Multiple regression and its alternatives
Bibliographic Data
| ID | 12008025 |
|---|---|
| Authors | Stephen L Bieber (University of Wyoming, corresponding author) |
| Year | 1988 |
| Volume | 25 |
| Issue | 1 |
| Pages | 1-19 |
| Publication date | 1988-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Social Science Journal (JOURNAL) |
| Journal identifiers | ISSN: 0362-3319 • E-ISSN: 1873-5355 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1016/0362-3319(88)90050-x |
| OpenAlex | W2069789390 |
| Language | EN |
| Citations received | 4 |
| References cited | 11 |
Although multiple regression is popular with social and behavioral scientists, the technique is not appropriate in most cases due to the level of variable measurement or multicollinearity. This article examines seven methods: multiple regression, discriminant analysis, logistic regression, analysis of variance, logit analysis, factor analysis, and multidimensional scaling. It then compares them using computer-generated data for a hypothetical election
Cross-sectional regression · Econometrics · Economics · Factor regression model · Linear discriminant analysis · Logistic regression · Logit · Multicollinearity · Multinomial logistic regression · Polynomial regression · Proper linear model · Regression · Regression analysis · Regression diagnostic · Statistics · Variables · Variance (accounting · Variance inflation factor · Advanced Statistical Methods and Models · Computer Science · Mathematics
| Citations per year | 0.11 |
|---|---|
| Citation span | 1990 - 1995 (6) |
| Highly cited | No |