Aggregation in Data Tables
Implications for Evaluating Criminal Justice Statistics
Bibliographic Data
| ID | 4138657 |
|---|---|
| Authors | Lawrence J Hubert (University of California, Santa Barbara), Thomas Kenney (0000-0002-4984-8251, University of California, Santa Barbara), R G Golledge (University of California, Santa Barbara), C Michael Costanzo (University of California, Santa Barbara) |
| Year | 1981 |
| Volume | 13 |
| Issue | 2 |
| Pages | 185-199 |
| Publication date | 1981-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1068/a130185 |
| OpenAlex | W2056387420 |
| Language | EN |
| Citations received | 1 |
| References cited | 11 |
Data collected on a set of objects (cities, for instance) over a set of attributes (time points, for instance) can be subjected to a variety of aggregation schemes. For example, if a hypothesized pattern over the attributes is to be confirmed (a temporal increase in homicide rate, for instance), the data could be first aggregated over cities and then compared to the hypothesized pattern. Alternatively, the correspondence for each city could be separately assessed and the individual city indices aggregated. The stage at which aggregation takes place affects the size of the final measure of confirmation as well as its significance, but, unfortunately, in opposite ways. Preliminary data aggregation typically leads to larger summary statistics and larger significance levels. The conflicting notions of size and significance are first formalized in detail when the basic data are single numerical values obtained for each object-attribute pair. Extensions are presented to multigroup concordance, hierarchical aggregation schemes, and to object data defined by pairwise proximities between the attributes
Data mining · Data set · Econometrics · Pairwise comparison · Statistics · Advanced Statistical Methods and Models · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,02 |
| Citation span | 1981 - 1981 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |