Analisi delle preferenze per le diverse categorie di pomodoro trasformato nel food service

Il pomodoro è una delle colture principali del panorama agro-alimentare italiano e rappresenta un ingrediente base della tradizione culinaria nazionale. Il pomodoro lavorato dall’industria conserviera può essere trasformato in diverse tipologie merceologiche, che si differenziano in base alla tecniche di lavorazione impiegate ed alle caratteristiche del prodotto finito. la percentuale di spesa totale destinata all’acquisto di cibo fuori casa è in aumento a livello globale e l’interesse dell’industria alimentare nei confronti di questo canale di vendita è quindi crescente. Mentre sono numerose le indagine in letteratura che studiano i processi di acquisto dei consumatori finali, non ci sono evidenze di studi simili condotti sugli operatori del Food Service. Obiettivo principale della ricerca è quello di valutare le preferenze dei responsabili acquisti del settore Food Service per diverse tipologie di pomodoro trasformato, in relazione ad una gamma di attributi rilevanti del prodotto e di caratteristiche del cliente. La raccolta dei dati è avvenuta attraverso un esperimento di scelta ipotetico realizzato in Italia e alcuni mercati esteri. Dai risultati ottenuti dall’indagine emerge che i Pelati sono la categoria di pomodoro trasformato preferita dai responsabili degli acquisti del settore Food Service intervistati, con il 35% delle preferenze dichiarate nell'insieme dei contesti di scelta proposti, seguita dalla Polpa (25%), dalla Passata (20%) e dal Concentrato (15%). Dai risultati ottenuti dalla stima del modello econometrico Logit a parametri randomizzati è emerso che alcuni attributi qualitativi di fiducia (credence), spesso impiegati nelle strategie di differenziazione e posizionamento da parte dell’industria alimentare nel mercato Retail, possono rivestire un ruolo importante anche nell’influenzare le preferenze degli operatori del Food Service. Questo potrebbe quindi essere un interessante filone di ricerca da sviluppare nel futuro, possibilmente con l'impiego congiunto di metodologie di analisi basate su esperimenti di scelta ipotetici e non ipotetici.

America's Shifting Color Line? Reexamining Determinants of Latino Racial Self-Identification

Objectives. Latinos are the nation's largest minority group and will double in size by 2050. Their size coupled with the fact that Latinos do not constitute a separate race raises questions about Latinos' incorporation into the U. S. racial hierarchy. This article explores patterns of Latino racial identity formation, examining the determinants of racial identity. Methods. Using the 2006 Latino National Survey, I estimate multinomial logit and ordered probit models of identification choices. Results. Latino racial identity is strongly associated with several factors, including socioeconomic status, measures of perceived discrimination and commonality, and measures of acculturation/assimilation. Most Latinos have a broader, more complex understanding of race. Furthermore, some Latinos do believe that they occupy a unique position in the racial hierarchy. Conclusions. The results suggest that the color line W. E. DuBois argued has long divided our nation may eventually shift.

Does Mental Illness Affect Consumer Direction of Community-Based Care? Lessons From the Arkansas Cash and Counseling Program

Purpose: Previous research from the Cash and Counseling Demonstration and Evaluation (CCDE) in Arkansas, New Jersey, and Florida suggests that giving consumers control over their personal care greatly increases their satisfaction and improves their outlook on life. Still, some argue that consumerdirected care may not be appropriate for consumers with intellectual disabilities or mental health diagnoses. This study examined how Cash and Counseling— a new option allowing consumers to manage an individualized budget equivalent to what agencies would have spent on their care—changes the way consumers with mental health diagnoses meet their personal care needs and how that affects their wellbeing. Design and Methods: Using the Arkansas CCDE baseline and the 9-month follow-up data for individuals in the treatment and control groups, we compared and contrasted the experience of elderly consumers with and without mental health diagnoses utilizing logit regression. Results: After examining several outcome measures, including satisfaction with care arrangements and the paid caregiver’s reliability and schedule, unmet needs, and satisfaction with the relationship with paid caregivers, this study found evidence that, from the perspective of consumers, the Cash and Counseling program works well for participants with mental health diagnoses. Implications: Considering the growing need for long-term-care services and the limited resources available, a consumer-directed option makes sense, and it can be a valuable alternative for persons with mental health needs.

The Supportive Role of the Family in Solving Real and Projected Problems of Everyday Life

In this research the supportive role of the family in coping with everyday problems was studied using two large data sets. The results show the importance of the structural aspect of social support. Mapping individual preferences to support referents showed the crucial role of spouse and parents in solving everyday problems. The individual choices of particular support referents could be fairly accurately predicted from knowledge of the composition of the family, in both categorical regression and logit models. The far lower predictability of the criterion variable was shown using a wide range of socioeconomic, social and demographic indicators. Residence in small cities and indicators of extreme occupational strata were particularly predictive of the choice of support referent. The supportive role of the family was also traced in the personal projects of young adults, which were seen as ecological, natural and dynamic middle-level units of analysis of personality. Different aspects of personal projects, including reliance on social support referents, turned out to be highly interrelated. One the one hand, expectations of support were determined by the content of the project, and on the other, expected social support also influences the content of the project. Sivuha sees this as one of the ways others can enter self-structures.

A unified approach to modeling multivariate binary data using copulas over partitions

Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

Electronic medical record systems, data quality and loss to follow-up: survey of antiretroviral therapy programmes in resource-limited settings

OBJECTIVE: To describe the electronic medical databases used in antiretroviral therapy (ART) programmes in lower-income countries and assess the measures such programmes employ to maintain and improve data quality and reduce the loss of patients to follow-up. METHODS: In 15 countries of Africa, South America and Asia, a survey was conducted from December 2006 to February 2007 on the use of electronic medical record systems in ART programmes. Patients enrolled in the sites at the time of the survey but not seen during the previous 12 months were considered lost to follow-up. The quality of the data was assessed by computing the percentage of missing key variables (age, sex, clinical stage of HIV infection, CD4+ lymphocyte count and year of ART initiation). Associations between site characteristics (such as number of staff members dedicated to data management), measures to reduce loss to follow-up (such as the presence of staff dedicated to tracing patients) and data quality and loss to follow-up were analysed using multivariate logit models. FINDINGS: Twenty-one sites that together provided ART to 50 060 patients were included (median number of patients per site: 1000; interquartile range, IQR: 72-19 320). Eighteen sites (86%) used an electronic database for medical record-keeping; 15 (83%) such sites relied on software intended for personal or small business use. The median percentage of missing data for key variables per site was 10.9% (IQR: 2.0-18.9%) and declined with training in data management (odds ratio, OR: 0.58; 95% confidence interval, CI: 0.37-0.90) and weekly hours spent by a clerk on the database per 100 patients on ART (OR: 0.95; 95% CI: 0.90-0.99). About 10 weekly hours per 100 patients on ART were required to reduce missing data for key variables to below 10%. The median percentage of patients lost to follow-up 1 year after starting ART was 8.5% (IQR: 4.2-19.7%). Strategies to reduce loss to follow-up included outreach teams, community-based organizations and checking death registry data. Implementation of all three strategies substantially reduced losses to follow-up (OR: 0.17; 95% CI: 0.15-0.20). CONCLUSION: The quality of the data collected and the retention of patients in ART treatment programmes are unsatisfactory for many sites involved in the scale-up of ART in resource-limited settings, mainly because of insufficient staff trained to manage data and trace patients lost to follow-up.

Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .

Modelling zero-inflated count data when exposure varies: with an application to tumor counts)

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a modified zero-inflated count data model where the probability of an extra zero is derived from an underlying duration model with Weibull hazard rate. The new model is compared to the standard Poisson model with logit zero inflation in an application to the effect of treatment with thiotepa on the number of new bladder tumors.

Pre-Election Positions and Voting Behaviour in Parliament: Consistency among Swiss MPs

This article examines the determinants of positional incongruence between pre-election statements and post-election behaviour in the Swiss parliament between 2003 and 2009. The question is examined at the individual MP level, which is appropriate for dispersion-of-powers systems like Switzerland. While the overall rate of political congruence reaches about 85%, a multilevel logit analysis detects the underlying factors which push or curb a candidate's propensity to change his or her mind once elected. The results show that positional changes are more likely when (1) MPs are freshmen, (2) individual voting behaviour is invisible to the public, (3) the electoral district magnitude is not small, (4) the vote is not about a party's core issue, (5) the MP belongs to a party which is located in the political centre, and (6) if the pre-election statement dissents from the majority position of the legislative party group. Of these factors, the last one is paramount.

The Psychological Outcome of Religious Coping with Stressful Life Events in a Swiss Sample of Church Attendees

Background: Recent research suggested thatreligious coping, based on dispositional religiousness and spirituality (R/S), is an important modulating factor in the process of dealing with adversity. In contrast to the United States, the effect of R/S on psychological adjustment to stress is a widely unexplored area in Europe. Methods: We examined a Swiss sample of 328 church attendees in the aftermath of stressful life events to explore associations of positive or negative religious coping with the psychological outcome. Applying a cross-sectional design, we used Huber’s Centrality Scale to specify religiousness and Pargament’s measure of religious coping (RCOPE) for the assessment of positive and negative religious coping. Depressive symptoms and anxiety as outcome variables were examined by the Brief Symptom Inventory. The Stress-Related Growth Scale and the Marburg questionnaire for the assessment of well-being were used to assess positive outcome aspects. We conducted Mann-Whitney tests for group comparisons and cumulative logit analysis for the assessmentof associations of religious coping with our outcome variables. Results: Both forms of religious coping were positively associated with stress-related growth (p < 0.01). However, negative religious coping additionally reduced well-being (p = 0.05, β = 0.52, 95% CI = 0.27–0.99) and increased anxiety (p = 0.02, β = 1.94, 95% CI = 1.10–3.39) and depressive symptoms (p = 0.01, β = 2.27, 95% CI = 1.27–4.06). Conclusions: The effects of religious coping on the psychological adjustment to stressful life events seem relevant. These findings should be confirmed in prospective studies.

Now You See it, Now You Don't: Why Do Real Estate Agents Withhold Available Houses from Black Customers?

Potential home buyers may initiate contact with a real estate agent by asking to see a particular advertised house. This paper asks whether an agent's response to such a request depends on the race of the potential buyer or on whether the house is located in an integrated neighborhood. We build on previous research about the causes of discrimination in housing by using data from fair housing audits, a matched-pair technique for comparing the treatment of equllay qualified black and white home buyers. However, we shift the focus from differences in the treatment of paired buyers to agent decisions concerning an individual housing unit using a sample of all houses seen during he 1989 Housing Discrimination study. We estimate a random effect, multinomial logit model to explain a real estate agent's joint decisions concerning whether to show each unit to a black auditor and to a white auditor. We find evidence that agents withhold houses in suburban, integrated neighborhoods from all customers (redlining), that agents' decisions to show houses in integrated neighborhoods are not the same for black and white customers (steering), and that the houses agents show are more likely to deviate from the initial request when the customeris black than when the customer is white. These deviations are consistent with the possibility that agents act upon the belief that some types of transactions are relatively unlikely for black customers (statistical discrimination).

Shirking and Motivation in Firms: Survey Evidence on Worker Attitudes

In an extensive national survey, 82.7% of the respondents report that they are very likely to keep an agreement to work hard if they agreed to, even if it was almost impossible for their employer to monitor them. Based on mean responses, the rank order of motivations in descending importance is: moral, intrinsic, peer-pressure, and positive incentives. Respondents also report that fairness considerations are important and that they are especially likely to keep agreements to do a good job with honest employers. Logit analysis indicates that increases in moral and intrinsic motivations increase the likelihood of keeping agreements to provide effort. The evidence suggests that we need to re-examine a foundational assumption underlying the theory of the firm.

Logistic regression models for ordinal response: A study of self-efficacy in colorectal cancer screening

The ordinal logistic regression models are used to analyze the dependant variable with multiple outcomes that can be ranked, but have been underutilized. In this study, we describe four logistic regression models for analyzing the ordinal response variable. ^ In this methodological study, the four regression models are proposed. The first model uses the multinomial logistic model. The second is adjacent-category logit model. The third is the proportional odds model and the fourth model is the continuation-ratio model. We illustrate and compare the fit of these models using data from the survey designed by the University of Texas, School of Public Health research project PCCaSO (Promoting Colon Cancer Screening in people 50 and Over), to study the patient’s confidence in the completion colorectal cancer screening (CRCS). ^ The purpose of this study is two fold: first, to provide a synthesized review of models for analyzing data with ordinal response, and second, to evaluate their usefulness in epidemiological research, with particular emphasis on model formulation, interpretation of model coefficients, and their implications. Four ordinal logistic models that are used in this study include (1) Multinomial logistic model, (2) Adjacent-category logistic model [9], (3) Continuation-ratio logistic model [10], (4) Proportional logistic model [11]. We recommend that the analyst performs (1) goodness-of-fit tests, (2) sensitivity analysis by fitting and comparing different models.^