A “Hetero-statistical” and Graphical Approach for Massive Survey Data – The PointG Software. In sociology, one is often confronted with the problem of graphic presentations based on processing massive quantities of data from questionnaire surveys. Moreover, potential users can be of low statistical expertise, thus requiring relatively easy to use software such as PointG with drop-down menus. It is aimed at facing the difficulties due to the mass of collected quantitative data, both at the strategic, tactical and operational levels of statistical processing. It offers conventional ways of analysis going from univariate to multivariate data, but also automates treatments on groups of variables and calculates the size of overall and local effects. Apart from the graphic aspects, its distinctive “hetero-statistic” approach permits the user not to worry about the nature of the variables in bivariate analyses and during regression and factor analysis. PointG was developed within the powerful distribution free R programming environment.
BeckerRAWilksARBrownriggRMinkaTP (2011) Maps : Draw Geographical Maps, version 2.2-2. Disponible à : http://CRAN.R-project.org/.
3.
BlössTGrossettiM (1999) Introduction aux méthodes statistiques en sociologie. Paris : Presses Universitaires de France.
4.
ChampelyS (2012) RcmdrPlugin.pointG: Rcmdr Graphical Point of View for Questionnaire Data Plug-In, version 0.2.1. Disponible à : http://CRAN.R-project.org/.
5.
ChesselDDufourABThioulouseJ (2004) The Ade4 Package-I- One Table Methods. R News4 : 5–10.
6.
CiboisP (1993) Le PEM, pourcentage de l'écart maximum - Un indice de liaison entre modalités d'un tableau de contingence. Bulletin de Méthodologie Sociologique40 : 43–63.
7.
CiboisP (2009) Les Méthodes d’Analyse d’Enquêtes. Paris : PUF, Que sais-je, n. 3782 (épuisé), version corrigée disponible àhttp://cibois.pagesperso-orange.fr/.
ChambersJM (2010) Software for Data Analysis. New-York: Springer.
10.
CohenJ (1988) Statistical Power Analysis for the Behavioral Sciences (Second Edition). Hillsdale, NJ : Lawrence Erlbaum.
11.
CookDSwayneDF (2007) Interactive and Dynamics Graphics for Data Analysis. New-York: Springer.
12.
DavenportECEl-SanhurryNA (1991) Phi/phimax: Review and Synthesis. Educational and Psychological Measurement51 : 821–28.
13.
DeepayanS (2008) Lattice: Multivariate Data Visualization with R. New York : Springer.
14.
Des NétumièresF (1997) Méthodes de régression et analyse factorielle. Histoire et mesure12 : 271–97.
15.
DetrezC (2002) La construction sociale du corps. Paris : Seuil.
16.
EhrenbergASC (1981) Rudiments of Numeracy. American Statistician35 : 67–71.
17.
EmersonJWGreenWA (2010) YaleToolkit: Data Exploration Tools from Yale University, version 3.2. Disponible à : http://CRAN.R-project.org/.
18.
EmersonJWGreenWA (2011) Gpairs: The Generalized Pairs Plot, version 1.0. Disponible à : http://CRAN.R-project.org.
19.
EpskampSCramerAOJWaldorpLJSchmittmannVDBorsboomD (2011) Qgraph: Network Representations of Relationships in Data, version 0.5.3. Disponible à : http://CRAN.R-project.org/.
20.
EscoffierBPagèsJ (1994) Multiple Factor Analysis (AFMULT package). Computational Statistics and Data Analysis18 : 121–40.
21.
FalissardB (2012) Analysis of Questionnaire Data with R. Boca Raton, FL : CRC Press.
22.
FoxJ (2003) Effect Displays in R for Generalised Linear Models. Journal of Statistical Software8(15) : 1–27.
23.
FoxJWeisbergS (2011) An {R} Companion to Applied Regression, Second Edition. Thousand Oaks CA: Sage.
FoxW (1999) Statistiques Sociales. Paris : DeBoeck Université.
26.
FerezSLomoA (eds) (sous presses). Sport et VIH. Un corps sous contrainte médicale. Paris : Téraèdre.
27.
GanassaliS (2007) Les enquêtes par questionnaire avec Sphinx. Paris : Pearson Education France.
28.
HarrellJR FE. (2011) Hmisc: Harrell Miscellaneous, version 3.9-0. Disponible àhttp://CRAN.R-project.org/.
29.
HillMOSmithAJE (1976) Principal Component Analysis of Taxonomic Data with Multi-state Discrete Characters. Taxon25 : 249–55.
30.
HuberPJ (2011) Data Analysis. What Can be Learned from the Past 50 Years. Hoboken, NJ: Wiley.
31.
HussonFJosseJLeSMazetJ (2011) FactoMineR: Multivariate Exploratory Data Analysis and Data Mining with R, version 1.16. Disponible à : http://CRAN.R-project.org/.
32.
MartinO (2005) L’analyse de données quantitatives (L’enquête et ses méthodes). Paris: Armand Colin.
33.
McCullaghPNelderJA (1989) Generalized Linear Models. London : Chapman and Hall.
34.
MeyerDZeileisDHornikK (2006) The Strucplot Framework: Visualizing Multi-way Contingency Tables with Vcd. Journal of Statistical Software17(3) : 1–48.
35.
MurrellP (2002) The Grid Graphics Package. R News2 : 14–19.
36.
NakacheJPConfiasJ (2004) Approche pragmatique de la classification - Arbres hiérarchiques et partitionnements. Paris : Technip.
37.
NeuwirthE (2011) RColorBrewer: ColorBrewer Palettes, version 1.0-5. Disponible à : http://CRAN.R-project.org/.
38.
R Development Core Team (2011) R - A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Disponible àhttp://www.R-project.org/.
39.
SelzMMaillochonF (2009) Le raisonnement statistique en sociologie. Paris : Presses Universitaires de France.
40.
TempleMAlfonsAKowarikA (2011) VIM - Visualization and Imputation of Missing Values, version 2.0.4. Disponible à : http://CRAN.Rproject.org/.
41.
TufteER (1983) The Visual Display of Quantitative Information. Cheshire, CT : Graphics Press.
