Impact of survey design in the estimation of habitual food consumption

Troubat, Nathalie; Grünberger, Klaus

doi:10.1016/j.foodpol.2017.08.019

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…The role of recall error in survey data has been explored in a range of topics, including consumption expenditure and food intake (e.g. Beegle et al, 2012b;Troubat and Grünberger, 2017;Backiny-Yetna et al, 2017;Brzozowski et al, 2017;Engle-Stone et al, 2017;D'Alessio, 2017;Schündeln, 2018;Zezza et al, 2017), household enterprises (De Mel et al, 2009;Liedholm, 1991) and income measurement (Moore et al, 2000).…”

Section: Background and Related Literaturementioning

confidence: 99%

Recall length and measurement error in agricultural surveys

2021

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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Section: Background and Related Literaturementioning

confidence: 99%

Recall length and measurement error in agricultural surveys

2021

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“…It is not immediately clear why over-reporting would affect multi-person households to a lesser extent than single-person households, but this may have to do with accuracy that is incurred by the cognitive exercise of distinguishing and dividing consumption among each household member in a multi-person household, while those living alone might rely on less enumerative rules-of-thumb. Recall error is mitigated by the use of prospective instruments such as the HSES-HH’s consumption diary, but these are conversely more likely to be affected by underreporting due to respondent burden [ 21 ]. Both retrospective and prospective assessment methods are also subject to some measure of social desirability bias (the latter by way of respondent “self-monitoring”) [ 38 ], however, research is lacking on the importance of this bias in assessing diet and household food consumption in Mongolia.…”

Section: Discussionmentioning

confidence: 99%

“…Efforts to improve the accuracy of household food consumption measurements are ongoing and have considered such cognitive and survey design [ 7 , 39 ]. In Mongolia, recent analysis by Troubat and colleagues suggests that the HSES-HH’s diary instrument could be satisfactorily substituted with a less costly consumption recall combined with measurement of changes in household foods stocks and acquisitions [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

“…(Throughout this paper, “urban” is used to refer to the capital municipality of Ulaanbaatar and province (aimag) and county (soum) centers, while “rural” refers to more remote settlements and the countryside). Although pooling data from all three HSES-HH reference periods would allow for more precise long-term estimates of household consumption, later periods (the last 20 days of record collection) have been observed to be more prone to underreporting in prior survey waves [ 21 ] (as we also found in exploratory analyses of the 2012–2014 waves), in part due to fatigue incurred by maintaining a diary of household consumption for an entire month. Considering this, this study analyzed HSES-HH household food consumption data from the earlier first 10 days only.…”

Section: Methodsmentioning

confidence: 99%

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Comparison of Methods for Estimating Dietary Food and Nutrient Intakes and Intake Densities from Household Consumption and Expenditure Data in Mongolia

et al. 2018

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Household consumption and expenditure surveys are frequently conducted around the world and they usually include data on household food consumption, but their applicability to nutrition research is limited by their collection at the household level. Using data from Mongolia, this study evaluated four approaches for estimating diet from household surveys: direct inference from per-capita household consumption; disaggregation of household consumption using a statistical method and the “adult male equivalent” method, and direct prediction of dietary intake. Per-capita household consumption overestimated dietary energy in single- and multi-person households by factors of 2.63 and 1.89, respectively. Performance of disaggregation methods was variable across two household surveys analyzed, while the statistical method exhibited less bias in estimating intake densities (per 100 kcal) of most dietary components in both of the surveys. Increasingly complex prediction models explained 54% to 72% of in-sample variation in dietary energy, with consistent benefits incurred by inclusion of basic dietary measurements. In conclusion, in Mongolia and elsewhere, differences in how household and dietary measurements are recorded make their comparison challenging. Validity of disaggregation methods depends on household survey characteristics and the dietary components that are considered. Relatively precise prediction models of dietary intake can be achieved by integrating basic dietary assessment into household surveys.

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“…Economic factors include income, development, urban-rural economic differences, price, and markets (Zhai et al, 2009;Zhen et al, 2010a;Baquedano and Liefert, 2014;Csutora and Vetőné Mózner, 2014;Chaves et al, 2017); social factors include those related to an aging population, urbanization, cultural diversity, and festivals (Han, 2013;Hu, 2016;Deng et al, 2018;Nikolay, 2018); ecological factors include seasonal variations, land-use diversity, climate, and the environment (de Ruiter et al, 2014;Hu, 2016;. Data from statistics, questionnaires, and remote sensing are the most commonly used data sources for studies on food consumption impact factors (Rasmussen et al, 2016;Troubat and Grünberger, 2017;Zhen et al, 2018;Li et al, 2019). Nevertheless, studies on the perception of food consumption impact factors of stakeholders remain insufficient, and in the Xilin Gol Grassland there is an obvious lack of qualitative and quantitative analyses on food consumption impact factors applying effective approaches.…”

Section: Introductionmentioning

confidence: 99%

Perceptive Factors That Affect Household Food Consumption Among Different Grassland Areas: A Case Study Based on Fuzzy Cognitive Map

Yang

Zhen

Wei

et al. 2021

Front. Environ. Sci.

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Food consumption acts as an intermediary between ecosystems and human systems in grassland areas. In this study, we generated a fuzzy cognitive map (FCM) to obtain a semi-quantitative assessment of impact factors on food consumption in the Xilin Gol Grassland, China, from the perception of local rural households. Based on it, the food consumption impact factors networks of households in Xilin Gol Grassland were created. Results showed that household recognition of food consumption impact factors differed among grassland transects in both amount and category, and the results of principal component analysis (PCA) ascertain such differences and transitions. Livestock breeding, income, local economic development, consumption habit, age, food price, and cost were the most important impact factors mentioned by both farmers and herdsmen. From south to north in the grassland transects, households' cognition of ecological and economic factors increased, and the cognition of personal and social factors decreased. Differences in supply, culture, customs, and socioeconomic characteristics affected the cognition of households and their livelihoods, which influenced impact factors as well as associated connections. The role of livestock breeding also validated the connection between human actives and the grassland ecosystem. Results from this study can be used as a reference for policymakers during decision-making processes respective of regional sustainable development.

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Impact of survey design in the estimation of habitual food consumption

Cited by 12 publications

References 11 publications

Recall length and measurement error in agricultural surveys

Recall length and measurement error in agricultural surveys

Comparison of Methods for Estimating Dietary Food and Nutrient Intakes and Intake Densities from Household Consumption and Expenditure Data in Mongolia

Perceptive Factors That Affect Household Food Consumption Among Different Grassland Areas: A Case Study Based on Fuzzy Cognitive Map

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