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This open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error. The parameters of interest concern the earnings distribution, but despite this outcome of interest, the discussion is generalizable to any question in a random sample survey of households or firms. This book then investigates questionnaire design and item nonresponse by building a response propensity model for the employee income question in two South African labour market surveys: the October Household Survey (OHS, 1997-1999) and the Labour Force Survey (LFS, 2000-2003). This time period isolates a period of changing questionnaire design for the income question. Finally, this book is concerned with how to employee income data with a mixture of continuous data, bounded response data and nonresponse. A variable with this mixture of data types is called coarse data. Because the income question consists of two parts -- an initial, exact income question and a bounded income follow-up question -- the resulting statistical distribution of employee income is both continuous and discrete. The book shows researchers how to appropriately deal with coarse income data using multiple imputation. The take-home message from this book is that researchers have a responsibility to treat data quality concerns in a statistically sound manner, rather than making adjustments to public-use data in arbitrary ways, often underpinned by undefensible assumptions about an implicit unobservable loss function in the data. The demonstration of how this can be done provides a replicable concept map with applicable methods that can be utilised in any sample survey.

This open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error. The parameters of interest concern the earnings distribution, but despite this outcome of interest, the discussion is generalizable to any question in a random sample survey of households or firms. This book then investigates questionnaire design and item nonresponse by building a response propensity model for the employee income question in two South African labour market surveys: the October Household Survey (OHS, 1997-1999) and the Labour Force Survey (LFS, 2000-2003). This time period isolates a period of changing questionnaire design for the income question. Finally, this book is concerned with how to employee income data with a mixture of continuous data, bounded response data and nonresponse. A variable with this mixture of data types is called coarse data. Because the income question consists of two parts -- an initial, exact income question and a bounded income follow-up question -- the resulting statistical distribution of employee income is both continuous and discrete. The book shows researchers how to appropriately deal with coarse income data using multiple imputation. The take-home message from this book is that researchers have a responsibility to treat data quality concerns in a statistically sound manner, rather than making adjustments to public-use data in arbitrary ways, often underpinned by undefensible assumptions about an implicit unobservable loss function in the data. The demonstration of how this can be done provides a replicable concept map with applicable methods that can be utilised in any sample survey.

AbstractIt is challenging for survey researchers to investigate sensitive topics due to concerns about socially desirable responding (SDR). The susceptibility to social desirability bias may vary not only between individuals (e.g., different perceptions about social norms) but also within individuals (e.g., perceived sensitivity of different items). Thus, controlling for SDR is particularly challenging when analyzing multidimensional constructs that are measured via multiple groups of items with varying degrees of sensitivity. In this research, we address this challenge using a combination of a randomized response (RR) approach for data collection and a multiscale item response theory (IRT) model for data analysis. While the RR approach protects the anonymity of respondents at the item level, the multiscale IRT approach accounts for the multidimensional nature of the construct and explicitly models the item-level differences in the measurement of its dimensions. We empirically demonstrate the benefits of the model using a multidimensional self-report instrument for the assessment of academic misconduct of university students. Based on an experiment with random assignment, our results uncover considerable differences in the perceived sensitivity, both between the construct dimensions and between their measurement items. These findings support the view that individuals engage in SDR to varying degrees depending on the perceived sensitivity of the specific items and groups of items. In contrast, a social desirability scale that treats SDR as a stable personality trait is not found to capture meaningful differences in response style. Finally, we show how structural models can be incorporated into the framework to link the latent construct's dimensions to individual-level explanatory variables.

Despite the economic turmoil of the time, a typical study of vote choice in the 2008 US Presidential Election would (falsely) find little evidence that voters' opinions about the future state of the economy affected their vote choice. We argue that this misleading conclusion results from serious measurement error in the standard prospective economic evaluations survey question. Relying instead on a revised question, included for the first time in the 2008 American National Election Study, we find that most respondents condition their prospective economic evaluations on potential election outcomes, and that these evaluations are an important determinant of vote choice. A replication in a very different political context - the 2008 Ghanaian election - yields similar results. [Copyright Elsevier Ltd.]

AbstractSoil contamination by trace elements like copper (Cu) can affect soil functioning. Environmental policies with guidelines and soil survey measurements still refer to the total content of Cu in soils. However, Cu content in soil solution or free Cu content have been shown to be better proxies of risks of Cu mobility or (bio-)availability for soil organisms. Several empirical equations have been defined at the local scale to predict the amount of Cu in soil solution based on both total soil Cu content and main soil parameters involved in the soil/solution partitioning. Nevertheless, despite the relevance for risk assessment, these equations are not applied at a large spatial scale due to difficulties to perform changes from local to regional. To progress in this challenge, we collected several empirical equations from literature and selected those allowing estimation of the amount of Cu in solution, used as a proxy of available Cu, from the knowledge of both total soil Cu content and soil parameters. We did the same for the estimation of free Cu in solution, used as a proxy of bio-available Cu. These equations were used to provide European maps of (bio-)available Cu based on the one of total soil Cu over Europe. Results allowed comparing the maps of available and bio-available Cu at the European scale. This was done with respective median values of each form of Cu to identify specific areas of risks linked to these two proxies. Higher discrepancies were highlighted between the map of bio-available Cu and the map of soil total Cu compared to the Cu available map. Such results can be used to assess environmental-related issues for land use planning.

"Schwartz's theory of human values, as operationalized using different instruments such as the Portrait Values Questionnaire (PVQ), was confirmed by multiple studies using Smallest Space Analysis (SSA). Because of its success, a short version of the PVQ was introduced in the European Social Survey (ESS). However, initial tests using Confirmatory Factor Analysis (CFA) pointed to low discriminant validity of the 10 basic values: The correlations between values next to each other in the two-dimensional space described by SSA were close to or greater than 1. In response, one research stream suggested combining the factors with low discriminant validity. Another stream suggested that the problem was not low discriminant validity but rather misspecifications in the model. Analyses of the short Portrait Values Questionnaire of the ESS confirmed the latter view. This paper demonstrates that the problems of the short version of the PVQ exist in the full 40-item PVQ as well. Based on SEM analyses of the items of the full PVQ, we propose that it can provide measures of 15 more narrowly defined values with good discriminant validity. The author's proposal respects the conceptual complexity of the values theory while avoiding contamination of composite scores. It can be expected that the improved measurement of 15 values will increase their predictive power. The presence of some single items suggests the extension of the value theory and scales to encompass more than 15 values. Implications for further development of the scale are drawn." (author's abstract)

Examines results of a nationwide survey of legislators and staff exploring their understanding of environmental risk and risk assessment; since 1990, chiefly; US.

Investigates how the fear of conventional and nuclear war relates to social values held; based on a survey of 183 Israeli and 400 German students, from the Hebrew University of Jerusalem, the Free University of Berlin, and the University of Technology Chemnitz-Zwickau, conducted in the Summer of 1994.

A criticism of research by Frank R. Baumgartner & Jack L. Walker (see SA 37:5/89U9660), which claims that membership in voluntary organizations has risen since the 1950s, using evidence on the increase in new groups & from national surveys. This claim is refuted, highlighting methodological problems with the research, eg, differences in context, data measurement errors, & noncomparable question wordings in existing survey data. Baumgartner's & Walker's article also affirms that the standard question on group membership masks membership increases since the 1974 General Social Survey (GSS). It is shown here that this is only partially correct: although the GSS question does not count multiple memberships within the 16 group categories, other factors must be taken into consideration. In Response to Smith's "Trends in Voluntary Group Membership: Comments on Baumgartner and Walker": Measurement Validity and the Continuity of Results in Survey Research, Baumgartner (Texas A&M U, College Station) & Walker state that this debate involves a trade-off between continuity of results (ie, preserving reliability of growing time series by not changing the wording of the questions) & validity (ie, fine-tuning the survey questions to increase relevance). They claim that holding on to the seriously outdated standard questions results in serious measurement error. 3 Tables, 1 Appendix, 45 References. C. Grindle

Knowledge of water depth is a crucial for planning military amphibious operations. Bathymetry from remote sensing with multispectral or hyperspectral imagery provides an opportunity to acquire water depth data faster than traditional hydrographic survey methods without the need to deploy a hydrographic survey vessel. It also provides a means of collecting bathymetric data covertly. This research explores two techniques for deriving bathymetry and assesses them for use by those involved in providing support to military operations. To support this aim a fieldwork campaign was undertaken in May, 2000, in northern Queensland. The fieldwork collected various inherent and apparent water optical properties and was concurrent with airborne hyperspectral imagery collection, space-based multispectral imagery collection and a hydrographic survey. The water optical properties were used to characterise the water and to understand how they affect deriving bathymetry from imagery. The hydrographic data was used to assess the performance of the bathymetric techniques. Two methods for deriving bathymetry were trialled. One uses a ratio of subsurface irradiance reflectance at two wavelengths and then tunes the result with known water depths. The other inverts the radiative transfer equation utilising the optical properties of the water to derive water depth. Both techniques derived water depth down to approximately six to seven metres. At that point the Cowley Beach waters became optically deep. Sensitivity analysis of the inversion method found that it was most sensitive to errors in vertical attenuation Kd and to errors in transforming the imagery into subsurface irradiance reflectance, R(0-) units. Both techniques require a priori knowledge to derive depth and a more sophisticated approach would be required to determine water depth without prior knowledge of the area of interest. This research demonstrates that water depth can be accurately mapped with optical techniques in less than ideal optical conditions. It also ...

Using data from the Consumer Expenditure Survey and the March Current Population Survey, we calculate historical poverty estimates based on the new Supplemental Poverty Measure (SPM) from 1967 to 2012. During this period, poverty as officially measured has stagnated. However, the official poverty measure (OPM) does not account for the effect of near-cash transfers on the financial resources available to families, an important omission since such transfers have become an increasingly important part of government anti-poverty policy. Applying the SPM, which does count such transfers, we find that historical trends in poverty have been more favorable than the OPM suggests and that government policies have played an important and growing role in reducing poverty --- a role that is not evident when the OPM is used to assess poverty. We also find that government programs have played a particularly important role in alleviating child poverty and deep poverty, especially during economic downturns.

Seasonal hunger may result from seasonality of agriculture when households fail to smooth income and consumption. Using household survey data from the north-west region of Bangladesh, this article examines alternative measures of seasonal hunger, and provides some evidence to support policies and programmes needed to mitigate seasonal hunger. The results suggest that a large majority of food-vulnerable households are the perpetual poor, as opposed to a small percentage of households who are subject to food deprivation only during the lean period. Findings suggest that government safety net programmes and microcredit provide a cushion for the poor to stave off seasonal hunger.

The debate over the relative importance of ideology versus party for vote choice in France is enduring. Resolution of the debate would have much value, for the light shed on sources of stability and change in multiparty electoral systems generally. The main reason the debate continues is that previous studies examining that question have been plagued by difficulties pertaining to variable measurement, model specification, election type, and research design. We address these problems and provide new evidence from the 2002 French Electoral Panel. Most notably, these data allow stronger causal inference because party identification and ideological identification are both measured in the first wave of the survey, that is, before the declaration of vote actually occurs. We estimate a multi-equation model of first-round legislative vote intention as measured in the second wave of the panel using two-stage least squares, ordered logit, as well as binomial and multinomial logit techniques. The results indicate that ideological identification systematically outweighs party identification in shaping the French voter's choice. Adapted from the source document.