Emerging Trends in The Social and Behavioral Sciences

The Role of Data in Research and Policy

Item

Title

The Role of Data in Research and Policy

Author

Anderson, Barbara A.

Research Area

Special Areas of Interdisciplinary Study

Topic

Applications of Social Science Knowledge to Policy

Abstract

Data are essential for scientific research and policy planning. However, there needs to be attention to data quality and to the estimates and models based on those data. In addition, data need to be freely available for researchers to test new ideas and validate the work of others through replication, while respondents who provide data need to be protected. Three issues concerning data are addressed: (i) availability and accuracy of data for new research and reanalysis while protecting human subjects, (ii) problems with the estimation of indicators based on flawed or nongeneralizable data, and (iii) the use of data to develop models for projecting the future, the assumptions on which those models are based, and the assessment of the accuracy of past projections. In each of these areas, increased attention is necessary on how data are used, interpreted, and made available to the scholarly and policy community.

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Identifier

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extracted text

The Role of Data in Research
and Policy
BARBARA A. ANDERSON

Abstract
Data are essential for scientific research and policy planning. However, there needs
to be attention to data quality and to the estimates and models based on those data.
In addition, data need to be freely available for researchers to test new ideas and validate the work of others through replication, while respondents who provide data
need to be protected. Three issues concerning data are addressed: (i) availability and
accuracy of data for new research and reanalysis while protecting human subjects,
(ii) problems with the estimation of indicators based on flawed or nongeneralizable
data, and (iii) the use of data to develop models for projecting the future, the assumptions on which those models are based, and the assessment of the accuracy of past
projections. In each of these areas, increased attention is necessary on how data are
used, interpreted, and made available to the scholarly and policy community.

INTRODUCTION
Data are essential for scientific research and policy planning. In light of this,
there has been an increasing call for evidence-based policy in numerous
areas. However, there needs to be attention to data quality and to the
characteristics of estimates and models based on those data.
Three issues of special concern are as follows:
Availability and accuracy of data for new research and reanalysis while protecting human subjects. There are impediments to data sharing for new research
and for replicability of findings, and there are inherent tensions between data
access and protection of human subjects.
Problems with the estimation of models and indicators based on flawed or nongeneralizable data. Indicators must be estimated from sufficiently accurate and
unbiased data so that a misleading picture of the situation is not given. Methods of estimating indicators are often developed based on relationships in
settings where there are high-quality data. The indicator might not have the

Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

same relation to the data in settings in which less is known, but this is very
difficult to determine. The effort to use more appropriate data can lead to
additional problems.
The use of data to develop models for projecting the future, the assumptions on
which those models are based, and the assessment of the accuracy of past projections.
Models of the trajectory of demographic changes are necessarily constructed
based on situations where a particular change has already occurred. Projecting the future requires assumptions that can be arbitrary and can change
radically as the situation in demographically advanced countries changes,
producing an unstable situation for users of the projections.
In each of these areas, increased attention is necessary on data are used,
interpreted, and made available to the scholarly and policy community.
FOUNDATIONAL RESEARCH
Some of the main principles of data collection and analysis are as follows:
Data should be available for replicating the work of others and for testing new ideas.
There has been an increasing standard that data must be made available to
others in the scientific community for reanalysis, and an increasing number
of scholarly journals require that data used in an article be available at an
accessible data repository (Anderson, Greene, McCullough, & Vinod, 2008).
Data that are collected using U. S. government funds are to be made available
to other researchers in some form, usually within 2 years of the completion
of data collection.
The identity, answers, and other data regarding respondents need to be effectively
protected. Concern with protection of human subjects increased in response to
Nazi medical experiments as well as disclosure of egregious violation of the
rights of patients in the United States. The Tuskegee Study from 1932 through
1972 studied 600 poor African-American men in Tuskegee, Alabama, 399 of
whom already had syphilis. Participants were not told they had syphilis and
were not treated for syphilis, even after penicillin was accepted as an effective
syphilis treatment in 1947. Many participants and their wives and children
died of syphilis. In 1972, information about the study was leaked, which led
to the study’s termination (Fairchild & Bayer, 1999; Jones, 1981). Disgust at
the Tuskegee Study motivated the Belmont Report, which is the basis for the
Institutional Review Board guidelines that govern all academic research in
the United States.
It is better to know something rather than nothing in a given situation. Models of
behavior are often based on the assembly of all available high-quality data.
Then, a model is fit to those data, which then can be used in other situations. If
the model is intended for use in estimating indicators for situations in which
the most appropriate data are not available, then the input data should be

The Role of Data in Research and Policy

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readily available in those situations in which one wants to produce estimates.
There is always tension between including as much data as possible in the
development of the model and being sure not to include low-quality data or
biased data.
An example of this is the Coale–Demeny regional model life tables (1966).
These mortality models were based on 326 life tables that were thought to
have high-quality data. Of these, 324 were from Europe or North America.
Almost all the life tables with high mortality were from historical Europe.
There was awareness that the life tables used might not represent all of
world experience, but it was viewed as too risky to include life tables from
low-income questions where the data quality might be poor.
Sometimes, the only guide to the future is what has happened in the past. Modeling mortality as addressed in the previous point has its pitfalls, but modeling
fertility can be even more risky. Family planning programs in Taiwan and
elsewhere in much of Asia in the 1960s and 1970s were quite successful. These
programs seemed to indicate that once easy-to-use, effective contraceptives
were made available, fertility would rapidly fall. That view was called into
question when fertility in sub-Saharan Africa and some parts of Latin America seemed much more resistant to change (Caldwell & Caldwell, 1988).
Population projections are important in and of themselves for planning the
future and are also part of the input information for many other purposes,
such as estimating the adequacy of future food supply in a region. There have
been noted instances in the past when projections were very far off, because
of an unknown changeable future.
CUTTING-EDGE RESEARCH
Research has pointed out shortcomings and considerations about data,
models, and estimation. Such studies highlight further work that needs to
be done.
AVAILABILITY AND ACCURACY OF DATA FOR NEW RESEARCH AND REANALYSIS WHILE
PROTECTING HUMAN SUBJECTS
Data access for original research and for replicability of findings remains an
issue in both low-income and high-income countries. In both settings, issues
of researcher or institutional hoarding and of protection of respondent confidentiality arise.
In many low-income countries, there remains lack of high-quality demographic data upon which to base population estimates and to look at interrelations between demographic and social variables. To address this problem,
49 demographic surveillance sites have been established in 20 low-income

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

countries, especially in sub-Saharan Africa. This is called the INDEPTH Network (2014). In these sites, intensive efforts are made to collect information
on demographic events at frequent intervals.
There is concern about access to data from demographic surveillance sites.
Data collection requires a huge effort. Persons directing and conducting data
collection are often reluctant to turn the data over to external researchers who
have not devoted the same amount of energy to this effort. On the other hand,
scientific standards require that data be available for independent examination and validation. If other researchers do not have access to the data, it is
not possible for alternative explanations to be investigated, and it can lead
to questioning of the value of research results based on the data. In addition, since data collection is so time intensive, researchers often need to turn
their attention to the next round of data collection as soon as one round is
completed. Thus, much data from such sites are analyzed only locally to a
limited extent. These issues have led to a lively debate about the conditions
under which data from a demographic surveillance site should be available
to the larger scholarly community (Baiden, Hodgson, & Binka, 2006; Carrel &
Rennie, 2008; Chandramohan et al., 2008).
The balance between protecting respondents, giving those who collect data
a fair chance to benefit from publication, and allowing replicability remains
a source of tension. In the INDEPTH research sites in many low-income
countries, there has been a debate concerned with whether in principle,
researchers who were not involved in data collection should have access and
whether their research aims should be required to be in line with or whether
they should be required to collaborate with project researchers. These
potential requirements conflict with principles of independent assessment
and replicability in science.
There also remain concerns about sharing of data in the United States. Even
as more and more journals require deposit of data in an archive, this had not
at least in 2009 affected many open-access journals, which are advocates for
total openness in research (McCullough, 2009). Even when data have been
deposited in an archive, McCullough, McGeary, and Harrison (2008) found
it was only possible to replicate the work in 14 out of 62 articles.
As more and more analyses for policy purposes are based on surveys and
microdata from censuses, there is concern with protecting the identity of
individuals. This often leads to masking data in various ways, including
perturbation of data by introduction of a random factor, grouping dates of
occurrence of events into 5-year or 10-year periods, and masking aggregating geographic location to a fairly large area. These respondent-protection
measures can lead to erroneous conclusions, though, in analysis of publicly
available data.

The Role of Data in Research and Policy

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A slight adjustment of ages through introduction of a random factor
can sometimes lead to anomalous results, such as unreasonable sex ratios
(Alexander, Davern, & Stevenson, 2010; U. S. Census Bureau, 2010). Adjustment of the reported data to mask the identity of those over age 65 can
also lead to inaccurate estimates of characteristics of the elderly, such as
their income (Fisher, 2010). On event history analysis, the detailed dating of
events and the sequencing of events are important, which are not possible
with aggregated times of occurrence (Freedman, Thornton, Camburn,
Alwin, & Young-DeMarco, 1988), a common way of masking identity
through grouping time into 5- or 10-year periods, and reporting data for
fairly large geographic units calls for rethinking of how respondent identity
should be masked.
Masking geographic detail can help protect respondents (Sherman &
Fetters, 2007). However, researchers have increasingly incorporated detailed
information about the characteristics of small geographic areas in order
to identify clusters of people with particular diseases or who are studying attitudes or behavior need very detailed geographic information to
do so (Berg, Stewart, Stewart, & Simons, 2013; Armstrong, Rushton and
Zimmerman, 1999). A researcher can apply to the body that controls the
data and ask for more detailed information. If the controlling body sees
the proposed research as sufficiently valuable, the researcher could obtain
the more detailed data, but the approval process can take a long time and is
often not successful.
PROBLEMS WITH THE ESTIMATION OF INDICATORS BASED ON NONGENERALIZABLE OR FLAWED
DATA
Estimates of the number of persons with a disease are sometimes based
on results of a survey. In order for the estimates to be accurate, the survey
respondents must be representative of the population as a whole or the way
in which the respondents differ from the population as a whole must be well
understood so that estimates for the entire population can be made.
UNAIDS revised downward its estimate of the number of HIV-positive
people in India from 5.7 million for 2006 to 2.5 million for 2007. This downward revision was not due to an actual enormous decline in HIV, but rather
due to a change in the basis for the estimates. UNAIDS changed the basis
of their estimates from clinic data for high-risk groups (pregnant women,
injection drug users, commercial sex workers) to a more representative
population-based survey. It was clear that the earlier estimates had greatly
overestimated the prevalence of HIV in the general Indian population
(Steinbrook, 2008; UNAIDS, 2007, 2008).

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

The new estimates are clearly more accurate than the old estimates. However, some are not happy about this change because they think it could lead
to less attention and less money being allocated to fight HIV. Also, some
interpret this reported change as real and thus exaggerate the extent of real
declines in HIV. This example shows that how data are collected and how survey respondents are chosen for collection of the data can have a large impact
on what conclusions are drawn.
The United Nations (1982) developed models of mortality patterns based
on data from 22 less-developed countries. These new mortality models were
intended to improve upon the earlier Coale–Demeny models that were
mainly based on data from Europe or North America. They were intended
to provide models of mortality that were more relevant to the situation
in low-income countries. The United Nations developed a General Model
based on data from all 22 countries, as well as four additional models based
on data from subsets of the countries. Unfortunately, it was later concluded
that the male model for the Latin American pattern was substantially a
model of data error, due to problems with the data for males at the older
ages in the contributing life tables (Dechter & Preston, 1991).
ASSESSMENT OF THE ACCURACY OF PAST PROJECTIONS, THE USE OF DATA TO DEVELOP
MODELS FOR PROJECTING THE FUTURE, AND THE ASSUMPTIONS ON WHICH THOSE MODELS
ARE BASED
Despite the importance of population projections, there has been fairly little
work assessing their accuracy. Keilman’s (1998) research is especially interesting. He assessed the accuracy of United Nations population projections
for 1951–1988. Sometimes, the assessment was inaccurate because of error
in the estimation of the population at the first time. After the results of the
1953 Chinese Census were released in 1954, the estimate of the population
of the world for 1950 was increased, because it was seen that the population
of China was more than 100 million larger than had earlier been thought.
At other times, assumptions about the future were inaccurate. Throughout
the world, mortality declined more rapidly than had been projected. Also,
fertility declined more quickly after the 1970s than had been expected,
partially owing to policies implemented because of alarm about high rates
of population growth in many low-income countries in the 1960s and 1970s
(Keilman, 1998).
Keilman (2008) also showed that in the period 1950–2001, population
projection of the expected future demographic situation (total population,
mortality, fertility, and international migration) done by European national
statistical offices did not become more accurate. This was true even as the

The Role of Data in Research and Policy

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amount and the quality of the data on which these forecasts were based
improved.
The United Nations Population Division is the main producer of authoritative estimates and projections of the total population and of demographic
processes, such as mortality and fertility. The most important part of a population projection is the future fertility assumption. Between 2004 and 2012,
the UN Population Division changed its basic assumption about the course
of fertility decline to a level that would result in zero population growth and
about the fertility trajectory after that four times (Anderson, 2014; Basten,
2013b). With low mortality, zero population growth would require a total
fertility rate (TFR) of 2.07. TFR resulting in eventual zero population growth
is also called replacement fertility.1
These changes were based on observation of the history of some
high-income, low-fertility countries. Before 2004, the UN Population
Division had long projected TFR to asymptotically reach replacement level,
TFR = 2.07. This assumed that all countries would eventually have low
mortality and low fertility stationary populations.
In the 1990s, many countries had sustained below replacement fertility
(TFR < 2.07), sometimes falling to lowest-low fertility (TFR ≤ 1.3). After
extensive consultation, fertility projection assumptions were changed in
2004, and all countries were then projected to asymptotically approach
TFR = 1.86, which implies long-term population decline. This was a major
departure from the earlier eventual zero population growth assumption.
Through the 2000s, TFR increased across at least two 5-year time periods
in 21 below replacement fertility countries. On the basis of fertility increases
in those countries, assumptions were again changed in 2010, so that in
the new model TFR in below replacement fertility countries increased
toward replacement, with the pace of increase more rapidly the farther TFR
was below replacement. For countries with above replacement fertility in
2005–2010 such as Algeria, TFR was projected to fall below replacement
and then increase toward replacement. This marked a return to the eventual
stationary population assumption.
There was a loud outcry about the unreasonableness of the fertility projections for some Asian countries where fertility was very low and where
there had been no indication of any increase (Basten, 2013a, 2013b; Basten,
Coleman & Gu, 2012).
Partly in response to complaints about the 2010 estimates, in 2012, TFR projection assumptions were again changed. By 2012, TFR had increased across
at least three 5-year periods in 25 low-fertility countries. The new low-fertility
1. The TFR is the number of children a woman would have in her life if she survived to age 50 and at
every age had children at the rate in that population at that time.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

projection model for many individual countries was based both on the experience of these 25 countries and on the TFR record of the individual country.
The 2012 projections resulted in less extreme departures from earlier projected TFRs than occurred between the 2004 and 2010 projections.
KEY ISSUES FOR FUTURE RESEARCH
Progress of the issues highlighted here requires scientific research but also
discourse and discussion in philosophy and ethics. Just as in a trial, there is
usually some merit on each side of an argument and there are conflicting considerations and values so that a perfect resolution in many areas is probably
not possible.
AVAILABILITY AND ACCURACY OF DATA FOR NEW RESEARCH AND REANALYSIS WHILE
PROTECTING HUMAN SUBJECTS
Despite rules by journals about access to data, Tenopir et al. (2011) document that data hoarding is still common. There needs to be consideration
about what further steps could be taken while avoiding negative unintended
consequences, such as discouraging data collection by researchers. Similarly,
although there exists a procedure for external researchers to apply for use of
INDEPTH data, the process is complicated and it is yet to be seen how open
access will be.
The US Census Bureau has established Research Data Centers (RDCs) at
more than 15 universities and research centers. At these centers, researchers
with approved projects can research results of computer runs based on
analysis of detailed individual data that are not available in as detailed a
form in public use data sets. The RDCs help resolve the issue of data access
and respondent confidentiality, but they are flawed by the requirement that
projects using an RDC must “provide benefit to Census Bureau programs
(U. S. Census Bureau, 2012).” This is an impediment to free scientific work
and to the range of studies that can be pursued. More thought needs to
go into this program, which seems to be affected by some of the same
inclinations that have impeded data sharing among researchers.
The balance between data access and protection of respondents is a
value-laden issue of public policy. More discussion between those concerned with research and those concerned with ethics could be fruitful to
clarify what the guiding principles should be. These discussions seem even
more necessary with increasing emphasis on “big data” to address many
scientific and policy questions (Schuurman, 2000; United States. White
House. Office of Science and Technology Policy, 2012).

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9

PROBLEMS WITH THE ESTIMATION OF INDICATORS BASED ON NONGENERALIZABLE OR FLAWED
DATA
The development of indicators and models, such as for mortality, that are
relevant to the situation in parts of the world where data are lacking or of
poor quality remains a challenge. There is an understandable urge to include
all appropriate data in developing ways to make estimates, but there remains
the danger of including data that include serious error.
The need for some basis for estimates, such as of mortality, is clear. A
country wants to know things such as the average length of life (also called
expectation of life at birth) for many purposes. However, in 2012, The United
Nations Population Division reported that 26% of the countries in the world
and 60% of the countries in Africa had no reliable data on adult mortality,
and two countries had no reliable data on mortality at any age. In these
situations, estimates based on the situation elsewhere are essential (United
Nations, 2014, p. 14).
ASSESSMENT OF THE ACCURACY OF PAST PROJECTIONS, THE USE OF DATA TO DEVELOP
MODELS FOR PROJECTING THE FUTURE, AND THE ASSUMPTIONS ON WHICH THOSE MODELS
ARE BASED
How to use the past and thoughts about the future to model the future is a
difficult problem. Additional assessment of the accuracy of past projections
of the total population as well as of fertility and mortality could contribute
to more informed decisions. In any case, probably, it is not wise to change
assumptions frequently in a major way, as users of the results would easily
assume real change where there has been none. In 2008, the UN projected that
the TFR in Singapore in 2040–2045 would be 1.59; in 2010, with new fertility
assumptions, it projected that the TFR in Singapore in 2040–2045 would be
higher than was thought only 2 years earlier at 1.80; in 2012, the UN projected
that the TFR in 2040–2045 would be lower than had been thought 2 years
earlier at 1.38. Across the period 2008–2012, the estimated TFR in Singapore
declined from 1.33 to 1.25. These changes in projected TFR had no relation to
actual fertility changes in Singapore (Anderson, 2014). What changed over
time was thinking about the future of fertility, based on the trajectory in less
than 1∕2 of the low-fertility countries rather than an observed change in many
countries for which fertility was projected.
ACKNOWLEDGMENTS
Theresa Anderson, Heather King, Emily Marshall, Emily Merchant, John H.
Romani, Michelle Steinmetz, and Victoria Velkoff have made helpful comments and suggestions.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

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FURTHER READING
Coale, A. J., & Trussell, T. J. (1996). The development and use of demographic models.
Population Studies, 50, 469–484.
National Academy of Science (1995). On being a scientist: Responsible conduct of research
(2nd ed.). Washington, DC: National Academies Press.
Preston, S. H. (1993). The contours of demography: Estimates and projections.
Demography, 30, 593–606.
United States, National Commission for the Protection of Human Subjects of
Biomedical and Behavioral Research (1979). The Belmont Report: Ethical principles and guidelines for the protection of human subjects of research. Retrieved
from http://www.fda.gov/ohrms/dockets/ac/05/briefing/2005-4178b_09_02_
Belmont%20Report.pdf.
VanWey, L, Rindfuss, R. R., Gutman, M. P., Entwisle, B., & Balk, D. L. (2005). Confidentiality and spatially explicit data: Concerns and challenges. Proceedings of
the National Academy of Sciences of the United States of America, 102: 15337–15342.
Retrieved from http://www.pnas.org/content/102/43/15337.full

BARBARA A. ANDERSON SHORT BIOGRAPHY
Barbara A. Anderson is the Ronald Freedman Collegiate Professor of Sociology and Population Studies at the University of Michigan. She holds an
AB degree in mathematics from the University of Chicago and a PhD degree
in sociology from Princeton University. She has been a faculty member at
Yale University and Brown University, a visiting member at the Institute
for Advanced Study, and a fellow at the Center for Advanced Study in the
Behavioral Sciences. She has conducted extensive research on the relation of
population and development and the role of data and data quality in these
areas. She has consulted on data and research with the governments of Estonia, China, and South Africa. She is a member of the U.S. Census Scientific
Advisory Committee and has served on the NSF Review Panel on Sociology and the NICHD Population Research Committee. She has published or
edited six books and more than 100 articles and chapters.

The Role of Data in Research and Policy

13

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