Understanding selection bias,time-lags and measurement bias in secondary data sources: Putting the Encyclopedia of Associations database in broader context

Secondary data gathered for purposes other than research play an important role in the social sciences. A recent data release has made an important source of publicly available data on associational interests, the Encyclopedia of Associations (EA), readily accessible to scholars (www.policyagendas.org). In this paper we introduce these new data and systematically investigate issues of lag between events and subsequent reporting in the EA, as these have important but under-appreciated effects on time-series statistical models. We further analyze the accuracy and coverage of the database in numerous ways. Our study serves as a guide to potential users of this database, but we also reflect upon a number of issues that should concern all researchers who use secondary data such as newspaper records, IRS reports and FBI Uniform Crime Reports.     

Sample Selection and Treatment Effect Estimation of Lender of Last Resort Policies

This article develops a framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology deals with several important issues prevalent in policy and program evaluation, including application and approval stages, nonrandom treatment assignment, endogeneity, and discrete outcomes. This article presents a computationally efficient estimation algorithm and techniques for model comparison and treatment effects. The framework is applied to evaluate the effectiveness of bank recapitalization programs and their ability to resuscitate the financial system. The analysis of lender of last resort (LOLR) policies is not only complicated due to econometric challenges, but also because regulator data are not easily obtainable. Motivated by these difficulties, this article constructs a novel bank-level dataset and employs the new methodology to jointly model a bank’s decision to apply for assistance, the LOLR’s decision to approve or decline the assistance, and the bank’s performance following the disbursements. The article offers practical estimation tools to unveil new answers to important regulatory and policy questions.     

A temporal investigation of the direct,interactive, and reverse relations between demand and control and affective strain

Abstract

Few studies have tested how stressors affect outcomes over time. We sought to extend the literature by means of a longitudinal study testing for direct, interactive, and causal relations between demands and control and affective strain. We extended prior work testing causal relationships for Karasek's (1979 Karasek, R.A. Jr. 1979. Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24: 285–307. [Crossref], [Web of Science ®] , [Google Scholar]) Job Demand-Control (JDC) model by examining both the effects of demands and control on strain and in turn the effects of strain on demand and control. We tested our hypotheses using hierarchical linear modelling with a military sample of 1539 soldiers who completed six waves of survey data at 3-month time lags. The results replicate earlier cross-sectional studies reporting effects of work characteristics on strain; however, in our study these effects did not persist past three months. The results also provide evidence for reverse causal effects such that higher strain was associated with higher subsequent work overload and lower control over a six month time period. Similar to past research, we did not find support for the interactive effects of work overload and control on strain. We discuss the implications of our findings for theory and practice (such as the optimum time for applying interventions during the management of change), especially in terms of understanding the specific time lags for different stress–strain associations and the need for additional theories to explain reverse relationships.     

Understanding Policy Reform in Colorado's Domestic Violence Offender Treatment Standards

This study examines the perceived conditions leading to Colorado adopting revised Standards for treating domestic violence offenders from the perspective of Kingdon's multiple streams theory. A within‐case analysis of ten semi‐structured interviews explores the Domestic Violence Offender Management Board in Colorado's process for revising Colorado's 2001 Treatment Standards. Findings suggest a benign political stream, supported by technically feasible and value‐accepted policy ideas, feedback and indicators of the problem, promoted a favourable setting for change. At least two policy entrepreneurs championed the decision‐making process. Agenda setting and policy formulation depended on three elements: feedback from existing programmes, indicators of continued difficulty in effectively treating offenders, and the existence of a suitable policy alternative. This study contributes a rare application of multiple streams theory to a criminal justice context in a sub‐national government. Future research should explore whether the presence of a benign political stream influences the development and revision of domestic violence policy in other policy environments.     

Local therapy facilitators working with children with developmental delay in rural and remote areas of western New South Wales,Australia: the ‘Outback’ service delivery model

Australia's dispersed population in rural areas contributes to poor access to therapy services and the inability of the existing rural therapy workforce to meet demand. As a result, rural children with a developmental delay wait a long time for therapy. This paper describes participant perceptions of a therapy facilitation service model that has worked to improve access to therapy for children in these circumstances. The model, given the pseudonym ‘Outback’, operates in rural and remote areas of western New South Wales. ‘Outback’ employs local people to work under the guidance of therapists based in larger centres to provide preschool children with developmental delays with access to therapy interventions they might not otherwise receive. A two‐stage case study design involving focus groups and interviews with the director, four therapy facilitators, nine therapists, and seven carers was used. Three themes were identified as central to the service model: 1) being part of the local community; 2) developing therapy facilitator knowledge and skills; 3) improving access to therapy intervention for children in rural and remote areas. The ‘Outback’ model demonstrates that appropriately supported, local therapy facilitators provide a flexible workforce adjunct that expands the reach of therapists into rural and remote communities and enhances service access for children and their families.     

Different estimators of the spectral matrix: an empirical comparison testing a new shrinkage estimator

ABSTRACT

In this paper we propose a new non parametric estimator of the spectral matrix of a multivariate stationary stochastic process, with the main goal to locally improve the deficiencies of the smoothed periodogram in terms of mean square error of the estimates. Our estimator is based on a convex linear combination of the frequency averaged periodogram and an estimate of the true mean spectral matrix across frequencies. In a wide simulation study we show that our estimator turns out to be able to markedly improve the frequency averaged periodogram especially at central frequencies.     

Improved population-based probability of developing cancer when direct estimates of the cancer-free population are available

Age-conditional probabilities of developing a first cancer represent the transition from being cancer-free to developing a first cancer. Natural inputs into their calculation are rates of first cancer per person-years alive and cancer-free. However these rates are not readily available because they require information on the cancer-free population. Instead rates of first cancer per person-years alive, calculated using as denominator the mid-year populations, available from census data, can be easily calculated from cancer registry data. Methods have been developed to estimate age-conditional probabilities of developing cancer based on these easily available rates per person-years alive that do not directly account for the cancer-free population. In the last few years models (Merrill et al., Int J Epidemiol 29(2):197-207, 2000; Mariotto et al., SEER Cancer Statistics Review, 2002; Clegg et al., Biometrics 58(3):684-688, 2002; Gigli et al., Stat Methods Med Res 15(3):235-253, 2006, and software (ComPrev:Complete Prevalence Software, Version 1.0, 2005) have been developed that allow estimation of cancer prevalence (DevCan: Probability of Developing or Dying of Cancer Software, Version 6.0, 2005). Estimates of population-based cancer prevalence allows for the estimation of the cancer-free population and consequently of rates per person-years alive and cancer-free. In this paper we present a method that directly estimates the age-conditional probabilities of developing a first cancer using rates per person-years alive and cancer-free obtained from prevalence estimates. We explore conditions when the previous and the new estimators give similar or different values using real data from the Surveillance, Epidemiology and End Results (SEER) program.     

A skew-normal factor model for the analysis of student satisfaction towards university courses

Classical factor analysis relies on the assumption of normally distributed factors that guarantees the model to be estimated via the maximum likelihood method. Even when the assumption of Gaussian factors is not explicitly formulated and estimation is performed via the iterated principal factors’ method, the interest is actually mainly focussed on the linear structure of the data, since only moments up to the second ones are involved. In many real situations, the factors could not be adequately described by the first two moments only. For example, skewness characterizing most latent variables in social analysis can be properly measured by the third moment: the factors are not normally distributed and covariance is no longer a sufficient statistic. In this work we propose a factor model characterized by skew-normally distributed factors. Skew-normal refers to a parametric class of probability distributions, that extends the normal distribution by an additional shape parameter regulating the skewness. The model estimation can be solved by the generalized EM algorithm, in which the iterative Newthon–Raphson procedure is needed in the M-step to estimate the factor shape parameter. The proposed skew-normal factor analysis is applied to the study of student satisfaction towards university courses, in order to identify the factors representing different aspects of the latent overall satisfaction.