Comparer des méthodes

	Recherche quantitative comparative exploratoire	Recherche quantitative exploratoire
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	Mid-to-late 20th century	Mid-20th century (codified in social research methods texts c. 1950s–1970s)
Auteur d'origine	No single originator; codified in quantitative research methodology traditions (20th century)	Earl Babbie; John Creswell (systematic codification in social science methods)
Type	Quantitative research design	Non-experimental quantitative research design
Source fondatrice	Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications. ISBN: 978-1452226101	Babbie, E. (2021). The Practice of Social Research (15th ed.). Cengage Learning. ISBN: 978-0357360767
Alias	exploratory comparative quantitative design, comparative exploratory survey research, quantitative comparative exploration, CEQR design	quantitative exploratory design, exploratory survey research, initial quantitative investigation, preliminary quantitative study
Apparentées	3	4
Résumé	Comparative exploratory quantitative research is a design that uses structured numerical data collection to discover patterns, differences, and relationships across two or more distinct groups or conditions — without a fully specified hypothesis in advance. It sits at the intersection of exploratory intent and comparative structure: the researcher does not enter the field with a predetermined answer but organises the inquiry around a comparison that will generate quantitative insights. The design is common in social, educational, and behavioural sciences when a phenomenon is insufficiently understood to permit confirmatory testing but structured group comparison is still feasible and informative.	Exploratory quantitative research is a non-experimental design used when a phenomenon is insufficiently understood to support formal hypothesis testing. The researcher collects numerical data — typically through surveys, structured observation, or existing records — to describe distributions, detect patterns, and generate hypotheses that more targeted confirmatory studies can subsequently test. It occupies the first stage of a cumulative quantitative research programme.