Critical Realism,Dialectics, and Qualitative Research Methods

Critical realism has been an important advance in social science methodology because it develops a qualitative theory of causality which avoids some of the pitfalls of empiricist theories of causality. But while there has been ample work exploring the relationship between critical realism and qualitative research methods there has been noticeably less work exploring the relationship between dialectical critical realism and qualitative research methods. This seems strange especially since the founder of the philosophy of critical realism, Roy Bhaskar, employs and develops a range of dialectical concepts in his later work in order to extend the main tenets of critical realism. The aim of this paper is to draw on Bhaskar's later work, as well as Marxism, to reorient a critical realist methodology towards a dialectical approach for qualitative research. In particular, the paper demonstrates how dialectical critical realism can begin to provide answers to three common criticisms made against original critical realist methodology: that the qualitative theory of causal powers and structures developed by critical realists is problematic; that critical realist methodology contains values which prove damaging to empirical research; and that critical realists often have difficulties in researching everyday qualitative dilemmas that people face in their daily lives.     

Compulsory income management: Combatting or compounding the underlying causes of homelessness?

Compulsory Income Management (CIM) is a form of conditional welfare that involves the mandatory quarantining of a portion of welfare recipients’ social security payments. Quarantined funds are accessible via a government-issued debit card, with restrictions surrounding where and on what funds can be spent. Official justifications of CIM have framed these policies as attempts to combat substance abuse and gambling problems, and to thus secure better outcomes for welfare recipients and their families. Central to this narrative has been the argument that welfare quarantining will ensure more money is spent on ‘essentials’, including accommodation. No existing studies, however, have specifically interrogated the impacts of CIM on housing security. This article responds to this gap in the literature by reviewing existing research concerning CIM's impacts and locating this research within broader debates regarding the causes of homelessness and the efficacy of individualised policy interventions. In doing so, it highlights CIM's potential to exacerbate housing insecurity not only through technical issues such as rental transfer failures, but also by contributing to underlying stressors such as economic disadvantage; relationship difficulties, poor health and addiction; and social stigma. The article concludes that – far from addressing the structural causes of homelessness – CIM has enflamed them.     

Stakeholder value disclosures: anchoring on primacy and importance of financial and nonfinancial performance measures

In the growing debate about stakeholder values, there has been little discussion about information overload or whether the requested disclosures can be effectively used. Stakeholder advocates call for complicated and massive environmental and related social disclosures while not considering how information overload might affect the discourse about corporate performance. Stakeholders, including shareholders, plead for more transparency in financial statements, management discussion and analysis (MDA), and other corporate disclosures. As we know, shareholders and boards of directors are most concerned with the ‘Holy Trinity’ of earnings per share, dividends and market value changes. We believe that managers and stakeholders involved in performance evaluations have multiple interests that extend beyond traditional shareholder value measures. We note that the Balanced Scorecard (BSC) was developed as one tool to reflect and communicate these multiple measures. We test how managers use (or ignore) multiple performance measures and we posit that stakeholders will face many of the same constraints when using and processing multiple disclosures including Corporate Social Reports (CSR), environmental, or similar disclosures. While we do not directly test a wide variety of stakeholder disclosures, we examine eight (four for a single subject) shareholder values (financial measures) and four stakeholder values (nonfinancial measures). The eight measures included in our research instruments serve as proxies for the multiple concerns that might be of interest to many stakeholders. Note that stakeholders are likely to be extremely interested in nonfinancial performance measures, while many shareholders will likely concentrate on financial performance measures. Field research has reported managers tend to favor financial measures while discounting or ignoring nonfinancial measures when evaluating subordinates, making it difficult to align performance evaluations and incentives with corporate strategies (Ittner et al. Account Rev 78:725–758, 2003). In this study, we find the relative weights managers place on financial and nonfinancial performance measures are influenced by both (1) presentation order and (2) the relative importance of specific measures. When financial measures are presented first, the manager who performs better on financial measures is rated higher than the manager who performs better on nonfinancial measures. However, when nonfinancial measures are presented first, managers who excel on nonfinancial measures are rated higher. Reports that include financial measures that are relatively more (less) important also produce higher (lower) ratings for the manager who excels on financial measures. Thus, the relative weights that superiors place on financial and nonfinancial measures in evaluating corporate managers’ performance are substantially anchored both by the order in which measures are presented as well as by the importance of the specific performance measures employed. Other stakeholder disclosures are likely to be similarly anchored, perhaps biased, by primacy and a priori importance rankings.     

Discovering classes in microarray data using island counts

We present a new biclustering algorithm to simultaneously discover tissue classes and identify a set of genes that well-characterize these classes from DNA microarray data sets. We employ a combinatorial optimization approach where the object is to simultaneously identify an interesting set of genes and a partition of the array samples that optimizes a certain score based on a novel color island statistic. While this optimization problem is NP-complete in general, we are effectively able to solve problems of interest to optimality using a branch-and-bound algorithm. We have tested the algorithm on a 30 sample Cutaneous T-cell Lymphoma data set; it was able to almost perfectly discriminate short-term survivors from long-term survivors and normal controls. Another useful feature of our method is that can easily handle missing expression data.     

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes

We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation.     

Problems of Growth in a High Technology Firm: Moving between Conscious and Unconscious Accounts of Organizational Processes

This paper explores an infrequently discussed methodological divide within qualitative research; that between conscious and unconscious accounts of organizational processes. The paper makes use of an empirical case study of problems encountered in the growth of a high-technology company. The conscious accounts of growth treat members of the firm as knowledgeable agents whose understandings are then drawn upon to generate an account of growth in terms of interrelated processes of power, meaning and legitimacy. These conscious accounts are then complemented through an exploration of unconscious dynamics in the personality of the entrepreneur, the work group and the `family' structure of the firm. It is argued that the key shift in moving between conscious and unconscious interpretation, involves the bracketing of the reality claims implicit in conscious rationalizations, and a re-listening to research material as a largely unconscious projection of individuals' `inner worlds'. With unconscious interpretations, the character of language, its emotional content and in particular the sources of individual and group anxiety all realize a central importance. Despite the established divide in the literature between these two forms of interpretation, it is argued that the case suggests the value and necessity of their integration, particularly for the understanding of creativity.     

A case study in non-centering for data augmentation: Stochastic epidemics

In this paper, we introduce non-centered and partially non-centered MCMC algorithms for stochastic epidemic models. Centered algorithms previously considered in the literature perform adequately well for small data sets. However, due to the high dependence inherent in the models between the missing data and the parameters, the performance of the centered algorithms gets appreciably worse when larger data sets are considered. Therefore non-centered and partially non-centered algorithms are introduced and are shown to out perform the existing centered algorithms.     

Contributions of the National Incident-Based Reporting System to Substantive Knowledge in Criminology: A Review of Research

The National Incident-Based Reporting System (NIBRS) data have been available to the research community for about a decade. The advantages of NIBRS over other official statistics such as summary Uniform Crime Reports, Supplementary Homicide Reports, and the National Crime Victimization Survey have contributed to better evaluation of a number of theoretical perspectives in criminology and criminal justice, and have helped in suggesting possible policy implications. This paper reviews studies using the NIBRS data in the areas of crime clearance, crime rates disaggregated by situational context (including domestic violence, race-specific crime, and gun crime), and lethal and non-lethal outcomes of violent encounters. The paper also discusses limitations of the NIBRS data and cautions for its use, along with future research possibilities.     

The Dynamics of Disaster Recovery: Resilience and Entropy in Hurricane Response Systems 2005–2008

The challenge for policy makers and disaster managers is to achieve a balance between two dynamics — resilience and entropy — in order to develop sustainable risk reduction. Achieving an appropriate balance between resilience and entropy in any given community requires a systematic exploration of both dynamics. The recent hurricanes that struck Louisiana, Hurricane Katrina on August 29, 2005 and Hurricane Gustav, on September 1, 2008, offer an unusual opportunity to assess the degree to which both dynamics operated following Hurricane Katrina.