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  • Comment: Subject appears notable. But most of the claims are unsourced. This should help to build a good Wikipedia page of this subject. Twinkle1990 ( talk) 09:29, 13 April 2024 (UTC)


Joseph Waksberg (1915–2006) was an American statistician.  While at the United States Census Bureau and Westat, he developed methods for area sampling and telephone sampling and made contributions in many areas of surveys and censuses. Much of the material below is based on a 2000 interview with Waksberg in the journal Statistical Science. [1]

Joseph Waksberg
Born1915
Died2006
Alma mater City University of New York B.A. 1936
Scientific career
FieldsSurvey sampling, Statistics
Institutions US Census, Westat

Early Life

Waksberg was born on September 20, 1915, in Kielce in what is now Poland. His family emigrated to the United States in 1921. He attended the City University of New York (CUNY) because it was a free college for New York residents, and his family, like many others in the Depression, had no money to pay for a college education. He graduated from CUNY in 1936 with a degree in mathematics and then moved to Washington, DC to join the Navy Department as a mathematician. He joined the Census Bureau in 1940 as a clerk after (according to Leslie Kish, one of the founders of survey sampling) receiving the highest grade in the nation on the Civil Service examination. While there, he worked closely with Morris Hansen, another of survey sampling’s founders. Among the other clerks at the Census Bureau were Benjamin Tepping, Joseph Steinberg, Samuel Greenhouse, William N. Hurwitz, Margaret Gurney, and Marvin Schneiderman [2]—all of whom became distinguished statisticians.

U.S. Census Bureau 1940–1973

Waksberg mainly worked on sample design issues, but his thinking was not limited to mathematical considerations. Depending on the application, he adapted methods to account for practicalities. In the early 1960’s he and John Neter studied memory recall errors in a consumer survey of home repair costs (Neter and Waksberg, 1964 [3]). Although response errors in expenditure surveys were a known problem (e.g., see Cole and Utting, 1956 [4]; Ferber, 1955 [5]), it had not often been studied directly. Neter and Waksberg conducted an experiment sponsored by the United States Census Bureau to study the tendency of people to misreport the time period when expenditures occurred. Large expenditures, in particular, were often reported to have occurred nearer to the present than when they actually occurred, i.e., they were telescoped forward. Based on their findings, they were the first to propose bounded recall as a potential solution. In the second or later interview in a continuing survey the respondent is told the expenditures that had been reported in the previous interview then asked for the additional expenditures since then. The telescoping effect was later recognized in cognitive psychology as a common memory problem in the recall of past effects, e.g., see Tourangeau, Rips, and Rasinski (2000) [6]. Waksberg and Neter (1964) [3] are credited with doing the original work on the concept of telescoping Their work is also relevant to conditioning effects in panel surveys where participants' reports of their characteristics may (incorrectly) change over time, leading to biases in estimates. [7] [8] [9]

Faulty data used in designing a sample was another topic he studied. When he became the head statistician on the US Current Population Survey (CPS) in the early 1960’s, the area probability methods were well established. But the survey had to face new problems caused by the expanding American economy. The migration to the suburbs from cities was in full swing and data from the 1960 census was becoming progressively staler. Maps being used for fieldwork were outdated, and some area segments (small geographic areas) that had a few farm houses in the census were found with major housing developments built on them. Such fast-growing neighborhoods led to bad measures of size used for probability proportional to size sampling based on the last census, which, in turn, led to intolerably expensive workloads if the original sampling plan was implemented. This led to his instituting the use of building permit samples to identify new construction in advance and avoid such ”surprise” sample segments.

In the 1950 Census an interviewer variance study was conducted in which randomized assignments were given to interviewers. Waksberg and colleagues measured the total variance between the sample areas and the within-area variance to measure the effect of interviewers.  Two interviewers had randomized assignments within a set of small geographic areas. The results showed high interviewer effects for many items. The between-interviewer variance so dominated the total, it became obvious that the Census Bureau was wasting money by obtaining most of its data from the whole population. This research led to much of the information being collected from a sample of persons rather than the full population. In the 1960 US census, data collection was conducted by mail thereby eliminating the issue of undesirable variation among interviewers.

Coverage errors were recognized problems for censuses and surveys on which he also led research. In the 1960s decade, while he was head of the Current Population Survey, that survey and others at the Census Bureau introduced address-list sampling as a way of reducing the number of households inadvertently omitted by field listers. Their method for compiling an address list began with purchasing one from a private vendor of lists. As Waksberg explained in Morganstein and Marker (2000) [1], “the post office had the mailing addresses in little slots. Dummy mailing pieces were prepared for all addresses on the commercial list and the postal carriers put the mail into these little slots and checked for missing addresses, filling out a card for each missing address.” With this method plus some special procedures, like checking buildings that had been converted into apartments but with no apartment number designated, they compiled a more complete list to use for sampling within selected areas. This kind of inventiveness was characteristic of the way that he, Morris Hansen, and colleagues at Census solved practical problems.

Later Years

After 33 years of service, Waksberg retired from the Census Bureau and joined Westat, a statistical research firm in Rockville MD USA. He worked at Westat for another 33 years, being appointed Chairman of the Board of Westat in 1990.

While at Westat he and Warren Mitofsky developed the Mitofsky-Waksberg (MW) [10] method of random digit dialing (Waksberg, 1978). This article has been cited in various statistical and social science journals nearly 2,000 times. Kalton and Anderson (1986) [11] note that the method is especially useful for sampling rare populations. The article has also been cited many times in text and reference books on survey sampling methodology and social science research, e.g., Fowler (2014) [12], Groves and Alexander (2001) [13], Groves, et al. (2009) [14], Smith and Kluegel (2017) [15], Lee (1993) [16], Lohr (2021) [17], and Kalton and Moser (2017) [18]. The MW method has been particularly useful in identifying persons to use as controls from the general population in case-control studies. [19]

In the early 1970s unrestricted random sampling of telephone numbers in the US was extremely inefficient for household sampling since about 80% of 10-digit phone numbers were assigned to businesses, institutions, government agencies, or were unassigned. The MW method treated the first eight digits in the sorted list of phone numbers as clusters (known as 100-banks), screened clusters by phoning a randomly selected number in a sample 100-bank and retaining a cluster only if the contacted number was residential. In a retained cluster additional 2-digit numbers were appended to the 8-digit cluster number and phoned to obtain the desired sample size. The MW method does not require knowledge of either the first- or second-stage selection probabilities but does produce an equal probability sample of telephone numbers. Because a high percentage of 100-banks had no residential numbers, MW sampling was substantially more cost efficient than unrestricted random sampling. Since the 1990s the MW method has been superseded by more direct sampling using commercially available lists of residential numbers.

In 1967 he was recruited by Mitofsky to consult for the CBS television network on election night predictions, a post he maintained through the 1994 elections. These predictions were originally based on the official tallies in a sample of precincts, but then evolved into exit polls. In 1966 CBS based its predictions on set of key precincts in every state. In most states, the system worked well but gave poor predictions in a few states like Maryland.  The issue in Maryland was that precincts whose party vote-split changed substantially from the previous election were thrown out as being either outliers or errors. The reported vote in those precincts turned out to be correct, and their removal produced an incorrect prediction in the governor's race. Based on the recommendation of Waksberg and Mitofsky, CBS switched to probability samples of precincts with none being replaced.

Honors and Professional Service

Waksberg served the profession of statistics in many roles and received numerous awards, including the Department of Commerce Gold Medal, the Roger Herriot Memorial Award from the American Statistical Association (ASA), and election as a Fellow of the American Statistical Association in 1964. He served on the ASA Board of Directors as chairs of both the Survey Research Methods Section and the Social Statistics Section and on a number of committees. He has been president of the Washington Statistical Society and was an Associate Editor of the journal Survey Methodology. Throughout his career at the Census Bureau and Westat, he had a commitment to mentoring young statisticians. The journal Survey Methodology has established an annual invited paper series in his honor to recognize his contributions to survey methodology. Each year a prominent survey statistician is chosen to write a paper that reviews the development and current state of an important topic in the field of survey methodology.

Notes

  1. ^ a b Morganstein, David; Marker, David; Waksberg, Joseph (2000). "A Conversation with Joseph Waksberg". Statistical Science. 15 (3): 299–312. ISSN  0883-4237. JSTOR  2676667.
  2. ^ Simon, Richard M. (1997-05-01). "A conversation with Marvin A. Schneiderman". Statistical Science. 12 (2). doi: 10.1214/ss/1029963425. ISSN  0883-4237.
  3. ^ a b Neter, John; Waksberg, Joseph (1964). "A Study of Response Errors in Expenditures Data from Household Interviews". Journal of the American Statistical Association. 59 (305): 18–55. doi: 10.1080/01621459.1964.10480699. ISSN  0162-1459.
  4. ^ Cole, D.; Uttig, J.E.G. (1956). "Estimating expenditure, saving and income from household budgets". Journal of the Royal Statistical Society, Series A. 119 (4): 371–392. doi: 10.2307/2342576. JSTOR  2342576.
  5. ^ Ferber, Robert (1955). "On the reliability of responses secured in sample surveys". Journal of the American Statistical Association. 50: 788–810.
  6. ^ Tourangeau, Roger; Rips, Lance J.; Rasinski, Kenneth A. (2000). The psychology of survey response. Cambridge, U.K. ; New York: Cambridge University Press. ISBN  978-0-521-57246-0.
  7. ^ Neter, John; Waksberg, Joseph (1964). "Conditioning Effects from Repeated Household Interviews". Journal of Marketing. 28 (2): 51–56. doi: 10.1177/002224296402800211. ISSN  0022-2429.
  8. ^ Halpern-Manners, Andrew; Warren, John Robert (2012-11-01). "Panel Conditioning in Longitudinal Studies: Evidence From Labor Force Items in the Current Population Survey". Demography. 49 (4): 1499–1519. doi: 10.1007/s13524-012-0124-x. ISSN  0070-3370. PMC  3648659. PMID  22893185.
  9. ^ Lynn, Peter (2009). Methodology of longitudinal surveys. Wiley series in survey methodology. Chichester, UK: John Wiley & Sons. ISBN  978-0-470-01871-2. OCLC  298612199.
  10. ^ Waksberg, Joseph (1978). "Sampling Methods for Random Digit Dialing". Journal of the American Statistical Association. 73 (361): 40–46. doi: 10.1080/01621459.1978.10479995. ISSN  0162-1459.
  11. ^ Kalton, Graham; Anderson, Dallas W. (1986). "Sampling Rare Populations". Journal of the Royal Statistical Society. Series A (General). 149 (1): 65–82. doi: 10.2307/2981886. hdl: 2027.42/147111. ISSN  0035-9238. JSTOR  2981886.
  12. ^ Fowler, Floyd J. (2014). Survey research methods. Applied social research methods series (5th ed.). Los Angeles, Calif.: SAGE. ISBN  978-1-4522-5900-0.
  13. ^ Groves, Robert M.; Alexander, Charles H., eds. (2001). Telephone survey methodology. Wiley series in survey methodology (Paperback ed.). New York, Weinheim: Wiley. ISBN  978-0-471-20956-0.
  14. ^ Groves, Robert M.; Fowler, Floyd J.; Couper, Mick; Lepkowski, James M.; Singer, Eleanor; Tourangeau, Roger (2009). Survey methodology. Wiley series in survey methodology (2nd ed.). Hoboken, NJ: Wiley. ISBN  978-0-470-46546-2.
  15. ^ Smith, James R. Kluegel, Eliot R. (2017-10-31). Beliefs about Inequality: Americans' Views of What is and What Ought to be. New York: Routledge. doi: 10.4324/9781351329002. ISBN  978-1-351-32900-2.{{ cite book}}: CS1 maint: multiple names: authors list ( link)
  16. ^ Lee, Raymond M. (1993). Doing research on sensitive topics. London ; Newbury Park, Calif: Sage Publications. ISBN  978-0-8039-8860-6.
  17. ^ Lohr, Sharon L. (2021-11-30). Sampling: Design and Analysis (3rd ed.). New York: Chapman and Hall/CRC. doi: 10.1201/9780429298899. ISBN  978-0-429-29889-9.
  18. ^ Kalton, C. A. Moser, G. (2017-01-05). Survey Methods in Social Investigation. London: Routledge. doi: 10.4324/9781315241999. ISBN  978-1-315-24199-9.{{ cite book}}: CS1 maint: multiple names: authors list ( link)
  19. ^ Hartge, P.; Brinton, L. A.; Rosenthal, J. F.; Cahill, J. I.; Hoover, R. N.; Waksberg, J. (1984-12-01). "Random Digit Dialing in Selecting a Population-Based Control Group". American Journal of Epidemiology. 120 (6): 825–833. doi: 10.1093/oxfordjournals.aje.a113955. ISSN  0002-9262. PMID  6334439.

References

  • Cole, D. and Utting, J. E. G. (1956). Estimating expenditure, saving and income from household budgets. Journal of the Royal Statistical Society, Series A, 119:371–392.
  • Ferber, R. (1955). On the reliability of responses secured in sample surveys. Journal of the American Statistical Association, 50:788–810.
  • Fowler, F.J. (2014). Survey Research Methods. Applied social research methods series, 5th edition. Los Angeles: SAGE. ISBN: 9781452259000.
  • Groves, Robert M.; Alexander, Charles H., eds. (2001). Telephone survey methodology. Wiley series in survey methodology (Paperback ed.). New York, Weinheim: Wiley. ISBN 978-0-471-20956-0.
  • Groves, Robert M.; Fowler, Floyd J.; Couper, Mick; Lepkowski, James M.; Singer, Eleanor; Tourangeau, Roger (2009). Survey methodology. Wiley series in survey methodology (2nd ed.). Hoboken, NJ: Wiley. ISBN 978-0-470-46546-2.
  • Halpern-Manners, Andrew; Warren, John Robert (2012-11-01). Panel Conditioning in Longitudinal Studies: Evidence From Labor Force Items in the Current Population Survey. Demography, 49 (4): 1499–1519. doi:10.1007/s13524-012-0124-x. ISSN 0070-3370. PMC 3648659. PMID 22893185.
  • Hartge, P.; Brinton, L. A.; Rosenthal, J. F.; Cahill, J. I.; Hoover, R. N.; Waksberg, J. (1984-12-01). Random digit dialing in selecting a population-based control group. American Journal of Epidemiology. 120 (6): 825–833. doi:10.1093/oxfordjournals.aje.a113955. ISSN 0002-9262.
  • Kalton, G. and Anderson, D.W. (1986). Sampling Rare Populations. Journal of the Royal Statistical Society A, Vol. 149, pp. 65-82. https://doi.org/10.2307/2981886
  • Kalton, C. A. Moser, G. (2017-01-05). Survey Methods in Social Investigation. London: Routledge. doi:10.4324/9781315241999. ISBN 978-1-315-24199-9.
  • Lee, Raymond M. (1993). Doing research on sensitive topics. London ; Newbury Park, Calif: Sage Publications. ISBN 978-0-8039-8860-6.
  • Lohr, S.L. (2021). Sampling: Design and Analysis, 3rd edition. New York: Chapman & Hall/CRC.
  • Lynn, Peter (2009). Methodology of longitudinal surveys. Wiley series in survey methodology. Chichester, UK: John Wiley & Sons. ISBN 978-0-470-01871-2. OCLC 298612199.
  • Morganstein, David R. and Marker, David A. (2000). A Conversation with Joseph Waksberg.  Statistical Science, Vol. 15, No. 3, pp. 299–312.
  • Neter, J. and Waksberg, J. (1964). A study of response errors in expenditures data from household interviews. Journal of the American Statistical Association, 59(305):18–55. DOI:10.1080/01621459.1964.10480699
  • .Neter, John; Waksberg, Joseph (1964). "Conditioning Effects from Repeated Household Interviews". Journal of Marketing. 28 (2): 51–56. doi:10.1177/002224296402800211. ISSN 0022-2429.
  • Simon, Richard M. (1997). A conversation with Marvin A. Schneiderman. Statistical Science, Vol. 12, No. 2: pp 98-102.  DOI: 10.1214/ss/1029963425.
  • Smith, James R. Kluegel, Eliot R. (2017-10-31). Beliefs about Inequality: Americans' Views of What is and What Ought to be. New York: Routledge. doi:10.4324/9781351329002. ISBN 978-1-351-32900-2
  • Tourangeau, Roger; Rips, Lance J.; Rasinski, Kenneth A. (2000). The psychology of survey response. Cambridge, U.K. ; New York: Cambridge University Press. ISBN 978-0-521-57246-0.
Retrieved from " https://en.wikipedia.org/?title=Draft:Joseph_Waksberg&oldid=1218770158"
From Wikipedia, the free encyclopedia
  • Comment: Subject appears notable. But most of the claims are unsourced. This should help to build a good Wikipedia page of this subject. Twinkle1990 ( talk) 09:29, 13 April 2024 (UTC)


Joseph Waksberg (1915–2006) was an American statistician.  While at the United States Census Bureau and Westat, he developed methods for area sampling and telephone sampling and made contributions in many areas of surveys and censuses. Much of the material below is based on a 2000 interview with Waksberg in the journal Statistical Science. [1]

Joseph Waksberg
Born1915
Died2006
Alma mater City University of New York B.A. 1936
Scientific career
FieldsSurvey sampling, Statistics
Institutions US Census, Westat

Early Life

Waksberg was born on September 20, 1915, in Kielce in what is now Poland. His family emigrated to the United States in 1921. He attended the City University of New York (CUNY) because it was a free college for New York residents, and his family, like many others in the Depression, had no money to pay for a college education. He graduated from CUNY in 1936 with a degree in mathematics and then moved to Washington, DC to join the Navy Department as a mathematician. He joined the Census Bureau in 1940 as a clerk after (according to Leslie Kish, one of the founders of survey sampling) receiving the highest grade in the nation on the Civil Service examination. While there, he worked closely with Morris Hansen, another of survey sampling’s founders. Among the other clerks at the Census Bureau were Benjamin Tepping, Joseph Steinberg, Samuel Greenhouse, William N. Hurwitz, Margaret Gurney, and Marvin Schneiderman [2]—all of whom became distinguished statisticians.

U.S. Census Bureau 1940–1973

Waksberg mainly worked on sample design issues, but his thinking was not limited to mathematical considerations. Depending on the application, he adapted methods to account for practicalities. In the early 1960’s he and John Neter studied memory recall errors in a consumer survey of home repair costs (Neter and Waksberg, 1964 [3]). Although response errors in expenditure surveys were a known problem (e.g., see Cole and Utting, 1956 [4]; Ferber, 1955 [5]), it had not often been studied directly. Neter and Waksberg conducted an experiment sponsored by the United States Census Bureau to study the tendency of people to misreport the time period when expenditures occurred. Large expenditures, in particular, were often reported to have occurred nearer to the present than when they actually occurred, i.e., they were telescoped forward. Based on their findings, they were the first to propose bounded recall as a potential solution. In the second or later interview in a continuing survey the respondent is told the expenditures that had been reported in the previous interview then asked for the additional expenditures since then. The telescoping effect was later recognized in cognitive psychology as a common memory problem in the recall of past effects, e.g., see Tourangeau, Rips, and Rasinski (2000) [6]. Waksberg and Neter (1964) [3] are credited with doing the original work on the concept of telescoping Their work is also relevant to conditioning effects in panel surveys where participants' reports of their characteristics may (incorrectly) change over time, leading to biases in estimates. [7] [8] [9]

Faulty data used in designing a sample was another topic he studied. When he became the head statistician on the US Current Population Survey (CPS) in the early 1960’s, the area probability methods were well established. But the survey had to face new problems caused by the expanding American economy. The migration to the suburbs from cities was in full swing and data from the 1960 census was becoming progressively staler. Maps being used for fieldwork were outdated, and some area segments (small geographic areas) that had a few farm houses in the census were found with major housing developments built on them. Such fast-growing neighborhoods led to bad measures of size used for probability proportional to size sampling based on the last census, which, in turn, led to intolerably expensive workloads if the original sampling plan was implemented. This led to his instituting the use of building permit samples to identify new construction in advance and avoid such ”surprise” sample segments.

In the 1950 Census an interviewer variance study was conducted in which randomized assignments were given to interviewers. Waksberg and colleagues measured the total variance between the sample areas and the within-area variance to measure the effect of interviewers.  Two interviewers had randomized assignments within a set of small geographic areas. The results showed high interviewer effects for many items. The between-interviewer variance so dominated the total, it became obvious that the Census Bureau was wasting money by obtaining most of its data from the whole population. This research led to much of the information being collected from a sample of persons rather than the full population. In the 1960 US census, data collection was conducted by mail thereby eliminating the issue of undesirable variation among interviewers.

Coverage errors were recognized problems for censuses and surveys on which he also led research. In the 1960s decade, while he was head of the Current Population Survey, that survey and others at the Census Bureau introduced address-list sampling as a way of reducing the number of households inadvertently omitted by field listers. Their method for compiling an address list began with purchasing one from a private vendor of lists. As Waksberg explained in Morganstein and Marker (2000) [1], “the post office had the mailing addresses in little slots. Dummy mailing pieces were prepared for all addresses on the commercial list and the postal carriers put the mail into these little slots and checked for missing addresses, filling out a card for each missing address.” With this method plus some special procedures, like checking buildings that had been converted into apartments but with no apartment number designated, they compiled a more complete list to use for sampling within selected areas. This kind of inventiveness was characteristic of the way that he, Morris Hansen, and colleagues at Census solved practical problems.

Later Years

After 33 years of service, Waksberg retired from the Census Bureau and joined Westat, a statistical research firm in Rockville MD USA. He worked at Westat for another 33 years, being appointed Chairman of the Board of Westat in 1990.

While at Westat he and Warren Mitofsky developed the Mitofsky-Waksberg (MW) [10] method of random digit dialing (Waksberg, 1978). This article has been cited in various statistical and social science journals nearly 2,000 times. Kalton and Anderson (1986) [11] note that the method is especially useful for sampling rare populations. The article has also been cited many times in text and reference books on survey sampling methodology and social science research, e.g., Fowler (2014) [12], Groves and Alexander (2001) [13], Groves, et al. (2009) [14], Smith and Kluegel (2017) [15], Lee (1993) [16], Lohr (2021) [17], and Kalton and Moser (2017) [18]. The MW method has been particularly useful in identifying persons to use as controls from the general population in case-control studies. [19]

In the early 1970s unrestricted random sampling of telephone numbers in the US was extremely inefficient for household sampling since about 80% of 10-digit phone numbers were assigned to businesses, institutions, government agencies, or were unassigned. The MW method treated the first eight digits in the sorted list of phone numbers as clusters (known as 100-banks), screened clusters by phoning a randomly selected number in a sample 100-bank and retaining a cluster only if the contacted number was residential. In a retained cluster additional 2-digit numbers were appended to the 8-digit cluster number and phoned to obtain the desired sample size. The MW method does not require knowledge of either the first- or second-stage selection probabilities but does produce an equal probability sample of telephone numbers. Because a high percentage of 100-banks had no residential numbers, MW sampling was substantially more cost efficient than unrestricted random sampling. Since the 1990s the MW method has been superseded by more direct sampling using commercially available lists of residential numbers.

In 1967 he was recruited by Mitofsky to consult for the CBS television network on election night predictions, a post he maintained through the 1994 elections. These predictions were originally based on the official tallies in a sample of precincts, but then evolved into exit polls. In 1966 CBS based its predictions on set of key precincts in every state. In most states, the system worked well but gave poor predictions in a few states like Maryland.  The issue in Maryland was that precincts whose party vote-split changed substantially from the previous election were thrown out as being either outliers or errors. The reported vote in those precincts turned out to be correct, and their removal produced an incorrect prediction in the governor's race. Based on the recommendation of Waksberg and Mitofsky, CBS switched to probability samples of precincts with none being replaced.

Honors and Professional Service

Waksberg served the profession of statistics in many roles and received numerous awards, including the Department of Commerce Gold Medal, the Roger Herriot Memorial Award from the American Statistical Association (ASA), and election as a Fellow of the American Statistical Association in 1964. He served on the ASA Board of Directors as chairs of both the Survey Research Methods Section and the Social Statistics Section and on a number of committees. He has been president of the Washington Statistical Society and was an Associate Editor of the journal Survey Methodology. Throughout his career at the Census Bureau and Westat, he had a commitment to mentoring young statisticians. The journal Survey Methodology has established an annual invited paper series in his honor to recognize his contributions to survey methodology. Each year a prominent survey statistician is chosen to write a paper that reviews the development and current state of an important topic in the field of survey methodology.

Notes

  1. ^ a b Morganstein, David; Marker, David; Waksberg, Joseph (2000). "A Conversation with Joseph Waksberg". Statistical Science. 15 (3): 299–312. ISSN  0883-4237. JSTOR  2676667.
  2. ^ Simon, Richard M. (1997-05-01). "A conversation with Marvin A. Schneiderman". Statistical Science. 12 (2). doi: 10.1214/ss/1029963425. ISSN  0883-4237.
  3. ^ a b Neter, John; Waksberg, Joseph (1964). "A Study of Response Errors in Expenditures Data from Household Interviews". Journal of the American Statistical Association. 59 (305): 18–55. doi: 10.1080/01621459.1964.10480699. ISSN  0162-1459.
  4. ^ Cole, D.; Uttig, J.E.G. (1956). "Estimating expenditure, saving and income from household budgets". Journal of the Royal Statistical Society, Series A. 119 (4): 371–392. doi: 10.2307/2342576. JSTOR  2342576.
  5. ^ Ferber, Robert (1955). "On the reliability of responses secured in sample surveys". Journal of the American Statistical Association. 50: 788–810.
  6. ^ Tourangeau, Roger; Rips, Lance J.; Rasinski, Kenneth A. (2000). The psychology of survey response. Cambridge, U.K. ; New York: Cambridge University Press. ISBN  978-0-521-57246-0.
  7. ^ Neter, John; Waksberg, Joseph (1964). "Conditioning Effects from Repeated Household Interviews". Journal of Marketing. 28 (2): 51–56. doi: 10.1177/002224296402800211. ISSN  0022-2429.
  8. ^ Halpern-Manners, Andrew; Warren, John Robert (2012-11-01). "Panel Conditioning in Longitudinal Studies: Evidence From Labor Force Items in the Current Population Survey". Demography. 49 (4): 1499–1519. doi: 10.1007/s13524-012-0124-x. ISSN  0070-3370. PMC  3648659. PMID  22893185.
  9. ^ Lynn, Peter (2009). Methodology of longitudinal surveys. Wiley series in survey methodology. Chichester, UK: John Wiley & Sons. ISBN  978-0-470-01871-2. OCLC  298612199.
  10. ^ Waksberg, Joseph (1978). "Sampling Methods for Random Digit Dialing". Journal of the American Statistical Association. 73 (361): 40–46. doi: 10.1080/01621459.1978.10479995. ISSN  0162-1459.
  11. ^ Kalton, Graham; Anderson, Dallas W. (1986). "Sampling Rare Populations". Journal of the Royal Statistical Society. Series A (General). 149 (1): 65–82. doi: 10.2307/2981886. hdl: 2027.42/147111. ISSN  0035-9238. JSTOR  2981886.
  12. ^ Fowler, Floyd J. (2014). Survey research methods. Applied social research methods series (5th ed.). Los Angeles, Calif.: SAGE. ISBN  978-1-4522-5900-0.
  13. ^ Groves, Robert M.; Alexander, Charles H., eds. (2001). Telephone survey methodology. Wiley series in survey methodology (Paperback ed.). New York, Weinheim: Wiley. ISBN  978-0-471-20956-0.
  14. ^ Groves, Robert M.; Fowler, Floyd J.; Couper, Mick; Lepkowski, James M.; Singer, Eleanor; Tourangeau, Roger (2009). Survey methodology. Wiley series in survey methodology (2nd ed.). Hoboken, NJ: Wiley. ISBN  978-0-470-46546-2.
  15. ^ Smith, James R. Kluegel, Eliot R. (2017-10-31). Beliefs about Inequality: Americans' Views of What is and What Ought to be. New York: Routledge. doi: 10.4324/9781351329002. ISBN  978-1-351-32900-2.{{ cite book}}: CS1 maint: multiple names: authors list ( link)
  16. ^ Lee, Raymond M. (1993). Doing research on sensitive topics. London ; Newbury Park, Calif: Sage Publications. ISBN  978-0-8039-8860-6.
  17. ^ Lohr, Sharon L. (2021-11-30). Sampling: Design and Analysis (3rd ed.). New York: Chapman and Hall/CRC. doi: 10.1201/9780429298899. ISBN  978-0-429-29889-9.
  18. ^ Kalton, C. A. Moser, G. (2017-01-05). Survey Methods in Social Investigation. London: Routledge. doi: 10.4324/9781315241999. ISBN  978-1-315-24199-9.{{ cite book}}: CS1 maint: multiple names: authors list ( link)
  19. ^ Hartge, P.; Brinton, L. A.; Rosenthal, J. F.; Cahill, J. I.; Hoover, R. N.; Waksberg, J. (1984-12-01). "Random Digit Dialing in Selecting a Population-Based Control Group". American Journal of Epidemiology. 120 (6): 825–833. doi: 10.1093/oxfordjournals.aje.a113955. ISSN  0002-9262. PMID  6334439.

References

  • Cole, D. and Utting, J. E. G. (1956). Estimating expenditure, saving and income from household budgets. Journal of the Royal Statistical Society, Series A, 119:371–392.
  • Ferber, R. (1955). On the reliability of responses secured in sample surveys. Journal of the American Statistical Association, 50:788–810.
  • Fowler, F.J. (2014). Survey Research Methods. Applied social research methods series, 5th edition. Los Angeles: SAGE. ISBN: 9781452259000.
  • Groves, Robert M.; Alexander, Charles H., eds. (2001). Telephone survey methodology. Wiley series in survey methodology (Paperback ed.). New York, Weinheim: Wiley. ISBN 978-0-471-20956-0.
  • Groves, Robert M.; Fowler, Floyd J.; Couper, Mick; Lepkowski, James M.; Singer, Eleanor; Tourangeau, Roger (2009). Survey methodology. Wiley series in survey methodology (2nd ed.). Hoboken, NJ: Wiley. ISBN 978-0-470-46546-2.
  • Halpern-Manners, Andrew; Warren, John Robert (2012-11-01). Panel Conditioning in Longitudinal Studies: Evidence From Labor Force Items in the Current Population Survey. Demography, 49 (4): 1499–1519. doi:10.1007/s13524-012-0124-x. ISSN 0070-3370. PMC 3648659. PMID 22893185.
  • Hartge, P.; Brinton, L. A.; Rosenthal, J. F.; Cahill, J. I.; Hoover, R. N.; Waksberg, J. (1984-12-01). Random digit dialing in selecting a population-based control group. American Journal of Epidemiology. 120 (6): 825–833. doi:10.1093/oxfordjournals.aje.a113955. ISSN 0002-9262.
  • Kalton, G. and Anderson, D.W. (1986). Sampling Rare Populations. Journal of the Royal Statistical Society A, Vol. 149, pp. 65-82. https://doi.org/10.2307/2981886
  • Kalton, C. A. Moser, G. (2017-01-05). Survey Methods in Social Investigation. London: Routledge. doi:10.4324/9781315241999. ISBN 978-1-315-24199-9.
  • Lee, Raymond M. (1993). Doing research on sensitive topics. London ; Newbury Park, Calif: Sage Publications. ISBN 978-0-8039-8860-6.
  • Lohr, S.L. (2021). Sampling: Design and Analysis, 3rd edition. New York: Chapman & Hall/CRC.
  • Lynn, Peter (2009). Methodology of longitudinal surveys. Wiley series in survey methodology. Chichester, UK: John Wiley & Sons. ISBN 978-0-470-01871-2. OCLC 298612199.
  • Morganstein, David R. and Marker, David A. (2000). A Conversation with Joseph Waksberg.  Statistical Science, Vol. 15, No. 3, pp. 299–312.
  • Neter, J. and Waksberg, J. (1964). A study of response errors in expenditures data from household interviews. Journal of the American Statistical Association, 59(305):18–55. DOI:10.1080/01621459.1964.10480699
  • .Neter, John; Waksberg, Joseph (1964). "Conditioning Effects from Repeated Household Interviews". Journal of Marketing. 28 (2): 51–56. doi:10.1177/002224296402800211. ISSN 0022-2429.
  • Simon, Richard M. (1997). A conversation with Marvin A. Schneiderman. Statistical Science, Vol. 12, No. 2: pp 98-102.  DOI: 10.1214/ss/1029963425.
  • Smith, James R. Kluegel, Eliot R. (2017-10-31). Beliefs about Inequality: Americans' Views of What is and What Ought to be. New York: Routledge. doi:10.4324/9781351329002. ISBN 978-1-351-32900-2
  • Tourangeau, Roger; Rips, Lance J.; Rasinski, Kenneth A. (2000). The psychology of survey response. Cambridge, U.K. ; New York: Cambridge University Press. ISBN 978-0-521-57246-0.
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