This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered-categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions such as the underlying population distribution, the number of items, the level of random error, and characteristics of items and item-sets. Each of these datasets is factor analysed in a variety of ways that are frequently used in the extant literature, or that are recommended in current methodological texts. These include exploratory factor retention heuristics such as Kaiser’s criterion, Parallel Analysis and a non-graphical scree test, and (for exploratory and confirmatory analyses) evaluations of model fit. These analyses are conducted on the basis of Pearson and polychoric correlations. We find that, irrespective of the particular mode of analysis, factor analysis applied to ordered-categorical survey data very often leads to over-dimensionalisation. The magnitude of this risk depends on the specific way in which factor analysis is conducted, the number of items, the properties of the set of items, and the underlying population distribution. The paper concludes with a discussion of the consequences of over-dimensionalisation, and a brief mention of alternative modes of analysis that are much less prone to such problems.

:The financial cost of corruption has recently been estimated at more than 5 per cent of global GDP. Yet, despite the widespread agreement that corruption is one of the most pressing policy challenges facing world leaders, it remains as widespread today, possibly even more so, as it was when concerted international attention began being devoted to the issue following the end of the Cold War. In reality, we still have a relatively weak understanding of how best to measure corruption and how to develop effective guides to action from such measurement. This paper provides a detailed review of existing approaches to measuring corruption, focusing in particular on perception-based and nonperceptual approaches. We highlight a gap between the conceptualisation of corruption and its measurement, and argue that there is a tension between the demands of policy-makers and anti-corruption activists on the one hand, and the motivations of academic researchers on the other. The search for actionable answers on the part of the former sits uncomfortably with the latter's focus on the inherent complexity of corruption.

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This paper examines the relief provided by the Home Owners’ Loan Corporation (HOLC), a New Deal program that purchased and refinanced over 1 million distressed residential mortgages. I document that the HOLC paid relatively high prices for its mortgages, most likely in an effort to encourage lender participation and stimulate the housing market. The consequence was that lenders were able to remove poorly performing assets from their balance sheets at attractive prices. While this meant the HOLC’s ability to seek principal reductions was somewhat limited, borrowers still received significant relief through the terms of the HOLC’s more modern and forgiving mortgage contracts.