Search results

The contents and function of a computer package to fit reliability models for computer software are outlined. Parameters in the models are, in the first place, estimated by maximum likelihood estimation procedures. Bayesian estimation methods are also used and are shown to give estimates with a smaller variance than their MLE counterparts. An example of the application to a particular set of failure times is given.

In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

Surveys measuring consumers’ preferences for sustainable food might suffer from socially desirable responding. Social desirability stems in part from social norms about sustainable lifestyles, when respondents need approval from others and when privacy is not guaranteed during survey completion. While various studies showed this phenomenon through laboratory experiments and by comparing different modes of survey administration, no research adopted factorial survey experiments (FSEs) to measure which factors are perceived by consumers as critical for socially desirable answering. The purpose of this paper is to fill this gap, at least for young consumers in a case study with organic fruit.

In total, 143 under-30 respondents were involved in an FSE. Each respondent evaluated six hypothetical scenarios (n=858) describing a consumer surveyed about his/her preferences for organic fruit. Respondents indicated whether they believed participants would have answered honestly or not to the survey described in each scenario. Generalized linear mixed models were used to model how scenario attributes were perceived to influence honest answering.

Respondents believe that people are more prone to bias their answers the more they seek approval from others. Moreover, the presence of acquaintances during survey completion is another critical driver of survey misreporting.

This study, by using a novel robust quasi-experimental approach, confirms that social desirability could lead consumers to misreport their preferences when surveyed about an organic fruit. This confirms that well-designed surveys, adopting proper remedies for social desirability should be adopted even for those food products, like fruit, which are usually deemed to be less subjected to misreporting. It also introduces FSEs as a flexible tool for collecting insights from consumers about potential antecedents of their behavior.

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T² control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T² control chart.

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

Observations in a dataset are rarely missing at random. One can control for this non-random selection of the data by introducing fixed effects or other nuisance parameters. This chapter deals with consistent estimation the presence of many nuisance parameters. It derives a new orthogonality concept that gives sufficient conditions for consistent estimation of the parameters of interest. It also shows how this orthogonality concept can be used to derive and compare estimators. The chapter then shows how to use the orthogonality concept to derive estimators for unbalanced panels and incomplete data sets (missing data).

Research shows that teams often fail to surface and use unique information to evaluate decision alternatives. Under a condition known as the hidden profile, each member uniquely possesses a critical clue needed to uncover the superior solution. Failure to share and adequately evaluate this information will result in poor decision quality. The aim of this paper is to examine the impact of the devil's advocacy technique on the decision quality of hidden profile teams.

In order to mitigate this team decision‐making bias, the present study utilizes experimental research to examine the impact of the devil's advocacy technique on the decision quality of hidden profile teams.

Results show that devil's advocacy groups achieved higher decision quality than groups under free discussion. However, devil's advocacy teams also had higher levels of affective conflict. As a result, while they selected the best solution, devil's advocacy introduced conditions that may hinder the solution's implementation

Similar experiments with advocacy techniques suggest that the positive effect on decision quality found here may be reduced in the presence of stronger hidden profiles.

While the devil's advocacy technique has the potential to uncover hidden profiles and improve group decision making, the paper recommends that managers use this technique only in teams with strong critical thinking norms that foster constructive conflict.

To the authors' knowledge, no study has examined the impact of devil's advocacy in groups where information is not shared equally prior to deliberations.

The purpose of this paper is to examine the issue of racial profiling when the traffic stop outcome is a search using focal concerns theory as a theoretical explanation for police officer decision making and propensity score matching (PSM) as a better analysis to understand the race of the driver.

The data for this study come from traffic stops conducted by the Louisville Police Department between January 1 and December 31, 2002.

The results show that the elements of focal concerns theory matter most when it comes to if a traffic stop that resulted in a search even though racial profiling was evident. The use of PSM provides evidence that it is a better statistical technique when studying racial profiling. The gender of the driver was significant for male drivers but not for female drivers.

The data for this study are cross-sectional and are self-report data from the police officer.

This paper serves as a theoretical explanation that other researchers could use when studying racial profiling along with a better type of statistical analysis being PSM.

The findings based on focal concerns theory could provide an explanation for police officer decision making that police departments could use to help citizens understand why a traffic stop search took place.

This is the first study of its kind to the researcher’s knowledge to apply focal concerns theory with PSM to understand traffic stop searches.

The environment surrounding U.S. higher education has changed substantially over the past 40 years. However, we have a limited understanding of what these changes mean for the higher education organizations (HEOs) that occupy this organizational field. In this paper, I use descriptive statistics and multilevel latent class analysis (MLCA) to analyze the financial behaviors of public four-year HEOs from 1986 to 2010 to evaluate how HEOs adapt financially to their changing environments. I advance the current conceptual and empirical understanding of public HEO behaviors by evaluating how public HEOs utilize combinations of revenue and spending streams to accomplish their mission and the extent to which the revenues and spending patterns of these institutions are related. Descriptive results confirm the shift away from state funding toward tuition revenues and the relative stability in spending patterns. MLCA results, which allow for the investigation of how combinations of revenue and spending streams work together, indicate that public HEOs are changing the combinations of revenues they rely on in different ways, revealing multiple specific pathways for how public HEOs adapt to their changing environments. The spending profiles, in contrast, remain stable with only a few HEOs changing their profile over time. I argue that the loose coupling between revenues and spending and discontinuity in their patterns of change over time suggests that public HEOs are able to establish a buffer between their environment and spending or activities that allows them to continue engaging in the same broad set of activities despite environmental changes.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

This study aims to provide insights on how different sources of information concerning a quality assurance program (QAP) influence consumers’ wine purchase likelihood, profiles of consumers most likely to use QAPs (demographics, wine consumption and expenditures, wine involvement behaviors) and consumer attitudes toward QAPs.

Data are from a 2021 survey of 1,191 wine consumers in Tennessee and other US states. A multiple indicators multiple causes model is used to estimate how consumer demographics, wine consumption and expenditure patterns and several wine-involvement measures influence likelihood of using QAPs from eight provider sources when making wine purchase decisions. Sources include university, government, third-party certifiers, wineries and wine associations at the state, regional, national and international levels.

Wine consumers have an interest in QAP information when making wine purchase decisions. Not all QAP provider information is used equally, with almost 69% of the sample indicating the use of state wine association QAPs, but less than 44% indicating the use of government agency QAPs or third-party QAPs. Wine consumers’ demographics also influence the use of QAP information. Males, higher income consumers, residing outside of Tennessee and more wine-involved consumers are more likely to use QAPs. Consumers view QAPs as indicators of overall wine quality, ingredient quality and wine consistency rather than necessarily a means of building knowledge about local wines.

To the best of the authors’ knowledge, this paper is the first to examine not only QAP use from multiple providers but also how demographics, wine consumption, wine expenditures and wine-involvement impact QAP use.