Conversion of Verbal Response Scales: Robustness Across Demographic Categories

Happiness and life satisfaction have traditionally been measured using verbal response scales, however, these verbal scales have not kept up with the present trend to use numerical response scales. A switch from a verbal scale to a numerical scale, however, causes a severe problem for trend analyses, due to the incomparability of the old and new measurements. The Reference Distribution Method is a method that has been developed recently to deal with this comparison problem. In this method use is made of a reference distribution based on responses to a numerical scale which is used to decide at which point verbally labelled response options transit from one state to another, for example from ‘happy’ to ‘very happy’. Next, for each wave of the time series in which the verbal scale is used, a population mean is estimated for the beta distribution that fits best to these transition points and the responses in this wave. These estimates are on a level that is comparable to that of the mean of the reference distribution and are appropriate for use in an extended time series based on the responses measured using a verbal and a numerical scale. In this paper we address the question of whether the transition points derived for the general population can be used for demographic categories to produce reliable, extended time series to monitor differences in trends among these categories. We conclude that this is possible and that it is not necessary to derive transition points for each demographic category separately.     

Optimally Scaled Quality-of-Life Indicators

When studying quality of life, researchers have to rely on the subjective evaluations, which are typically categorized, collected in surveys. When statistical analysis are applied to these data, they used to apply the simplistic approach including (i) direct quantification, which assigns discrete numerical values to ordinal response scale, and (ii) complete-case analysis, which discards all observations selecting any of the off-scales choices like Dont Know No Answer from the analysis. The present paper examines the disadvantages of this approach and introduces the optimal scaling method as a remedy. The new scheme attempts to restore the continuity property of the measurements as well as provide estimates for most of the missing responses. Application of the new scheme to the Hong Kong QOL data illustrates how the scheme works, demonstrates its advantages and shows how the QOL indicators, the global QOL indicator as well as its inherited indicators, can be constructed from series of principal component analyses. Factor analysis of the 20 life domain indicators verifies Wan's opinion (1992) that the global sense of well-being can best be captured by two dimensions, namely the personal well-being and the societal well-being. Although previous QOL studies had seldom included perceptions towards various societal conditions to identify life satisfaction in general, our data analysis shows that satisfaction on these conditions do constitute an important component of the global QOL.     

AN INVESTIGATION OF GENERALIZED RAKINGIN THE SYNTHETIC ESTIMATION OFPOPULATION SIZE

The problem of counting a population that is cross-classified with respect to demographic and geographic attributes is considered. A census is conducted in which individuals are “captured” with probabilities that are believed to be relatively constant within demographic categories. The census is followed by a random sample in which individuals are “recaptured” independently of the census. Using the two counts, capture-recapture estimates of the demographic category populations are obtained. A synthetic estimate of population size for a geographic entity is obtained by summing the corresponding adjustment factors (capture-recapture estimates divided by census counts) across all individuals captured by the census in the entity. The use of generalized raking is considered as a method for smoothing adjustment factors. It is found that generalized raking differs little from a class of weighted least squares regression models. This suggests that generalized raking does not offer an improvement over regression for smoothing adjustment factors. The efficiency loss of generalized raking relative to the best regression-based procedures can be substantial.     

The Pre- and Post-1997 Well-Being of Hong Kong Residents

Since July 1997, Hong Kong has become a Special Administrative Region (SAR) under the sovereignty of China. This paper compares the social well-being of Hong Kong residents before and after the change of sovereignty by analyzing six sets of survey data collected between 1990 and 2001. These are six biennial territory wide Social Indicator Surveys conducted to collect subjective assessments of well-being of Hong Kong residents. Analysis of the survey data which are typically categorized subjective evaluations has to deal with the commonly encountered problems of ‘direct quantification’ (Schuessler and Fisher, 1985) and ‘incomplete data’ (Little R.J.A. and Rubin D.B., 2002, Statistical Analysis with Missing data (Wiley-Interscience, New Jersy). The problems are handled by applying ‘optimal scaling’ and ‘hot-deck imputation’ as recommended by Shen S.M. and Lai Y.L. (2001, Social Indicators Research 55, pp. 121-166). Different life domain measures as well as compound measures in the form of indices are studied. The data analysis indicates that the residents of Hong Kong were most satisfied in 1997 and least satisfied in 1995. It reveals that the sovereignty change had major impact on many life domains of the people of Hong Kong and the effect was compounded by the economic situation of the territory.     

Bridging 1990 and 2000 census race data: Fractional assignment of multiracial populations

In contrast to previous censuses, Census 2000 permitted individuals to mark more than one race. Because the new race tables include both single-race and mixed-race categories, measuring change during the 1990s requires some method of bridging between the two data sets.To accomplish this bridging, we first identified biracial populations as of 1990 through the race and ancestry responses of individuals in the PUMS file. With race responses assumed to represent a person's primary race identity, we then determined the percentage of each biracial group that preferred each race as the primary identity. The same percentages can be used to assign biracial persons from Census 2000 into single-race categories. We also provide fractional assignment percentages for selected states and for the larger specific nationality groups of mixed-race Asians.Comparison of our 1990 estimates of the numbers in leading biracial groups with those reported in Census 2000 suggests that our fractional assignment values are reasonable for biracial groups other than those involving American Indians and Alaska Natives. For the latter biracial groups and for all groups representing three or more races, we recommend equal fractional assignment into the appropriate single-race categories.     

Do Cultural Values Affect Quality of Life Evaluation?

Schwartz defines cultural values as motivational types, where each value reflects goals and objectives to be achieved. According to Schwartz, cultural values are related to an orientation that is individualistic (values referred to as power, achievement, hedonism, stimulation and self-direction), collectivistic (benevolence, tradition and conformity) or mixed (security and universalism). Today, there is a theoretical consensus that cultural values are mediators in the evaluation of quality of life (QOL); nonetheless, there are few published studies to date relating them to QOL. To determine whether a significant relationship exits between cultural values and QOL in three Spanish-speaking countries. A total of 821 persons participated: 321 from Chile, 200 from Spain and 300 from Cuba. The Schwartz Cultural Values Survey and the WHOQOL-BREF Quality of Life Scale were used. Analysis of variance, and correlation and regression analyses were preformed after collecting data. Only hedonism was significantly correlated with the global evaluation of QOL in Spain and Chile. Few correlations were found in all three countries between cultural values and the QOL domains evaluated, with the exceptions of the value of self-direction, which was related to physical well-being, and the value stimulation, which was correlated with psychological as well as social well-being in all three countries. Certain values may be associated with a better perception of QOL, depending on the particular culture of the population evaluated.     

Dealing with omitted answers in a survey on social integration of immigrants in Italy

ABSTRACT

Surveys are used to infer the level of social integration of immigrants. Item response theory helps to describe the relationship among responses to test items and latent traits of interest. However, in the presence of nonignorable missing data, which are omitted responses depending on the latent traits to be measured, estimates of the model parameters are biased. To account for nonignorable missing data, the quantity and quality of contacts between immigrants and natives (so called “social integration”) are taken into account through a linear function of the response propensity. Higher education, no intention to migrate again, young age, Albanian nationality, and declaring a non-Muslim religion or none, comparatively favor social integration.     

How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050

Sex‐based discrimination has resulted in severe demographic imbalances between males and females, culminating in a large number of “missing women” in several countries around the world. We provide new estimates and projections of the number of missing females and of the roles played by prenatal and postnatal factors in this imbalance. We estimate time series of the number of missing females, the number of excess female deaths, and the number of missing female births for the world and selected countries. Estimates are provided for 1970–2010 and projections are made from 2010 to 2050. We show that the estimates of these different indicators are consistent with one another and account for the dynamics of the population of missing females over time. We conclude that the number of missing females has steadily risen in the past decades, reaching 126 million in 2010, and the number is expected to peak at 150 million in 2035. Excess mortality was the dominant cause of missing females in the past, and this is expected to remain the case in future decades in spite of the recent rise of prenatal sex selection. The annual number of newly missing females reached 3.4 million in 2010 and is expected to remain above 3 million every year until 2050.     

Interpolation and forecasting of population census data

Since population censuses are not annually implemented, population estimates are needed for the intercensal period. This paper describes simultaneous implementations of the temporal interpolation and forecasting of the population census data, aggregated by age and period. Since age equals period minus cohort, age-period-cohort decomposition suffers from the identification problem. In order to overcome this problem, the Bayesian cohort (BC) model is applied. The efficacy of the BC model for temporal interpolation is examined in comparison with official Japanese population estimates. Empirical results suggest that the BC model is expected to work well in temporal interpolation. Regarding the age-period-cohort decomposition of the Japanese census data, it is shown that the cohort effect is the largest while the other two effects are very small but not negligible. With regard to the forecasting of the Japanese population, the official population forecast considerably outperforms the BC forecast in most forecast horizons. However, the pace of increase in root mean square error for longer-term forecasting is larger in the official population forecast than in the BC forecasts. As a result, a variant of the BC forecast is best for 10-year forecast.     

Multidimensional Life Table Estimation of the Total Fertility Rate and Its Components

Using discrete-time survival models of parity progression and illustrative data from the Philippines, this article develops a multivariate multidimensional life table of nuptiality and fertility, the dimensions of which are age, parity, and duration in parity. The measures calculated from this life table include total fertility rate (TRF), total marital fertility rate (TMFR), parity progression ratios (PPR), age-specific fertility rates, mean and median ages at first marriage, mean and median closed birth intervals, and mean and median ages at childbearing by child’s birth order and for all birth orders combined. These measures are referred to collectively as “TFR and its components.” Because the multidimensional life table is multivariate, all measures derived from it are also multivariate in the sense that they can be tabulated by categories or selected values of one socioeconomic variable while controlling for other socioeconomic variables. The methodology is applied to birth history data, in the form of actual birth histories from a fertility survey or reconstructed birth histories derived from a census or household survey. The methodology yields period estimates as well as cohort estimates of the aforementioned measures.     

Using administrative data to estimate population displacement and resettlement following a catastrophic U.S. disaster

After a large scale evacuation, authorities need to know the new and frequently changing population distributions in order to meet needs for housing, schools, health care, and other services. This paper reviews literature from the fields of demography and other disciplines to identify available administrative data sets that can form the basis of sound, relevant, and timely county-level population estimates following a catastrophic U.S. event. The most appropriate data to estimate population in damaged counties will be disaster-specific data such as housing damage estimates and FEMA applicant counts initially, and later electric accounts and USPS active residences. In heavily damaged counties, data on electric accounts and USPS active residences may not be consistently collected for many months, during which time sample surveys may be needed. For counties that receive an influx of population, school enrollment data provide the most appropriate basis for population estimates. Population estimates for large, heavily damaged counties are highly uncertain. Sensitivity analysis when using estimates for planning in these areas is recommended. The Census Bureau can build on this research by codifying recommendations to local authorities for developing frequent post-disaster population estimates.     

Coverage of the population in Census 2000: Results from Demographic Analysis

Since the 1960 Census, Demographic Analysis (DA) has been used by the Census Bureau to evaluate the coverage of the population. Administrative statistics on births, deaths, immigration and Medicare enrollments as well as estimates of legal emigration and net undocumented immigration are used to produce demographic analysis estimates of the population for the census date. These estimates are compared to the Census 2000 data to evaluate coverage in the census. The results are also compared to measures of undercount obtained from dual system estimation. The DA measures substantial reduction in net undercount in Census 2000 compared to 1990. The reductions occur among all demographic categories: all broad age groups, males and females, Blacks and Non-Blacks.     

The Use of Common Area <Emphasis Type="Italic">k</Emphasis>-Sample Test in Evaluating Targeting Methodologies: An Application to the Case of Egypt

The quality of life (QOL) is a measure of social wellbeing and life satisfaction of individuals in an area. Measuring its spatial dynamics is of great significance as it can assist the policy makers and practitioners in improving the balance between urbanization and living environment. This study proposes an approach to spatially map and examine the relationships between QOL, land use/land cover (LULC) and population density in an urban environment. The city of Lahore, Pakistan was selected as the case study area. The QOL was evaluated through the data related to physical health, psychological, social relationships, environment (natural and built), economic condition and development, and access to facilities and services. The weights/relative importance of each QOL domain was determined through the analytic hierarchy process by processing the data collected from local field experts. Overall QOL was computed by applying the domain weights to the data; spatial mapping of QOL domains and overall QOL was conducted afterwards. The spatial dynamics of QOL were examined, and its interrelationships with LULC and population density were analyzed. The relationship between these three variables turned out to be spatially dynamic. The proposed approach assists the spatial mapping and analyses of QOL, LULC and population, and by examining the spatial dynamics of these variables, contributes to devising appropriate land management and QOL improvement strategies and policies in the metropolitan regions.     

Estimating historic population data for small geographies using census housing information

Historical population data for small geographies (e.g. blocks, block-groups, and census tracts) are not available for periods earlier than 1980. In this research note, we propose a geographically-constrained housing unit method (GHUM) to estimate historic population for small geographies using housing age data available in the 1980–2000 censuses. The GHUM is a two-stage method. The first stage follows a traditional housing unit method and provides initial household and group quarter population estimates for small geographies. The second stage takes advantage of the availability of historic data for larger geographies (e.g. counties, states) to adjust the first stage estimates and to provide final estimates. The GHUM is used to estimate 1940–1990 county population and census tract population in Kentucky. The quality of the population estimates is assessed. A two-sample Kolmogorov–Smirnov test indicates that these estimates are statistically reliable at the 10 % significance level.     

Quality of Life Predictors and Normative Data

This study identifies predictors and normative data for quality of life (QOL) in a sample of Portuguese adults from general population. A cross-sectional correlational study was undertaken with two hundred and fifty-five (N = 255) individuals from Portuguese general population (mean age 43 years, range 25–84 years; 148 females, 107 males). Participants completed the European Portuguese version of the World Health Organization Quality of Life short-form instrument and the European Portuguese version of the Center for Epidemiologic Studies Depression Scale. Demographic information was also collected. Portuguese adults reported their QOL as good. The physical, psychological and environmental domains predicted 44 % of the variance of QOL. The strongest predictor was the physical domain and the weakest was social relationships. Age, educational level, socioeconomic status and emotional status were significantly correlated with QOL and explained 25 % of the variance of QOL. The strongest predictor of QOL was emotional status followed by education and age. QOL was significantly different according to: marital status; living place (mainland or islands); type of cohabitants; occupation; health. The sample of adults from general Portuguese population reported high levels of QOL. The life domain that better explained QOL was the physical domain. Among other variables, emotional status best predicted QOL. Further variables influenced overall QOL. These findings inform our understanding on adults from Portuguese general population QOL and can be helpful for researchers and practitioners using this assessment tool to compare their results with normative data.     

New techniques in small area population estimates by demographic characteristics

The increasing demand for small area population estimates calls for both innovative ways of using existing data and new techniques suitable for small area estimates. This paper explores the methods for population estimates by age, sex, race, and Hispanic origin at the census tract level for Multnomah County, Oregon. New techniques include employing building permits to indirectly estimate migration and examining the changes in age/sex structure using the American Community Survey (ACS). A practical method for bridging the race categories is also developed. Finally, the paper discusses some reflections on small area estimates and the potentials of using ACS to track the changes of the demographic characteristics for the sub-county level.     

An evaluation of bridging methods using race data from Census 2000

The question on race from Census 2000 was different from previous censuses because it allowed respondents to select one or more races to indicate their racial identities. Because of this change, the race data from Census 2000 are not directly comparable with data from earlier censuses. Researchers can use `bridging' methods to assign more than one race respondents to single race categories to maximize the comparability of Census 2000 race data with earlier censuses. This paper uses several bridging methods to generate race population estimates and analyzes the variability in those estimates across six single race groups.     

Happy life-expectancy

One of the aims of social indicator research is to develop a comprehensive measure of quality-of-life in nations that is analogous to GNP in economic indicator research. For that purpose, several multi dimensional indexes have been proposed. In addition to economic performance, these also acknowledge the nation's success in matters like schooling and social equality. The most current indicator of this type is the ‘Human Development Index’. In this approach QOL is measured by input; the degree to which society provides conditions deemed beneficial (‘presumed’ QOL). The basic problem is that one never knows to what extent the cherished provisions are really good for people. An alternative is measuring QOL in nations by output, and consider how well people actually flourish in the country. This ‘apparent’ QOL can be measured by the degree to which citizens live long and happily. This conception is operationalized by combining registration based estimates of length-of-life, with survey data on subjective appreciation-of-life. Life-expectancy in years is multiplied by average happiness on a 0–1 scale. The product is named ‘Happy Life-Expectancy’ (HLE), and can be interpreted as the number of years the average citizen in a country lives happily at a certain time. HLE was assessed in 48 nations in the early 1990's. It appears to be highest in North-West European nations (about 60) and lowest in Africa (below 35). HLE scores are systematically higher in nations that are most affluent, free, educated, and tolerant. Together, these country-characteristics explain 70% of the statistical variance in HLE. Yet HLE is not significantly related to unemployment, state welfare and income equality, nor to religiousness and trust in institutions. HLE does not differ either with military dominance and population pressure. The conclusion is that HLE qualifies as the envisioned comprehensive social indicator. It has both clear substantive meaning (happy life-years) and theoretical significance (ultimate output measure). HLE differentiates well. Its correlations fit most assumptions about required input, but also challenge some. The indicator is likely to have political appeal.     

Research note: imputing large group averages for missing data,using rural-urban continuum codes for density driven industry sectors

Understanding the effects and consequences of missing data imputation is vital to the ability to obtain meaningful and reliable statistics and coefficients in the examination of any quantitatively-based phenomena. Over time a series of sophisticated methods have been developed to handle the issue of missing data imputation however, these sophisticated methods may not always be appropriate or attainable. In these specific cases more traditional approaches to missing data imputation must be employed and driven by the research project, theoretical framework, and the data. In this research note we offer a brief account of one such instance, implementing a large-group mean imputation approach to handling missing data. The analysis is drawn from a much larger project and shows the effect of proper group selection in terms of mean imputation using a cross-validation approach based on the imputed data’s relation to known values. Ultimately, the results show that the use of Rural-Urban Continuum codes are superior to currently used group-means in the U.S., thus introducing a new, and more efficient, approach to the handling of missing data using group-mean imputation.