Compara mètodes

	Investigació quantitativa exploratòria comparativa	Disseny d'investigació per enquesta
Camp	Disseny de recerca	Disseny de recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	Mid-to-late 20th century	Late 19th century; methodologically systematised 1940s–1960s
Autor original	No single originator; codified in quantitative research methodology traditions (20th century)	Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s
Tipus	Quantitative research design	Quantitative (and mixed) non-experimental design
Font seminal	Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications. ISBN: 978-1452226101	Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000
Àlies	exploratory comparative quantitative design, comparative exploratory survey research, quantitative comparative exploration, CEQR design	survey methodology, questionnaire research, survey design, survey study
Relacionats	3	4
Resum	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.	Survey research is a quantitative (and sometimes mixed-methods) design in which a researcher collects standardised self-report data from a sample drawn from a defined population, using a questionnaire or structured interview. It is the dominant non-experimental strategy for describing population characteristics, estimating prevalence, mapping attitude distributions, and testing bivariate or multivariate associations across social, behavioural, and health sciences.