The effect of undercount on the accuracy of small-area population estimates: Implications for the use of administrative data for improving ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1995-06

AUTHORS

Steve H. Murdock, Md Nazrul Hoque

ABSTRACT

The effects of underenumeration on the accuracy of alternative methods of population estimation have not been sufficiently analyzed. Although the US Bureau of the Census has decided not to adjust either the counts or its estimates for underenumeration in 1990, the extent to which local population estimates may account for underenumeration is of importance both for those who may wish to adjust existing estimates and in anticipation of future census adjustments. This paper examines the accuracy of small-area population estimation methods with and without adjustment. Mean Percent Errors, Mean Absolute Percent Errors, and Mean Percent Absolute Differences between local estimates for 1990 and 1990 adjusted and unadjusted census counts are computed. Population estimates for 1990 made using housing unit, ratio correlation, and component methods are compared for 451 counties and 2,633 places in the states of California, Florida, Texas, and Wisconsin. An analysis of the data for counties shows little indication that local estimates more accurately estimate the adjusted than the unadjusted population counts. The results for places show clear improvements in accuracy for places in Florida and Texas. Implications of the findings for issues related to undercount adjustment and local population estimates are discussed. More... »

PAGES

251-271

Journal

TITLE

Population Research and Policy Review

ISSUE

2

VOLUME

14

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01074461

DOI

http://dx.doi.org/10.1007/bf01074461

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026460705


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Econometrics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Texas A&M University", 
          "id": "https://www.grid.ac/institutes/grid.264756.4", 
          "name": [
            "Department of Rural Sociology, Texas A & M University, 77843-2125, College Station, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Murdock", 
        "givenName": "Steve H.", 
        "id": "sg:person.010733675235.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010733675235.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Texas A&M University", 
          "id": "https://www.grid.ac/institutes/grid.264756.4", 
          "name": [
            "Department of Rural Sociology, Texas A & M University, 77843-2125, College Station, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hoque", 
        "givenName": "Md Nazrul", 
        "id": "sg:person.01355240052.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01355240052.68"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.2307/2061167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002080192", 
          "https://doi.org/10.2307/2061167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01944369108975518", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014808783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2307/2061279", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051385993", 
          "https://doi.org/10.2307/2061279"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1986.10478272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303249"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1995-06", 
    "datePublishedReg": "1995-06-01", 
    "description": "The effects of underenumeration on the accuracy of alternative methods of population estimation have not been sufficiently analyzed. Although the US Bureau of the Census has decided not to adjust either the counts or its estimates for underenumeration in 1990, the extent to which local population estimates may account for underenumeration is of importance both for those who may wish to adjust existing estimates and in anticipation of future census adjustments. This paper examines the accuracy of small-area population estimation methods with and without adjustment. Mean Percent Errors, Mean Absolute Percent Errors, and Mean Percent Absolute Differences between local estimates for 1990 and 1990 adjusted and unadjusted census counts are computed. Population estimates for 1990 made using housing unit, ratio correlation, and component methods are compared for 451 counties and 2,633 places in the states of California, Florida, Texas, and Wisconsin. An analysis of the data for counties shows little indication that local estimates more accurately estimate the adjusted than the unadjusted population counts. The results for places show clear improvements in accuracy for places in Florida and Texas. Implications of the findings for issues related to undercount adjustment and local population estimates are discussed.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf01074461", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1094213", 
        "issn": [
          "0167-5923", 
          "1573-7829"
        ], 
        "name": "Population Research and Policy Review", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "14"
      }
    ], 
    "name": "The effect of undercount on the accuracy of small-area population estimates: Implications for the use of administrative data for improving population enumeration", 
    "pagination": "251-271", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7e0ecb5ffb16619c7ae179e0bcd23bcac26133944020f8c36b258a06ea7abbdc"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf01074461"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1026460705"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf01074461", 
      "https://app.dimensions.ai/details/publication/pub.1026460705"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:27", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000370_0000000370/records_46738_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF01074461"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/bf01074461'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bf01074461'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf01074461'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf01074461'


 

This table displays all metadata directly associated to this object as RDF triples.

82 TRIPLES      21 PREDICATES      31 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf01074461 schema:about anzsrc-for:14
2 anzsrc-for:1403
3 schema:author Nf3c6bf88c25f4c209e109c0af1466970
4 schema:citation sg:pub.10.2307/2061167
5 sg:pub.10.2307/2061279
6 https://doi.org/10.1080/01621459.1986.10478272
7 https://doi.org/10.1080/01944369108975518
8 schema:datePublished 1995-06
9 schema:datePublishedReg 1995-06-01
10 schema:description The effects of underenumeration on the accuracy of alternative methods of population estimation have not been sufficiently analyzed. Although the US Bureau of the Census has decided not to adjust either the counts or its estimates for underenumeration in 1990, the extent to which local population estimates may account for underenumeration is of importance both for those who may wish to adjust existing estimates and in anticipation of future census adjustments. This paper examines the accuracy of small-area population estimation methods with and without adjustment. Mean Percent Errors, Mean Absolute Percent Errors, and Mean Percent Absolute Differences between local estimates for 1990 and 1990 adjusted and unadjusted census counts are computed. Population estimates for 1990 made using housing unit, ratio correlation, and component methods are compared for 451 counties and 2,633 places in the states of California, Florida, Texas, and Wisconsin. An analysis of the data for counties shows little indication that local estimates more accurately estimate the adjusted than the unadjusted population counts. The results for places show clear improvements in accuracy for places in Florida and Texas. Implications of the findings for issues related to undercount adjustment and local population estimates are discussed.
11 schema:genre research_article
12 schema:inLanguage en
13 schema:isAccessibleForFree false
14 schema:isPartOf N4093a00b45674f9b9366f48bba3a920d
15 N98ce3826b5cc49519693385cab5f8df3
16 sg:journal.1094213
17 schema:name The effect of undercount on the accuracy of small-area population estimates: Implications for the use of administrative data for improving population enumeration
18 schema:pagination 251-271
19 schema:productId N91656df4a02640bcb0086ccfef12bc5d
20 Nd1ba8b65dbbf4d3b8d1cdeb3791704d1
21 Ndb39a95421a34bb8b6419e3bd95b326a
22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026460705
23 https://doi.org/10.1007/bf01074461
24 schema:sdDatePublished 2019-04-11T13:27
25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
26 schema:sdPublisher Ndceecf99458f480bbbd6e47f6109d116
27 schema:url http://link.springer.com/10.1007/BF01074461
28 sgo:license sg:explorer/license/
29 sgo:sdDataset articles
30 rdf:type schema:ScholarlyArticle
31 N4093a00b45674f9b9366f48bba3a920d schema:volumeNumber 14
32 rdf:type schema:PublicationVolume
33 N91656df4a02640bcb0086ccfef12bc5d schema:name readcube_id
34 schema:value 7e0ecb5ffb16619c7ae179e0bcd23bcac26133944020f8c36b258a06ea7abbdc
35 rdf:type schema:PropertyValue
36 N98ce3826b5cc49519693385cab5f8df3 schema:issueNumber 2
37 rdf:type schema:PublicationIssue
38 Nb40e3b53f98e475c8001f8fd5277bc71 rdf:first sg:person.01355240052.68
39 rdf:rest rdf:nil
40 Nd1ba8b65dbbf4d3b8d1cdeb3791704d1 schema:name dimensions_id
41 schema:value pub.1026460705
42 rdf:type schema:PropertyValue
43 Ndb39a95421a34bb8b6419e3bd95b326a schema:name doi
44 schema:value 10.1007/bf01074461
45 rdf:type schema:PropertyValue
46 Ndceecf99458f480bbbd6e47f6109d116 schema:name Springer Nature - SN SciGraph project
47 rdf:type schema:Organization
48 Nf3c6bf88c25f4c209e109c0af1466970 rdf:first sg:person.010733675235.05
49 rdf:rest Nb40e3b53f98e475c8001f8fd5277bc71
50 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
51 schema:name Economics
52 rdf:type schema:DefinedTerm
53 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
54 schema:name Econometrics
55 rdf:type schema:DefinedTerm
56 sg:journal.1094213 schema:issn 0167-5923
57 1573-7829
58 schema:name Population Research and Policy Review
59 rdf:type schema:Periodical
60 sg:person.010733675235.05 schema:affiliation https://www.grid.ac/institutes/grid.264756.4
61 schema:familyName Murdock
62 schema:givenName Steve H.
63 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010733675235.05
64 rdf:type schema:Person
65 sg:person.01355240052.68 schema:affiliation https://www.grid.ac/institutes/grid.264756.4
66 schema:familyName Hoque
67 schema:givenName Md Nazrul
68 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01355240052.68
69 rdf:type schema:Person
70 sg:pub.10.2307/2061167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002080192
71 https://doi.org/10.2307/2061167
72 rdf:type schema:CreativeWork
73 sg:pub.10.2307/2061279 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051385993
74 https://doi.org/10.2307/2061279
75 rdf:type schema:CreativeWork
76 https://doi.org/10.1080/01621459.1986.10478272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303249
77 rdf:type schema:CreativeWork
78 https://doi.org/10.1080/01944369108975518 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014808783
79 rdf:type schema:CreativeWork
80 https://www.grid.ac/institutes/grid.264756.4 schema:alternateName Texas A&M University
81 schema:name Department of Rural Sociology, Texas A & M University, 77843-2125, College Station, TX, USA
82 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...