Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Лонгитюдный веб-скрейпинг× | Лонгитюдное (продольное) исследование× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2000s–2010s | 1940s (panel survey tradition); longitudinal designs codified mid-20th century |
| Автор метода≠ | Emergent practice in computational social science; formalized across internet research community | Established tradition; formalized in social science by Paul Lazarsfeld and colleagues (1940s panel studies) |
| Тип≠ | Automated longitudinal data collection | Quantitative / mixed-methods survey design |
| Основополагающий источник≠ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 978-0691158648 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922292 |
| Другие названия | repeated web scraping, time-series web data collection, longitudinal crawling, panel web scraping | panel survey, repeated-measures survey, longitudinal panel study, wave survey |
| Связанные≠ | 5 | 3 |
| Сводка≠ | Longitudinal web scraping is a data collection technique that uses automated scripts to extract content from websites at multiple, predefined time points. By revisiting the same web sources repeatedly, researchers build a time-series dataset that captures how online content, prices, discourse, or behavior evolves. It is widely used in computational social science, economics, political science, health research, and digital humanities to study change without relying on retrospective self-report. | A longitudinal survey collects structured questionnaire data from the same individuals or units at two or more distinct points in time. By tracking the same respondents across waves, researchers can distinguish genuine change from stable individual differences, establish temporal ordering between variables, and model trajectories of attitudes, behaviors, or outcomes in ways that a single cross-sectional snapshot cannot support. |
| ScholarGateНабор данных ↗ |
|
|