Jaime R. S. Fonseca

Coming of (Old) Age in the Digital Age: ICT Usage and Non-Usage Among Older Adults

Most developed countries are in the midst of two significant societal trends: the first is an aging population; the second is the uptake of Information and Communication Technologies (ICT) by large segments of society. But research shows a strong association between age and the so-called digital divide: older adults are less likely to use ICT when compared to other age groups. If we consider the social affordances of the Internet and the online migration of several public and private services, the lack of access or of digital literacy might be increasing age-related inequality. Consequently, we studied adoption, usage, and non-usage of ICT (mobile phones, computers, and the Internet) by Portuguese older adults. For that, we surveyed a random stratified sample of 500 individuals over 64 years of age living in Lisbon. Of this sample, 77% owned a mobile phone, 13% used computers, and 10% used the Internet. The main reasons for non-usage were functional and attitudinal, rather than physical or associated with age. But usage of mobile phones and computers was predicted by age and education, whereas the usage of the Internet was only predicted by education. We followed up the survey with 10 qualitative interviews, using a mixed methods strategy. The qualitative data showed a general positive perception of ICT as well as the importance of family and intergenerational relationships for technology adoption and use.

Clustering in the field of social sciences: that is your choice

Clustering seeks to identify a finite set of clusters to describe data. Cluster analysis is partitioning similar objects into meaningful classes, when both the number of classes and their composition are to be determined. Nowadays, we often see illustrations concerning the use of latent class models (LCM) in the field of cluster analysis. They provide a useful probabilistic/statistical method for grouping observations into clusters. In this approach to clustering, each different cluster in the population is assumed to be described by a different probability distribution, which may belong to the same family but differ in the values they take for the parameters of the distribution. The goal of this research is cluster analysis and LCM comparison, and methodologically we considered three data-sets: one with solely continuous variables, one with only binary variables and one with mixed variables. In all situations, LCM performed reasonably well; in contrast, cluster analysis achieved both the best (90.7%, only continuous variables) and the worst performance (40%, mixed variables).

Retail clients latent segments

Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the best criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and

Latent Class Models in action: Bridging social capital & Internet usage

{\mathcal L}

On The contribution of using computers in the classroom in teaching/learning statistics

criteria are selected as the best performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients’ segmentation. The best information criteria yield two segments: Preferential Clients and Occasional Clients.

Segmenting and profiling the Portuguese festival-goers through the most ancient form of music retailing: the music festivals.

This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in ones social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research.

Why does Segmentation Matter? Using Mixed Methodology to Identify Market Segments

We must recognise a distinct difference between teaching students with computers, and teaching students about computers. Nowadays, computers are used in secondary schools for information skills courses, such as Word processing and Powerpoint presentations, which are called Information and Communication Technology (ICT). This paper addresses the first situation, the issue of using the computer in the classroom to assist the teaching/learning of the discipline of Statistics. This study aims to find out the effect of the use of the computer in the classroom, in teaching/learning the discipline of Statistics, and more than that it really wants to know if this use can reduce the effect of negative past experiences of learning Mathematics at secondary school. From the data set, based on a questionnaire which involved students from the Technical University of Lisbon, we first built a profile of the students, based on Latent Class Models; second, we concluded that the negative attitude towards the learning of Mathematics (liking or disliking Maths) at secondary school, influenced their performance in the discipline of Quantitative Methods, at secondary school, but the same did not happen with their performance in the discipline of Statistics in the first year at University (using the computer in the classroom).

Social capital and Internet use in an age-comparative perspective with a focus on later life

This article intends to segment and profile the festival-goers in Portugal, and through a mixed mode survey of 657 Portuguese citizens using latent segments models, a three-segment solution was achieved: music lovers, 53%; networkers, 33%; and tourists, 14%. The majority of the festival-goers, the music lovers, consider Optimus Alive the most memorable festival because of the music and these results should be conveniently used for music retailing purposes. The model identified the demographic factors that contributed the most to the solution, and according to each segment, several recommendations to festivals organizations are presented.

Mixture Modeling and Information Criteria for Discovering Patterns in Continuous Data

The purpose of this chapter is to describe how markets can be segmented. In other words, it studies ways of grouping customers for the most effective targeting by means of a new conceptual model which combines the use of latent segment models with a mixed research scheme (merging qualitative and quantitative research methods). A particular retail market segmentation solution depends on both market segmentation base variables and a specific segmentation procedure providing a better understanding of the market. Knowledge of segment structure is extremely important in marketing because of its managerial utility, particularly with regard to targeting and positioning. Companies that identify underserved segments can then outperform the competition by developing uniquely appealing products and services. This research begins with an overview of segmentation aspects and aims, and uses a mixed research scheme to present an application with a latent segment model (LSM) procedure for retail market segmentation and information criteria AIC3 and AICu for model selection, in order to uncover the segment structure underlying a dataset from retail chain customers.

Can We Reduce Students' Negative Attitude Towards Math?

Older adults (aged 65+) are still less likely to adopt the Internet when compared to other age groups, although their usage is increasing. To explore the societal effects of Internet usage, scholars have been using social capital as an analytical tool. Social capital pertains to the resources that are potentially available in one’s social ties. As the Internet becomes a prominent source of information, communication, and participation in industrialized countries, it is critical to study how it affects social resources from an age-comparative perspective. Research has found a positive association between Internet use and social capital, though limited attention has been paid to older adults. Studies have also found a positive association between social capital and wellbeing, health, sociability, and social support amongst older adults. However, little is known about how Internet usage or lack thereof relates to their social capital. To address this gap, we used a mixed-methods approach to examine the relationship between Internet usage and social capital and whether and how it differs by age. For this, we surveyed a representative sample of 417 adults (18+) living in Lisbon, Portugal, of which 118 are older adults. Social capital was measured through bonding, bridging, and specific resources, and analyzed with Latent Class Modeling and logistic regressions. Internet usage was measured through frequency and type of use. Fourteen follow-up semi-structured interviews helped contextualize the survey data. Our findings show that social capital decreased with age but varied for each type of Internet user. Older adults were less likely to have a high level of social capital; yet within this age group, frequent Internet users had higher levels than other users and non-users. On the one hand, the Internet seems to help maintain, accrue, and even mobilize social capital. On the other hand, it also seems to reinforce social inequality and accumulated advantage (known as the Matthew effect).