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The use of choice-based, matched, and other stratified sample designs is common in auditing research. However, it is not widely appreciated that the data analysis for these studies has to take into account the non-random nature of sample selection in these designs. A choice-based, matched or otherwise stratified sample is a nonrandom sample that must be analyzed using conditional analysis techniques. We review five research streams in the auditing area. These streams include work on determinants of audit litigation, audit fees, auditor reporting in financially distressed firms, audit quality and auditor switches. Cram, Karan, and Stuart (CKS) (2009) demonstrated the accuracy of conditional analysis, compared to unconditional analysis, of nonrandom samples through the use of simulations, replications, and mathematical proofs. Papers since published have continued to rely upon questionable research, however, and it is hard for researchers to identify what is the reliability of a given work. We complement and extend CKS (2009) by identifying audit papers in selected research streams whose results will likely differ if the data gathered are analyzed using conditional analysis techniques. Thus research can be advanced either by replication and reanalysis, or by refocus of new research upon issues that should no longer be viewed as settled.

Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

This paper aims to extend the work by Vafeas and Lie and Lie by developing an empirical model of choice among four alternative mechanisms for distributing cash from corporations to shareholders: a fixed‐price self‐tender offer, a Dutch auction self‐tender offer, an open market share repurchase, and a special dividend.

A multinomial logit (MNL) model adapted for choice‐based sampling is used to examine the factors that influence a firm's choice among the four methods.

Firms with a high degree of heterogeneity in shareholder valuations tend to select an open market repurchase, while firms with low levels of heterogeneity choose a special dividend. Firms already paying high dividends are more likely to issue a special dividend than institute an open market repurchase. A firm with poor stock performance prior to the announcement is more likely to choose a fixed‐price self‐tender offer or open market share repurchase. On the other hand, firms are more likely to follow strong performance with a special dividend. Contrary to Persons' model, it is found that firms facing a takeover threat are more likely to choose a fixed‐price tender offer than a Dutch auction.

It is shown that the ownership structure, current payout level; the size of the distribution, and the degree of stock undervaluation are among the most important determinants of a firm's choice among alternative payout methods.

This study adds to the existing literature by developing the first empirical model of choice among all four one‐time (or infrequent) corporate cash disbursement methods. It is also the first to adjust the MNL estimates for the choice‐based sampling method used to collect the data.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

Much of the research conducted in logistics/SCM has focused on satisfaction/retention of customers. This has left a critical gap for managers: before customers can be satisfied and ultimately retained, a purchase choice of logistics services has to occur. To date, very little research has addressed how logistics customers make purchase choice decisions about logistics services. The purpose of this paper, using logistics research methods, is to introduce adaptive choice modelling (ACM) to address this gap and put forth a research method that is useful for academic researchers and logistics/SCM managers.

This paper provides an overview of ACM, along with a discussion of its important research advantages, limitations, and practical applications. Additionally, an empirical demonstration of this research technique is provided to illustrate how academic researchers and logistics managers can use ACM to better understand the decision‐making process of customers when selecting logistics services.

In order to demonstrate this research technique, a research project was designed and implemented that analyzed the choice process of consumers selecting parcel carriers to ship a textbook. The results show that price, speed of delivery, and tracking are the three most important variables in the selection decision. The results also show that consumers are not homogeneous, but can be divided into five distinct need‐based segments. Recognizing and understanding the nature of these segments should help managers better meet the needs of parcel shippers.

The main research limitation with this study is that it is based on a convenience sample; thus future research will need to replicate this study to confirm the research findings. However, the ultimate purpose of the study is to present a new research method and discuss how to apply this method, so that logistics/SCM practitioners and academic researchers can better understand customers of logistics/SCM services. Thus, while the nature of the sample is a limitation, it should be viewed in this context.

While conjoint analysis has existed for decades, this technique has rarely been implemented by logistics/SCM researchers and practitioners. Instead, logistics/SCM researchers and practitioners have focused more on retention methods and have virtually ignored modelling the actual purchase choice of logistics/SCM services. New advancements in conjoint analysis, specifically the ACM approach, have many important and unique advantages and applications for logistics/SCM researchers and practitioners. ACM has not been used in a logistics/SCM context.

The purpose of this paper is to empirically test legitimacy theory by comparing the corporate social responsibility (CSR) disclosures of tax aggressive corporations with those of non‐tax aggressive corporations in Australia.

A unique sample of 20 Australian corporations accused by the Australian Taxation Office of engaging in tax aggressive activities during the 2001‐2006 period was hand‐collected. These 20 tax aggressive corporations were then matched with 20 non‐tax aggressive corporations (based on industry classification, corporation size and time period). This process generated a choice‐based sample of 40 corporations for empirical analysis. Using content analysis techniques, financial accounting data were gathered from the Aspect‐Huntley database and CSR disclosures were individually measured for each corporation in the sample. Various statistical techniques were then used (e.g. paired sample statistics, Pearson correlation analysis and ordinary least squares regression analysis) to test legitimacy theory.

Overall, the empirical results consistently show a positive and statistically significant association between corporate tax aggressiveness and CSR disclosure, thereby confirming legitimacy theory in the context of corporate tax aggressiveness.

The paper provides empirical evidence in support of legitimacy theory as an explanation for why specific corporations disclose more CSR‐related information than others. Additionally, to the best of the authors' knowledge, the paper is one of the first to document an empirical association between corporate tax aggressiveness and CSR in the literature.

Prior analyses of racial bias in the New York City’s Stop-and-Frisk program implicitly assumed that potential bias of police officers did not vary by crime type and that their decision of which type of crime to report as the basis for the stop did not exhibit any bias. In this paper, we first extend the hit rates model to consider crime type heterogeneity in racial bias and police officer decisions of reported crime type. Second, we reevaluate the program while accounting for heterogeneity in bias along crime types and for the sample selection which may arise from conditioning on crime type. We present evidence that differences in biases across crime types are substantial and specification tests support incorporating corrections for selective crime reporting. However, the main findings on racial bias do not differ sharply once accounting for this choice-based selection.