Сравнение на методи

	Сравнително проучване чрез анкетиране	Проучване чрез анкети
Област	Дизайн на изследването	Дизайн на изследването
Семейство	Process / pipeline	Process / pipeline
Година на възникване	Mid-20th century onward	Late 19th century; methodologically systematised 1940s–1960s
Създател	Rooted in survey methodology traditions (Gallup, Likert, Lazarsfeld mid-20th century); comparative extension codified in social science research methods literature	Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s
Тип	Quantitative non-experimental research design	Quantitative (and mixed) non-experimental design
Основополагащ източник	Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000	Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000
Други названия	comparative survey design, cross-group survey, multi-group survey research, comparative questionnaire study	survey methodology, questionnaire research, survey design, survey study
Свързани	4	4
Резюме	Comparative survey research is a quantitative non-experimental design that systematically collects structured survey data from two or more clearly defined groups, populations, or contexts in order to identify, describe, and analyze similarities and differences among them. It extends basic survey research by making comparison the explicit organizing logic: rather than characterizing a single population, the goal is to detect how attitudes, behaviors, or outcomes vary across groups defined by nationality, culture, profession, demographic category, or time period.	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.