Latent Class Analysis for Marketing Scales Development.

Measurement scales are a crucial instrument for research in marketing in order to measure unobservable variables as attitudes, opinions, beliefs. In using, evaluating, or developing multi-item scales, a number of guidelines and procedures are recommended to ensure that the measure is psychometrically robust. These procedures have been outlined in the psychometric literature since the late seventies and are composed of steps which refer to construct definition, domain and scale validity, reliability, dimensionality, and generalizability. Various statistical instruments are used in the scale developing process, these almost always refer to metric variables (interval or ratio scales). Items forming scales are instead rarely measured on an metric level, frequently items are ordinal, in some rare cases, nominal. In this paper, it is shown how the implementation of latent class analysis may improve the process of measurement scale development since it explicitly considers that items generate ordinal or even nominal variables. Specifically, applying appropriate latent class models allows to assess scale validity and reliability more soundly than the methods traditionally used

Latent Class Models for Marketing: An Application to Pharmaceuticals.

In this paper some extensions of the latent class (LC) approach are applied to analyze the Italian pharmaceutical market. This sector is characterized by a high level of competitiveness, more limited economic budgets than years ago and, at the same time, expensive sales and promotion activities; in this context, it is very important to know the reference market, so to design appropriate marketing strategies. The paper has two aims: (i) identifying groups of doctors homogeneous for attitude towards pharmaceutical representatives’ activities; (ii) verify which aspects of the promotional activity may be significant in order to influence prescription quantities

Dynamic clustering to evaluate satisfaction with teaching at university,

Purpose In this paper, students’ satisfaction with the didactics in a large Italian university, that of Padua, is measured, giving special attention to its evolution over time in consecutive academic years. The overall level of the quality of the didactics is examined and its change over time is modeled. Moreover, the effect of courses’ and teachers’ variables on it is estimated. Methodology Latent cluster lass models and mixture latent class Markov models are estimated in order to identify groups of courses that are homogeneous for the level of the quality of the didactics. Evolution over the three academic years of satisfaction is monitored. The effect on the clustering and its dynamics of potential covariates is also examined. Findings Results of model estimation reveal some interesting evidences that are important indications for the university management to define targeted strategies to elevate teaching quality. Originality The paper gives its original contribution both on the side of methods applied to analyze data collected with students evaluation of teaching and on the evidences obtained for a large university

Longitudinal models for dynamic segmentation in financial markets

Purpose: Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation. Design/methodology/approach: The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers\u2019 characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers\u2019 characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author\u2019s case, market segments. Findings: The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market. Originality/value: The proposed statistical models are new in the field of financial markets

Inconsistencies in Reported Employment Characteristics among Employed Stayers

The paper deals with measurement error, and its potentially distorting role, in information on industry and professional status collected by labour force surveys. The focus of our analyses is on inconsistent information on these employment characteristics resulting from yearly transition matrices for workers who were continuously employed over the year and who did not change job. As a case-study we use yearly panel data for the period from April 1993 to April 2003 collected by the Italian Quarterly Labour Force Survey. The analysis goes through four steps: (i) descriptive indicators of (dis)agreement; (ii) testing whether the consistency of repeated information significantly increases when the number of categories is collapsed; (iii) examination of the pattern of inconsistencies among response categories by means of Goodman's quasi-independence model; (iv) comparisons of alternative classifications. Results document sizable measurement error, which is only moderately reduced by more aggregated classifications. They suggest that even cross-section estimates of employment by industry and/or professional status are affected by non-random measurement error.industry, professional status, measurement errors, survey data

Experience goods and customer satisfaction measurement.

Aim of this paper is to develop an instrument to measure customer satisfaction with reference to the entire consumption experience of an experience good. Experience is defined as a new dimension of offer: a combination of goods and services enriched by sensations. Experiential marketing has innovative features. This has effects on all phases constituting a consumption experience. We look for relevant aspects in the consumption process, related to satisfaction, through a literature review and an exploratory survey. A list of items is proposed to a sample and the scale is evaluated for validity and reliability with satisfactory results

Latent class analysis for evaluating a multi-item scale to measure customer satisfaction with reference to a shopping good: a pair of branded jeans

In the field of marketing many objects of interest exist that are not directly observable, nevertheless they can be measured through multi-item measurement scales. As a consequence, this kind of instruments are extremely useful and their importance requires an accurate development and validation procedure. The traditional marketing literature highlights specific protocols along with statistical instruments and techniques to be used for achieving this goal. For example, correlation coefficients, univariate and multivariate analysis of variance and factorial analysis are widely employed with this purpose. However, these kind of statistical tools are usually suited for metric variables but they are adopted even when the nature of the observed variables is different, as it often occurs, since in many cases the variables measured by the items of which the scale is made up are ordinal. On the contrary, latent class analysis takes explicitly into account the ordinal nature of the observed variables and also the fact that the object of interest, that has to be measured, is unobservable. The aim of this paper is showing how latent class analysis can improve the procedures for developing and validating a multi-item measurement scale for measuring customer satisfaction with reference to a shopping good that is a good characterized by a high level of involvement and an emotional learning, linked to the lifestyle of the customer. This latent class approach explicitly considers both the ordinal nature of the observed variables and the fact that the construct to be measured is not directly observable. Especially, applying appropriate latent class models, important features such as scale dimensionality, criterion and construct validity can be better assessed while evaluating the scale

Survival of farms in Veneto Region 1999-2004.

We model at a micro level farms’ survival in Veneto Region (Italy) for the period 1999- 2004. We use a new database which links information on survival histories contained in the Official Registers of the Chamber of Commerce with information on socio-economic and structural characteristics of farms and farmers collected in the 2000 Census of Agriculture. First, we consider the cohort of farms born in year 1999, then we extend the analysis to all farms registered at the end of year 1999. We find that the model performs quite well in selecting farms with a high probability of survival