Missing data - Revision history

Livesadmin at 08:47, 10 December 2025

2025-12-10T08:47:41Z

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Livesadmin at 14:07, 20 April 2021

2021-04-20T14:07:03Z

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	Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis of statistical tests (Molenberghs et al., 2014).<br>		Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis of statistical tests (Molenberghs et al., 2014).<br>
	<br>		<br>
	In a survey, missing data can be partial (only one part of the answers of an individual are missing) or complete (all information is missing for this individual). In longitudinal designs especially, when a respondent drops out of the study after a given wave, then this individual will have only complete MD in all subsequent waves. This is a very important problem for the analysis of [[Vulnerability\|vulnerability]], since research shows that vulnerable people tend to drop out more frequently of a longitudinal survey than other people do, leading to an under-representativeness of such people and to an underestimation of their proportion in the general population (Rothenbühler & Voorpostel, 2016). <br>		In a survey, missing data can be partial (only one part of the answers of an individual are missing) or complete (all information is missing for this individual). In [[Longitudinal Data\|longitudinal]] designs especially, when a respondent drops out of the study after a given wave, then this individual will have only complete MD in all subsequent waves. This is a very important problem for the analysis of [[Vulnerability\|vulnerability]], since research shows that vulnerable people tend to drop out more frequently of a longitudinal survey than other people do, leading to an under-representativeness of such people and to an underestimation of their proportion in the general population (Rothenbühler & Voorpostel, 2016). <br>
	<br>		<br>
	In many situations, MD are apparent (we see in the database that the answer to a question is missing), but sometimes we cannot know whether an answer is missing or not. A good example is provided by retrospective data collected through a life history calendar. In such a survey design, every effort is done to enhance the recall memory of participants, but we can never be certain that all relevant information was collected (Morselli et al., 2016). Therefore, MD can be very complicated to identify, hence to treat. Finally, MD are sometimes planned in advance. For example, to reduce the burden on participants in a longitudinal design, only subsets of participants are surveyed in each wave, with the remainder considered missing. In this case, the MD process can be, under certain conditions, considered random and explicitly taken into account during the statistical analysis phase (Brandmaier et al., 2020; Rhemtulla & Little, 2012).<br>		In many situations, MD are apparent (we see in the database that the answer to a question is missing), but sometimes we cannot know whether an answer is missing or not. A good example is provided by retrospective data collected through a life history calendar. In such a survey design, every effort is done to enhance the recall memory of participants, but we can never be certain that all relevant information was collected (Morselli et al., 2016). Therefore, MD can be very complicated to identify, hence to treat. Finally, MD are sometimes planned in advance. For example, to reduce the burden on participants in a longitudinal design, only subsets of participants are surveyed in each wave, with the remainder considered missing. In this case, the MD process can be, under certain conditions, considered random and explicitly taken into account during the statistical analysis phase (Brandmaier et al., 2020; Rhemtulla & Little, 2012).<br>

Livesadmin: /* Semantic network visualisation */

2021-03-09T14:39:04Z

Semantic network visualisation

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	==Semantic network visualisation==		==Semantic network visualisation==
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Livesadmin: /* semantic network visualisation */

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semantic network visualisation

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	van Buuren, S. (2018). ''Flexible Imputation of Missing Data (2nd New edition)''. CRC Press.<br>		van Buuren, S. (2018). ''Flexible Imputation of Missing Data (2nd New edition)''. CRC Press.<br>
	<br>		<br>
	==~~semantic~~ network visualisation==		==Semantic network visualisation==
	Click to activate zoom- and drag-fonctionnality		Click to activate zoom- and drag-fonctionnality
	{{#network:		{{#network:

Livesadmin at 14:22, 9 March 2021

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 Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis  of statistical tests (Molenberghs et al., 2014).<br>
 <br>
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 van Buuren, S. (2018). ''Flexible Imputation of Missing Data (2nd New edition)''. CRC Press.<br>
 <br>

Livesadmin at 13:23, 4 March 2021

2021-03-04T13:23:47Z

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	Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis of statistical tests (Molenberghs et al., 2014).<br>		Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis of statistical tests (Molenberghs et al., 2014).<br>
	<br>		<br>

Livesadmin at 09:22, 18 February 2021

2021-02-18T09:22:42Z

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	<br>		<br>
	<br>		<br>
			Author: André Berchtold

	==References==		==References==

Widmere at 12:54, 8 October 2020

2020-10-08T12:54:31Z

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	van Buuren, S. (2018). ''Flexible Imputation of Missing Data (2nd New edition)''. CRC Press.<br>		van Buuren, S. (2018). ''Flexible Imputation of Missing Data (2nd New edition)''. CRC Press.<br>
	<br>		<br>

	~~[[Category:CCI4]]~~

Imstepft at 12:43, 8 October 2020

2020-10-08T12:43:56Z

← Older revision		Revision as of 12:43, 8 October 2020
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	Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis of statistical tests (Molenberghs et al., 2014).<br>		Missing data (MD) are data that were planned to be collected but could not be obtained in the end. Their causes are multiple, they concern all research fields, and their presence in a database can be considered as the rule, not the exception. The first consequence of MD is a reduced sample size, implying a loss in statistical power. Moreover, since MD rarely occur in a completely random fashion, they generally also imply biased coefficient estimates and reduced variance, what in turn leads to an under estimation of confidence intervals and a too high probability of rejecting the null hypothesis of statistical tests (Molenberghs et al., 2014).<br>
	<br>		<br>
	In a survey, missing data can be partial (only one part of the answers of an individual are missing) or complete (all information is missing for this individual). In longitudinal designs especially, when a respondent drops out of the study after a given wave, then this individual will have only complete MD in all subsequent waves. This is a very important problem for the analysis of vulnerability, since research shows that vulnerable people tend to drop out more frequently of a longitudinal survey than other people do, leading to an under-representativeness of such people and to an underestimation of their proportion in the general population (Rothenbühler & Voorpostel, 2016). <br>		In a survey, missing data can be partial (only one part of the answers of an individual are missing) or complete (all information is missing for this individual). In longitudinal designs especially, when a respondent drops out of the study after a given wave, then this individual will have only complete MD in all subsequent waves. This is a very important problem for the analysis of [[Vulnerability\|vulnerability]], since research shows that vulnerable people tend to drop out more frequently of a longitudinal survey than other people do, leading to an under-representativeness of such people and to an underestimation of their proportion in the general population (Rothenbühler & Voorpostel, 2016). <br>
	<br>		<br>
	In many situations, MD are apparent (we see in the database that the answer to a question is missing), but sometimes we cannot know whether an answer is missing or not. A good example is provided by retrospective data collected through a life history calendar. In such a survey design, every effort is done to enhance the recall memory of participants, but we can never be certain that all relevant information was collected (Morselli et al., 2016). Therefore, MD can be very complicated to identify, hence to treat. Finally, MD are sometimes planned in advance. For example, to reduce the burden on participants in a longitudinal design, only subsets of participants are surveyed in each wave, with the remainder considered missing. In this case, the MD process can be, under certain conditions, considered random and explicitly taken into account during the statistical analysis phase (Brandmaier et al., 2020; Rhemtulla & Little, 2012).<br>		In many situations, MD are apparent (we see in the database that the answer to a question is missing), but sometimes we cannot know whether an answer is missing or not. A good example is provided by retrospective data collected through a life history calendar. In such a survey design, every effort is done to enhance the recall memory of participants, but we can never be certain that all relevant information was collected (Morselli et al., 2016). Therefore, MD can be very complicated to identify, hence to treat. Finally, MD are sometimes planned in advance. For example, to reduce the burden on participants in a longitudinal design, only subsets of participants are surveyed in each wave, with the remainder considered missing. In this case, the MD process can be, under certain conditions, considered random and explicitly taken into account during the statistical analysis phase (Brandmaier et al., 2020; Rhemtulla & Little, 2012).<br>

Imstepft at 10:54, 8 October 2020

2020-10-08T10:54:13Z

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	==References==		==References==
	Berchtold, A. (2019). Treatment and reporting of item-level missing data in social science research. International Journal of Social Research Methodology, 22(5), 431–439. https://doi.org/10.1080/13645579.2018.1563978<br>		Berchtold, A. (2019). Treatment and reporting of item-level missing data in social science research. ''International Journal of Social Research Methodology'', 22(5), 431–439. https://doi.org/10.1080/13645579.2018.1563978<br>
	Brandmaier, A. M., Ghisletta, P., & Oertzen, T. von. (2020). Optimal planned missing data design for linear latent growth curve models. Behavior Research Methods, 52(4), 1445–1458. https://doi.org/10.3758/s13428-019-01325-y<br>		Brandmaier, A. M., Ghisletta, P., & Oertzen, T. von. (2020). Optimal planned missing data design for linear latent growth curve models. ''Behavior Research Methods'', 52(4), 1445–1458. https://doi.org/10.3758/s13428-019-01325-y<br>
	Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). Wiley.<br>		Little, R. J. A., & Rubin, D. B. (2019). ''Statistical Analysis with Missing Data (3rd ed.)''. Wiley.<br>
	Molenberghs, G., Fitzmaurice, G., Kenward, M. G., Tsiatis, A., & Verbeke, G. (Eds.). (2014). Handbook of Missing Data Methodology (1 edition). Chapman and Hall/CRC.<br>		Molenberghs, G., Fitzmaurice, G., Kenward, M. G., Tsiatis, A., & Verbeke, G. (Eds.). (2014). ''Handbook of Missing Data Methodology (1 edition)''. Chapman and Hall/CRC.<br>
	Morselli, D., Berchtold, A., Suris, J.-C., & Berchtold, A. (2016). On-line life history calendar and sensitive topics: A pilot study. Computers in Human Behavior, 58, 141–149. https://doi.org/10.1016/j.chb.2015.12.068<br>		Morselli, D., Berchtold, A., Suris, J.-C., & Berchtold, A. (2016). On-line life history calendar and sensitive topics: A pilot study. ''Computers in Human Behavior'', 58, 141–149. https://doi.org/10.1016/j.chb.2015.12.068<br>
	Rhemtulla, M., & Little, T. D. (2012). Planned Missing Data Designs for Research in Cognitive Development. Journal of Cognition and Development, 13(4), 425–438. https://doi.org/10.1080/15248372.2012.717340<br>		Rhemtulla, M., & Little, T. D. (2012). Planned Missing Data Designs for Research in Cognitive Development. ''Journal of Cognition and Development'', 13(4), 425–438. https://doi.org/10.1080/15248372.2012.717340<br>
	Rothenbühler, M., & Voorpostel, M. (2016). Attrition in the Swiss Household Panel: Are Vulnerable Groups more Affected than Others? In M. Oris, C. Roberts, D. Joye, & M. Ernst Stähli (Eds.), Surveying Human Vulnerabilities across the Life Course (pp. 223–244). Springer International Publishing. https://doi.org/10.1007/978-3-319-24157-9_10<br>		Rothenbühler, M., & Voorpostel, M. (2016). Attrition in the Swiss Household Panel: Are Vulnerable Groups more Affected than Others? In ''M. Oris, C. Roberts, D. Joye, & M. Ernst Stähli (Eds.), Surveying Human Vulnerabilities across the Life Course'' (pp. 223–244). Springer International Publishing. https://doi.org/10.1007/978-3-319-24157-9_10<br>
	Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.<br>		Rubin, D. B. (1987). ''Multiple Imputation for Nonresponse in Surveys''. Wiley.<br>
	Stähli, M. E., Joye, D., & Roberts, C. (2016). Mixing modes of data collection in Swiss social surveys: Methodological Report of the LIVES-FORS Mixed Mode Experiment. https://doi.org/10.12682/LIVES.2296-1658.2016.48<br>		Stähli, M. E., Joye, D., & Roberts, C. (2016). Mixing modes of data collection in Swiss social surveys: Methodological Report of the LIVES-FORS Mixed Mode Experiment. ''LIVES Working Paper'', 48, 1-42. https://doi.org/10.12682/LIVES.2296-1658.2016.48<br>
	van Buuren, S. (2018). Flexible Imputation of Missing Data (2nd New edition). CRC Press.<br>		van Buuren, S. (2018). ''Flexible Imputation of Missing Data (2nd New edition)''. CRC Press.<br>
	<br>		<br>

	[[Category:CCI4]]		[[Category:CCI4]]