Data Harmonization, Data Documentation, and Dissemination

Granda, Peter

doi:10.1002/9781118884997.ch42

Cited by 4 publications

(5 citation statements)

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“…Ex post survey harmonization is a useful approach to overcome limited coverage over periods and countries by creating a single data set that pools and adjusts variables from different surveys that were not designed to be compared in the first place (Granda & Blasczyk, 2016; Tomescu-Dubrow & Slomczynski, 2016). The SDR project has constructed a large data set that harmonizes the survey items about attending demonstrations and signing petitions from 19 international survey projects for 136 countries and territories from 1966 to 2013 (Slomczynski et al, 2016; Slomczynski et al, this issue).…”

Section: Intersurvey Methodological Variability In Survey Data Harmon...mentioning

confidence: 99%

Measuring and Analyzing Protest Potential From a Survey Data Recycling Framework

Kwak

2021

American Behavioral Scientist

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As active involvement in protest has been legitimized as an acceptable form of political activity, citizens’ protest potential has become an important measure to understand contemporary democratic politics. However, the arbitrary use of a forced-choice question, which prevents those who have previously participated in protests from expressing willingness to engage in future protest, and the limited coverage of international surveys across countries and years have impeded comparative research on protest potential. This research develops a new systematic weighting method for the measurement of protest potential for comparative research. Using the 1996 International Social Survey Program survey, which asks two separate questions about “have done” and “would do” demonstrations, I create a weighting scale for the forced-choice question by estimating the predicted probabilities of protest potential for those who have already participated in demonstrations. Capitalizing on the survey data recycling framework, this study also controls for harmonization procedures and the quality of surveys, thereby expanding the cross-national and temporal coverage beyond the affluent Western democracies. The results show that this weighting scale provides a valid measure of protest potential, and the survey data recycling framework improves comparability between surveys.

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Section: Intersurvey Methodological Variability In Survey Data Harmon...mentioning

confidence: 99%

Measuring and Analyzing Protest Potential From a Survey Data Recycling Framework

Kwak

2021

American Behavioral Scientist

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“…However, such data are faced with a threat of being lost at the end of the project. Globally, it has been advocated that proper preservation of such data can guarantee easy access, browsing, consultation, and future use (Tripathi et al, 2017), and also can facilitate technology transfer, and innovation in agriculture (Ng’eno and Mutula, 2018a), creation of opportunities for access, sharing, and reuse by other researchers beyond a given geographical area (Brouder et al, 2019; Dileepkumar, 2014; Eckes et al, 2017; Granda and Blasczyk, 2011; Kirub, 2016; Zhao and Wang, 2015). The choice of the best preservation method is a pre-requisites for its success (Ng’eno and Mutula, 2018a).…”

Section: Introductionmentioning

confidence: 99%

Data preservation practices for enhancing agricultural research data usage among agricultural researchers in Tanzania

Mwinami

Dulle

Mtega

2022

Journal of Librarianship and Information Science

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The objective of this study was to investigate the role of research data preservation for enhanced data usage among agricultural researchers in Tanzania. Specifically, the study aimed to examine the data preservation methods used by agriculture researchers, find out how long agriculture researchers preserve their agriculture research data, and determine factors that influence agriculture researchers on their choice of data preservation methods for use. The study employed a cross-sectional research design. The study employed both qualitative and quantitative approaches. A survey was conducted to collect data in 11 research institutions. A simple random sampling technique was used to select 204 respondents from the study area while purposive sampling techniques were used to select 11 agriculture research institutions including 10 Tanzanian Agricultural Research Institution (TARI) centers, and Sokoine University of Agriculture (SUA). Also, 12 respondents were selected purposively for an in-depth interview as key informants. The study adopted Data Curation Centre (DCC) Lifecycle Model to explain data preservation process. Findings indicated that a majority of more than 90% of researchers preferred to preserve their data using different storage devices such as field notebooks, computers, and institutional libraries. Moreover, findings indicated that about 74% of agricultural researchers preferred to preserve their data for more than 6 years after the end of the project. Findings also indicated factors that influence researchers in the choice of data preservation methods to be easy to reach, cost-effective storage devices, support to use the devices, adequate infrastructure for data preservation, and reliable power supply. It can be concluded that there is yet a great role of research data preservation in enhancing data usage among researchers in Tanzania. It is recommended that the government should establish an agricultural research data bank to guarantee permanent availability of data at all times when needed.

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“…To broaden the scope of comparison without fielding more surveys, social scientists increasingly turn to reprocessing information from different cross-national data sets into new, integrated databases. In doing so, researchers rely on ex-post harmonization methods (Granda & Blasczyk, 2016; Granda et al, 2010; Günther, 2003; Slomczynski et al, 2016) to strengthen the comparability of answers from respondents interviewed about the same issue but in projects whose methodology can vary considerably (Blasius & Thiessen, 2012; Oleksiyenko et al, 2018; Thiessen & Blasius, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers pool information on the same concept from sources not a priori designed as comparative, transform it to increase the comparability of answers from respondents interviewed in different populations and periods, and create a new data set whose coverage—of individuals, countries, and years—is much wider than that of its constituents. The literature refers to these methods as ex-post harmonization, to the original survey data sets and variables as source data sets and source variables, respectively, and to the harmonized, common, measures produced from the source variables, as target variables (Ehling & Rendtel, 2006; Granda & Blasczyk, 2016; Granda et al, 2010; Günther, 2003; Minkel, 2004). Recent examples of cross-national analyses of micro- and macrolevel determinants of protest participation using harmonized data sets include Slomczynski et al (2016) and Kołczyńska (2020).…”

Section: Introductionmentioning

confidence: 99%

“…As the interdisciplinary field of survey data harmonization grows (e.g., Dubrow & Tomescu-Dubrow, 2016), so do efforts to strengthen and integrate the methodology of ex-post harmonization (e.g., Granda & Blasczyk, 2016; Kołczyńska & Schoene, 2018; Oleksiyenko et al, 2018; Slomczynski et al, 2016; Slomczynski & Tomescu-Dubrow, 2018; Wolf et al, 2016). An important area of development deals with methodological variability among international survey projects that differences in the properties of source variables measuring the same concept, introduce.…”

Section: Introductionmentioning

confidence: 99%

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Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

Slomczynski

Tomescu-Dubrow

Wysmulek

2021

American Behavioral Scientist

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This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

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Data Harmonization, Data Documentation, and Dissemination

Cited by 4 publications

References 1 publication

Measuring and Analyzing Protest Potential From a Survey Data Recycling Framework

Measuring and Analyzing Protest Potential From a Survey Data Recycling Framework

Data preservation practices for enhancing agricultural research data usage among agricultural researchers in Tanzania

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

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