loglinear methods. In addition, interest has grown in the collection of panel data. These methods have transformed the field, though simple univariate & bivariate analyses remain the most widespread methods. 106 References. W. H. Stoddard

Multilevel data are structures that consist of multiple units of analysis, one nested within the other. Such data are becoming quite common in political science & provide numerous opportunities for theory testing & development. Unfortunately, this type of data typically generates a number of statistical problems, of which clustering is particularly important. To exploit the opportunities offered by multilevel data, & to solve the statistical problems inherent in them, special statistical techniques are required. In this article, we focus on a technique that has become popular in educational statistics & sociology -- multilevel analysis. In multilevel analysis, researchers build models that capture the layered structure of multilevel data, & determine how layers interact & impact a dependent variable of interest. Our objective in this article is to introduce the logic & statistical theory behind multilevel models, to illustrate how such models can be applied in political science, & to call attention to some of the pitfalls in multilevel analysis. 5 Tables, 96 References. Adapted from the source document.

The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.