Póster de la ULL y el ISTAC en la 4th International Conference of the ERCIM WG on COMPUTING & STATISTICS en Londres

1. Adaptation to the survey ICT-H in Canary Islands of the
Dual Frame methodology
González-Dávila, Enrique1; and and González-Yanes, Jesús Alberto2
1egonzale@ull.es, 2jesusalberto.gonzalezyanes@gobiernodecanarias.org
1Departamento de Estadística, Investigación Operativa y Computación (Universidad de La Laguna, Spain)
2Instituto Canario de Estadística (ISTAC, Spain)
Abstract
QUESTION 2 and 3: Using Dual-Frame Estimators
The combination of information obtained in person and by telephone of a survey usually includes the definition of two Dual-Frame methodology is adapted to the requirements of a survey which combines multiple scenarios with the
scenarios, one associated with the census or population register, and another to phonebook. The application of dual intent to cover the total population and lower implementation costs. We only introduce the situation of two scenarios
frame methodology to surveys of availability or use of new technologies in households where there are target variables with one totally contained in the other.
that can match, have different degrees of association, or be independent with the scenarios defined, are of particular Let be A and B such frames, and in particular:
interest. In this work this methodology is adapted to the particular case of the ICT-H survey conducted in the Canary  A: the total housing.
Islands, where one of the scenarios is contained entirely in the other. Dual frame estimator used is the pseudo  B: households with fixed telephone.
maximum likelihood estimator introduced by Skinner and Rao. The results are compared with those offered by the Then independent samples of size nA and nB are considered for each frame respectively with and the inverse of
direct estimator, and the estimates given by the ICT-H survey conducted by the Spanish National Statistics Institute for the inclusion probabilities in each frame. We consider that survey is conducted by in-person interview in frame A and
the Canaries. Additionally, a simulation study of such survey, on an artificial population similar to the actual by telephone interview in frame B.
A In this case, it creates two mutually
a
population, allows us to evaluate the efficiency of its application. a B exclusive domains, a and ab, formed
ab
as: units of A that are not in B, and
Keywords: Dual-Frame estimators; pseudo maximum likelihood estimator; Relative Bias; Relative Mean Squared ab units that are in both frame to time,
respectively.
error.
1. Description of the surveys Hartley Estimator
ICT-H Survey (INE): 1
Scale-load Estimator (Rao 1983)
 Provides information of equipment and use of new technologies in household at the Autonomous Community ,
level. with:
Its calculation is very simple but is
 Follows a stratified three stage random sampling design in each province, with primary sampling units the The calculation of variances and covariance highly influenced by the sample size.
census sections, secondary units the dwellings and tertiary units the people. can be complex and depends on the type of
 120 sections are sampled (8 dwellings by section, approximately 808 dwellings). sampling is performed.
 Each year renews a quarter of dwellings (rotating panels from 2004).
Pseudo-Maximum Likelihood (PML) Estimator (Skinner and Rao, 1996)
 It uses direct estimates of reason with calibrated weights, wj .
,
,
 All households at the first visit are surveyed by personal interview (CAPI), then those ones that have fixed
telephone, in subsequent visits are surveyed through telephone interviews (CATI). where , is the smallest root of the quadratic equation:
0
 The variables of interest, among others, are: availability of fixed telephone, mobile phone, only fixed
telephone, desktop computer, portable computer, internet, etc. The optimal choice needs to estimate the variances of and . We consider the definition with the samples sizes.
ICT-H Canary Survey (ISTAC): Additionally, you can define a new variable of weights and work with the typical structure of a direct estimator usual in
Statistical Institutes, as:
 Similar to the ICT-H survey (INE) but it’s a light survey in the questionnaire and provides information at the ,
∈
,
island level (performed only in 2006 and 2010). ∈
,
∈
 Follows a stratified three stage random sampling design.
 180 sections are sampled (20 dwellings by section, approximately 3,500 dwellings).
 It uses direct estimates of reason with calibrated weights, wj .
 70% of the survey is conducted through telephone interview (CATI) and 30% with personal interview.
2. Questions
1. If we use a similar survey to the one conducted by ISTAC: how does the use of telephone interview
affect in the direct estimation of the target variables?
2. Assuming that the direct estimations of the target variables will be biased, is it possible to maintain a
high percentage of telephone interview (low cost) to avoid or correct this bias?
3. How does the telephone interview affect the Dual-Frame estimation of target variables, which can
match, or have different degrees of association, or be independent of the availability of phone?
The PML estimator remains unbiased and fairly stable. By decreasing p4 (more
QUESTION 1: How does the telephone interview affect? independent is the variable respect to phone) the RMSE of both (direct and PML)
is more similar. When the percentage of in-person interview is very small (less
Conducting a survey is very expensive and the use of telephone interview reduces the cost. But, in this than 20%) PML estimator variance increases, being more serious when p4 is
survey, most of the target variables are related to the availability of phone at home. smaller (more independent is the variable respect to phone).
 We build an artificial population of households in the Canary Islands, departing of the Housing Census 2001 of that
community, and generating the variables of interest under different conditions.
 Simulate the extraction of surveys with the same methodology that ICT-H Canary survey, being able to vary the 4. Implementation real on ICT-H Canary survey (2010)
degree of in person interview (500 simulations). We obtain the relative mean square errors and biases of different
estimators. A: Continuous Census (Total households)
The target variables considered are generated using Bernoulli distribution with different parameter. B: Phone Directory (Not total households with fixed telephone).
 p1: probability of availability of fixed telephone. We consider that the availability of computer is independent of the fixed telephone. This
allows us to evaluate the performance of an independent variable to the type of interview. The domain a, units of A that are not in B, is defined as the households without fixed telephone plus households
 p2: probability of availability of desktop computer.
with fixed telephone that are not included in the phone directory. Because in this survey, the variable “is in
The variable “only fixed telephone” enable us to evaluate the performance
 p3: probability of only fixed telephone for those with fixed telephone. of a variable contained entirely within the telephone framework. phone directory?” is not considered, we use an estimated proportion, p, of households that are not included in
The variable “internet” allows us to evaluate variables that are closely
phone directory on households with fixed telephone, and we denote as PML(p%).
 p4: probability of availability of internet connection for those with fixed telephone.
related to the availability of fixed telephone, but that is not fully contained ICT-H-INE ICT-HC-ISTAC 2010
 p5: probability of availability of internet connection for those with not fixed telephone. within the phone framework.
2010 Direct PML(20%)
Households with Computer 67,0 76,7 68,6
Table Computer 51,0 55,5 47,0
Telephone Computer Only fixed Telephone Internet Portable Computer 37,6 44,2 40,2
Households with access to Internet 58,4 70,4 60,0
Households with fixed telephone 74,7 89,1 70,6
Households with mobile telephone 94,2 92,3 92,2
Acknowledgments
This work was supported by the Instituto Canario de Estadística (ISTAC) and Agencia Canaria de Investigación e Innovación y Sociedad de la Información
(ACIISI) and partially supported by the Spanish MICINN Proyect MTM2010-16828.
ERCIM 2011
4th International Conference of the ERCIM on Computing & Statistics
Londres, December 17-19, 2011