General Linear Model
Bibliographic Data
| ID | 7077689 |
|---|---|
| Authors | Xitao Fan (0000-0001-8527-2563, corresponding author) |
| Year | 2017 |
| Pages | 1-2 |
| Publication date | 2017-09-14 |
| Open Access | Yes |
| Type | OTHER |
| Venue | The Blackwell Encyclopedia of Sociology (JOURNAL) |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/9781405165518.wbeosg029 |
| OpenAlex | W4206227787 |
| ISBN | 9781405165518 |
| Language | EN |
| References cited | 3 |
The social science researcher's repertoire of statistical tools ranges from simple techniques (e.g., t ‐test) to sophisticated multivariate analytic techniques (e.g., structural equation modeling). Many of these statistical techniques appear to be different analytic systems designed for different research situations. The distinctions, however, are often superficial. Fundamentally, many techniques are related to each other to such a degree that they can be considered as variations of a more general statistical model. For quantitative researchers in the social sciences, understanding the relationship between the seemingly different analytic techniques is important because such understanding helps researchers make judicious choices of analytic techniques in research
Data science · Epistemology · Machine learning · Management science · Multivariate statistics · Simple (philosophy · Statistical hypothesis testing · Statistical model · Statistics · Test (biology · Advanced Statistical Methods and Models · Computer Science · Engineering · Mathematics · Statistical Methods and Applications · Artificial Intelligence · Ecology
| Highly cited | No |
|---|