Quantitative and Statistical Data in Education

From Data Collection to Data Processing

Michel Larini, Angela Barthes

• Publisher: Wiley-ISTE
• Year: 2018
• ISBN:
  9781119563396
• Edition: 1
• Topic: Education
• Subtopic: Research in Education

This book presents different data collection and representation techniques: elementary descriptive statistics, confirmatory statistics, multivariate approaches and statistical modeling. It exposes the possibility of giving more robustness to the classical methodologies of education sciences by adding a quantitative approach. The fundamentals of each approach and the reasons behind them are methodically analyzed, and both simple and advanced examples are given to demonstrate how to use them. Subsequently, this book can be used both as a course for the uninitiated and as an accompaniment for researchers who are already familiar with these concepts.

1
Data Collection in Education

1.1. Use of existing databases in education

A piece of data is an elementary description of a reality. It might be, for instance, an observation or measurement. A data point is not subject to any reasoning, supposition, observation or probability, even though the reason for and method of its collection are non-neutral. Indisputable or undisputed, it serves as the basis for a search, any examination, expressed in terms of a problem. Data analysis is normally the result of prior work on the raw data, imbuing them with meaning in relation to a particular problem, and thereby obtaining information. Data are a set of measurable values or qualitative criteria, taken in relation to a reference standard or to epistemological positions identified in an analysis grid. The reference grid used and the way in which the raw data are processed are explicit or implicit interpretations, which can transform (or skew) the final interpretation. For example, discretizing data (classifying them by establishing boundaries) in a graph enables an analyst to associate a meaning (an interpretation) with those data, and thus create new information in relation to a given problem.

Societies are restructuring to become “knowledge societies” in a context where in the global economy, innovations are based on the storage and exploitation of knowledge (value creation), and on the training and qualifications of actors in knowledge exploitation. Today, our knowledge society is faced with an explosion in the volume of data. The volume of the data created and copied is increasing exponentially, and the actors in civil society and in economics are taking great pains to find solutions allowing them to manage, store and secure those data. The tendency toward the use of Big Data to obtain a competitive edge and help organizations to achieve their objectives requires the collection of new types of information (comments posted on websites, pharmaceutical testing data, researchers’ results, to cite only a few examples) and examination of the data from every possible angle in order to reach an understanding and find solutions. Education is no exception to this rule, and burgeoning quantities of data are available in this field. Though this is not an exhaustive list, we could point to international databases with which practitioners, students, researchers and businesses can work. Data enables us to act on knowledge, which is a determining factor in the exercise of power. It should also be noted that in today’s world, data transmission is subject to rigorous frameworks (for example, issues of data privacy).

Below, we give a few examples of the databases that can be used.

1.1.1. International databases

The available international databases are, primarily, those maintained by intergovernmental organizations. For example, the UNESCO Institute for Statistics and the World Bank run a paying service for statistical monitoring in education. They facilitate international comparisons. The main access points are listed in Table 1.1.

Table 1.1. International databases

UNESCO database: education	http://www.uis.unesco.org/education
World Bank database: education	http://datatopics.worldbank.org/education/
OECD database: education	http://www.oecd.org/education/
United Nations database: for teachers	https://www.gapminder.org/for-teachers/
Eurostat database: education and training for Europe-wide comparisons and longitudinal monitoring	http://ec.europa.eu/eurostat/web/education-and-training/data/database
Eurydice database: institutional information network on educational policy and systems in Europe	http://www.eurydice.org/
Canadian databases on education the world over	https://www.wes.org/ca/wedb/
Database of National Center for Education Statistics	https://en.wikipedia.org/wiki/National_Center_for_Education_Statistics_

Table 1.2. French national databases

Educsol database: education	http://eduscol.education.fr/numerique/dossier/archives/ressources-en-ligne/bases/bases-donnees
INSEE database: teaching and education by commune	http://www.insee.fr/fr/themes/theme.asp?theme=7
Databases of the Ministry for National Education and the Ministry for Higher Education and Research	http://www.education.gouv.fr/bcp/mainFrame.jsp?p=1 https://data.enseignementsup-recherche.gouv.fr/explore/?sort=modified
Statistical data from CAPES (French teacher training qualification) examination	http://www.devenirenseignant.gouv.fr/cid98479/donnees-statistiques-des-concours-du-capes-de-la-session-2015.html

1.1.2. Compound databases

The list offered here is by no means exhaustive, and the situation is constantly changing. It shows the main databases compiled by organizations, communes and researchers.

Table 1.3. Compound databases

Institutional databases: e.g. that of the Académie d’Aix-Marseille	https://fichetab.ac-aix-marseille.fr/
Emmanuelle database: compiles all editions of textbooks published in France since 1789, for all disciplines and all levels of education	http://www.inrp.fr/emma/web/index.php

Researchers establish numerous databases. A few examples are presented in Table 1.4.

Table 1.4. Databases established by researchers

Database of the Center for Study and Research on qualifications	http://mimosa.cereq.fr/reflet/index.php?lien=educ_pres
Databases of rural schools of the Observatory for Education and Territory, longitudinal examination	http://observatoireecoleetterritoire

An analyst working with the data can then construct their own statistical tables and apply their own processes to obtain an answer to a given question.

1.2. Survey questionnaire

In the field of education, it is very common to use questionnaires to gather data regarding educational situations. Think, for example, about the areas of guidance counselling, for school and for work; about the paths taken by the pupils; the social make-up of the community of teachers and parents; knowledge about the establishments; etc.

In all cases, in order to write a useful question...

Table of contents

• Cover
• Table of Contents
• Introduction
• 1 Data Collection in Education
• 2 Elementary Descriptive Statistics and Data Representation
• 3 Confirmatory Statistics
• 4 Multivariate Analyses
• 5 Statistical Modeling
• 6 Toward the Robustness in Studies in Education by the Quantitative Approach
• References
• Index
• End User License Agreement