Research issue

The thesis focuses on the factors that are in connection with a particular vulnera-bility, and that I call here factors of vulnerability². By a literal interpretation, the term factor of vulnerability refers to an observable situation (factor) that (a) in-creases or dein-creases the likelihood of experiencing a particular vulnerability or (b) changes the level of vulnerability of an already vulnerable individual. For example, the factor “type of employment contract” is a potential factor of vulnerability to unemployment as one of its possible values, “fixed-term contract”, may increase the probability of experiencing unemployment.

As Spini et al. (2013) note, vulnerability pertains to the interaction of indi-vidual and contextual dimensions. For example, having a fixed-term contract in times of full employmentdoes not per se make individuals vulnerable to unemploy-ment. Therefore, a better factor of vulnerability would result from the interaction between the individual resource “type of employment contract” and the contextual resource “unemployment rate of the area”.

In addition, as Adger (2006) notes, social processes are complex and with many linkages that are difficult to pin down. Therefore, I postulate that most of the relevant factors of vulnerability lie in the interaction of several variables.

But factors of vulnerability resulting from interaction effects are more difficult to identify for the researcher in social sciences as they are not directly available as

2I formalize the definition of a factor of vulnerability in Section3.1.1

Table 1.1 –Information system five-layer model of Long´ep´e (2009).

Layer Description

Strategic

The strategic layer defines the business objectives of the com-pany. The business objectives concern both external objectives such as new services to be provided or performance objectives to be achieved, as well as internal objectives such as organiza-tional changes or reducing operating costs.

Process

The process layer defines the different activities required to achieve the business objectives defined in the strategic layer as well as the respective functions and skills needed to complete each activity. The activities are organized and orchestrated to-gether by identifying both their sequence and sequencing con-ditions.

Functional

The functional layer defines the hierarchical structure of the different functions performing the activities defined in the pro-cess layer. For example, the responsibilities of the financial function include invoice and payment management, account-ing management, and budget plannaccount-ing. This general function is broken down into more specific sub-functions related to each of the activities to be executed.

Applicative

The applicative layer defines the operations that have been automated by software units. Software units include the ap-plications and services used by the functions of the company as well as the libraries that allow applications to work. The dependency relationships between each software component as well as data structures and data flows are modeled.

Infrastructure

The infrastructure layer defines the set of hardware resources required for the proper execution of the software units. The hardware resources include data storage components, network components, and physical servers. The physical storage lo-cations of each element are referenced, and the connections between each component are modeled. Each software unit is linked to the hardware it requires.

a single variable in data – this is especially the case when factors of vulnerability result from high-level interactions, such as those involving more than three vari-ables. In addition, interaction effects may be buried under main effects of some strong covariates. For those reasons, I refer to such factors asunderlying factors of vulnerability.

The thesis addresses the issue of the discovery by researchers in social sciences of the underlying factors associated with vulnerability.

As a starting point, it has to be asked where this discovery stage takes place in

1.2. Research framework 5 the quantitative research process followed by researchers in social sciences. From a general point a view, all scientific research is an iterative process of observation, ra-tionalization, and validation (Bhattacherjee,2012). The observation phase consists in observing a natural or social phenomenon, event, or behavior that deserves con-sideration. The rationalization phase consists in logically connecting the different pieces of the puzzle that has been observed and integrating them into an existing theory with the purpose of building a new one that includes additional hypotheses.

Finally, the validation phase consists in testing the new theory by using a scientific method through a process of data analysis, and in doing so, possibly, validating the new theory. The two first steps of observation and rationalization call for both an inductive reasoning and a deductive reasoning. An inductive reasoning takes place when starting from some observations and then attempting to rationalize them. A deductive reasoning takes place when starting from an ex-ante rationalization or theory and attempting to integrate new hypotheses based on the observations. By conducting both stages in parallel or iteratively, the researcher ends with a concep-tual model. To validate the model empirically, data are required. To acquire the data needed for this stage, either new data is collected or existing data previously collected are retrieved. Especially when the study focuses on a large sample of individuals such as a national or international population, like in demography and sociology, researchers opt for data collected by a professional or national survey in-stitution. In this case, a first stage for the researcher is to understand the data to be able to correctly prepare them (Wirth and Hipp,2000). Once data is prepared in the statistical software, one could directly go to confirmatory analyses. But in the context of looking for relevant factors of vulnerability, it is much more appro-priate to go through an exploratory analysis. Here, the exploratory analysis must not be confused with descriptive analysis. While performing descriptive statistics is a passive stage limited to extract some indicators in order to have a first under-standing of the data or to compare them with other data, exploratory analysis is an active stage involving a series of predictive analyses with a bottom-up strategy to extract the factors that impact the most the variables of interest (Kuonen,2015).

Although often under-used, the exploratory analysis is a very important stage: as Tukey (1980) notes, new ideas come more often from previous explorations than from lightning strokes. Thanks to the exploratory analysis, the researcher may end with a refined model that goes further than the initial hypotheses. Then, the confirmatory analysis is performed on this final model.

This quantitative research process is illustrated in Figure1.1. It is clear that the issue of discovering factors of vulnerability belongs to the earlier stages of this process. More precisely, I postulate that discovering appropriate factors in connection with a particular vulnerability, a researcher in social sciences has to focus on the steps A to C and E to G reported on the figure. Steps A to C refer to the observation stage and rationalization stage. Steps E to G refer to the data exploration stage. The thesis focuses on the data exploration stage by postulating that a more effective data exploration stage would facilitate the discovery of the underlying factors in connection with the vulnerability studied.

As shown by the steps E to G in Figure1.1, this stage involves understanding data, preparing data, and the use of exploratory analyses.

Figure 1.1 – An example of traditional quantitative research process based on Bhattacherjee (2012) and Wirth and Hipp (2000). Stages of concern in the context of discovering some factors of vulnerability are reported in red.

The core of the thesis focuses on the exploratory step (G/G’) in the context of the discovery of underlying factors of vulnerability in life courses. I address this issue through two research questions. As information systems are designed to sup-port processes, my first research question focuses on the possibility of formalizing a model of the process of the vulnerability in life courses. To facilitate the design of information system strategies, the objective pursued is to provide researchers with an operationalizable model of the vulnerability in life courses. I introduce this first research question (Q1) in Section 1.2.2.1. Once such a model is set up, my second research question focuses on supporting researchers in social sciences in exploring variable interactions to identify potential factors of vulnerability. As I postulate that outcomes resulting from vulnerable situations may be infrequent in a general population, I focus in identifying interaction effects with infrequent outcomes. I introduce this second research question (Q2) in Section1.2.2.2.

But to address the issue globally, I also aim to investigate the data under-standing step (E) and the data preparation step (F). The NCCR LIVES gathers together a high number of researchers in social sciences. Taking advantage of this opportunity, my strategy is to immerse myself in the role of a social scientist by collaborating on real social science studies and to observe what technical difficulties researchers experience when working on data, and the understanding and prepara-tion of it. In this second work, my aim is not to assess but to identify and explore some strategies that could be studied in future works. Therefore, they will not be discussed in the assessment section of the thesis (Section6). Instead, I made some of these strategies available to the scientific community by adding them within

1.2. Research framework 7 the software developed for the assessment of my conceptual model (Section 5). I introduce these immersions in Section1.2.3.