Answering Ordinal Questions with Ordinal Data Using Ordinal Statistics
Bibliographic Data
| ID | 19290223 |
|---|---|
| Authors | Norman Cliff (corresponding author) |
| Year | 1996 |
| Volume | 31 |
| Issue | 3 |
| Pages | 331-350 |
| Publication date | 1996-07-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1207/s15327906mbr3103_4 |
| PMID | 26741071 |
| OpenAlex | W2052388428 |
| Language | EN |
| Citations received | 11 |
| References cited | 4 |
It is argued that ordinal statistical methods are often more appropriate than their more common counterparts for three types of reasons: Conclusions from them will be unaffected by monotonic transformation of the variables, they are statistically more robust when used appropriately, and they often correspond more closely to the goals of the investigator. Kendall's tau (Kendall, 1970) and its counterpart delta are recommended as having wide I applicability and good statistical behavior. It is recommended that they be estimated as population parameters and their standard errors estimated form the data. Ways in which they can then substitute for Pearson correlations and mean comparisons in a number of research contexts are suggested
Econometrics · Monotonic function · Ordinal data · Ordinal optimization · Ordinal regression · Ordinal Scale · Population · Statistics · Advanced Statistical Methods and Models · Computer Science · Demography · Forecasting Techniques and Applications · Mathematics · Water Quality and Resources Studies
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| Citations per year | 1 |
|---|---|
| Citation span | 2015 - 2026 (12) |
| Highly cited | No |